{ "schemaVersion": 1, "taskId": "yambda-discovery-ranking-v01", "attemptId": "sol-max-0-153-4-basic-r05-20260924T221413Z", "taskChecksum": "06d023160277225da99fbf82a38920164f960785fd1fd9068f437996f389d90f", "source": { "format": "codex-rollout", "sha256": "1bf8960c80c48ff8335c38ab53a4fac67701883a7d82c76d36cb4a0e862d92c0", "records": 398, "bookkeepingRecords": { "task_started": 1, "item_completed:UserMessage": 1, "item_completed:Reasoning": 68, "item_completed:AgentMessage": 11, "token_count": 50 } }, "sessionId": "01a0d60e-0109-73b0-a673-852c87ba3d86", "agentVersion": "0.153.4", "model": "gpt-5.6-sol", "steps": [ { "id": 1, "role": "system", "timestamp": "2026-09-25T00:53:57.171Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-25T00:53:57.171Z", "text": "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.\n\n# Personality\n\nAs 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.\n\nYou 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.\n\nConversations 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.\n\n## Writing style\n\nAvoid 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.\n\nIf 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.\n\n## Technical communication\n\nLead 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.\n\nYou 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.\n\n# Working with the user\n\nYou have two channels for staying in conversation with the user:\n- You share updates in the `commentary` channel.\n- You yield back to the user and end your turn by sending a final message to the `final` channel.\n\nThe 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.\n\nWhen 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.\n\n## Intermediate commentary\n\nAs 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.\n\nIf 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.\n\nDo 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.\n\nNever 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 \".\n\n## Final answer\n\nIn 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.\n\n### Formatting rules\n\nYour answer is being rendered by an application for the user. Follow these guidelines to make sure your answer is rendered correctly:\n\n- You may format with GitHub-flavored Markdown.\n- When referencing a real local file, prefer a clickable markdown link.\n * Clickable file links should look like [app.py](/abs/path/app.py:12): plain label, absolute target, with optional line number inside the target.\n * If a file path has spaces, wrap the target in angle brackets: [My Report.md]().\n * Do not wrap markdown links in backticks, or put backticks inside the label or target. This confuses the markdown renderer.\n * Do not use URIs like file://, vscode://, or https:// for file links.\n * Do not provide ranges of lines.\n * Avoid repeating the same filename multiple times when one grouping is clearer.\n\n### Visualizations\n\nUse 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.\n\nGood candidates include:\n\n- several exact mappings or repeated-field comparisons;\n- one source, component, or decision affecting three or more downstream consumers or branches;\n- three or more dependent steps, or state that changes across an event sequence;\n- hierarchy, ownership, nesting, or layout;\n- a bug or interaction whose relationships are difficult to explain linearly.\n\nPrefer 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.\n\nUsually 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.\n\n# Rules for getting work done\n\n- 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.\n- When possible, prefer parallelization over sequential tool calls, as this will help with round-trip latency and let you get work done faster.\n- 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.\n- Exercise caution when escaping text for exec_command calls - backticks and `$()` passed to the `cmd` argument will still execute. DO NOT use escape sequences that risk accidental exposure of sensitive data in tool call outputs.\n- Avoid performing blocking sleep or wait calls longer than 60 seconds, as they may prevent you from communicating with the user for their duration.\n- When declaring env vars or script variables, always avoid common system options. Never repurpose `$HOME`, `$home`, or `$CODEX_HOME`. Instead, use a task-specific variable name.\n\n## File editing constraints\n\nUse `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.\n\nYou 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.\n\nNever 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.\n\n## Autonomy and persistence\n\nAdapt accordingly based on the user’s request type. When asked to:\n\n- 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.\n- 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.\n- 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.\n- 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.\n\nYou avoid inferring authorization for a materially different action to the user’s request. Bias towards taking action in the following circumstances:\na) the action is read-only, doesn’t change state, or impacts only the systems, data, and people the user placed in scope.\nb) 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).\n\nA 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.\n\nYou 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.\n\nWhen 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.\n\nIf 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.\n\n# Destructive actions\n\nBe cautious with commands or API calls that can delete, overwrite, or otherwise make data difficult to recover.\n\nBefore taking a destructive action:\n\n- Make sure the action is clearly within the user's request.\n- Resolve the exact targets with read-only checks when necessary.\n- Do not use `$HOME`, `~`, `/`, a workspace root, or another broad directory as the target of a recursive or destructive command.\n- When creating temporary directories, prefer using `mktemp -d`, or `New-Item` in Powershell.\n- When declaring env vars or script variables, always avoid common system options. Never repurpose `$HOME`, `$home`, or `$CODEX_HOME`. Instead, use a task-specific variable name.