yambda-discovery-ranking-v01 / sol-max-0-153-4-basic-r05-20260924T221413Z Recorded model: gpt-5.6-sol ## Step 1 · system · 2026-09-25T00:53:57.171Z 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 · 2026-09-25T00:53:58.578Z ## 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 · 2026-09-25T00:53:58.578Z 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 · 2026-09-25T00:53:58.578Z 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 · 2026-09-25T00:53:58.579Z 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 2026-09-25 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": "2026-09-25", "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": "01a0d60e-0117-7221-a9b9-1654a97420c3", "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "cwd": "/app", "workspace_roots": [ "/app" ], "current_date": "2026-09-25", "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": "max", "developer_instructions": null } }, "multi_agent_version": "v2", "realtime_active": false, "effort": "max", "summary": "auto" } ## Step 6 · user · 2026-09-25T00:53:58.598Z # 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 · 2026-09-25T00:54:01.346Z ### Thinking unavailable — no text recorded I’ll first profile the data, eligibility density, and event timelines, then build a leakage-safe validation pipeline and iterate against the public evaluator. I’ll keep a valid submission saved early, and all generated files will stay under `/app/solution/`. ### exec · 2026-09-25T00:54:03.823Z { "code": "const r = await tools.exec_command({cmd:\"ls -lh /task/data && sed -n '1,220p' /task/requirements.txt && sed -n '1,260p' /task/ranking_evaluation.py\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:30000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:54:03.889Z Script completed Wall time 0.1 seconds Output: total 340M -r--r--r-- 1 root root 342K Sep 25 00:46 candidate_catalog.parquet -r--r--r-- 1 root root 83K Sep 25 00:46 target_users.parquet -r--r--r-- 1 root root 7.4M Sep 25 00:46 test_eligible_candidates.npz -r--r--r-- 1 root root 1.1M Sep 25 00:47 test_preference_state.parquet -r--r--r-- 1 root root 838K Sep 25 00:46 train_dislikes.parquet -r--r--r-- 1 root root 5.2M Sep 25 00:46 train_likes.parquet -r--r--r-- 1 root root 305M Sep 25 00:46 train_listens.parquet -r--r--r-- 1 root root 153K Sep 25 00:46 train_undislikes.parquet -r--r--r-- 1 root root 1.8M Sep 25 00:46 train_unlikes.parquet -r--r--r-- 1 root root 36K Sep 25 00:46 val_dislikes.parquet -r--r--r-- 1 root root 292K Sep 25 00:46 val_likes.parquet -r--r--r-- 1 root root 9.5M Sep 25 00:46 val_listens.parquet -r--r--r-- 1 root root 7.7K Sep 25 00:46 val_undislikes.parquet -r--r--r-- 1 root root 71K Sep 25 00:46 val_unlikes.parquet -r--r--r-- 1 root root 7.3M Sep 25 00:46 validation_eligible_candidates.npz -r--r--r-- 1 root root 1.1M Sep 25 00:47 validation_preference_state.parquet # Exact third-party distribution inventory for yambda-discovery-ranking-v01. # # This is an input to `uv pip sync`, not a list of minimum requirements. A # conforming task environment contains exactly these third-party Python # distributions and no others. The same inventory is used for source preparation, # task building, reference scoring, judging, and solver attempts. Python itself # is pinned by task.toml and both Harbor image Dockerfiles. attrs==26.1.0 bleach==6.4.0 certifi==2026.7.22 chardet==7.6.0 charset-normalizer==3.5.1 fastjsonschema==2.22.2 idna==3.18 joblib==1.5.3 jsonschema==4.26.0 jsonschema-specifications==2025.9.1 jupyter-core==5.9.1 jupytext==1.19.5 kaggle==2.2.4 kagglesdk==0.1.37 markdown-it-py==4.2.0 mdit-py-plugins==0.6.1 mdurl==0.1.2 nbformat==5.11.1 numpy==2.2.6 packaging==26.3 pandas==2.3.3 pillow==12.3.0 pip==26.0.1 platformdirs==4.11.3 protobuf==7.35.1 pyarrow==18.1.0 python-dateutil==2.9.0.post0 python-dotenv==1.2.3 python-slugify==8.0.4 pytz==2026.3.post1 PyYAML==6.0.3 referencing==0.37.0 reportlab==4.2.5 requests==2.34.2 rpds-py==2026.6.3 scikit-learn==1.7.2 scipy==1.15.3 six==1.17.0 text-unidecode==1.3 threadpoolctl==3.6.0 tqdm==4.70.0 traitlets==5.16.1 typing-extensions==4.16.0 tzdata==2026.3 urllib3==2.7.0 webencodings==0.6.1 """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), } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "response_id": "resp_0fdbf3e20a3ba82b016ab5c626c98487d284dfdb6e39233a73", "usage": { "input_tokens": 14337, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 224, "reasoning_output_tokens": 83, "total_tokens": 14561 }, "turn_token_usage": { "input_tokens": 14337, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 224, "reasoning_output_tokens": 83, "total_tokens": 14561 }, "thread_token_usage": { "input_tokens": 14337, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 224, "reasoning_output_tokens": 83, "total_tokens": 14561 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6993c271-a937-4c93-b39e-3a7553ce5356", "process_id": "32443", "command": [ "/bin/sh", "-lc", "ls -lh /task/data && sed -n '1,220p' /task/requirements.txt && sed -n '1,260p' /task/ranking_evaluation.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "list_files", "cmd": "ls -lh /task/data", "path": "data" }, { "type": "read", "cmd": "sed -n '1,220p' /task/requirements.txt", "name": "requirements.txt", "path": "/task/requirements.txt" }, { "type": "read", "cmd": "sed -n '1,260p' /task/ranking_evaluation.py", "name": "ranking_evaluation.py", "path": "/task/ranking_evaluation.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "total 340M\n-r--r--r-- 1 root root 342K Sep 25 00:46 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 25 00:46 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 25 00:46 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:47 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 25 00:46 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 25 00:46 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 25 00:46 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 25 00:46 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 25 00:46 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 25 00:46 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 25 00:46 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 25 00:46 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 25 00:46 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 25 00:46 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 25 00:46 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:47 validation_preference_state.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\"\"\"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 }\n", "stderr": "", "aggregated_output": "total 340M\n-r--r--r-- 1 root root 342K Sep 25 00:46 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 25 00:46 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 25 00:46 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:47 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 25 00:46 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 25 00:46 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 25 00:46 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 25 00:46 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 25 00:46 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 25 00:46 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 25 00:46 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 25 00:46 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 25 00:46 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 25 00:46 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 25 00:46 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:47 validation_preference_state.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\"\"\"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 }\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4870 }, "formatted_output": "total 340M\n-r--r--r-- 1 root root 342K Sep 25 00:46 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 25 00:46 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 25 00:46 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:47 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 25 00:46 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 25 00:46 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 25 00:46 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 25 00:46 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 25 00:46 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 25 00:46 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 25 00:46 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 25 00:46 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 25 00:46 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 25 00:46 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 25 00:46 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:47 validation_preference_state.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\"\"\"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 }\n" } } ## Step 8 · agent · 2026-09-25T00:54:05.917Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:54:10.393Z { "code": "const r = await tools.exec_command({cmd:\"sed -n '1,280p' /task/evaluate_public.py && python - <<'PY'\\nimport pandas as pd, numpy as np, os\\np='/task/data'\\nfor f in sorted(os.listdir(p)):\\n if f.endswith('.parquet'):\\n x=pd.read_parquet(f)\\n print(f, x.shape, x.dtypes.to_dict())\\n print(x.head(2).to_dict('records'))\\nfor f in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\\n z=np.load(p+'/'+f)\\n print(f,{k:(z[k].shape,z[k].dtype) for k in z.files})\\n print('uid range',z['uids'][:5],z['uids'][-5:],'items',z['item_ids'][:10], 'density',np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(z['item_ids'])].mean())\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:30000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:54:10.455Z 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() Traceback (most recent call last): File "", line 1, in ModuleNotFoundError: No module named 'pandas' ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-0d925e80-02e9-4298-8ef3-0fd3fb59cc3f", "process_id": "7712", "command": [ "/bin/sh", "-lc", "sed -n '1,280p' /task/evaluate_public.py && python - <<'PY'\nimport pandas as pd, numpy as np, os\np='/task/data'\nfor f in sorted(os.listdir(p)):\n if f.endswith('.parquet'):\n x=pd.read_parquet(f)\n print(f, x.shape, x.dtypes.to_dict())\n print(x.head(2).to_dict('records'))\nfor f in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n z=np.load(p+'/'+f)\n print(f,{k:(z[k].shape,z[k].dtype) for k in z.files})\n print('uid range',z['uids'][:5],z['uids'][-5:],'items',z['item_ids'][:10], 'density',np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(z['item_ids'])].mean())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "sed -n '1,280p' /task/evaluate_public.py && python - <<'PY'\nimport pandas as pd, numpy as np, os\np='/task/data'\nfor f in sorted(os.listdir(p)):\n if f.endswith('.parquet'):\n x=pd.read_parquet(f)\n print(f, x.shape, x.dtypes.to_dict())\n print(x.head(2).to_dict('records'))\nfor f in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n z=np.load(p+'/'+f)\n print(f,{k:(z[k].shape,z[k].dtype) for k in z.files})\n print('uid range',z['uids'][:5],z['uids'][-5:],'items',z['item_ids'][:10], 'density',np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(z['item_ids'])].mean())\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()\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()\nTraceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pandas'\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 5061 }, "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()\nTraceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pandas'\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "response_id": "resp_0fdbf3e20a3ba82b016ab5c62c2d2087d2959035b66aaec015", "usage": { "input_tokens": 17476, "cached_input_tokens": 14208, "cache_write_input_tokens": 0, "output_tokens": 271, "reasoning_output_tokens": 13, "total_tokens": 17747 }, "turn_token_usage": { "input_tokens": 31813, "cached_input_tokens": 25344, "cache_write_input_tokens": 0, "output_tokens": 495, "reasoning_output_tokens": 96, "total_tokens": 32308 }, "thread_token_usage": { "input_tokens": 31813, "cached_input_tokens": 25344, "cache_write_input_tokens": 0, "output_tokens": 495, "reasoning_output_tokens": 96, "total_tokens": 32308 } } ## Step 9 · agent · 2026-09-25T00:54:14.121Z ### Thinking unavailable — no text recorded The system `python` executable is not the task environment despite the provided package inventory. I’m locating the preinstalled task interpreter (without installing anything) before continuing the data profile. ### exec · 2026-09-25T00:54:16.933Z { "code": "const r = await tools.exec_command({cmd:\"command -v python; command -v python3; python --version; python -m pip list 2>/dev/null | head -30; ls -la /app; ls -la /task | sed -n '1,160p'; ls -la /task/.venv/bin 2>/dev/null | sed -n '1,80p'\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:54:17.688Z Script completed Wall time 0.8 seconds Output: /usr/local/bin/python /usr/local/bin/python3 Python 3.12.13 Package Version ------- ------- pip 25.0.1 total 12 drwxr-xr-x 3 agent agent 4096 Sep 25 00:50 . drwxr-xr-x 1 root root 4096 Sep 25 00:53 .. drwxr-xr-x 2 agent agent 4096 Sep 25 00:50 solution total 36 dr-xr-xr-x 1 root root 4096 Sep 25 00:50 . drwxr-xr-x 1 root root 4096 Sep 25 00:53 .. dr-xr-xr-x 1 root root 4096 Sep 25 00:47 data -r--r--r-- 1 root root 5774 Sep 25 00:46 evaluate_public.py -r--r--r-- 1 root root 8265 Sep 25 00:46 ranking_evaluation.py -r--r--r-- 1 root root 1278 Sep 25 00:46 requirements.txt ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "response_id": "resp_0fdbf3e20a3ba82b016ab5c632ac3887d29df15686ab90cda1", "usage": { "input_tokens": 19104, "cached_input_tokens": 17280, "cache_write_input_tokens": 0, "output_tokens": 260, "reasoning_output_tokens": 97, "total_tokens": 19364 }, "turn_token_usage": { "input_tokens": 50917, "cached_input_tokens": 42624, "cache_write_input_tokens": 0, "output_tokens": 755, "reasoning_output_tokens": 193, "total_tokens": 51672 }, "thread_token_usage": { "input_tokens": 50917, "cached_input_tokens": 42624, "cache_write_input_tokens": 0, "output_tokens": 755, "reasoning_output_tokens": 193, "total_tokens": 51672 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-5d605f7e-b067-4f77-b7dc-0c7a3b93f0bb", "process_id": "93559", "command": [ "/bin/sh", "-lc", "command -v python; command -v python3; python --version; python -m pip list 2>/dev/null | head -30; ls -la /app; ls -la /task | sed -n '1,160p'; ls -la /task/.venv/bin 2>/dev/null | sed -n '1,80p'" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "command -v python; command -v python3; python --version; python -m pip list 2>/dev/null | head -30; ls -la /app; ls -la /task | sed -n '1,160p'; ls -la /task/.venv/bin 2>/dev/null | sed -n '1,80p'" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "Package Version\n------- -------\npip 25.0.1\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 25 00:50 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:53 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 25 00:50 solution\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 25 00:50 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:53 ..\ndr-xr-xr-x 1 root root 4096 Sep 25 00:47 data\n-r--r--r-- 1 root root 5774 Sep 25 00:46 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 25 00:46 ranking_evaluation.py\n-r--r--r-- 1 root root 1278 Sep 25 00:46 requirements.txt\n", "stderr": "", "aggregated_output": "Package Version\n------- -------\npip 25.0.1\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 25 00:50 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:53 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 25 00:50 solution\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 25 00:50 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:53 ..\ndr-xr-xr-x 1 root root 4096 Sep 25 00:47 data\n-r--r--r-- 1 root root 5774 Sep 25 00:46 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 25 00:46 ranking_evaluation.py\n-r--r--r-- 1 root root 1278 Sep 25 00:46 requirements.txt\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 587055587 }, "formatted_output": "Package Version\n------- -------\npip 25.0.1\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 25 00:50 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:53 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 25 00:50 solution\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 25 00:50 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:53 ..\ndr-xr-xr-x 1 root root 4096 Sep 25 00:47 data\n-r--r--r-- 1 root root 5774 Sep 25 00:46 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 25 00:46 ranking_evaluation.py\n-r--r--r-- 1 root root 1278 Sep 25 00:46 requirements.txt\n" } } ## Step 10 · agent · 2026-09-25T00:54:23.733Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:54:25.959Z { "code": "const r = await tools.exec_command({cmd:\"command -v uv || true; command -v parquet-tools || true; python - <<'PY'\\nimport sys\\nprint('\\\\n'.join(sys.path))\\nPY\\nls -la /opt 2>/dev/null; ls -d /opt/*/bin/python* /usr/local/lib/python*/site-packages/pandas /usr/local/lib/python*/dist-packages/pandas 2>/dev/null || true\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:54:26.016Z Script completed Wall time 0.1 seconds Output: /usr/local/lib/python312.zip /usr/local/lib/python3.12 /usr/local/lib/python3.12/lib-dynload /usr/local/lib/python3.12/site-packages total 12 drwxr-xr-x 1 root root 4096 Sep 25 00:50 . drwxr-xr-x 1 root root 4096 Sep 25 00:53 .. dr-xr-xr-x 1 root root 4096 Sep 25 00:50 task-venv /opt/task-venv/bin/python /opt/task-venv/bin/python3 /opt/task-venv/bin/python3.12 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "response_id": "resp_0fdbf3e20a3ba82b016ab5c639e45487d2a0444f97cb52e3c4", "usage": { "input_tokens": 19630, "cached_input_tokens": 18944, "cache_write_input_tokens": 0, "output_tokens": 376, "reasoning_output_tokens": 243, "total_tokens": 20006 }, "turn_token_usage": { "input_tokens": 70547, "cached_input_tokens": 61568, "cache_write_input_tokens": 0, "output_tokens": 1131, "reasoning_output_tokens": 436, "total_tokens": 71678 }, "thread_token_usage": { "input_tokens": 70547, "cached_input_tokens": 61568, "cache_write_input_tokens": 0, "output_tokens": 1131, "reasoning_output_tokens": 436, "total_tokens": 71678 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a17ec75f-380d-4eaf-9071-7b9265938bcd", "process_id": "34301", "command": [ "/bin/sh", "-lc", "command -v uv || true; command -v parquet-tools || true; python - <<'PY'\nimport sys\nprint('\\n'.join(sys.path))\nPY\nls -la /opt 2>/dev/null; ls -d /opt/*/bin/python* /usr/local/lib/python*/site-packages/pandas /usr/local/lib/python*/dist-packages/pandas 2>/dev/null || true" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "command -v uv || true; command -v parquet-tools || true; python - <<'PY'\nimport sys\nprint('\\n'.join(sys.path))\nPY\nls -la /opt 2>/dev/null; ls -d /opt/*/bin/python* /usr/local/lib/python*/site-packages/pandas /usr/local/lib/python*/dist-packages/pandas 2>/dev/null || true" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "\n/usr/local/lib/python312.zip\n/usr/local/lib/python3.12\n/usr/local/lib/python3.12/lib-dynload\n/usr/local/lib/python3.12/site-packages\ntotal 12\ndrwxr-xr-x 1 root root 4096 Sep 25 00:50 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:53 ..\ndr-xr-xr-x 1 root root 4096 Sep 25 00:50 task-venv\n/opt/task-venv/bin/python\n/opt/task-venv/bin/python3\n/opt/task-venv/bin/python3.12\n", "stderr": "", "aggregated_output": "\n/usr/local/lib/python312.zip\n/usr/local/lib/python3.12\n/usr/local/lib/python3.12/lib-dynload\n/usr/local/lib/python3.12/site-packages\ntotal 12\ndrwxr-xr-x 1 root root 4096 Sep 25 00:50 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:53 ..\ndr-xr-xr-x 1 root root 4096 Sep 25 00:50 task-venv\n/opt/task-venv/bin/python\n/opt/task-venv/bin/python3\n/opt/task-venv/bin/python3.12\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 1971 }, "formatted_output": "\n/usr/local/lib/python312.zip\n/usr/local/lib/python3.12\n/usr/local/lib/python3.12/lib-dynload\n/usr/local/lib/python3.12/site-packages\ntotal 12\ndrwxr-xr-x 1 root root 4096 Sep 25 00:50 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:53 ..\ndr-xr-xr-x 1 root root 4096 Sep 25 00:50 task-venv\n/opt/task-venv/bin/python\n/opt/task-venv/bin/python3\n/opt/task-venv/bin/python3.12\n" } } ## Step 11 · agent · 2026-09-25T00:54:28.791Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:54:34.795Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd, numpy as np, os\\np='/task/data'\\nfor f in sorted(os.listdir(p)):\\n if f.endswith('.parquet'):\\n x=pd.read_parquet(p+'/'+f)\\n print('\\\\n',f, x.shape, x.dtypes.to_dict())\\n print(x.head(2).to_dict('records'))\\n for c in ['uid','item_id','timestamp','is_organic','played_ratio_pct']:\\n if c in x: print(c,'nuniq',x[c].nunique(),'min',x[c].min(),'max',x[c].max())\\nfor f in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\\n z=np.load(p+'/'+f)\\n bits=np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(z['item_ids'])]\\n print('\\\\n',f,{k:(z[k].shape,z[k].dtype) for k in z.files})\\n print('uid range',z['uids'][:5],z['uids'][-5:],'items first',z['item_ids'][:10], 'density',bits.mean(),'eligible/user',bits.sum(1).min(),np.median(bits.sum(1)),bits.sum(1).max())\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:30000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:54:39.178Z Script completed Wall time 4.4 seconds Output: candidate_catalog.parquet (30000, 4) {'item_id': dtype('uint32'), 'positive_listens': dtype('int64'), 'organic_positive_listens': dtype('uint64'), 'popularity_rank': dtype('int32')} [{'item_id': 5862961, 'positive_listens': 26615, 'organic_positive_listens': 20620, 'popularity_rank': 1}, {'item_id': 6901374, 'positive_listens': 25536, 'organic_positive_listens': 17347, 'popularity_rank': 2}] item_id nuniq 30000 min 206 max 9390294 target_users.parquet (8456, 3) {'uid': dtype('uint32'), 'recent_positive_listens': dtype('float64'), 'history_positive_listens': dtype('int64')} [{'uid': 100, 'recent_positive_listens': 844.0, 'history_positive_listens': 2604}, {'uid': 200, 'recent_positive_listens': 50.0, 'history_positive_listens': 286}] uid nuniq 8456 min 100 max 1000000 test_preference_state.parquet (484776, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')} [{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}] uid nuniq 7092 min 100 max 1000000 item_id nuniq 28863 min 206 max 9390294 train_dislikes.parquet (101075, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')} [{'uid': 100, 'timestamp': 3087560, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 3936560, 'item_id': 8661238, 'is_organic': 1}] uid nuniq 5732 min 100 max 1000000 item_id nuniq 50876 min 105 max 9390541 timestamp nuniq 67779 min 75 max 24786580 is_organic nuniq 2 min 0 max 1 train_likes.parquet (814831, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')} [{'uid': 100, 'timestamp': 44755, 'item_id': 732449, 'is_organic': 1}, {'uid': 100, 'timestamp': 1155860, 'item_id': 6568592, 'is_organic': 0}] uid nuniq 8144 min 100 max 1000000 item_id nuniq 172303 min 26 max 9390522 timestamp nuniq 562774 min 80 max 24786790 is_organic nuniq 2 min 0 max 1 train_listens.parquet (43484159, 6) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')} [{'uid': 100, 'timestamp': 39420, 'item_id': 8326270, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 170}, {'uid': 100, 'timestamp': 39420, 'item_id': 1441281, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 105}] uid nuniq 9238 min 100 max 1000000 item_id nuniq 845395 min 22 max 9390623 timestamp nuniq 4806896 min 0 max 24786795 is_organic nuniq 2 min 0 max 1 played_ratio_pct nuniq 160 min 0 max 159 train_undislikes.parquet (19960, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')} [{'uid': 100, 'timestamp': 7895900, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 7895900, 'item_id': 5038504, 'is_organic': 1}] uid nuniq 2769 min 100 max 999900 item_id nuniq 14725 min 178 max 9390240 timestamp nuniq 6956 min 15950 max 24785395 is_organic nuniq 2 min 0 max 1 train_unlikes.parquet (292665, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')} [{'uid': 100, 'timestamp': 3087555, 'item_id': 5038504, 'is_organic': 1}, {'uid': 100, 'timestamp': 3087555, 'item_id': 9170134, 'is_organic': 1}] uid nuniq 6197 min 100 max 1000000 item_id nuniq 113072 min 26 max 9390601 timestamp nuniq 87635 min 250 max 24786570 is_organic nuniq 2 min 0 max 1 val_dislikes.parquet (3563, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')} [{'uid': 1600, 'timestamp': 25163525, 'item_id': 6747257, 'is_organic': 0}, {'uid': 1900, 'timestamp': 24877510, 'item_id': 5832822, 'is_organic': 1}] uid nuniq 955 min 1600 max 998800 item_id nuniq 3135 min 651 max 9388630 timestamp nuniq 2386 min 24789920 max 25393280 is_organic nuniq 2 min 0 max 1 val_likes.parquet (37795, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')} [{'uid': 100, 'timestamp': 25249855, 'item_id': 3526521, 'is_organic': 1}, {'uid': 300, 'timestamp': 25046245, 'item_id': 4867327, 'is_organic': 1}] uid nuniq 4114 min 100 max 999900 item_id nuniq 22902 min 209 max 9390461 timestamp nuniq 19790 min 24788815 max 25393390 is_organic nuniq 2 min 0 max 1 val_listens.parquet (1501697, 6) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')} [{'uid': 100, 'timestamp': 24943430, 'item_id': 9321269, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 195}, {'uid': 100, 'timestamp': 24943605, 'item_id': 5452713, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 175}] uid nuniq 8196 min 100 max 1000000 item_id nuniq 191747 min 50 max 9390541 timestamp nuniq 119851 min 24788600 max 25393395 is_organic nuniq 2 min 0 max 1 played_ratio_pct nuniq 160 min 0 max 159 val_undislikes.parquet (606, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')} [{'uid': 900, 'timestamp': 25364045, 'item_id': 6092542, 'is_organic': 1}, {'uid': 900, 'timestamp': 25364045, 'item_id': 6501913, 'is_organic': 1}] uid nuniq 205 min 900 max 998800 item_id nuniq 579 min 15149 max 9385279 timestamp nuniq 242 min 24810715 max 25393035 is_organic nuniq 2 min 0 max 1 val_unlikes.parquet (9346, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')} [{'uid': 1100, 'timestamp': 24861880, 'item_id': 6901050, 'is_organic': 1}, {'uid': 1100, 'timestamp': 24861880, 'item_id': 5421761, 'is_organic': 1}] uid nuniq 1313 min 1100 max 998800 item_id nuniq 8080 min 1928 max 9390163 timestamp nuniq 2941 min 24789005 max 25393385 is_organic nuniq 2 min 0 max 1 validation_preference_state.parquet (469124, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')} [{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}] uid nuniq 7038 min 100 max 1000000 item_id nuniq 28814 min 206 max 9390294 validation_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))} uid range [100 200 300 600 700] [ 999600 999700 999800 999900 1000000] items first [5862961 6901374 5635052 3542184 9378983 5463340 8213481 906358 2859641 3033749] density 0.9689102097130243 eligible/user 24657 29242.0 30000 test_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))} uid range [100 200 300 600 700] [ 999600 999700 999800 999900 1000000] items first [5862961 6901374 5635052 3542184 9378983 5463340 8213481 906358 2859641 3033749] density 0.9680439293598234 eligible/user 24552 29220.0 30000 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "response_id": "resp_0fdbf3e20a3ba82b016ab5c6426cd087d2b2b9cbe529bc36d4", "usage": { "input_tokens": 20170, "cached_input_tokens": 19456, "cache_write_input_tokens": 0, "output_tokens": 358, "reasoning_output_tokens": 16, "total_tokens": 20528 }, "turn_token_usage": { "input_tokens": 90717, "cached_input_tokens": 81024, "cache_write_input_tokens": 0, "output_tokens": 1489, "reasoning_output_tokens": 452, "total_tokens": 92206 }, "thread_token_usage": { "input_tokens": 90717, "cached_input_tokens": 81024, "cache_write_input_tokens": 0, "output_tokens": 1489, "reasoning_output_tokens": 452, "total_tokens": 92206 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-5f3b7d9b-d5ff-4793-a172-d61711b30d2b", "process_id": "40954", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np, os\np='/task/data'\nfor f in sorted(os.listdir(p)):\n if f.endswith('.parquet'):\n x=pd.read_parquet(p+'/'+f)\n print('\\n',f, x.shape, x.dtypes.to_dict())\n print(x.head(2).to_dict('records'))\n for c in ['uid','item_id','timestamp','is_organic','played_ratio_pct']:\n if c in x: print(c,'nuniq',x[c].nunique(),'min',x[c].min(),'max',x[c].max())\nfor f in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n z=np.load(p+'/'+f)\n bits=np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(z['item_ids'])]\n print('\\n',f,{k:(z[k].shape,z[k].dtype) for k in z.files})\n print('uid range',z['uids'][:5],z['uids'][-5:],'items first',z['item_ids'][:10], 'density',bits.mean(),'eligible/user',bits.sum(1).min(),np.median(bits.sum(1)),bits.sum(1).max())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np, os\np='/task/data'\nfor f in sorted(os.listdir(p)):\n if f.endswith('.parquet'):\n x=pd.read_parquet(p+'/'+f)\n print('\\n',f, x.shape, x.dtypes.to_dict())\n print(x.head(2).to_dict('records'))\n for c in ['uid','item_id','timestamp','is_organic','played_ratio_pct']:\n if c in x: print(c,'nuniq',x[c].nunique(),'min',x[c].min(),'max',x[c].max())\nfor f in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n z=np.load(p+'/'+f)\n bits=np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(z['item_ids'])]\n print('\\n',f,{k:(z[k].shape,z[k].dtype) for k in z.files})\n print('uid range',z['uids'][:5],z['uids'][-5:],'items first',z['item_ids'][:10], 'density',bits.mean(),'eligible/user',bits.sum(1).min(),np.median(bits.sum(1)),bits.sum(1).max())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "\n candidate_catalog.parquet (30000, 4) {'item_id': dtype('uint32'), 'positive_listens': dtype('int64'), 'organic_positive_listens': dtype('uint64'), 'popularity_rank': dtype('int32')}\n[{'item_id': 5862961, 'positive_listens': 26615, 'organic_positive_listens': 20620, 'popularity_rank': 1}, {'item_id': 6901374, 'positive_listens': 25536, 'organic_positive_listens': 17347, 'popularity_rank': 2}]\nitem_id nuniq 30000 min 206 max 9390294\n\n target_users.parquet (8456, 3) {'uid': dtype('uint32'), 'recent_positive_listens': dtype('float64'), 'history_positive_listens': dtype('int64')}\n[{'uid': 100, 'recent_positive_listens': 844.0, 'history_positive_listens': 2604}, {'uid': 200, 'recent_positive_listens': 50.0, 'history_positive_listens': 286}]\nuid nuniq 8456 min 100 max 1000000\n\n test_preference_state.parquet (484776, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\n[{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}]\nuid nuniq 7092 min 100 max 1000000\nitem_id nuniq 28863 min 206 max 9390294\n\n train_dislikes.parquet (101075, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 100, 'timestamp': 3087560, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 3936560, 'item_id': 8661238, 'is_organic': 1}]\nuid nuniq 5732 min 100 max 1000000\nitem_id nuniq 50876 min 105 max 9390541\ntimestamp nuniq 67779 min 75 max 24786580\nis_organic nuniq 2 min 0 max 1\n\n train_likes.parquet (814831, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 100, 'timestamp': 44755, 'item_id': 732449, 'is_organic': 1}, {'uid': 100, 'timestamp': 1155860, 'item_id': 6568592, 'is_organic': 0}]\nuid nuniq 8144 min 100 max 1000000\nitem_id nuniq 172303 min 26 max 9390522\ntimestamp nuniq 562774 min 80 max 24786790\nis_organic nuniq 2 min 0 max 1\n\n train_listens.parquet (43484159, 6) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')}\n[{'uid': 100, 'timestamp': 39420, 'item_id': 8326270, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 170}, {'uid': 100, 'timestamp': 39420, 'item_id': 1441281, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 105}]\nuid nuniq 9238 min 100 max 1000000\nitem_id nuniq 845395 min 22 max 9390623\ntimestamp nuniq 4806896 min 0 max 24786795\nis_organic nuniq 2 min 0 max 1\nplayed_ratio_pct nuniq 160 min 0 max 159\n\n train_undislikes.parquet (19960, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 100, 'timestamp': 7895900, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 7895900, 'item_id': 5038504, 'is_organic': 1}]\nuid nuniq 2769 min 100 max 999900\nitem_id nuniq 14725 min 178 max 9390240\ntimestamp nuniq 6956 min 15950 max 24785395\nis_organic nuniq 2 min 0 max 1\n\n train_unlikes.parquet (292665, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 100, 'timestamp': 3087555, 'item_id': 5038504, 'is_organic': 1}, {'uid': 100, 'timestamp': 3087555, 'item_id': 9170134, 'is_organic': 1}]\nuid nuniq 6197 min 100 max 1000000\nitem_id nuniq 113072 min 26 max 9390601\ntimestamp nuniq 87635 min 250 max 24786570\nis_organic nuniq 2 min 0 max 1\n\n val_dislikes.parquet (3563, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 1600, 'timestamp': 25163525, 'item_id': 6747257, 'is_organic': 0}, {'uid': 1900, 'timestamp': 24877510, 'item_id': 5832822, 'is_organic': 1}]\nuid nuniq 955 min 1600 max 998800\nitem_id nuniq 3135 min 651 max 9388630\ntimestamp nuniq 2386 min 24789920 max 25393280\nis_organic nuniq 2 min 0 max 1\n\n val_likes.parquet (37795, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 100, 'timestamp': 25249855, 'item_id': 3526521, 'is_organic': 1}, {'uid': 300, 'timestamp': 25046245, 'item_id': 4867327, 'is_organic': 1}]\nuid nuniq 4114 min 100 max 999900\nitem_id nuniq 22902 min 209 max 9390461\ntimestamp nuniq 19790 min 24788815 max 25393390\nis_organic nuniq 2 min 0 max 1\n\n val_listens.parquet (1501697, 6) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')}\n[{'uid': 100, 'timestamp': 24943430, 'item_id': 9321269, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 195}, {'uid': 100, 'timestamp': 24943605, 'item_id': 5452713, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 175}]\nuid nuniq 8196 min 100 max 1000000\nitem_id nuniq 191747 min 50 max 9390541\ntimestamp nuniq 119851 min 24788600 max 25393395\nis_organic nuniq 2 min 0 max 1\nplayed_ratio_pct nuniq 160 min 0 max 159\n\n val_undislikes.parquet (606, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 900, 'timestamp': 25364045, 'item_id': 6092542, 'is_organic': 1}, {'uid': 900, 'timestamp': 25364045, 'item_id': 6501913, 'is_organic': 1}]\nuid nuniq 205 min 900 max 998800\nitem_id nuniq 579 min 15149 max 9385279\ntimestamp nuniq 242 min 24810715 max 25393035\nis_organic nuniq 2 min 0 max 1\n\n val_unlikes.parquet (9346, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 1100, 'timestamp': 24861880, 'item_id': 6901050, 'is_organic': 1}, {'uid': 1100, 'timestamp': 24861880, 'item_id': 5421761, 'is_organic': 1}]\nuid nuniq 1313 min 1100 max 998800\nitem_id nuniq 8080 min 1928 max 9390163\ntimestamp nuniq 2941 min 24789005 max 25393385\nis_organic nuniq 2 min 0 max 1\n\n validation_preference_state.parquet (469124, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\n[{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}]\nuid nuniq 7038 min 100 max 1000000\nitem_id nuniq 28814 min 206 max 9390294\n\n validation_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\nuid range [100 200 300 600 700] [ 999600 999700 999800 999900 1000000] items first [5862961 6901374 5635052 3542184 9378983 5463340 8213481 906358 2859641\n 3033749] density 0.9689102097130243 eligible/user 24657 29242.0 30000\n\n test_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\nuid range [100 200 300 600 700] [ 999600 999700 999800 999900 1000000] items first [5862961 6901374 5635052 3542184 9378983 5463340 8213481 906358 2859641\n 3033749] density 0.9680439293598234 eligible/user 24552 29220.0 30000\n", "stderr": "", "aggregated_output": "\n candidate_catalog.parquet (30000, 4) {'item_id': dtype('uint32'), 'positive_listens': dtype('int64'), 'organic_positive_listens': dtype('uint64'), 'popularity_rank': dtype('int32')}\n[{'item_id': 5862961, 'positive_listens': 26615, 'organic_positive_listens': 20620, 'popularity_rank': 1}, {'item_id': 6901374, 'positive_listens': 25536, 'organic_positive_listens': 17347, 'popularity_rank': 2}]\nitem_id nuniq 30000 min 206 max 9390294\n\n target_users.parquet (8456, 3) {'uid': dtype('uint32'), 'recent_positive_listens': dtype('float64'), 'history_positive_listens': dtype('int64')}\n[{'uid': 100, 'recent_positive_listens': 844.0, 'history_positive_listens': 2604}, {'uid': 200, 'recent_positive_listens': 50.0, 'history_positive_listens': 286}]\nuid nuniq 8456 min 100 max 1000000\n\n test_preference_state.parquet (484776, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\n[{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}]\nuid nuniq 7092 min 100 max 1000000\nitem_id nuniq 28863 min 206 max 9390294\n\n train_dislikes.parquet (101075, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 100, 'timestamp': 3087560, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 3936560, 'item_id': 8661238, 'is_organic': 1}]\nuid nuniq 5732 min 100 max 1000000\nitem_id nuniq 50876 min 105 max 9390541\ntimestamp nuniq 67779 min 75 max 24786580\nis_organic nuniq 2 min 0 max 1\n\n train_likes.parquet (814831, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 100, 'timestamp': 44755, 'item_id': 732449, 'is_organic': 1}, {'uid': 100, 'timestamp': 1155860, 'item_id': 6568592, 'is_organic': 0}]\nuid nuniq 8144 min 100 max 1000000\nitem_id nuniq 172303 min 26 max 9390522\ntimestamp nuniq 562774 min 80 max 24786790\nis_organic nuniq 2 min 0 max 1\n\n train_listens.parquet (43484159, 6) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')}\n[{'uid': 100, 'timestamp': 39420, 'item_id': 8326270, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 170}, {'uid': 100, 'timestamp': 39420, 'item_id': 1441281, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 105}]\nuid nuniq 9238 min 100 max 1000000\nitem_id nuniq 845395 min 22 max 9390623\ntimestamp nuniq 4806896 min 0 max 24786795\nis_organic nuniq 2 min 0 max 1\nplayed_ratio_pct nuniq 160 min 0 max 159\n\n train_undislikes.parquet (19960, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 100, 'timestamp': 7895900, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 7895900, 'item_id': 5038504, 'is_organic': 1}]\nuid nuniq 2769 min 100 max 999900\nitem_id nuniq 14725 min 178 max 9390240\ntimestamp nuniq 6956 min 15950 max 24785395\nis_organic nuniq 2 min 0 max 1\n\n train_unlikes.parquet (292665, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 100, 'timestamp': 3087555, 'item_id': 5038504, 'is_organic': 1}, {'uid': 100, 'timestamp': 3087555, 'item_id': 9170134, 'is_organic': 1}]\nuid nuniq 6197 min 100 max 1000000\nitem_id nuniq 113072 min 26 max 9390601\ntimestamp nuniq 87635 min 250 max 24786570\nis_organic nuniq 2 min 0 max 1\n\n val_dislikes.parquet (3563, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 1600, 'timestamp': 25163525, 'item_id': 6747257, 'is_organic': 0}, {'uid': 1900, 'timestamp': 24877510, 'item_id': 5832822, 'is_organic': 1}]\nuid nuniq 955 min 1600 max 998800\nitem_id nuniq 3135 min 651 max 9388630\ntimestamp nuniq 2386 min 24789920 max 25393280\nis_organic nuniq 2 min 0 max 1\n\n val_likes.parquet (37795, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 100, 'timestamp': 25249855, 'item_id': 3526521, 'is_organic': 1}, {'uid': 300, 'timestamp': 25046245, 'item_id': 4867327, 'is_organic': 1}]\nuid nuniq 4114 min 100 max 999900\nitem_id nuniq 22902 min 209 max 9390461\ntimestamp nuniq 19790 min 24788815 max 25393390\nis_organic nuniq 2 min 0 max 1\n\n val_listens.parquet (1501697, 6) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')}\n[{'uid': 100, 'timestamp': 24943430, 'item_id': 9321269, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 195}, {'uid': 100, 'timestamp': 24943605, 'item_id': 5452713, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 175}]\nuid nuniq 8196 min 100 max 1000000\nitem_id nuniq 191747 min 50 max 9390541\ntimestamp nuniq 119851 min 24788600 max 25393395\nis_organic nuniq 2 min 0 max 1\nplayed_ratio_pct nuniq 160 min 0 max 159\n\n val_undislikes.parquet (606, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 900, 'timestamp': 25364045, 'item_id': 6092542, 'is_organic': 1}, {'uid': 900, 'timestamp': 25364045, 'item_id': 6501913, 'is_organic': 1}]\nuid nuniq 205 min 900 max 998800\nitem_id nuniq 579 min 15149 max 9385279\ntimestamp nuniq 242 min 24810715 max 25393035\nis_organic nuniq 2 min 0 max 1\n\n val_unlikes.parquet (9346, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 1100, 'timestamp': 24861880, 'item_id': 6901050, 'is_organic': 1}, {'uid': 1100, 'timestamp': 24861880, 'item_id': 5421761, 'is_organic': 1}]\nuid nuniq 1313 min 1100 max 998800\nitem_id nuniq 8080 min 1928 max 9390163\ntimestamp nuniq 2941 min 24789005 max 25393385\nis_organic nuniq 2 min 0 max 1\n\n validation_preference_state.parquet (469124, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\n[{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}]\nuid nuniq 7038 min 100 max 1000000\nitem_id nuniq 28814 min 206 max 9390294\n\n validation_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\nuid range [100 200 300 600 700] [ 999600 999700 999800 999900 1000000] items first [5862961 6901374 5635052 3542184 9378983 5463340 8213481 906358 2859641\n 3033749] density 0.9689102097130243 eligible/user 24657 29242.0 30000\n\n test_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\nuid range [100 200 300 600 700] [ 999600 999700 999800 999900 1000000] items first [5862961 6901374 5635052 3542184 9378983 5463340 8213481 906358 2859641\n 3033749] density 0.9680439293598234 eligible/user 24552 29220.0 30000\n", "exit_code": 0, "duration": { "secs": 4, "nanos": 215466631 }, "formatted_output": "\n candidate_catalog.parquet (30000, 4) {'item_id': dtype('uint32'), 'positive_listens': dtype('int64'), 'organic_positive_listens': dtype('uint64'), 'popularity_rank': dtype('int32')}\n[{'item_id': 5862961, 'positive_listens': 26615, 'organic_positive_listens': 20620, 'popularity_rank': 1}, {'item_id': 6901374, 'positive_listens': 25536, 'organic_positive_listens': 17347, 'popularity_rank': 2}]\nitem_id nuniq 30000 min 206 max 9390294\n\n target_users.parquet (8456, 3) {'uid': dtype('uint32'), 'recent_positive_listens': dtype('float64'), 'history_positive_listens': dtype('int64')}\n[{'uid': 100, 'recent_positive_listens': 844.0, 'history_positive_listens': 2604}, {'uid': 200, 'recent_positive_listens': 50.0, 'history_positive_listens': 286}]\nuid nuniq 8456 min 100 max 1000000\n\n test_preference_state.parquet (484776, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\n[{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}]\nuid nuniq 7092 min 100 max 1000000\nitem_id nuniq 28863 min 206 max 9390294\n\n train_dislikes.parquet (101075, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 100, 'timestamp': 3087560, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 3936560, 'item_id': 8661238, 'is_organic': 1}]\nuid nuniq 5732 min 100 max 1000000\nitem_id nuniq 50876 min 105 max 9390541\ntimestamp nuniq 67779 min 75 max 24786580\nis_organic nuniq 2 min 0 max 1\n\n train_likes.parquet (814831, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 100, 'timestamp': 44755, 'item_id': 732449, 'is_organic': 1}, {'uid': 100, 'timestamp': 1155860, 'item_id': 6568592, 'is_organic': 0}]\nuid nuniq 8144 min 100 max 1000000\nitem_id nuniq 172303 min 26 max 9390522\ntimestamp nuniq 562774 min 80 max 24786790\nis_organic nuniq 2 min 0 max 1\n\n train_listens.parquet (43484159, 6) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')}\n[{'uid': 100, 'timestamp': 39420, 'item_id': 8326270, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 170}, {'uid': 100, 'timestamp': 39420, 'item_id': 1441281, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 105}]\nuid nuniq 9238 min 100 max 1000000\nitem_id nuniq 845395 min 22 max 9390623\ntimestamp nuniq 4806896 min 0 max 24786795\nis_organic nuniq 2 min 0 max 1\nplayed_ratio_pct nuniq 160 min 0 max 159\n\n train_undislikes.parquet (19960, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 100, 'timestamp': 7895900, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 7895900, 'item_id': 5038504, 'is_organic': 1}]\nuid nuniq 2769 min 100 max 999900\nitem_id nuniq 14725 min 178 max 9390240\ntimestamp nuniq 6956 min 15950 max 24785395\nis_organic nuniq 2 min 0 max 1\n\n train_unlikes.parquet (292665, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 100, 'timestamp': 3087555, 'item_id': 5038504, 'is_organic': 1}, {'uid': 100, 'timestamp': 3087555, 'item_id': 9170134, 'is_organic': 1}]\nuid nuniq 6197 min 100 max 1000000\nitem_id nuniq 113072 min 26 max 9390601\ntimestamp nuniq 87635 min 250 max 24786570\nis_organic nuniq 2 min 0 max 1\n\n val_dislikes.parquet (3563, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 1600, 'timestamp': 25163525, 'item_id': 6747257, 'is_organic': 0}, {'uid': 1900, 'timestamp': 24877510, 'item_id': 5832822, 'is_organic': 1}]\nuid nuniq 955 min 1600 max 998800\nitem_id nuniq 3135 min 651 max 9388630\ntimestamp nuniq 2386 min 24789920 max 25393280\nis_organic nuniq 2 min 0 max 1\n\n val_likes.parquet (37795, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 100, 'timestamp': 25249855, 'item_id': 3526521, 'is_organic': 1}, {'uid': 300, 'timestamp': 25046245, 'item_id': 4867327, 'is_organic': 1}]\nuid nuniq 4114 min 100 max 999900\nitem_id nuniq 22902 min 209 max 9390461\ntimestamp nuniq 19790 min 24788815 max 25393390\nis_organic nuniq 2 min 0 max 1\n\n val_listens.parquet (1501697, 6) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')}\n[{'uid': 100, 'timestamp': 24943430, 'item_id': 9321269, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 195}, {'uid': 100, 'timestamp': 24943605, 'item_id': 5452713, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 175}]\nuid nuniq 8196 min 100 max 1000000\nitem_id nuniq 191747 min 50 max 9390541\ntimestamp nuniq 119851 min 24788600 max 25393395\nis_organic nuniq 2 min 0 max 1\nplayed_ratio_pct nuniq 160 min 0 max 159\n\n val_undislikes.parquet (606, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 900, 'timestamp': 25364045, 'item_id': 6092542, 'is_organic': 1}, {'uid': 900, 'timestamp': 25364045, 'item_id': 6501913, 'is_organic': 1}]\nuid nuniq 205 min 900 max 998800\nitem_id nuniq 579 min 15149 max 9385279\ntimestamp nuniq 242 min 24810715 max 25393035\nis_organic nuniq 2 min 0 max 1\n\n val_unlikes.parquet (9346, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 1100, 'timestamp': 24861880, 'item_id': 6901050, 'is_organic': 1}, {'uid': 1100, 'timestamp': 24861880, 'item_id': 5421761, 'is_organic': 1}]\nuid nuniq 1313 min 1100 max 998800\nitem_id nuniq 8080 min 1928 max 9390163\ntimestamp nuniq 2941 min 24789005 max 25393385\nis_organic nuniq 2 min 0 max 1\n\n validation_preference_state.parquet (469124, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\n[{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}]\nuid nuniq 7038 min 100 max 1000000\nitem_id nuniq 28814 min 206 max 9390294\n\n validation_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\nuid range [100 200 300 600 700] [ 999600 999700 999800 999900 1000000] items first [5862961 6901374 5635052 3542184 9378983 5463340 8213481 906358 2859641\n 3033749] density 0.9689102097130243 eligible/user 24657 29242.0 30000\n\n test_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\nuid range [100 200 300 600 700] [ 999600 999700 999800 999900 1000000] items first [5862961 6901374 5635052 3542184 9378983 5463340 8213481 906358 2859641\n 3033749] density 0.9680439293598234 eligible/user 24552 29220.0 30000\n" } } ## Step 12 · agent · 2026-09-25T00:54:50.528Z ### Thinking unavailable — no text recorded ## Step 13 · agent · 2026-09-25T00:55:00.195Z ### Thinking unavailable — no text recorded The candidate boundary is broad (median 29,242 of 30,000 tracks per user), so ranking quality will come from personalization rather than eligibility alone. I’m measuring how much validation relevance comes from repeat listening, explicit likes, recency, and global popularity; that will determine whether to emphasize a repeat model, collaborative discovery, or both. ### exec · 2026-09-25T00:55:21.379Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd, numpy as np, gc, time\\nP='/task/data'; cutoff=24786800\\nT=set(pd.read_parquet(P+'/target_users.parquet',columns=['uid']).uid.astype(int))\\ncat=pd.read_parquet(P+'/candidate_catalog.parquet')\\ncatset=set(cat.item_id.astype(int)); prank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\\nz=np.load(P+'/validation_eligible_candidates.npz'); uidrow={int(x):i for i,x in enumerate(z['uids'])}; itcol={int(x):i for i,x in enumerate(z['item_ids'])}\\ndef eligible(u,i):\\n c=itcol.get(int(i)); r=uidrow.get(int(u)); return r is not None and c is not None and ((int(z['packed_eligible'][r,c//8])>>(c%8))&1)\\nvl=pd.read_parquet(P+'/val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\\nvg=vl[(vl.uid.isin(T))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); vg['grade']=1\\nvlike=pd.read_parquet(P+'/val_likes.parquet',columns=['uid','item_id','is_organic']); q=vlike[(vlike.uid.isin(T))&(vlike.is_organic==1)][['uid','item_id']].drop_duplicates(); q['grade']=3\\ntruth=pd.concat([vg,q]).groupby(['uid','item_id'],as_index=False).grade.max(); truth=truth[[eligible(u,i) for u,i in truth[['uid','item_id']].itertuples(index=False,name=None)]].copy()\\nprint('truth pairs/users/grade',len(truth),truth.uid.nunique(),truth.grade.value_counts().to_dict())\\ntruth['poprank']=truth.item_id.map(prank)\\nprint('poprank quantiles',truth.poprank.quantile([0,.1,.25,.5,.75,.9,1]).to_dict(),'missing',truth.poprank.isna().sum())\\nprint('labels/user quantiles',truth.groupby('uid').size().quantile([0,.25,.5,.75,.9,.99,1]).to_dict())\\nps=pd.read_parquet(P+'/validation_preference_state.parquet'); truth=truth.merge(ps.assign(in_state=1),on=['uid','item_id'],how='left')\\nprint('truth active pref',truth.groupby(['grade','state'],dropna=False).size())\\n# eligibility known state percentages\\nps['eligible']=[eligible(u,i) for u,i in ps[['uid','item_id']].itertuples(index=False,name=None)]\\nprint('preference eligible by state',ps.groupby('state').eligible.agg(['mean','sum','count']))\\nprint('load train relevant joins')\\ntl=pd.read_parquet(P+'/train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\\n# restrict target+catalog first\\nx=tl[tl.uid.isin(T)&tl.item_id.isin(catset)].copy(); del tl; gc.collect(); print('candidate train events',len(x))\\nx['pos50']=x.played_ratio_pct>=50; x['strong']=x.played_ratio_pct>=80; x['orgstrong']=(x.is_organic==1)&x.strong\\n# aggregate only truth pair merge events for repeat diagnostic\\nte=x.merge(truth[['uid','item_id','grade']],on=['uid','item_id'],how='inner')\\nagg=te.groupby(['uid','item_id','grade']).agg(n=('timestamp','size'),pos=('pos50','sum'),strong=('strong','sum'),org=('is_organic','sum'),orgstrong=('orgstrong','sum'),last=('timestamp','max')).reset_index(); truth2=truth[['uid','item_id','grade','poprank']].merge(agg,on=['uid','item_id','grade'],how='left').fillna({'n':0,'pos':0,'strong':0,'org':0,'orgstrong':0,'last':0})\\nfor col in ['n','pos','strong','org','orgstrong']:\\n print('repeat',col,truth2.groupby('grade')[col].apply(lambda s:pd.Series({'any':(s>0).mean(),'>=2':(s>=2).mean(),'>=5':(s>=5).mean(),'median':s.median()})).unstack().to_dict('index'))\\nfor days in [1,3,7,14,30,60,90,180,300]:\\n print('last within',days,'days',truth2.groupby('grade')['last'].apply(lambda s:(s>=cutoff-days*86400).mean()).to_dict())\\n# all grade-weighted relevance capture union prior and popular top K\\nfor k in [10,30,100,300,1000,3000,10000]:\\n print('pop<=',k,truth2.groupby('grade').poprank.apply(lambda s:(s<=k).mean()).to_dict())\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:20000});\nif(r.session_id){text(`SESSION_ID=${r.session_id}`);text(r.output);}else{text(r.output);}\n" } ### Tool output · 2026-09-25T00:55:51.558Z Script completed Wall time 30.2 seconds Output: SESSION_ID=16403 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "response_id": "resp_0fdbf3e20a3ba82b016ab5c64f5ad887d29823c30142bbb426", "usage": { "input_tokens": 23169, "cached_input_tokens": 19968, "cache_write_input_tokens": 0, "output_tokens": 2276, "reasoning_output_tokens": 1006, "total_tokens": 25445 }, "turn_token_usage": { "input_tokens": 113886, "cached_input_tokens": 100992, "cache_write_input_tokens": 0, "output_tokens": 3765, "reasoning_output_tokens": 1458, "total_tokens": 117651 }, "thread_token_usage": { "input_tokens": 113886, "cached_input_tokens": 100992, "cache_write_input_tokens": 0, "output_tokens": 3765, "reasoning_output_tokens": 1458, "total_tokens": 117651 } } ## Step 14 · agent · 2026-09-25T00:55:54.051Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:55:54.727Z { "code": "const r = await tools.write_stdin({session_id:16403,chars:\"\",yield_time_ms:30000,max_output_tokens:20000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:56:24.735Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "response_id": "resp_0fdbf3e20a3ba82b016ab5c697c00087d2a8ec785f697abd5f", "usage": { "input_tokens": 25473, "cached_input_tokens": 23040, "cache_write_input_tokens": 0, "output_tokens": 61, "reasoning_output_tokens": 14, "total_tokens": 25534 }, "turn_token_usage": { "input_tokens": 139359, "cached_input_tokens": 124032, "cache_write_input_tokens": 0, "output_tokens": 3826, "reasoning_output_tokens": 1472, "total_tokens": 143185 }, "thread_token_usage": { "input_tokens": 139359, "cached_input_tokens": 124032, "cache_write_input_tokens": 0, "output_tokens": 3826, "reasoning_output_tokens": 1472, "total_tokens": 143185 } } ## Step 15 · agent · 2026-09-25T00:56:26.659Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:56:27.379Z { "code": "const r = await tools.write_stdin({session_id:16403,chars:\"\",yield_time_ms:30000,max_output_tokens:20000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:56:57.388Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "response_id": "resp_0fdbf3e20a3ba82b016ab5c6b920b887d288a10db109a14f96", "usage": { "input_tokens": 25557, "cached_input_tokens": 25344, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 8, "total_tokens": 25612 }, "turn_token_usage": { "input_tokens": 164916, "cached_input_tokens": 149376, "cache_write_input_tokens": 0, "output_tokens": 3881, "reasoning_output_tokens": 1480, "total_tokens": 168797 }, "thread_token_usage": { "input_tokens": 164916, "cached_input_tokens": 149376, "cache_write_input_tokens": 0, "output_tokens": 3881, "reasoning_output_tokens": 1480, "total_tokens": 168797 } } ## Step 16 · agent · 2026-09-25T00:56:58.914Z ### Thinking unavailable — no text recorded The full listen log is large enough that the repeat-history join is still running. While it completes, the key validation labels are already established in memory; next I’ll turn the strongest signals into a valid baseline immediately, then layer in collaborative and temporal components only where they improve NDCG@10. ### exec · 2026-09-25T00:57:00.885Z { "code": "const r = await tools.write_stdin({session_id:16403,chars:\"\",yield_time_ms:30000,max_output_tokens:20000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:57:30.893Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "response_id": "resp_0fdbf3e20a3ba82b016ab5c6d99b3887d2b4a08775deade502", "usage": { "input_tokens": 25635, "cached_input_tokens": 25344, "cache_write_input_tokens": 0, "output_tokens": 138, "reasoning_output_tokens": 24, "total_tokens": 25773 }, "turn_token_usage": { "input_tokens": 190551, "cached_input_tokens": 174720, "cache_write_input_tokens": 0, "output_tokens": 4019, "reasoning_output_tokens": 1504, "total_tokens": 194570 }, "thread_token_usage": { "input_tokens": 190551, "cached_input_tokens": 174720, "cache_write_input_tokens": 0, "output_tokens": 4019, "reasoning_output_tokens": 1504, "total_tokens": 194570 } } ## Step 17 · agent · 2026-09-25T00:57:33.386Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:57:34.060Z { "code": "const r = await tools.write_stdin({session_id:16403,chars:\"\",yield_time_ms:30000,max_output_tokens:20000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:58:04.068Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "response_id": "resp_0fdbf3e20a3ba82b016ab5c6fb0f9087d28f4a5e13dcb09c0e", "usage": { "input_tokens": 25796, "cached_input_tokens": 25472, "cache_write_input_tokens": 0, "output_tokens": 69, "reasoning_output_tokens": 22, "total_tokens": 25865 }, "turn_token_usage": { "input_tokens": 216347, "cached_input_tokens": 200192, "cache_write_input_tokens": 0, "output_tokens": 4088, "reasoning_output_tokens": 1526, "total_tokens": 220435 }, "thread_token_usage": { "input_tokens": 216347, "cached_input_tokens": 200192, "cache_write_input_tokens": 0, "output_tokens": 4088, "reasoning_output_tokens": 1526, "total_tokens": 220435 } } ## Step 18 · agent · 2026-09-25T00:58:06.262Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:58:07.557Z { "code": "const r = await tools.exec_command({cmd:\"ps -o pid,etime,%cpu,%mem,rss,vsz,stat,cmd -C python -C python3 | sed -n '1,20p'; free -h\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:5000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:58:07.629Z Script completed Wall time 0.0 seconds Output: /bin/sh: 1: ps: not found /bin/sh: 1: free: not found ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b71da5c5-2fde-4f47-831d-bbd0f387cc88", "process_id": "52839", "command": [ "/bin/sh", "-lc", "ps -o pid,etime,%cpu,%mem,rss,vsz,stat,cmd -C python -C python3 | sed -n '1,20p'; free -h" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ps -o pid,etime,%cpu,%mem,rss,vsz,stat,cmd -C python -C python3 | sed -n '1,20p'; free -h" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "/bin/sh: 1: ps: not found\n/bin/sh: 1: free: not found\n", "stderr": "", "aggregated_output": "/bin/sh: 1: ps: not found\n/bin/sh: 1: free: not found\n", "exit_code": 127, "duration": { "secs": 0, "nanos": 3960 }, "formatted_output": "/bin/sh: 1: ps: not found\n/bin/sh: 1: free: not found\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "response_id": "resp_0fdbf3e20a3ba82b016ab5c71c3e3087d2bd847fb73d6cf71d", "usage": { "input_tokens": 25888, "cached_input_tokens": 25600, "cache_write_input_tokens": 0, "output_tokens": 102, "reasoning_output_tokens": 18, "total_tokens": 25990 }, "turn_token_usage": { "input_tokens": 242235, "cached_input_tokens": 225792, "cache_write_input_tokens": 0, "output_tokens": 4190, "reasoning_output_tokens": 1544, "total_tokens": 246425 }, "thread_token_usage": { "input_tokens": 242235, "cached_input_tokens": 225792, "cache_write_input_tokens": 0, "output_tokens": 4190, "reasoning_output_tokens": 1544, "total_tokens": 246425 } } ## Step 19 · agent · 2026-09-25T00:58:09.856Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:58:10.544Z { "code": "const r = await tools.write_stdin({session_id:16403,chars:\"\",yield_time_ms:30000,max_output_tokens:20000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:58:40.551Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "response_id": "resp_0fdbf3e20a3ba82b016ab5c71febf887d2a873b0adf6244ddc", "usage": { "input_tokens": 26035, "cached_input_tokens": 25728, "cache_write_input_tokens": 0, "output_tokens": 56, "reasoning_output_tokens": 9, "total_tokens": 26091 }, "turn_token_usage": { "input_tokens": 268270, "cached_input_tokens": 251520, "cache_write_input_tokens": 0, "output_tokens": 4246, "reasoning_output_tokens": 1553, "total_tokens": 272516 }, "thread_token_usage": { "input_tokens": 268270, "cached_input_tokens": 251520, "cache_write_input_tokens": 0, "output_tokens": 4246, "reasoning_output_tokens": 1553, "total_tokens": 272516 } } ## Step 20 · agent · 2026-09-25T00:58:43.324Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:58:44.005Z { "code": "const r = await tools.write_stdin({session_id:16403,chars:\"\",yield_time_ms:30000,max_output_tokens:20000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:59:14.014Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "response_id": "resp_0fdbf3e20a3ba82b016ab5c740cbc887d285d6eb984f131f78", "usage": { "input_tokens": 26114, "cached_input_tokens": 25856, "cache_write_input_tokens": 0, "output_tokens": 61, "reasoning_output_tokens": 14, "total_tokens": 26175 }, "turn_token_usage": { "input_tokens": 294384, "cached_input_tokens": 277376, "cache_write_input_tokens": 0, "output_tokens": 4307, "reasoning_output_tokens": 1567, "total_tokens": 298691 }, "thread_token_usage": { "input_tokens": 294384, "cached_input_tokens": 277376, "cache_write_input_tokens": 0, "output_tokens": 4307, "reasoning_output_tokens": 1567, "total_tokens": 298691 } } ## Step 21 · agent · 2026-09-25T00:59:16.014Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:59:16.773Z { "code": "const r = await tools.write_stdin({session_id:16403,chars:\"\\u0003\",yield_time_ms:1000,max_output_tokens:20000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T00:59:16.850Z Script completed Wall time 0.1 seconds Output: Traceback (most recent call last): File "", line 12, in File "", line 8, in eligible 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 1098, in _read1 self._update_crc(data) File "/usr/local/lib/python3.12/zipfile/__init__.py", line 1023, in _update_crc self._running_crc = crc32(newdata, self._running_crc) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ KeyboardInterrupt ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "response_id": "resp_0fdbf3e20a3ba82b016ab5c7623c1087d28617413059fb6fcb", "usage": { "input_tokens": 26198, "cached_input_tokens": 25984, "cache_write_input_tokens": 0, "output_tokens": 93, "reasoning_output_tokens": 42, "total_tokens": 26291 }, "turn_token_usage": { "input_tokens": 320582, "cached_input_tokens": 303360, "cache_write_input_tokens": 0, "output_tokens": 4400, "reasoning_output_tokens": 1609, "total_tokens": 324982 }, "thread_token_usage": { "input_tokens": 320582, "cached_input_tokens": 303360, "cache_write_input_tokens": 0, "output_tokens": 4400, "reasoning_output_tokens": 1609, "total_tokens": 324982 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-3ae11c2b-b5e7-4f8e-943b-d74f8f9f612a", "process_id": "16403", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np, gc, time\nP='/task/data'; cutoff=24786800\nT=set(pd.read_parquet(P+'/target_users.parquet',columns=['uid']).uid.astype(int))\ncat=pd.read_parquet(P+'/candidate_catalog.parquet')\ncatset=set(cat.item_id.astype(int)); prank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\nz=np.load(P+'/validation_eligible_candidates.npz'); uidrow={int(x):i for i,x in enumerate(z['uids'])}; itcol={int(x):i for i,x in enumerate(z['item_ids'])}\ndef eligible(u,i):\n c=itcol.get(int(i)); r=uidrow.get(int(u)); return r is not None and c is not None and ((int(z['packed_eligible'][r,c//8])>>(c%8))&1)\nvl=pd.read_parquet(P+'/val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nvg=vl[(vl.uid.isin(T))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); vg['grade']=1\nvlike=pd.read_parquet(P+'/val_likes.parquet',columns=['uid','item_id','is_organic']); q=vlike[(vlike.uid.isin(T))&(vlike.is_organic==1)][['uid','item_id']].drop_duplicates(); q['grade']=3\ntruth=pd.concat([vg,q]).groupby(['uid','item_id'],as_index=False).grade.max(); truth=truth[[eligible(u,i) for u,i in truth[['uid','item_id']].itertuples(index=False,name=None)]].copy()\nprint('truth pairs/users/grade',len(truth),truth.uid.nunique(),truth.grade.value_counts().to_dict())\ntruth['poprank']=truth.item_id.map(prank)\nprint('poprank quantiles',truth.poprank.quantile([0,.1,.25,.5,.75,.9,1]).to_dict(),'missing',truth.poprank.isna().sum())\nprint('labels/user quantiles',truth.groupby('uid').size().quantile([0,.25,.5,.75,.9,.99,1]).to_dict())\nps=pd.read_parquet(P+'/validation_preference_state.parquet'); truth=truth.merge(ps.assign(in_state=1),on=['uid','item_id'],how='left')\nprint('truth active pref',truth.groupby(['grade','state'],dropna=False).size())\n# eligibility known state percentages\nps['eligible']=[eligible(u,i) for u,i in ps[['uid','item_id']].itertuples(index=False,name=None)]\nprint('preference eligible by state',ps.groupby('state').eligible.agg(['mean','sum','count']))\nprint('load train relevant joins')\ntl=pd.read_parquet(P+'/train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\n# restrict target+catalog first\nx=tl[tl.uid.isin(T)&tl.item_id.isin(catset)].copy(); del tl; gc.collect(); print('candidate train events',len(x))\nx['pos50']=x.played_ratio_pct>=50; x['strong']=x.played_ratio_pct>=80; x['orgstrong']=(x.is_organic==1)&x.strong\n# aggregate only truth pair merge events for repeat diagnostic\nte=x.merge(truth[['uid','item_id','grade']],on=['uid','item_id'],how='inner')\nagg=te.groupby(['uid','item_id','grade']).agg(n=('timestamp','size'),pos=('pos50','sum'),strong=('strong','sum'),org=('is_organic','sum'),orgstrong=('orgstrong','sum'),last=('timestamp','max')).reset_index(); truth2=truth[['uid','item_id','grade','poprank']].merge(agg,on=['uid','item_id','grade'],how='left').fillna({'n':0,'pos':0,'strong':0,'org':0,'orgstrong':0,'last':0})\nfor col in ['n','pos','strong','org','orgstrong']:\n print('repeat',col,truth2.groupby('grade')[col].apply(lambda s:pd.Series({'any':(s>0).mean(),'>=2':(s>=2).mean(),'>=5':(s>=5).mean(),'median':s.median()})).unstack().to_dict('index'))\nfor days in [1,3,7,14,30,60,90,180,300]:\n print('last within',days,'days',truth2.groupby('grade')['last'].apply(lambda s:(s>=cutoff-days*86400).mean()).to_dict())\n# all grade-weighted relevance capture union prior and popular top K\nfor k in [10,30,100,300,1000,3000,10000]:\n print('pop<=',k,truth2.groupby('grade').poprank.apply(lambda s:(s<=k).mean()).to_dict())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np, gc, time\nP='/task/data'; cutoff=24786800\nT=set(pd.read_parquet(P+'/target_users.parquet',columns=['uid']).uid.astype(int))\ncat=pd.read_parquet(P+'/candidate_catalog.parquet')\ncatset=set(cat.item_id.astype(int)); prank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\nz=np.load(P+'/validation_eligible_candidates.npz'); uidrow={int(x):i for i,x in enumerate(z['uids'])}; itcol={int(x):i for i,x in enumerate(z['item_ids'])}\ndef eligible(u,i):\n c=itcol.get(int(i)); r=uidrow.get(int(u)); return r is not None and c is not None and ((int(z['packed_eligible'][r,c//8])>>(c%8))&1)\nvl=pd.read_parquet(P+'/val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nvg=vl[(vl.uid.isin(T))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); vg['grade']=1\nvlike=pd.read_parquet(P+'/val_likes.parquet',columns=['uid','item_id','is_organic']); q=vlike[(vlike.uid.isin(T))&(vlike.is_organic==1)][['uid','item_id']].drop_duplicates(); q['grade']=3\ntruth=pd.concat([vg,q]).groupby(['uid','item_id'],as_index=False).grade.max(); truth=truth[[eligible(u,i) for u,i in truth[['uid','item_id']].itertuples(index=False,name=None)]].copy()\nprint('truth pairs/users/grade',len(truth),truth.uid.nunique(),truth.grade.value_counts().to_dict())\ntruth['poprank']=truth.item_id.map(prank)\nprint('poprank quantiles',truth.poprank.quantile([0,.1,.25,.5,.75,.9,1]).to_dict(),'missing',truth.poprank.isna().sum())\nprint('labels/user quantiles',truth.groupby('uid').size().quantile([0,.25,.5,.75,.9,.99,1]).to_dict())\nps=pd.read_parquet(P+'/validation_preference_state.parquet'); truth=truth.merge(ps.assign(in_state=1),on=['uid','item_id'],how='left')\nprint('truth active pref',truth.groupby(['grade','state'],dropna=False).size())\n# eligibility known state percentages\nps['eligible']=[eligible(u,i) for u,i in ps[['uid','item_id']].itertuples(index=False,name=None)]\nprint('preference eligible by state',ps.groupby('state').eligible.agg(['mean','sum','count']))\nprint('load train relevant joins')\ntl=pd.read_parquet(P+'/train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\n# restrict target+catalog first\nx=tl[tl.uid.isin(T)&tl.item_id.isin(catset)].copy(); del tl; gc.collect(); print('candidate train events',len(x))\nx['pos50']=x.played_ratio_pct>=50; x['strong']=x.played_ratio_pct>=80; x['orgstrong']=(x.is_organic==1)&x.strong\n# aggregate only truth pair merge events for repeat diagnostic\nte=x.merge(truth[['uid','item_id','grade']],on=['uid','item_id'],how='inner')\nagg=te.groupby(['uid','item_id','grade']).agg(n=('timestamp','size'),pos=('pos50','sum'),strong=('strong','sum'),org=('is_organic','sum'),orgstrong=('orgstrong','sum'),last=('timestamp','max')).reset_index(); truth2=truth[['uid','item_id','grade','poprank']].merge(agg,on=['uid','item_id','grade'],how='left').fillna({'n':0,'pos':0,'strong':0,'org':0,'orgstrong':0,'last':0})\nfor col in ['n','pos','strong','org','orgstrong']:\n print('repeat',col,truth2.groupby('grade')[col].apply(lambda s:pd.Series({'any':(s>0).mean(),'>=2':(s>=2).mean(),'>=5':(s>=5).mean(),'median':s.median()})).unstack().to_dict('index'))\nfor days in [1,3,7,14,30,60,90,180,300]:\n print('last within',days,'days',truth2.groupby('grade')['last'].apply(lambda s:(s>=cutoff-days*86400).mean()).to_dict())\n# all grade-weighted relevance capture union prior and popular top K\nfor k in [10,30,100,300,1000,3000,10000]:\n print('pop<=',k,truth2.groupby('grade').poprank.apply(lambda s:(s<=k).mean()).to_dict())\nPY" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "Traceback (most recent call last):\n File \"\", line 12, in \n File \"\", line 8, in eligible\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 1098, in _read1\n self._update_crc(data)\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1023, in _update_crc\n self._running_crc = crc32(newdata, self._running_crc)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nKeyboardInterrupt\n", "stderr": "", "aggregated_output": "Traceback (most recent call last):\n File \"\", line 12, in \n File \"\", line 8, in eligible\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 1098, in _read1\n self._update_crc(data)\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1023, in _update_crc\n self._running_crc = crc32(newdata, self._running_crc)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nKeyboardInterrupt\n", "exit_code": 130, "duration": { "secs": 235, "nanos": 291883895 }, "formatted_output": "Traceback (most recent call last):\n File \"\", line 12, in \n File \"\", line 8, in eligible\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 1098, in _read1\n self._update_crc(data)\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1023, in _update_crc\n self._running_crc = crc32(newdata, self._running_crc)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nKeyboardInterrupt\n" } } ## Step 22 · agent · 2026-09-25T00:59:20.718Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T00:59:41.920Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u - <<'PY'\\nimport pandas as pd, numpy as np, gc, time\\nP='/task/data'; cutoff=24786800\\ntarget=pd.read_parquet(P+'/target_users.parquet',columns=['uid']).uid.to_numpy(np.uint32); T=set(map(int,target))\\ncat=pd.read_parquet(P+'/candidate_catalog.parquet'); prank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\\nwith np.load(P+'/validation_eligible_candidates.npz') as z:\\n zu=z['uids'].copy(); zi=z['item_ids'].copy(); packed=z['packed_eligible'].copy()\\nuidrow={int(x):i for i,x in enumerate(zu)}; itcol={int(x):i for i,x in enumerate(zi)}\\ndef eligvec(df):\\n rr=np.array([uidrow.get(int(x),-1) for x in df.uid],dtype=np.int32); cc=np.array([itcol.get(int(x),-1) for x in df.item_id],dtype=np.int32); ok=(rr>=0)&(cc>=0); out=np.zeros(len(df),bool); j=np.flatnonzero(ok); out[j]=((packed[rr[j],cc[j]//8]>>(cc[j]%8))&1).astype(bool); return out\\nvl=pd.read_parquet(P+'/val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\\nvg=vl[(vl.uid.isin(T))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); vg['grade']=1\\nvlike=pd.read_parquet(P+'/val_likes.parquet',columns=['uid','item_id','is_organic']); q=vlike[(vlike.uid.isin(T))&(vlike.is_organic==1)][['uid','item_id']].drop_duplicates(); q['grade']=3\\ntruth=pd.concat([vg,q]).groupby(['uid','item_id'],as_index=False).grade.max(); truth=truth[eligvec(truth)].copy()\\nprint('truth pairs/users/grade',len(truth),truth.uid.nunique(),truth.grade.value_counts().to_dict(),flush=True)\\ntruth['poprank']=truth.item_id.map(prank)\\nprint('poprank quantiles',truth.poprank.quantile([0,.1,.25,.5,.75,.9,1]).to_dict(),'missing',truth.poprank.isna().sum(),flush=True)\\nprint('labels/user quantiles',truth.groupby('uid').size().quantile([0,.25,.5,.75,.9,.99,1]).to_dict(),flush=True)\\nps=pd.read_parquet(P+'/validation_preference_state.parquet'); truth=truth.merge(ps.assign(in_state=1),on=['uid','item_id'],how='left')\\nprint('truth active pref\\\\n',truth.groupby(['grade','state'],dropna=False).size(),flush=True)\\nps['eligible']=eligvec(ps)\\nprint('preference eligible by state\\\\n',ps.groupby('state').eligible.agg(['mean','sum','count']),flush=True)\\nprint('load train relevant joins',flush=True)\\ntl=pd.read_parquet(P+'/train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\\nprint('train loaded',len(tl),flush=True)\\nte=tl.merge(truth[['uid','item_id','grade']],on=['uid','item_id'],how='inner'); del tl; gc.collect(); print('matched events',len(te),flush=True)\\nte['pos']=te.played_ratio_pct>=50; te['strong']=te.played_ratio_pct>=80; te['orgstrong']=(te.is_organic==1)&te.strong\\nagg=te.groupby(['uid','item_id','grade']).agg(n=('timestamp','size'),pos=('pos','sum'),strong=('strong','sum'),org=('is_organic','sum'),orgstrong=('orgstrong','sum'),last=('timestamp','max')).reset_index(); truth2=truth[['uid','item_id','grade','poprank']].merge(agg,on=['uid','item_id','grade'],how='left').fillna({'n':0,'pos':0,'strong':0,'org':0,'orgstrong':0,'last':0})\\nfor col in ['n','pos','strong','org','orgstrong']:\\n print('repeat',col,truth2.groupby('grade')[col].apply(lambda s:pd.Series({'any':(s>0).mean(),'>=2':(s>=2).mean(),'>=5':(s>=5).mean(),'median':s.median()})).unstack().to_dict('index'),flush=True)\\nfor days in [1,3,7,14,30,60,90,180,300]: print('last within',days,'days',truth2.groupby('grade')['last'].apply(lambda s:(s>=cutoff-days*86400).mean()).to_dict(),flush=True)\\nfor k in [10,30,100,300,1000,3000,10000]: print('pop<=',k,truth2.groupby('grade').poprank.apply(lambda s:(s<=k).mean()).to_dict(),flush=True)\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:20000});\nif(r.session_id){text(`SESSION_ID=${r.session_id}`);text(r.output);}else{text(r.output);}\n" } ### Tool output · 2026-09-25T00:59:48.279Z Script completed Wall time 6.4 seconds Output: truth pairs/users/grade 37769 4263 {1: 33352, 3: 4417} poprank quantiles {0.0: 1.0, 0.1: 428.0, 0.25: 1733.0, 0.5: 5622.0, 0.75: 13374.0, 0.9: 21680.200000000004, 1.0: 30000.0} missing 0 labels/user quantiles {0.0: 1.0, 0.25: 2.0, 0.5: 4.0, 0.75: 10.0, 0.9: 21.0, 0.99: 63.0, 1.0: 442.0} truth active pref grade state 1 NaN 33352 3 NaN 4417 dtype: int64 preference eligible by state mean sum count state disliked 0.0 0 50689 liked 0.0 0 418435 load train relevant joins train loaded 43484159 matched events 0 repeat n {1: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}, 3: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}} repeat pos {1: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}, 3: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}} repeat strong {1: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}, 3: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}} repeat org {1: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}, 3: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}} repeat orgstrong {1: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}, 3: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}} last within 1 days {1: 0.0, 3: 0.0} last within 3 days {1: 0.0, 3: 0.0} last within 7 days {1: 0.0, 3: 0.0} last within 14 days {1: 0.0, 3: 0.0} last within 30 days {1: 0.0, 3: 0.0} last within 60 days {1: 0.0, 3: 0.0} last within 90 days {1: 0.0, 3: 0.0} last within 180 days {1: 0.0, 3: 0.0} last within 300 days {1: 1.0, 3: 1.0} pop<= 10 {1: 0.00509714559846486, 3: 0.004527960153950645} pop<= 30 {1: 0.013042696090189494, 3: 0.00996151233869142} pop<= 100 {1: 0.03307147997121612, 3: 0.022866198777450757} pop<= 300 {1: 0.07897577356680259, 3: 0.052524337785827484} pop<= 1000 {1: 0.17947949148476852, 3: 0.14421553090332806} pop<= 3000 {1: 0.35521108179419525, 3: 0.3205795788997057} pop<= 10000 {1: 0.6623290957064044, 3: 0.6361784016300657} ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "response_id": "resp_0fdbf3e20a3ba82b016ab5c76515ac87d2b11a5d6b83817df4", "usage": { "input_tokens": 26630, "cached_input_tokens": 25984, "cache_write_input_tokens": 0, "output_tokens": 1309, "reasoning_output_tokens": 105, "total_tokens": 27939 }, "turn_token_usage": { "input_tokens": 347212, "cached_input_tokens": 329344, "cache_write_input_tokens": 0, "output_tokens": 5709, "reasoning_output_tokens": 1714, "total_tokens": 352921 }, "thread_token_usage": { "input_tokens": 347212, "cached_input_tokens": 329344, "cache_write_input_tokens": 0, "output_tokens": 5709, "reasoning_output_tokens": 1714, "total_tokens": 352921 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-500ef9f0-47fb-45e0-a584-393d51575b1c", "process_id": "82035", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u - <<'PY'\nimport pandas as pd, numpy as np, gc, time\nP='/task/data'; cutoff=24786800\ntarget=pd.read_parquet(P+'/target_users.parquet',columns=['uid']).uid.to_numpy(np.uint32); T=set(map(int,target))\ncat=pd.read_parquet(P+'/candidate_catalog.parquet'); prank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\nwith np.load(P+'/validation_eligible_candidates.npz') as z:\n zu=z['uids'].copy(); zi=z['item_ids'].copy(); packed=z['packed_eligible'].copy()\nuidrow={int(x):i for i,x in enumerate(zu)}; itcol={int(x):i for i,x in enumerate(zi)}\ndef eligvec(df):\n rr=np.array([uidrow.get(int(x),-1) for x in df.uid],dtype=np.int32); cc=np.array([itcol.get(int(x),-1) for x in df.item_id],dtype=np.int32); ok=(rr>=0)&(cc>=0); out=np.zeros(len(df),bool); j=np.flatnonzero(ok); out[j]=((packed[rr[j],cc[j]//8]>>(cc[j]%8))&1).astype(bool); return out\nvl=pd.read_parquet(P+'/val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nvg=vl[(vl.uid.isin(T))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); vg['grade']=1\nvlike=pd.read_parquet(P+'/val_likes.parquet',columns=['uid','item_id','is_organic']); q=vlike[(vlike.uid.isin(T))&(vlike.is_organic==1)][['uid','item_id']].drop_duplicates(); q['grade']=3\ntruth=pd.concat([vg,q]).groupby(['uid','item_id'],as_index=False).grade.max(); truth=truth[eligvec(truth)].copy()\nprint('truth pairs/users/grade',len(truth),truth.uid.nunique(),truth.grade.value_counts().to_dict(),flush=True)\ntruth['poprank']=truth.item_id.map(prank)\nprint('poprank quantiles',truth.poprank.quantile([0,.1,.25,.5,.75,.9,1]).to_dict(),'missing',truth.poprank.isna().sum(),flush=True)\nprint('labels/user quantiles',truth.groupby('uid').size().quantile([0,.25,.5,.75,.9,.99,1]).to_dict(),flush=True)\nps=pd.read_parquet(P+'/validation_preference_state.parquet'); truth=truth.merge(ps.assign(in_state=1),on=['uid','item_id'],how='left')\nprint('truth active pref\\n',truth.groupby(['grade','state'],dropna=False).size(),flush=True)\nps['eligible']=eligvec(ps)\nprint('preference eligible by state\\n',ps.groupby('state').eligible.agg(['mean','sum','count']),flush=True)\nprint('load train relevant joins',flush=True)\ntl=pd.read_parquet(P+'/train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\nprint('train loaded',len(tl),flush=True)\nte=tl.merge(truth[['uid','item_id','grade']],on=['uid','item_id'],how='inner'); del tl; gc.collect(); print('matched events',len(te),flush=True)\nte['pos']=te.played_ratio_pct>=50; te['strong']=te.played_ratio_pct>=80; te['orgstrong']=(te.is_organic==1)&te.strong\nagg=te.groupby(['uid','item_id','grade']).agg(n=('timestamp','size'),pos=('pos','sum'),strong=('strong','sum'),org=('is_organic','sum'),orgstrong=('orgstrong','sum'),last=('timestamp','max')).reset_index(); truth2=truth[['uid','item_id','grade','poprank']].merge(agg,on=['uid','item_id','grade'],how='left').fillna({'n':0,'pos':0,'strong':0,'org':0,'orgstrong':0,'last':0})\nfor col in ['n','pos','strong','org','orgstrong']:\n print('repeat',col,truth2.groupby('grade')[col].apply(lambda s:pd.Series({'any':(s>0).mean(),'>=2':(s>=2).mean(),'>=5':(s>=5).mean(),'median':s.median()})).unstack().to_dict('index'),flush=True)\nfor days in [1,3,7,14,30,60,90,180,300]: print('last within',days,'days',truth2.groupby('grade')['last'].apply(lambda s:(s>=cutoff-days*86400).mean()).to_dict(),flush=True)\nfor k in [10,30,100,300,1000,3000,10000]: print('pop<=',k,truth2.groupby('grade').poprank.apply(lambda s:(s<=k).mean()).to_dict(),flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport pandas as pd, numpy as np, gc, time\nP='/task/data'; cutoff=24786800\ntarget=pd.read_parquet(P+'/target_users.parquet',columns=['uid']).uid.to_numpy(np.uint32); T=set(map(int,target))\ncat=pd.read_parquet(P+'/candidate_catalog.parquet'); prank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\nwith np.load(P+'/validation_eligible_candidates.npz') as z:\n zu=z['uids'].copy(); zi=z['item_ids'].copy(); packed=z['packed_eligible'].copy()\nuidrow={int(x):i for i,x in enumerate(zu)}; itcol={int(x):i for i,x in enumerate(zi)}\ndef eligvec(df):\n rr=np.array([uidrow.get(int(x),-1) for x in df.uid],dtype=np.int32); cc=np.array([itcol.get(int(x),-1) for x in df.item_id],dtype=np.int32); ok=(rr>=0)&(cc>=0); out=np.zeros(len(df),bool); j=np.flatnonzero(ok); out[j]=((packed[rr[j],cc[j]//8]>>(cc[j]%8))&1).astype(bool); return out\nvl=pd.read_parquet(P+'/val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nvg=vl[(vl.uid.isin(T))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); vg['grade']=1\nvlike=pd.read_parquet(P+'/val_likes.parquet',columns=['uid','item_id','is_organic']); q=vlike[(vlike.uid.isin(T))&(vlike.is_organic==1)][['uid','item_id']].drop_duplicates(); q['grade']=3\ntruth=pd.concat([vg,q]).groupby(['uid','item_id'],as_index=False).grade.max(); truth=truth[eligvec(truth)].copy()\nprint('truth pairs/users/grade',len(truth),truth.uid.nunique(),truth.grade.value_counts().to_dict(),flush=True)\ntruth['poprank']=truth.item_id.map(prank)\nprint('poprank quantiles',truth.poprank.quantile([0,.1,.25,.5,.75,.9,1]).to_dict(),'missing',truth.poprank.isna().sum(),flush=True)\nprint('labels/user quantiles',truth.groupby('uid').size().quantile([0,.25,.5,.75,.9,.99,1]).to_dict(),flush=True)\nps=pd.read_parquet(P+'/validation_preference_state.parquet'); truth=truth.merge(ps.assign(in_state=1),on=['uid','item_id'],how='left')\nprint('truth active pref\\n',truth.groupby(['grade','state'],dropna=False).size(),flush=True)\nps['eligible']=eligvec(ps)\nprint('preference eligible by state\\n',ps.groupby('state').eligible.agg(['mean','sum','count']),flush=True)\nprint('load train relevant joins',flush=True)\ntl=pd.read_parquet(P+'/train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\nprint('train loaded',len(tl),flush=True)\nte=tl.merge(truth[['uid','item_id','grade']],on=['uid','item_id'],how='inner'); del tl; gc.collect(); print('matched events',len(te),flush=True)\nte['pos']=te.played_ratio_pct>=50; te['strong']=te.played_ratio_pct>=80; te['orgstrong']=(te.is_organic==1)&te.strong\nagg=te.groupby(['uid','item_id','grade']).agg(n=('timestamp','size'),pos=('pos','sum'),strong=('strong','sum'),org=('is_organic','sum'),orgstrong=('orgstrong','sum'),last=('timestamp','max')).reset_index(); truth2=truth[['uid','item_id','grade','poprank']].merge(agg,on=['uid','item_id','grade'],how='left').fillna({'n':0,'pos':0,'strong':0,'org':0,'orgstrong':0,'last':0})\nfor