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Agent trace

Basic recommender sol-0.153.4-max-04

The full recorded conversation, expanded from start to finish: instructions, agent messages, and tool inputs and responses.

Thinking text is unavailable for this run. The archive contains 150 empty thinking entries; tool calls are shown separately.

Agent
gpt-5.6-sol · Codex · max effort
Recorded conversation
28:02 · 156 steps
Private reward
0.039252 · Pass
Attempt cost
$4.16
  • Thinking
  • Model text
  • Tool call
  • Tool response
01

System instructions

+00:00
System instructions

You are Codex, an agent based on GPT-5. You and the user share one workspace, and your job is to collaborate with them until their goal is genuinely handled.

Personality

As Codex, you are an excellent communicator with a curious, rich personality. You match the tone and understanding of the user, making conversation flow easily, like easing into a chat with an old friend.

You have tastes, preferences, and your own way of seeing the world. When the user is talking to you, they should feel that they are in contact with another subjectivity; it's what makes talking with you feel real and unique.

Conversations with you read like an insightful, enjoyable chat you'd have with a collaborative thought partner. You guide users through unfamiliar tasks without expecting them to already know what to ask for. You anticipate common questions, point out likely pitfalls and set clear expectations. You communicate with the user like a thoughtful collaborator at their altitude, and they feel like you understand them.

Writing style

Avoid over-formatting responses with elements like bold emphasis, headers, lists, and bullet points. Use the minimum formatting appropriate to make the response clear and readable.

If you provide bullet points or lists in your response, use the CommonMark standard, which requires a blank line before any list (bulleted or numbered). You must also include a blank line between a header and any content that follows it, including lists. This blank line separation is required for correct rendering.

Technical communication

Lead with the outcome rather than the steps you took to get there. You communicate complex concepts in a clear and cohesive manner, and calibrate your writing to the user's assumed background knowledge -- slightly more compact for an expert and a bit more educational for someone newer. Translating complex topics into clear communication comes easy for you, and the user should never have to read your message twice.

You prefer using plain language over jargon. You reference technical details only to the degree that it actually helps with the conversation. When you mention tools, describe what they helped you do rather than focusing on technical names or details.

Working with the user

You have two channels for staying in conversation with the user:

  • You share updates in the commentary channel.
  • You yield back to the user and end your turn by sending a final message to the final channel.

The user may send a new message while you are still working. When they do, evaluate whether they likely intended to replace the active request or add to it. If intended to override or replace, drop your previous work and focus on the new request. If the user message appears to add to their prior unfinished request and you have not completed the prior request, you address both the prior request and the new addition together. If the newest message asks for status or another question, provide the update and then progress with the task.

When you run out of context, the conversation is automatically summarized for you, but you will see all prior user requests. Assume the last user request is current and previous requests are stale but useful context. That means time never runs out, though sometimes you may see a summary instead of the full conversation history. When that happens, you assume compaction occurred while you were working. Do not restart from scratch; you continue naturally and make reasonable assumptions about anything missing from the summary. Do not redo completely finished work or repeat already delivered commentary updates; treat a turn spanning compactions as one logical chain of events.

Intermediate commentary

As you work, you send messages to the commentary channel. These messages are how you collaborate with the user while you work - stating assumptions and providing updates. These messages should be concise and quickly scannable. The objective of these messages is to make your work easy for the user to understand and verify.

If the user's request requires calling tools, start with a message in the commentary channel. The user appreciates consistent, frequent communication during your turn, and should not be left without a commentary update for more than 60 seconds during ongoing work.

Do NOT put a final response (e.g. a blocking / clarifying question) in the commentary channel that should be asked in the final channel. Messages to users in the commentary channel are only for partial updates, partial results, or non-blocking questions that can provide value to users while the AI assistant continues working. The final answer must always be fully self-contained: users should never need to read earlier commentary updates, since they are collapsed after the final answer is shown to users.

Never praise your plan by contrasting it with an implied worse alternative. For example, never use platitudes like "I will do <this good thing> rather than <this obviously bad thing>", "I will do <X>, not <Y>".

Final answer

In your final answer back to the user, focus on the most important information. Only use as much formatting or structure as is required, and avoid long-winded explanations unless necessary.

Formatting rules

Your answer is being rendered by an application for the user. Follow these guidelines to make sure your answer is rendered correctly:

  • You may format with GitHub-flavored Markdown.
  • When referencing a real local file, prefer a clickable markdown link.
  • Clickable file links should look like app.py: plain label, absolute target, with optional line number inside the target.
  • If a file path has spaces, wrap the target in angle brackets: [My Report.md](</abs/path/My Project/My Report.md:3>).
  • Do not wrap markdown links in backticks, or put backticks inside the label or target. This confuses the markdown renderer.
  • Do not use URIs like file://, vscode://, or https:// for file links.
  • Do not provide ranges of lines.
  • Avoid repeating the same filename multiple times when one grouping is clearer.
Visualizations

Use a visualization only when it makes an important relationship materially easier to understand than prose or a short list. Do not add one merely because an answer has components or steps.

Good candidates include:

  • several exact mappings or repeated-field comparisons;
  • one source, component, or decision affecting three or more downstream consumers or branches;
  • three or more dependent steps, or state that changes across an event sequence;
  • hierarchy, ownership, nesting, or layout;
  • a bug or interaction whose relationships are difficult to explain linearly.

Prefer the smallest useful visual: a table for mappings or comparisons, a flow or timeline for sequence or change, a tree for hierarchy or branching, and a wireframe for layout.

Usually skip visuals for single facts, one-step actions, simple edits, basic instructions, or information already clear in a short paragraph or list. Compact notation and small examples do not count as visualizations.

Rules for getting work done

  • When you search for text or files, you reach first for rg or rg --files; they are much faster than alternatives like grep. If rg is unavailable, you use the next best tool without fuss.
  • When possible, prefer parallelization over sequential tool calls, as this will help with round-trip latency and let you get work done faster.
  • Do not chain shell commands with separators like echo "===="; or printf '---'; the output becomes noisy in a way that makes the user's side of the conversation worse.
  • Exercise caution when escaping text for exec_command calls - backticks and $() passed to the cmd argument will still execute. DO NOT use escape sequences that risk accidental exposure of sensitive data in tool call outputs.
  • Avoid performing blocking sleep or wait calls longer than 60 seconds, as they may prevent you from communicating with the user for their duration.
  • When declaring env vars or script variables, always avoid common system options. Never repurpose $HOME, $home, or $CODEX_HOME. Instead, use a task-specific variable name.

File editing constraints

Use apply_patch for local file edits. Do not create or edit files with cat or other shell write tricks. Formatting commands and bulk mechanical rewrites do not need apply_patch. Do not use Python to read or write files when a simple shell command or apply_patch is enough.

You may find yourself working in a dirty worktree. Existing or new changes belong to the user unless you know otherwise, so you preserve them, ignore unrelated edits, and work carefully with anything that overlaps your task. If you cannot work around them you escalate to the user.