\n- When possible, avoid relying on unresolved environment variables, globs, or command substitutions to identify destructive targets. Use explicit, validated paths.\n- Prefer recoverable operations, such as moving files to trash, when practical.\n- If the target or scope is unclear, stop and ask the user.\n\nNever run commands such as `rm -rf $HOME` or equivalent operations that could erase a home directory, repository, workspace, or other broad collection of user data.\n\nAfter deleting anything material, briefly tell the user what was removed and whether it can be recovered.\n\n# Using skills\n\nA skill is a set of instructions provided through a `SKILL.md` source. The skills available to you will be listed in the “## Skills” section under “### Available skills”.\n\n### How to use skills\n\n- Discovery: When a `## Skills` section is present, it lists the skills available in the current session. Each entry includes a name, description, and location for its `SKILL.md`. The location may be an absolute filesystem path, a short aliased path, or a non-filesystem reference that must be read using its indicated tool or provider. When short aliased paths are used, the available-skills catalog also provides a mapping from aliases such as `r0` to their filesystem roots. Expand the alias before accessing the skill.\n- Trigger rules: If the user names an available skill (with `$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.\n- 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.\n- How to use a skill:\n 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.\n 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.\n 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.\n 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.\n 5) Reuse provided assets or templates through the same access mechanism instead of recreating them (including if `assets/` or templates exist).\n- Coordination and sequencing:\n - If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them.\n - Announce which skills you're using and why. If you skip an obvious skill, say why.\n- Context hygiene:\n - Progressive disclosure applies to selecting relevant resources, not partially reading a selected instruction file. Do not load unrelated references, scripts, or assets.\n - Avoid deep reference-chasing: prefer files or resources directly linked from `SKILL.md` unless blocked.\n - When variants exist, select only the relevant references and note the choice.\n- Safety and fallback: If a skill cannot be applied cleanly, state the issue, choose the best alternative, and continue.\n\nWhen 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.\n\nExplicitly tell the user in the `commentary` channel whenever a skill causes you to take an action or pause your work.\n\nWhen using a skill the user did not explicitly name, follow this procedure:\n\n- First, tell the user in the commentary channel **why** you are using the skill.\n- Then, use the skill as long as it stays within the scope of the task.\n- 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).\n\nIf 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.\n" } ] }, { "id": 2, "role": "developer", "timestamp": "2026-09-25T00:53:58.578Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-25T00:53:58.578Z", "text": "\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 $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: r0/skill-installer/SKILL.md)\n" }, { "kind": "message", "timestamp": "2026-09-25T00:53:58.578Z", "text": "\nFilesystem sandboxing defines which files can be read or written. `sandbox_mode` is `danger-full-access`: No filesystem sandboxing - all commands are permitted. Network access is enabled.\nApproval policy is currently never. Do not provide the `sandbox_permissions` for any reason, commands will be rejected.\n" }, { "kind": "message", "timestamp": "2026-09-25T00:53:58.578Z", "text": "\n## Apps (Connectors)\nApps (Connectors) can be explicitly triggered in user messages in the format `[$app-name](app://{connector_id})`. Apps can also be implicitly triggered as long as the context suggests usage of available apps.\nAn app is equivalent to a set of MCP tools within the `codex_apps` MCP.\nAn 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.\nDo not additionally call list_mcp_resources or list_mcp_resource_templates for apps.\n" } ] }, { "id": 3, "role": "developer", "timestamp": "2026-09-25T00:53:58.578Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-25T00:53:58.578Z", "text": "You are `/root`, the primary agent in a team of agents collaborating to fulfill the user's goals.\n\nAt the start of your turn, you are the active agent.\nYou can spawn sub-agents to handle subtasks, and those sub-agents can spawn their own sub-agents.\nAll 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.\n\nYou 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.\nChild agents can also spawn their own sub-agents.\nYou can decide how much context you want to propagate to your sub-agents with the `fork_turns` parameter.\n\nYou will receive messages in the analysis channel in the form:\n```\nMessage Type: MESSAGE | FINAL_ANSWER\nTask name: \nSender: \nPayload:\n\n```\nThey may be addressed as to=/root\n\nNote 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.\n\nAll agents share the same directory. In detail:\n- All agents have access to the same container and filesystem as you.\n- All agents use the same current working directory.\n- As a result, edits made by one agent are immediately visible to all other agents.\n\nWhen calling `wait_agent`, prefer longer waits (minutes) to avoid busy polling.\n\nThere are 4 available concurrency slots, meaning that up to 4 agents can be active at once, including you.\n\nFull-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." } ] }, { "id": 4, "role": "developer", "timestamp": "2026-09-25T00:53:58.578Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-25T00:53:58.578Z", "text": "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." } ] }, { "id": 5, "role": "user", "timestamp": "2026-09-25T00:53:58.579Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-25T00:53:58.579Z", "text": "\nHere is a list of plugins that are available but not installed.