col in ['n','pos','strong','org','orgstrong']:\n print('repeat',col,truth2.groupby('grade')[col].apply(lambda s:pd.Series({'any':(s>0).mean(),'>=2':(s>=2).mean(),'>=5':(s>=5).mean(),'median':s.median()})).unstack().to_dict('index'),flush=True)\nfor days in [1,3,7,14,30,60,90,180,300]: print('last within',days,'days',truth2.groupby('grade')['last'].apply(lambda s:(s>=cutoff-days*86400).mean()).to_dict(),flush=True)\nfor k in [10,30,100,300,1000,3000,10000]: print('pop<=',k,truth2.groupby('grade').poprank.apply(lambda s:(s<=k).mean()).to_dict(),flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "truth pairs/users/grade 37769 4263 {1: 33352, 3: 4417}\npoprank quantiles {0.0: 1.0, 0.1: 428.0, 0.25: 1733.0, 0.5: 5622.0, 0.75: 13374.0, 0.9: 21680.200000000004, 1.0: 30000.0} missing 0\nlabels/user quantiles {0.0: 1.0, 0.25: 2.0, 0.5: 4.0, 0.75: 10.0, 0.9: 21.0, 0.99: 63.0, 1.0: 442.0}\ntruth active pref\n grade state\n1 NaN 33352\n3 NaN 4417\ndtype: int64\npreference eligible by state\n mean sum count\nstate \ndisliked 0.0 0 50689\nliked 0.0 0 418435\nload train relevant joins\ntrain loaded 43484159\nmatched events 0\nrepeat n {1: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}, 3: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}}\nrepeat pos {1: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}, 3: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}}\nrepeat strong {1: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}, 3: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}}\nrepeat org {1: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}, 3: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}}\nrepeat orgstrong {1: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}, 3: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}}\nlast within 1 days {1: 0.0, 3: 0.0}\nlast within 3 days {1: 0.0, 3: 0.0}\nlast within 7 days {1: 0.0, 3: 0.0}\nlast within 14 days {1: 0.0, 3: 0.0}\nlast within 30 days {1: 0.0, 3: 0.0}\nlast within 60 days {1: 0.0, 3: 0.0}\nlast within 90 days {1: 0.0, 3: 0.0}\nlast within 180 days {1: 0.0, 3: 0.0}\nlast within 300 days {1: 1.0, 3: 1.0}\npop<= 10 {1: 0.00509714559846486, 3: 0.004527960153950645}\npop<= 30 {1: 0.013042696090189494, 3: 0.00996151233869142}\npop<= 100 {1: 0.03307147997121612, 3: 0.022866198777450757}\npop<= 300 {1: 0.07897577356680259, 3: 0.052524337785827484}\npop<= 1000 {1: 0.17947949148476852, 3: 0.14421553090332806}\npop<= 3000 {1: 0.35521108179419525, 3: 0.3205795788997057}\npop<= 10000 {1: 0.6623290957064044, 3: 0.6361784016300657}\n", "stderr": "", "aggregated_output": "truth pairs/users/grade 37769 4263 {1: 33352, 3: 4417}\npoprank quantiles {0.0: 1.0, 0.1: 428.0, 0.25: 1733.0, 0.5: 5622.0, 0.75: 13374.0, 0.9: 21680.200000000004, 1.0: 30000.0} missing 0\nlabels/user quantiles {0.0: 1.0, 0.25: 2.0, 0.5: 4.0, 0.75: 10.0, 0.9: 21.0, 0.99: 63.0, 1.0: 442.0}\ntruth active pref\n grade state\n1 NaN 33352\n3 NaN 4417\ndtype: int64\npreference eligible by state\n mean sum count\nstate \ndisliked 0.0 0 50689\nliked 0.0 0 418435\nload train relevant joins\ntrain loaded 43484159\nmatched events 0\nrepeat n {1: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}, 3: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}}\nrepeat pos {1: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}, 3: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}}\nrepeat strong {1: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}, 3: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}}\nrepeat org {1: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}, 3: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}}\nrepeat orgstrong {1: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}, 3: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}}\nlast within 1 days {1: 0.0, 3: 0.0}\nlast within 3 days {1: 0.0, 3: 0.0}\nlast within 7 days {1: 0.0, 3: 0.0}\nlast within 14 days {1: 0.0, 3: 0.0}\nlast within 30 days {1: 0.0, 3: 0.0}\nlast within 60 days {1: 0.0, 3: 0.0}\nlast within 90 days {1: 0.0, 3: 0.0}\nlast within 180 days {1: 0.0, 3: 0.0}\nlast within 300 days {1: 1.0, 3: 1.0}\npop<= 10 {1: 0.00509714559846486, 3: 0.004527960153950645}\npop<= 30 {1: 0.013042696090189494, 3: 0.00996151233869142}\npop<= 100 {1: 0.03307147997121612, 3: 0.022866198777450757}\npop<= 300 {1: 0.07897577356680259, 3: 0.052524337785827484}\npop<= 1000 {1: 0.17947949148476852, 3: 0.14421553090332806}\npop<= 3000 {1: 0.35521108179419525, 3: 0.3205795788997057}\npop<= 10000 {1: 0.6623290957064044, 3: 0.6361784016300657}\n", "exit_code": 0, "duration": { "secs": 6, "nanos": 191153308 }, "formatted_output": "truth pairs/users/grade 37769 4263 {1: 33352, 3: 4417}\npoprank quantiles {0.0: 1.0, 0.1: 428.0, 0.25: 1733.0, 0.5: 5622.0, 0.75: 13374.0, 0.9: 21680.200000000004, 1.0: 30000.0} missing 0\nlabels/user quantiles {0.0: 1.0, 0.25: 2.0, 0.5: 4.0, 0.75: 10.0, 0.9: 21.0, 0.99: 63.0, 1.0: 442.0}\ntruth active pref\n grade state\n1 NaN 33352\n3 NaN 4417\ndtype: int64\npreference eligible by state\n mean sum count\nstate \ndisliked 0.0 0 50689\nliked 0.0 0 418435\nload train relevant joins\ntrain loaded 43484159\nmatched events 0\nrepeat n {1: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}, 3: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}}\nrepeat pos {1: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}, 3: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}}\nrepeat strong {1: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}, 3: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}}\nrepeat org {1: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}, 3: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}}\nrepeat orgstrong {1: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}, 3: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}}\nlast within 1 days {1: 0.0, 3: 0.0}\nlast within 3 days {1: 0.0, 3: 0.0}\nlast within 7 days {1: 0.0, 3: 0.0}\nlast within 14 days {1: 0.0, 3: 0.0}\nlast within 30 days {1: 0.0, 3: 0.0}\nlast within 60 days {1: 0.0, 3: 0.0}\nlast within 90 days {1: 0.0, 3: 0.0}\nlast within 180 days {1: 0.0, 3: 0.0}\nlast within 300 days {1: 1.0, 3: 1.0}\npop<= 10 {1: 0.00509714559846486, 3: 0.004527960153950645}\npop<= 30 {1: 0.013042696090189494, 3: 0.00996151233869142}\npop<= 100 {1: 0.03307147997121612, 3: 0.022866198777450757}\npop<= 300 {1: 0.07897577356680259, 3: 0.052524337785827484}\npop<= 1000 {1: 0.17947949148476852, 3: 0.14421553090332806}\npop<= 3000 {1: 0.35521108179419525, 3: 0.3205795788997057}\npop<= 10000 {1: 0.6623290957064044, 3: 0.6361784016300657}\n" } } ## Step 23 · agent · 2026-09-25T01:00:02.240Z ### Thinking unavailable — no text recorded ## Step 24 · agent · 2026-09-25T01:00:13.663Z ### Thinking unavailable — no text recorded ## Step 25 · agent · 2026-09-25T01:00:26.036Z ### Thinking unavailable — no text recorded ## Step 26 · agent · 2026-09-25T01:00:32.581Z ### Thinking unavailable — no text recorded A decisive property emerged: validation-relevant user–track pairs have no prior training interaction, and every active liked/disliked pair is ineligible. This is a true discovery task, so repeat/favorite heuristics are useless. I’m saving a popularity fallback now, then focusing on collaborative generalization to unseen tracks and recent organic trends. ### exec · 2026-09-25T01:00:43.359Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/build_ranker.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Build cutoff-correct music discovery rankings.\\n+\\n+The script is intentionally self contained. With no arguments it refits on\\n+train + validation history and writes ``submission.parquet``. ``--stage val``\\n+uses training history only and writes ``validation.parquet``.\\n+\\\"\\\"\\\"\\n+from __future__ import annotations\\n+\\n+import argparse\\n+from pathlib import Path\\n+\\n+import numpy as np\\n+import pandas as pd\\n+\\n+\\n+DATA = Path(\\\"/task/data\\\")\\n+OUT = Path(\\\"/app/solution\\\")\\n+TOP_N = 100\\n+\\n+\\n+def load_candidates(stage: str) -> tuple[np.ndarray, np.ndarray, np.ndarray]:\\n+ path = DATA / (\\\"validation_eligible_candidates.npz\\\" if stage == \\\"val\\\" else \\\"test_eligible_candidates.npz\\\")\\n+ with np.load(path, allow_pickle=False) as z:\\n+ return z[\\\"uids\\\"].copy(), z[\\\"item_ids\\\"].copy(), z[\\\"packed_eligible\\\"].copy()\\n+\\n+\\n+def is_eligible(packed_row: np.ndarray, column: int) -> bool:\\n+ return bool((int(packed_row[column // 8]) >> (column % 8)) & 1)\\n+\\n+\\n+def rank_global(stage: str, ordered_items: np.ndarray) -> pd.DataFrame:\\n+ \\\"\\\"\\\"Apply a global item order while respecting each user's candidate mask.\\\"\\\"\\\"\\n+ uids, candidate_items, packed = load_candidates(stage)\\n+ item_col = {int(item): col for col, item in enumerate(candidate_items)}\\n+ ordered_cols = np.asarray([item_col[int(item)] for item in ordered_items], dtype=np.int32)\\n+ out_uid = np.repeat(uids.astype(np.int64), TOP_N)\\n+ out_item = np.empty(len(uids) * TOP_N, dtype=np.int64)\\n+ for row in range(len(uids)):\\n+ chosen: list[int] = []\\n+ for col in ordered_cols:\\n+ if is_eligible(packed[row], int(col)):\\n+ chosen.append(int(candidate_items[col]))\\n+ if len(chosen) == TOP_N:\\n+ break\\n+ if len(chosen) != TOP_N:\\n+ raise RuntimeError(f\\\"only {len(chosen)} eligible items for uid={uids[row]}\\\")\\n+ out_item[row * TOP_N : (row + 1) * TOP_N] = chosen\\n+ return pd.DataFrame(\\n+ {\\n+ \\\"uid\\\": out_uid,\\n+ \\\"item_id\\\": out_item,\\n+ \\\"rank\\\": np.tile(np.arange(1, TOP_N + 1, dtype=np.int64), len(uids)),\\n+ }\\n+ )\\n+\\n+\\n+def main() -> None:\\n+ parser = argparse.ArgumentParser()\\n+ parser.add_argument(\\\"--stage\\\", choices=(\\\"val\\\", \\\"test\\\"), default=\\\"test\\\")\\n+ args = parser.parse_args()\\n+ catalog = pd.read_parquet(DATA / \\\"candidate_catalog.parquet\\\")\\n+ ranking = rank_global(args.stage, catalog.sort_values(\\\"popularity_rank\\\").item_id.to_numpy())\\n+ path = OUT / (\\\"validation.parquet\\\" if args.stage == \\\"val\\\" else \\\"submission.parquet\\\")\\n+ ranking.to_parquet(path, index=False)\\n+ print(f\\\"wrote {len(ranking):,} rows to {path}\\\")\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** Add File: /app/solution/README.md\\n+# Music-discovery ranker\\n+\\n+Run with the task environment:\\n+\\n+```sh\\n+/opt/task-venv/bin/python /app/solution/build_ranker.py\\n+```\\n+\\n+This refits the selected ranker at the test cutoff and writes\\n+`submission.parquet`. Use `--stage val` to construct the leakage-safe public\\n+validation ranking from training history only.\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-25T01:00:43.405Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-bb736bb9-da32-4d47-8f1a-6ef09c2af4b2", "changes": { "/app/solution/README.md": { "type": "add", "content": "# Music-discovery ranker\n\nRun with the task environment:\n\n```sh\n/opt/task-venv/bin/python /app/solution/build_ranker.py\n```\n\nThis refits the selected ranker at the test cutoff and writes\n`submission.parquet`. Use `--stage val` to construct the leakage-safe public\nvalidation ranking from training history only.\n" }, "/app/solution/build_ranker.py": { "type": "add", "content": "#!/usr/bin/env python3\n\"\"\"Build cutoff-correct music discovery rankings.\n\nThe script is intentionally self contained. With no arguments it refits on\ntrain + validation history and writes ``submission.parquet``. ``--stage val``\nuses training history only and writes ``validation.parquet``.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\nDATA = Path(\"/task/data\")\nOUT = Path(\"/app/solution\")\nTOP_N = 100\n\n\ndef load_candidates(stage: str) -> tuple[np.ndarray, np.ndarray, np.ndarray]:\n path = DATA / (\"validation_eligible_candidates.npz\" if stage == \"val\" else \"test_eligible_candidates.npz\")\n with np.load(path, allow_pickle=False) as z:\n return z[\"uids\"].copy(), z[\"item_ids\"].copy(), z[\"packed_eligible\"].copy()\n\n\ndef is_eligible(packed_row: np.ndarray, column: int) -> bool:\n return bool((int(packed_row[column // 8]) >> (column % 8)) & 1)\n\n\ndef rank_global(stage: str, ordered_items: np.ndarray) -> pd.DataFrame:\n \"\"\"Apply a global item order while respecting each user's candidate mask.\"\"\"\n uids, candidate_items, packed = load_candidates(stage)\n item_col = {int(item): col for col, item in enumerate(candidate_items)}\n ordered_cols = np.asarray([item_col[int(item)] for item in ordered_items], dtype=np.int32)\n out_uid = np.repeat(uids.astype(np.int64), TOP_N)\n out_item = np.empty(len(uids) * TOP_N, dtype=np.int64)\n for row in range(len(uids)):\n chosen: list[int] = []\n for col in ordered_cols:\n if is_eligible(packed[row], int(col)):\n chosen.append(int(candidate_items[col]))\n if len(chosen) == TOP_N:\n break\n if len(chosen) != TOP_N:\n raise RuntimeError(f\"only {len(chosen)} eligible items for uid={uids[row]}\")\n out_item[row * TOP_N : (row + 1) * TOP_N] = chosen\n return pd.DataFrame(\n {\n \"uid\": out_uid,\n \"item_id\": out_item,\n \"rank\": np.tile(np.arange(1, TOP_N + 1, dtype=np.int64), len(uids)),\n }\n )\n\n\ndef main() -> None:\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--stage\", choices=(\"val\", \"test\"), default=\"test\")\n args = parser.parse_args()\n catalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n ranking = rank_global(args.stage, catalog.sort_values(\"popularity_rank\").item_id.to_numpy())\n path = OUT / (\"validation.parquet\" if args.stage == \"val\" else \"submission.parquet\")\n ranking.to_parquet(path, index=False)\n print(f\"wrote {len(ranking):,} rows to {path}\")\n\n\nif __name__ == \"__main__\":\n main()\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/build_ranker.py\nA /app/solution/README.md\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "response_id": "resp_0fdbf3e20a3ba82b016ab5c78478bc87d2a09194bd5b9fb6e5", "usage": { "input_tokens": 29022, "cached_input_tokens": 26496, "cache_write_input_tokens": 0, "output_tokens": 2909, "reasoning_output_tokens": 1862, "total_tokens": 31931 }, "turn_token_usage": { "input_tokens": 376234, "cached_input_tokens": 355840, "cache_write_input_tokens": 0, "output_tokens": 8618, "reasoning_output_tokens": 3576, "total_tokens": 384852 }, "thread_token_usage": { "input_tokens": 376234, "cached_input_tokens": 355840, "cache_write_input_tokens": 0, "output_tokens": 8618, "reasoning_output_tokens": 3576, "total_tokens": 384852 } } ## Step 27 · agent · 2026-09-25T01:00:49.576Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:00:51.507Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python /app/solution/build_ranker.py --stage val && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet && /opt/task-venv/bin/python /app/solution/build_ranker.py --stage test && ls -lh /app/solution\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:00:57.523Z Script completed Wall time 6.0 seconds Output: wrote 845,600 rows to /app/solution/validation.parquet { "model": "/app/solution/validation.parquet", "scores": { "quality_ndcg@10": 0.01071, "quality_users": 4263 } } wrote 845,600 rows to /app/solution/submission.parquet total 1.5M -rw-r--r-- 1 agent agent 312 Sep 25 01:00 README.md -rw-r--r-- 1 agent agent 2.6K Sep 25 01:00 build_ranker.py -rw-r--r-- 1 agent agent 719K Sep 25 01:00 submission.parquet -rw-r--r-- 1 agent agent 712K Sep 25 01:00 validation.parquet ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "response_id": "resp_0fdbf3e20a3ba82b016ab5c7bbb7f087d2a83b133bf5f70e65", "usage": { "input_tokens": 31955, "cached_input_tokens": 28800, "cache_write_input_tokens": 0, "output_tokens": 135, "reasoning_output_tokens": 19, "total_tokens": 32090 }, "turn_token_usage": { "input_tokens": 408189, "cached_input_tokens": 384640, "cache_write_input_tokens": 0, "output_tokens": 8753, "reasoning_output_tokens": 3595, "total_tokens": 416942 }, "thread_token_usage": { "input_tokens": 408189, "cached_input_tokens": 384640, "cache_write_input_tokens": 0, "output_tokens": 8753, "reasoning_output_tokens": 3595, "total_tokens": 416942 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-34cc24a3-1b90-4bbc-8c54-9a012f151b0c", "process_id": "66959", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/build_ranker.py --stage val && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet && /opt/task-venv/bin/python /app/solution/build_ranker.py --stage test && ls -lh /app/solution" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/build_ranker.py --stage val && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet && /opt/task-venv/bin/python /app/solution/build_ranker.py --stage test && ls -lh /app/solution" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "wrote 845,600 rows to /app/solution/validation.parquet\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\nwrote 845,600 rows to /app/solution/submission.parquet\ntotal 1.5M\n-rw-r--r-- 1 agent agent 312 Sep 25 01:00 README.md\n-rw-r--r-- 1 agent agent 2.6K Sep 25 01:00 build_ranker.py\n-rw-r--r-- 1 agent agent 719K Sep 25 01:00 submission.parquet\n-rw-r--r-- 1 agent agent 712K Sep 25 01:00 validation.parquet\n", "stderr": "", "aggregated_output": "wrote 845,600 rows to /app/solution/validation.parquet\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\nwrote 845,600 rows to /app/solution/submission.parquet\ntotal 1.5M\n-rw-r--r-- 1 agent agent 312 Sep 25 01:00 README.md\n-rw-r--r-- 1 agent agent 2.6K Sep 25 01:00 build_ranker.py\n-rw-r--r-- 1 agent agent 719K Sep 25 01:00 submission.parquet\n-rw-r--r-- 1 agent agent 712K Sep 25 01:00 validation.parquet\n", "exit_code": 0, "duration": { "secs": 5, "nanos": 836948960 }, "formatted_output": "wrote 845,600 rows to /app/solution/validation.parquet\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\nwrote 845,600 rows to /app/solution/submission.parquet\ntotal 1.5M\n-rw-r--r-- 1 agent agent 312 Sep 25 01:00 README.md\n-rw-r--r-- 1 agent agent 2.6K Sep 25 01:00 build_ranker.py\n-rw-r--r-- 1 agent agent 719K Sep 25 01:00 submission.parquet\n-rw-r--r-- 1 agent agent 712K Sep 25 01:00 validation.parquet\n" } } ## Step 28 · agent · 2026-09-25T01:01:15.639Z ### Thinking unavailable — no text recorded ## Step 29 · agent · 2026-09-25T01:01:26.402Z ### Thinking unavailable — no text recorded ## Step 30 · agent · 2026-09-25T01:01:28.365Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:01:44.474Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u - <<'PY'\\nimport sys,time\\nsys.path.insert(0,'/task')\\nfrom evaluate_public import validation_truth\\nfrom ranking_evaluation import CandidateSet, score_rankings\\nimport pyarrow.parquet as pq, pandas as pd, numpy as np\\nP='/task/data'; CUTOFF=24786800; DAY=86400\\ncat=pd.read_parquet(P+'/candidate_catalog.parquet'); ids=cat.item_id.to_numpy(np.int64); n=len(ids); sid=np.sort(ids); s2c=np.argsort(ids)\\n# map item ids to catalog column\\ndef cols(a):\\n p=np.searchsorted(sid,a); ok=(p=50,ratio>=80,(ratio>=50)&(org==1),(ratio>=80)&(org==1)]\\n for j,m in enumerate(masks):\\n cc=c[m]; aa=ages[m]\\n for wi,w in enumerate(wins): F[j,wi]+=np.bincount(cc[aa<=w],minlength=n)\\n print('batch',bi,flush=True)\\n# likes counts same windows organic and all\\nLF=np.zeros((2,len(wins),n),np.float64)\\nfor fam in ['likes']:\\n d=pd.read_parquet(P+'/train_'+fam+'.parquet',columns=['timestamp','item_id','is_organic']); c,ok=cols(d.item_id.to_numpy(np.int64)); ts=d.timestamp.to_numpy(np.int64)[ok]; org=d.is_organic.to_numpy(np.uint8)[ok]; ages=(CUTOFF-ts)/DAY\\n for j,m in enumerate([np.ones(len(c),bool),org==1]):\\n for wi,w in enumerate(wins): LF[j,wi]+=np.bincount(c[m&(ages<=w)],minlength=n)\\n# truth and evaluator\\nelig=CandidateSet(__import__('pathlib').Path(P+'/validation_eligible_candidates.npz')); targets=set(pd.read_parquet(P+'/target_users.parquet').uid.astype(int)); gold=validation_truth(elig,targets)\\ndef evaluate(scores):\\n order=np.lexsort((cat.popularity_rank.to_numpy(),-scores)); ranked={}\\n for u in gold:\\n row=[]\\n for j in order:\\n it=int(ids[j])\\n if elig.is_eligible(u,it): row.append(it)\\n if len(row)==10: break\\n ranked[u]=row\\n return score_rankings(ranked,gold)['quality_ndcg@10']\\nres=[]\\nres.append(('catalog_positive',evaluate(cat.positive_listens.to_numpy())))\\nres.append(('catalog_orgpositive',evaluate(cat.organic_positive_listens.to_numpy())))\\nfor j,cn in enumerate(conds):\\n for wi,wn in enumerate(names): res.append((cn+'_'+wn,evaluate(F[j,wi])))\\nfor j,cn in enumerate(['likeall','likeorg']):\\n for wi,wn in enumerate(names): res.append((cn+'_'+wn,evaluate(LF[j,wi])))\\nfor x in sorted(res,key=lambda x:-x[1]): print(x)\\nnp.savez_compressed('/app/solution/global_features.npz',F=F,LF=LF,wins=wins)\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:20000});\nif(r.session_id){text(`SESSION_ID=${r.session_id}`);text(r.output);}else{text(r.output);}\n" } ### Tool output · 2026-09-25T01:02:14.640Z Script completed Wall time 30.2 seconds Output: SESSION_ID=85654 batch 0 batch 1 batch 2 batch 3 batch 4 batch 5 batch 6 batch 7 batch 8 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "response_id": "resp_0fdbf3e20a3ba82b016ab5c7c9c30487d28c8e107578214ff8", "usage": { "input_tokens": 32297, "cached_input_tokens": 31744, "cache_write_input_tokens": 0, "output_tokens": 2177, "reasoning_output_tokens": 1137, "total_tokens": 34474 }, "turn_token_usage": { "input_tokens": 440486, "cached_input_tokens": 416384, "cache_write_input_tokens": 0, "output_tokens": 10930, "reasoning_output_tokens": 4732, "total_tokens": 451416 }, "thread_token_usage": { "input_tokens": 440486, "cached_input_tokens": 416384, "cache_write_input_tokens": 0, "output_tokens": 10930, "reasoning_output_tokens": 4732, "total_tokens": 451416 } } ## Step 31 · agent · 2026-09-25T01:02:20.845Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:02:21.542Z { "code": "const r = await tools.write_stdin({session_id:85654,chars:\"\",yield_time_ms:30000,max_output_tokens:20000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:02:51.552Z Script completed Wall time 30.0 seconds Output: batch 9 batch 10 batch 11 batch 12 batch 13 batch 14 batch 15 batch 16 batch 17 batch 18 batch 19 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "response_id": "resp_0fdbf3e20a3ba82b016ab5c816d56087d286dbe51245a725d2", "usage": { "input_tokens": 34538, "cached_input_tokens": 32128, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 34591 }, "turn_token_usage": { "input_tokens": 475024, "cached_input_tokens": 448512, "cache_write_input_tokens": 0, "output_tokens": 10983, "reasoning_output_tokens": 4738, "total_tokens": 486007 }, "thread_token_usage": { "input_tokens": 475024, "cached_input_tokens": 448512, "cache_write_input_tokens": 0, "output_tokens": 10983, "reasoning_output_tokens": 4738, "total_tokens": 486007 } } ## Step 32 · agent · 2026-09-25T01:02:56.794Z ### Thinking unavailable — no text recorded The baseline scores 0.010710 NDCG@10. The popularity sweep is processing several cutoff-safe variants (organic vs. recommendation-surface activity, 50% vs. 80% completion, and multiple recency windows). This gives both a stronger fallback and a calibrated global prior for the personalized model. ### exec · 2026-09-25T01:02:58.874Z { "code": "const r = await tools.write_stdin({session_id:85654,chars:\"\",yield_time_ms:30000,max_output_tokens:20000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:03:04.122Z Script completed Wall time 5.2 seconds Output: batch 20 batch 21 ('all50_7d', 0.030933) ('likeorg_3d', 0.030503) ('likeall_3d', 0.030269) ('likeall_7d', 0.030187) ('all80_7d', 0.030107) ('likeorg_7d', 0.029297) ('all50_3d', 0.028592) ('org50_7d', 0.028456) ('org80_7d', 0.028131) ('org50_14d', 0.026849) ('likeorg_14d', 0.026726) ('org50_3d', 0.026298) ('org80_3d', 0.025721) ('all50_14d', 0.025124) ('all80_3d', 0.023621) ('org80_14d', 0.022878) ('all80_14d', 0.022238) ('likeall_14d', 0.020652) ('likeorg_30d', 0.018429) ('all50_1d', 0.018312) ('org50_1d', 0.018231) ('all80_1d', 0.017614) ('all50_30d', 0.017182) ('all80_60d', 0.016953) ('all50_60d', 0.016892) ('likeall_30d', 0.016812) ('org80_1d', 0.016755) ('likeorg_60d', 0.016741) ('all80_30d', 0.016688) ('likeorg_1d', 0.016286) ('likeorg_90d', 0.016217) ('all80_90d', 0.01598) ('all50_90d', 0.015972) ('org50_60d', 0.015967) ('org80_90d', 0.015822) ('org80_60d', 0.015802) ('org50_90d', 0.015801) ('likeall_60d', 0.015481) ('likeall_90d', 0.015093) ('org50_30d', 0.01431) ('org80_30d', 0.013314) ('likeall_180d', 0.01324) ('likeorg_180d', 0.013) ('likeall_1d', 0.01253) ('likeall_all', 0.012135) ('all50_180d', 0.012116) ('likeorg_all', 0.012054) ('all80_180d', 0.01204) ('org50_180d', 0.011913) ('org80_180d', 0.011908) ('all80_all', 0.010727) ('catalog_positive', 0.01071) ('all50_all', 0.01071) ('org50_all', 0.010577) ('org80_all', 0.010551) ('catalog_orgpositive', 3.2e-05) ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "response_id": "resp_0fdbf3e20a3ba82b016ab5c83bbf1c87d28c64f07400f1aff3", "usage": { "input_tokens": 34658, "cached_input_tokens": 34304, "cache_write_input_tokens": 0, "output_tokens": 130, "reasoning_output_tokens": 12, "total_tokens": 34788 }, "turn_token_usage": { "input_tokens": 509682, "cached_input_tokens": 482816, "cache_write_input_tokens": 0, "output_tokens": 11113, "reasoning_output_tokens": 4750, "total_tokens": 520795 }, "thread_token_usage": { "input_tokens": 509682, "cached_input_tokens": 482816, "cache_write_input_tokens": 0, "output_tokens": 11113, "reasoning_output_tokens": 4750, "total_tokens": 520795 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-3ed7e05b-d904-4b97-a5e9-087a92717615", "process_id": "85654", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u - <<'PY'\nimport sys,time\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\nimport pyarrow.parquet as pq, pandas as pd, numpy as np\nP='/task/data'; CUTOFF=24786800; DAY=86400\ncat=pd.read_parquet(P+'/candidate_catalog.parquet'); ids=cat.item_id.to_numpy(np.int64); n=len(ids); sid=np.sort(ids); s2c=np.argsort(ids)\n# map item ids to catalog column\ndef cols(a):\n p=np.searchsorted(sid,a); ok=(p=50,ratio>=80,(ratio>=50)&(org==1),(ratio>=80)&(org==1)]\n for j,m in enumerate(masks):\n cc=c[m]; aa=ages[m]\n for wi,w in enumerate(wins): F[j,wi]+=np.bincount(cc[aa<=w],minlength=n)\n print('batch',bi,flush=True)\n# likes counts same windows organic and all\nLF=np.zeros((2,len(wins),n),np.float64)\nfor fam in ['likes']:\n d=pd.read_parquet(P+'/train_'+fam+'.parquet',columns=['timestamp','item_id','is_organic']); c,ok=cols(d.item_id.to_numpy(np.int64)); ts=d.timestamp.to_numpy(np.int64)[ok]; org=d.is_organic.to_numpy(np.uint8)[ok]; ages=(CUTOFF-ts)/DAY\n for j,m in enumerate([np.ones(len(c),bool),org==1]):\n for wi,w in enumerate(wins): LF[j,wi]+=np.bincount(c[m&(ages<=w)],minlength=n)\n# truth and evaluator\nelig=CandidateSet(__import__('pathlib').Path(P+'/validation_eligible_candidates.npz')); targets=set(pd.read_parquet(P+'/target_users.parquet').uid.astype(int)); gold=validation_truth(elig,targets)\ndef evaluate(scores):\n order=np.lexsort((cat.popularity_rank.to_numpy(),-scores)); ranked={}\n for u in gold:\n row=[]\n for j in order:\n it=int(ids[j])\n if elig.is_eligible(u,it): row.append(it)\n if len(row)==10: break\n ranked[u]=row\n return score_rankings(ranked,gold)['quality_ndcg@10']\nres=[]\nres.append(('catalog_positive',evaluate(cat.positive_listens.to_numpy())))\nres.append(('catalog_orgpositive',evaluate(cat.organic_positive_listens.to_numpy())))\nfor j,cn in enumerate(conds):\n for wi,wn in enumerate(names): res.append((cn+'_'+wn,evaluate(F[j,wi])))\nfor j,cn in enumerate(['likeall','likeorg']):\n for wi,wn in enumerate(names): res.append((cn+'_'+wn,evaluate(LF[j,wi])))\nfor x in sorted(res,key=lambda x:-x[1]): print(x)\nnp.savez_compressed('/app/solution/global_features.npz',F=F,LF=LF,wins=wins)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport sys,time\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\nimport pyarrow.parquet as pq, pandas as pd, numpy as np\nP='/task/data'; CUTOFF=24786800; DAY=86400\ncat=pd.read_parquet(P+'/candidate_catalog.parquet'); ids=cat.item_id.to_numpy(np.int64); n=len(ids); sid=np.sort(ids); s2c=np.argsort(ids)\n# map item ids to catalog column\ndef cols(a):\n p=np.searchsorted(sid,a); ok=(p=50,ratio>=80,(ratio>=50)&(org==1),(ratio>=80)&(org==1)]\n for j,m in enumerate(masks):\n cc=c[m]; aa=ages[m]\n for wi,w in enumerate(wins): F[j,wi]+=np.bincount(cc[aa<=w],minlength=n)\n print('batch',bi,flush=True)\n# likes counts same windows organic and all\nLF=np.zeros((2,len(wins),n),np.float64)\nfor fam in ['likes']:\n d=pd.read_parquet(P+'/train_'+fam+'.parquet',columns=['timestamp','item_id','is_organic']); c,ok=cols(d.item_id.to_numpy(np.int64)); ts=d.timestamp.to_numpy(np.int64)[ok]; org=d.is_organic.to_numpy(np.uint8)[ok]; ages=(CUTOFF-ts)/DAY\n for j,m in enumerate([np.ones(len(c),bool),org==1]):\n for wi,w in enumerate(wins): LF[j,wi]+=np.bincount(c[m&(ages<=w)],minlength=n)\n# truth and evaluator\nelig=CandidateSet(__import__('pathlib').Path(P+'/validation_eligible_candidates.npz')); targets=set(pd.read_parquet(P+'/target_users.parquet').uid.astype(int)); gold=validation_truth(elig,targets)\ndef evaluate(scores):\n order=np.lexsort((cat.popularity_rank.to_numpy(),-scores)); ranked={}\n for u in gold:\n row=[]\n for j in order:\n it=int(ids[j])\n if elig.is_eligible(u,it): row.append(it)\n if len(row)==10: break\n ranked[u]=row\n return score_rankings(ranked,gold)['quality_ndcg@10']\nres=[]\nres.append(('catalog_positive',evaluate(cat.positive_listens.to_numpy())))\nres.append(('catalog_orgpositive',evaluate(cat.organic_positive_listens.to_numpy())))\nfor j,cn in enumerate(conds):\n for wi,wn in enumerate(names): res.append((cn+'_'+wn,evaluate(F[j,wi])))\nfor j,cn in enumerate(['likeall','likeorg']):\n for wi,wn in enumerate(names): res.append((cn+'_'+wn,evaluate(LF[j,wi])))\nfor x in sorted(res,key=lambda x:-x[1]): print(x)\nnp.savez_compressed('/app/solution/global_features.npz',F=F,LF=LF,wins=wins)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "batch 0\nbatch 1\nbatch 2\nbatch 3\nbatch 4\nbatch 5\nbatch 6\nbatch 7\nbatch 8\nbatch 9\nbatch 10\nbatch 11\nbatch 12\nbatch 13\nbatch 14\nbatch 15\nbatch 16\nbatch 17\nbatch 18\nbatch 19\nbatch 20\nbatch 21\n('all50_7d', 0.030933)\n('likeorg_3d', 0.030503)\n('likeall_3d', 0.030269)\n('likeall_7d', 0.030187)\n('all80_7d', 0.030107)\n('likeorg_7d', 0.029297)\n('all50_3d', 0.028592)\n('org50_7d', 0.028456)\n('org80_7d', 0.028131)\n('org50_14d', 0.026849)\n('likeorg_14d', 0.026726)\n('org50_3d', 0.026298)\n('org80_3d', 0.025721)\n('all50_14d', 0.025124)\n('all80_3d', 0.023621)\n('org80_14d', 0.022878)\n('all80_14d', 0.022238)\n('likeall_14d', 0.020652)\n('likeorg_30d', 0.018429)\n('all50_1d', 0.018312)\n('org50_1d', 0.018231)\n('all80_1d', 0.017614)\n('all50_30d', 0.017182)\n('all80_60d', 0.016953)\n('all50_60d', 0.016892)\n('likeall_30d', 0.016812)\n('org80_1d', 0.016755)\n('likeorg_60d', 0.016741)\n('all80_30d', 0.016688)\n('likeorg_1d', 0.016286)\n('likeorg_90d', 0.016217)\n('all80_90d', 0.01598)\n('all50_90d', 0.015972)\n('org50_60d', 0.015967)\n('org80_90d', 0.015822)\n('org80_60d', 0.015802)\n('org50_90d', 0.015801)\n('likeall_60d', 0.015481)\n('likeall_90d', 0.015093)\n('org50_30d', 0.01431)\n('org80_30d', 0.013314)\n('likeall_180d', 0.01324)\n('likeorg_180d', 0.013)\n('likeall_1d', 0.01253)\n('likeall_all', 0.012135)\n('all50_180d', 0.012116)\n('likeorg_all', 0.012054)\n('all80_180d', 0.01204)\n('org50_180d', 0.011913)\n('org80_180d', 0.011908)\n('all80_all', 0.010727)\n('catalog_positive', 0.01071)\n('all50_all', 0.01071)\n('org50_all', 0.010577)\n('org80_all', 0.010551)\n('catalog_orgpositive', 3.2e-05)\n", "stderr": "", "aggregated_output": "batch 0\nbatch 1\nbatch 2\nbatch 3\nbatch 4\nbatch 5\nbatch 6\nbatch 7\nbatch 8\nbatch 9\nbatch 10\nbatch 11\nbatch 12\nbatch 13\nbatch 14\nbatch 15\nbatch 16\nbatch 17\nbatch 18\nbatch 19\nbatch 20\nbatch 21\n('all50_7d', 0.030933)\n('likeorg_3d', 0.030503)\n('likeall_3d', 0.030269)\n('likeall_7d', 0.030187)\n('all80_7d', 0.030107)\n('likeorg_7d', 0.029297)\n('all50_3d', 0.028592)\n('org50_7d', 0.028456)\n('org80_7d', 0.028131)\n('org50_14d', 0.026849)\n('likeorg_14d', 0.026726)\n('org50_3d', 0.026298)\n('org80_3d', 0.025721)\n('all50_14d', 0.025124)\n('all80_3d', 0.023621)\n('org80_14d', 0.022878)\n('all80_14d', 0.022238)\n('likeall_14d', 0.020652)\n('likeorg_30d', 0.018429)\n('all50_1d', 0.018312)\n('org50_1d', 0.018231)\n('all80_1d', 0.017614)\n('all50_30d', 0.017182)\n('all80_60d', 0.016953)\n('all50_60d', 0.016892)\n('likeall_30d', 0.016812)\n('org80_1d', 0.016755)\n('likeorg_60d', 0.016741)\n('all80_30d', 0.016688)\n('likeorg_1d', 0.016286)\n('likeorg_90d', 0.016217)\n('all80_90d', 0.01598)\n('all50_90d', 0.015972)\n('org50_60d', 0.015967)\n('org80_90d', 0.015822)\n('org80_60d', 0.015802)\n('org50_90d', 0.015801)\n('likeall_60d', 0.015481)\n('likeall_90d', 0.015093)\n('org50_30d', 0.01431)\n('org80_30d', 0.013314)\n('likeall_180d', 0.01324)\n('likeorg_180d', 0.013)\n('likeall_1d', 0.01253)\n('likeall_all', 0.012135)\n('all50_180d', 0.012116)\n('likeorg_all', 0.012054)\n('all80_180d', 0.01204)\n('org50_180d', 0.011913)\n('org80_180d', 0.011908)\n('all80_all', 0.010727)\n('catalog_positive', 0.01071)\n('all50_all', 0.01071)\n('org50_all', 0.010577)\n('org80_all', 0.010551)\n('catalog_orgpositive', 3.2e-05)\n", "exit_code": 0, "duration": { "secs": 79, "nanos": 481555672 }, "formatted_output": "batch 0\nbatch 1\nbatch 2\nbatch 3\nbatch 4\nbatch 5\nbatch 6\nbatch 7\nbatch 8\nbatch 9\nbatch 10\nbatch 11\nbatch 12\nbatch 13\nbatch 14\nbatch 15\nbatch 16\nbatch 17\nbatch 18\nbatch 19\nbatch 20\nbatch 21\n('all50_7d', 0.030933)\n('likeorg_3d', 0.030503)\n('likeall_3d', 0.030269)\n('likeall_7d', 0.030187)\n('all80_7d', 0.030107)\n('likeorg_7d', 0.029297)\n('all50_3d', 0.028592)\n('org50_7d', 0.028456)\n('org80_7d', 0.028131)\n('org50_14d', 0.026849)\n('likeorg_14d', 0.026726)\n('org50_3d', 0.026298)\n('org80_3d', 0.025721)\n('all50_14d', 0.025124)\n('all80_3d', 0.023621)\n('org80_14d', 0.022878)\n('all80_14d', 0.022238)\n('likeall_14d', 0.020652)\n('likeorg_30d', 0.018429)\n('all50_1d', 0.018312)\n('org50_1d', 0.018231)\n('all80_1d', 0.017614)\n('all50_30d', 0.017182)\n('all80_60d', 0.016953)\n('all50_60d', 0.016892)\n('likeall_30d', 0.016812)\n('org80_1d', 0.016755)\n('likeorg_60d', 0.016741)\n('all80_30d', 0.016688)\n('likeorg_1d', 0.016286)\n('likeorg_90d', 0.016217)\n('all80_90d', 0.01598)\n('all50_90d', 0.015972)\n('org50_60d', 0.015967)\n('org80_90d', 0.015822)\n('org80_60d', 0.015802)\n('org50_90d', 0.015801)\n('likeall_60d', 0.015481)\n('likeall_90d', 0.015093)\n('org50_30d', 0.01431)\n('org80_30d', 0.013314)\n('likeall_180d', 0.01324)\n('likeorg_180d', 0.013)\n('likeall_1d', 0.01253)\n('likeall_all', 0.012135)\n('all50_180d', 0.012116)\n('likeorg_all', 0.012054)\n('all80_180d', 0.01204)\n('org50_180d', 0.011913)\n('org80_180d', 0.011908)\n('all80_all', 0.010727)\n('catalog_positive', 0.01071)\n('all50_all', 0.01071)\n('org50_all', 0.010577)\n('org80_all', 0.010551)\n('catalog_orgpositive', 3.2e-05)\n" } } ## Step 33 · agent · 2026-09-25T01:03:21.225Z ### Thinking unavailable — no text recorded ## Step 34 · agent · 2026-09-25T01:03:31.594Z ### Thinking unavailable — no text recorded ## Step 35 · agent · 2026-09-25T01:03:43.246Z ### Thinking unavailable — no text recorded ## Step 36 · agent · 2026-09-25T01:03:57.672Z ### Thinking unavailable — no text recorded ## Step 37 · agent · 2026-09-25T01:04:13.124Z ### Thinking unavailable — no text recorded ## Step 38 · agent · 2026-09-25T01:04:18.847Z ### Thinking unavailable — no text recorded Recent seven-day positive activity is much stronger than lifetime popularity: 0.030933 versus 0.010710. I’m now testing neighborhood collaborative filtering over the cutoff-specific active-like graph—precisely the signal suited to unseen eligible tracks—while retaining that seven-day trend as the cold-start prior. ### exec · 2026-09-25T01:04:37.669Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u - <<'PY'\\nimport sys,time\\nsys.path.insert(0,'/task')\\nfrom evaluate_public import validation_truth\\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\\nfrom pathlib import Path\\nimport pandas as pd,numpy as np\\nfrom scipy import sparse\\nP='/task/data'; cat=pd.read_parquet(P+'/candidate_catalog.parquet'); items=cat.item_id.to_numpy(np.int64); nitem=len(items)\\ntarget=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64); nuser=len(target); ur={int(x):i for i,x in enumerate(target)}; ic={int(x):i for i,x in enumerate(items)}\\nps=pd.read_parquet(P+'/validation_preference_state.parquet'); ps=ps[ps.state=='liked']; rr=np.array([ur.get(int(x),-1) for x in ps.uid],np.int32); cc=np.array([ic.get(int(x),-1) for x in ps.item_id],np.int32); ok=(rr>=0)&(cc>=0); X=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(rr[ok],cc[ok])),shape=(nuser,nitem)); X.data[:]=1\\nprint('X',X.shape,X.nnz,'users',np.sum(np.diff(X.indptr)>0),flush=True)\\nelig=CandidateSet(Path(P+'/validation_eligible_candidates.npz')); gold=validation_truth(elig,set(map(int,target))); qrows=np.array([ur[u] for u in gold],np.int32); quid=[int(target[r]) for r in qrows]\\n# bits loaded aligned exactly target/items\\nwith np.load(P+'/validation_eligible_candidates.npz') as z: bits=np.unpackbits(z['packed_eligible'],axis=1,count=nitem,bitorder='little').astype(bool)\\ng=np.load('/app/solution/global_features.npz'); global7=g['F'][0,2].astype(np.float32); global7=np.log1p(global7); global7/=global7.max()\\n# vector scoring from sparse P\\ndef evalP(Pmat,blend):\\n vals=[]\\n for qi,(r,u) in enumerate(zip(qrows,quid)):\\n row=Pmat.getrow(qi); scores=global7*blend\\n if row.nnz:\\n mx=row.data.max(); scores=scores.copy(); scores[row.indices]+=row.data/mx\\n scores=np.where(bits[r],scores,-np.inf)\\n top=np.argpartition(scores,-10)[-10:]; top=top[np.argsort(scores[top])[::-1]]\\n vals.append(ndcg_at_k(items[top].tolist(),gold[u],10))\\n return np.mean(vals)\\ndef topk_rows(S,k):\\n ind=[]; dat=[]; ptr=[0]\\n for i in range(S.shape[0]):\\n a,b=S.indptr[i],S.indptr[i+1]; ix=S.indices[a:b]; va=S.data[a:b]; keep=ix!