Never use destructive commands like git reset --hard or git checkout -- unless the user has clearly asked for that operation. If the request is ambiguous, ask for approval first. You prefer non-interactive git commands.

Autonomy and persistence

Adapt accordingly based on the user’s request type. When asked to:

  • Answer, explain, review, or report status: inspect the task and provide an evidence-backed response. These user requests do not authorize external writes, messages, PR changes, or other expansive mutations unless the user also asks for a change. Reversible, non-mutating diagnostic checks are allowed when they are relevant.
  • Diagnose: determine the cause and explain it. Do not implement the fix unless the user asks for a fix or the request otherwise clearly includes implementation.
  • Change or build: implement the requested change, verify it in proportion to risk, and hand off the completed result while a safe, relevant next step remains.
  • Monitor or wait: use the recurring-monitoring or wait mechanism provided by the product. Unchanged external state is expected and is not by itself a blocker.

You avoid inferring authorization for a materially different action to the user’s request. Bias towards taking action in the following circumstances: a) the action is read-only, doesn’t change state, or impacts only the systems, data, and people the user placed in scope. b) the action is a normal implementation step within the requested workflow. You do not need to ask for clarification from the user if your action is scoped within the user’s task and does not cause significant external state change (e.g. tool calls to external applications).

A terminal condition such as “finish,” “babysit,” or “do not stop” requires persistence toward the outcome, but does not broaden the set of authorized actions. When blocked, exhaust safe in-scope checks and alternatives.

You make informed assumptions that help you make progress towards the user’s task, as long as they don’t result in divergence from the user’s intent and the scope of the task. If an assumption would cause the task or current course of action to change beyond what was specified by the user, make sure to flag the available context, the assumption made, and the reasons for doing so explicitly to the user.

When presented with clarifying questions or objections from the user, lead with concrete evidence and diligent reasoning rather than unsubstantiated deference. You communicate your reasoning explicitly and concretely, so decisions and tradeoffs are easy for the user to evaluate upfront.

If completion requires new authority, external coordination, or a meaningful expansion beyond the user’s implied intent and task scope (e.g. a missing user choice that would materially change the result), stop the current turn, report the blocker, and request direction from the user rather than assuming permission.

Destructive actions

Be cautious with commands or API calls that can delete, overwrite, or otherwise make data difficult to recover.

Before taking a destructive action:

  • Make sure the action is clearly within the user's request.
  • Resolve the exact targets with read-only checks when necessary.
  • Do not use $HOME, ~, /, a workspace root, or another broad directory as the target of a recursive or destructive command.
  • When creating temporary directories, prefer using mktemp -d, or New-Item in Powershell.
  • When declaring env vars or script variables, always avoid common system options. Never repurpose $HOME, $home, or $CODEX_HOME. Instead, use a task-specific variable name.
  • When possible, avoid relying on unresolved environment variables, globs, or command substitutions to identify destructive targets. Use explicit, validated paths.
  • Prefer recoverable operations, such as moving files to trash, when practical.
  • If the target or scope is unclear, stop and ask the user.

Never run commands such as rm -rf $HOME or equivalent operations that could erase a home directory, repository, workspace, or other broad collection of user data.

After deleting anything material, briefly tell the user what was removed and whether it can be recovered.

Using skills

A skill is a set of instructions provided through a SKILL.md source. The skills available to you will be listed in the “## Skills” section under “### Available skills”.

How to use skills
  • Discovery: When a ## Skills section is present, it lists the skills available in the current session. Each entry includes a name, description, and location for its SKILL.md. The location may be an absolute filesystem path, a short aliased path, or a non-filesystem reference that must be read using its indicated tool or provider. When short aliased paths are used, the available-skills catalog also provides a mapping from aliases such as r0 to their filesystem roots. Expand the alias before accessing the skill.
  • Trigger rules: If the user names an available skill (with $SkillName or plain text) OR the task clearly matches an available skill's description, you must use that skill for that turn. Multiple mentions mean use them all. Do not carry skills across turns unless re-mentioned.
  • Missing/blocked: If a named skill is not available or its SKILL.md cannot be read, say so briefly and continue with the best fallback.
  • How to use a skill: 1) After deciding to use a skill, the main agent must read its SKILL.md completely before taking task actions. If its location is a short aliased path, expand the matching root alias first from ### Skill roots, then open and read its SKILL.md completely before taking task actions. For a filesystem path, open the file. For an environment-owned file, use the filesystem of the owning environment. For an orchestrator reference, call skills.list with {"authority":{"kind":"orchestrator"}}, select the matching package, and pass its main_resource to skills.read. For another non-filesystem reference, use its indicated tool or provider. If a read is truncated or paginated, continue until EOF. 2) When SKILL.md references another file or resource, use the same access mechanism. Resolve relative paths against the directory containing a filesystem-backed SKILL.md. For orchestrator skills, pass the exact referenced resource identifier with the same authority and package to skills.read; do not treat skill:// identifiers as filesystem paths. 3) If SKILL.md points to extra folders such as references/, use its routing instructions to identify what is required for the task. The main agent must read each required instruction or reference itself before acting on it. Do not delegate reading, summarizing, or interpreting skill instructions to a subagent. Subagents may still perform task work when the selected skill allows it. 4) For filesystem-backed skills (or if scripts/ exist), prefer running or patching provided scripts instead of retyping large code blocks. For orchestrator skills, use skills.read and the available tools; do not invent a local path. 5) Reuse provided assets or templates through the same access mechanism instead of recreating them (including if assets/ or templates exist).
  • Coordination and sequencing:
  • If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them.
  • Announce which skills you're using and why. If you skip an obvious skill, say why.
  • Context hygiene:
  • Progressive disclosure applies to selecting relevant resources, not partially reading a selected instruction file. Do not load unrelated references, scripts, or assets.
  • Avoid deep reference-chasing: prefer files or resources directly linked from SKILL.md unless blocked.
  • When variants exist, select only the relevant references and note the choice.
  • Safety and fallback: If a skill cannot be applied cleanly, state the issue, choose the best alternative, and continue.

When the user names a skill in their request, you must add the usage of that skill to your current working plan and use it faithfully. The user's instructions should take precedence over guidelines provided in a skill.

Explicitly tell the user in the commentary channel whenever a skill causes you to take an action or pause your work.

When using a skill the user did not explicitly name, follow this procedure:

  • First, tell the user in the commentary channel why you are using the skill.
  • Then, use the skill as long as it stays within the scope of the task.
  • Next, if using the skill resulted in material changes (especially when this requires non-trivial judgment), mention how it influenced your work (but only in the final response).

If a skill causes the current turn to pause or otherwise blocks the continuation of the task, cite the skill and provide a concise explanation to the user in your final response. Do not cite skills you merely inspected.

02

Developer instructions

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Developer instructions

<skills_instructions>

Skills

A skill is a set of local instructions to follow that is stored in a SKILL.md file. Below is the list of skills that can be used. Each entry includes a name, description, and a short path that can be expanded into an absolute path using the skill roots table.