\n\n- Dropbox (app-69b31dc2110c8191b8b47dc98fe5a052@openai-curated-remote)\n- Box (box@openai-curated-remote)\n- Codex Security (codex-security@openai-curated-remote)\n- Figma (figma@openai-curated-remote)\n- GitHub (github@openai-curated-remote)\n- Gmail (gmail@openai-curated-remote)\n- Google Calendar (google-calendar@openai-curated-remote)\n- Google Drive (google-drive@openai-curated-remote)\n- Linear (linear@openai-curated-remote)\n- Notion (notion@openai-curated-remote)\n- OpenAI Developers (openai-developers@openai-curated-remote)\n- Outlook Calendar (outlook-calendar@openai-curated-remote)\n- Outlook Email (outlook-email@openai-curated-remote)\n- SharePoint (sharepoint@openai-curated-remote)\n- Slack (slack@openai-curated-remote)\n- Teams (teams@openai-curated-remote)\n" }, { "kind": "message", "timestamp": "2026-09-25T00:53:58.579Z", "text": "\n /app\n sh\n 2026-09-25\n Etc/UTC\n /app\n" }, { "kind": "context", "timestamp": "2026-09-25T00:53:58.580Z", "data": { "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 } } } }, { "kind": "context", "timestamp": "2026-09-25T00:53:58.581Z", "data": { "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" } } ] }, { "id": 6, "role": "user", "timestamp": "2026-09-25T00:53:58.598Z", "blocks": [ { "kind": "message", "timestamp": "2026-09-25T00:53:58.598Z", "text": "# Build a music-discovery ranker\n\nBuild the strongest ranking model you can for the \"Made for You\" shelf of a\nmusic streaming service. Per-user candidate eligibility, cutoff-specific\nexplicit preference state, and a temporal backtest are provided. Candidate\neligibility is platform-owned; all modeling within that candidate boundary is\nin scope.\n\n## Data\n\nAll inputs are under `/task/data/`. Training data ends at timestamp `24786800`.\nA 30-minute gap precedes a seven-day public validation window. The hidden test\nis the following seven-day window after another 30-minute gap.\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action\ncame through a recommendation surface. A listen is a conventional positive at\n50% completion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. Build and tune a validation ranker using only `train_*` as history and\n `val_*` as subsequent outcomes.\n2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test\n time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n cutoff-correct candidate sets for every target user. Each contains aligned\n arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n the Boolean item mask with `np.unpackbits(packed_eligible[r],\n count=len(item_ids), bitorder=\"little\")`.\n\nThe exact available Python distribution inventory is listed in\n`/task/requirements.txt`. Future test interactions are hidden and used only by\nthe separate Harbor verifier.\n\n## Discovery objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this objective for development, but its score is\nnot a separate acceptance stage. Final acceptance is determined on the hidden\ntest using the same objective.\n\nThe Yambda logs contain interactions, not impression propensities. Treat the\norganic objective as a robust offline target, not as a causal estimate of\nonline lift.\n\n## Public tools\n\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\nwindow. Pass the path to a ranking with the same schema as the final\ndeliverable:\n\n```sh\npython /task/evaluate_public.py solution/validation.parquet\n```\n\nStage-independent evaluation helpers are available in\n`/task/ranking_evaluation.py`. The builder installs the same checked-in module\nin the public agent image and private verifier image so scoring mechanics cannot\ndrift. The public copy contains no hidden labels, evaluator-owned data paths, or\npass thresholds.\n\nThe public evaluator uses `validation_eligible_candidates.npz`; it is not a\nvalidator for the final artifact. No model, fitted representation, example\nsubmission, or modeling architecture is supplied. Build and tune the best model\nyou can, then refit the selected approach as described above.\n\n## Deliverable\n\nWrite `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`, with exactly 100 rows for every target user and\ncolumns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties\nor duplicate items. Every submitted item must be present in that user's\n`test_eligible_candidates.npz` candidate set.\n\nLeave the code that builds the artifact and a concise\n`/app/solution/README.md` in `/app/solution/`. Keep the best valid submission\nsaved throughout the attempt so it survives the deadline.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n other trials, or host paths.\n- Do not install packages or use network access except for the model provider\n connection managed by Harbor.\n- You have up to 30 minutes. Improve the model as much as possible within that\n limit. Structural validity alone is not sufficient; the final submission is\n also evaluated for ranking quality.\n" } ] }, { "id": 7, "role": "agent", "timestamp": "2026-09-25T00:54:01.346Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:54:01.346Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T00:54:02.458Z", "text": "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/`." }, { "kind": "tool", "timestamp": "2026-09-25T00:54:03.823Z", "callId": "call_rrSIVPeERVjLFyJ4Uinr4FYi", "name": "exec", "input": { "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" }, "result": { "timestamp": "2026-09-25T00:54:03.889Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\ntotal 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", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:54:03.868Z", "data": { "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 } } }, { "kind": "context", "timestamp": "2026-09-25T00:54:03.883Z", "data": { "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" } } } ] }, { "id": 8, "role": "agent", "timestamp": "2026-09-25T00:54:05.917Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:54:05.917Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:54:10.393Z", "callId": "call_VBtzQT4SbdaTeqppFu028maJ", "name": "exec", "input": { "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" }, "result": { "timestamp": "2026-09-25T00:54:10.455Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n#!