=qrows[i]; ix=ix[keep];va=va[keep]\\n if len(va)>k:\\n z=np.argpartition(va,-k)[-k:];ix=ix[z];va=va[z]\\n ind.append(ix);dat.append(va);ptr.append(ptr[-1]+len(ix))\\n return sparse.csr_matrix((np.concatenate(dat),np.concatenate(ind),np.array(ptr)),shape=(len(qrows),nuser))\\ndf=np.bincount(X.indices,minlength=nitem)\\nfor alpha in [0,0.5,1.0]:\\n W=X.copy(); W.data*=np.power(np.log1p(nuser/(1+df[W.indices])),alpha).astype(np.float32)\\n norm=np.sqrt(np.asarray(W.multiply(W).sum(1)).ravel()); norm[norm==0]=1; W=sparse.diags(1/norm)@W\\n print('sim alpha',alpha,flush=True); S=(W[qrows]@W.T).tocsr(); print('S nnz',S.nnz,flush=True)\\n for k in [20,50,100,200]:\\n Sk=topk_rows(S,k); Pred=(Sk@X).tocsr(); print('pred',alpha,k,Pred.nnz,flush=True)\\n for blend in [0,0.01,0.03,0.1,0.3,1.0]: print('RESULT',alpha,k,blend,evalP(Pred,blend),flush=True)\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:30000});\nif(r.session_id){text(`SESSION_ID=${r.session_id}`);text(r.output);}else{text(r.output);}\n" } ### Tool output · 2026-09-25T01:05:07.865Z Script completed Wall time 30.2 seconds Output: SESSION_ID=58575 X (8456, 30000) 418435 users 6769 sim alpha 0 S nnz 5208759 pred 0 20 4120533 RESULT 0 20 0 0.010189151174046571 RESULT 0 20 0.01 0.014625009228479428 RESULT 0 20 0.03 0.014953498427857191 RESULT 0 20 0.1 0.015603920122833 RESULT 0 20 0.3 0.01818227165559802 RESULT 0 20 1.0 0.02614888619836062 pred 0 50 9791912 RESULT 0 50 0 0.01278979557371587 RESULT 0 50 0.01 0.016931792254098226 RESULT 0 50 0.03 0.017241748496562945 RESULT 0 50 0.1 0.01803826865470088 RESULT 0 50 0.3 0.020647610156042415 RESULT 0 50 1.0 0.027850455696709697 pred 0 100 17824538 RESULT 0 100 0 0.013942372413151364 RESULT 0 100 0.01 0.01819974565864306 RESULT 0 100 0.03 0.018239386259612918 RESULT 0 100 0.1 0.019052189606065792 RESULT 0 100 0.3 0.020466507261134904 RESULT 0 100 1.0 0.027936251913010045 pred 0 200 30005716 RESULT 0 200 0 0.013695124373878454 RESULT 0 200 0.01 0.017881347029397733 RESULT 0 200 0.03 0.01811718282971806 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "response_id": "resp_0fdbf3e20a3ba82b016ab5c8484e7087d284c40396c5b31925", "usage": { "input_tokens": 35531, "cached_input_tokens": 34432, "cache_write_input_tokens": 0, "output_tokens": 3929, "reasoning_output_tokens": 2810, "total_tokens": 39460 }, "turn_token_usage": { "input_tokens": 545213, "cached_input_tokens": 517248, "cache_write_input_tokens": 0, "output_tokens": 15042, "reasoning_output_tokens": 7560, "total_tokens": 560255 }, "thread_token_usage": { "input_tokens": 545213, "cached_input_tokens": 517248, "cache_write_input_tokens": 0, "output_tokens": 15042, "reasoning_output_tokens": 7560, "total_tokens": 560255 } } ## Step 39 · agent · 2026-09-25T01:05:11.014Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:05:11.434Z { "code": "const r = await tools.write_stdin({session_id:58575,chars:\"\",yield_time_ms:30000,max_output_tokens:30000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:05:41.442Z Script completed Wall time 30.0 seconds Output: RESULT 0 200 0.1 0.018644844509053415 RESULT 0 200 0.3 0.020052867129975376 RESULT 0 200 1.0 0.026666550127912775 sim alpha 0.5 S nnz 5208759 pred 0.5 20 4347325 RESULT 0.5 20 0 0.009756102751931808 RESULT 0.5 20 0.01 0.014195539354608162 RESULT 0.5 20 0.03 0.014518028075190427 RESULT 0.5 20 0.1 0.015447652588324439 RESULT 0.5 20 0.3 0.017621824838038123 RESULT 0.5 20 1.0 0.025838365552665308 pred 0.5 50 10304043 RESULT 0.5 50 0 0.013099753371926605 RESULT 0.5 50 0.01 0.017274966699935747 RESULT 0.5 50 0.03 0.017498331713449052 RESULT 0.5 50 0.1 0.018290733147372267 RESULT 0.5 50 0.3 0.02008576358616745 RESULT 0.5 50 1.0 0.027616094791895238 pred 0.5 100 18578173 RESULT 0.5 100 0 0.01419749188073614 RESULT 0.5 100 0.01 0.01832402281210433 RESULT 0.5 100 0.03 0.018379772386347838 RESULT 0.5 100 0.1 0.018727698022948797 RESULT 0.5 100 0.3 0.020277147016509896 RESULT 0.5 100 1.0 0.026940600252948747 pred 0.5 200 30925926 RESULT 0.5 200 0 0.014042554321452811 RESULT 0.5 200 0.01 0.01815658036900156 RESULT 0.5 200 0.03 0.018243893834594564 RESULT 0.5 200 0.1 0.01876376044262871 RESULT 0.5 200 0.3 0.020022159840145572 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "response_id": "resp_0fdbf3e20a3ba82b016ab5c8c414d887d298654b3bc06562ff", "usage": { "input_tokens": 39948, "cached_input_tokens": 35328, "cache_write_input_tokens": 0, "output_tokens": 85, "reasoning_output_tokens": 38, "total_tokens": 40033 }, "turn_token_usage": { "input_tokens": 585161, "cached_input_tokens": 552576, "cache_write_input_tokens": 0, "output_tokens": 15127, "reasoning_output_tokens": 7598, "total_tokens": 600288 }, "thread_token_usage": { "input_tokens": 585161, "cached_input_tokens": 552576, "cache_write_input_tokens": 0, "output_tokens": 15127, "reasoning_output_tokens": 7598, "total_tokens": 600288 } } ## Step 40 · agent · 2026-09-25T01:05:44.337Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:05:45.052Z { "code": "const r = await tools.write_stdin({session_id:58575,chars:\"\",yield_time_ms:30000,max_output_tokens:30000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:06:13.356Z Script completed Wall time 28.3 seconds Output: RESULT 0.5 200 1.0 0.026467739303177605 sim alpha 1.0 S nnz 5208759 pred 1.0 20 4487569 RESULT 1.0 20 0 0.009764170700602641 RESULT 1.0 20 0.01 0.014275421240727157 RESULT 1.0 20 0.03 0.014364684560321889 RESULT 1.0 20 0.1 0.015258290966744893 RESULT 1.0 20 0.3 0.017116595795726507 RESULT 1.0 20 1.0 0.024692198262097922 pred 1.0 50 10759896 RESULT 1.0 50 0 0.01329800262093432 RESULT 1.0 50 0.01 0.017526661009645172 RESULT 1.0 50 0.03 0.01792440496909016 RESULT 1.0 50 0.1 0.0183000230729711 RESULT 1.0 50 0.3 0.019963966051010137 RESULT 1.0 50 1.0 0.026295988978744727 pred 1.0 100 19366482 RESULT 1.0 100 0 0.014071639205553531 RESULT 1.0 100 0.01 0.01821762953880885 RESULT 1.0 100 0.03 0.01835130151965709 RESULT 1.0 100 0.1 0.018807116302041852 RESULT 1.0 100 0.3 0.019953840882952198 RESULT 1.0 100 1.0 0.02602265501423322 pred 1.0 200 31970762 RESULT 1.0 200 0 0.014013773550960614 RESULT 1.0 200 0.01 0.018208368760460475 RESULT 1.0 200 0.03 0.018266319931025912 RESULT 1.0 200 0.1 0.018716109353217568 RESULT 1.0 200 0.3 0.019838782353696058 RESULT 1.0 200 1.0 0.02549782542888643 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "response_id": "resp_0fdbf3e20a3ba82b016ab5c8e68d2087d2b6c21c53f5dc05a1", "usage": { "input_tokens": 40651, "cached_input_tokens": 39808, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 40704 }, "turn_token_usage": { "input_tokens": 625812, "cached_input_tokens": 592384, "cache_write_input_tokens": 0, "output_tokens": 15180, "reasoning_output_tokens": 7604, "total_tokens": 640992 }, "thread_token_usage": { "input_tokens": 625812, "cached_input_tokens": 592384, "cache_write_input_tokens": 0, "output_tokens": 15180, "reasoning_output_tokens": 7604, "total_tokens": 640992 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ef833f12-2eb5-4c60-8441-6ac2c9e94697", "process_id": "58575", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u - <<'PY'\nimport sys,time\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nP='/task/data'; cat=pd.read_parquet(P+'/candidate_catalog.parquet'); items=cat.item_id.to_numpy(np.int64); nitem=len(items)\ntarget=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64); nuser=len(target); ur={int(x):i for i,x in enumerate(target)}; ic={int(x):i for i,x in enumerate(items)}\nps=pd.read_parquet(P+'/validation_preference_state.parquet'); ps=ps[ps.state=='liked']; rr=np.array([ur.get(int(x),-1) for x in ps.uid],np.int32); cc=np.array([ic.get(int(x),-1) for x in ps.item_id],np.int32); ok=(rr>=0)&(cc>=0); X=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(rr[ok],cc[ok])),shape=(nuser,nitem)); X.data[:]=1\nprint('X',X.shape,X.nnz,'users',np.sum(np.diff(X.indptr)>0),flush=True)\nelig=CandidateSet(Path(P+'/validation_eligible_candidates.npz')); gold=validation_truth(elig,set(map(int,target))); qrows=np.array([ur[u] for u in gold],np.int32); quid=[int(target[r]) for r in qrows]\n# bits loaded aligned exactly target/items\nwith np.load(P+'/validation_eligible_candidates.npz') as z: bits=np.unpackbits(z['packed_eligible'],axis=1,count=nitem,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz'); global7=g['F'][0,2].astype(np.float32); global7=np.log1p(global7); global7/=global7.max()\n# vector scoring from sparse P\ndef evalP(Pmat,blend):\n vals=[]\n for qi,(r,u) in enumerate(zip(qrows,quid)):\n row=Pmat.getrow(qi); scores=global7*blend\n if row.nnz:\n mx=row.data.max(); scores=scores.copy(); scores[row.indices]+=row.data/mx\n scores=np.where(bits[r],scores,-np.inf)\n top=np.argpartition(scores,-10)[-10:]; top=top[np.argsort(scores[top])[::-1]]\n vals.append(ndcg_at_k(items[top].tolist(),gold[u],10))\n return np.mean(vals)\ndef topk_rows(S,k):\n ind=[]; dat=[]; ptr=[0]\n for i in range(S.shape[0]):\n a,b=S.indptr[i],S.indptr[i+1]; ix=S.indices[a:b]; va=S.data[a:b]; keep=ix!=qrows[i]; ix=ix[keep];va=va[keep]\n if len(va)>k:\n z=np.argpartition(va,-k)[-k:];ix=ix[z];va=va[z]\n ind.append(ix);dat.append(va);ptr.append(ptr[-1]+len(ix))\n return sparse.csr_matrix((np.concatenate(dat),np.concatenate(ind),np.array(ptr)),shape=(len(qrows),nuser))\ndf=np.bincount(X.indices,minlength=nitem)\nfor alpha in [0,0.5,1.0]:\n W=X.copy(); W.data*=np.power(np.log1p(nuser/(1+df[W.indices])),alpha).astype(np.float32)\n norm=np.sqrt(np.asarray(W.multiply(W).sum(1)).ravel()); norm[norm==0]=1; W=sparse.diags(1/norm)@W\n print('sim alpha',alpha,flush=True); S=(W[qrows]@W.T).tocsr(); print('S nnz',S.nnz,flush=True)\n for k in [20,50,100,200]:\n Sk=topk_rows(S,k); Pred=(Sk@X).tocsr(); print('pred',alpha,k,Pred.nnz,flush=True)\n for blend in [0,0.01,0.03,0.1,0.3,1.0]: print('RESULT',alpha,k,blend,evalP(Pred,blend),flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport sys,time\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nP='/task/data'; cat=pd.read_parquet(P+'/candidate_catalog.parquet'); items=cat.item_id.to_numpy(np.int64); nitem=len(items)\ntarget=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64); nuser=len(target); ur={int(x):i for i,x in enumerate(target)}; ic={int(x):i for i,x in enumerate(items)}\nps=pd.read_parquet(P+'/validation_preference_state.parquet'); ps=ps[ps.state=='liked']; rr=np.array([ur.get(int(x),-1) for x in ps.uid],np.int32); cc=np.array([ic.get(int(x),-1) for x in ps.item_id],np.int32); ok=(rr>=0)&(cc>=0); X=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(rr[ok],cc[ok])),shape=(nuser,nitem)); X.data[:]=1\nprint('X',X.shape,X.nnz,'users',np.sum(np.diff(X.indptr)>0),flush=True)\nelig=CandidateSet(Path(P+'/validation_eligible_candidates.npz')); gold=validation_truth(elig,set(map(int,target))); qrows=np.array([ur[u] for u in gold],np.int32); quid=[int(target[r]) for r in qrows]\n# bits loaded aligned exactly target/items\nwith np.load(P+'/validation_eligible_candidates.npz') as z: bits=np.unpackbits(z['packed_eligible'],axis=1,count=nitem,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz'); global7=g['F'][0,2].astype(np.float32); global7=np.log1p(global7); global7/=global7.max()\n# vector scoring from sparse P\ndef evalP(Pmat,blend):\n vals=[]\n for qi,(r,u) in enumerate(zip(qrows,quid)):\n row=Pmat.getrow(qi); scores=global7*blend\n if row.nnz:\n mx=row.data.max(); scores=scores.copy(); scores[row.indices]+=row.data/mx\n scores=np.where(bits[r],scores,-np.inf)\n top=np.argpartition(scores,-10)[-10:]; top=top[np.argsort(scores[top])[::-1]]\n vals.append(ndcg_at_k(items[top].tolist(),gold[u],10))\n return np.mean(vals)\ndef topk_rows(S,k):\n ind=[]; dat=[]; ptr=[0]\n for i in range(S.shape[0]):\n a,b=S.indptr[i],S.indptr[i+1]; ix=S.indices[a:b]; va=S.data[a:b]; keep=ix!=qrows[i]; ix=ix[keep];va=va[keep]\n if len(va)>k:\n z=np.argpartition(va,-k)[-k:];ix=ix[z];va=va[z]\n ind.append(ix);dat.append(va);ptr.append(ptr[-1]+len(ix))\n return sparse.csr_matrix((np.concatenate(dat),np.concatenate(ind),np.array(ptr)),shape=(len(qrows),nuser))\ndf=np.bincount(X.indices,minlength=nitem)\nfor alpha in [0,0.5,1.0]:\n W=X.copy(); W.data*=np.power(np.log1p(nuser/(1+df[W.indices])),alpha).astype(np.float32)\n norm=np.sqrt(np.asarray(W.multiply(W).sum(1)).ravel()); norm[norm==0]=1; W=sparse.diags(1/norm)@W\n print('sim alpha',alpha,flush=True); S=(W[qrows]@W.T).tocsr(); print('S nnz',S.nnz,flush=True)\n for k in [20,50,100,200]:\n Sk=topk_rows(S,k); Pred=(Sk@X).tocsr(); print('pred',alpha,k,Pred.nnz,flush=True)\n for blend in [0,0.01,0.03,0.1,0.3,1.0]: print('RESULT',alpha,k,blend,evalP(Pred,blend),flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "X (8456, 30000) 418435 users 6769\nsim alpha 0\nS nnz 5208759\npred 0 20 4120533\nRESULT 0 20 0 0.010189151174046571\nRESULT 0 20 0.01 0.014625009228479428\nRESULT 0 20 0.03 0.014953498427857191\nRESULT 0 20 0.1 0.015603920122833\nRESULT 0 20 0.3 0.01818227165559802\nRESULT 0 20 1.0 0.02614888619836062\npred 0 50 9791912\nRESULT 0 50 0 0.01278979557371587\nRESULT 0 50 0.01 0.016931792254098226\nRESULT 0 50 0.03 0.017241748496562945\nRESULT 0 50 0.1 0.01803826865470088\nRESULT 0 50 0.3 0.020647610156042415\nRESULT 0 50 1.0 0.027850455696709697\npred 0 100 17824538\nRESULT 0 100 0 0.013942372413151364\nRESULT 0 100 0.01 0.01819974565864306\nRESULT 0 100 0.03 0.018239386259612918\nRESULT 0 100 0.1 0.019052189606065792\nRESULT 0 100 0.3 0.020466507261134904\nRESULT 0 100 1.0 0.027936251913010045\npred 0 200 30005716\nRESULT 0 200 0 0.013695124373878454\nRESULT 0 200 0.01 0.017881347029397733\nRESULT 0 200 0.03 0.01811718282971806\nRESULT 0 200 0.1 0.018644844509053415\nRESULT 0 200 0.3 0.020052867129975376\nRESULT 0 200 1.0 0.026666550127912775\nsim alpha 0.5\nS nnz 5208759\npred 0.5 20 4347325\nRESULT 0.5 20 0 0.009756102751931808\nRESULT 0.5 20 0.01 0.014195539354608162\nRESULT 0.5 20 0.03 0.014518028075190427\nRESULT 0.5 20 0.1 0.015447652588324439\nRESULT 0.5 20 0.3 0.017621824838038123\nRESULT 0.5 20 1.0 0.025838365552665308\npred 0.5 50 10304043\nRESULT 0.5 50 0 0.013099753371926605\nRESULT 0.5 50 0.01 0.017274966699935747\nRESULT 0.5 50 0.03 0.017498331713449052\nRESULT 0.5 50 0.1 0.018290733147372267\nRESULT 0.5 50 0.3 0.02008576358616745\nRESULT 0.5 50 1.0 0.027616094791895238\npred 0.5 100 18578173\nRESULT 0.5 100 0 0.01419749188073614\nRESULT 0.5 100 0.01 0.01832402281210433\nRESULT 0.5 100 0.03 0.018379772386347838\nRESULT 0.5 100 0.1 0.018727698022948797\nRESULT 0.5 100 0.3 0.020277147016509896\nRESULT 0.5 100 1.0 0.026940600252948747\npred 0.5 200 30925926\nRESULT 0.5 200 0 0.014042554321452811\nRESULT 0.5 200 0.01 0.01815658036900156\nRESULT 0.5 200 0.03 0.018243893834594564\nRESULT 0.5 200 0.1 0.01876376044262871\nRESULT 0.5 200 0.3 0.020022159840145572\nRESULT 0.5 200 1.0 0.026467739303177605\nsim alpha 1.0\nS nnz 5208759\npred 1.0 20 4487569\nRESULT 1.0 20 0 0.009764170700602641\nRESULT 1.0 20 0.01 0.014275421240727157\nRESULT 1.0 20 0.03 0.014364684560321889\nRESULT 1.0 20 0.1 0.015258290966744893\nRESULT 1.0 20 0.3 0.017116595795726507\nRESULT 1.0 20 1.0 0.024692198262097922\npred 1.0 50 10759896\nRESULT 1.0 50 0 0.01329800262093432\nRESULT 1.0 50 0.01 0.017526661009645172\nRESULT 1.0 50 0.03 0.01792440496909016\nRESULT 1.0 50 0.1 0.0183000230729711\nRESULT 1.0 50 0.3 0.019963966051010137\nRESULT 1.0 50 1.0 0.026295988978744727\npred 1.0 100 19366482\nRESULT 1.0 100 0 0.014071639205553531\nRESULT 1.0 100 0.01 0.01821762953880885\nRESULT 1.0 100 0.03 0.01835130151965709\nRESULT 1.0 100 0.1 0.018807116302041852\nRESULT 1.0 100 0.3 0.019953840882952198\nRESULT 1.0 100 1.0 0.02602265501423322\npred 1.0 200 31970762\nRESULT 1.0 200 0 0.014013773550960614\nRESULT 1.0 200 0.01 0.018208368760460475\nRESULT 1.0 200 0.03 0.018266319931025912\nRESULT 1.0 200 0.1 0.018716109353217568\nRESULT 1.0 200 0.3 0.019838782353696058\nRESULT 1.0 200 1.0 0.02549782542888643\n", "stderr": "", "aggregated_output": "X (8456, 30000) 418435 users 6769\nsim alpha 0\nS nnz 5208759\npred 0 20 4120533\nRESULT 0 20 0 0.010189151174046571\nRESULT 0 20 0.01 0.014625009228479428\nRESULT 0 20 0.03 0.014953498427857191\nRESULT 0 20 0.1 0.015603920122833\nRESULT 0 20 0.3 0.01818227165559802\nRESULT 0 20 1.0 0.02614888619836062\npred 0 50 9791912\nRESULT 0 50 0 0.01278979557371587\nRESULT 0 50 0.01 0.016931792254098226\nRESULT 0 50 0.03 0.017241748496562945\nRESULT 0 50 0.1 0.01803826865470088\nRESULT 0 50 0.3 0.020647610156042415\nRESULT 0 50 1.0 0.027850455696709697\npred 0 100 17824538\nRESULT 0 100 0 0.013942372413151364\nRESULT 0 100 0.01 0.01819974565864306\nRESULT 0 100 0.03 0.018239386259612918\nRESULT 0 100 0.1 0.019052189606065792\nRESULT 0 100 0.3 0.020466507261134904\nRESULT 0 100 1.0 0.027936251913010045\npred 0 200 30005716\nRESULT 0 200 0 0.013695124373878454\nRESULT 0 200 0.01 0.017881347029397733\nRESULT 0 200 0.03 0.01811718282971806\nRESULT 0 200 0.1 0.018644844509053415\nRESULT 0 200 0.3 0.020052867129975376\nRESULT 0 200 1.0 0.026666550127912775\nsim alpha 0.5\nS nnz 5208759\npred 0.5 20 4347325\nRESULT 0.5 20 0 0.009756102751931808\nRESULT 0.5 20 0.01 0.014195539354608162\nRESULT 0.5 20 0.03 0.014518028075190427\nRESULT 0.5 20 0.1 0.015447652588324439\nRESULT 0.5 20 0.3 0.017621824838038123\nRESULT 0.5 20 1.0 0.025838365552665308\npred 0.5 50 10304043\nRESULT 0.5 50 0 0.013099753371926605\nRESULT 0.5 50 0.01 0.017274966699935747\nRESULT 0.5 50 0.03 0.017498331713449052\nRESULT 0.5 50 0.1 0.018290733147372267\nRESULT 0.5 50 0.3 0.02008576358616745\nRESULT 0.5 50 1.0 0.027616094791895238\npred 0.5 100 18578173\nRESULT 0.5 100 0 0.01419749188073614\nRESULT 0.5 100 0.01 0.01832402281210433\nRESULT 0.5 100 0.03 0.018379772386347838\nRESULT 0.5 100 0.1 0.018727698022948797\nRESULT 0.5 100 0.3 0.020277147016509896\nRESULT 0.5 100 1.0 0.026940600252948747\npred 0.5 200 30925926\nRESULT 0.5 200 0 0.014042554321452811\nRESULT 0.5 200 0.01 0.01815658036900156\nRESULT 0.5 200 0.03 0.018243893834594564\nRESULT 0.5 200 0.1 0.01876376044262871\nRESULT 0.5 200 0.3 0.020022159840145572\nRESULT 0.5 200 1.0 0.026467739303177605\nsim alpha 1.0\nS nnz 5208759\npred 1.0 20 4487569\nRESULT 1.0 20 0 0.009764170700602641\nRESULT 1.0 20 0.01 0.014275421240727157\nRESULT 1.0 20 0.03 0.014364684560321889\nRESULT 1.0 20 0.1 0.015258290966744893\nRESULT 1.0 20 0.3 0.017116595795726507\nRESULT 1.0 20 1.0 0.024692198262097922\npred 1.0 50 10759896\nRESULT 1.0 50 0 0.01329800262093432\nRESULT 1.0 50 0.01 0.017526661009645172\nRESULT 1.0 50 0.03 0.01792440496909016\nRESULT 1.0 50 0.1 0.0183000230729711\nRESULT 1.0 50 0.3 0.019963966051010137\nRESULT 1.0 50 1.0 0.026295988978744727\npred 1.0 100 19366482\nRESULT 1.0 100 0 0.014071639205553531\nRESULT 1.0 100 0.01 0.01821762953880885\nRESULT 1.0 100 0.03 0.01835130151965709\nRESULT 1.0 100 0.1 0.018807116302041852\nRESULT 1.0 100 0.3 0.019953840882952198\nRESULT 1.0 100 1.0 0.02602265501423322\npred 1.0 200 31970762\nRESULT 1.0 200 0 0.014013773550960614\nRESULT 1.0 200 0.01 0.018208368760460475\nRESULT 1.0 200 0.03 0.018266319931025912\nRESULT 1.0 200 0.1 0.018716109353217568\nRESULT 1.0 200 0.3 0.019838782353696058\nRESULT 1.0 200 1.0 0.02549782542888643\n", "exit_code": 0, "duration": { "secs": 95, "nanos": 490699237 }, "formatted_output": "X (8456, 30000) 418435 users 6769\nsim alpha 0\nS nnz 5208759\npred 0 20 4120533\nRESULT 0 20 0 0.010189151174046571\nRESULT 0 20 0.01 0.014625009228479428\nRESULT 0 20 0.03 0.014953498427857191\nRESULT 0 20 0.1 0.015603920122833\nRESULT 0 20 0.3 0.01818227165559802\nRESULT 0 20 1.0 0.02614888619836062\npred 0 50 9791912\nRESULT 0 50 0 0.01278979557371587\nRESULT 0 50 0.01 0.016931792254098226\nRESULT 0 50 0.03 0.017241748496562945\nRESULT 0 50 0.1 0.01803826865470088\nRESULT 0 50 0.3 0.020647610156042415\nRESULT 0 50 1.0 0.027850455696709697\npred 0 100 17824538\nRESULT 0 100 0 0.013942372413151364\nRESULT 0 100 0.01 0.01819974565864306\nRESULT 0 100 0.03 0.018239386259612918\nRESULT 0 100 0.1 0.019052189606065792\nRESULT 0 100 0.3 0.020466507261134904\nRESULT 0 100 1.0 0.027936251913010045\npred 0 200 30005716\nRESULT 0 200 0 0.013695124373878454\nRESULT 0 200 0.01 0.017881347029397733\nRESULT 0 200 0.03 0.01811718282971806\nRESULT 0 200 0.1 0.018644844509053415\nRESULT 0 200 0.3 0.020052867129975376\nRESULT 0 200 1.0 0.026666550127912775\nsim alpha 0.5\nS nnz 5208759\npred 0.5 20 4347325\nRESULT 0.5 20 0 0.009756102751931808\nRESULT 0.5 20 0.01 0.014195539354608162\nRESULT 0.5 20 0.03 0.014518028075190427\nRESULT 0.5 20 0.1 0.015447652588324439\nRESULT 0.5 20 0.3 0.017621824838038123\nRESULT 0.5 20 1.0 0.025838365552665308\npred 0.5 50 10304043\nRESULT 0.5 50 0 0.013099753371926605\nRESULT 0.5 50 0.01 0.017274966699935747\nRESULT 0.5 50 0.03 0.017498331713449052\nRESULT 0.5 50 0.1 0.018290733147372267\nRESULT 0.5 50 0.3 0.02008576358616745\nRESULT 0.5 50 1.0 0.027616094791895238\npred 0.5 100 18578173\nRESULT 0.5 100 0 0.01419749188073614\nRESULT 0.5 100 0.01 0.01832402281210433\nRESULT 0.5 100 0.03 0.018379772386347838\nRESULT 0.5 100 0.1 0.018727698022948797\nRESULT 0.5 100 0.3 0.020277147016509896\nRESULT 0.5 100 1.0 0.026940600252948747\npred 0.5 200 30925926\nRESULT 0.5 200 0 0.014042554321452811\nRESULT 0.5 200 0.01 0.01815658036900156\nRESULT 0.5 200 0.03 0.018243893834594564\nRESULT 0.5 200 0.1 0.01876376044262871\nRESULT 0.5 200 0.3 0.020022159840145572\nRESULT 0.5 200 1.0 0.026467739303177605\nsim alpha 1.0\nS nnz 5208759\npred 1.0 20 4487569\nRESULT 1.0 20 0 0.009764170700602641\nRESULT 1.0 20 0.01 0.014275421240727157\nRESULT 1.0 20 0.03 0.014364684560321889\nRESULT 1.0 20 0.1 0.015258290966744893\nRESULT 1.0 20 0.3 0.017116595795726507\nRESULT 1.0 20 1.0 0.024692198262097922\npred 1.0 50 10759896\nRESULT 1.0 50 0 0.01329800262093432\nRESULT 1.0 50 0.01 0.017526661009645172\nRESULT 1.0 50 0.03 0.01792440496909016\nRESULT 1.0 50 0.1 0.0183000230729711\nRESULT 1.0 50 0.3 0.019963966051010137\nRESULT 1.0 50 1.0 0.026295988978744727\npred 1.0 100 19366482\nRESULT 1.0 100 0 0.014071639205553531\nRESULT 1.0 100 0.01 0.01821762953880885\nRESULT 1.0 100 0.03 0.01835130151965709\nRESULT 1.0 100 0.1 0.018807116302041852\nRESULT 1.0 100 0.3 0.019953840882952198\nRESULT 1.0 100 1.0 0.02602265501423322\npred 1.0 200 31970762\nRESULT 1.0 200 0 0.014013773550960614\nRESULT 1.0 200 0.01 0.018208368760460475\nRESULT 1.0 200 0.03 0.018266319931025912\nRESULT 1.0 200 0.1 0.018716109353217568\nRESULT 1.0 200 0.3 0.019838782353696058\nRESULT 1.0 200 1.0 0.02549782542888643\n" } } ## Step 41 · agent · 2026-09-25T01:06:24.631Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:06:39.517Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u - <<'PY'\\nimport sys\\nsys.path.insert(0,'/task')\\nfrom evaluate_public import validation_truth\\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\\nfrom pathlib import Path\\nimport numpy as np,pandas as pd,itertools\\nP='/task/data'; cat=pd.read_parquet(P+'/candidate_catalog.parquet'); items=cat.item_id.to_numpy(np.int64); poprank=cat.popularity_rank.to_numpy(); n=len(items)\\nE=CandidateSet(Path(P+'/validation_eligible_candidates.npz')); target=set(pd.read_parquet(P+'/target_users.parquet').uid.astype(int)); gold=validation_truth(E,target)\\nuidrow=E.uid_row\\nG=np.load('/app/solution/global_features.npz');F=G['F'];L=G['LF']\\n# exact but optimized global evaluation\\ndef ev(s):\\n order=np.lexsort((poprank,-s))\\n vals=[]\\n for u,g in gold.items():\\n r=uidrow[u]; chosen=[]\\n for j in order[:500]:\\n if (int(E.packed[r,j//8])>>(j%8))&1: chosen.append(int(items[j]))\\n if len(chosen)==10: break\\n vals.append(ndcg_at_k(chosen,g,10))\\n return float(np.mean(vals))\\nbase=F[0,2]\\nresults=[]\\ndef add(name,s): results.append((ev(np.asarray(s,dtype=float)),name))\\nadd('base_all50_7d',base)\\n# cumulative time feature blends\\nfor a in [-.75,-.5,-.25,.25,.5,1,2,4]: add(f'F7 + {a} F3',base+a*F[0,1])\\nfor a in [-.75,-.5,-.25,.1,.25,.5,1,2]: add(f'F7 + {a} F14',base+a*F[0,3])\\nfor a in [.01,.03,.1,.3,1,3]: add(f'F7 + {a} F30',base+a*F[0,4])\\nfor a in [.1,.3,1,3,10,30,100]: add(f'F7 + {a} LikeOrg3',base+a*L[1,1])\\nfor a in [.1,.3,1,3,10,30,100]: add(f'F7 + {a} LikeAll7',base+a*L[0,2])\\nfor a in [.1,.3,1,3,10]: add(f'F7 + {a} Org50_7',base+a*F[2,2])\\nfor a in [.1,.3,1,3,10]: add(f'F7 + {a} StrongAll7',base+a*F[1,2])\\n# rank fusion (best=1); normalized reverse percentile\\ndef rankscore(x):\\n order=np.lexsort((poprank,-x)); z=np.empty(n);z[order]=np.arange(n);return 1-z/n\\nrb=rankscore(base)\\nfor feat,name in [(L[1,1],'LikeOrg3'),(L[0,2],'LikeAll7'),(F[2,2],'Org50_7'),(F[1,2],'All80_7'),(F[0,1],'All50_3'),(F[0,3],'All50_14')]:\\n rf=rankscore(feat)\\n for a in [.05,.1,.2,.3,.5,1,2]: add(f'rank F7 + {a} {name}',rb+a*rf)\\n# log fusion\\nfor feat,name in [(L[1,1],'LikeOrg3'),(L[0,2],'LikeAll7'),(F[2,2],'Org50_7'),(F[0,3],'All50_14')]:\\n x=np.log1p(feat);x/=x.max()\\n y=np.log1p(base);y/=y.max()\\n for a in [.05,.1,.2,.3,.5,1]:add(f'log F7 + {a} {name}',y+a*x)\\nfor x in sorted(results,reverse=True)[:35]:print(x)\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:12000});\nif(r.session_id){text(`SESSION_ID=${r.session_id}`);text(r.output);}else{text(r.output);}\n" } ### Tool output · 2026-09-25T01:06:55.565Z Script completed Wall time 16.0 seconds Output: (0.034957401307281685, 'rank F7 + 2 LikeOrg3') (0.03478906582342955, 'rank F7 + 1 LikeOrg3') (0.03436086491344594, 'log F7 + 0.3 LikeOrg3') (0.03429570802518156, 'log F7 + 0.5 LikeOrg3') (0.03421016414251796, 'log F7 + 0.2 LikeOrg3') (0.0342037438050043, 'rank F7 + 0.2 LikeOrg3') (0.033963631976805006, 'rank F7 + 0.5 LikeOrg3') (0.03393315911693131, 'rank F7 + 0.3 LikeOrg3') (0.033787105886709724, 'rank F7 + 0.1 LikeOrg3') (0.033577766869050334, 'F7 + 100 LikeOrg3') (0.033459262947387595, 'log F7 + 1 LikeOrg3') (0.03326640989113371, 'F7 + 30 LikeOrg3') (0.03324830605264237, 'rank F7 + 0.05 LikeOrg3') (0.03323864773018212, 'log F7 + 0.1 LikeOrg3') (0.03303446457964477, 'log F7 + 0.2 LikeAll7') (0.03301407343142128, 'rank F7 + 0.3 LikeAll7') (0.032992502245060944, 'rank F7 + 0.5 LikeAll7') (0.03296717813655677, 'log F7 + 0.1 LikeAll7') (0.032964118087312456, 'rank F7 + 0.2 LikeAll7') (0.03296207749721871, 'rank F7 + 1 LikeAll7') (0.03294124369615301, 'log F7 + 0.3 LikeAll7') (0.032898299180800554, 'log F7 + 0.5 LikeAll7') (0.03267579072906117, 'rank F7 + 0.1 LikeAll7') (0.032656385582347736, 'F7 + 10 LikeAll7') (0.03262728734120741, 'log F7 + 0.05 LikeOrg3') (0.03240378572193071, 'rank F7 + 2 LikeAll7') (0.03232675455530496, 'F7 + 30 LikeAll7') (0.032248437646257566, 'rank F7 + 0.05 LikeAll7') (0.03222491551007138, 'log F7 + 0.05 LikeAll7') (0.03198543638504063, 'log F7 + 1 LikeAll7') (0.03190467328496499, 'F7 + 10 LikeOrg3') (0.0317579672030315, 'F7 + 3 LikeAll7') (0.0312735133385832, 'rank F7 + 0.1 All50_3') (0.0312514732279822, 'rank F7 + 0.05 All50_3') (0.03124045903603493, 'F7 + -0.75 F3') ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "response_id": "resp_0fdbf3e20a3ba82b016ab5c9058c4087d2a90887a30b67d18b", "usage": { "input_tokens": 41307, "cached_input_tokens": 40448, "cache_write_input_tokens": 0, "output_tokens": 1378, "reasoning_output_tokens": 357, "total_tokens": 42685 }, "turn_token_usage": { "input_tokens": 667119, "cached_input_tokens": 632832, "cache_write_input_tokens": 0, "output_tokens": 16558, "reasoning_output_tokens": 7961, "total_tokens": 683677 }, "thread_token_usage": { "input_tokens": 667119, "cached_input_tokens": 632832, "cache_write_input_tokens": 0, "output_tokens": 16558, "reasoning_output_tokens": 7961, "total_tokens": 683677 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2ed2da1c-3760-44e9-a10f-998242d5175a", "process_id": "72683", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u - <<'PY'\nimport sys\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport numpy as np,pandas as pd,itertools\nP='/task/data'; cat=pd.read_parquet(P+'/candidate_catalog.parquet'); items=cat.item_id.to_numpy(np.int64); poprank=cat.popularity_rank.to_numpy(); n=len(items)\nE=CandidateSet(Path(P+'/validation_eligible_candidates.npz')); target=set(pd.read_parquet(P+'/target_users.parquet').uid.astype(int)); gold=validation_truth(E,target)\nuidrow=E.uid_row\nG=np.load('/app/solution/global_features.npz');F=G['F'];L=G['LF']\n# exact but optimized global evaluation\ndef ev(s):\n order=np.lexsort((poprank,-s))\n vals=[]\n for u,g in gold.items():\n r=uidrow[u]; chosen=[]\n for j in order[:500]:\n if (int(E.packed[r,j//8])>>(j%8))&1: chosen.append(int(items[j]))\n if len(chosen)==10: break\n vals.append(ndcg_at_k(chosen,g,10))\n return float(np.mean(vals))\nbase=F[0,2]\nresults=[]\ndef add(name,s): results.append((ev(np.asarray(s,dtype=float)),name))\nadd('base_all50_7d',base)\n# cumulative time feature blends\nfor a in [-.75,-.5,-.25,.25,.5,1,2,4]: add(f'F7 + {a} F3',base+a*F[0,1])\nfor a in [-.75,-.5,-.25,.1,.25,.5,1,2]: add(f'F7 + {a} F14',base+a*F[0,3])\nfor a in [.01,.03,.1,.3,1,3]: add(f'F7 + {a} F30',base+a*F[0,4])\nfor a in [.1,.3,1,3,10,30,100]: add(f'F7 + {a} LikeOrg3',base+a*L[1,1])\nfor a in [.1,.3,1,3,10,30,100]: add(f'F7 + {a} LikeAll7',base+a*L[0,2])\nfor a in [.1,.3,1,3,10]: add(f'F7 + {a} Org50_7',base+a*F[2,2])\nfor a in [.1,.3,1,3,10]: add(f'F7 + {a} StrongAll7',base+a*F[1,2])\n# rank fusion (best=1); normalized reverse percentile\ndef rankscore(x):\n order=np.lexsort((poprank,-x)); z=np.empty(n);z[order]=np.arange(n);return 1-z/n\nrb=rankscore(base)\nfor feat,name in [(L[1,1],'LikeOrg3'),(L[0,2],'LikeAll7'),(F[2,2],'Org50_7'),(F[1,2],'All80_7'),(F[0,1],'All50_3'),(F[0,3],'All50_14')]:\n rf=rankscore(feat)\n for a in [.05,.1,.2,.3,.5,1,2]: add(f'rank F7 + {a} {name}',rb+a*rf)\n# log fusion\nfor feat,name in [(L[1,1],'LikeOrg3'),(L[0,2],'LikeAll7'),(F[2,2],'Org50_7'),(F[0,3],'All50_14')]:\n x=np.log1p(feat);x/=x.max()\n y=np.log1p(base);y/=y.max()\n for a in [.05,.1,.2,.3,.5,1]:add(f'log F7 + {a} {name}',y+a*x)\nfor x in sorted(results,reverse=True)[:35]:print(x)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport sys\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport numpy as np,pandas as pd,itertools\nP='/task/data'; cat=pd.read_parquet(P+'/candidate_catalog.parquet'); items=cat.item_id.to_numpy(np.int64); poprank=cat.popularity_rank.to_numpy(); n=len(items)\nE=CandidateSet(Path(P+'/validation_eligible_candidates.npz')); target=set(pd.read_parquet(P+'/target_users.parquet').uid.astype(int)); gold=validation_truth(E,target)\nuidrow=E.uid_row\nG=np.load('/app/solution/global_features.npz');F=G['F'];L=G['LF']\n# exact but optimized global evaluation\ndef ev(s):\n order=np.lexsort((poprank,-s))\n vals=[]\n for u,g in gold.items():\n r=uidrow[u]; chosen=[]\n for j in order[:500]:\n if (int(E.packed[r,j//8])>>(j%8))&1: chosen.append(int(items[j]))\n if len(chosen)==10: break\n vals.append(ndcg_at_k(chosen,g,10))\n return float(np.mean(vals))\nbase=F[0,2]\nresults=[]\ndef add(name,s): results.append((ev(np.asarray(s,dtype=float)),name))\nadd('base_all50_7d',base)\n# cumulative time feature blends\nfor a in [-.75,-.5,-.25,.25,.5,1,2,4]: add(f'F7 + {a} F3',base+a*F[0,1])\nfor a in [-.75,-.5,-.25,.1,.25,.5,1,2]: add(f'F7 + {a} F14',base+a*F[0,3])\nfor a in [.01,.03,.1,.3,1,3]: add(f'F7 + {a} F30',base+a*F[0,4])\nfor a in [.1,.3,1,3,10,30,100]: add(f'F7 + {a} LikeOrg3',base+a*L[1,1])\nfor a in [.1,.3,1,3,10,30,100]: add(f'F7 + {a} LikeAll7',base+a*L[0,2])\nfor a in [.1,.3,1,3,10]: add(f'F7 + {a} Org50_7',base+a*F[2,2])\nfor a in [.1,.3,1,3,10]: add(f'F7 + {a} StrongAll7',base+a*F[1,2])\n# rank fusion (best=1); normalized reverse percentile\ndef rankscore(x):\n order=np.lexsort((poprank,-x)); z=np.empty(n);z[order]=np.arange(n);return 1-z/n\nrb=rankscore(base)\nfor feat,name in [(L[1,1],'LikeOrg3'),(L[0,2],'LikeAll7'),(F[2,2],'Org50_7'),(F[1,2],'All80_7'),(F[0,1],'All50_3'),(F[0,3],'All50_14')]:\n rf=rankscore(feat)\n for a in [.05,.1,.2,.3,.5,1,2]: add(f'rank F7 + {a} {name}',rb+a*rf)\n# log fusion\nfor feat,name in [(L[1,1],'LikeOrg3'),(L[0,2],'LikeAll7'),(F[2,2],'Org50_7'),(F[0,3],'All50_14')]:\n x=np.log1p(feat);x/=x.max()\n y=np.log1p(base);y/=y.max()\n for a in [.05,.1,.2,.3,.5,1]:add(f'log F7 + {a} {name}',y+a*x)\nfor x in sorted(results,reverse=True)[:35]:print(x)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "(0.034957401307281685, 'rank F7 + 2 LikeOrg3')\n(0.03478906582342955, 'rank F7 + 1 LikeOrg3')\n(0.03436086491344594, 'log F7 + 0.3 LikeOrg3')\n(0.03429570802518156, 'log F7 + 0.5 LikeOrg3')\n(0.03421016414251796, 'log F7 + 0.2 LikeOrg3')\n(0.0342037438050043, 'rank F7 + 0.2 LikeOrg3')\n(0.033963631976805006, 'rank F7 + 0.5 LikeOrg3')\n(0.03393315911693131, 'rank F7 + 0.3 LikeOrg3')\n(0.033787105886709724, 'rank F7 + 0.1 LikeOrg3')\n(0.033577766869050334, 'F7 + 100 LikeOrg3')\n(0.033459262947387595, 'log F7 + 1 LikeOrg3')\n(0.03326640989113371, 'F7 + 30 LikeOrg3')\n(0.03324830605264237, 'rank F7 + 0.05 LikeOrg3')\n(0.03323864773018212, 'log F7 + 0.1 LikeOrg3')\n(0.03303446457964477, 'log F7 + 0.2 LikeAll7')\n(0.03301407343142128, 'rank F7 + 0.3 LikeAll7')\n(0.032992502245060944, 'rank F7 + 0.5 LikeAll7')\n(0.03296717813655677, 'log F7 + 0.1 LikeAll7')\n(0.032964118087312456, 'rank F7 + 0.2 LikeAll7')\n(0.03296207749721871, 'rank F7 + 1 LikeAll7')\n(0.03294124369615301, 'log F7 + 0.3 LikeAll7')\n(0.032898299180800554, 'log F7 + 0.5 LikeAll7')\n(0.03267579072906117, 'rank F7 + 0.1 LikeAll7')\n(0.032656385582347736, 'F7 + 10 LikeAll7')\n(0.03262728734120741, 'log F7 + 0.05 LikeOrg3')\n(0.03240378572193071, 'rank F7 + 2 LikeAll7')\n(0.03232675455530496, 'F7 + 30 LikeAll7')\n(0.032248437646257566, 'rank F7 + 0.05 LikeAll7')\n(0.03222491551007138, 'log F7 + 0.05 LikeAll7')\n(0.03198543638504063, 'log F7 + 1 LikeAll7')\n(0.03190467328496499, 'F7 + 10 LikeOrg3')\n(0.0317579672030315, 'F7 + 3 LikeAll7')\n(0.0312735133385832, 'rank F7 + 0.1 All50_3')\n(0.0312514732279822, 'rank F7 + 0.05 All50_3')\n(0.03124045903603493, 'F7 + -0.75 F3')\n", "stderr": "", "aggregated_output": "(0.034957401307281685, 'rank F7 + 2 LikeOrg3')\n(0.03478906582342955, 'rank F7 + 1 LikeOrg3')\n(0.03436086491344594, 'log F7 + 0.3 LikeOrg3')\n(0.03429570802518156, 'log F7 + 0.5 LikeOrg3')\n(0.03421016414251796, 'log F7 + 0.2 LikeOrg3')\n(0.0342037438050043, 'rank F7 + 0.2 LikeOrg3')\n(0.033963631976805006, 'rank F7 + 0.5 LikeOrg3')\n(0.03393315911693131, 'rank F7 + 0.3 LikeOrg3')\n(0.033787105886709724, 'rank F7 + 0.1 LikeOrg3')\n(0.033577766869050334, 'F7 + 100 LikeOrg3')\n(0.033459262947387595, 'log F7 + 1 LikeOrg3')\n(0.03326640989113371, 'F7 + 30 LikeOrg3')\n(0.03324830605264237, 'rank F7 + 0.05 LikeOrg3')\n(0.03323864773018212, 'log F7 + 0.1 LikeOrg3')\n(0.03303446457964477, 'log F7 + 0.2 LikeAll7')\n(0.03301407343142128, 'rank F7 + 0.3 LikeAll7')\n(0.032992502245060944, 'rank F7 + 0.5 LikeAll7')\n(0.03296717813655677, 'log F7 + 0.1 LikeAll7')\n(0.032964118087312456, 'rank F7 + 0.2 LikeAll7')\n(0.03296207749721871, 'rank F7 + 1 LikeAll7')\n(0.03294124369615301, 'log F7 + 0.3 LikeAll7')\n(0.032898299180800554, 'log F7 + 0.5 LikeAll7')\n(0.03267579072906117, 'rank F7 + 0.1 LikeAll7')\n(0.032656385582347736, 'F7 + 10 LikeAll7')\n(0.03262728734120741, 'log F7 + 0.05 LikeOrg3')\n(0.03240378572193071, 'rank F7 + 2 LikeAll7')\n(0.03232675455530496, 'F7 + 30 LikeAll7')\n(0.032248437646257566, 'rank F7 + 0.05 LikeAll7')\n(0.03222491551007138, 'log F7 + 0.05 LikeAll7')\n(0.03198543638504063, 'log F7 + 1 LikeAll7')\n(0.03190467328496499, 'F7 + 10 LikeOrg3')\n(0.0317579672030315, 'F7 + 3 LikeAll7')\n(0.0312735133385832, 'rank F7 + 0.1 All50_3')\n(0.0312514732279822, 'rank F7 + 0.05 All50_3')\n(0.03124045903603493, 'F7 + -0.75 F3')\n", "exit_code": 0, "duration": { "secs": 15, "nanos": 880264343 }, "formatted_output": "(0.034957401307281685, 'rank F7 + 2 LikeOrg3')\n(0.03478906582342955, 'rank F7 + 1 LikeOrg3')\n(0.03436086491344594, 'log F7 + 0.3 LikeOrg3')\n(0.03429570802518156, 'log F7 + 0.5 LikeOrg3')\n(0.03421016414251796, 'log F7 + 0.2 LikeOrg3')\n(0.0342037438050043, 'rank F7 + 0.2 LikeOrg3')\n(0.033963631976805006, 'rank F7 + 0.5 LikeOrg3')\n(0.03393315911693131, 'rank F7 + 0.3 LikeOrg3')\n(0.033787105886709724, 'rank F7 + 0.1 LikeOrg3')\n(0.033577766869050334, 'F7 + 100 LikeOrg3')\n(0.033459262947387595, 'log F7 + 1 LikeOrg3')\n(0.03326640989113371, 'F7 + 30 LikeOrg3')\n(0.03324830605264237, 'rank F7 + 0.05 LikeOrg3')\n(0.03323864773018212, 'log F7 + 0.1 LikeOrg3')\n(0.03303446457964477, 'log F7 + 0.2 LikeAll7')\n(0.03301407343142128, 'rank F7 + 0.3 LikeAll7')\n(0.032992502245060944, 'rank F7 + 0.5 LikeAll7')\n(0.03296717813655677, 'log F7 + 0.1 LikeAll7')\n(0.032964118087312456, 'rank F7 + 0.2 LikeAll7')\n(0.03296207749721871, 'rank F7 + 1 LikeAll7')\n(0.03294124369615301, 'log F7 + 0.3 LikeAll7')\n(0.032898299180800554, 'log F7 + 0.5 LikeAll7')\n(0.03267579072906117, 'rank F7 + 0.1 LikeAll7')\n(0.032656385582347736, 'F7 + 10 LikeAll7')\n(0.03262728734120741, 'log F7 + 0.05 LikeOrg3')\n(0.03240378572193071, 'rank F7 + 2 LikeAll7')\n(0.03232675455530496, 'F7 + 30 LikeAll7')\n(0.032248437646257566, 'rank F7 + 0.05 LikeAll7')\n(0.03222491551007138, 'log F7 + 0.05 LikeAll7')\n(0.03198543638504063, 'log F7 + 1 LikeAll7')\n(0.03190467328496499, 'F7 + 10 LikeOrg3')\n(0.0317579672030315, 'F7 + 3 LikeAll7')\n(0.0312735133385832, 'rank F7 + 0.1 All50_3')\n(0.0312514732279822, 'rank F7 + 0.05 All50_3')\n(0.03124045903603493, 'F7 + -0.75 F3')\n" } } ## Step 42 · agent · 2026-09-25T01:07:09.011Z ### Thinking unavailable — no text recorded ## Step 43 · agent · 2026-09-25T01:07:17.458Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:07:28.957Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u - <<'PY'\\nimport sys,time\\nsys.path.insert(0,'/task')\\nfrom evaluate_public import validation_truth\\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\\nfrom pathlib import Path\\nimport pandas as pd,numpy as np\\nfrom scipy import sparse\\nfrom sklearn.decomposition import TruncatedSVD\\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();nitem=len(items)\\ntarget=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64); ur={int(x):i for i,x in enumerate(target)};ic={int(x):i for i,x in enumerate(items)}\\nps=pd.read_parquet(P+'/validation_preference_state.parquet');ps=ps[ps.state=='liked'];rr=np.array([ur.get(int(x),-1) for x in ps.uid],np.int32);cc=np.array([ic.get(int(x),-1) for x in ps.item_id],np.int32);ok=(rr>=0)&(cc>=0);X=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(rr[ok],cc[ok])),shape=(len(target),nitem));X.data[:]=1\\nE=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qrows=np.array([ur[u] for u in gold]);quid=[int(target[r]) for r in qrows]\\nwith np.load(P+'/validation_eligible_candidates.npz') as z: bits=np.unpackbits(z['packed_eligible'],axis=1,count=nitem,bitorder='little').astype(bool)\\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\\ndef rankscore(x):o=np.lexsort((pr,-x));z=np.empty(nitem,np.float32);z[o]=1-np.arange(nitem)/nitem;return z\\ngb=rankscore(F[0,2])+2*rankscore(L[1,1])\\nalphas=[0,.01,.03,.05,.1,.2,.3,.5,1]; vals={a:[] for a in alphas}\\nfor nc in [32,64,128]:\\n print('fit',nc,flush=True);sv=TruncatedSVD(n_components=nc,n_iter=7,random_state=42);U=sv.fit_transform(X);V=sv.components_;print('variance',sv.explained_variance_ratio_.sum(),flush=True)\\n for st in range(0,len(qrows),128):\\n qr=qrows[st:st+128];S=(U[qr]@V).astype(np.float32);S-=S.mean(1)[:,None];sd=S.std(1);sd[sd==0]=1;S/=sd[:,None]\\n S[~bits[qr]]=-np.inf\\n for a in alphas:\\n Z=gb[None,:]+a*S if a else np.broadcast_to(gb,S.shape).copy();Z[~bits[qr]]=-np.inf\\n top=np.argpartition(Z,-10,axis=1)[:,-10:]\\n for j,row in enumerate(top):\\n row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[a].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\\n for a in alphas:print('RES',nc,a,np.mean(vals[a]),flush=True)\\n vals={a:[] for a in alphas}\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:20000});\nif(r.session_id){text(`SESSION_ID=${r.session_id}`);text(r.output);}else{text(r.output);}\n" } ### Tool output · 2026-09-25T01:07:56.018Z Script completed Wall time 27.1 seconds Output: fit 32 variance 0.11988419 RES 32 0 0.03495591411760116 RES 32 0.01 0.024748579646264566 RES 32 0.03 0.02183208445854038 RES 32 0.05 0.020925747937378815 RES 32 0.1 0.01990034332098162 RES 32 0.2 0.019802510935443572 RES 32 0.3 0.01946900450982573 RES 32 0.5 0.019094940574087935 RES 32 1 0.018764380033371834 fit 64 variance 0.16763306 RES 64 0 0.03495591411760116 RES 64 0.01 0.02416820275801751 RES 64 0.03 0.02155272475849179 RES 64 0.05 0.020676271508330072 RES 64 0.1 0.02021373227437422 RES 64 0.2 0.019845774823983888 RES 64 0.3 0.01946677981747845 RES 64 0.5 0.019178314654645673 RES 64 1 0.019090399536859086 fit 128 variance 0.23857811 RES 128 0 0.03495591411760116 RES 128 0.01 0.024084694866554294 RES 128 0.03 0.02123484317464594 RES 128 0.05 0.020576848528793597 RES 128 0.1 0.019645424416012094 RES 128 0.2 0.019256679279768662 RES 128 0.3 0.01899265541444578 RES 128 0.5 0.018630611718205715 RES 128 1 0.01854198263504278 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "response_id": "resp_0fdbf3e20a3ba82b016ab5c92fc3bc87d2b281491a484e4b77", "usage": { "input_tokens": 43483, "cached_input_tokens": 41088, "cache_write_input_tokens": 0, "output_tokens": 1755, "reasoning_output_tokens": 895, "total_tokens": 45238 }, "turn_token_usage": { "input_tokens": 710602, "cached_input_tokens": 673920, "cache_write_input_tokens": 0, "output_tokens": 18313, "reasoning_output_tokens": 8856, "total_tokens": 728915 }, "thread_token_usage": { "input_tokens": 710602, "cached_input_tokens": 673920, "cache_write_input_tokens": 0, "output_tokens": 18313, "reasoning_output_tokens": 8856, "total_tokens": 728915 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ac1fd492-036b-43f1-99d6-aa5560cef8b3", "process_id": "43255", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u - <<'PY'\nimport sys,time\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();nitem=len(items)\ntarget=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64); ur={int(x):i for i,x in enumerate(target)};ic={int(x):i for i,x in enumerate(items)}\nps=pd.read_parquet(P+'/validation_preference_state.parquet');ps=ps[ps.state=='liked'];rr=np.array([ur.get(int(x),-1) for x in ps.uid],np.int32);cc=np.array([ic.get(int(x),-1) for x in ps.item_id],np.int32);ok=(rr>=0)&(cc>=0);X=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(rr[ok],cc[ok])),shape=(len(target),nitem));X.data[:]=1\nE=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qrows=np.array([ur[u] for u in gold]);quid=[int(target[r]) for r in qrows]\nwith np.load(P+'/validation_eligible_candidates.npz') as z: bits=np.unpackbits(z['packed_eligible'],axis=1,count=nitem,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rankscore(x):o=np.lexsort((pr,-x));z=np.empty(nitem,np.float32);z[o]=1-np.arange(nitem)/nitem;return z\ngb=rankscore(F[0,2])+2*rankscore(L[1,1])\nalphas=[0,.01,.03,.05,.1,.2,.3,.5,1]; vals={a:[] for a in alphas}\nfor nc in [32,64,128]:\n print('fit',nc,flush=True);sv=TruncatedSVD(n_components=nc,n_iter=7,random_state=42);U=sv.fit_transform(X);V=sv.components_;print('variance',sv.explained_variance_ratio_.sum(),flush=True)\n for st in range(0,len(qrows),128):\n qr=qrows[st:st+128];S=(U[qr]@V).astype(np.float32);S-=S.mean(1)[:,None];sd=S.std(1);sd[sd==0]=1;S/=sd[:,None]\n S[~bits[qr]]=-np.inf\n for a in alphas:\n Z=gb[None,:]+a*S if a else np.broadcast_to(gb,S.shape).copy();Z[~bits[qr]]=-np.inf\n top=np.argpartition(Z,-10,axis=1)[:,-10:]\n for j,row in enumerate(top):\n row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[a].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\n for a in alphas:print('RES',nc,a,np.mean(vals[a]),flush=True)\n vals={a:[] for a in alphas}\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport sys,time\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();nitem=len(items)\ntarget=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64); ur={int(x):i for i,x in enumerate(target)};ic={int(x):i for i,x in enumerate(items)}\nps=pd.read_parquet(P+'/validation_preference_state.parquet');ps=ps[ps.state=='liked'];rr=np.array([ur.get(int(x),-1) for x in ps.uid],np.int32);cc=np.array([ic.get(int(x),-1) for x in ps.item_id],np.int32);ok=(rr>=0)&(cc>=0);X=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(rr[ok],cc[ok])),shape=(len(target),nitem));X.data[:]=1\nE=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qrows=np.array([ur[u] for u in gold]);quid=[int(target[r]) for r in qrows]\nwith np.load(P+'/validation_eligible_candidates.npz') as z: bits=np.unpackbits(z['packed_eligible'],axis=1,count=nitem,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rankscore(x):o=np.lexsort((pr,-x));z=np.empty(nitem,np.float32);z[o]=1-np.arange(nitem)/nitem;return z\ngb=rankscore(F[0,2])+2*rankscore(L[1,1])\nalphas=[0,.01,.03,.05,.1,.2,.3,.5,1]; vals={a:[] for a in alphas}\nfor nc in [32,64,128]:\n print('fit',nc,flush=True);sv=TruncatedSVD(n_components=nc,n_iter=7,random_state=42);U=sv.fit_transform(X);V=sv.components_;print('variance',sv.explained_variance_ratio_.sum(),flush=True)\n for st in range(0,len(qrows),128):\n qr=qrows[st:st+128];S=(U[qr]@V).astype(np.float32);S-=S.mean(1)[:,None];sd=S.std(1);sd[sd==0]=1;S/=sd[:,None]\n S[~bits[qr]]=-np.inf\n for a in alphas:\n Z=gb[None,:]+a*S if a else np.broadcast_to(gb,S.shape).copy();Z[~bits[qr]]=-np.inf\n top=np.argpartition(Z,-10,axis=1)[:,-10:]\n for j,row in enumerate(top):\n row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[a].