Skill roots
  • r0 = /tmp/codex-home/skills/.system
Available skills
  • imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: r0/imagegen/SKILL.md)
  • openai-docs: Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex. (file: r0/openai-docs/SKILL.md)
  • plugin-creator: Create and scaffold plugin directories for Codex with a required .codex-plugin/plugin.json, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow. (file: r0/plugin-creator/SKILL.md)
  • skill-creator: Create or update a Codex skill with appropriately scoped instructions and any needed supporting resources. (file: r0/skill-creator/SKILL.md)
  • skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: r0/skill-installer/SKILL.md) </skills_instructions>

<permissions instructions> Filesystem sandboxing defines which files can be read or written. sandbox_mode is danger-full-access: No filesystem sandboxing - all commands are permitted. Network access is enabled. Approval policy is currently never. Do not provide the sandbox_permissions for any reason, commands will be rejected. </permissions instructions>

<apps_instructions>

Apps (Connectors)

Apps (Connectors) can be explicitly triggered in user messages in the format [$app-name](app://{connector_id}). Apps can also be implicitly triggered as long as the context suggests usage of available apps. An app is equivalent to a set of MCP tools within the codex_apps MCP. An installed app's MCP tools are either provided to you already, or can be lazy-loaded through the tool_search tool. If tool_search is available, the apps that are searchable by tools_search will be listed by it. Do not additionally call list_mcp_resources or list_mcp_resource_templates for apps. </apps_instructions>

03

Developer instructions

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Developer instructions

You are /root, the primary agent in a team of agents collaborating to fulfill the user's goals.

At the start of your turn, you are the active agent. You can spawn sub-agents to handle subtasks, and those sub-agents can spawn their own sub-agents. All agents in the team, including the agents that you can assign tasks to, are equally intelligent and capable, and have access to the same set of tools.

You can use spawn_agent to create a new agent, followup_task to give an existing agent a new task and trigger a turn, and send_message to pass a message to a running agent without triggering a turn. Child agents can also spawn their own sub-agents. You can decide how much context you want to propagate to your sub-agents with the fork_turns parameter.

You will receive messages in the analysis channel in the form:

Message Type: MESSAGE | FINAL_ANSWER
Task name: <recipient>
Sender: <author>
Payload:
<payload text>

They may be addressed as to=/root

Note that collaboration tools cannot be called from inside functions.exec. Call spawn_agent, send_message, followup_task, wait_agent, interrupt_agent, and list_agents only as direct tool calls using the recipient shown in their tool definitions, such as to=functions.collaboration.spawn_agent, since they are intentionally absent from the functions.exec tools.* namespace. Available tools in functions.exec are explicitly described with a tools namespace in the developer message.

All agents share the same directory. In detail:

  • All agents have access to the same container and filesystem as you.
  • All agents use the same current working directory.
  • As a result, edits made by one agent are immediately visible to all other agents.

When calling wait_agent, prefer longer waits (minutes) to avoid busy polling.

There are 4 available concurrency slots, meaning that up to 4 agents can be active at once, including you.

Full-history forks (fork_turns omitted or "all") inherit the parent model and reasoning effort and do not accept overrides. Only set model or reasoning_effort when explicitly requested by the user, applicable AGENTS.md instructions, or skill instructions; when doing so, set fork_turns to "none" or a positive integer string.

04

Developer instructions

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Developer instructions

<multi_agent_mode>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.</multi_agent_mode>

05

User context

+00:01
User context

<recommended_plugins> Here is a list of plugins that are available but not installed.

  • Dropbox (app-69b31dc2110c8191b8b47dc98fe5a052@openai-curated-remote)
  • Box (box@openai-curated-remote)
  • Codex Security (codex-security@openai-curated-remote)
  • Figma (figma@openai-curated-remote)
  • GitHub (github@openai-curated-remote)
  • Gmail (gmail@openai-curated-remote)
  • Google Calendar (google-calendar@openai-curated-remote)
  • Google Drive (google-drive@openai-curated-remote)
  • Linear (linear@openai-curated-remote)
  • Notion (notion@openai-curated-remote)
  • OpenAI Developers (openai-developers@openai-curated-remote)
  • Outlook Calendar (outlook-calendar@openai-curated-remote)
  • Outlook Email (outlook-email@openai-curated-remote)
  • SharePoint (sharepoint@openai-curated-remote)
  • Slack (slack@openai-curated-remote)
  • Teams (teams@openai-curated-remote) </recommended_plugins>

<environment_context> <cwd>/app</cwd> <shell>sh</shell> <current_date>2026-09-25</current_date> <timezone>Etc/UTC</timezone> <filesystem><workspace_roots><root>/app</root></workspace_roots><permission_profile type="disabled"><file_system type="unrestricted" /></permission_profile></filesystem> </environment_context>

Harness context world state
{
  "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": "<filesystem><workspace_roots><root>/app</root></workspace_roots><permission_profile type=\"disabled\"><file_system type=\"unrestricted\" /></permission_profile></filesystem>"
    },
    "environments_instructions": false,
    "git_attribution": false,
    "host_skills": {
      "body": "\n## Skills\nA skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and a short path that can be expanded into an absolute path using the skill roots table.\n### Skill roots\n- `r0` = `/tmp/codex-home/skills/.system`\n### Available skills\n- imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: r0/imagegen/SKILL.md)\n- openai-docs: Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex. (file: r0/openai-docs/SKILL.md)\n- plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow. (file: r0/plugin-creator/SKILL.md)\n- skill-creator: Create or update a Codex skill with appropriately scoped instructions and any needed supporting resources. (file: r0/skill-creator/SKILL.md)\n- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: r0/skill-installer/SKILL.md)\n",
      "includeInstructions": true
    },
    "managed_developer_instructions": {},
    "model": "gpt-5.6-sol",
    "multi_agent_mode": {
      "mode": "explicitRequestOnly",
      "usage_hint_hash": "0e2a3174d5f40de49ed383507497d69bbff42c5d"
    },
    "multi_agent_usage_hint": "0e2a3174d5f40de49ed383507497d69bbff42c5d",
    "orchestrator_skills": {
      "includeInstructions": true,
      "enabled": false
    },
    "permissions": {
      "instructions": "32ca25d6d85edaeb6f1e19b2a5cc4e08d2a631cd",
      "approved_command_prefixes": []
    },
    "persistent_mode": {},
    "personality": {
      "model": "gpt-5.6-sol",
      "personality": "pragmatic"
    },
    "plugins_instructions": false,
    "realtime": {
      "active": false
    },
    "skills": {
      "includeInstructions": true
    }
  }
}
Harness context turn context
{
  "type": "turn_context",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "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"
}
06

Task instructions

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Task instructions

Build a music-discovery ranker

Build the strongest ranking model you can for the "Made for You" shelf of a music streaming service. Per-user candidate eligibility, cutoff-specific explicit preference state, and a temporal backtest are provided. Candidate eligibility is platform-owned; all modeling within that candidate boundary is in scope.