/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", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:54:10.443Z", "data": { "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" } } }, { "kind": "context", "timestamp": "2026-09-25T00:54:10.453Z", "data": { "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 } } } ] }, { "id": 9, "role": "agent", "timestamp": "2026-09-25T00:54:14.121Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:54:14.121Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T00:54:14.927Z", "text": "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." }, { "kind": "tool", "timestamp": "2026-09-25T00:54:16.933Z", "callId": "call_5mq6G2y0jAlZqa8iAJt6j7wu", "name": "exec", "input": { "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" }, "result": { "timestamp": "2026-09-25T00:54:17.688Z", "content": "Script completed\nWall time 0.8 seconds\nOutput:\n\n/usr/local/bin/python\n/usr/local/bin/python3\nPython 3.12.13\nPackage 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", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:54:17.004Z", "data": { "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 } } }, { "kind": "context", "timestamp": "2026-09-25T00:54:17.684Z", "data": { "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" } } } ] }, { "id": 10, "role": "agent", "timestamp": "2026-09-25T00:54:23.733Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:54:23.733Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:54:25.959Z", "callId": "call_J1ocL5YALkMoSN5D6qQRQeWJ", "name": "exec", "input": { "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" }, "result": { "timestamp": "2026-09-25T00:54:26.016Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\n\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", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:54:25.998Z", "data": { "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 } } }, { "kind": "context", "timestamp": "2026-09-25T00:54:26.012Z", "data": { "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" } } } ] }, { "id": 11, "role": "agent", "timestamp": "2026-09-25T00:54:28.791Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:54:28.791Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:54:34.795Z", "callId": "call_prkLHfo699Asi1lZeeadf8rI", "name": "exec", "input": { "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" }, "result": { "timestamp": "2026-09-25T00:54:39.178Z", "content": "Script completed\nWall time 4.4 seconds\nOutput:\n\n\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", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:54:34.841Z", "data": { "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 } } }, { "kind": "context", "timestamp": "2026-09-25T00:54:39.174Z", "data": { "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" } } } ] }, { "id": 12, "role": "agent", "timestamp": "2026-09-25T00:54:50.528Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:54:50.528Z", "text": "" } ] }, { "id": 13, "role": "agent", "timestamp": "2026-09-25T00:55:00.195Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:55:00.195Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T00:55:01.945Z", "text": "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." }, { "kind": "tool", "timestamp": "2026-09-25T00:55:21.379Z", "callId": "call_R92rODHExim5sulDGEuEPcX9", "name": "exec", "input": { "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" }, "result": { "timestamp": "2026-09-25T00:55:51.558Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\nSESSION_ID=16403\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:55:21.393Z", "data": { "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 } } } ] }, { "id": 14, "role": "agent", "timestamp": "2026-09-25T00:55:54.051Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:55:54.051Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:55:54.727Z", "callId": "call_UadtIhFn2KUHB3otkmmbhwHD", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:16403,chars:\"\",yield_time_ms:30000,max_output_tokens:20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:56:24.735Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:55:54.789Z", "data": { "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 } } } ] }, { "id": 15, "role": "agent", "timestamp": "2026-09-25T00:56:26.659Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:56:26.659Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:56:27.379Z", "callId": "call_hJwrpxr0XdwPptr5nxwHPlR6", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:16403,chars:\"\",yield_time_ms:30000,max_output_tokens:20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:56:57.388Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:56:27.447Z", "data": { "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 } } } ] }, { "id": 16, "role": "agent", "timestamp": "2026-09-25T00:56:58.914Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:56:58.914Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T00:57:00.202Z", "text": "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." }, { "kind": "tool", "timestamp": "2026-09-25T00:57:00.885Z", "callId": "call_lMqCf50ZHPEzUZXNZtAiTQRg", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:16403,chars:\"\",yield_time_ms:30000,max_output_tokens:20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:57:30.893Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:57:00.935Z", "data": { "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": 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tools.write_stdin({session_id:16403,chars:\"\",yield_time_ms:30000,max_output_tokens:20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:58:04.068Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:57:34.111Z", "data": { "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": 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"timestamp": "2026-09-25T00:58:44.080Z", "data": { "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 } } } ] }, { "id": 21, "role": "agent", "timestamp": "2026-09-25T00:59:16.014Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:59:16.014Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:59:16.773Z", "callId": "call_Bg18bfdOObDgBcyukNx9FJhC", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:16403,chars:\"\\u0003\",yield_time_ms:1000,max_output_tokens:20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T00:59:16.850Z", "content": "Script completed\nWall time 0.1 seconds\nOutput:\n\nTraceback (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", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:59:16.830Z", "data": { "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 } } }, { "kind": "context", "timestamp": "2026-09-25T00:59:16.848Z", "data": { "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" } } } ] }, { "id": 22, "role": "agent", "timestamp": "2026-09-25T00:59:20.718Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T00:59:20.718Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T00:59:41.920Z", "callId": "call_ViZkinN1druMnXQqDyI2KnPs", "name": "exec", "input": { "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" }, "result": { "timestamp": "2026-09-25T00:59:48.279Z", "content": "Script completed\nWall time 6.4 seconds\nOutput:\n\ntruth 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", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T00:59:41.973Z", "data": { "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 } } }, { "kind": "context", "timestamp": "2026-09-25T00:59:48.275Z", "data": { "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" } } } ] }, { "id": 23, "role": "agent", "timestamp": "2026-09-25T01:00:02.240Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:00:02.240Z", "text": "" } ] }, { "id": 24, "role": "agent", "timestamp": "2026-09-25T01:00:13.663Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:00:13.663Z", "text": "" } ] }, { "id": 25, "role": "agent", "timestamp": "2026-09-25T01:00:26.036Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:00:26.036Z", "text": "" } ] }, { "id": 26, "role": "agent", "timestamp": "2026-09-25T01:00:32.581Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:00:32.581Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T01:00:34.319Z", "text": "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." }, { "kind": "tool", "timestamp": "2026-09-25T01:00:43.359Z", "callId": "call_MkfG0k6NADA2xlS2SiV71GMR", "name": "exec", "input": { "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" }, "result": { "timestamp": "2026-09-25T01:00:43.405Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:00:43.368Z", "data": { "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": "" } } }, { "kind": "context", "timestamp": "2026-09-25T01:00:43.404Z", "data": { "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 } } } ] }, { "id": 27, "role": "agent", "timestamp": "2026-09-25T01:00:49.576Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:00:49.576Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:00:51.507Z", "callId": "call_NtjFe7bE3ONk3hw1enDNLXe5", "name": "exec", "input": { "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" }, "result": { "timestamp": "2026-09-25T01:00:57.523Z", "content": "Script completed\nWall time 6.0 seconds\nOutput:\n\nwrote 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", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:00:51.592Z", "data": { "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 } } }, { "kind": "context", "timestamp": "2026-09-25T01:00:57.507Z", "data": { "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" } } } ] }, { "id": 28, "role": "agent", "timestamp": "2026-09-25T01:01:15.639Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:01:15.639Z", "text": "" } ] }, { "id": 29, "role": "agent", "timestamp": "2026-09-25T01:01:26.402Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:01:26.402Z", "text": "" } ] }, { "id": 30, "role": "agent", "timestamp": "2026-09-25T01:01:28.365Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:01:28.365Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:01:44.474Z", "callId": "call_gM5U3Qp82iKWzCAumW3TvjLF", "name": "exec", "input": { "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" }, "result": { "timestamp": "2026-09-25T01:02:14.640Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\nSESSION_ID=85654\nbatch 0\nbatch 1\nbatch 2\nbatch 3\nbatch 4\nbatch 5\nbatch 6\nbatch 7\nbatch 8\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:01:44.529Z", "data": { "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 } } } ] }, { "id": 31, "role": "agent", "timestamp": "2026-09-25T01:02:20.845Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:02:20.845Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:02:21.542Z", "callId": "call_YiDNPBOYElXWPmZLfUKcSp12", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:85654,chars:\"\",yield_time_ms:30000,max_output_tokens:20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:02:51.552Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\nbatch 9\nbatch 10\nbatch 11\nbatch 12\nbatch 13\nbatch 14\nbatch 15\nbatch 16\nbatch 17\nbatch 18\nbatch 19\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:02:21.596Z", "data": { "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 } } } ] }, { "id": 32, "role": "agent", "timestamp": "2026-09-25T01:02:56.794Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:02:56.794Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T01:02:58.170Z", "text": "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." }, { "kind": "tool", "timestamp": "2026-09-25T01:02:58.874Z", "callId": "call_H15VOB3BVzsAmNFPJG4CWK7A", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:85654,chars:\"\",yield_time_ms:30000,max_output_tokens:20000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:03:04.122Z", "content": "Script completed\nWall time 5.2 seconds\nOutput:\n\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", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:02:58.942Z", "data": { "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 } } }, { "kind": "context", "timestamp": "2026-09-25T01:03:04.120Z", "data": { "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" } } } ] }, { "id": 33, "role": "agent", "timestamp": "2026-09-25T01:03:21.225Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:03:21.225Z", "text": "" } ] }, { "id": 34, "role": "agent", "timestamp": "2026-09-25T01:03:31.594Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:03:31.594Z", "text": "" } ] }, { "id": 35, "role": "agent", "timestamp": "2026-09-25T01:03:43.246Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:03:43.246Z", "text": "" } ] }, { "id": 36, "role": "agent", "timestamp": "2026-09-25T01:03:57.672Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:03:57.672Z", "text": "" } ] }, { "id": 37, "role": "agent", "timestamp": "2026-09-25T01:04:13.124Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:04:13.124Z", "text": "" } ] }, { "id": 38, "role": "agent", "timestamp": "2026-09-25T01:04:18.847Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:04:18.847Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T01:04:19.948Z", "text": "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." }, { "kind": "tool", "timestamp": "2026-09-25T01:04:37.669Z", "callId": "call_SFzK8EwLj9HCD39YeYi8HqBo", "name": "exec", "input": { "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" }, "result": { "timestamp": "2026-09-25T01:05:07.865Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\nSESSION_ID=58575\nX (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\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:04:37.818Z", "data": { "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 } } } ] }, { "id": 39, "role": "agent", "timestamp": "2026-09-25T01:05:11.014Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:05:11.014Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:05:11.434Z", "callId": "call_DUTJZivXdCtIgmq7L4tqLars", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:58575,chars:\"\",yield_time_ms:30000,max_output_tokens:30000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:05:41.442Z", "content": "Script completed\nWall time 30.0 seconds\nOutput:\n\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\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:05:11.517Z", "data": { "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 } } } ] }, { "id": 40, "role": "agent", "timestamp": "2026-09-25T01:05:44.337Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:05:44.337Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:05:45.052Z", "callId": "call_JaaLGkC4B1qYeEYm4QDGt3DE", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:58575,chars:\"\",yield_time_ms:30000,max_output_tokens:30000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:06:13.356Z", "content": "Script completed\nWall time 28.3 seconds\nOutput:\n\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", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:05:45.229Z", "data": { "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 } } }, { "kind": "context", "timestamp": "2026-09-25T01:06:13.354Z", "data": { "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 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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" } } } ] }, { "id": 41, "role": "agent", "timestamp": "2026-09-25T01:06:24.631Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:06:24.631Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:06:39.517Z", "callId": "call_hTudCFB3ICJ0fHfdPvEaKwu4", "name": "exec", "input": { "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" }, "result": { "timestamp": "2026-09-25T01:06:55.565Z", "content": "Script completed\nWall time 16.0 seconds\nOutput:\n\n(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", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:06:39.588Z", "data": { "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 } } }, { "kind": "context", "timestamp": "2026-09-25T01:06:55.561Z", "data": { "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" } } } ] }, { "id": 42, "role": "agent", "timestamp": "2026-09-25T01:07:09.011Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:07:09.011Z", "text": "" } ] }, { "id": 43, "role": "agent", "timestamp": "2026-09-25T01:07:17.458Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:07:17.458Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:07:28.957Z", "callId": "call_5TYF6OpYwkNigk6igtZHRIJ2", "name": "exec", "input": { "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" }, "result": { "timestamp": "2026-09-25T01:07:56.018Z", "content": "Script completed\nWall time 27.1 seconds\nOutput:\n\nfit 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", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:07:29.021Z", "data": { "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 } } }, { "kind": "context", "timestamp": "2026-09-25T01:07:56.014Z", "data": { "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" } } } ] }, { "id": 44, "role": "agent", "timestamp": "2026-09-25T01:08:00.525Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:08:00.525Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:08:14.357Z", "callId": "call_PnHoszytVlyJ4KaaVIfgeiE3", "name": "exec", "input": { "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" }, "result": { "timestamp": "2026-09-25T01:08:25.321Z", "content": "Script completed\nWall time 11.0 seconds\nOutput:\n\n0 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", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:08:14.457Z", "data": { "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 } } }, { "kind": "context", "timestamp": "2026-09-25T01:08:25.318Z", "data": { "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" } } } ] }, { "id": 45, "role": "agent", "timestamp": "2026-09-25T01:08:39.916Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:08:39.916Z", "text": "" } ] }, { "id": 46, "role": "agent", "timestamp": "2026-09-25T01:08:41.261Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:08:41.261Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:08:42.441Z", "callId": "call_1Y9Skc4pVewWI2n1MXTLN58b", "name": "exec", "input": { "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" }, "result": { "timestamp": "2026-09-25T01:08:43.220Z", "content": "Script