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\n for a in alphas:print('RES',nc,a,np.mean(vals[a]),flush=True)\n vals={a:[] for a in alphas}\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "fit 32\nvariance 0.11988419\nRES 32 0 0.03495591411760116\nRES 32 0.01 0.024748579646264566\nRES 32 0.03 0.02183208445854038\nRES 32 0.05 0.020925747937378815\nRES 32 0.1 0.01990034332098162\nRES 32 0.2 0.019802510935443572\nRES 32 0.3 0.01946900450982573\nRES 32 0.5 0.019094940574087935\nRES 32 1 0.018764380033371834\nfit 64\nvariance 0.16763306\nRES 64 0 0.03495591411760116\nRES 64 0.01 0.02416820275801751\nRES 64 0.03 0.02155272475849179\nRES 64 0.05 0.020676271508330072\nRES 64 0.1 0.02021373227437422\nRES 64 0.2 0.019845774823983888\nRES 64 0.3 0.01946677981747845\nRES 64 0.5 0.019178314654645673\nRES 64 1 0.019090399536859086\nfit 128\nvariance 0.23857811\nRES 128 0 0.03495591411760116\nRES 128 0.01 0.024084694866554294\nRES 128 0.03 0.02123484317464594\nRES 128 0.05 0.020576848528793597\nRES 128 0.1 0.019645424416012094\nRES 128 0.2 0.019256679279768662\nRES 128 0.3 0.01899265541444578\nRES 128 0.5 0.018630611718205715\nRES 128 1 0.01854198263504278\n", "stderr": "", "aggregated_output": "fit 32\nvariance 0.11988419\nRES 32 0 0.03495591411760116\nRES 32 0.01 0.024748579646264566\nRES 32 0.03 0.02183208445854038\nRES 32 0.05 0.020925747937378815\nRES 32 0.1 0.01990034332098162\nRES 32 0.2 0.019802510935443572\nRES 32 0.3 0.01946900450982573\nRES 32 0.5 0.019094940574087935\nRES 32 1 0.018764380033371834\nfit 64\nvariance 0.16763306\nRES 64 0 0.03495591411760116\nRES 64 0.01 0.02416820275801751\nRES 64 0.03 0.02155272475849179\nRES 64 0.05 0.020676271508330072\nRES 64 0.1 0.02021373227437422\nRES 64 0.2 0.019845774823983888\nRES 64 0.3 0.01946677981747845\nRES 64 0.5 0.019178314654645673\nRES 64 1 0.019090399536859086\nfit 128\nvariance 0.23857811\nRES 128 0 0.03495591411760116\nRES 128 0.01 0.024084694866554294\nRES 128 0.03 0.02123484317464594\nRES 128 0.05 0.020576848528793597\nRES 128 0.1 0.019645424416012094\nRES 128 0.2 0.019256679279768662\nRES 128 0.3 0.01899265541444578\nRES 128 0.5 0.018630611718205715\nRES 128 1 0.01854198263504278\n", "exit_code": 0, "duration": { "secs": 26, "nanos": 893513205 }, "formatted_output": "fit 32\nvariance 0.11988419\nRES 32 0 0.03495591411760116\nRES 32 0.01 0.024748579646264566\nRES 32 0.03 0.02183208445854038\nRES 32 0.05 0.020925747937378815\nRES 32 0.1 0.01990034332098162\nRES 32 0.2 0.019802510935443572\nRES 32 0.3 0.01946900450982573\nRES 32 0.5 0.019094940574087935\nRES 32 1 0.018764380033371834\nfit 64\nvariance 0.16763306\nRES 64 0 0.03495591411760116\nRES 64 0.01 0.02416820275801751\nRES 64 0.03 0.02155272475849179\nRES 64 0.05 0.020676271508330072\nRES 64 0.1 0.02021373227437422\nRES 64 0.2 0.019845774823983888\nRES 64 0.3 0.01946677981747845\nRES 64 0.5 0.019178314654645673\nRES 64 1 0.019090399536859086\nfit 128\nvariance 0.23857811\nRES 128 0 0.03495591411760116\nRES 128 0.01 0.024084694866554294\nRES 128 0.03 0.02123484317464594\nRES 128 0.05 0.020576848528793597\nRES 128 0.1 0.019645424416012094\nRES 128 0.2 0.019256679279768662\nRES 128 0.3 0.01899265541444578\nRES 128 0.5 0.018630611718205715\nRES 128 1 0.01854198263504278\n" } } ## Step 44 · agent · 2026-09-25T01:08:00.525Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:08:14.357Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u - <<'PY'\\nimport sys\\nsys.path.insert(0,'/task')\\nfrom evaluate_public import validation_truth\\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\\nfrom pathlib import Path\\nimport pandas as pd,numpy as np\\nfrom scipy import sparse\\nfrom sklearn.decomposition import TruncatedSVD\\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();nitem=len(items);target=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64);ur={int(x):i for i,x in enumerate(target)};ic={int(x):i for i,x in enumerate(items)}\\nps=pd.read_parquet(P+'/validation_preference_state.parquet');ps=ps[ps.state=='liked'];rr=np.array([ur.get(int(x),-1) for x in ps.uid]);cc=np.array([ic.get(int(x),-1) for x in ps.item_id]);ok=(rr>=0)&(cc>=0);X=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(rr[ok],cc[ok])),shape=(len(target),nitem));X.data[:]=1\\nE=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qrows=np.array([ur[u] for u in gold]);quid=[int(target[r]) for r in qrows]\\nwith np.load(P+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=nitem,bitorder='little').astype(bool)\\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(nitem,np.float32);z[o]=1-np.arange(nitem)/nitem;return z\\ngb=rs(F[0,2])+2*rs(L[1,1]);sv=TruncatedSVD(n_components=32,n_iter=7,random_state=42);U=sv.fit_transform(X);V=sv.components_\\nalphas=[0,1e-7,3e-7,1e-6,3e-6,1e-5,3e-5,1e-4,3e-4,1e-3];vals={a:[] for a in alphas}\\nfor st in range(0,len(qrows),128):\\n qr=qrows[st:st+128];S=U[qr]@V;S-=S.mean(1)[:,None];sd=S.std(1);sd[sd==0]=1;S/=sd[:,None]\\n for a in alphas:\\n Z=gb[None,:]+a*S;Z[~bits[qr]]=-np.inf;top=np.argpartition(Z,-10,axis=1)[:,-10:]\\n for j,row in enumerate(top):row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[a].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\\nfor a in alphas:print(a,np.mean(vals[a]))\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:12000});\nif(r.session_id){text(`SESSION_ID=${r.session_id}`);text(r.output);}else{text(r.output);}\n" } ### Tool output · 2026-09-25T01:08:25.321Z Script completed Wall time 11.0 seconds Output: 0 0.03495591411760116 1e-07 0.03495608415986733 3e-07 0.03495608415986733 1e-06 0.03495608415986733 3e-06 0.03495114874763764 1e-05 0.03526063346151137 3e-05 0.03542977359260937 0.0001 0.035877024136109305 0.0003 0.03568162253059627 0.001 0.033818241776565706 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "response_id": "resp_0fdbf3e20a3ba82b016ab5c96c376487d2aa7f5d150a6f31a9", "usage": { "input_tokens": 45744, "cached_input_tokens": 43264, "cache_write_input_tokens": 0, "output_tokens": 907, "reasoning_output_tokens": 124, "total_tokens": 46651 }, "turn_token_usage": { "input_tokens": 756346, "cached_input_tokens": 717184, "cache_write_input_tokens": 0, "output_tokens": 19220, "reasoning_output_tokens": 8980, "total_tokens": 775566 }, "thread_token_usage": { "input_tokens": 756346, "cached_input_tokens": 717184, "cache_write_input_tokens": 0, "output_tokens": 19220, "reasoning_output_tokens": 8980, "total_tokens": 775566 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d5123a2f-db26-42a3-8cd8-31dcd05167d3", "process_id": "57191", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u - <<'PY'\nimport sys\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();nitem=len(items);target=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64);ur={int(x):i for i,x in enumerate(target)};ic={int(x):i for i,x in enumerate(items)}\nps=pd.read_parquet(P+'/validation_preference_state.parquet');ps=ps[ps.state=='liked'];rr=np.array([ur.get(int(x),-1) for x in ps.uid]);cc=np.array([ic.get(int(x),-1) for x in ps.item_id]);ok=(rr>=0)&(cc>=0);X=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(rr[ok],cc[ok])),shape=(len(target),nitem));X.data[:]=1\nE=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qrows=np.array([ur[u] for u in gold]);quid=[int(target[r]) for r in qrows]\nwith np.load(P+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=nitem,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(nitem,np.float32);z[o]=1-np.arange(nitem)/nitem;return z\ngb=rs(F[0,2])+2*rs(L[1,1]);sv=TruncatedSVD(n_components=32,n_iter=7,random_state=42);U=sv.fit_transform(X);V=sv.components_\nalphas=[0,1e-7,3e-7,1e-6,3e-6,1e-5,3e-5,1e-4,3e-4,1e-3];vals={a:[] for a in alphas}\nfor st in range(0,len(qrows),128):\n qr=qrows[st:st+128];S=U[qr]@V;S-=S.mean(1)[:,None];sd=S.std(1);sd[sd==0]=1;S/=sd[:,None]\n for a in alphas:\n Z=gb[None,:]+a*S;Z[~bits[qr]]=-np.inf;top=np.argpartition(Z,-10,axis=1)[:,-10:]\n for j,row in enumerate(top):row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[a].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\nfor a in alphas:print(a,np.mean(vals[a]))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport sys\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();nitem=len(items);target=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64);ur={int(x):i for i,x in enumerate(target)};ic={int(x):i for i,x in enumerate(items)}\nps=pd.read_parquet(P+'/validation_preference_state.parquet');ps=ps[ps.state=='liked'];rr=np.array([ur.get(int(x),-1) for x in ps.uid]);cc=np.array([ic.get(int(x),-1) for x in ps.item_id]);ok=(rr>=0)&(cc>=0);X=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(rr[ok],cc[ok])),shape=(len(target),nitem));X.data[:]=1\nE=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qrows=np.array([ur[u] for u in gold]);quid=[int(target[r]) for r in qrows]\nwith np.load(P+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=nitem,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(nitem,np.float32);z[o]=1-np.arange(nitem)/nitem;return z\ngb=rs(F[0,2])+2*rs(L[1,1]);sv=TruncatedSVD(n_components=32,n_iter=7,random_state=42);U=sv.fit_transform(X);V=sv.components_\nalphas=[0,1e-7,3e-7,1e-6,3e-6,1e-5,3e-5,1e-4,3e-4,1e-3];vals={a:[] for a in alphas}\nfor st in range(0,len(qrows),128):\n qr=qrows[st:st+128];S=U[qr]@V;S-=S.mean(1)[:,None];sd=S.std(1);sd[sd==0]=1;S/=sd[:,None]\n for a in alphas:\n Z=gb[None,:]+a*S;Z[~bits[qr]]=-np.inf;top=np.argpartition(Z,-10,axis=1)[:,-10:]\n for j,row in enumerate(top):row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[a].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\nfor a in alphas:print(a,np.mean(vals[a]))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "0 0.03495591411760116\n1e-07 0.03495608415986733\n3e-07 0.03495608415986733\n1e-06 0.03495608415986733\n3e-06 0.03495114874763764\n1e-05 0.03526063346151137\n3e-05 0.03542977359260937\n0.0001 0.035877024136109305\n0.0003 0.03568162253059627\n0.001 0.033818241776565706\n", "stderr": "", "aggregated_output": "0 0.03495591411760116\n1e-07 0.03495608415986733\n3e-07 0.03495608415986733\n1e-06 0.03495608415986733\n3e-06 0.03495114874763764\n1e-05 0.03526063346151137\n3e-05 0.03542977359260937\n0.0001 0.035877024136109305\n0.0003 0.03568162253059627\n0.001 0.033818241776565706\n", "exit_code": 0, "duration": { "secs": 10, "nanos": 762249920 }, "formatted_output": "0 0.03495591411760116\n1e-07 0.03495608415986733\n3e-07 0.03495608415986733\n1e-06 0.03495608415986733\n3e-06 0.03495114874763764\n1e-05 0.03526063346151137\n3e-05 0.03542977359260937\n0.0001 0.035877024136109305\n0.0003 0.03568162253059627\n0.001 0.033818241776565706\n" } } ## Step 45 · agent · 2026-09-25T01:08:39.916Z ### Thinking unavailable — no text recorded ## Step 46 · agent · 2026-09-25T01:08:41.261Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:08:42.441Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd\\nx=pd.read_parquet('/task/data/candidate_catalog.parquet')\\nprint(x[['positive_listens','organic_positive_listens']].sum())\\nprint(x[['positive_listens','organic_positive_listens']].quantile([.5,.9,.99,1]))\\nPY\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:5000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:08:43.220Z Script completed Wall time 0.8 seconds Output: positive_listens 20056329.0 organic_positive_listens 9417793.0 dtype: float64 positive_listens organic_positive_listens 0.50 354.00 162.00 0.90 1367.00 640.00 0.99 5052.16 2548.08 1.00 26615.00 20620.00 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "response_id": "resp_0fdbf3e20a3ba82b016ab5c989d9ac87d29b0baf9dc3b315f2", "usage": { "input_tokens": 46810, "cached_input_tokens": 45568, "cache_write_input_tokens": 0, "output_tokens": 700, "reasoning_output_tokens": 574, "total_tokens": 47510 }, "turn_token_usage": { "input_tokens": 803156, "cached_input_tokens": 762752, "cache_write_input_tokens": 0, "output_tokens": 19920, "reasoning_output_tokens": 9554, "total_tokens": 823076 }, "thread_token_usage": { "input_tokens": 803156, "cached_input_tokens": 762752, "cache_write_input_tokens": 0, "output_tokens": 19920, "reasoning_output_tokens": 9554, "total_tokens": 823076 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4c38087a-e9e4-4569-96a8-4edb742e53f6", "process_id": "61431", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd\nx=pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint(x[['positive_listens','organic_positive_listens']].sum())\nprint(x[['positive_listens','organic_positive_listens']].quantile([.5,.9,.99,1]))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd\nx=pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint(x[['positive_listens','organic_positive_listens']].sum())\nprint(x[['positive_listens','organic_positive_listens']].quantile([.5,.9,.99,1]))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "positive_listens 20056329.0\norganic_positive_listens 9417793.0\ndtype: float64\n positive_listens organic_positive_listens\n0.50 354.00 162.00\n0.90 1367.00 640.00\n0.99 5052.16 2548.08\n1.00 26615.00 20620.00\n", "stderr": "", "aggregated_output": "positive_listens 20056329.0\norganic_positive_listens 9417793.0\ndtype: float64\n positive_listens organic_positive_listens\n0.50 354.00 162.00\n0.90 1367.00 640.00\n0.99 5052.16 2548.08\n1.00 26615.00 20620.00\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 557417630 }, "formatted_output": "positive_listens 20056329.0\norganic_positive_listens 9417793.0\ndtype: float64\n positive_listens organic_positive_listens\n0.50 354.00 162.00\n0.90 1367.00 640.00\n0.99 5052.16 2548.08\n1.00 26615.00 20620.00\n" } } ## Step 47 · agent · 2026-09-25T01:08:56.257Z ### Thinking unavailable — no text recorded Active-like SVD provides a real, if modest, lift: the best blended validation score is now about 0.03588. I’m expanding the representation to the full positive-listen graph; it contains roughly 20 million candidate-track events and should capture taste structure that sparse explicit likes miss. ### exec · 2026-09-25T01:09:04.680Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u - <<'PY'\\nimport pyarrow.parquet as pq, pandas as pd, numpy as np\\nfrom scipy import sparse\\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');ids=cat.item_id.to_numpy(np.int64);n=len(ids);sid=np.sort(ids);s2c=np.argsort(ids)\\nrows=[];cols=[];orgs=[]\\npf=pq.ParquetFile(P+'/train_listens.parquet')\\nfor bi,b in enumerate(pf.iter_batches(batch_size=2_000_000,columns=['uid','item_id','is_organic','played_ratio_pct'])):\\n a=b.to_pydict();ratio=np.asarray(a['played_ratio_pct'],np.uint16);m=ratio>=50;it=np.asarray(a['item_id'],np.int64)[m];p=np.searchsorted(sid,it);ok=(p=50;it=np.asarray(a['item_id'],np.int64)[m];p=np.searchsorted(sid,it);ok=(p=50;it=np.asarray(a['item_id'],np.int64)[m];p=np.searchsorted(sid,it);ok=(p>(j%8))&1:a.append(int(it[j]))\\n if len(a)==10:break\\n v.append(ndcg_at_k(a,gg,10))\\n return np.mean(v)\\nr7=rs(F[0,2]);\\nfeatures=[('likeorg3',rs(L[1,1])),('likeorg7',rs(L[1,2])),('likeall3',rs(L[0,1])),('likeblend',rs(L[1,1])+0.3*rs(L[1,2])),('likeorg3_rawtie',rs(L[1,1],-F[0,2]))]\\nfor name,r in features:\\n for a in [.5,.75,1,1.25,1.5,1.75,2,2.25,2.5,3,4,5,7,10]: print(name,a,ev(r7+a*r))\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:12000});\nif(r.session_id){text(`SESSION_ID=${r.session_id}`);text(r.output);}else{text(r.output);}\n" } ### Tool output · 2026-09-25T01:11:47.269Z Script completed Wall time 10.2 seconds Output: likeorg3 0.5 0.033963631976805006 likeorg3 0.75 0.03413670092434041 likeorg3 1 0.03478906582342955 likeorg3 1.25 0.03481941028493119 likeorg3 1.5 0.0348618509450571 likeorg3 1.75 0.034886024028319376 likeorg3 2 0.034957401307281685 likeorg3 2.25 0.03453638825389476 likeorg3 2.5 0.0342649138091844 likeorg3 3 0.03410615910380185 likeorg3 4 0.03386415382699404 likeorg3 5 0.03361298271630871 likeorg3 7 0.033363475304940986 likeorg3 10 0.03312374318430003 likeorg7 0.5 0.03300521469452717 likeorg7 0.75 0.032714753661767 likeorg7 1 0.03215135411560338 likeorg7 1.25 0.031975020627536085 likeorg7 1.5 0.03187190903460598 likeorg7 1.75 0.03166283041459479 likeorg7 2 0.031683216162269004 likeorg7 2.25 0.03175334192483856 likeorg7 2.5 0.031735352730559725 likeorg7 3 0.03165587557469718 likeorg7 4 0.031219386489485065 likeorg7 5 0.03118128422998665 likeorg7 7 0.030461659029017908 likeorg7 10 0.030506893990931268 likeall3 0.5 0.034027207369405336 likeall3 0.75 0.03421020290779107 likeall3 1 0.03447505310436017 likeall3 1.25 0.03448857389772924 likeall3 1.5 0.034469006991614586 likeall3 1.75 0.03413722504781785 likeall3 2 0.03418546580534684 likeall3 2.25 0.03414946999074894 likeall3 2.5 0.034291482503797974 likeall3 3 0.03391757674421942 likeall3 4 0.03311989908179825 likeall3 5 0.03288138166394025 likeall3 7 0.03294791771345674 likeall3 10 0.03226151795615059 likeblend 0.5 0.03409316057929037 likeblend 0.75 0.03426779707219418 likeblend 1 0.03460844146056106 likeblend 1.25 0.0346986227865933 likeblend 1.5 0.03433258726225442 likeblend 1.75 0.034233449048533565 likeblend 2 0.0342574963923634 likeblend 2.25 0.03421577784838721 likeblend 2.5 0.03384182230443158 likeblend 3 0.0334504252092217 likeblend 4 0.033291197097675446 likeblend 5 0.033092381999362604 likeblend 7 0.03257359081248193 likeblend 10 0.0325665198876556 likeorg3_rawtie 0.5 0.033880949387223004 likeorg3_rawtie 0.75 0.03385470160526581 likeorg3_rawtie 1 0.03409585099446252 likeorg3_rawtie 1.25 0.03430167766570564 likeorg3_rawtie 1.5 0.034557518727019704 likeorg3_rawtie 1.75 0.0341957909376445 likeorg3_rawtie 2 0.03410001970826477 likeorg3_rawtie 2.25 0.03415115970020574 likeorg3_rawtie 2.5 0.0341368847678014 likeorg3_rawtie 3 0.034302700665116176 likeorg3_rawtie 4 0.03401202335041622 likeorg3_rawtie 5 0.03369932438727578 likeorg3_rawtie 7 0.033007819899746244 likeorg3_rawtie 10 0.032861566939448 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "response_id": "resp_0fdbf3e20a3ba82b016ab5ca3cc13087d2b7c39a21e67de4ac", "usage": { "input_tokens": 51320, "cached_input_tokens": 50688, "cache_write_input_tokens": 0, "output_tokens": 575, "reasoning_output_tokens": 46, "total_tokens": 51895 }, "turn_token_usage": { "input_tokens": 1099980, "cached_input_tokens": 1054080, "cache_write_input_tokens": 0, "output_tokens": 23048, "reasoning_output_tokens": 10597, "total_tokens": 1123028 }, "thread_token_usage": { "input_tokens": 1099980, "cached_input_tokens": 1054080, "cache_write_input_tokens": 0, "output_tokens": 23048, "reasoning_output_tokens": 10597, "total_tokens": 1123028 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f3dd3635-31e4-4bd5-aa95-c066fb14b9ed", "process_id": "16197", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u - <<'PY'\nimport sys\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');it=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();n=len(it);E=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(pd.read_parquet(P+'/target_users.parquet').uid.astype(int)));g=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x,tie=pr):o=np.lexsort((tie,-x));z=np.empty(n);z[o]=1-np.arange(n)/n;return z\ndef ev(s):\n o=np.lexsort((pr,-s));v=[]\n for u,gg in gold.items():\n a=[];r=E.uid_row[u]\n for j in o[:100]:\n if (int(E.packed[r,j//8])>>(j%8))&1:a.append(int(it[j]))\n if len(a)==10:break\n v.append(ndcg_at_k(a,gg,10))\n return np.mean(v)\nr7=rs(F[0,2]);\nfeatures=[('likeorg3',rs(L[1,1])),('likeorg7',rs(L[1,2])),('likeall3',rs(L[0,1])),('likeblend',rs(L[1,1])+0.3*rs(L[1,2])),('likeorg3_rawtie',rs(L[1,1],-F[0,2]))]\nfor name,r in features:\n for a in [.5,.75,1,1.25,1.5,1.75,2,2.25,2.5,3,4,5,7,10]: print(name,a,ev(r7+a*r))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport sys\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');it=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();n=len(it);E=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(pd.read_parquet(P+'/target_users.parquet').uid.astype(int)));g=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x,tie=pr):o=np.lexsort((tie,-x));z=np.empty(n);z[o]=1-np.arange(n)/n;return z\ndef ev(s):\n o=np.lexsort((pr,-s));v=[]\n for u,gg in gold.items():\n a=[];r=E.uid_row[u]\n for j in o[:100]:\n if (int(E.packed[r,j//8])>>(j%8))&1:a.append(int(it[j]))\n if len(a)==10:break\n v.append(ndcg_at_k(a,gg,10))\n return np.mean(v)\nr7=rs(F[0,2]);\nfeatures=[('likeorg3',rs(L[1,1])),('likeorg7',rs(L[1,2])),('likeall3',rs(L[0,1])),('likeblend',rs(L[1,1])+0.3*rs(L[1,2])),('likeorg3_rawtie',rs(L[1,1],-F[0,2]))]\nfor name,r in features:\n for a in [.5,.75,1,1.25,1.5,1.75,2,2.25,2.5,3,4,5,7,10]: print(name,a,ev(r7+a*r))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "likeorg3 0.5 0.033963631976805006\nlikeorg3 0.75 0.03413670092434041\nlikeorg3 1 0.03478906582342955\nlikeorg3 1.25 0.03481941028493119\nlikeorg3 1.5 0.0348618509450571\nlikeorg3 1.75 0.034886024028319376\nlikeorg3 2 0.034957401307281685\nlikeorg3 2.25 0.03453638825389476\nlikeorg3 2.5 0.0342649138091844\nlikeorg3 3 0.03410615910380185\nlikeorg3 4 0.03386415382699404\nlikeorg3 5 0.03361298271630871\nlikeorg3 7 0.033363475304940986\nlikeorg3 10 0.03312374318430003\nlikeorg7 0.5 0.03300521469452717\nlikeorg7 0.75 0.032714753661767\nlikeorg7 1 0.03215135411560338\nlikeorg7 1.25 0.031975020627536085\nlikeorg7 1.5 0.03187190903460598\nlikeorg7 1.75 0.03166283041459479\nlikeorg7 2 0.031683216162269004\nlikeorg7 2.25 0.03175334192483856\nlikeorg7 2.5 0.031735352730559725\nlikeorg7 3 0.03165587557469718\nlikeorg7 4 0.031219386489485065\nlikeorg7 5 0.03118128422998665\nlikeorg7 7 0.030461659029017908\nlikeorg7 10 0.030506893990931268\nlikeall3 0.5 0.034027207369405336\nlikeall3 0.75 0.03421020290779107\nlikeall3 1 0.03447505310436017\nlikeall3 1.25 0.03448857389772924\nlikeall3 1.5 0.034469006991614586\nlikeall3 1.75 0.03413722504781785\nlikeall3 2 0.03418546580534684\nlikeall3 2.25 0.03414946999074894\nlikeall3 2.5 0.034291482503797974\nlikeall3 3 0.03391757674421942\nlikeall3 4 0.03311989908179825\nlikeall3 5 0.03288138166394025\nlikeall3 7 0.03294791771345674\nlikeall3 10 0.03226151795615059\nlikeblend 0.5 0.03409316057929037\nlikeblend 0.75 0.03426779707219418\nlikeblend 1 0.03460844146056106\nlikeblend 1.25 0.0346986227865933\nlikeblend 1.5 0.03433258726225442\nlikeblend 1.75 0.034233449048533565\nlikeblend 2 0.0342574963923634\nlikeblend 2.25 0.03421577784838721\nlikeblend 2.5 0.03384182230443158\nlikeblend 3 0.0334504252092217\nlikeblend 4 0.033291197097675446\nlikeblend 5 0.033092381999362604\nlikeblend 7 0.03257359081248193\nlikeblend 10 0.0325665198876556\nlikeorg3_rawtie 0.5 0.033880949387223004\nlikeorg3_rawtie 0.75 0.03385470160526581\nlikeorg3_rawtie 1 0.03409585099446252\nlikeorg3_rawtie 1.25 0.03430167766570564\nlikeorg3_rawtie 1.5 0.034557518727019704\nlikeorg3_rawtie 1.75 0.0341957909376445\nlikeorg3_rawtie 2 0.03410001970826477\nlikeorg3_rawtie 2.25 0.03415115970020574\nlikeorg3_rawtie 2.5 0.0341368847678014\nlikeorg3_rawtie 3 0.034302700665116176\nlikeorg3_rawtie 4 0.03401202335041622\nlikeorg3_rawtie 5 0.03369932438727578\nlikeorg3_rawtie 7 0.033007819899746244\nlikeorg3_rawtie 10 0.032861566939448\n", "stderr": "", "aggregated_output": "likeorg3 0.5 0.033963631976805006\nlikeorg3 0.75 0.03413670092434041\nlikeorg3 1 0.03478906582342955\nlikeorg3 1.25 0.03481941028493119\nlikeorg3 1.5 0.0348618509450571\nlikeorg3 1.75 0.034886024028319376\nlikeorg3 2 0.034957401307281685\nlikeorg3 2.25 0.03453638825389476\nlikeorg3 2.5 0.0342649138091844\nlikeorg3 3 0.03410615910380185\nlikeorg3 4 0.03386415382699404\nlikeorg3 5 0.03361298271630871\nlikeorg3 7 0.033363475304940986\nlikeorg3 10 0.03312374318430003\nlikeorg7 0.5 0.03300521469452717\nlikeorg7 0.75 0.032714753661767\nlikeorg7 1 0.03215135411560338\nlikeorg7 1.25 0.031975020627536085\nlikeorg7 1.5 0.03187190903460598\nlikeorg7 1.75 0.03166283041459479\nlikeorg7 2 0.031683216162269004\nlikeorg7 2.25 0.03175334192483856\nlikeorg7 2.5 0.031735352730559725\nlikeorg7 3 0.03165587557469718\nlikeorg7 4 0.031219386489485065\nlikeorg7 5 0.03118128422998665\nlikeorg7 7 0.030461659029017908\nlikeorg7 10 0.030506893990931268\nlikeall3 0.5 0.034027207369405336\nlikeall3 0.75 0.03421020290779107\nlikeall3 1 0.03447505310436017\nlikeall3 1.25 0.03448857389772924\nlikeall3 1.5 0.034469006991614586\nlikeall3 1.75 0.03413722504781785\nlikeall3 2 0.03418546580534684\nlikeall3 2.25 0.03414946999074894\nlikeall3 2.5 0.034291482503797974\nlikeall3 3 0.03391757674421942\nlikeall3 4 0.03311989908179825\nlikeall3 5 0.03288138166394025\nlikeall3 7 0.03294791771345674\nlikeall3 10 0.03226151795615059\nlikeblend 0.5 0.03409316057929037\nlikeblend 0.75 0.03426779707219418\nlikeblend 1 0.03460844146056106\nlikeblend 1.25 0.0346986227865933\nlikeblend 1.5 0.03433258726225442\nlikeblend 1.75 0.034233449048533565\nlikeblend 2 0.0342574963923634\nlikeblend 2.25 0.03421577784838721\nlikeblend 2.5 0.03384182230443158\nlikeblend 3 0.0334504252092217\nlikeblend 4 0.033291197097675446\nlikeblend 5 0.033092381999362604\nlikeblend 7 0.03257359081248193\nlikeblend 10 0.0325665198876556\nlikeorg3_rawtie 0.5 0.033880949387223004\nlikeorg3_rawtie 0.75 0.03385470160526581\nlikeorg3_rawtie 1 0.03409585099446252\nlikeorg3_rawtie 1.25 0.03430167766570564\nlikeorg3_rawtie 1.5 0.034557518727019704\nlikeorg3_rawtie 1.75 0.0341957909376445\nlikeorg3_rawtie 2 0.03410001970826477\nlikeorg3_rawtie 2.25 0.03415115970020574\nlikeorg3_rawtie 2.5 0.0341368847678014\nlikeorg3_rawtie 3 0.034302700665116176\nlikeorg3_rawtie 4 0.03401202335041622\nlikeorg3_rawtie 5 0.03369932438727578\nlikeorg3_rawtie 7 0.033007819899746244\nlikeorg3_rawtie 10 0.032861566939448\n", "exit_code": 0, "duration": { "secs": 9, "nanos": 990549708 }, "formatted_output": "likeorg3 0.5 0.033963631976805006\nlikeorg3 0.75 0.03413670092434041\nlikeorg3 1 0.03478906582342955\nlikeorg3 1.25 0.03481941028493119\nlikeorg3 1.5 0.0348618509450571\nlikeorg3 1.75 0.034886024028319376\nlikeorg3 2 0.034957401307281685\nlikeorg3 2.25 0.03453638825389476\nlikeorg3 2.5 0.0342649138091844\nlikeorg3 3 0.03410615910380185\nlikeorg3 4 0.03386415382699404\nlikeorg3 5 0.03361298271630871\nlikeorg3 7 0.033363475304940986\nlikeorg3 10 0.03312374318430003\nlikeorg7 0.5 0.03300521469452717\nlikeorg7 0.75 0.032714753661767\nlikeorg7 1 0.03215135411560338\nlikeorg7 1.25 0.031975020627536085\nlikeorg7 1.5 0.03187190903460598\nlikeorg7 1.75 0.03166283041459479\nlikeorg7 2 0.031683216162269004\nlikeorg7 2.25 0.03175334192483856\nlikeorg7 2.5 0.031735352730559725\nlikeorg7 3 0.03165587557469718\nlikeorg7 4 0.031219386489485065\nlikeorg7 5 0.03118128422998665\nlikeorg7 7 0.030461659029017908\nlikeorg7 10 0.030506893990931268\nlikeall3 0.5 0.034027207369405336\nlikeall3 0.75 0.03421020290779107\nlikeall3 1 0.03447505310436017\nlikeall3 1.25 0.03448857389772924\nlikeall3 1.5 0.034469006991614586\nlikeall3 1.75 0.03413722504781785\nlikeall3 2 0.03418546580534684\nlikeall3 2.25 0.03414946999074894\nlikeall3 2.5 0.034291482503797974\nlikeall3 3 0.03391757674421942\nlikeall3 4 0.03311989908179825\nlikeall3 5 0.03288138166394025\nlikeall3 7 0.03294791771345674\nlikeall3 10 0.03226151795615059\nlikeblend 0.5 0.03409316057929037\nlikeblend 0.75 0.03426779707219418\nlikeblend 1 0.03460844146056106\nlikeblend 1.25 0.0346986227865933\nlikeblend 1.5 0.03433258726225442\nlikeblend 1.75 0.034233449048533565\nlikeblend 2 0.0342574963923634\nlikeblend 2.25 0.03421577784838721\nlikeblend 2.5 0.03384182230443158\nlikeblend 3 0.0334504252092217\nlikeblend 4 0.033291197097675446\nlikeblend 5 0.033092381999362604\nlikeblend 7 0.03257359081248193\nlikeblend 10 0.0325665198876556\nlikeorg3_rawtie 0.5 0.033880949387223004\nlikeorg3_rawtie 0.75 0.03385470160526581\nlikeorg3_rawtie 1 0.03409585099446252\nlikeorg3_rawtie 1.25 0.03430167766570564\nlikeorg3_rawtie 1.5 0.034557518727019704\nlikeorg3_rawtie 1.75 0.0341957909376445\nlikeorg3_rawtie 2 0.03410001970826477\nlikeorg3_rawtie 2.25 0.03415115970020574\nlikeorg3_rawtie 2.5 0.0341368847678014\nlikeorg3_rawtie 3 0.034302700665116176\nlikeorg3_rawtie 4 0.03401202335041622\nlikeorg3_rawtie 5 0.03369932438727578\nlikeorg3_rawtie 7 0.033007819899746244\nlikeorg3_rawtie 10 0.032861566939448\n" } } ## Step 53 · agent · 2026-09-25T01:11:51.995Z ### Thinking unavailable — no text recorded The full listen graph improves the blended score again, to roughly 0.03699. Its best behavior uses a broad 96-dimensional taste representation with a deliberately small reranking weight, so the strong release-trend prior remains dominant. I’m checking count weighting and an ensemble of implicit and explicit taste before locking the final refit. ### exec · 2026-09-25T01:12:07.769Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u - <<'PY'\\nimport sys\\nsys.path.insert(0,'/task')\\nfrom evaluate_public import validation_truth\\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\\nfrom pathlib import Path\\nimport pandas as pd,numpy as np\\nfrom scipy import sparse\\nfrom sklearn.decomposition import TruncatedSVD\\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();nitem=len(items);target=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64);qm={int(x):i for i,x in enumerate(target)}\\nX=sparse.load_npz('/app/solution/listen_counts_train.npz').astype(np.float32);X.data=np.log1p(X.data)\\nE=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qidx=np.array([qm[u] for u in gold]);xrows=(target[qidx]//100-1).astype(int);quid=[int(target[r]) for r in qidx]\\nwith np.load(P+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=nitem,bitorder='little').astype(bool)\\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(nitem,np.float32);z[o]=1-np.arange(nitem)/nitem;return z\\ngb=rs(F[0,2])+2*rs(L[1,1]);print('fit log',flush=True);sv=TruncatedSVD(n_components=96,n_iter=5,random_state=42);U=sv.fit_transform(X);V=sv.components_;print('var',sv.explained_variance_ratio_.sum(),flush=True)\\nalphas=[0,3e-5,1e-4,2e-4,3e-4,5e-4,8e-4,1e-3,1.5e-3,2e-3];\\nfor nc in [32,64,96]:\\n vals={a:[] for a in alphas}\\n for st in range(0,len(xrows),128):\\n xr=xrows[st:st+128];br=qidx[st:st+128];S=U[xr,:nc]@V[:nc];S-=S.mean(1)[:,None];sd=S.std(1);sd[sd==0]=1;S/=sd[:,None]\\n for a in alphas:\\n Z=gb[None,:]+a*S;Z[~bits[br]]=-np.inf;top=np.argpartition(Z,-10,axis=1)[:,-10:]\\n for j,row in enumerate(top):row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[a].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\\n for a in alphas:print('RES',nc,a,np.mean(vals[a]),flush=True)\\nnp.savez_compressed('/app/solution/svd_log_train.npz',U=U.astype(np.float32),V=V.astype(np.float32))\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:18000});\nif(r.session_id){text(`SESSION_ID=${r.session_id}`);text(r.output);}else{text(r.output);}\n" } ### Tool output · 2026-09-25T01:12:36.852Z Script completed Wall time 29.1 seconds Output: fit log var 0.3575546 RES 32 0 0.03495591411760116 RES 32 3e-05 0.0353726078481892 RES 32 0.0001 0.03605401797775986 RES 32 0.0002 0.0359450069399237 RES 32 0.0003 0.03603601478047241 RES 32 0.0005 0.03552715065926786 RES 32 0.0008 0.03428268122781672 RES 32 0.001 0.03398355523132804 RES 32 0.0015 0.03283249076932839 RES 32 0.002 0.031165942662446152 RES 64 0 0.03495591411760116 RES 64 3e-05 0.03536834239899224 RES 64 0.0001 0.036017760504718166 RES 64 0.0002 0.03645014265578708 RES 64 0.0003 0.03653031242431469 RES 64 0.0005 0.03635567310962588 RES 64 0.0008 0.03549897760970315 RES 64 0.001 0.0354733726813475 RES 64 0.0015 0.034946425240903294 RES 64 0.002 0.034490170162109084 RES 96 0 0.03495591411760116 RES 96 3e-05 0.035316829966982925 RES 96 0.0001 0.03591987355758362 RES 96 0.0002 0.03639998885755811 RES 96 0.0003 0.03661170809011704 RES 96 0.0005 0.03650224844968985 RES 96 0.0008 0.03580003695449044 RES 96 0.001 0.035307308547128796 RES 96 0.0015 0.03502323382880352 RES 96 0.002 0.03454097055740437 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "response_id": "resp_0fdbf3e20a3ba82b016ab5ca53bed087d2acdbbad4553ad3a7", "usage": { "input_tokens": 53050, "cached_input_tokens": 51200, "cache_write_input_tokens": 0, "output_tokens": 971, "reasoning_output_tokens": 93, "total_tokens": 54021 }, "turn_token_usage": { "input_tokens": 1153030, "cached_input_tokens": 1105280, "cache_write_input_tokens": 0, "output_tokens": 24019, "reasoning_output_tokens": 10690, "total_tokens": 1177049 }, "thread_token_usage": { "input_tokens": 1153030, "cached_input_tokens": 1105280, "cache_write_input_tokens": 0, "output_tokens": 24019, "reasoning_output_tokens": 10690, "total_tokens": 1177049 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e7f40801-ee92-44aa-9716-5f071982fabb", "process_id": "8836", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u - <<'PY'\nimport sys\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();nitem=len(items);target=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64);qm={int(x):i for i,x in enumerate(target)}\nX=sparse.load_npz('/app/solution/listen_counts_train.npz').astype(np.float32);X.data=np.log1p(X.data)\nE=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qidx=np.array([qm[u] for u in gold]);xrows=(target[qidx]//100-1).astype(int);quid=[int(target[r]) for r in qidx]\nwith np.load(P+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=nitem,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(nitem,np.float32);z[o]=1-np.arange(nitem)/nitem;return z\ngb=rs(F[0,2])+2*rs(L[1,1]);print('fit log',flush=True);sv=TruncatedSVD(n_components=96,n_iter=5,random_state=42);U=sv.fit_transform(X);V=sv.components_;print('var',sv.explained_variance_ratio_.sum(),flush=True)\nalphas=[0,3e-5,1e-4,2e-4,3e-4,5e-4,8e-4,1e-3,1.5e-3,2e-3];\nfor nc in [32,64,96]:\n vals={a:[] for a in alphas}\n for st in range(0,len(xrows),128):\n xr=xrows[st:st+128];br=qidx[st:st+128];S=U[xr,:nc]@V[:nc];S-=S.mean(1)[:,None];sd=S.std(1);sd[sd==0]=1;S/=sd[:,None]\n for a in alphas:\n Z=gb[None,:]+a*S;Z[~bits[br]]=-np.inf;top=np.argpartition(Z,-10,axis=1)[:,-10:]\n for j,row in enumerate(top):row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[a].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\n for a in alphas:print('RES',nc,a,np.mean(vals[a]),flush=True)\nnp.savez_compressed('/app/solution/svd_log_train.npz',U=U.astype(np.float32),V=V.astype(np.float32))\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport sys\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();nitem=len(items);target=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64);qm={int(x):i for i,x in enumerate(target)}\nX=sparse.load_npz('/app/solution/listen_counts_train.npz').astype(np.float32);X.data=np.log1p(X.data)\nE=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qidx=np.array([qm[u] for u in gold]);xrows=(target[qidx]//100-1).astype(int);quid=[int(target[r]) for r in qidx]\nwith np.load(P+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=nitem,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(nitem,np.float32);z[o]=1-np.arange(nitem)/nitem;return z\ngb=rs(F[0,2])+2*rs(L[1,1]);print('fit log',flush=True);sv=TruncatedSVD(n_components=96,n_iter=5,random_state=42);U=sv.fit_transform(X);V=sv.components_;print('var',sv.explained_variance_ratio_.sum(),flush=True)\nalphas=[0,3e-5,1e-4,2e-4,3e-4,5e-4,8e-4,1e-3,1.5e-3,2e-3];\nfor nc in [32,64,96]:\n vals={a:[] for a in alphas}\n for st in range(0,len(xrows),128):\n xr=xrows[st:st+128];br=qidx[st:st+128];S=U[xr,:nc]@V[:nc];S-=S.mean(1)[:,None];sd=S.std(1);sd[sd==0]=1;S/=sd[:,None]\n for a in alphas:\n Z=gb[None,:]+a*S;Z[~bits[br]]=-np.inf;top=np.argpartition(Z,-10,axis=1)[:,-10:]\n for j,row in enumerate(top):row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[a].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\n for a in alphas:print('RES',nc,a,np.mean(vals[a]),flush=True)\nnp.savez_compressed('/app/solution/svd_log_train.npz',U=U.astype(np.float32),V=V.astype(np.float32))\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "fit log\nvar 0.3575546\nRES 32 0 0.03495591411760116\nRES 32 3e-05 0.0353726078481892\nRES 32 0.0001 0.03605401797775986\nRES 32 0.0002 0.0359450069399237\nRES 32 0.0003 0.03603601478047241\nRES 32 0.0005 0.03552715065926786\nRES 32 0.0008 0.03428268122781672\nRES 32 0.001 0.03398355523132804\nRES 32 0.0015 0.03283249076932839\nRES 32 0.002 0.031165942662446152\nRES 64 0 0.03495591411760116\nRES 64 3e-05 0.03536834239899224\nRES 64 0.0001 0.036017760504718166\nRES 64 0.0002 0.03645014265578708\nRES 64 0.0003 0.03653031242431469\nRES 64 0.0005 0.03635567310962588\nRES 64 0.0008 0.03549897760970315\nRES 64 0.001 0.0354733726813475\nRES 64 0.0015 0.034946425240903294\nRES 64 0.002 0.034490170162109084\nRES 96 0 0.03495591411760116\nRES 96 3e-05 0.035316829966982925\nRES 96 0.0001 0.03591987355758362\nRES 96 0.0002 0.03639998885755811\nRES 96 0.0003 0.03661170809011704\nRES 96 0.0005 0.03650224844968985\nRES 96 0.0008 0.03580003695449044\nRES 96 0.001 0.035307308547128796\nRES 96 0.0015 0.03502323382880352\nRES 96 0.002 0.03454097055740437\n", "stderr": "", "aggregated_output": "fit log\nvar 0.3575546\nRES 32 0 0.03495591411760116\nRES 32 3e-05 0.0353726078481892\nRES 32 0.0001 0.03605401797775986\nRES 32 0.0002 0.0359450069399237\nRES 32 0.0003 0.03603601478047241\nRES 32 0.0005 0.03552715065926786\nRES 32 0.0008 0.03428268122781672\nRES 32 0.001 0.03398355523132804\nRES 32 0.0015 0.03283249076932839\nRES 32 0.002 0.031165942662446152\nRES 64 0 0.03495591411760116\nRES 64 3e-05 0.03536834239899224\nRES 64 0.0001 0.036017760504718166\nRES 64 0.0002 0.03645014265578708\nRES 64 0.0003 0.03653031242431469\nRES 64 0.0005 0.03635567310962588\nRES 64 0.0008 0.03549897760970315\nRES 64 0.001 0.0354733726813475\nRES 64 0.0015 0.034946425240903294\nRES 64 0.002 0.034490170162109084\nRES 96 0 0.03495591411760116\nRES 96 3e-05 0.035316829966982925\nRES 96 0.0001 0.03591987355758362\nRES 96 0.0002 0.03639998885755811\nRES 96 0.0003 0.03661170809011704\nRES 96 0.0005 0.03650224844968985\nRES 96 0.0008 0.03580003695449044\nRES 96 0.001 0.035307308547128796\nRES 96 0.0015 0.03502323382880352\nRES 96 0.002 0.03454097055740437\n", "exit_code": 0, "duration": { "secs": 28, "nanos": 893231268 }, "formatted_output": "fit log\nvar 0.3575546\nRES 32 0 0.03495591411760116\nRES 32 3e-05 0.0353726078481892\nRES 32 0.0001 0.03605401797775986\nRES 32 0.0002 0.0359450069399237\nRES 32 0.0003 0.03603601478047241\nRES 32 0.0005 0.03552715065926786\nRES 32 0.0008 0.03428268122781672\nRES 32 0.001 0.03398355523132804\nRES 32 0.0015 0.03283249076932839\nRES 32 0.002 0.031165942662446152\nRES 64 0 0.03495591411760116\nRES 64 3e-05 0.03536834239899224\nRES 64 0.0001 0.036017760504718166\nRES 64 0.0002 0.03645014265578708\nRES 64 0.0003 0.03653031242431469\nRES 64 0.0005 0.03635567310962588\nRES 64 0.0008 0.03549897760970315\nRES 64 0.001 0.0354733726813475\nRES 64 0.0015 0.034946425240903294\nRES 64 0.002 0.034490170162109084\nRES 96 0 0.03495591411760116\nRES 96 3e-05 0.035316829966982925\nRES 96 0.0001 0.03591987355758362\nRES 96 0.0002 0.03639998885755811\nRES 96 0.0003 0.03661170809011704\nRES 96 0.0005 0.03650224844968985\nRES 96 0.0008 0.03580003695449044\nRES 96 0.001 0.035307308547128796\nRES 96 0.0015 0.03502323382880352\nRES 96 0.002 0.03454097055740437\n" } } ## Step 54 · agent · 2026-09-25T01:12:50.249Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:13:11.012Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u - <<'PY'\\nimport sys,itertools\\nsys.path.insert(0,'/task')\\nfrom evaluate_public import validation_truth\\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\\nfrom pathlib import Path\\nimport pandas as pd,numpy as np\\nfrom scipy import sparse\\nfrom sklearn.decomposition import TruncatedSVD\\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();nitem=len(items);target=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64);qm={int(x):i for i,x in enumerate(target)};ic={int(x):i for i,x in enumerate(items)}\\nE=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qidx=np.array([qm[u] for u in gold]);xrows=(target[qidx]//100-1).astype(int);quid=[int(target[r]) for r in qidx]\\nwith np.load(P+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=nitem,bitorder='little').astype(bool)\\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(nitem,np.float32);z[o]=1-np.arange(nitem)/nitem;return z\\ngb=rs(F[0,2])+2*rs(L[1,1]);B=np.load('/app/solution/svd_binary_train.npz');UB=B['U'];VB=B['V'];D=np.load('/app/solution/svd_log_train.npz');UL=D['U'];VL=D['V']\\n# active-like SVD\\nps=pd.read_parquet(P+'/validation_preference_state.parquet');ps=ps[ps.state=='liked'];ur={int(x):i for i,x in enumerate(target)};rr=np.array([ur.get(int(x),-1) for x in ps.uid]);cc=np.array([ic.get(int(x),-1) for x in ps.item_id]);ok=(rr>=0)&(cc>=0);XA=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(rr[ok],cc[ok])),shape=(len(target),nitem));XA.data[:]=1\\nsv=TruncatedSVD(n_components=32,n_iter=7,random_state=42);UA=sv.fit_transform(XA);VA=sv.components_;np.savez_compressed('/app/solution/svd_active_train.npz',U=UA.astype(np.float32),V=VA.astype(np.float32))\\n# first binary-log grid\\nvariants=list(itertools.product([.0004,.0006,.0008,.0010],[0,.00005,.0001,.0002,.0003]));vals={x:[] for x in variants}\\ndef std(S):S-=S.mean(1)[:,None];d=S.std(1);d[d==0]=1;S/=d[:,None];return S\\nfor st in range(0,len(xrows),128):\\n xr=xrows[st:st+128];br=qidx[st:st+128];SB=std(UB[xr,:96]@VB[:96]);SL=std(UL[xr,:96]@VL[:96])\\n for v in variants:\\n Z=gb[None,:]+v[0]*SB+v[1]*SL;Z[~bits[br]]=-np.inf;top=np.argpartition(Z,-10,axis=1)[:,-10:]\\n for j,row in enumerate(top):row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[v].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\\nres=sorted([(np.mean(x),v) for v,x in vals.items()],reverse=True);print('BEST BL',res[:10],flush=True);best=res[0][1]\\n# add active grid around best\\nvariants=[(best[0],best[1],a) for a in [-.0001,-.00003,0,.00003,.0001,.0002,.0003]];vals={x:[] for x in variants}\\nfor st in range(0,len(xrows),128):\\n xr=xrows[st:st+128];br=qidx[st:st+128];SB=std(UB[xr,:96]@VB[:96]);SL=std(UL[xr,:96]@VL[:96]);SA=std(UA[qidx[st:st+128]]@VA)\\n for v in variants:\\n Z=gb[None,:]+v[0]*SB+v[1]*SL+v[2]*SA;Z[~bits[br]]=-np.inf;top=np.argpartition(Z,-10,axis=1)[:,-10:]\\n for j,row in enumerate(top):row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[v].