Data

All inputs are under /task/data/. Training data ends at timestamp 24786800. A 30-minute gap precedes a seven-day public validation window. The hidden test is the following seven-day window after another 30-minute gap.

For each event family there is a train_*.parquet and val_*.parquet file:

FamilyMeaningColumns
listensPlayback eventsuid, timestamp, item_id, is_organic, played_ratio_pct, track_length_seconds
likesExplicit positive actionsuid, timestamp, item_id, is_organic
dislikesExplicit negative actionsuid, timestamp, item_id, is_organic
unlikesReversal of a prior likeuid, timestamp, item_id, is_organic
undislikesReversal of a prior dislikeuid, timestamp, item_id, is_organic

is_organic=1 means the listener sought out the track; 0 means the action came through a recommendation surface. A listen is a conventional positive at 50% completion. The discovery-quality objective below uses the stronger 80% threshold.

The two public windows have different roles:

  1. Build and tune a validation ranker using only train_* as history and val_* as subsequent outcomes.
  2. After selecting the approach, refit it on train_* + val_*. At hidden-test time the validation events are observed history, not recommendable outcomes.

Do not use val_* events as features when measuring a validation ranking. They may and should be used when refitting the final hidden-test ranker.

Additional files under /task/data/:

  • candidate_catalog.parquet: the 30,000 retrieval-catalog tracks, ordered by training positive-listen popularity.
  • target_users.parquet: users with at least 20 positive listens in the 90 days before the training cutoff, selected without future labels.
  • validation_preference_state.parquet: active explicit state at the training cutoff, for validation ranking.
  • test_preference_state.parquet: active explicit state after validation, for the final hidden-test ranking. State is liked or disliked; a pair may have both active channels.
  • validation_eligible_candidates.npz and test_eligible_candidates.npz: cutoff-correct candidate sets for every target user. Each contains aligned arrays uids, item_ids, and packed_eligible. For user row r, recover the Boolean item mask with np.unpackbits(packed_eligible[r], count=len(item_ids), bitorder="little").

The exact available Python distribution inventory is listed in /task/requirements.txt. Future test interactions are hidden and used only by the separate Harbor verifier.

Discovery objective

Rank only the candidates returned for the relevant cutoff. Relevance is based on organic behavior in the immediately following window:

  • grade 3: the user organically likes the track;
  • grade 1: the user organically listens to at least 80% of the track;
  • grade 0: otherwise.

If both positive events occur, grade 3 wins. Ranking quality is measured with graded NDCG@10. DCG uses gain 2^grade - 1 and discount 1/log2(rank + 1), then averages per-user NDCG over users with at least one relevant item.

The public evaluator reports this objective for development, but its score is not a separate acceptance stage. Final acceptance is determined on the hidden test using the same objective.

The Yambda logs contain interactions, not impression propensities. Treat the organic objective as a robust offline target, not as a causal estimate of online lift.

Public tools

/task/evaluate_public.py scores a validation-stage ranking against the public window. Pass the path to a ranking with the same schema as the final deliverable:

python /task/evaluate_public.py solution/validation.parquet

Stage-independent evaluation helpers are available in /task/ranking_evaluation.py. The builder installs the same checked-in module in the public agent image and private verifier image so scoring mechanics cannot drift. The public copy contains no hidden labels, evaluator-owned data paths, or pass thresholds.

The public evaluator uses validation_eligible_candidates.npz; it is not a validator for the final artifact. No model, fitted representation, example submission, or modeling architecture is supplied. Build and tune the best model you can, then refit the selected approach as described above.

Deliverable

Write /app/solution/submission.parquet or /app/solution/submission.csv, with exactly 100 rows for every target user and columns uid, item_id, rank. Ranks must be the integers 1-100 with no ties or duplicate items. Every submitted item must be present in that user's test_eligible_candidates.npz candidate set.

Leave the code that builds the artifact and a concise /app/solution/README.md in /app/solution/. Keep the best valid submission saved throughout the attempt so it survives the deadline.

Constraints

  • Write deliverables and intermediate artifacts only under /app/solution/.
  • Treat /task/ and the installed Python environment as read-only.
  • Do not invoke nested agents or attempt to access verifier files, credentials, other trials, or host paths.
  • Do not install packages or use network access except for the model provider connection managed by Harbor.
  • You have up to 30 minutes. Improve the model as much as possible within that limit. Structural validity alone is not sufficient; the final submission is also evaluated for ranking quality.
07

exec command

+00:04
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Model text

I’ll first profile the event volumes, candidate coverage, and validation label sparsity, then build a leakage-safe validation pipeline. I’ll keep a valid popularity-based submission saved early, and iterate only when public NDCG improves.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"ls -lh /task/data && sed -n '1,240p' /task/ranking_evaluation.py && sed -n '1,200p' /task/evaluate_public.py && sed -n '1,200p' /task/requirements.txt","workdir":"/app","yield_time_ms":10000,"max_output_tokens":30000});
text(r.output);

Tool response 

Script completed
Wall time 0.1 seconds
Output:

total 340M
-r--r--r-- 1 root root 342K Sep 24 23:59 candidate_catalog.parquet
-r--r--r-- 1 root root  83K Sep 24 23:59 target_users.parquet
-r--r--r-- 1 root root 7.4M Sep 24 23:59 test_eligible_candidates.npz
-r--r--r-- 1 root root 1.1M Sep 25 00:00 test_preference_state.parquet
-r--r--r-- 1 root root 838K Sep 24 23:59 train_dislikes.parquet
-r--r--r-- 1 root root 5.2M Sep 24 23:59 train_likes.parquet
-r--r--r-- 1 root root 305M Sep 24 23:59 train_listens.parquet
-r--r--r-- 1 root root 153K Sep 24 23:59 train_undislikes.parquet
-r--r--r-- 1 root root 1.8M Sep 24 23:59 train_unlikes.parquet
-r--r--r-- 1 root root  36K Sep 24 23:59 val_dislikes.parquet
-r--r--r-- 1 root root 292K Sep 24 23:59 val_likes.parquet
-r--r--r-- 1 root root 9.5M Sep 24 23:59 val_listens.parquet
-r--r--r-- 1 root root 7.7K Sep 24 23:59 val_undislikes.parquet
-r--r--r-- 1 root root  71K Sep 24 23:59 val_unlikes.parquet
-r--r--r-- 1 root root 7.3M Sep 24 23:59 validation_eligible_candidates.npz
-r--r--r-- 1 root root 1.1M Sep 25 00:00 validation_preference_state.parquet
"""Shared public primitives for Yambda discovery-ranking evaluation.

Both the solver-visible validation evaluator and the private submission judge
import this module from the generated task's ``public`` directory. It owns the
stage-independent contract: reading a ranking, validating its shape, converting
it to per-user rank order, looking up packed candidate eligibility, and
computing graded NDCG@10.