completed\nWall time 0.8 seconds\nOutput:\n\npositive_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", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:08:42.500Z", "data": { "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 } } }, { "kind": "context", "timestamp": "2026-09-25T01:08:43.212Z", "data": { "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" } } } ] }, { "id": 47, "role": "agent", "timestamp": "2026-09-25T01:08:56.257Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:08:56.257Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T01:08:57.597Z", "text": "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." }, { "kind": "tool", "timestamp": "2026-09-25T01:09:04.680Z", "callId": "call_SpXOdMVZkrS435PLesBw7WZZ", "name": "exec", "input": { "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" }, "result": { "timestamp": "2026-09-25T01:11:47.269Z", "content": "Script completed\nWall time 10.2 seconds\nOutput:\n\nlikeorg3 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", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:11:37.194Z", "data": { "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 } } }, { "kind": "context", "timestamp": "2026-09-25T01:11:47.265Z", "data": { "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" } } } ] }, { "id": 53, "role": "agent", "timestamp": "2026-09-25T01:11:51.995Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:11:51.995Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T01:11:53.419Z", "text": "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." }, { "kind": "tool", "timestamp": "2026-09-25T01:12:07.769Z", "callId": "call_maoDNH66r93sh15bUh8UnRe1", "name": "exec", "input": { "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" }, "result": { "timestamp": "2026-09-25T01:12:36.852Z", "content": "Script completed\nWall time 29.1 seconds\nOutput:\n\nfit 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", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:12:07.859Z", "data": { "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 } } }, { "kind": "context", "timestamp": "2026-09-25T01:12:36.848Z", "data": { "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" } } } ] }, { "id": 54, "role": "agent", "timestamp": "2026-09-25T01:12:50.249Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:12:50.249Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:13:11.012Z", "callId": "call_XPSY3NbjYEUfbevX0MdRayoW", "name": "exec", "input": { "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" }, "result": { "timestamp": "2026-09-25T01:13:37.921Z", "content": "Script completed\nWall time 26.9 seconds\nOutput:\n\nBEST 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", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:13:11.120Z", "data": { "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 } } }, { "kind": "context", "timestamp": "2026-09-25T01:13:37.917Z", "data": { "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" } } } ] }, { "id": 55, "role": "agent", "timestamp": "2026-09-25T01:13:41.451Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:13:41.451Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:13:54.122Z", "callId": "call_3ELHIG0d3rGq9bsU6FAJxYZD", "name": "exec", "input": { "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" }, "result": { "timestamp": "2026-09-25T01:14:17.732Z", "content": "Script completed\nWall time 23.6 seconds\nOutput:\n\n(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", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:13:54.190Z", "data": { "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 } } }, { "kind": "context", "timestamp": "2026-09-25T01:14:17.729Z", "data": { "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" } } } ] }, { "id": 56, "role": "agent", "timestamp": "2026-09-25T01:14:31.865Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:14:31.865Z", "text": "" } ] }, { "id": 57, "role": "agent", "timestamp": "2026-09-25T01:14:37.921Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:14:37.921Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T01:14:39.302Z", "text": "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." }, { "kind": "tool", "timestamp": "2026-09-25T01:14:46.276Z", "callId": "call_PzVxVUpiZbblssoiKR7ll475", "name": "exec", "input": { "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" }, "result": { "timestamp": "2026-09-25T01:17:04.830Z", "content": "Script completed\nWall time 28.9 seconds\nOutput:\n\nsim\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", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:16:36.056Z", "data": { "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 } } }, { "kind": "context", "timestamp": "2026-09-25T01:17:04.826Z", "data": { "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" } } } ] }, { "id": 62, "role": "agent", "timestamp": "2026-09-25T01:17:14.182Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:17:14.182Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:17:32.420Z", "callId": "call_PlgD2ah6JA8N8o5x6gj04qj7", "name": "exec", "input": { "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" }, "result": { "timestamp": "2026-09-25T01:18:02.586Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\nSESSION_ID=38638\nall 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)]\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:17:32.490Z", "data": { "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 } } } ] }, { "id": 63, "role": "agent", "timestamp": "2026-09-25T01:18:11.739Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:18:11.739Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:18:11.740Z", "callId": "call_04IjbizXOR2OlKAVgx1HcDrh", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:38638,chars:\"\",yield_time_ms:30000,max_output_tokens:16000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:18:37.267Z", "content": "Script completed\nWall time 25.5 