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\\nprint('BEST BLA',sorted([(np.mean(x),v) for v,x in vals.items()],reverse=True),flush=True)\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:20000});\nif(r.session_id){text(`SESSION_ID=${r.session_id}`);text(r.output);}else{text(r.output);}\n" } ### Tool output · 2026-09-25T01:13:37.921Z Script completed Wall time 26.9 seconds Output: BEST BL [(np.float64(0.036988795291506504), (0.0008, 0)), (np.float64(0.03689014685453151), (0.0006, 0)), (np.float64(0.03682229202876586), (0.0006, 5e-05)), (np.float64(0.03682127356016855), (0.0004, 5e-05)), (np.float64(0.03679496237083738), (0.0004, 0)), (np.float64(0.03677617681782175), (0.0006, 0.0001)), (np.float64(0.03677462555123591), (0.0004, 0.0001)), (np.float64(0.036599303890385994), (0.0004, 0.0002)), (np.float64(0.036528893650655106), (0.0006, 0.0002)), (np.float64(0.036510611219199574), (0.0004, 0.0003))] BEST BLA [(np.float64(0.03772549162296406), (0.0008, 0, 0.0003)), (np.float64(0.03769434742153713), (0.0008, 0, 0.0002)), (np.float64(0.036988795291506504), (0.0008, 0, 0)), (np.float64(0.03696522278774561), (0.0008, 0, 0.0001)), (np.float64(0.03678322775707024), (0.0008, 0, 3e-05)), (np.float64(0.03668518393893282), (0.0008, 0, -3e-05)), (np.float64(0.03610501520478487), (0.0008, 0, -0.0001))] ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "response_id": "resp_0fdbf3e20a3ba82b016ab5ca85d70887d2ab2c426c5585ce99", "usage": { "input_tokens": 54580, "cached_input_tokens": 52864, "cache_write_input_tokens": 0, "output_tokens": 1641, "reasoning_output_tokens": 400, "total_tokens": 56221 }, "turn_token_usage": { "input_tokens": 1207610, "cached_input_tokens": 1158144, "cache_write_input_tokens": 0, "output_tokens": 25660, "reasoning_output_tokens": 11090, "total_tokens": 1233270 }, "thread_token_usage": { "input_tokens": 1207610, "cached_input_tokens": 1158144, "cache_write_input_tokens": 0, "output_tokens": 25660, "reasoning_output_tokens": 11090, "total_tokens": 1233270 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-928accae-13d2-4254-971a-8bccab4580e2", "process_id": "20587", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u - <<'PY'\nimport sys,itertools\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();nitem=len(items);target=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64);qm={int(x):i for i,x in enumerate(target)};ic={int(x):i for i,x in enumerate(items)}\nE=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qidx=np.array([qm[u] for u in gold]);xrows=(target[qidx]//100-1).astype(int);quid=[int(target[r]) for r in qidx]\nwith np.load(P+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=nitem,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(nitem,np.float32);z[o]=1-np.arange(nitem)/nitem;return z\ngb=rs(F[0,2])+2*rs(L[1,1]);B=np.load('/app/solution/svd_binary_train.npz');UB=B['U'];VB=B['V'];D=np.load('/app/solution/svd_log_train.npz');UL=D['U'];VL=D['V']\n# active-like SVD\nps=pd.read_parquet(P+'/validation_preference_state.parquet');ps=ps[ps.state=='liked'];ur={int(x):i for i,x in enumerate(target)};rr=np.array([ur.get(int(x),-1) for x in ps.uid]);cc=np.array([ic.get(int(x),-1) for x in ps.item_id]);ok=(rr>=0)&(cc>=0);XA=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(rr[ok],cc[ok])),shape=(len(target),nitem));XA.data[:]=1\nsv=TruncatedSVD(n_components=32,n_iter=7,random_state=42);UA=sv.fit_transform(XA);VA=sv.components_;np.savez_compressed('/app/solution/svd_active_train.npz',U=UA.astype(np.float32),V=VA.astype(np.float32))\n# first binary-log grid\nvariants=list(itertools.product([.0004,.0006,.0008,.0010],[0,.00005,.0001,.0002,.0003]));vals={x:[] for x in variants}\ndef std(S):S-=S.mean(1)[:,None];d=S.std(1);d[d==0]=1;S/=d[:,None];return S\nfor st in range(0,len(xrows),128):\n xr=xrows[st:st+128];br=qidx[st:st+128];SB=std(UB[xr,:96]@VB[:96]);SL=std(UL[xr,:96]@VL[:96])\n for v in variants:\n Z=gb[None,:]+v[0]*SB+v[1]*SL;Z[~bits[br]]=-np.inf;top=np.argpartition(Z,-10,axis=1)[:,-10:]\n for j,row in enumerate(top):row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[v].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\nres=sorted([(np.mean(x),v) for v,x in vals.items()],reverse=True);print('BEST BL',res[:10],flush=True);best=res[0][1]\n# add active grid around best\nvariants=[(best[0],best[1],a) for a in [-.0001,-.00003,0,.00003,.0001,.0002,.0003]];vals={x:[] for x in variants}\nfor st in range(0,len(xrows),128):\n xr=xrows[st:st+128];br=qidx[st:st+128];SB=std(UB[xr,:96]@VB[:96]);SL=std(UL[xr,:96]@VL[:96]);SA=std(UA[qidx[st:st+128]]@VA)\n for v in variants:\n Z=gb[None,:]+v[0]*SB+v[1]*SL+v[2]*SA;Z[~bits[br]]=-np.inf;top=np.argpartition(Z,-10,axis=1)[:,-10:]\n for j,row in enumerate(top):row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[v].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\nprint('BEST BLA',sorted([(np.mean(x),v) for v,x in vals.items()],reverse=True),flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport sys,itertools\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();nitem=len(items);target=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64);qm={int(x):i for i,x in enumerate(target)};ic={int(x):i for i,x in enumerate(items)}\nE=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qidx=np.array([qm[u] for u in gold]);xrows=(target[qidx]//100-1).astype(int);quid=[int(target[r]) for r in qidx]\nwith np.load(P+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=nitem,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(nitem,np.float32);z[o]=1-np.arange(nitem)/nitem;return z\ngb=rs(F[0,2])+2*rs(L[1,1]);B=np.load('/app/solution/svd_binary_train.npz');UB=B['U'];VB=B['V'];D=np.load('/app/solution/svd_log_train.npz');UL=D['U'];VL=D['V']\n# active-like SVD\nps=pd.read_parquet(P+'/validation_preference_state.parquet');ps=ps[ps.state=='liked'];ur={int(x):i for i,x in enumerate(target)};rr=np.array([ur.get(int(x),-1) for x in ps.uid]);cc=np.array([ic.get(int(x),-1) for x in ps.item_id]);ok=(rr>=0)&(cc>=0);XA=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(rr[ok],cc[ok])),shape=(len(target),nitem));XA.data[:]=1\nsv=TruncatedSVD(n_components=32,n_iter=7,random_state=42);UA=sv.fit_transform(XA);VA=sv.components_;np.savez_compressed('/app/solution/svd_active_train.npz',U=UA.astype(np.float32),V=VA.astype(np.float32))\n# first binary-log grid\nvariants=list(itertools.product([.0004,.0006,.0008,.0010],[0,.00005,.0001,.0002,.0003]));vals={x:[] for x in variants}\ndef std(S):S-=S.mean(1)[:,None];d=S.std(1);d[d==0]=1;S/=d[:,None];return S\nfor st in range(0,len(xrows),128):\n xr=xrows[st:st+128];br=qidx[st:st+128];SB=std(UB[xr,:96]@VB[:96]);SL=std(UL[xr,:96]@VL[:96])\n for v in variants:\n Z=gb[None,:]+v[0]*SB+v[1]*SL;Z[~bits[br]]=-np.inf;top=np.argpartition(Z,-10,axis=1)[:,-10:]\n for j,row in enumerate(top):row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[v].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\nres=sorted([(np.mean(x),v) for v,x in vals.items()],reverse=True);print('BEST BL',res[:10],flush=True);best=res[0][1]\n# add active grid around best\nvariants=[(best[0],best[1],a) for a in [-.0001,-.00003,0,.00003,.0001,.0002,.0003]];vals={x:[] for x in variants}\nfor st in range(0,len(xrows),128):\n xr=xrows[st:st+128];br=qidx[st:st+128];SB=std(UB[xr,:96]@VB[:96]);SL=std(UL[xr,:96]@VL[:96]);SA=std(UA[qidx[st:st+128]]@VA)\n for v in variants:\n Z=gb[None,:]+v[0]*SB+v[1]*SL+v[2]*SA;Z[~bits[br]]=-np.inf;top=np.argpartition(Z,-10,axis=1)[:,-10:]\n for j,row in enumerate(top):row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[v].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\nprint('BEST BLA',sorted([(np.mean(x),v) for v,x in vals.items()],reverse=True),flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "BEST BL [(np.float64(0.036988795291506504), (0.0008, 0)), (np.float64(0.03689014685453151), (0.0006, 0)), (np.float64(0.03682229202876586), (0.0006, 5e-05)), (np.float64(0.03682127356016855), (0.0004, 5e-05)), (np.float64(0.03679496237083738), (0.0004, 0)), (np.float64(0.03677617681782175), (0.0006, 0.0001)), (np.float64(0.03677462555123591), (0.0004, 0.0001)), (np.float64(0.036599303890385994), (0.0004, 0.0002)), (np.float64(0.036528893650655106), (0.0006, 0.0002)), (np.float64(0.036510611219199574), (0.0004, 0.0003))]\nBEST BLA [(np.float64(0.03772549162296406), (0.0008, 0, 0.0003)), (np.float64(0.03769434742153713), (0.0008, 0, 0.0002)), (np.float64(0.036988795291506504), (0.0008, 0, 0)), (np.float64(0.03696522278774561), (0.0008, 0, 0.0001)), (np.float64(0.03678322775707024), (0.0008, 0, 3e-05)), (np.float64(0.03668518393893282), (0.0008, 0, -3e-05)), (np.float64(0.03610501520478487), (0.0008, 0, -0.0001))]\n", "stderr": "", "aggregated_output": "BEST BL [(np.float64(0.036988795291506504), (0.0008, 0)), (np.float64(0.03689014685453151), (0.0006, 0)), (np.float64(0.03682229202876586), (0.0006, 5e-05)), (np.float64(0.03682127356016855), (0.0004, 5e-05)), (np.float64(0.03679496237083738), (0.0004, 0)), (np.float64(0.03677617681782175), (0.0006, 0.0001)), (np.float64(0.03677462555123591), (0.0004, 0.0001)), (np.float64(0.036599303890385994), (0.0004, 0.0002)), (np.float64(0.036528893650655106), (0.0006, 0.0002)), (np.float64(0.036510611219199574), (0.0004, 0.0003))]\nBEST BLA [(np.float64(0.03772549162296406), (0.0008, 0, 0.0003)), (np.float64(0.03769434742153713), (0.0008, 0, 0.0002)), (np.float64(0.036988795291506504), (0.0008, 0, 0)), (np.float64(0.03696522278774561), (0.0008, 0, 0.0001)), (np.float64(0.03678322775707024), (0.0008, 0, 3e-05)), (np.float64(0.03668518393893282), (0.0008, 0, -3e-05)), (np.float64(0.03610501520478487), (0.0008, 0, -0.0001))]\n", "exit_code": 0, "duration": { "secs": 26, "nanos": 740822271 }, "formatted_output": "BEST BL [(np.float64(0.036988795291506504), (0.0008, 0)), (np.float64(0.03689014685453151), (0.0006, 0)), (np.float64(0.03682229202876586), (0.0006, 5e-05)), (np.float64(0.03682127356016855), (0.0004, 5e-05)), (np.float64(0.03679496237083738), (0.0004, 0)), (np.float64(0.03677617681782175), (0.0006, 0.0001)), (np.float64(0.03677462555123591), (0.0004, 0.0001)), (np.float64(0.036599303890385994), (0.0004, 0.0002)), (np.float64(0.036528893650655106), (0.0006, 0.0002)), (np.float64(0.036510611219199574), (0.0004, 0.0003))]\nBEST BLA [(np.float64(0.03772549162296406), (0.0008, 0, 0.0003)), (np.float64(0.03769434742153713), (0.0008, 0, 0.0002)), (np.float64(0.036988795291506504), (0.0008, 0, 0)), (np.float64(0.03696522278774561), (0.0008, 0, 0.0001)), (np.float64(0.03678322775707024), (0.0008, 0, 3e-05)), (np.float64(0.03668518393893282), (0.0008, 0, -3e-05)), (np.float64(0.03610501520478487), (0.0008, 0, -0.0001))]\n" } } ## Step 55 · agent · 2026-09-25T01:13:41.451Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:13:54.122Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u - <<'PY'\\nimport sys,itertools\\nsys.path.insert(0,'/task')\\nfrom evaluate_public import validation_truth\\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\\nfrom pathlib import Path\\nimport pandas as pd,numpy as np\\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();n=len(items);target=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64);qm={int(x):i for i,x in enumerate(target)};E=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qidx=np.array([qm[u] for u in gold]);xrows=(target[qidx]//100-1).astype(int);quid=[int(target[r]) for r in qidx]\\nwith np.load(P+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=n,bitorder='little').astype(bool)\\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(n,np.float32);z[o]=1-np.arange(n)/n;return z\\ngb=rs(F[0,2])+2*rs(L[1,1]);B=np.load('/app/solution/svd_binary_train.npz');UB=B['U'];VB=B['V'];A=np.load('/app/solution/svd_active_train.npz');UA=A['U'];VA=A['V']\\nvariants=list(itertools.product([.0005,.00065,.0008,.00095,.0011],[.00015,.00025,.00035,.0005,.0007,.001]));vals={v:[] for v in variants}\\ndef std(S):S-=S.mean(1)[:,None];d=S.std(1);d[d==0]=1;return S/d[:,None]\\nfor st in range(0,len(xrows),128):\\n br=qidx[st:st+128];SB=std(UB[xrows[st:st+128],:96]@VB[:96]);SA=std(UA[br]@VA)\\n for v in variants:\\n Z=gb[None,:]+v[0]*SB+v[1]*SA;Z[~bits[br]]=-np.inf;top=np.argpartition(Z,-10,axis=1)[:,-10:]\\n for j,row in enumerate(top):row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[v].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\\nfor x in sorted([(np.mean(z),v) for v,z in vals.items()],reverse=True)[:20]:print(x)\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000});\nif(r.session_id){text(`SESSION_ID=${r.session_id}`);text(r.output);}else{text(r.output);}\n" } ### Tool output · 2026-09-25T01:14:17.732Z Script completed Wall time 23.6 seconds Output: (np.float64(0.03782265903114937), (0.00065, 0.00025)) (np.float64(0.03781290415473602), (0.0008, 0.00025)) (np.float64(0.03770811927597809), (0.0005, 0.00025)) (np.float64(0.03768039062853601), (0.0005, 0.00015)) (np.float64(0.037452960897849094), (0.0005, 0.00035)) (np.float64(0.037356719459346724), (0.0008, 0.00035)) (np.float64(0.03732225456871879), (0.0008, 0.00015)) (np.float64(0.0373168331199789), (0.00095, 0.00025)) (np.float64(0.03729623751340539), (0.00095, 0.00035)) (np.float64(0.03728535629281436), (0.00065, 0.00035)) (np.float64(0.037214379473454545), (0.00065, 0.00015)) (np.float64(0.03715755832409045), (0.0011, 0.00035)) (np.float64(0.03714900328281677), (0.0011, 0.0005)) (np.float64(0.03711433724666826), (0.0005, 0.0005)) (np.float64(0.03708395282693272), (0.0011, 0.00025)) (np.float64(0.03707408450072468), (0.00095, 0.00015)) (np.float64(0.03698574720843331), (0.00095, 0.0005)) (np.float64(0.03698054797417537), (0.00065, 0.0005)) (np.float64(0.036941817992516966), (0.0011, 0.00015)) (np.float64(0.03688764507977994), (0.0008, 0.0005)) ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "response_id": "resp_0fdbf3e20a3ba82b016ab5cac229d087d2b3178e49f23f75f4", "usage": { "input_tokens": 56700, "cached_input_tokens": 54400, "cache_write_input_tokens": 0, "output_tokens": 779, "reasoning_output_tokens": 67, "total_tokens": 57479 }, "turn_token_usage": { "input_tokens": 1264310, "cached_input_tokens": 1212544, "cache_write_input_tokens": 0, "output_tokens": 26439, "reasoning_output_tokens": 11157, "total_tokens": 1290749 }, "thread_token_usage": { "input_tokens": 1264310, "cached_input_tokens": 1212544, "cache_write_input_tokens": 0, "output_tokens": 26439, "reasoning_output_tokens": 11157, "total_tokens": 1290749 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-41cdc66c-605a-4910-8986-37078843fbcf", "process_id": "97807", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u - <<'PY'\nimport sys,itertools\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();n=len(items);target=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64);qm={int(x):i for i,x in enumerate(target)};E=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qidx=np.array([qm[u] for u in gold]);xrows=(target[qidx]//100-1).astype(int);quid=[int(target[r]) for r in qidx]\nwith np.load(P+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=n,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(n,np.float32);z[o]=1-np.arange(n)/n;return z\ngb=rs(F[0,2])+2*rs(L[1,1]);B=np.load('/app/solution/svd_binary_train.npz');UB=B['U'];VB=B['V'];A=np.load('/app/solution/svd_active_train.npz');UA=A['U'];VA=A['V']\nvariants=list(itertools.product([.0005,.00065,.0008,.00095,.0011],[.00015,.00025,.00035,.0005,.0007,.001]));vals={v:[] for v in variants}\ndef std(S):S-=S.mean(1)[:,None];d=S.std(1);d[d==0]=1;return S/d[:,None]\nfor st in range(0,len(xrows),128):\n br=qidx[st:st+128];SB=std(UB[xrows[st:st+128],:96]@VB[:96]);SA=std(UA[br]@VA)\n for v in variants:\n Z=gb[None,:]+v[0]*SB+v[1]*SA;Z[~bits[br]]=-np.inf;top=np.argpartition(Z,-10,axis=1)[:,-10:]\n for j,row in enumerate(top):row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[v].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\nfor x in sorted([(np.mean(z),v) for v,z in vals.items()],reverse=True)[:20]:print(x)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport sys,itertools\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();n=len(items);target=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64);qm={int(x):i for i,x in enumerate(target)};E=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qidx=np.array([qm[u] for u in gold]);xrows=(target[qidx]//100-1).astype(int);quid=[int(target[r]) for r in qidx]\nwith np.load(P+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=n,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(n,np.float32);z[o]=1-np.arange(n)/n;return z\ngb=rs(F[0,2])+2*rs(L[1,1]);B=np.load('/app/solution/svd_binary_train.npz');UB=B['U'];VB=B['V'];A=np.load('/app/solution/svd_active_train.npz');UA=A['U'];VA=A['V']\nvariants=list(itertools.product([.0005,.00065,.0008,.00095,.0011],[.00015,.00025,.00035,.0005,.0007,.001]));vals={v:[] for v in variants}\ndef std(S):S-=S.mean(1)[:,None];d=S.std(1);d[d==0]=1;return S/d[:,None]\nfor st in range(0,len(xrows),128):\n br=qidx[st:st+128];SB=std(UB[xrows[st:st+128],:96]@VB[:96]);SA=std(UA[br]@VA)\n for v in variants:\n Z=gb[None,:]+v[0]*SB+v[1]*SA;Z[~bits[br]]=-np.inf;top=np.argpartition(Z,-10,axis=1)[:,-10:]\n for j,row in enumerate(top):row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[v].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\nfor x in sorted([(np.mean(z),v) for v,z in vals.items()],reverse=True)[:20]:print(x)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "(np.float64(0.03782265903114937), (0.00065, 0.00025))\n(np.float64(0.03781290415473602), (0.0008, 0.00025))\n(np.float64(0.03770811927597809), (0.0005, 0.00025))\n(np.float64(0.03768039062853601), (0.0005, 0.00015))\n(np.float64(0.037452960897849094), (0.0005, 0.00035))\n(np.float64(0.037356719459346724), (0.0008, 0.00035))\n(np.float64(0.03732225456871879), (0.0008, 0.00015))\n(np.float64(0.0373168331199789), (0.00095, 0.00025))\n(np.float64(0.03729623751340539), (0.00095, 0.00035))\n(np.float64(0.03728535629281436), (0.00065, 0.00035))\n(np.float64(0.037214379473454545), (0.00065, 0.00015))\n(np.float64(0.03715755832409045), (0.0011, 0.00035))\n(np.float64(0.03714900328281677), (0.0011, 0.0005))\n(np.float64(0.03711433724666826), (0.0005, 0.0005))\n(np.float64(0.03708395282693272), (0.0011, 0.00025))\n(np.float64(0.03707408450072468), (0.00095, 0.00015))\n(np.float64(0.03698574720843331), (0.00095, 0.0005))\n(np.float64(0.03698054797417537), (0.00065, 0.0005))\n(np.float64(0.036941817992516966), (0.0011, 0.00015))\n(np.float64(0.03688764507977994), (0.0008, 0.0005))\n", "stderr": "", "aggregated_output": "(np.float64(0.03782265903114937), (0.00065, 0.00025))\n(np.float64(0.03781290415473602), (0.0008, 0.00025))\n(np.float64(0.03770811927597809), (0.0005, 0.00025))\n(np.float64(0.03768039062853601), (0.0005, 0.00015))\n(np.float64(0.037452960897849094), (0.0005, 0.00035))\n(np.float64(0.037356719459346724), (0.0008, 0.00035))\n(np.float64(0.03732225456871879), (0.0008, 0.00015))\n(np.float64(0.0373168331199789), (0.00095, 0.00025))\n(np.float64(0.03729623751340539), (0.00095, 0.00035))\n(np.float64(0.03728535629281436), (0.00065, 0.00035))\n(np.float64(0.037214379473454545), (0.00065, 0.00015))\n(np.float64(0.03715755832409045), (0.0011, 0.00035))\n(np.float64(0.03714900328281677), (0.0011, 0.0005))\n(np.float64(0.03711433724666826), (0.0005, 0.0005))\n(np.float64(0.03708395282693272), (0.0011, 0.00025))\n(np.float64(0.03707408450072468), (0.00095, 0.00015))\n(np.float64(0.03698574720843331), (0.00095, 0.0005))\n(np.float64(0.03698054797417537), (0.00065, 0.0005))\n(np.float64(0.036941817992516966), (0.0011, 0.00015))\n(np.float64(0.03688764507977994), (0.0008, 0.0005))\n", "exit_code": 0, "duration": { "secs": 23, "nanos": 442914717 }, "formatted_output": "(np.float64(0.03782265903114937), (0.00065, 0.00025))\n(np.float64(0.03781290415473602), (0.0008, 0.00025))\n(np.float64(0.03770811927597809), (0.0005, 0.00025))\n(np.float64(0.03768039062853601), (0.0005, 0.00015))\n(np.float64(0.037452960897849094), (0.0005, 0.00035))\n(np.float64(0.037356719459346724), (0.0008, 0.00035))\n(np.float64(0.03732225456871879), (0.0008, 0.00015))\n(np.float64(0.0373168331199789), (0.00095, 0.00025))\n(np.float64(0.03729623751340539), (0.00095, 0.00035))\n(np.float64(0.03728535629281436), (0.00065, 0.00035))\n(np.float64(0.037214379473454545), (0.00065, 0.00015))\n(np.float64(0.03715755832409045), (0.0011, 0.00035))\n(np.float64(0.03714900328281677), (0.0011, 0.0005))\n(np.float64(0.03711433724666826), (0.0005, 0.0005))\n(np.float64(0.03708395282693272), (0.0011, 0.00025))\n(np.float64(0.03707408450072468), (0.00095, 0.00015))\n(np.float64(0.03698574720843331), (0.00095, 0.0005))\n(np.float64(0.03698054797417537), (0.00065, 0.0005))\n(np.float64(0.036941817992516966), (0.0011, 0.00015))\n(np.float64(0.03688764507977994), (0.0008, 0.0005))\n" } } ## Step 56 · agent · 2026-09-25T01:14:31.865Z ### Thinking unavailable — no text recorded ## Step 57 · agent · 2026-09-25T01:14:37.921Z ### Thinking unavailable — no text recorded The explicit/implicit taste ensemble reaches about 0.03782. One higher-value idea remains: model the audience of tracks gaining traction in the final seven training days, then favor trending tracks among taste-similar listeners. That directly combines the strongest temporal signal with personalization instead of treating them independently. ### exec · 2026-09-25T01:14:46.276Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u - <<'PY'\\nimport pyarrow.parquet as pq,pandas as pd,numpy as np\\nfrom scipy import sparse\\nP='/task/data';cut=24786800;cat=pd.read_parquet(P+'/candidate_catalog.parquet');ids=cat.item_id.to_numpy(np.int64);n=len(ids);sid=np.sort(ids);s2c=np.argsort(ids);rows=[];cols=[];dat=[];org=[]\\npf=pq.ParquetFile(P+'/train_listens.parquet')\\nfor bi,b in enumerate(pf.iter_batches(batch_size=2_000_000,columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])):\\n a=b.to_pydict();ts=np.asarray(a['timestamp'],np.int64);ratio=np.asarray(a['played_ratio_pct'],np.uint16);m=(ratio>=50)&(ts>=cut-7*86400);it=np.asarray(a['item_id'],np.int64)[m];p=np.searchsorted(sid,it);ok=(p=50)&(ts>=cut-7*86400);it=np.asarray(a['item_id'],np.int64)[m];p=np.searchsorted(sid,it);ok=(p=50)&(ts>=cut-7*86400);it=np.asarray(a['item_id'],np.int64)[m];p=np.searchsorted(sid,it);ok=(p=0)&(cc>=0);X=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(rr[ok],cc[ok])),shape=(len(target),n));X.data[:]=1;norm=np.sqrt(np.asarray(X.multiply(X).sum(1)).ravel());norm[norm==0]=1;XN=sparse.diags(1/norm)@X\\nprint('sim',flush=True);S=(XN[qidx]@XN.T).tocsr();R=sparse.load_npz('/app/solution/recent7_train.npz')[(target//100-1).astype(int)]\\ndef topk(S,k):\\n ix=[];da=[];pt=[0]\\n for i in range(S.shape[0]):\\n a,b=S.indptr[i:i+2];j=S.indices[a:b];v=S.data[a:b];m=j!=qidx[i];j=j[m];v=v[m]\\n if len(v)>k:z=np.argpartition(v,-k)[-k:];j=j[z];v=v[z]\\n ix.append(j);da.append(v);pt.append(pt[-1]+len(j))\\n return sparse.csr_matrix((np.concatenate(da),np.concatenate(ix),np.array(pt)),shape=S.shape)\\nalphas=[0,3e-6,1e-5,3e-5,1e-4,3e-4,1e-3,3e-3,1e-2,.03,.1]\\nfor k in [20,50,100,200]:\\n PRED=(topk(S,k)@R).tocsr();vals={a:[] for a in alphas}\\n for i,(r,u) in enumerate(zip(qidx,quid)):\\n a,b=PRED.indptr[i:i+2];ix=PRED.indices[a:b];pv=PRED.data[a:b];mx=pv.max() if len(pv) else 1\\n for al in alphas:\\n z=gb.copy();z[ix]+=al*pv/mx;z[~bits[r]]=-np.inf;top=np.argpartition(z,-10)[-10:];top=top[np.argsort(z[top])[::-1]];vals[al].append(ndcg_at_k(items[top].tolist(),gold[u],10))\\n print('K',k,[(a,float(np.mean(vals[a]))) for a in alphas],flush=True)\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:14000});\nif(r.session_id){text(`SESSION_ID=${r.session_id}`);text(r.output);}else{text(r.output);}\n" } ### Tool output · 2026-09-25T01:17:04.830Z Script completed Wall time 28.9 seconds Output: sim K 20 [(0, 0.03495591411760116), (3e-06, 0.03495608415986733), (1e-05, 0.03495608415986733), (3e-05, 0.03495608415986733), (0.0001, 0.03493599649868167), (0.0003, 0.035197933279644975), (0.001, 0.03525900208900905), (0.003, 0.03453605279868152), (0.01, 0.03438422463494998), (0.03, 0.033694902312559026), (0.1, 0.03095152699165364)] K 50 [(0, 0.03495591411760116), (3e-06, 0.03495608415986733), (1e-05, 0.03495608415986733), (3e-05, 0.03495608415986733), (0.0001, 0.03498275460342701), (0.0003, 0.035225190124442585), (0.001, 0.035208124241811724), (0.003, 0.03552790049430244), (0.01, 0.035664974237640513), (0.03, 0.03528358481765686), (0.1, 0.03305843525932005)] K 100 [(0, 0.03495591411760116), (3e-06, 0.03495591411760116), (1e-05, 0.03495591411760116), (3e-05, 0.03495591411760116), (0.0001, 0.03499758774598571), (0.0003, 0.03524563100460555), (0.001, 0.03574551898994348), (0.003, 0.036176808197157365), (0.01, 0.03705005279359867), (0.03, 0.037668884633164805), (0.1, 0.035774176384964586)] K 200 [(0, 0.03495591411760116), (3e-06, 0.03495591411760116), (1e-05, 0.03495591411760116), (3e-05, 0.03495591411760116), (0.0001, 0.035007902595195366), (0.0003, 0.03533415687628328), (0.001, 0.036073673976050054), (0.003, 0.03653309527603927), (0.01, 0.03820523891853365), (0.03, 0.038739373079525144), (0.1, 0.037853640727801285)] ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "response_id": "resp_0fdbf3e20a3ba82b016ab5cb5a16fc87d282687245732cf6f9", "usage": { "input_tokens": 59882, "cached_input_tokens": 59648, "cache_write_input_tokens": 0, "output_tokens": 1364, "reasoning_output_tokens": 390, "total_tokens": 61246 }, "turn_token_usage": { "input_tokens": 1561057, "cached_input_tokens": 1505280, "cache_write_input_tokens": 0, "output_tokens": 29454, "reasoning_output_tokens": 12456, "total_tokens": 1590511 }, "thread_token_usage": { "input_tokens": 1561057, "cached_input_tokens": 1505280, "cache_write_input_tokens": 0, "output_tokens": 29454, "reasoning_output_tokens": 12456, "total_tokens": 1590511 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-358d7ddb-d1a7-43c3-997d-f2ceac48a76b", "process_id": "21545", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u - <<'PY'\nimport sys\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();n=len(items);target=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64);ur={int(x):i for i,x in enumerate(target)};ic={int(x):i for i,x in enumerate(items)}\nE=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qidx=np.array([ur[u] for u in gold]);quid=[int(target[r]) for r in qidx]\nwith np.load(P+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=n,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(n,np.float32);z[o]=1-np.arange(n)/n;return z\ngb=rs(F[0,2])+2*rs(L[1,1]);ps=pd.read_parquet(P+'/validation_preference_state.parquet');ps=ps[ps.state=='liked'];rr=np.array([ur.get(int(x),-1) for x in ps.uid]);cc=np.array([ic.get(int(x),-1) for x in ps.item_id]);ok=(rr>=0)&(cc>=0);X=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(rr[ok],cc[ok])),shape=(len(target),n));X.data[:]=1;norm=np.sqrt(np.asarray(X.multiply(X).sum(1)).ravel());norm[norm==0]=1;XN=sparse.diags(1/norm)@X\nprint('sim',flush=True);S=(XN[qidx]@XN.T).tocsr();R=sparse.load_npz('/app/solution/recent7_train.npz')[(target//100-1).astype(int)]\ndef topk(S,k):\n ix=[];da=[];pt=[0]\n for i in range(S.shape[0]):\n a,b=S.indptr[i:i+2];j=S.indices[a:b];v=S.data[a:b];m=j!=qidx[i];j=j[m];v=v[m]\n if len(v)>k:z=np.argpartition(v,-k)[-k:];j=j[z];v=v[z]\n ix.append(j);da.append(v);pt.append(pt[-1]+len(j))\n return sparse.csr_matrix((np.concatenate(da),np.concatenate(ix),np.array(pt)),shape=S.shape)\nalphas=[0,3e-6,1e-5,3e-5,1e-4,3e-4,1e-3,3e-3,1e-2,.03,.1]\nfor k in [20,50,100,200]:\n PRED=(topk(S,k)@R).tocsr();vals={a:[] for a in alphas}\n for i,(r,u) in enumerate(zip(qidx,quid)):\n a,b=PRED.indptr[i:i+2];ix=PRED.indices[a:b];pv=PRED.data[a:b];mx=pv.max() if len(pv) else 1\n for al in alphas:\n z=gb.copy();z[ix]+=al*pv/mx;z[~bits[r]]=-np.inf;top=np.argpartition(z,-10)[-10:];top=top[np.argsort(z[top])[::-1]];vals[al].append(ndcg_at_k(items[top].tolist(),gold[u],10))\n print('K',k,[(a,float(np.mean(vals[a]))) for a in alphas],flush=True)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport sys\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();n=len(items);target=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64);ur={int(x):i for i,x in enumerate(target)};ic={int(x):i for i,x in enumerate(items)}\nE=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qidx=np.array([ur[u] for u in gold]);quid=[int(target[r]) for r in qidx]\nwith np.load(P+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=n,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(n,np.float32);z[o]=1-np.arange(n)/n;return z\ngb=rs(F[0,2])+2*rs(L[1,1]);ps=pd.read_parquet(P+'/validation_preference_state.parquet');ps=ps[ps.state=='liked'];rr=np.array([ur.get(int(x),-1) for x in ps.uid]);cc=np.array([ic.get(int(x),-1) for x in ps.item_id]);ok=(rr>=0)&(cc>=0);X=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(rr[ok],cc[ok])),shape=(len(target),n));X.data[:]=1;norm=np.sqrt(np.asarray(X.multiply(X).sum(1)).ravel());norm[norm==0]=1;XN=sparse.diags(1/norm)@X\nprint('sim',flush=True);S=(XN[qidx]@XN.T).tocsr();R=sparse.load_npz('/app/solution/recent7_train.npz')[(target//100-1).astype(int)]\ndef topk(S,k):\n ix=[];da=[];pt=[0]\n for i in range(S.shape[0]):\n a,b=S.indptr[i:i+2];j=S.indices[a:b];v=S.data[a:b];m=j!=qidx[i];j=j[m];v=v[m]\n if len(v)>k:z=np.argpartition(v,-k)[-k:];j=j[z];v=v[z]\n ix.append(j);da.append(v);pt.append(pt[-1]+len(j))\n return sparse.csr_matrix((np.concatenate(da),np.concatenate(ix),np.array(pt)),shape=S.shape)\nalphas=[0,3e-6,1e-5,3e-5,1e-4,3e-4,1e-3,3e-3,1e-2,.03,.1]\nfor k in [20,50,100,200]:\n PRED=(topk(S,k)@R).tocsr();vals={a:[] for a in alphas}\n for i,(r,u) in enumerate(zip(qidx,quid)):\n a,b=PRED.indptr[i:i+2];ix=PRED.indices[a:b];pv=PRED.data[a:b];mx=pv.max() if len(pv) else 1\n for al in alphas:\n z=gb.copy();z[ix]+=al*pv/mx;z[~bits[r]]=-np.inf;top=np.argpartition(z,-10)[-10:];top=top[np.argsort(z[top])[::-1]];vals[al].append(ndcg_at_k(items[top].tolist(),gold[u],10))\n print('K',k,[(a,float(np.mean(vals[a]))) for a in alphas],flush=True)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "sim\nK 20 [(0, 0.03495591411760116), (3e-06, 0.03495608415986733), (1e-05, 0.03495608415986733), (3e-05, 0.03495608415986733), (0.0001, 0.03493599649868167), (0.0003, 0.035197933279644975), (0.001, 0.03525900208900905), (0.003, 0.03453605279868152), (0.01, 0.03438422463494998), (0.03, 0.033694902312559026), (0.1, 0.03095152699165364)]\nK 50 [(0, 0.03495591411760116), (3e-06, 0.03495608415986733), (1e-05, 0.03495608415986733), (3e-05, 0.03495608415986733), (0.0001, 0.03498275460342701), (0.0003, 0.035225190124442585), (0.001, 0.035208124241811724), (0.003, 0.03552790049430244), (0.01, 0.035664974237640513), (0.03, 0.03528358481765686), (0.1, 0.03305843525932005)]\nK 100 [(0, 0.03495591411760116), (3e-06, 0.03495591411760116), (1e-05, 0.03495591411760116), (3e-05, 0.03495591411760116), (0.0001, 0.03499758774598571), (0.0003, 0.03524563100460555), (0.001, 0.03574551898994348), (0.003, 0.036176808197157365), (0.01, 0.03705005279359867), (0.03, 0.037668884633164805), (0.1, 0.035774176384964586)]\nK 200 [(0, 0.03495591411760116), (3e-06, 0.03495591411760116), (1e-05, 0.03495591411760116), (3e-05, 0.03495591411760116), (0.0001, 0.035007902595195366), (0.0003, 0.03533415687628328), (0.001, 0.036073673976050054), (0.003, 0.03653309527603927), (0.01, 0.03820523891853365), (0.03, 0.038739373079525144), (0.1, 0.037853640727801285)]\n", "stderr": "", "aggregated_output": "sim\nK 20 [(0, 0.03495591411760116), (3e-06, 0.03495608415986733), (1e-05, 0.03495608415986733), (3e-05, 0.03495608415986733), (0.0001, 0.03493599649868167), (0.0003, 0.035197933279644975), (0.001, 0.03525900208900905), (0.003, 0.03453605279868152), (0.01, 0.03438422463494998), (0.03, 0.033694902312559026), (0.1, 0.03095152699165364)]\nK 50 [(0, 0.03495591411760116), (3e-06, 0.03495608415986733), (1e-05, 0.03495608415986733), (3e-05, 0.03495608415986733), (0.0001, 0.03498275460342701), (0.0003, 0.035225190124442585), (0.001, 0.035208124241811724), (0.003, 0.03552790049430244), (0.01, 0.035664974237640513), (0.03, 0.03528358481765686), (0.1, 0.03305843525932005)]\nK 100 [(0, 0.03495591411760116), (3e-06, 0.03495591411760116), (1e-05, 0.03495591411760116), (3e-05, 0.03495591411760116), (0.0001, 0.03499758774598571), (0.0003, 0.03524563100460555), (0.001, 0.03574551898994348), (0.003, 0.036176808197157365), (0.01, 0.03705005279359867), (0.03, 0.037668884633164805), (0.1, 0.035774176384964586)]\nK 200 [(0, 0.03495591411760116), (3e-06, 0.03495591411760116), (1e-05, 0.03495591411760116), (3e-05, 0.03495591411760116), (0.0001, 0.035007902595195366), (0.0003, 0.03533415687628328), (0.001, 0.036073673976050054), (0.003, 0.03653309527603927), (0.01, 0.03820523891853365), (0.03, 0.038739373079525144), (0.1, 0.037853640727801285)]\n", "exit_code": 0, "duration": { "secs": 28, "nanos": 670769215 }, "formatted_output": "sim\nK 20 [(0, 0.03495591411760116), (3e-06, 0.03495608415986733), (1e-05, 0.03495608415986733), (3e-05, 0.03495608415986733), (0.0001, 0.03493599649868167), (0.0003, 0.035197933279644975), (0.001, 0.03525900208900905), (0.003, 0.03453605279868152), (0.01, 0.03438422463494998), (0.03, 0.033694902312559026), (0.1, 0.03095152699165364)]\nK 50 [(0, 0.03495591411760116), (3e-06, 0.03495608415986733), (1e-05, 0.03495608415986733), (3e-05, 0.03495608415986733), (0.0001, 0.03498275460342701), (0.0003, 0.035225190124442585), (0.001, 0.035208124241811724), (0.003, 0.03552790049430244), (0.01, 0.035664974237640513), (0.03, 0.03528358481765686), (0.1, 0.03305843525932005)]\nK 100 [(0, 0.03495591411760116), (3e-06, 0.03495591411760116), (1e-05, 0.03495591411760116), (3e-05, 0.03495591411760116), (0.0001, 0.03499758774598571), (0.0003, 0.03524563100460555), (0.001, 0.03574551898994348), (0.003, 0.036176808197157365), (0.01, 0.03705005279359867), (0.03, 0.037668884633164805), (0.1, 0.035774176384964586)]\nK 200 [(0, 0.03495591411760116), (3e-06, 0.03495591411760116), (1e-05, 0.03495591411760116), (3e-05, 0.03495591411760116), (0.0001, 0.035007902595195366), (0.0003, 0.03533415687628328), (0.001, 0.036073673976050054), (0.003, 0.03653309527603927), (0.01, 0.03820523891853365), (0.03, 0.038739373079525144), (0.1, 0.037853640727801285)]\n" } } ## Step 62 · agent · 2026-09-25T01:17:14.182Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:17:32.420Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u - <<'PY'\\nimport sys,itertools\\nsys.path.insert(0,'/task')\\nfrom evaluate_public import validation_truth\\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\\nfrom pathlib import Path\\nimport pandas as pd,numpy as np\\nfrom scipy import sparse\\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();n=len(items);target=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64);ur={int(x):i for i,x in enumerate(target)};ic={int(x):i for i,x in enumerate(items)}\\nE=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qidx=np.array([ur[u] for u in gold]);quid=[int(target[r]) for r in qidx]\\nwith np.load(P+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=n,bitorder='little').astype(bool)\\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(n,np.float32);z[o]=1-np.arange(n)/n;return z\\ngb=rs(F[0,2])+2*rs(L[1,1]);ps=pd.read_parquet(P+'/validation_preference_state.parquet');ps=ps[ps.state=='liked'];rr=np.array([ur.get(int(x),-1) for x in ps.uid]);cc=np.array([ic.get(int(x),-1) for x in ps.item_id]);ok=(rr>=0)&(cc>=0);X=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(rr[ok],cc[ok])),shape=(len(target),n));X.data[:]=1;norm=np.sqrt(np.asarray(X.multiply(X).sum(1)).ravel());norm[norm==0]=1;XN=sparse.diags(1/norm)@X;S=(XN[qidx]@XN.T).tocsr();Rall=sparse.load_npz('/app/solution/recent7_train.npz')[(target//100-1).astype(int)];Rorg=sparse.load_npz('/app/solution/recent7org_train.npz')[(target//100-1).astype(int)]\\ndef topk(S,k):\\n ix=[];da=[];pt=[0]\\n for i in range(S.shape[0]):\\n a,b=S.indptr[i:i+2];j=S.indices[a:b];v=S.data[a:b];m=j!=qidx[i];j=j[m];v=v[m]\\n if len(v)>k:z=np.argpartition(v,-k)[-k:];j=j[z];v=v[z]\\n ix.append(j);da.append(v);pt.append(pt[-1]+len(j))\\n return sparse.csr_matrix((np.concatenate(da),np.concatenate(ix),np.array(pt)),shape=S.shape)\\ndef ev(P,alphas):\\n vals={a:[] for a in alphas}\\n for i,(r,u) in enumerate(zip(qidx,quid)):\\n lo,hi=P.indptr[i:i+2];ix=P.indices[lo:hi];pv=P.data[lo:hi];mx=pv.max() if len(pv) else 1\\n for al in alphas:\\n z=gb.copy();z[ix]+=al*pv/mx;z[~bits[r]]=-np.inf;t=np.argpartition(z,-10)[-10:];t=t[np.argsort(z[t])[::-1]];vals[al].append(ndcg_at_k(items[t].tolist(),gold[u],10))\\n return [(a,float(np.mean(vals[a]))) for a in alphas]\\nalphas=[.005,.01,.015,.02,.025,.03,.04,.05,.07,.1]\\nfor k in [200,300,500,1000]:\\n sk=topk(S,k)\\n for name,R in [('all',Rall),('org',Rorg)]:print(name,k,ev((sk@R).tocsr(),alphas),flush=True)\\n# save active similarity for final combos\\nsparse.save_npz('/app/solution/active_sim_q_train.npz',S)\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:16000});\nif(r.session_id){text(`SESSION_ID=${r.session_id}`);text(r.output);}else{text(r.output);}\n" } ### Tool output · 2026-09-25T01:18:02.586Z Script completed Wall time 30.2 seconds Output: SESSION_ID=38638 all 200 [(0.005, 0.03692219892752741), (0.01, 0.03820523891853365), (0.015, 0.03889850115844483), (0.02, 0.03914918396634148), (0.025, 0.03883897856003798), (0.03, 0.038739373079525144), (0.04, 0.03834609233828177), (0.05, 0.03840393475740443), (0.07, 0.03795927802852888), (0.1, 0.037853640727801285)] org 200 [(0.005, 0.03630562979527513), (0.01, 0.03720208356953695), (0.015, 0.03794864451871575), (0.02, 0.0378859088193051), (0.025, 0.037833123085761755), (0.03, 0.03763516163289139), (0.04, 0.037592962216435925), (0.05, 0.0375450725877074), (0.07, 0.03711393145359014), (0.1, 0.03621016808631632)] all 300 [(0.005, 0.037108066295842776), (0.01, 0.03796299471940949), (0.015, 0.03887657013193053), (0.02, 0.03891868810062882), (0.025, 0.038950698347121886), (0.03, 0.03878764129546214), (0.04, 0.038903958547439964), (0.05, 0.038736237793520555), (0.07, 0.03885513569245522), (0.1, 0.03837531829044672)] org 300 [(0.005, 0.03633373264361668), (0.01, 0.03753485140901134), (0.015, 0.0376789394150024), (0.02, 0.037689270987863094), (0.025, 0.0380125373888752), (0.03, 0.03787157865638546), (0.04, 0.03782868170148931), (0.05, 0.03748884861423028), (0.07, 0.03758573745405), (0.1, 0.036934922950726035)] ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "response_id": "resp_0fdbf3e20a3ba82b016ab5cb9105fc87d2bbc5386b88e04c67", "usage": { "input_tokens": 61955, "cached_input_tokens": 59648, "cache_write_input_tokens": 0, "output_tokens": 1471, "reasoning_output_tokens": 436, "total_tokens": 63426 }, "turn_token_usage": { "input_tokens": 1623012, "cached_input_tokens": 1564928, "cache_write_input_tokens": 0, "output_tokens": 30925, "reasoning_output_tokens": 12892, "total_tokens": 1653937 }, "thread_token_usage": { "input_tokens": 1623012, "cached_input_tokens": 1564928, "cache_write_input_tokens": 0, "output_tokens": 30925, "reasoning_output_tokens": 12892, "total_tokens": 1653937 } } ## Step 63 · agent · 2026-09-25T01:18:11.739Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:18:11.740Z { "code": "const r = await tools.write_stdin({session_id:38638,chars:\"\",yield_time_ms:30000,max_output_tokens:16000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:18:37.267Z Script completed Wall time 25.5 seconds Output: all 500 [(0.005, 0.03735766429925665), (0.01, 0.03836926951464056), (0.015, 0.038775988350233354), (0.02, 0.03879498381586903), (0.025, 0.038708260734915255), (0.03, 0.038768532427306726), (0.04, 0.038506216925910906), (0.05, 0.038473527103831964), (0.07, 0.03892529059806298), (0.1, 0.038723804499457705)] org 500 [(0.005, 0.03675847401314856), (0.01, 0.037350422570086604), (0.015, 0.03760682921284848), (0.02, 0.03750031822682396), (0.025, 0.037899077489761554), (0.03, 0.037930159481323285), (0.04, 0.03790294178047534), (0.05, 0.03767664632227021), (0.07, 0.03730120155011157), (0.1, 0.03697620123948642)] all 1000 [(0.005, 0.03725378012517222), (0.01, 0.038132666771878716), (0.015, 0.0386906935015919), (0.02, 0.038642641484461136), (0.025, 0.038563163472943134), (0.03, 0.03870686182348705), (0.04, 0.03879800321980825), (0.05, 0.03869984631108141), (0.07, 0.03867231938473162), (0.1, 0.038372894082663415)] org 1000 [(0.005, 0.03678256993121768), (0.01, 0.03727507158495396), (0.015, 0.03763773685817453), (0.02, 0.03772999268002472), (0.025, 0.037870437773240104), (0.03, 0.03763820197676112), (0.04, 0.037952669907349475), (0.05, 0.03763333019489976), (0.07, 0.03758792608191734), (0.1, 0.03739068779833278)] ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "response_id": "resp_0fdbf3e20a3ba82b016ab5cbcaeda487d2bb3e9f6b8131b1b8", "usage": { "input_tokens": 64065, "cached_input_tokens": 61824, "cache_write_input_tokens": 0, "output_tokens": 55, "reasoning_output_tokens": 8, "total_tokens": 64120 }, "turn_token_usage": { "input_tokens": 1687077, "cached_input_tokens": 1626752, "cache_write_input_tokens": 0, "output_tokens": 30980, "reasoning_output_tokens": 12900, "total_tokens": 1718057 }, "thread_token_usage": { "input_tokens": 1687077, "cached_input_tokens": 1626752, "cache_write_input_tokens": 0, "output_tokens": 30980, "reasoning_output_tokens": 12900, "total_tokens": 1718057 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e2d5c0d7-942d-4362-968e-227e2a114bd4", "process_id": "38638", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u - <<'PY'\nimport sys,itertools\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();n=len(items);target=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64);ur={int(x):i for i,x in enumerate(target)};ic={int(x):i for i,x in enumerate(items)}\nE=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qidx=np.array([ur[u] for u in gold]);quid=[int(target[r]) for r in qidx]\nwith np.load(P+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=n,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(n,np.float32);z[o]=1-np.arange(n)/n;return z\ngb=rs(F[0,2])+2*rs(L[1,1]);ps=pd.read_parquet(P+'/validation_preference_state.parquet');ps=ps[ps.state=='liked'];rr=np.array([ur.get(int(x),-1) for x in ps.uid]);cc=np.array([ic.get(int(x),-1) for x in ps.item_id]);ok=(rr>=0)&(cc>=0);X=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(rr[ok],cc[ok])),shape=(len(target),n));X.data[:]=1;norm=np.sqrt(np.asarray(X.multiply(X).sum(1)).ravel());norm[norm==0]=1;XN=sparse.diags(1/norm)@X;S=(XN[qidx]@XN.T).tocsr();Rall=sparse.load_npz('/app/solution/recent7_train.npz')[(target//100-1).astype(int)];Rorg=sparse.load_npz('/app/solution/recent7org_train.npz')[(target//100-1).astype(int)]\ndef topk(S,k):\n ix=[];da=[];pt=[0]\n for i in range(S.shape[0]):\n a,b=S.indptr[i:i+2];j=S.indices[a:b];v=S.data[a:b];m=j!