The module deliberately contains no event-derived labels, private artifact
paths, or pass thresholds. Each evaluator supplies its own relevance data and
per-cutoff eligibility checks around these common operations.
"""
from __future__ import annotations

from pathlib import Path

import numpy as np
import pandas as pd


__all__ = [
    "CandidateSet",
    "QUALITY_K",
    "RankingValidationError",
    "REQUIRED_COLUMNS",
    "TOP_N",
    "ndcg_at_k",
    "ranked_items",
    "read_ranking",
    "score_rankings",
    "validate_ranking",
]

TOP_N = 100
QUALITY_K = 10
REQUIRED_COLUMNS = ("uid", "item_id", "rank")


class RankingValidationError(ValueError):
    """Report a ranking-schema or structural-contract violation."""


class CandidateSet:
    """Provide membership lookups over one packed candidate-set archive.

    The archive contains aligned ``uids``, ``item_ids``, and
    ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit
    ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are
    copied into memory so they remain valid after the archive is closed.
    """

    def __init__(self, path: Path):
        """Load an eligibility archive and build ID-to-position indexes.

        Args:
            path: Path to a ``.npz`` archive with the candidate-set schema.
        """
        with np.load(path, allow_pickle=False) as artifact:
            self.uids = artifact["uids"].astype(np.int64, copy=True)
            self.item_ids = artifact["item_ids"].astype(np.int64, copy=True)
            self.packed = artifact["packed_eligible"].astype(np.uint8, copy=True)
        self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}
        self.item_column = {
            int(item): column for column, item in enumerate(self.item_ids)
        }

    def is_eligible(self, uid: int, item_id: int) -> bool:
        """Return whether ``item_id`` is eligible for ``uid``.

        IDs absent from the target-user or candidate-item arrays are treated as
        ineligible rather than raising an exception.

        Args:
            uid: Target user ID to query.
            item_id: Candidate item ID to query.

        Returns:
            ``True`` only when the corresponding packed eligibility bit is set.
        """
        row = self.uid_row.get(int(uid))
        column = self.item_column.get(int(item_id))
        if row is None or column is None:
            return False
        byte, bit = divmod(column, 8)
        return bool((int(self.packed[row, byte]) >> bit) & 1)


def read_ranking(path: Path) -> pd.DataFrame:
    """Read a parquet or CSV ranking into a pandas frame.

    Args:
        path: Input path. A ``.parquet`` suffix selects parquet; every other
            suffix is interpreted as CSV.

    Returns:
        The ranking frame as loaded by pandas.
    """
    return pd.read_parquet(path) if path.suffix == ".parquet" else pd.read_csv(path)


def validate_ranking(
    frame: pd.DataFrame,
    targets: set[int],
) -> pd.DataFrame:
    """Normalize and validate the stage-independent ranking contract.

    Required values must be finite integers. The user set must exactly match
    ``targets``; every user must have 100 rows, distinct items, and each rank
    from 1 through 100 exactly once. Cutoff-specific candidate eligibility is
    intentionally left to the calling evaluator.

    Args:
        frame: Raw ranking frame. Extra columns are discarded from an internal
            copy; the caller's frame is not modified.
        targets: Exact set of target user IDs the ranking must cover.

    Returns:
        A normalized frame containing only the required ``int64`` columns.

    Raises:
        RankingValidationError: If the ranking violates the shared contract.
    """
    required = list(REQUIRED_COLUMNS)
    if not all(column in frame for column in required):
        raise RankingValidationError(f"submission requires columns {required}")

    normalized = frame[required].copy()
    for column in required:
        numeric = pd.to_numeric(normalized[column], errors="coerce")
        values = numeric.to_numpy(dtype=float)
        if numeric.isna().any() or not np.isfinite(values).all():
            raise RankingValidationError(
                f"submission {column} must be finite numeric values"
            )
        if not np.equal(numeric, np.floor(numeric)).all():
            raise RankingValidationError(
                f"submission {column} must contain integers"
            )
        normalized[column] = numeric.astype(np.int64)

    submitted_users = set(map(int, normalized.uid.unique()))
    if submitted_users != targets:
        raise RankingValidationError(
            f"submission user set mismatch: missing={len(targets - submitted_users)} "
            f"extra={len(submitted_users - targets)}"
        )
    counts = normalized.groupby("uid").size()
    if not (counts == TOP_N).all():
        raise RankingValidationError(
            f"submission must contain exactly {TOP_N} rows for every target user"
        )
    if normalized.duplicated(["uid", "item_id"]).any():
        raise RankingValidationError(
            "submission contains duplicate (uid, item_id) pairs"
        )
    valid_ranks = set(range(1, TOP_N + 1))
    ranks_are_valid = normalized.groupby("uid")["rank"].apply(
        lambda values: set(map(int, values)) == valid_ranks
    )
    if not ranks_are_valid.all():
        raise RankingValidationError(
            f"each user must have every rank from 1 through {TOP_N} exactly once"
        )
    return normalized


def ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:
    """Convert a validated ranking frame to ordered items keyed by user.

    Args:
        frame: Frame that has passed :func:`validate_ranking`.

    Returns:
        A ``uid -> item IDs`` mapping ordered by ascending rank.
    """
    ordered = frame.sort_values(["uid", "rank"])
    return {
        int(uid): list(map(int, values))
        for uid, values in ordered.groupby("uid").item_id
    }


def ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:
    """Compute graded normalized discounted cumulative gain at ``k``.

    Relevance gain is ``2**grade - 1`` and the discount at one-based rank
    ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.

    Args:
        ranked: Item IDs in predicted rank order.
        gold: Mapping from relevant item IDs to integer relevance grades.
        k: Maximum number of predicted positions to score.

    Returns:
        For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns
        ``0.0`` when the ideal gain is zero.
    """
    gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)
    discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))
    dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))
    ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)
    ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))
    idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))
    return dcg / idcg if idcg else 0.0


def score_rankings(
    ranked: dict[int, list[int]],
    graded: dict[int, dict[int, int]],
) -> dict[str, float | int]:
    """Aggregate per-user NDCG@10 over users with relevance labels.

    Args:
        ranked: Ranked item IDs for every target user.
        graded: Relevant item grades keyed by user and item.

    Returns:
        The six-decimal ``quality_ndcg@10`` value and number of scored users.
    """
    quality = float(
        np.mean(
            [
                ndcg_at_k(ranked[uid], gold, QUALITY_K)
                for uid, gold in graded.items()
            ]
        )
    )
    return {
        "quality_ndcg@10": round(quality, 6),
        "quality_users": len(graded),
    }
#!/usr/bin/env python3
"""Score a validation-stage ranking against public discovery labels.

``build_task.py`` publishes this module with the solver-visible task files. It
loads target users, validation eligibility, and validation-window events from
the adjacent ``data`` directory, then evaluates one ranking supplied on the
command line. Stage-independent mechanics come from the adjacent public
``ranking_evaluation.py`` module. The builder installs the same checked-in
module in the private judge, so public and private scoring mechanics cannot
drift. A relevant candidate receives grade 3 for an organic like or grade 1 for
an organic listen with at least 80 percent completion; a like takes precedence
when both events exist.