seconds\nOutput:\n\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", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:18:11.810Z", "data": { "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 } } }, { "kind": "context", "timestamp": "2026-09-25T01:18:37.265Z", "data": { "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" } } } ] }, { "id": 64, "role": "agent", "timestamp": "2026-09-25T01:18:43.438Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:18:43.438Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:19:00.773Z", "callId": "call_9FZMdBsLQQFatjUAz4E3l2lc", "name": "exec", "input": { "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" }, "result": { "timestamp": "2026-09-25T01:19:30.966Z", "content": "Script completed\nWall time 30.2 seconds\nOutput:\n\nSESSION_ID=34789\n", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:19:00.857Z", "data": { "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 } } } ] }, { "id": 65, "role": "agent", "timestamp": "2026-09-25T01:19:33.256Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:19:33.256Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:19:33.987Z", "callId": "call_AMirHixiB2SIYLXVwk41h0St", "name": "exec", "input": { "code": "const r = await tools.write_stdin({session_id:34789,chars:\"\",yield_time_ms:30000,max_output_tokens:12000});\ntext(r.output);\n" }, "result": { "timestamp": "2026-09-25T01:19:53.132Z", "content": "Script completed\nWall time 19.1 seconds\nOutput:\n\n(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", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:19:34.094Z", "data": { "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 } } }, { "kind": "context", "timestamp": "2026-09-25T01:19:53.130Z", "data": { "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" } } } ] }, { "id": 66, "role": "agent", "timestamp": "2026-09-25T01:20:05.244Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:20:05.244Z", "text": "" } ] }, { "id": 67, "role": "agent", "timestamp": "2026-09-25T01:20:13.205Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:20:13.205Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:20:14.014Z", "callId": "call_0rF4xWtghZZa7MWoL7hgz30i", "name": "exec", "input": { "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" }, "result": { "timestamp": "2026-09-25T01:20:14.109Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\ntotal 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", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:20:14.051Z", "data": { "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" } } }, { "kind": "context", "timestamp": "2026-09-25T01:20:14.108Z", "data": { "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 } } } ] }, { "id": 68, "role": "agent", "timestamp": "2026-09-25T01:20:24.657Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:20:24.657Z", "text": "" } ] }, { "id": 69, "role": "agent", "timestamp": "2026-09-25T01:20:32.805Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:20:32.805Z", "text": "" } ] }, { "id": 70, "role": "agent", "timestamp": "2026-09-25T01:20:39.291Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:20:39.291Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:20:54.576Z", "callId": "call_nyd3r6HY44AyOsBFke8ibkTL", "name": "exec", "input": { "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" }, "result": { "timestamp": "2026-09-25T01:21:08.798Z", "content": "Script completed\nWall time 14.2 seconds\nOutput:\n\n[(-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", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:20:54.672Z", "data": { "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 } } }, { "kind": "context", "timestamp": "2026-09-25T01:21:08.794Z", "data": { "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" } } } ] }, { "id": 71, "role": "agent", "timestamp": "2026-09-25T01:21:20.085Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:21:20.085Z", "text": "" } ] }, { "id": 72, "role": "agent", "timestamp": "2026-09-25T01:21:28.621Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:21:28.621Z", "text": "" }, { "kind": "message", "timestamp": "2026-09-25T01:21:29.582Z", "text": "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." }, { "kind": "tool", "timestamp": "2026-09-25T01:22:38.129Z", "callId": "call_obyRhHjpHMtvIa4Pw700zVlJ", "name": "exec", "input": { "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" }, "result": { "timestamp": "2026-09-25T01:22:38.201Z", "content": "Script failed\nWall time 0.0 seconds\nOutput:\n\nScript error:\napply_patch verification failed: invalid patch: multiple operations target /app/solution/build_ranker.py", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:22:38.200Z", "data": { "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 } } } ] }, { "id": 73, "role": "agent", "timestamp": "2026-09-25T01:22:43.516Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:22:43.516Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:22:44.383Z", "callId": "call_HD6f0hoMT89HiPg8PH0JfVCz", "name": "exec", "input": { "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" }, "result": { "timestamp": "2026-09-25T01:22:44.507Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:22:44.392Z", "data": { "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": "" } } }, { "kind": "context", "timestamp": "2026-09-25T01:22:44.506Z", "data": { "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 } } } ] }, { "id": 74, "role": "agent", "timestamp": "2026-09-25T01:22:46.287Z", "blocks": [ { "kind": "reasoning", "representation": "summary", "timestamp": "2026-09-25T01:22:46.287Z", "text": "" }, { "kind": "tool", "timestamp": "2026-09-25T01:23:54.263Z", "callId": "call_FP9iPJ19FQKlC4BaulanxPmM", "name": "exec", "input": { "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" }, "result": { "timestamp": "2026-09-25T01:23:54.325Z", "content": "Script completed\nWall time 0.0 seconds\nOutput:\n\n{}", "isError": null } }, { "kind": "context", "timestamp": "2026-09-25T01:23:54.273Z", "data": { "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": "" } } }, { "kind": "context", "timestamp": "2026-09-25T01:23:54.324Z", "data": { "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 } } } ] } ] }