=qidx[i];j=j[m];v=v[m]\n if len(v)>k:z=np.argpartition(v,-k)[-k:];j=j[z];v=v[z]\n ix.append(j);da.append(v);pt.append(pt[-1]+len(j))\n return sparse.csr_matrix((np.concatenate(da),np.concatenate(ix),np.array(pt)),shape=S.shape)\ndef ev(P,alphas):\n vals={a:[] for a in alphas}\n for i,(r,u) in enumerate(zip(qidx,quid)):\n lo,hi=P.indptr[i:i+2];ix=P.indices[lo:hi];pv=P.data[lo:hi];mx=pv.max() if len(pv) else 1\n for al in alphas:\n z=gb.copy();z[ix]+=al*pv/mx;z[~bits[r]]=-np.inf;t=np.argpartition(z,-10)[-10:];t=t[np.argsort(z[t])[::-1]];vals[al].append(ndcg_at_k(items[t].tolist(),gold[u],10))\n return [(a,float(np.mean(vals[a]))) for a in alphas]\nalphas=[.005,.01,.015,.02,.025,.03,.04,.05,.07,.1]\nfor k in [200,300,500,1000]:\n sk=topk(S,k)\n for name,R in [('all',Rall),('org',Rorg)]:print(name,k,ev((sk@R).tocsr(),alphas),flush=True)\n# save active similarity for final combos\nsparse.save_npz('/app/solution/active_sim_q_train.npz',S)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport sys,itertools\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();n=len(items);target=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64);ur={int(x):i for i,x in enumerate(target)};ic={int(x):i for i,x in enumerate(items)}\nE=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qidx=np.array([ur[u] for u in gold]);quid=[int(target[r]) for r in qidx]\nwith np.load(P+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=n,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(n,np.float32);z[o]=1-np.arange(n)/n;return z\ngb=rs(F[0,2])+2*rs(L[1,1]);ps=pd.read_parquet(P+'/validation_preference_state.parquet');ps=ps[ps.state=='liked'];rr=np.array([ur.get(int(x),-1) for x in ps.uid]);cc=np.array([ic.get(int(x),-1) for x in ps.item_id]);ok=(rr>=0)&(cc>=0);X=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(rr[ok],cc[ok])),shape=(len(target),n));X.data[:]=1;norm=np.sqrt(np.asarray(X.multiply(X).sum(1)).ravel());norm[norm==0]=1;XN=sparse.diags(1/norm)@X;S=(XN[qidx]@XN.T).tocsr();Rall=sparse.load_npz('/app/solution/recent7_train.npz')[(target//100-1).astype(int)];Rorg=sparse.load_npz('/app/solution/recent7org_train.npz')[(target//100-1).astype(int)]\ndef topk(S,k):\n ix=[];da=[];pt=[0]\n for i in range(S.shape[0]):\n a,b=S.indptr[i:i+2];j=S.indices[a:b];v=S.data[a:b];m=j!=qidx[i];j=j[m];v=v[m]\n if len(v)>k:z=np.argpartition(v,-k)[-k:];j=j[z];v=v[z]\n ix.append(j);da.append(v);pt.append(pt[-1]+len(j))\n return sparse.csr_matrix((np.concatenate(da),np.concatenate(ix),np.array(pt)),shape=S.shape)\ndef ev(P,alphas):\n vals={a:[] for a in alphas}\n for i,(r,u) in enumerate(zip(qidx,quid)):\n lo,hi=P.indptr[i:i+2];ix=P.indices[lo:hi];pv=P.data[lo:hi];mx=pv.max() if len(pv) else 1\n for al in alphas:\n z=gb.copy();z[ix]+=al*pv/mx;z[~bits[r]]=-np.inf;t=np.argpartition(z,-10)[-10:];t=t[np.argsort(z[t])[::-1]];vals[al].append(ndcg_at_k(items[t].tolist(),gold[u],10))\n return [(a,float(np.mean(vals[a]))) for a in alphas]\nalphas=[.005,.01,.015,.02,.025,.03,.04,.05,.07,.1]\nfor k in [200,300,500,1000]:\n sk=topk(S,k)\n for name,R in [('all',Rall),('org',Rorg)]:print(name,k,ev((sk@R).tocsr(),alphas),flush=True)\n# save active similarity for final combos\nsparse.save_npz('/app/solution/active_sim_q_train.npz',S)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "all 200 [(0.005, 0.03692219892752741), (0.01, 0.03820523891853365), (0.015, 0.03889850115844483), (0.02, 0.03914918396634148), (0.025, 0.03883897856003798), (0.03, 0.038739373079525144), (0.04, 0.03834609233828177), (0.05, 0.03840393475740443), (0.07, 0.03795927802852888), (0.1, 0.037853640727801285)]\norg 200 [(0.005, 0.03630562979527513), (0.01, 0.03720208356953695), (0.015, 0.03794864451871575), (0.02, 0.0378859088193051), (0.025, 0.037833123085761755), (0.03, 0.03763516163289139), (0.04, 0.037592962216435925), (0.05, 0.0375450725877074), (0.07, 0.03711393145359014), (0.1, 0.03621016808631632)]\nall 300 [(0.005, 0.037108066295842776), (0.01, 0.03796299471940949), (0.015, 0.03887657013193053), (0.02, 0.03891868810062882), (0.025, 0.038950698347121886), (0.03, 0.03878764129546214), (0.04, 0.038903958547439964), (0.05, 0.038736237793520555), (0.07, 0.03885513569245522), (0.1, 0.03837531829044672)]\norg 300 [(0.005, 0.03633373264361668), (0.01, 0.03753485140901134), (0.015, 0.0376789394150024), (0.02, 0.037689270987863094), (0.025, 0.0380125373888752), (0.03, 0.03787157865638546), (0.04, 0.03782868170148931), (0.05, 0.03748884861423028), (0.07, 0.03758573745405), (0.1, 0.036934922950726035)]\nall 500 [(0.005, 0.03735766429925665), (0.01, 0.03836926951464056), (0.015, 0.038775988350233354), (0.02, 0.03879498381586903), (0.025, 0.038708260734915255), (0.03, 0.038768532427306726), (0.04, 0.038506216925910906), (0.05, 0.038473527103831964), (0.07, 0.03892529059806298), (0.1, 0.038723804499457705)]\norg 500 [(0.005, 0.03675847401314856), (0.01, 0.037350422570086604), (0.015, 0.03760682921284848), (0.02, 0.03750031822682396), (0.025, 0.037899077489761554), (0.03, 0.037930159481323285), (0.04, 0.03790294178047534), (0.05, 0.03767664632227021), (0.07, 0.03730120155011157), (0.1, 0.03697620123948642)]\nall 1000 [(0.005, 0.03725378012517222), (0.01, 0.038132666771878716), (0.015, 0.0386906935015919), (0.02, 0.038642641484461136), (0.025, 0.038563163472943134), (0.03, 0.03870686182348705), (0.04, 0.03879800321980825), (0.05, 0.03869984631108141), (0.07, 0.03867231938473162), (0.1, 0.038372894082663415)]\norg 1000 [(0.005, 0.03678256993121768), (0.01, 0.03727507158495396), (0.015, 0.03763773685817453), (0.02, 0.03772999268002472), (0.025, 0.037870437773240104), (0.03, 0.03763820197676112), (0.04, 0.037952669907349475), (0.05, 0.03763333019489976), (0.07, 0.03758792608191734), (0.1, 0.03739068779833278)]\n", "stderr": "", "aggregated_output": "all 200 [(0.005, 0.03692219892752741), (0.01, 0.03820523891853365), (0.015, 0.03889850115844483), (0.02, 0.03914918396634148), (0.025, 0.03883897856003798), (0.03, 0.038739373079525144), (0.04, 0.03834609233828177), (0.05, 0.03840393475740443), (0.07, 0.03795927802852888), (0.1, 0.037853640727801285)]\norg 200 [(0.005, 0.03630562979527513), (0.01, 0.03720208356953695), (0.015, 0.03794864451871575), (0.02, 0.0378859088193051), (0.025, 0.037833123085761755), (0.03, 0.03763516163289139), (0.04, 0.037592962216435925), (0.05, 0.0375450725877074), (0.07, 0.03711393145359014), (0.1, 0.03621016808631632)]\nall 300 [(0.005, 0.037108066295842776), (0.01, 0.03796299471940949), (0.015, 0.03887657013193053), (0.02, 0.03891868810062882), (0.025, 0.038950698347121886), (0.03, 0.03878764129546214), (0.04, 0.038903958547439964), (0.05, 0.038736237793520555), (0.07, 0.03885513569245522), (0.1, 0.03837531829044672)]\norg 300 [(0.005, 0.03633373264361668), (0.01, 0.03753485140901134), (0.015, 0.0376789394150024), (0.02, 0.037689270987863094), (0.025, 0.0380125373888752), (0.03, 0.03787157865638546), (0.04, 0.03782868170148931), (0.05, 0.03748884861423028), (0.07, 0.03758573745405), (0.1, 0.036934922950726035)]\nall 500 [(0.005, 0.03735766429925665), (0.01, 0.03836926951464056), (0.015, 0.038775988350233354), (0.02, 0.03879498381586903), (0.025, 0.038708260734915255), (0.03, 0.038768532427306726), (0.04, 0.038506216925910906), (0.05, 0.038473527103831964), (0.07, 0.03892529059806298), (0.1, 0.038723804499457705)]\norg 500 [(0.005, 0.03675847401314856), (0.01, 0.037350422570086604), (0.015, 0.03760682921284848), (0.02, 0.03750031822682396), (0.025, 0.037899077489761554), (0.03, 0.037930159481323285), (0.04, 0.03790294178047534), (0.05, 0.03767664632227021), (0.07, 0.03730120155011157), (0.1, 0.03697620123948642)]\nall 1000 [(0.005, 0.03725378012517222), (0.01, 0.038132666771878716), (0.015, 0.0386906935015919), (0.02, 0.038642641484461136), (0.025, 0.038563163472943134), (0.03, 0.03870686182348705), (0.04, 0.03879800321980825), (0.05, 0.03869984631108141), (0.07, 0.03867231938473162), (0.1, 0.038372894082663415)]\norg 1000 [(0.005, 0.03678256993121768), (0.01, 0.03727507158495396), (0.015, 0.03763773685817453), (0.02, 0.03772999268002472), (0.025, 0.037870437773240104), (0.03, 0.03763820197676112), (0.04, 0.037952669907349475), (0.05, 0.03763333019489976), (0.07, 0.03758792608191734), (0.1, 0.03739068779833278)]\n", "exit_code": 0, "duration": { "secs": 64, "nanos": 681057836 }, "formatted_output": "all 200 [(0.005, 0.03692219892752741), (0.01, 0.03820523891853365), (0.015, 0.03889850115844483), (0.02, 0.03914918396634148), (0.025, 0.03883897856003798), (0.03, 0.038739373079525144), (0.04, 0.03834609233828177), (0.05, 0.03840393475740443), (0.07, 0.03795927802852888), (0.1, 0.037853640727801285)]\norg 200 [(0.005, 0.03630562979527513), (0.01, 0.03720208356953695), (0.015, 0.03794864451871575), (0.02, 0.0378859088193051), (0.025, 0.037833123085761755), (0.03, 0.03763516163289139), (0.04, 0.037592962216435925), (0.05, 0.0375450725877074), (0.07, 0.03711393145359014), (0.1, 0.03621016808631632)]\nall 300 [(0.005, 0.037108066295842776), (0.01, 0.03796299471940949), (0.015, 0.03887657013193053), (0.02, 0.03891868810062882), (0.025, 0.038950698347121886), (0.03, 0.03878764129546214), (0.04, 0.038903958547439964), (0.05, 0.038736237793520555), (0.07, 0.03885513569245522), (0.1, 0.03837531829044672)]\norg 300 [(0.005, 0.03633373264361668), (0.01, 0.03753485140901134), (0.015, 0.0376789394150024), (0.02, 0.037689270987863094), (0.025, 0.0380125373888752), (0.03, 0.03787157865638546), (0.04, 0.03782868170148931), (0.05, 0.03748884861423028), (0.07, 0.03758573745405), (0.1, 0.036934922950726035)]\nall 500 [(0.005, 0.03735766429925665), (0.01, 0.03836926951464056), (0.015, 0.038775988350233354), (0.02, 0.03879498381586903), (0.025, 0.038708260734915255), (0.03, 0.038768532427306726), (0.04, 0.038506216925910906), (0.05, 0.038473527103831964), (0.07, 0.03892529059806298), (0.1, 0.038723804499457705)]\norg 500 [(0.005, 0.03675847401314856), (0.01, 0.037350422570086604), (0.015, 0.03760682921284848), (0.02, 0.03750031822682396), (0.025, 0.037899077489761554), (0.03, 0.037930159481323285), (0.04, 0.03790294178047534), (0.05, 0.03767664632227021), (0.07, 0.03730120155011157), (0.1, 0.03697620123948642)]\nall 1000 [(0.005, 0.03725378012517222), (0.01, 0.038132666771878716), (0.015, 0.0386906935015919), (0.02, 0.038642641484461136), (0.025, 0.038563163472943134), (0.03, 0.03870686182348705), (0.04, 0.03879800321980825), (0.05, 0.03869984631108141), (0.07, 0.03867231938473162), (0.1, 0.038372894082663415)]\norg 1000 [(0.005, 0.03678256993121768), (0.01, 0.03727507158495396), (0.015, 0.03763773685817453), (0.02, 0.03772999268002472), (0.025, 0.037870437773240104), (0.03, 0.03763820197676112), (0.04, 0.037952669907349475), (0.05, 0.03763333019489976), (0.07, 0.03758792608191734), (0.1, 0.03739068779833278)]\n" } } ## Step 64 · agent · 2026-09-25T01:18:43.438Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:19:00.773Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u - <<'PY'\\nimport sys,itertools\\nsys.path.insert(0,'/task')\\nfrom evaluate_public import validation_truth\\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\\nfrom pathlib import Path\\nimport pandas as pd,numpy as np\\nfrom scipy import sparse\\nPTH='/task/data';cat=pd.read_parquet(PTH+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();n=len(items);target=pd.read_parquet(PTH+'/target_users.parquet').uid.to_numpy(np.int64);ur={int(x):i for i,x in enumerate(target)};E=CandidateSet(Path(PTH+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qidx=np.array([ur[u] for u in gold]);xrows=(target[qidx]//100-1).astype(int);quid=[int(target[r]) for r in qidx]\\nwith np.load(PTH+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=n,bitorder='little').astype(bool)\\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(n,np.float32);z[o]=1-np.arange(n)/n;return z\\ngb=rs(F[0,2])+2*rs(L[1,1]);B=np.load('/app/solution/svd_binary_train.npz');UB=B['U'];VB=B['V'];A=np.load('/app/solution/svd_active_train.npz');UA=A['U'];VA=A['V'];S=sparse.load_npz('/app/solution/active_sim_q_train.npz');R=sparse.load_npz('/app/solution/recent7_train.npz')[(target//100-1).astype(int)]\\n# prune top 200\\nix=[];da=[];pt=[0]\\nfor i in range(S.shape[0]):\\n lo,hi=S.indptr[i:i+2];j=S.indices[lo:hi];v=S.data[lo:hi];m=j!=qidx[i];j=j[m];v=v[m]\\n if len(v)>200:z=np.argpartition(v,-200)[-200:];j=j[z];v=v[z]\\n ix.append(j);da.append(v);pt.append(pt[-1]+len(j))\\nSK=sparse.csr_matrix((np.concatenate(da),np.concatenate(ix),np.array(pt)),shape=S.shape);NP=(SK@R).tocsr();sparse.save_npz('/app/solution/neighbor_recent_train.npz',NP)\\nvariants=list(itertools.product([.01,.015,.02,.025,.03],[.0003,.0006,.0009],[0,.00015,.0003]));vals={v:[] for v in variants}\\ndef std(Z):Z-=Z.mean(1)[:,None];d=Z.std(1);d[d==0]=1;return Z/d[:,None]\\nfor st in range(0,len(qidx),128):\\n br=qidx[st:st+128];SB=std(UB[xrows[st:st+128],:96]@VB[:96]);SA=std(UA[br]@VA);PN=NP[st:st+128].toarray();mx=PN.max(1);mx[mx==0]=1;PN/=mx[:,None]\\n for v in variants:\\n Z=gb[None,:]+v[0]*PN+v[1]*SB+v[2]*SA;Z[~bits[br]]=-np.inf;top=np.argpartition(Z,-10,axis=1)[:,-10:]\\n for j,row in enumerate(top):row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[v].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\\nfor x in sorted([(np.mean(z),v) for v,z in vals.items()],reverse=True)[:25]:print(x)\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:12000});\nif(r.session_id){text(`SESSION_ID=${r.session_id}`);text(r.output);}else{text(r.output);}\n" } ### Tool output · 2026-09-25T01:19:30.966Z Script completed Wall time 30.2 seconds Output: SESSION_ID=34789 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "response_id": "resp_0fdbf3e20a3ba82b016ab5cbed831c87d29936731d2871efcd", "usage": { "input_tokens": 64757, "cached_input_tokens": 63872, "cache_write_input_tokens": 0, "output_tokens": 1215, "reasoning_output_tokens": 243, "total_tokens": 65972 }, "turn_token_usage": { "input_tokens": 1751834, "cached_input_tokens": 1690624, "cache_write_input_tokens": 0, "output_tokens": 32195, "reasoning_output_tokens": 13143, "total_tokens": 1784029 }, "thread_token_usage": { "input_tokens": 1751834, "cached_input_tokens": 1690624, "cache_write_input_tokens": 0, "output_tokens": 32195, "reasoning_output_tokens": 13143, "total_tokens": 1784029 } } ## Step 65 · agent · 2026-09-25T01:19:33.256Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:19:33.987Z { "code": "const r = await tools.write_stdin({session_id:34789,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:19:53.132Z Script completed Wall time 19.1 seconds Output: (np.float64(0.04129788096117011), (0.02, 0.0006, 0.0003)) (np.float64(0.04115708419337064), (0.015, 0.0006, 0.00015)) (np.float64(0.04108797955522227), (0.015, 0.0006, 0.0003)) (np.float64(0.04107968649340763), (0.01, 0.0006, 0.0003)) (np.float64(0.041069981008229006), (0.015, 0.0009, 0.00015)) (np.float64(0.04103696499739304), (0.02, 0.0009, 0.00015)) (np.float64(0.04103009805436581), (0.02, 0.0009, 0.0003)) (np.float64(0.040880197041195165), (0.025, 0.0009, 0.0003)) (np.float64(0.04086624182649273), (0.02, 0.0006, 0.00015)) (np.float64(0.0408578262021265), (0.015, 0.0009, 0.0003)) (np.float64(0.04082906313027645), (0.03, 0.0009, 0.0003)) (np.float64(0.04078799741458635), (0.025, 0.0006, 0.0003)) (np.float64(0.040772988959447136), (0.01, 0.0006, 0.00015)) (np.float64(0.04075338304407032), (0.025, 0.0009, 0.00015)) (np.float64(0.0407306075008462), (0.015, 0.0006, 0)) (np.float64(0.04068342318201982), (0.015, 0.0003, 0.0003)) (np.float64(0.04063743707113819), (0.015, 0.0003, 0.00015)) (np.float64(0.04062231686881125), (0.03, 0.0006, 0.0003)) (np.float64(0.04061632275882384), (0.02, 0.0003, 0.0003)) (np.float64(0.04061325343329181), (0.025, 0.0003, 0.0003)) (np.float64(0.040545913758730096), (0.02, 0.0006, 0)) (np.float64(0.040531547613006716), (0.03, 0.0006, 0.00015)) (np.float64(0.04051341388160154), (0.015, 0.0003, 0)) (np.float64(0.040494020628630924), (0.01, 0.0003, 0.0003)) (np.float64(0.04049225865695971), (0.015, 0.0009, 0)) ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "response_id": "resp_0fdbf3e20a3ba82b016ab5cc238ac087d2a757f2eed0468afd", "usage": { "input_tokens": 66000, "cached_input_tokens": 64640, "cache_write_input_tokens": 0, "output_tokens": 56, "reasoning_output_tokens": 9, "total_tokens": 66056 }, "turn_token_usage": { "input_tokens": 1817834, "cached_input_tokens": 1755264, "cache_write_input_tokens": 0, "output_tokens": 32251, "reasoning_output_tokens": 13152, "total_tokens": 1850085 }, "thread_token_usage": { "input_tokens": 1817834, "cached_input_tokens": 1755264, "cache_write_input_tokens": 0, "output_tokens": 32251, "reasoning_output_tokens": 13152, "total_tokens": 1850085 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2f40910e-1902-4157-8b29-3c50e42370a8", "process_id": "34789", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u - <<'PY'\nimport sys,itertools\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nPTH='/task/data';cat=pd.read_parquet(PTH+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();n=len(items);target=pd.read_parquet(PTH+'/target_users.parquet').uid.to_numpy(np.int64);ur={int(x):i for i,x in enumerate(target)};E=CandidateSet(Path(PTH+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qidx=np.array([ur[u] for u in gold]);xrows=(target[qidx]//100-1).astype(int);quid=[int(target[r]) for r in qidx]\nwith np.load(PTH+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=n,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(n,np.float32);z[o]=1-np.arange(n)/n;return z\ngb=rs(F[0,2])+2*rs(L[1,1]);B=np.load('/app/solution/svd_binary_train.npz');UB=B['U'];VB=B['V'];A=np.load('/app/solution/svd_active_train.npz');UA=A['U'];VA=A['V'];S=sparse.load_npz('/app/solution/active_sim_q_train.npz');R=sparse.load_npz('/app/solution/recent7_train.npz')[(target//100-1).astype(int)]\n# prune top 200\nix=[];da=[];pt=[0]\nfor i in range(S.shape[0]):\n lo,hi=S.indptr[i:i+2];j=S.indices[lo:hi];v=S.data[lo:hi];m=j!=qidx[i];j=j[m];v=v[m]\n if len(v)>200:z=np.argpartition(v,-200)[-200:];j=j[z];v=v[z]\n ix.append(j);da.append(v);pt.append(pt[-1]+len(j))\nSK=sparse.csr_matrix((np.concatenate(da),np.concatenate(ix),np.array(pt)),shape=S.shape);NP=(SK@R).tocsr();sparse.save_npz('/app/solution/neighbor_recent_train.npz',NP)\nvariants=list(itertools.product([.01,.015,.02,.025,.03],[.0003,.0006,.0009],[0,.00015,.0003]));vals={v:[] for v in variants}\ndef std(Z):Z-=Z.mean(1)[:,None];d=Z.std(1);d[d==0]=1;return Z/d[:,None]\nfor st in range(0,len(qidx),128):\n br=qidx[st:st+128];SB=std(UB[xrows[st:st+128],:96]@VB[:96]);SA=std(UA[br]@VA);PN=NP[st:st+128].toarray();mx=PN.max(1);mx[mx==0]=1;PN/=mx[:,None]\n for v in variants:\n Z=gb[None,:]+v[0]*PN+v[1]*SB+v[2]*SA;Z[~bits[br]]=-np.inf;top=np.argpartition(Z,-10,axis=1)[:,-10:]\n for j,row in enumerate(top):row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[v].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\nfor x in sorted([(np.mean(z),v) for v,z in vals.items()],reverse=True)[:25]:print(x)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport sys,itertools\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nPTH='/task/data';cat=pd.read_parquet(PTH+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();n=len(items);target=pd.read_parquet(PTH+'/target_users.parquet').uid.to_numpy(np.int64);ur={int(x):i for i,x in enumerate(target)};E=CandidateSet(Path(PTH+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qidx=np.array([ur[u] for u in gold]);xrows=(target[qidx]//100-1).astype(int);quid=[int(target[r]) for r in qidx]\nwith np.load(PTH+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=n,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(n,np.float32);z[o]=1-np.arange(n)/n;return z\ngb=rs(F[0,2])+2*rs(L[1,1]);B=np.load('/app/solution/svd_binary_train.npz');UB=B['U'];VB=B['V'];A=np.load('/app/solution/svd_active_train.npz');UA=A['U'];VA=A['V'];S=sparse.load_npz('/app/solution/active_sim_q_train.npz');R=sparse.load_npz('/app/solution/recent7_train.npz')[(target//100-1).astype(int)]\n# prune top 200\nix=[];da=[];pt=[0]\nfor i in range(S.shape[0]):\n lo,hi=S.indptr[i:i+2];j=S.indices[lo:hi];v=S.data[lo:hi];m=j!=qidx[i];j=j[m];v=v[m]\n if len(v)>200:z=np.argpartition(v,-200)[-200:];j=j[z];v=v[z]\n ix.append(j);da.append(v);pt.append(pt[-1]+len(j))\nSK=sparse.csr_matrix((np.concatenate(da),np.concatenate(ix),np.array(pt)),shape=S.shape);NP=(SK@R).tocsr();sparse.save_npz('/app/solution/neighbor_recent_train.npz',NP)\nvariants=list(itertools.product([.01,.015,.02,.025,.03],[.0003,.0006,.0009],[0,.00015,.0003]));vals={v:[] for v in variants}\ndef std(Z):Z-=Z.mean(1)[:,None];d=Z.std(1);d[d==0]=1;return Z/d[:,None]\nfor st in range(0,len(qidx),128):\n br=qidx[st:st+128];SB=std(UB[xrows[st:st+128],:96]@VB[:96]);SA=std(UA[br]@VA);PN=NP[st:st+128].toarray();mx=PN.max(1);mx[mx==0]=1;PN/=mx[:,None]\n for v in variants:\n Z=gb[None,:]+v[0]*PN+v[1]*SB+v[2]*SA;Z[~bits[br]]=-np.inf;top=np.argpartition(Z,-10,axis=1)[:,-10:]\n for j,row in enumerate(top):row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[v].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\nfor x in sorted([(np.mean(z),v) for v,z in vals.items()],reverse=True)[:25]:print(x)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "(np.float64(0.04129788096117011), (0.02, 0.0006, 0.0003))\n(np.float64(0.04115708419337064), (0.015, 0.0006, 0.00015))\n(np.float64(0.04108797955522227), (0.015, 0.0006, 0.0003))\n(np.float64(0.04107968649340763), (0.01, 0.0006, 0.0003))\n(np.float64(0.041069981008229006), (0.015, 0.0009, 0.00015))\n(np.float64(0.04103696499739304), (0.02, 0.0009, 0.00015))\n(np.float64(0.04103009805436581), (0.02, 0.0009, 0.0003))\n(np.float64(0.040880197041195165), (0.025, 0.0009, 0.0003))\n(np.float64(0.04086624182649273), (0.02, 0.0006, 0.00015))\n(np.float64(0.0408578262021265), (0.015, 0.0009, 0.0003))\n(np.float64(0.04082906313027645), (0.03, 0.0009, 0.0003))\n(np.float64(0.04078799741458635), (0.025, 0.0006, 0.0003))\n(np.float64(0.040772988959447136), (0.01, 0.0006, 0.00015))\n(np.float64(0.04075338304407032), (0.025, 0.0009, 0.00015))\n(np.float64(0.0407306075008462), (0.015, 0.0006, 0))\n(np.float64(0.04068342318201982), (0.015, 0.0003, 0.0003))\n(np.float64(0.04063743707113819), (0.015, 0.0003, 0.00015))\n(np.float64(0.04062231686881125), (0.03, 0.0006, 0.0003))\n(np.float64(0.04061632275882384), (0.02, 0.0003, 0.0003))\n(np.float64(0.04061325343329181), (0.025, 0.0003, 0.0003))\n(np.float64(0.040545913758730096), (0.02, 0.0006, 0))\n(np.float64(0.040531547613006716), (0.03, 0.0006, 0.00015))\n(np.float64(0.04051341388160154), (0.015, 0.0003, 0))\n(np.float64(0.040494020628630924), (0.01, 0.0003, 0.0003))\n(np.float64(0.04049225865695971), (0.015, 0.0009, 0))\n", "stderr": "", "aggregated_output": "(np.float64(0.04129788096117011), (0.02, 0.0006, 0.0003))\n(np.float64(0.04115708419337064), (0.015, 0.0006, 0.00015))\n(np.float64(0.04108797955522227), (0.015, 0.0006, 0.0003))\n(np.float64(0.04107968649340763), (0.01, 0.0006, 0.0003))\n(np.float64(0.041069981008229006), (0.015, 0.0009, 0.00015))\n(np.float64(0.04103696499739304), (0.02, 0.0009, 0.00015))\n(np.float64(0.04103009805436581), (0.02, 0.0009, 0.0003))\n(np.float64(0.040880197041195165), (0.025, 0.0009, 0.0003))\n(np.float64(0.04086624182649273), (0.02, 0.0006, 0.00015))\n(np.float64(0.0408578262021265), (0.015, 0.0009, 0.0003))\n(np.float64(0.04082906313027645), (0.03, 0.0009, 0.0003))\n(np.float64(0.04078799741458635), (0.025, 0.0006, 0.0003))\n(np.float64(0.040772988959447136), (0.01, 0.0006, 0.00015))\n(np.float64(0.04075338304407032), (0.025, 0.0009, 0.00015))\n(np.float64(0.0407306075008462), (0.015, 0.0006, 0))\n(np.float64(0.04068342318201982), (0.015, 0.0003, 0.0003))\n(np.float64(0.04063743707113819), (0.015, 0.0003, 0.00015))\n(np.float64(0.04062231686881125), (0.03, 0.0006, 0.0003))\n(np.float64(0.04061632275882384), (0.02, 0.0003, 0.0003))\n(np.float64(0.04061325343329181), (0.025, 0.0003, 0.0003))\n(np.float64(0.040545913758730096), (0.02, 0.0006, 0))\n(np.float64(0.040531547613006716), (0.03, 0.0006, 0.00015))\n(np.float64(0.04051341388160154), (0.015, 0.0003, 0))\n(np.float64(0.040494020628630924), (0.01, 0.0003, 0.0003))\n(np.float64(0.04049225865695971), (0.015, 0.0009, 0))\n", "exit_code": 0, "duration": { "secs": 52, "nanos": 173471616 }, "formatted_output": "(np.float64(0.04129788096117011), (0.02, 0.0006, 0.0003))\n(np.float64(0.04115708419337064), (0.015, 0.0006, 0.00015))\n(np.float64(0.04108797955522227), (0.015, 0.0006, 0.0003))\n(np.float64(0.04107968649340763), (0.01, 0.0006, 0.0003))\n(np.float64(0.041069981008229006), (0.015, 0.0009, 0.00015))\n(np.float64(0.04103696499739304), (0.02, 0.0009, 0.00015))\n(np.float64(0.04103009805436581), (0.02, 0.0009, 0.0003))\n(np.float64(0.040880197041195165), (0.025, 0.0009, 0.0003))\n(np.float64(0.04086624182649273), (0.02, 0.0006, 0.00015))\n(np.float64(0.0408578262021265), (0.015, 0.0009, 0.0003))\n(np.float64(0.04082906313027645), (0.03, 0.0009, 0.0003))\n(np.float64(0.04078799741458635), (0.025, 0.0006, 0.0003))\n(np.float64(0.040772988959447136), (0.01, 0.0006, 0.00015))\n(np.float64(0.04075338304407032), (0.025, 0.0009, 0.00015))\n(np.float64(0.0407306075008462), (0.015, 0.0006, 0))\n(np.float64(0.04068342318201982), (0.015, 0.0003, 0.0003))\n(np.float64(0.04063743707113819), (0.015, 0.0003, 0.00015))\n(np.float64(0.04062231686881125), (0.03, 0.0006, 0.0003))\n(np.float64(0.04061632275882384), (0.02, 0.0003, 0.0003))\n(np.float64(0.04061325343329181), (0.025, 0.0003, 0.0003))\n(np.float64(0.040545913758730096), (0.02, 0.0006, 0))\n(np.float64(0.040531547613006716), (0.03, 0.0006, 0.00015))\n(np.float64(0.04051341388160154), (0.015, 0.0003, 0))\n(np.float64(0.040494020628630924), (0.01, 0.0003, 0.0003))\n(np.float64(0.04049225865695971), (0.015, 0.0009, 0))\n" } } ## Step 66 · agent · 2026-09-25T01:20:05.244Z ### Thinking unavailable — no text recorded ## Step 67 · agent · 2026-09-25T01:20:13.205Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:20:14.014Z { "code": "const r = await tools.exec_command({cmd:\"ls -lh /app/solution\",workdir:\"/app\",yield_time_ms:10000,max_output_tokens:5000});\ntext(r.output);\n" } ### Tool output · 2026-09-25T01:20:14.109Z Script completed Wall time 0.0 seconds Output: total 171M -rw-r--r-- 1 agent agent 312 Sep 25 01:00 README.md -rw-r--r-- 1 agent agent 23M Sep 25 01:18 active_sim_q_train.npz -rw-r--r-- 1 agent agent 2.6K Sep 25 01:00 build_ranker.py -rw-r--r-- 1 agent agent 1.7M Sep 25 01:03 global_features.npz -rw-r--r-- 1 agent agent 14M Sep 25 01:10 listen_counts_train.npz -rw-r--r-- 1 agent agent 94M Sep 25 01:19 neighbor_recent_train.npz -rw-r--r-- 1 agent agent 5.4M Sep 25 01:10 organic_counts_train.npz -rw-r--r-- 1 agent agent 1.2M Sep 25 01:16 recent7_train.npz -rw-r--r-- 1 agent agent 489K Sep 25 01:16 recent7org_train.npz -rw-r--r-- 1 agent agent 719K Sep 25 01:00 submission.parquet -rw-r--r-- 1 agent agent 4.1M Sep 25 01:13 svd_active_train.npz -rw-r--r-- 1 agent agent 14M Sep 25 01:11 svd_binary_train.npz -rw-r--r-- 1 agent agent 14M Sep 25 01:12 svd_log_train.npz -rw-r--r-- 1 agent agent 712K Sep 25 01:00 validation.parquet ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ca05e54a-33de-4f02-8d72-714549373a07", "process_id": "39706", "command": [ "/bin/sh", "-lc", "ls -lh /app/solution" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "list_files", "cmd": "ls -lh /app/solution", "path": "solution" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "total 171M\n-rw-r--r-- 1 agent agent 312 Sep 25 01:00 README.md\n-rw-r--r-- 1 agent agent 23M Sep 25 01:18 active_sim_q_train.npz\n-rw-r--r-- 1 agent agent 2.6K Sep 25 01:00 build_ranker.py\n-rw-r--r-- 1 agent agent 1.7M Sep 25 01:03 global_features.npz\n-rw-r--r-- 1 agent agent 14M Sep 25 01:10 listen_counts_train.npz\n-rw-r--r-- 1 agent agent 94M Sep 25 01:19 neighbor_recent_train.npz\n-rw-r--r-- 1 agent agent 5.4M Sep 25 01:10 organic_counts_train.npz\n-rw-r--r-- 1 agent agent 1.2M Sep 25 01:16 recent7_train.npz\n-rw-r--r-- 1 agent agent 489K Sep 25 01:16 recent7org_train.npz\n-rw-r--r-- 1 agent agent 719K Sep 25 01:00 submission.parquet\n-rw-r--r-- 1 agent agent 4.1M Sep 25 01:13 svd_active_train.npz\n-rw-r--r-- 1 agent agent 14M Sep 25 01:11 svd_binary_train.npz\n-rw-r--r-- 1 agent agent 14M Sep 25 01:12 svd_log_train.npz\n-rw-r--r-- 1 agent agent 712K Sep 25 01:00 validation.parquet\n", "stderr": "", "aggregated_output": "total 171M\n-rw-r--r-- 1 agent agent 312 Sep 25 01:00 README.md\n-rw-r--r-- 1 agent agent 23M Sep 25 01:18 active_sim_q_train.npz\n-rw-r--r-- 1 agent agent 2.6K Sep 25 01:00 build_ranker.py\n-rw-r--r-- 1 agent agent 1.7M Sep 25 01:03 global_features.npz\n-rw-r--r-- 1 agent agent 14M Sep 25 01:10 listen_counts_train.npz\n-rw-r--r-- 1 agent agent 94M Sep 25 01:19 neighbor_recent_train.npz\n-rw-r--r-- 1 agent agent 5.4M Sep 25 01:10 organic_counts_train.npz\n-rw-r--r-- 1 agent agent 1.2M Sep 25 01:16 recent7_train.npz\n-rw-r--r-- 1 agent agent 489K Sep 25 01:16 recent7org_train.npz\n-rw-r--r-- 1 agent agent 719K Sep 25 01:00 submission.parquet\n-rw-r--r-- 1 agent agent 4.1M Sep 25 01:13 svd_active_train.npz\n-rw-r--r-- 1 agent agent 14M Sep 25 01:11 svd_binary_train.npz\n-rw-r--r-- 1 agent agent 14M Sep 25 01:12 svd_log_train.npz\n-rw-r--r-- 1 agent agent 712K Sep 25 01:00 validation.parquet\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 6680 }, "formatted_output": "total 171M\n-rw-r--r-- 1 agent agent 312 Sep 25 01:00 README.md\n-rw-r--r-- 1 agent agent 23M Sep 25 01:18 active_sim_q_train.npz\n-rw-r--r-- 1 agent agent 2.6K Sep 25 01:00 build_ranker.py\n-rw-r--r-- 1 agent agent 1.7M Sep 25 01:03 global_features.npz\n-rw-r--r-- 1 agent agent 14M Sep 25 01:10 listen_counts_train.npz\n-rw-r--r-- 1 agent agent 94M Sep 25 01:19 neighbor_recent_train.npz\n-rw-r--r-- 1 agent agent 5.4M Sep 25 01:10 organic_counts_train.npz\n-rw-r--r-- 1 agent agent 1.2M Sep 25 01:16 recent7_train.npz\n-rw-r--r-- 1 agent agent 489K Sep 25 01:16 recent7org_train.npz\n-rw-r--r-- 1 agent agent 719K Sep 25 01:00 submission.parquet\n-rw-r--r-- 1 agent agent 4.1M Sep 25 01:13 svd_active_train.npz\n-rw-r--r-- 1 agent agent 14M Sep 25 01:11 svd_binary_train.npz\n-rw-r--r-- 1 agent agent 14M Sep 25 01:12 svd_log_train.npz\n-rw-r--r-- 1 agent agent 712K Sep 25 01:00 validation.parquet\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "response_id": "resp_0fdbf3e20a3ba82b016ab5cc39ff6487d2961b99a07937b35f", "usage": { "input_tokens": 66817, "cached_input_tokens": 65792, "cache_write_input_tokens": 0, "output_tokens": 1006, "reasoning_output_tokens": 953, "total_tokens": 67823 }, "turn_token_usage": { "input_tokens": 1884651, "cached_input_tokens": 1821056, "cache_write_input_tokens": 0, "output_tokens": 33257, "reasoning_output_tokens": 14105, "total_tokens": 1917908 }, "thread_token_usage": { "input_tokens": 1884651, "cached_input_tokens": 1821056, "cache_write_input_tokens": 0, "output_tokens": 33257, "reasoning_output_tokens": 14105, "total_tokens": 1917908 } } ## Step 68 · agent · 2026-09-25T01:20:24.657Z ### Thinking unavailable — no text recorded ## Step 69 · agent · 2026-09-25T01:20:32.805Z ### Thinking unavailable — no text recorded ## Step 70 · agent · 2026-09-25T01:20:39.291Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:20:54.576Z { "code": "const r = await tools.exec_command({cmd:\"/opt/task-venv/bin/python -u - <<'PY'\\nimport sys\\nsys.path.insert(0,'/task')\\nfrom evaluate_public import validation_truth\\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\\nfrom pathlib import Path\\nimport pandas as pd,numpy as np\\nfrom scipy import sparse\\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();n=len(items);target=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64);ur={int(x):i for i,x in enumerate(target)};ic={int(x):i for i,x in enumerate(items)};E=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qidx=np.array([ur[u] for u in gold]);xrows=(target[qidx]//100-1).astype(int);quid=[int(target[r]) for r in qidx]\\nwith np.load(P+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=n,bitorder='little').astype(bool)\\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(n,np.float32);z[o]=1-np.arange(n)/n;return z\\ngb=rs(F[0,2])+2*rs(L[1,1]);B=np.load('/app/solution/svd_binary_train.npz');UB=B['U'];VB=B['V'];A=np.load('/app/solution/svd_active_train.npz');UA=A['U'];VA=A['V'];NP=sparse.load_npz('/app/solution/neighbor_recent_train.npz')\\nps=pd.read_parquet(P+'/validation_preference_state.parquet');pd_=ps[ps.state=='disliked'];rr=np.array([ur.get(int(x),-1) for x in pd_.uid]);cc=np.array([ic.get(int(x),-1) for x in pd_.item_id]);ok=(rr>=0)&(cc>=0);XD=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(rr[ok],cc[ok])),shape=(len(target),n));XD.data[:]=1;UD=(XD@VA.T).astype(np.float32)\\ndef std(Z):Z-=Z.mean(1)[:,None];d=Z.std(1);d[d==0]=1;return Z/d[:,None]\\ncoefs=[-.001,-.0007,-.0005,-.0003,-.0002,-.0001,-.00005,0,.00005,.0001,.0002];vals={c:[] for c in coefs}\\nfor st in range(0,len(qidx),128):\\n br=qidx[st:st+128];SB=std(UB[xrows[st:st+128],:96]@VB[:96]);SA=std(UA[br]@VA);SD=std(UD[br]@VA);PN=NP[st:st+128].toarray();mx=PN.max(1);mx[mx==0]=1;PN/=mx[:,None]\\n for c in coefs:\\n Z=gb[None,:]+.02*PN+.0006*SB+.0003*SA+c*SD;Z[~bits[br]]=-np.inf;top=np.argpartition(Z,-10,axis=1)[:,-10:]\\n for j,row in enumerate(top):row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[c].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\\nprint([(c,float(np.mean(vals[c]))) for c in coefs])\\nPY\",workdir:\"/app\",yield_time_ms:30000,max_output_tokens:10000});\nif(r.session_id){text(`SESSION_ID=${r.session_id}`);text(r.output);}else{text(r.output);}\n" } ### Tool output · 2026-09-25T01:21:08.798Z Script completed Wall time 14.2 seconds Output: [(-0.001, 0.03751401616451922), (-0.0007, 0.03875829585420797), (-0.0005, 0.039311597903167794), (-0.0003, 0.04015163011866139), (-0.0002, 0.04071082036559139), (-0.0001, 0.04097170096264719), (-5e-05, 0.0411363754471562), (0, 0.04129788096117011), (5e-05, 0.04122734667043429), (0.0001, 0.041307536916685234), (0.0002, 0.041164600067995535)] ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "response_id": "resp_0fdbf3e20a3ba82b016ab5cc4e70a087d28e324b3175b0b9f1", "usage": { "input_tokens": 68226, "cached_input_tokens": 66688, "cache_write_input_tokens": 0, "output_tokens": 2180, "reasoning_output_tokens": 1267, "total_tokens": 70406 }, "turn_token_usage": { "input_tokens": 1952877, "cached_input_tokens": 1887744, "cache_write_input_tokens": 0, "output_tokens": 35437, "reasoning_output_tokens": 15372, "total_tokens": 1988314 }, "thread_token_usage": { "input_tokens": 1952877, "cached_input_tokens": 1887744, "cache_write_input_tokens": 0, "output_tokens": 35437, "reasoning_output_tokens": 15372, "total_tokens": 1988314 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-07252fee-8b26-4dcd-b8b8-a195b4da702d", "process_id": "53056", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u - <<'PY'\nimport sys\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();n=len(items);target=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64);ur={int(x):i for i,x in enumerate(target)};ic={int(x):i for i,x in enumerate(items)};E=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qidx=np.array([ur[u] for u in gold]);xrows=(target[qidx]//100-1).astype(int);quid=[int(target[r]) for r in qidx]\nwith np.load(P+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=n,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(n,np.float32);z[o]=1-np.arange(n)/n;return z\ngb=rs(F[0,2])+2*rs(L[1,1]);B=np.load('/app/solution/svd_binary_train.npz');UB=B['U'];VB=B['V'];A=np.load('/app/solution/svd_active_train.npz');UA=A['U'];VA=A['V'];NP=sparse.load_npz('/app/solution/neighbor_recent_train.npz')\nps=pd.read_parquet(P+'/validation_preference_state.parquet');pd_=ps[ps.state=='disliked'];rr=np.array([ur.get(int(x),-1) for x in pd_.uid]);cc=np.array([ic.get(int(x),-1) for x in pd_.item_id]);ok=(rr>=0)&(cc>=0);XD=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(rr[ok],cc[ok])),shape=(len(target),n));XD.data[:]=1;UD=(XD@VA.T).astype(np.float32)\ndef std(Z):Z-=Z.mean(1)[:,None];d=Z.std(1);d[d==0]=1;return Z/d[:,None]\ncoefs=[-.001,-.0007,-.0005,-.0003,-.0002,-.0001,-.00005,0,.00005,.0001,.0002];vals={c:[] for c in coefs}\nfor st in range(0,len(qidx),128):\n br=qidx[st:st+128];SB=std(UB[xrows[st:st+128],:96]@VB[:96]);SA=std(UA[br]@VA);SD=std(UD[br]@VA);PN=NP[st:st+128].toarray();mx=PN.max(1);mx[mx==0]=1;PN/=mx[:,None]\n for c in coefs:\n Z=gb[None,:]+.02*PN+.0006*SB+.0003*SA+c*SD;Z[~bits[br]]=-np.inf;top=np.argpartition(Z,-10,axis=1)[:,-10:]\n for j,row in enumerate(top):row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[c].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\nprint([(c,float(np.mean(vals[c]))) for c in coefs])\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport sys\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();n=len(items);target=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64);ur={int(x):i for i,x in enumerate(target)};ic={int(x):i for i,x in enumerate(items)};E=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qidx=np.array([ur[u] for u in gold]);xrows=(target[qidx]//100-1).astype(int);quid=[int(target[r]) for r in qidx]\nwith np.load(P+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=n,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(n,np.float32);z[o]=1-np.arange(n)/n;return z\ngb=rs(F[0,2])+2*rs(L[1,1]);B=np.load('/app/solution/svd_binary_train.npz');UB=B['U'];VB=B['V'];A=np.load('/app/solution/svd_active_train.npz');UA=A['U'];VA=A['V'];NP=sparse.load_npz('/app/solution/neighbor_recent_train.npz')\nps=pd.read_parquet(P+'/validation_preference_state.parquet');pd_=ps[ps.state=='disliked'];rr=np.array([ur.get(int(x),-1) for x in pd_.uid]);cc=np.array([ic.get(int(x),-1) for x in pd_.item_id]);ok=(rr>=0)&(cc>=0);XD=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(rr[ok],cc[ok])),shape=(len(target),n));XD.data[:]=1;UD=(XD@VA.T).astype(np.float32)\ndef std(Z):Z-=Z.mean(1)[:,None];d=Z.std(1);d[d==0]=1;return Z/d[:,None]\ncoefs=[-.001,-.0007,-.0005,-.0003,-.0002,-.0001,-.00005,0,.00005,.0001,.0002];vals={c:[] for c in coefs}\nfor st in range(0,len(qidx),128):\n br=qidx[st:st+128];SB=std(UB[xrows[st:st+128],:96]@VB[:96]);SA=std(UA[br]@VA);SD=std(UD[br]@VA);PN=NP[st:st+128].toarray();mx=PN.max(1);mx[mx==0]=1;PN/=mx[:,None]\n for c in coefs:\n Z=gb[None,:]+.02*PN+.0006*SB+.0003*SA+c*SD;Z[~bits[br]]=-np.inf;top=np.argpartition(Z,-10,axis=1)[:,-10:]\n for j,row in enumerate(top):row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[c].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\nprint([(c,float(np.mean(vals[c]))) for c in coefs])\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "[(-0.001, 0.03751401616451922), (-0.0007, 0.03875829585420797), (-0.0005, 0.039311597903167794), (-0.0003, 0.04015163011866139), (-0.0002, 0.04071082036559139), (-0.0001, 0.04097170096264719), (-5e-05, 0.0411363754471562), (0, 0.04129788096117011), (5e-05, 0.04122734667043429), (0.0001, 0.041307536916685234), (0.0002, 0.041164600067995535)]\n", "stderr": "", "aggregated_output": "[(-0.001, 0.03751401616451922), (-0.0007, 0.03875829585420797), (-0.0005, 0.039311597903167794), (-0.0003, 0.04015163011866139), (-0.0002, 0.04071082036559139), (-0.0001, 0.04097170096264719), (-5e-05, 0.0411363754471562), (0, 0.04129788096117011), (5e-05, 0.04122734667043429), (0.0001, 0.041307536916685234), (0.0002, 0.041164600067995535)]\n", "exit_code": 0, "duration": { "secs": 14, "nanos": 38895489 }, "formatted_output": "[(-0.001, 0.03751401616451922), (-0.0007, 0.03875829585420797), (-0.0005, 0.039311597903167794), (-0.0003, 0.04015163011866139), (-0.0002, 0.04071082036559139), (-0.0001, 0.04097170096264719), (-5e-05, 0.0411363754471562), (0, 0.04129788096117011), (5e-05, 0.04122734667043429), (0.0001, 0.041307536916685234), (0.0002, 0.041164600067995535)]\n" } } ## Step 71 · agent · 2026-09-25T01:21:20.085Z ### Thinking unavailable — no text recorded ## Step 72 · agent · 2026-09-25T01:21:28.621Z ### Thinking unavailable — no text recorded The audience-propagation model materially improves the result: the combined validation score is about 0.04130, nearly four times the original popularity baseline. I’m locking this ensemble and refitting it at the test cutoff with validation events treated only as observed history, as required. ### exec · 2026-09-25T01:22:38.129Z { "code": "const patch = \"*** Begin Patch\\n*** Delete File: /app/solution/build_ranker.py\\n*** Add File: /app/solution/build_ranker.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Build the cutoff-correct Yambda discovery ranking.\\n+\\n+The model combines (1) very recent global momentum, (2) an implicit-listen\\n+SVD, (3) an active-like SVD, and (4) propagation of recent listens through an\\n+active-like nearest-neighbour graph. Validation history is included only for\\n+the test-stage refit.\\n+\\\"\\\"\\\"\\n+from __future__ import annotations\\n+\\n+import argparse\\n+from pathlib import Path\\n+\\n+import numpy as np\\n+import pandas as pd\\n+import pyarrow.parquet as pq\\n+from scipy import sparse\\n+from sklearn.decomposition import TruncatedSVD\\n+\\n+\\n+DATA = Path(\\\"/task/data\\\")\\n+OUT = Path(\\\"/app/solution\\\")\\n+TRAIN_CUTOFF = 24_786_800\\n+TEST_CUTOFF = 25_393_400\\n+DAY = 86_400\\n+TOP_N = 100\\n+N_UID_ROWS = 10_000 # Yambda user IDs in this extract are 100, 200, ...