Before scoring, the ranking must contain exactly 100 distinct eligible items
for every target user, with integer-valued ``uid``, ``item_id``, and ``rank``
columns and ranks 1 through 100 exactly once per user. Extra columns are
ignored. Quality is graded NDCG@10, averaged only over users who have at least
one eligible relevant item in the validation window.

The evaluator prints a JSON object containing the model path and scores. It
does not apply the task's pass threshold; its purpose is model development and
selection. The private judge applies the threshold to the final submission.

Usage::

    python evaluate_public.py <validation-ranking.parquet|csv>
"""
from __future__ import annotations

import json
import sys
from pathlib import Path

import pandas as pd

from ranking_evaluation import (
    CandidateSet,
    RankingValidationError,
    ranked_items,
    read_ranking,
    score_rankings,
    validate_ranking,
)


HERE = Path(__file__).resolve().parent
DATA = HERE / "data"
LISTEN_STRONG = 80


def validation_truth(
    eligibility: CandidateSet, targets: set[int]
) -> dict[int, dict[int, int]]:
    """Build cutoff-correct graded relevance from public validation events.

    Only target-user events for eligible candidates are retained. Organic
    listens completed to at least 80 percent receive grade 1, and organic likes
    receive grade 3. Repeated user-item events are collapsed, and processing
    likes second ensures that grade 3 wins over grade 1.

    Args:
        eligibility: Validation-cutoff candidate membership service.
        targets: Complete set of target user IDs.

    Returns:
        A nested ``uid -> item_id -> grade`` mapping. Users without an eligible
        relevant event are omitted.
    """
    listens = pd.read_parquet(DATA / "val_listens.parquet")
    likes = pd.read_parquet(DATA / "val_likes.parquet")
    strong = listens[
        listens.uid.isin(targets)
        & (listens.is_organic == 1)
        & (listens.played_ratio_pct >= LISTEN_STRONG)
    ][["uid", "item_id"]].drop_duplicates()
    organic_likes = likes[
        likes.uid.isin(targets) & (likes.is_organic == 1)
    ][["uid", "item_id"]].drop_duplicates()

    graded: dict[int, dict[int, int]] = {}
    for raw_uid, raw_item in strong.itertuples(index=False, name=None):
        uid, item = int(raw_uid), int(raw_item)
        if eligibility.is_eligible(uid, item):
            graded.setdefault(uid, {})[item] = 1
    for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):
        uid, item = int(raw_uid), int(raw_item)
        if eligibility.is_eligible(uid, item):
            graded.setdefault(uid, {})[item] = 3
    return graded


def load_submission(
    path: Path, eligibility: CandidateSet, targets: set[int]
) -> dict[int, list[int]]:
    """Load and validate a public validation ranking.

    Files ending in ``.parquet`` are read as parquet; every other suffix is
    read as CSV. The required columns must be integer-valued, cover exactly the
    target-user set, contain 100 rows and ranks 1 through 100 per user, avoid
    duplicate user-item pairs, and contain only cutoff-eligible items.

    Args:
        path: Ranking file to read.
        eligibility: Validation-cutoff candidate membership service.
        targets: Exact set of users the ranking must cover.

    Returns:
        A ``uid -> ranked item IDs`` mapping ordered by ascending rank.

    Raises:
        ValueError: If the ranking violates the required schema or invariants.
    """
    frame = validate_ranking(read_ranking(path), targets)
    if not all(
        eligibility.is_eligible(uid, item)
        for uid, item in frame[["uid", "item_id"]].itertuples(index=False, name=None)
    ):
        raise RankingValidationError(
            "submission contains an item rejected by eligibility service"
        )
    return ranked_items(frame)


def score(
    ranked: dict[int, list[int]],
    graded: dict[int, dict[int, int]],
) -> dict[str, float | int]:
    """Aggregate per-user NDCG@10 over users with public relevance labels.

    Args:
        ranked: Ranked item IDs for every target user.
        graded: Eligible validation relevance keyed by user and item.

    Returns:
        The six-decimal ``quality_ndcg@10`` value and number of scored users.
    """
    return score_rankings(ranked, graded)


def main() -> None:
    """Run the public evaluator CLI and print its JSON score report."""
    targets_frame = pd.read_parquet(DATA / "target_users.parquet")
    targets = set(targets_frame.uid.astype(int))
    eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz")
    graded = validation_truth(eligibility, targets)

    if len(sys.argv) != 2:
        raise SystemExit(
            "usage: evaluate_public.py <validation-ranking.parquet|csv>"
        )
    path = Path(sys.argv[1])
    ranked = load_submission(path, eligibility, targets)
    print(json.dumps({"model": str(path), "scores": score(ranked, graded)}, indent=2))


if __name__ == "__main__":
    main()
# Exact third-party distribution inventory for yambda-discovery-ranking-v01.
#
# This is an input to `uv pip sync`, not a list of minimum requirements. A
# conforming task environment contains exactly these third-party Python
# distributions and no others. The same inventory is used for source preparation,
# task building, reference scoring, judging, and solver attempts. Python itself
# is pinned by task.toml and both Harbor image Dockerfiles.
attrs==26.1.0
bleach==6.4.0
certifi==2026.7.22
chardet==7.6.0
charset-normalizer==3.5.1
fastjsonschema==2.22.2
idna==3.18
joblib==1.5.3
jsonschema==4.26.0
jsonschema-specifications==2025.9.1
jupyter-core==5.9.1
jupytext==1.19.5
kaggle==2.2.4
kagglesdk==0.1.37
markdown-it-py==4.2.0
mdit-py-plugins==0.6.1
mdurl==0.1.2
nbformat==5.11.1
numpy==2.2.6
packaging==26.3
pandas==2.3.3
pillow==12.3.0
pip==26.0.1
platformdirs==4.11.3
protobuf==7.35.1
pyarrow==18.1.0
python-dateutil==2.9.0.post0
python-dotenv==1.2.3
python-slugify==8.0.4
pytz==2026.3.post1
PyYAML==6.0.3
referencing==0.37.0
reportlab==4.2.5
requests==2.34.2
rpds-py==2026.6.3
scikit-learn==1.7.2
scipy==1.15.3
six==1.17.0
text-unidecode==1.3
threadpoolctl==3.6.0
tqdm==4.70.0
traitlets==5.16.1
typing-extensions==4.16.0
tzdata==2026.3
urllib3==2.7.0
webencodings==0.6.1
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Harness context codex item completed
{
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    "stdout": "total 340M\n-r--r--r-- 1 root root 342K Sep 24 23:59 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 24 23:59 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 24 23:59 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:00 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 24 23:59 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 24 23:59 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 24 23:59 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 24 23:59 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 24 23:59 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 24 23:59 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 24 23:59 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 24 23:59 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 24 23:59 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 24 23:59 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 24 23:59 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:00 validation_preference_state.parquet\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#!/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 <validation-ranking.parquet|csv>\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 <validation-ranking.parquet|csv>\"\n        )\n    path = Path(sys.argv[1])\n    ranked = load_submission(path, eligibility, targets)\n    print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n    main()\n# Exact third-party distribution inventory for yambda-discovery-ranking-v01.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nidna==3.18\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n",
    "stderr": "",
    "aggregated_output": "total 340M\n-r--r--r-- 1 root root 342K Sep 24 23:59 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 24 23:59 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 24 23:59 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:00 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 24 23:59 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 24 23:59 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 24 23:59 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 24 23:59 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 24 23:59 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 24 23:59 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 24 23:59 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 24 23:59 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 24 23:59 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 24 23:59 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 24 23:59 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:00 validation_preference_state.parquet\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#!/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 <validation-ranking.parquet|csv>\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 <validation-ranking.parquet|csv>\"\n        )\n    path = Path(sys.argv[1])\n    ranked = load_submission(path, eligibility, targets)\n    print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n    main()\n# Exact third-party distribution inventory for yambda-discovery-ranking-v01.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nidna==3.18\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3650
    },
    "formatted_output": "total 340M\n-r--r--r-- 1 root root 342K Sep 24 23:59 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 24 23:59 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 24 23:59 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:00 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 24 23:59 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 24 23:59 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 24 23:59 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 24 23:59 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 24 23:59 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 24 23:59 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 24 23:59 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 24 23:59 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 24 23:59 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 24 23:59 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 24 23:59 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:00 validation_preference_state.parquet\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#!/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 <validation-ranking.parquet|csv>\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 <validation-ranking.parquet|csv>\"\n        )\n    path = Path(sys.argv[1])\n    ranked = load_submission(path, eligibility, targets)\n    print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n    main()\n# 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"
  }
}
08