\\n+\\n+\\n+def item_mapper(items: np.ndarray):\\n+ sorted_items = np.sort(items)\\n+ sorted_to_catalog = np.argsort(items)\\n+\\n+ def map_items(raw: np.ndarray) -> tuple[np.ndarray, np.ndarray]:\\n+ pos = np.searchsorted(sorted_items, raw)\\n+ valid = pos < len(items)\\n+ valid &= sorted_items[np.minimum(pos, len(items) - 1)] == raw\\n+ return sorted_to_catalog[pos[valid]].astype(np.int32), valid\\n+\\n+ return map_items\\n+\\n+\\n+def scan_listens(\\n+ paths: list[Path], cutoff: int, map_items, n_items: int\\n+) -> tuple[sparse.csr_matrix, sparse.csr_matrix]:\\n+ \\\"\\\"\\\"Return positive-listen counts over all history and over the last 7d.\\\"\\\"\\\"\\n+ rows: list[np.ndarray] = []\\n+ cols: list[np.ndarray] = []\\n+ recent_rows: list[np.ndarray] = []\\n+ recent_cols: list[np.ndarray] = []\\n+ for path in paths:\\n+ parquet = pq.ParquetFile(path)\\n+ for batch in parquet.iter_batches(\\n+ batch_size=2_000_000,\\n+ columns=[\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"played_ratio_pct\\\"],\\n+ ):\\n+ uid = np.asarray(batch.column(\\\"uid\\\")).astype(np.int32, copy=False)\\n+ timestamp = np.asarray(batch.column(\\\"timestamp\\\")).astype(np.int64, copy=False)\\n+ item = np.asarray(batch.column(\\\"item_id\\\")).astype(np.int64, copy=False)\\n+ ratio = np.asarray(batch.column(\\\"played_ratio_pct\\\")).astype(np.uint16, copy=False)\\n+ positive = ratio >= 50\\n+ mapped, in_catalog = map_items(item[positive])\\n+ mapped_rows = (uid[positive][in_catalog] // 100 - 1).astype(np.int32)\\n+ mapped_time = timestamp[positive][in_catalog]\\n+ rows.append(mapped_rows)\\n+ cols.append(mapped)\\n+ recent = mapped_time >= cutoff - 7 * DAY\\n+ recent_rows.append(mapped_rows[recent])\\n+ recent_cols.append(mapped[recent])\\n+\\n+ row = np.concatenate(rows)\\n+ col = np.concatenate(cols)\\n+ history = sparse.coo_matrix(\\n+ (np.ones(len(row), dtype=np.float32), (row, col)),\\n+ shape=(N_UID_ROWS, n_items),\\n+ ).tocsr()\\n+ history.sum_duplicates()\\n+ rr = np.concatenate(recent_rows)\\n+ rc = np.concatenate(recent_cols)\\n+ recent = sparse.coo_matrix(\\n+ (np.ones(len(rr), dtype=np.float32), (rr, rc)),\\n+ shape=(N_UID_ROWS, n_items),\\n+ ).tocsr()\\n+ recent.sum_duplicates()\\n+ return history, recent\\n+\\n+\\n+def history_matrices(\\n+ stage: str, cutoff: int, map_items, n_items: int\\n+) -> tuple[sparse.csr_matrix, sparse.csr_matrix]:\\n+ \\\"\\\"\\\"Use optional local caches while retaining from-scratch reproducibility.\\\"\\\"\\\"\\n+ train_cache = OUT / \\\"listen_counts_train.npz\\\"\\n+ recent_cache = OUT / \\\"recent7_train.npz\\\"\\n+ if train_cache.exists():\\n+ train = sparse.load_npz(train_cache).astype(np.float32)\\n+ else:\\n+ train, train_recent = scan_listens(\\n+ [DATA / \\\"train_listens.parquet\\\"], TRAIN_CUTOFF, map_items, n_items\\n+ )\\n+ sparse.save_npz(train_cache, train, compressed=True)\\n+ sparse.save_npz(recent_cache, train_recent, compressed=True)\\n+\\n+ if stage == \\\"val\\\":\\n+ if recent_cache.exists():\\n+ recent = sparse.load_npz(recent_cache).astype(np.float32)\\n+ else:\\n+ _, recent = scan_listens(\\n+ [DATA / \\\"train_listens.parquet\\\"], cutoff, map_items, n_items\\n+ )\\n+ return train, recent\\n+\\n+ validation, recent = scan_listens(\\n+ [DATA / \\\"val_listens.parquet\\\"], cutoff, map_items, n_items\\n+ )\\n+ return (train + validation).tocsr(), recent\\n+\\n+\\n+def recent_organic_likes(\\n+ stage: str, cutoff: int, map_items, n_items: int\\n+) -> np.ndarray:\\n+ path = DATA / (\\\"train_likes.parquet\\\" if stage == \\\"val\\\" else \\\"val_likes.parquet\\\")\\n+ frame = pd.read_parquet(path, columns=[\\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\"])\\n+ frame = frame[(frame.is_organic == 1) & (frame.timestamp >= cutoff - 3 * DAY)]\\n+ col, valid = map_items(frame.item_id.to_numpy(np.int64))\\n+ return np.bincount(col, minlength=n_items).astype(np.float32)\\n+\\n+\\n+def active_matrix(\\n+ preference_path: Path,\\n+ target_users: np.ndarray,\\n+ map_items,\\n+ n_items: int,\\n+) -> sparse.csr_matrix:\\n+ preference = pd.read_parquet(preference_path)\\n+ preference = preference[preference.state == \\\"liked\\\"]\\n+ uid_row = {int(uid): row for row, uid in enumerate(target_users)}\\n+ raw_rows = np.fromiter(\\n+ (uid_row.get(int(uid), -1) for uid in preference.uid),\\n+ dtype=np.int32,\\n+ count=len(preference),\\n+ )\\n+ col, item_valid = map_items(preference.item_id.to_numpy(np.int64))\\n+ mapped_rows = raw_rows[item_valid]\\n+ row_valid = mapped_rows >= 0\\n+ matrix = sparse.csr_matrix(\\n+ (\\n+ np.ones(int(row_valid.sum()), dtype=np.float32),\\n+ (mapped_rows[row_valid], col[row_valid]),\\n+ ),\\n+ shape=(len(target_users), n_items),\\n+ )\\n+ matrix.sum_duplicates()\\n+ matrix.data[:] = 1.0\\n+ return matrix\\n+\\n+\\n+def rank_score(values: np.ndarray, popularity_rank: np.ndarray) -> np.ndarray:\\n+ order = np.lexsort((popularity_rank, -values))\\n+ score = np.empty(len(values), dtype=np.float32)\\n+ score[order] = 1.0 - np.arange(len(values), dtype=np.float32) / len(values)\\n+ return score\\n+\\n+\\n+def topk_similarity(similarity: sparse.csr_matrix, k: int) -> sparse.csr_matrix:\\n+ indices: list[np.ndarray] = []\\n+ values: list[np.ndarray] = []\\n+ indptr = [0]\\n+ for row in range(similarity.shape[0]):\\n+ lo, hi = similarity.indptr[row : row + 2]\\n+ idx = similarity.indices[lo:hi]\\n+ val = similarity.data[lo:hi]\\n+ keep = idx != row\\n+ idx, val = idx[keep], val[keep]\\n+ if len(val) > k:\\n+ selected = np.argpartition(val, -k)[-k:]\\n+ idx, val = idx[selected], val[selected]\\n+ indices.append(idx)\\n+ values.append(val)\\n+ indptr.append(indptr[-1] + len(idx))\\n+ return sparse.csr_matrix(\\n+ (np.concatenate(values), np.concatenate(indices), np.asarray(indptr)),\\n+ shape=similarity.shape,\\n+ )\\n+\\n+\\n+def standardize_rows(values: np.ndarray) -> np.ndarray:\\n+ values -= values.mean(axis=1, keepdims=True)\\n+ scale = values.std(axis=1)\\n+ scale[scale == 0] = 1.0\\n+ values /= scale[:, None]\\n+ return values\\n+\\n+\\n+def build(stage: str) -> pd.DataFrame:\\n+ cutoff = TRAIN_CUTOFF if stage == \\\"val\\\" else TEST_CUTOFF\\n+ eligible_name = (\\n+ \\\"validation_eligible_candidates.npz\\\"\\n+ if stage == \\\"val\\\"\\n+ else \\\"test_eligible_candidates.npz\\\"\\n+ )\\n+ preference_name = (\\n+ \\\"validation_preference_state.parquet\\\"\\n+ if stage == \\\"val\\\"\\n+ else \\\"test_preference_state.parquet\\\"\\n+ )\\n+ catalog = pd.read_parquet(DATA / \\\"candidate_catalog.parquet\\\")\\n+ items = catalog.item_id.to_numpy(np.int64)\\n+ popularity_rank = catalog.popularity_rank.to_numpy(np.int64)\\n+ n_items = len(items)\\n+ target_users = pd.read_parquet(DATA / \\\"target_users.parquet\\\").uid.to_numpy(np.int64)\\n+ map_items = item_mapper(items)\\n+\\n+ history, recent = history_matrices(stage, cutoff, map_items, n_items)\\n+ listen_momentum = np.asarray(recent.sum(axis=0)).ravel().astype(np.float32)\\n+ like_momentum = recent_organic_likes(stage, cutoff, map_items, n_items)\\n+ global_score = rank_score(listen_momentum, popularity_rank)\\n+ global_score += 2.0 * rank_score(like_momentum, popularity_rank)\\n+\\n+ # Broad implicit-taste representation. Counts are binarized deliberately:\\n+ # repeated plays should not swamp cross-track affinity.\\n+ history.data[:] = 1.0\\n+ listen_svd = TruncatedSVD(n_components=96, n_iter=5, random_state=42)\\n+ listen_users = listen_svd.fit_transform(history).astype(np.float32)\\n+ listen_items = listen_svd.components_.astype(np.float32)\\n+\\n+ active = active_matrix(DATA / preference_name, target_users, map_items, n_items)\\n+ active_svd = TruncatedSVD(n_components=32, n_iter=7, random_state=42)\\n+ active_users = active_svd.fit_transform(active).astype(np.float32)\\n+ active_items = active_svd.components_.astype(np.float32)\\n+\\n+ # Recommend tracks currently spreading among users with similar explicit\\n+ # tastes. The recent matrix is restricted to target-user neighbour rows,\\n+ # matching the population for which cutoff state is supplied.\\n+ norm = np.sqrt(np.asarray(active.multiply(active).sum(axis=1)).ravel())\\n+ norm[norm == 0] = 1.0\\n+ normalized_active = sparse.diags((1.0 / norm).astype(np.float32)) @ active\\n+ similarity = (normalized_active @ normalized_active.T).tocsr()\\n+ similarity = topk_similarity(similarity, 200)\\n+ uid_rows = (target_users // 100 - 1).astype(np.int32)\\n+ neighbour_recent = (similarity @ recent[uid_rows]).tocsr()\\n+\\n+ with np.load(DATA / eligible_name, allow_pickle=False) as artifact:\\n+ eligible_uids = artifact[\\\"uids\\\"].astype(np.int64, copy=True)\\n+ eligible_items = artifact[\\\"item_ids\\\"].astype(np.int64, copy=True)\\n+ packed = artifact[\\\"packed_eligible\\\"].copy()\\n+ if not np.array_equal(eligible_uids, target_users):\\n+ raise RuntimeError(\\\"eligibility users are not aligned to target_users\\\")\\n+ if not np.array_equal(eligible_items, items):\\n+ raise RuntimeError(\\\"eligibility items are not aligned to candidate catalog\\\")\\n+ eligible = np.unpackbits(packed, axis=1, count=n_items, bitorder=\\\"little\\\")\\n+\\n+ out_items = np.empty((len(target_users), TOP_N), dtype=np.int64)\\n+ block_size = 128\\n+ for start in range(0, len(target_users), block_size):\\n+ end = min(start + block_size, len(target_users))\\n+ listen_affinity = standardize_rows(\\n+ listen_users[uid_rows[start:end]] @ listen_items\\n+ )\\n+ active_affinity = standardize_rows(\\n+ active_users[start:end] @ active_items\\n+ )\\n+ propagation = neighbour_recent[start:end].toarray().astype(np.float32)\\n+ prop_scale = propagation.max(axis=1)\\n+ prop_scale[prop_scale == 0] = 1.0\\n+ propagation /= prop_scale[:, None]\\n+ score = (\\n+ global_score[None, :]\\n+ + 0.0200 * propagation\\n+ + 0.0006 * listen_affinity\\n+ + 0.0003 * active_affinity\\n+ )\\n+ score[eligible[start:end] == 0] = -np.inf\\n+ chosen = np.argpartition(score, -TOP_N, axis=1)[:, -TOP_N:]\\n+ for local_row in range(end - start):\\n+ cols = chosen[local_row]\\n+ cols = cols[np.argsort(score[local_row, cols])[::-1]]\\n+ out_items[start + local_row] = items[cols]\\n+\\n+ return pd.DataFrame(\\n+ {\\n+ \\\"uid\\\": np.repeat(target_users, TOP_N),\\n+ \\\"item_id\\\": out_items.ravel(),\\n+ \\\"rank\\\": np.tile(np.arange(1, TOP_N + 1, dtype=np.int64), len(target_users)),\\n+ }\\n+ )\\n+\\n+\\n+def main() -> None:\\n+ parser = argparse.ArgumentParser()\\n+ parser.add_argument(\\\"--stage\\\", choices=(\\\"val\\\", \\\"test\\\"), default=\\\"test\\\")\\n+ args = parser.parse_args()\\n+ ranking = build(args.stage)\\n+ final = OUT / (\\\"validation.parquet\\\" if args.stage == \\\"val\\\" else \\\"submission.parquet\\\")\\n+ temporary = OUT / (final.stem + \\\".tmp.parquet\\\")\\n+ ranking.to_parquet(temporary, index=False)\\n+ temporary.replace(final)\\n+ print(f\\\"wrote {len(ranking):,} rows to {final}\\\")\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** Delete File: /app/solution/README.md\\n*** Add File: /app/solution/README.md\\n+# Music-discovery ranker\\n+\\n+`build_ranker.py` produces the requested 100-track ranking. It uses only\\n+cutoff-available inputs and combines seven-day listen momentum, three-day\\n+organic-like momentum, implicit-listen and active-like SVD affinities, and\\n+recent-listen propagation across 200 active-like nearest neighbours.\\n+\\n+The public validation model uses training history only:\\n+\\n+```sh\\n+/opt/task-venv/bin/python /app/solution/build_ranker.py --stage val\\n+/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\\n+```\\n+\\n+The final refit treats validation events as history and writes the deliverable:\\n+\\n+```sh\\n+/opt/task-venv/bin/python /app/solution/build_ranker.py --stage test\\n+```\\n+\\n+The selected ensemble scores approximately **0.04130 NDCG@10** on the public\\n+temporal validation window (the popularity-only starting point scored 0.01071).\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-25T01:22:38.201Z Script failed Wall time 0.0 seconds Output: Script error: apply_patch verification failed: invalid patch: multiple operations target /app/solution/build_ranker.py ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "response_id": "resp_0fdbf3e20a3ba82b016ab5cc8532c887d2bbf41f0bb3cb25b5", "usage": { "input_tokens": 70602, "cached_input_tokens": 68096, "cache_write_input_tokens": 0, "output_tokens": 4854, "reasoning_output_tokens": 902, "total_tokens": 75456 }, "turn_token_usage": { "input_tokens": 2023479, "cached_input_tokens": 1955840, "cache_write_input_tokens": 0, "output_tokens": 40291, "reasoning_output_tokens": 16274, "total_tokens": 2063770 }, "thread_token_usage": { "input_tokens": 2023479, "cached_input_tokens": 1955840, "cache_write_input_tokens": 0, "output_tokens": 40291, "reasoning_output_tokens": 16274, "total_tokens": 2063770 } } ## Step 73 · agent · 2026-09-25T01:22:43.516Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:22:44.383Z { "code": "const p1 = \"*** Begin Patch\\n*** Delete File: /app/solution/build_ranker.py\\n*** Delete File: /app/solution/README.md\\n*** End Patch\";\ntext(await tools.apply_patch(p1));\n" } ### Tool output · 2026-09-25T01:22:44.507Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-803bfcc7-2c19-425f-8868-28d850dda76e", "changes": { "/app/solution/build_ranker.py": { "type": "delete", "content": "#!/usr/bin/env python3\n\"\"\"Build cutoff-correct music discovery rankings.\n\nThe script is intentionally self contained. With no arguments it refits on\ntrain + validation history and writes ``submission.parquet``. ``--stage val``\nuses training history only and writes ``validation.parquet``.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\nDATA = Path(\"/task/data\")\nOUT = Path(\"/app/solution\")\nTOP_N = 100\n\n\ndef load_candidates(stage: str) -> tuple[np.ndarray, np.ndarray, np.ndarray]:\n path = DATA / (\"validation_eligible_candidates.npz\" if stage == \"val\" else \"test_eligible_candidates.npz\")\n with np.load(path, allow_pickle=False) as z:\n return z[\"uids\"].copy(), z[\"item_ids\"].copy(), z[\"packed_eligible\"].copy()\n\n\ndef is_eligible(packed_row: np.ndarray, column: int) -> bool:\n return bool((int(packed_row[column // 8]) >> (column % 8)) & 1)\n\n\ndef rank_global(stage: str, ordered_items: np.ndarray) -> pd.DataFrame:\n \"\"\"Apply a global item order while respecting each user's candidate mask.\"\"\"\n uids, candidate_items, packed = load_candidates(stage)\n item_col = {int(item): col for col, item in enumerate(candidate_items)}\n ordered_cols = np.asarray([item_col[int(item)] for item in ordered_items], dtype=np.int32)\n out_uid = np.repeat(uids.astype(np.int64), TOP_N)\n out_item = np.empty(len(uids) * TOP_N, dtype=np.int64)\n for row in range(len(uids)):\n chosen: list[int] = []\n for col in ordered_cols:\n if is_eligible(packed[row], int(col)):\n chosen.append(int(candidate_items[col]))\n if len(chosen) == TOP_N:\n break\n if len(chosen) != TOP_N:\n raise RuntimeError(f\"only {len(chosen)} eligible items for uid={uids[row]}\")\n out_item[row * TOP_N : (row + 1) * TOP_N] = chosen\n return pd.DataFrame(\n {\n \"uid\": out_uid,\n \"item_id\": out_item,\n \"rank\": np.tile(np.arange(1, TOP_N + 1, dtype=np.int64), len(uids)),\n }\n )\n\n\ndef main() -> None:\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--stage\", choices=(\"val\", \"test\"), default=\"test\")\n args = parser.parse_args()\n catalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n ranking = rank_global(args.stage, catalog.sort_values(\"popularity_rank\").item_id.to_numpy())\n path = OUT / (\"validation.parquet\" if args.stage == \"val\" else \"submission.parquet\")\n ranking.to_parquet(path, index=False)\n print(f\"wrote {len(ranking):,} rows to {path}\")\n\n\nif __name__ == \"__main__\":\n main()\n" }, "/app/solution/README.md": { "type": "delete", "content": "# Music-discovery ranker\n\nRun with the task environment:\n\n```sh\n/opt/task-venv/bin/python /app/solution/build_ranker.py\n```\n\nThis refits the selected ranker at the test cutoff and writes\n`submission.parquet`. Use `--stage val` to construct the leakage-safe public\nvalidation ranking from training history only.\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nD /app/solution/build_ranker.py\nD /app/solution/README.md\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "response_id": "resp_0fdbf3e20a3ba82b016ab5ccdea34887d2adb4f5b0a2a97b35", "usage": { "input_tokens": 75501, "cached_input_tokens": 70400, "cache_write_input_tokens": 0, "output_tokens": 166, "reasoning_output_tokens": 109, "total_tokens": 75667 }, "turn_token_usage": { "input_tokens": 2098980, "cached_input_tokens": 2026240, "cache_write_input_tokens": 0, "output_tokens": 40457, "reasoning_output_tokens": 16383, "total_tokens": 2139437 }, "thread_token_usage": { "input_tokens": 2098980, "cached_input_tokens": 2026240, "cache_write_input_tokens": 0, "output_tokens": 40457, "reasoning_output_tokens": 16383, "total_tokens": 2139437 } } ## Step 74 · agent · 2026-09-25T01:22:46.287Z ### Thinking unavailable — no text recorded ### exec · 2026-09-25T01:23:54.263Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/build_ranker.py\\n+#!/usr/bin/env python3\\n+\\\"\\\"\\\"Build the cutoff-correct Yambda discovery ranking.\\n+\\n+The model combines (1) very recent global momentum, (2) an implicit-listen\\n+SVD, (3) an active-like SVD, and (4) propagation of recent listens through an\\n+active-like nearest-neighbour graph. Validation history is included only for\\n+the test-stage refit.\\n+\\\"\\\"\\\"\\n+from __future__ import annotations\\n+\\n+import argparse\\n+from pathlib import Path\\n+\\n+import numpy as np\\n+import pandas as pd\\n+import pyarrow.parquet as pq\\n+from scipy import sparse\\n+from sklearn.decomposition import TruncatedSVD\\n+\\n+\\n+DATA = Path(\\\"/task/data\\\")\\n+OUT = Path(\\\"/app/solution\\\")\\n+TRAIN_CUTOFF = 24_786_800\\n+TEST_CUTOFF = 25_393_400\\n+DAY = 86_400\\n+TOP_N = 100\\n+N_UID_ROWS = 10_000 # Yambda user IDs in this extract are 100, 200, ...\\n+\\n+\\n+def item_mapper(items: np.ndarray):\\n+ sorted_items = np.sort(items)\\n+ sorted_to_catalog = np.argsort(items)\\n+\\n+ def map_items(raw: np.ndarray) -> tuple[np.ndarray, np.ndarray]:\\n+ pos = np.searchsorted(sorted_items, raw)\\n+ valid = pos < len(items)\\n+ valid &= sorted_items[np.minimum(pos, len(items) - 1)] == raw\\n+ return sorted_to_catalog[pos[valid]].astype(np.int32), valid\\n+\\n+ return map_items\\n+\\n+\\n+def scan_listens(\\n+ paths: list[Path], cutoff: int, map_items, n_items: int\\n+) -> tuple[sparse.csr_matrix, sparse.csr_matrix]:\\n+ \\\"\\\"\\\"Return positive-listen counts over all history and over the last 7d.\\\"\\\"\\\"\\n+ rows: list[np.ndarray] = []\\n+ cols: list[np.ndarray] = []\\n+ recent_rows: list[np.ndarray] = []\\n+ recent_cols: list[np.ndarray] = []\\n+ for path in paths:\\n+ parquet = pq.ParquetFile(path)\\n+ for batch in parquet.iter_batches(\\n+ batch_size=2_000_000,\\n+ columns=[\\\"uid\\\", \\\"timestamp\\\", \\\"item_id\\\", \\\"played_ratio_pct\\\"],\\n+ ):\\n+ uid = np.asarray(batch.column(\\\"uid\\\")).astype(np.int32, copy=False)\\n+ timestamp = np.asarray(batch.column(\\\"timestamp\\\")).astype(np.int64, copy=False)\\n+ item = np.asarray(batch.column(\\\"item_id\\\")).astype(np.int64, copy=False)\\n+ ratio = np.asarray(batch.column(\\\"played_ratio_pct\\\")).astype(np.uint16, copy=False)\\n+ positive = ratio >= 50\\n+ mapped, in_catalog = map_items(item[positive])\\n+ mapped_rows = (uid[positive][in_catalog] // 100 - 1).astype(np.int32)\\n+ mapped_time = timestamp[positive][in_catalog]\\n+ rows.append(mapped_rows)\\n+ cols.append(mapped)\\n+ is_recent = mapped_time >= cutoff - 7 * DAY\\n+ recent_rows.append(mapped_rows[is_recent])\\n+ recent_cols.append(mapped[is_recent])\\n+\\n+ row = np.concatenate(rows)\\n+ col = np.concatenate(cols)\\n+ history = sparse.coo_matrix(\\n+ (np.ones(len(row), dtype=np.float32), (row, col)),\\n+ shape=(N_UID_ROWS, n_items),\\n+ ).tocsr()\\n+ history.sum_duplicates()\\n+ rr = np.concatenate(recent_rows)\\n+ rc = np.concatenate(recent_cols)\\n+ recent = sparse.coo_matrix(\\n+ (np.ones(len(rr), dtype=np.float32), (rr, rc)),\\n+ shape=(N_UID_ROWS, n_items),\\n+ ).tocsr()\\n+ recent.sum_duplicates()\\n+ return history, recent\\n+\\n+\\n+def history_matrices(\\n+ stage: str, cutoff: int, map_items, n_items: int\\n+) -> tuple[sparse.csr_matrix, sparse.csr_matrix]:\\n+ \\\"\\\"\\\"Use optional local caches while retaining from-scratch reproducibility.\\\"\\\"\\\"\\n+ train_cache = OUT / \\\"listen_counts_train.npz\\\"\\n+ recent_cache = OUT / \\\"recent7_train.npz\\\"\\n+ if train_cache.exists():\\n+ train = sparse.load_npz(train_cache).astype(np.float32)\\n+ else:\\n+ train, train_recent = scan_listens(\\n+ [DATA / \\\"train_listens.parquet\\\"], TRAIN_CUTOFF, map_items, n_items\\n+ )\\n+ sparse.save_npz(train_cache, train, compressed=True)\\n+ sparse.save_npz(recent_cache, train_recent, compressed=True)\\n+\\n+ if stage == \\\"val\\\":\\n+ if recent_cache.exists():\\n+ recent = sparse.load_npz(recent_cache).astype(np.float32)\\n+ else:\\n+ _, recent = scan_listens(\\n+ [DATA / \\\"train_listens.parquet\\\"], cutoff, map_items, n_items\\n+ )\\n+ return train, recent\\n+\\n+ validation, recent = scan_listens(\\n+ [DATA / \\\"val_listens.parquet\\\"], cutoff, map_items, n_items\\n+ )\\n+ return (train + validation).tocsr(), recent\\n+\\n+\\n+def recent_organic_likes(\\n+ stage: str, cutoff: int, map_items, n_items: int\\n+) -> np.ndarray:\\n+ path = DATA / (\\\"train_likes.parquet\\\" if stage == \\\"val\\\" else \\\"val_likes.parquet\\\")\\n+ frame = pd.read_parquet(path, columns=[\\\"timestamp\\\", \\\"item_id\\\", \\\"is_organic\\\"])\\n+ frame = frame[(frame.is_organic == 1) & (frame.timestamp >= cutoff - 3 * DAY)]\\n+ col, _ = map_items(frame.item_id.to_numpy(np.int64))\\n+ return np.bincount(col, minlength=n_items).astype(np.float32)\\n+\\n+\\n+def active_matrix(\\n+ preference_path: Path,\\n+ target_users: np.ndarray,\\n+ map_items,\\n+ n_items: int,\\n+) -> sparse.csr_matrix:\\n+ preference = pd.read_parquet(preference_path)\\n+ preference = preference[preference.state == \\\"liked\\\"]\\n+ uid_row = {int(uid): row for row, uid in enumerate(target_users)}\\n+ raw_rows = np.fromiter(\\n+ (uid_row.get(int(uid), -1) for uid in preference.uid),\\n+ dtype=np.int32,\\n+ count=len(preference),\\n+ )\\n+ col, item_valid = map_items(preference.item_id.to_numpy(np.int64))\\n+ mapped_rows = raw_rows[item_valid]\\n+ row_valid = mapped_rows >= 0\\n+ matrix = sparse.csr_matrix(\\n+ (\\n+ np.ones(int(row_valid.sum()), dtype=np.float32),\\n+ (mapped_rows[row_valid], col[row_valid]),\\n+ ),\\n+ shape=(len(target_users), n_items),\\n+ )\\n+ matrix.sum_duplicates()\\n+ matrix.data[:] = 1.0\\n+ return matrix\\n+\\n+\\n+def rank_score(values: np.ndarray, popularity_rank: np.ndarray) -> np.ndarray:\\n+ order = np.lexsort((popularity_rank, -values))\\n+ score = np.empty(len(values), dtype=np.float32)\\n+ score[order] = 1.0 - np.arange(len(values), dtype=np.float32) / len(values)\\n+ return score\\n+\\n+\\n+def topk_similarity(similarity: sparse.csr_matrix, k: int) -> sparse.csr_matrix:\\n+ indices: list[np.ndarray] = []\\n+ values: list[np.ndarray] = []\\n+ indptr = [0]\\n+ for row in range(similarity.shape[0]):\\n+ lo, hi = similarity.indptr[row : row + 2]\\n+ idx = similarity.indices[lo:hi]\\n+ val = similarity.data[lo:hi]\\n+ keep = idx != row\\n+ idx, val = idx[keep], val[keep]\\n+ if len(val) > k:\\n+ selected = np.argpartition(val, -k)[-k:]\\n+ idx, val = idx[selected], val[selected]\\n+ indices.append(idx)\\n+ values.append(val)\\n+ indptr.append(indptr[-1] + len(idx))\\n+ return sparse.csr_matrix(\\n+ (np.concatenate(values), np.concatenate(indices), np.asarray(indptr)),\\n+ shape=similarity.shape,\\n+ )\\n+\\n+\\n+def standardize_rows(values: np.ndarray) -> np.ndarray:\\n+ values -= values.mean(axis=1, keepdims=True)\\n+ scale = values.std(axis=1)\\n+ scale[scale == 0] = 1.0\\n+ values /= scale[:, None]\\n+ return values\\n+\\n+\\n+def build(stage: str) -> pd.DataFrame:\\n+ cutoff = TRAIN_CUTOFF if stage == \\\"val\\\" else TEST_CUTOFF\\n+ eligible_name = (\\n+ \\\"validation_eligible_candidates.npz\\\"\\n+ if stage == \\\"val\\\"\\n+ else \\\"test_eligible_candidates.npz\\\"\\n+ )\\n+ preference_name = (\\n+ \\\"validation_preference_state.parquet\\\"\\n+ if stage == \\\"val\\\"\\n+ else \\\"test_preference_state.parquet\\\"\\n+ )\\n+ catalog = pd.read_parquet(DATA / \\\"candidate_catalog.parquet\\\")\\n+ items = catalog.item_id.to_numpy(np.int64)\\n+ popularity_rank = catalog.popularity_rank.to_numpy(np.int64)\\n+ n_items = len(items)\\n+ target_users = pd.read_parquet(DATA / \\\"target_users.parquet\\\").uid.to_numpy(np.int64)\\n+ map_items = item_mapper(items)\\n+\\n+ history, recent = history_matrices(stage, cutoff, map_items, n_items)\\n+ listen_momentum = np.asarray(recent.sum(axis=0)).ravel().astype(np.float32)\\n+ like_momentum = recent_organic_likes(stage, cutoff, map_items, n_items)\\n+ global_score = rank_score(listen_momentum, popularity_rank)\\n+ global_score += 2.0 * rank_score(like_momentum, popularity_rank)\\n+\\n+ history.data[:] = 1.0\\n+ listen_svd = TruncatedSVD(n_components=96, n_iter=5, random_state=42)\\n+ listen_users = listen_svd.fit_transform(history).astype(np.float32)\\n+ listen_items = listen_svd.components_.astype(np.float32)\\n+\\n+ active = active_matrix(DATA / preference_name, target_users, map_items, n_items)\\n+ active_svd = TruncatedSVD(n_components=32, n_iter=7, random_state=42)\\n+ active_users = active_svd.fit_transform(active).astype(np.float32)\\n+ active_items = active_svd.components_.astype(np.float32)\\n+\\n+ norm = np.sqrt(np.asarray(active.multiply(active).sum(axis=1)).ravel())\\n+ norm[norm == 0] = 1.0\\n+ normalized_active = sparse.diags((1.0 / norm).astype(np.float32)) @ active\\n+ similarity = (normalized_active @ normalized_active.T).tocsr()\\n+ similarity = topk_similarity(similarity, 200)\\n+ uid_rows = (target_users // 100 - 1).astype(np.int32)\\n+ neighbour_recent = (similarity @ recent[uid_rows]).tocsr()\\n+\\n+ with np.load(DATA / eligible_name, allow_pickle=False) as artifact:\\n+ eligible_uids = artifact[\\\"uids\\\"].astype(np.int64, copy=True)\\n+ eligible_items = artifact[\\\"item_ids\\\"].astype(np.int64, copy=True)\\n+ packed = artifact[\\\"packed_eligible\\\"].copy()\\n+ if not np.array_equal(eligible_uids, target_users):\\n+ raise RuntimeError(\\\"eligibility users are not aligned to target_users\\\")\\n+ if not np.array_equal(eligible_items, items):\\n+ raise RuntimeError(\\\"eligibility items are not aligned to candidate catalog\\\")\\n+ eligible = np.unpackbits(packed, axis=1, count=n_items, bitorder=\\\"little\\\")\\n+\\n+ out_items = np.empty((len(target_users), TOP_N), dtype=np.int64)\\n+ block_size = 128\\n+ for start in range(0, len(target_users), block_size):\\n+ end = min(start + block_size, len(target_users))\\n+ listen_affinity = standardize_rows(\\n+ listen_users[uid_rows[start:end]] @ listen_items\\n+ )\\n+ active_affinity = standardize_rows(active_users[start:end] @ active_items)\\n+ propagation = neighbour_recent[start:end].toarray().astype(np.float32)\\n+ prop_scale = propagation.max(axis=1)\\n+ prop_scale[prop_scale == 0] = 1.0\\n+ propagation /= prop_scale[:, None]\\n+ score = (\\n+ global_score[None, :]\\n+ + 0.0200 * propagation\\n+ + 0.0006 * listen_affinity\\n+ + 0.0003 * active_affinity\\n+ )\\n+ score[eligible[start:end] == 0] = -np.inf\\n+ chosen = np.argpartition(score, -TOP_N, axis=1)[:, -TOP_N:]\\n+ for local_row in range(end - start):\\n+ cols = chosen[local_row]\\n+ cols = cols[np.argsort(score[local_row, cols])[::-1]]\\n+ out_items[start + local_row] = items[cols]\\n+\\n+ return pd.DataFrame(\\n+ {\\n+ \\\"uid\\\": np.repeat(target_users, TOP_N),\\n+ \\\"item_id\\\": out_items.ravel(),\\n+ \\\"rank\\\": np.tile(np.arange(1, TOP_N + 1, dtype=np.int64), len(target_users)),\\n+ }\\n+ )\\n+\\n+\\n+def main() -> None:\\n+ parser = argparse.ArgumentParser()\\n+ parser.add_argument(\\\"--stage\\\", choices=(\\\"val\\\", \\\"test\\\"), default=\\\"test\\\")\\n+ args = parser.parse_args()\\n+ ranking = build(args.stage)\\n+ final = OUT / (\\\"validation.parquet\\\" if args.stage == \\\"val\\\" else \\\"submission.parquet\\\")\\n+ temporary = OUT / (final.stem + \\\".tmp.parquet\\\")\\n+ ranking.to_parquet(temporary, index=False)\\n+ temporary.replace(final)\\n+ print(f\\\"wrote {len(ranking):,} rows to {final}\\\")\\n+\\n+\\n+if __name__ == \\\"__main__\\\":\\n+ main()\\n*** Add File: /app/solution/README.md\\n+# Music-discovery ranker\\n+\\n+`build_ranker.py` produces the requested 100-track ranking. It uses only\\n+cutoff-available inputs and combines seven-day listen momentum, three-day\\n+organic-like momentum, implicit-listen and active-like SVD affinities, and\\n+recent-listen propagation across 200 active-like nearest neighbours.\\n+\\n+The public validation model uses training history only:\\n+\\n+```sh\\n+/opt/task-venv/bin/python /app/solution/build_ranker.py --stage val\\n+/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\\n+```\\n+\\n+The final refit treats validation events as history and writes the deliverable:\\n+\\n+```sh\\n+/opt/task-venv/bin/python /app/solution/build_ranker.py --stage test\\n+```\\n+\\n+The selected ensemble scores approximately **0.04130 NDCG@10** on the public\\n+temporal validation window (the popularity-only starting point scored 0.01071).\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-25T01:23:54.325Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-643d60af-7404-4219-81a7-a32c0cbf1c76", "changes": { "/app/solution/README.md": { "type": "add", "content": "# Music-discovery ranker\n\n`build_ranker.py` produces the requested 100-track ranking. It uses only\ncutoff-available inputs and combines seven-day listen momentum, three-day\norganic-like momentum, implicit-listen and active-like SVD affinities, and\nrecent-listen propagation across 200 active-like nearest neighbours.\n\nThe public validation model uses training history only:\n\n```sh\n/opt/task-venv/bin/python /app/solution/build_ranker.py --stage val\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\n```\n\nThe final refit treats validation events as history and writes the deliverable:\n\n```sh\n/opt/task-venv/bin/python /app/solution/build_ranker.py --stage test\n```\n\nThe selected ensemble scores approximately **0.04130 NDCG@10** on the public\ntemporal validation window (the popularity-only starting point scored 0.01071).\n" }, "/app/solution/build_ranker.py": { "type": "add", "content": "#!/usr/bin/env python3\n\"\"\"Build the cutoff-correct Yambda discovery ranking.\n\nThe model combines (1) very recent global momentum, (2) an implicit-listen\nSVD, (3) an active-like SVD, and (4) propagation of recent listens through an\nactive-like nearest-neighbour graph. Validation history is included only for\nthe test-stage refit.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\n\n\nDATA = Path(\"/task/data\")\nOUT = Path(\"/app/solution\")\nTRAIN_CUTOFF = 24_786_800\nTEST_CUTOFF = 25_393_400\nDAY = 86_400\nTOP_N = 100\nN_UID_ROWS = 10_000 # Yambda user IDs in this extract are 100, 200, ...\n\n\ndef item_mapper(items: np.ndarray):\n sorted_items = np.sort(items)\n sorted_to_catalog = np.argsort(items)\n\n def map_items(raw: np.ndarray) -> tuple[np.ndarray, np.ndarray]:\n pos = np.searchsorted(sorted_items, raw)\n valid = pos < len(items)\n valid &= sorted_items[np.minimum(pos, len(items) - 1)] == raw\n return sorted_to_catalog[pos[valid]].astype(np.int32), valid\n\n return map_items\n\n\ndef scan_listens(\n paths: list[Path], cutoff: int, map_items, n_items: int\n) -> tuple[sparse.csr_matrix, sparse.csr_matrix]:\n \"\"\"Return positive-listen counts over all history and over the last 7d.\"\"\"\n rows: list[np.ndarray] = []\n cols: list[np.ndarray] = []\n recent_rows: list[np.ndarray] = []\n recent_cols: list[np.ndarray] = []\n for path in paths:\n parquet = pq.ParquetFile(path)\n for batch in parquet.iter_batches(\n batch_size=2_000_000,\n columns=[\"uid\", \"timestamp\", \"item_id\", \"played_ratio_pct\"],\n ):\n uid = np.asarray(batch.column(\"uid\")).astype(np.int32, copy=False)\n timestamp = np.asarray(batch.column(\"timestamp\")).astype(np.int64, copy=False)\n item = np.asarray(batch.column(\"item_id\")).astype(np.int64, copy=False)\n ratio = np.asarray(batch.column(\"played_ratio_pct\")).astype(np.uint16, copy=False)\n positive = ratio >= 50\n mapped, in_catalog = map_items(item[positive])\n mapped_rows = (uid[positive][in_catalog] // 100 - 1).astype(np.int32)\n mapped_time = timestamp[positive][in_catalog]\n rows.append(mapped_rows)\n cols.append(mapped)\n is_recent = mapped_time >= cutoff - 7 * DAY\n recent_rows.append(mapped_rows[is_recent])\n recent_cols.append(mapped[is_recent])\n\n row = np.concatenate(rows)\n col = np.concatenate(cols)\n history = sparse.coo_matrix(\n (np.ones(len(row), dtype=np.float32), (row, col)),\n shape=(N_UID_ROWS, n_items),\n ).tocsr()\n history.sum_duplicates()\n rr = np.concatenate(recent_rows)\n rc = np.concatenate(recent_cols)\n recent = sparse.coo_matrix(\n (np.ones(len(rr), dtype=np.float32), (rr, rc)),\n shape=(N_UID_ROWS, n_items),\n ).tocsr()\n recent.sum_duplicates()\n return history, recent\n\n\ndef history_matrices(\n stage: str, cutoff: int, map_items, n_items: int\n) -> tuple[sparse.csr_matrix, sparse.csr_matrix]:\n \"\"\"Use optional local caches while retaining from-scratch reproducibility.\"\"\"\n train_cache = OUT / \"listen_counts_train.npz\"\n recent_cache = OUT / \"recent7_train.npz\"\n if train_cache.exists():\n train = sparse.load_npz(train_cache).astype(np.float32)\n else:\n train, train_recent = scan_listens(\n [DATA / \"train_listens.parquet\"], TRAIN_CUTOFF, map_items, n_items\n )\n sparse.save_npz(train_cache, train, compressed=True)\n sparse.save_npz(recent_cache, train_recent, compressed=True)\n\n if stage == \"val\":\n if recent_cache.exists():\n recent = sparse.load_npz(recent_cache).astype(np.float32)\n else:\n _, recent = scan_listens(\n [DATA / \"train_listens.parquet\"], cutoff, map_items, n_items\n )\n return train, recent\n\n validation, recent = scan_listens(\n [DATA / \"val_listens.parquet\"], cutoff, map_items, n_items\n )\n return (train + validation).tocsr(), recent\n\n\ndef recent_organic_likes(\n stage: str, cutoff: int, map_items, n_items: int\n) -> np.ndarray:\n path = DATA / (\"train_likes.parquet\" if stage == \"val\" else \"val_likes.parquet\")\n frame = pd.read_parquet(path, columns=[\"timestamp\", \"item_id\", \"is_organic\"])\n frame = frame[(frame.is_organic == 1) & (frame.timestamp >= cutoff - 3 * DAY)]\n col, _ = map_items(frame.item_id.to_numpy(np.int64))\n return np.bincount(col, minlength=n_items).astype(np.float32)\n\n\ndef active_matrix(\n preference_path: Path,\n target_users: np.ndarray,\n map_items,\n n_items: int,\n) -> sparse.csr_matrix:\n preference = pd.read_parquet(preference_path)\n preference = preference[preference.state == \"liked\"]\n uid_row = {int(uid): row for row, uid in enumerate(target_users)}\n raw_rows = np.fromiter(\n (uid_row.get(int(uid), -1) for uid in preference.uid),\n dtype=np.int32,\n count=len(preference),\n )\n col, item_valid = map_items(preference.item_id.to_numpy(np.int64))\n mapped_rows = raw_rows[item_valid]\n row_valid = mapped_rows >= 0\n matrix = sparse.csr_matrix(\n (\n np.ones(int(row_valid.sum()), dtype=np.float32),\n (mapped_rows[row_valid], col[row_valid]),\n ),\n shape=(len(target_users), n_items),\n )\n matrix.sum_duplicates()\n matrix.data[:] = 1.0\n return matrix\n\n\ndef rank_score(values: np.ndarray, popularity_rank: np.ndarray) -> np.ndarray:\n order = np.lexsort((popularity_rank, -values))\n score = np.empty(len(values), dtype=np.float32)\n score[order] = 1.0 - np.arange(len(values), dtype=np.float32) / len(values)\n return score\n\n\ndef topk_similarity(similarity: sparse.csr_matrix, k: int) -> sparse.csr_matrix:\n indices: list[np.ndarray] = []\n values: list[np.ndarray] = []\n indptr = [0]\n for row in range(similarity.shape[0]):\n lo, hi = similarity.indptr[row : row + 2]\n idx = similarity.indices[lo:hi]\n val = similarity.data[lo:hi]\n keep = idx != row\n idx, val = idx[keep], val[keep]\n if len(val) > k:\n selected = np.argpartition(val, -k)[-k:]\n idx, val = idx[selected], val[selected]\n indices.append(idx)\n values.append(val)\n indptr.append(indptr[-1] + len(idx))\n return sparse.csr_matrix(\n (np.concatenate(values), np.concatenate(indices), np.asarray(indptr)),\n shape=similarity.shape,\n )\n\n\ndef standardize_rows(values: np.ndarray) -> np.ndarray:\n values -= values.mean(axis=1, keepdims=True)\n scale = values.std(axis=1)\n scale[scale == 0] = 1.0\n values /= scale[:, None]\n return values\n\n\ndef build(stage: str) -> pd.DataFrame:\n cutoff = TRAIN_CUTOFF if stage == \"val\" else TEST_CUTOFF\n eligible_name = (\n \"validation_eligible_candidates.npz\"\n if stage == \"val\"\n else \"test_eligible_candidates.npz\"\n )\n preference_name = (\n \"validation_preference_state.parquet\"\n if stage == \"val\"\n else \"test_preference_state.parquet\"\n )\n catalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n items = catalog.item_id.to_numpy(np.int64)\n popularity_rank = catalog.popularity_rank.to_numpy(np.int64)\n n_items = len(items)\n target_users = pd.read_parquet(DATA / \"target_users.parquet\").uid.to_numpy(np.int64)\n map_items = item_mapper(items)\n\n history, recent = history_matrices(stage, cutoff, map_items, n_items)\n listen_momentum = np.asarray(recent.sum(axis=0)).ravel().astype(np.float32)\n like_momentum = recent_organic_likes(stage, cutoff, map_items, n_items)\n global_score = rank_score(listen_momentum, popularity_rank)\n global_score += 2.0 * rank_score(like_momentum, popularity_rank)\n\n history.data[:] = 1.0\n listen_svd = TruncatedSVD(n_components=96, n_iter=5, random_state=42)\n listen_users = listen_svd.fit_transform(history).astype(np.float32)\n listen_items = listen_svd.components_.astype(np.float32)\n\n active = active_matrix(DATA / preference_name, target_users, map_items, n_items)\n active_svd = TruncatedSVD(n_components=32, n_iter=7, random_state=42)\n active_users = active_svd.fit_transform(active).astype(np.float32)\n active_items = active_svd.components_.astype(np.float32)\n\n norm = np.sqrt(np.asarray(active.multiply(active).sum(axis=1)).ravel())\n norm[norm == 0] = 1.0\n normalized_active = sparse.diags((1.0 / norm).astype(np.float32)) @ active\n similarity = (normalized_active @ normalized_active.T).tocsr()\n similarity = topk_similarity(similarity, 200)\n uid_rows = (target_users // 100 - 1).astype(np.int32)\n neighbour_recent = (similarity @ recent[uid_rows]).tocsr()\n\n with np.load(DATA / eligible_name, allow_pickle=False) as artifact:\n eligible_uids = artifact[\"uids\"].astype(np.int64, copy=True)\n eligible_items = artifact[\"item_ids\"].astype(np.int64, copy=True)\n packed = artifact[\"packed_eligible\"].copy()\n if not np.array_equal(eligible_uids, target_users):\n raise RuntimeError(\"eligibility users are not aligned to target_users\")\n if not np.array_equal(eligible_items, items):\n raise RuntimeError(\"eligibility items are not aligned to candidate catalog\")\n eligible = np.unpackbits(packed, axis=1, count=n_items, bitorder=\"little\")\n\n out_items = np.empty((len(target_users), TOP_N), dtype=np.int64)\n block_size = 128\n for start in range(0, len(target_users), block_size):\n end = min(start + block_size, len(target_users))\n listen_affinity = standardize_rows(\n listen_users[uid_rows[start:end]] @ listen_items\n )\n active_affinity = standardize_rows(active_users[start:end] @ active_items)\n propagation = neighbour_recent[start:end].toarray().astype(np.float32)\n prop_scale = propagation.max(axis=1)\n prop_scale[prop_scale == 0] = 1.0\n propagation /= prop_scale[:, None]\n score = (\n global_score[None, :]\n + 0.0200 * propagation\n + 0.0006 * listen_affinity\n + 0.0003 * active_affinity\n )\n score[eligible[start:end] == 0] = -np.inf\n chosen = np.argpartition(score, -TOP_N, axis=1)[:, -TOP_N:]\n for local_row in range(end - start):\n cols = chosen[local_row]\n cols = cols[np.argsort(score[local_row, cols])[::-1]]\n out_items[start + local_row] = items[cols]\n\n return pd.DataFrame(\n {\n \"uid\": np.repeat(target_users, TOP_N),\n \"item_id\": out_items.ravel(),\n \"rank\": np.tile(np.arange(1, TOP_N + 1, dtype=np.int64), len(target_users)),\n }\n )\n\n\ndef main() -> None:\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--stage\", choices=(\"val\", \"test\"), default=\"test\")\n args = parser.parse_args()\n ranking = build(args.stage)\n final = OUT / (\"validation.parquet\" if args.stage == \"val\" else \"submission.parquet\")\n temporary = OUT / (final.stem + \".tmp.parquet\")\n ranking.to_parquet(temporary, index=False)\n temporary.replace(final)\n print(f\"wrote {len(ranking):,} rows to {final}\")\n\n\nif __name__ == \"__main__\":\n main()\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/build_ranker.py\nA /app/solution/README.md\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86", "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3", "response_id": "resp_0fdbf3e20a3ba82b016ab5cce4d5e087d2987d66497a66d058", "usage": { "input_tokens": 75691, "cached_input_tokens": 75264, "cache_write_input_tokens": 0, "output_tokens": 3793, "reasoning_output_tokens": 9, "total_tokens": 79484 }, "turn_token_usage": { "input_tokens": 2174671, "cached_input_tokens": 2101504, "cache_write_input_tokens": 0, "output_tokens": 44250, "reasoning_output_tokens": 16392, "total_tokens": 2218921 }, "thread_token_usage": { "input_tokens": 2174671, "cached_input_tokens": 2101504, "cache_write_input_tokens": 0, "output_tokens": 44250, "reasoning_output_tokens": 16392, "total_tokens": 2218921 } }