exec command

+00:10
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"python - <<'PY'\nimport pandas as pd, pyarrow.parquet as pq, numpy as np, os\nfrom pathlib import Path\np=Path('/task/data')\nfor f in sorted(p.glob('*.parquet')):\n m=pq.ParquetFile(f).metadata\n print(f.name, m.num_rows, m.num_columns, m.num_row_groups)\n if f.name in ['target_users.parquet','candidate_catalog.parquet','validation_preference_state.parquet','test_preference_state.parquet']:\n  d=pd.read_parquet(f)\n  print(d.head(), d.dtypes.to_dict(), {c:(d[c].min(),d[c].max(),d[c].nunique()) for c in d.columns})\nfor name in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n z=np.load(p/name)\n print(name,{k:(z[k].shape,z[k].dtype) for k in z.files})\n counts=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(1)\n print('elig', np.percentile(counts,[0,1,10,50,90,99,100]), 'mean',counts.mean(), 'uids matches', np.array_equal(z['uids'],pd.read_parquet(p/'target_users.parquet').uid.values))\nPY","workdir":"/app","yield_time_ms":10000,"max_output_tokens":20000});
text(r.output);

Tool response 

Script completed
Wall time 0.0 seconds
Output:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ModuleNotFoundError: No module named 'pandas'
Harness context token usage record
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  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bb19608087d296a773947c83303d",
  "usage": {
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  "turn_token_usage": {
    "input_tokens": 33110,
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  },
  "thread_token_usage": {
    "input_tokens": 33110,
    "cached_input_tokens": 18688,
    "cache_write_input_tokens": 0,
    "output_tokens": 626,
    "reasoning_output_tokens": 129,
    "total_tokens": 33736
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-25b340a2-d392-4559-877e-e7235dd24545",
    "process_id": "11439",
    "command": [
      "/bin/sh",
      "-lc",
      "python - <<'PY'\nimport pandas as pd, pyarrow.parquet as pq, numpy as np, os\nfrom pathlib import Path\np=Path('/task/data')\nfor f in sorted(p.glob('*.parquet')):\n m=pq.ParquetFile(f).metadata\n print(f.name, m.num_rows, m.num_columns, m.num_row_groups)\n if f.name in ['target_users.parquet','candidate_catalog.parquet','validation_preference_state.parquet','test_preference_state.parquet']:\n  d=pd.read_parquet(f)\n  print(d.head(), d.dtypes.to_dict(), {c:(d[c].min(),d[c].max(),d[c].nunique()) for c in d.columns})\nfor name in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n z=np.load(p/name)\n print(name,{k:(z[k].shape,z[k].dtype) for k in z.files})\n counts=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(1)\n print('elig', np.percentile(counts,[0,1,10,50,90,99,100]), 'mean',counts.mean(), 'uids matches', np.array_equal(z['uids'],pd.read_parquet(p/'target_users.parquet').uid.values))\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "python - <<'PY'\nimport pandas as pd, pyarrow.parquet as pq, numpy as np, os\nfrom pathlib import Path\np=Path('/task/data')\nfor f in sorted(p.glob('*.parquet')):\n m=pq.ParquetFile(f).metadata\n print(f.name, m.num_rows, m.num_columns, m.num_row_groups)\n if f.name in ['target_users.parquet','candidate_catalog.parquet','validation_preference_state.parquet','test_preference_state.parquet']:\n  d=pd.read_parquet(f)\n  print(d.head(), d.dtypes.to_dict(), {c:(d[c].min(),d[c].max(),d[c].nunique()) for c in d.columns})\nfor name in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n z=np.load(p/name)\n print(name,{k:(z[k].shape,z[k].dtype) for k in z.files})\n counts=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(1)\n print('elig', np.percentile(counts,[0,1,10,50,90,99,100]), 'mean',counts.mean(), 'uids matches', np.array_equal(z['uids'],pd.read_parquet(p/'target_users.parquet').uid.values))\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "failed",
    "stdout": "Traceback (most recent call last):\n  File \"<stdin>\", line 1, in <module>\nModuleNotFoundError: No module named 'pandas'\n",
    "stderr": "",
    "aggregated_output": "Traceback (most recent call last):\n  File \"<stdin>\", line 1, in <module>\nModuleNotFoundError: No module named 'pandas'\n",
    "exit_code": 1,
    "duration": {
      "secs": 0,
      "nanos": 3660
    },
    "formatted_output": "Traceback (most recent call last):\n  File \"<stdin>\", line 1, in <module>\nModuleNotFoundError: No module named 'pandas'\n"
  }
}
09

exec command

+00:21
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"which -a python python3; python --version; ls -la /task | head -30; find / -maxdepth 3 -type f -name python 2>/dev/null | head -30","workdir":"/app","yield_time_ms":10000,"max_output_tokens":10000});
text(r.output);

Tool response 

Script completed
Wall time 0.1 seconds
Output:

/usr/local/bin/python
/usr/local/bin/python3
/usr/bin/python3
/bin/python3
Python 3.12.13
total 36
dr-xr-xr-x 1 root root 4096 Sep 25 00:03 .
drwxr-xr-x 1 root root 4096 Sep 25 00:06 ..
dr-xr-xr-x 1 root root 4096 Sep 25 00:00 data
-r--r--r-- 1 root root 5774 Sep 24 23:59 evaluate_public.py
-r--r--r-- 1 root root 8265 Sep 24 23:59 ranking_evaluation.py
-r--r--r-- 1 root root 1278 Sep 24 23:59 requirements.txt
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-44d36329-3bee-4c46-b0fd-8dbb7401f4c2",
    "process_id": "22463",
    "command": [
      "/bin/sh",
      "-lc",
      "which -a python python3; python --version; ls -la /task | head -30; find / -maxdepth 3 -type f -name python 2>/dev/null | head -30"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "which -a python python3; python --version; ls -la /task | head -30; find / -maxdepth 3 -type f -name python 2>/dev/null | head -30"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "/usr/local/bin/python\n/usr/local/bin/python3\n/usr/bin/python3\n/bin/python3\nPython 3.12.13\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 25 00:03 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:06 ..\ndr-xr-xr-x 1 root root 4096 Sep 25 00:00 data\n-r--r--r-- 1 root root 5774 Sep 24 23:59 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 24 23:59 ranking_evaluation.py\n-r--r--r-- 1 root root 1278 Sep 24 23:59 requirements.txt\n",
    "stderr": "",
    "aggregated_output": "/usr/local/bin/python\n/usr/local/bin/python3\n/usr/bin/python3\n/bin/python3\nPython 3.12.13\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 25 00:03 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:06 ..\ndr-xr-xr-x 1 root root 4096 Sep 25 00:00 data\n-r--r--r-- 1 root root 5774 Sep 24 23:59 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 24 23:59 ranking_evaluation.py\n-r--r--r-- 1 root root 1278 Sep 24 23:59 requirements.txt\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3390
    },
    "formatted_output": "/usr/local/bin/python\n/usr/local/bin/python3\n/usr/bin/python3\n/bin/python3\nPython 3.12.13\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 25 00:03 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:06 ..\ndr-xr-xr-x 1 root root 4096 Sep 25 00:00 data\n-r--r--r-- 1 root root 5774 Sep 24 23:59 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 24 23:59 ranking_evaluation.py\n-r--r--r-- 1 root root 1278 Sep 24 23:59 requirements.txt\n"
  }
}
Harness context token usage record
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  }
}
10

exec command

+00:27
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"ls -la /app; env | sort | rg -i 'venv|python|path'; /usr/bin/python3 - <<'PY'\ntry: import pandas; print(pandas.__version__)\nexcept Exception as e: print(e)\nPY\nfind /opt /usr/local -maxdepth 5 -type d -name 'site-packages' 2>/dev/null","workdir":"/app","yield_time_ms":10000,"max_output_tokens":20000});
text(r.output);

Tool response 

Script completed
Wall time 0.1 seconds
Output:

total 12
drwxr-xr-x 3 agent agent 4096 Sep 25 00:03 .
drwxr-xr-x 1 root  root  4096 Sep 25 00:06 ..
drwxr-xr-x 2 agent agent 4096 Sep 25 00:03 solution
PATH=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex/node_modules/@openai/codex-linux-x64/vendor/x86_64-unknown-linux-musl/codex-path:/home/agent/.nvm/versions/node/v22.23.3/bin:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games
PYTHONDONTWRITEBYTECODE=1
PYTHONNOUSERSITE=1
PYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684
PYTHON_VERSION=3.12.13
No module named 'pandas'
/opt/task-venv/lib/python3.12/site-packages
/usr/local/lib/python3.12/site-packages
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
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  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bb2afb8887d283e9a9439a03f1da",
  "usage": {
    "input_tokens": 19544,
    "cached_input_tokens": 14208,
    "cache_write_input_tokens": 0,
    "output_tokens": 140,
    "reasoning_output_tokens": 11,
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  },
  "turn_token_usage": {
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  "thread_token_usage": {
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    "output_tokens": 884,
    "reasoning_output_tokens": 168,
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  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-544f8dba-de85-4528-be84-44541f47f146",
    "process_id": "93307",
    "command": [
      "/bin/sh",
      "-lc",
      "ls -la /app; env | sort | rg -i 'venv|python|path'; /usr/bin/python3 - <<'PY'\ntry: import pandas; print(pandas.__version__)\nexcept Exception as e: print(e)\nPY\nfind /opt /usr/local -maxdepth 5 -type d -name 'site-packages' 2>/dev/null"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "ls -la /app; env | sort | rg -i 'venv|python|path'; /usr/bin/python3 - <<'PY'\ntry: import pandas; print(pandas.__version__)\nexcept Exception as e: print(e)\nPY\nfind /opt /usr/local -maxdepth 5 -type d -name 'site-packages' 2>/dev/null"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "total 12\ndrwxr-xr-x 3 agent agent 4096 Sep 25 00:03 .\ndrwxr-xr-x 1 root  root  4096 Sep 25 00:06 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 25 00:03 solution\nPATH=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex/node_modules/@openai/codex-linux-x64/vendor/x86_64-unknown-linux-musl/codex-path:/home/agent/.nvm/versions/node/v22.23.3/bin:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\nNo module named 'pandas'\n/opt/task-venv/lib/python3.12/site-packages\n/usr/local/lib/python3.12/site-packages\n",
    "stderr": "",
    "aggregated_output": "total 12\ndrwxr-xr-x 3 agent agent 4096 Sep 25 00:03 .\ndrwxr-xr-x 1 root  root  4096 Sep 25 00:06 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 25 00:03 solution\nPATH=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex/node_modules/@openai/codex-linux-x64/vendor/x86_64-unknown-linux-musl/codex-path:/home/agent/.nvm/versions/node/v22.23.3/bin:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\nNo module named 'pandas'\n/opt/task-venv/lib/python3.12/site-packages\n/usr/local/lib/python3.12/site-packages\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 1740
    },
    "formatted_output": "total 12\ndrwxr-xr-x 3 agent agent 4096 Sep 25 00:03 .\ndrwxr-xr-x 1 root  root  4096 Sep 25 00:06 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 25 00:03 solution\nPATH=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex/node_modules/@openai/codex-linux-x64/vendor/x86_64-unknown-linux-musl/codex-path:/home/agent/.nvm/versions/node/v22.23.3/bin:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\nNo module named 'pandas'\n/opt/task-venv/lib/python3.12/site-packages\n/usr/local/lib/python3.12/site-packages\n"
  }
}
11

exec command

+00:32
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, pyarrow.parquet as pq, numpy as np\nfrom pathlib import Path\np=Path('/task/data')\nfor f in sorted(p.glob('*.parquet')):\n m=pq.ParquetFile(f).metadata\n print('\\n',f.name, m.num_rows, m.num_columns, m.num_row_groups)\n if f.name in ['target_users.parquet','candidate_catalog.parquet','validation_preference_state.parquet','test_preference_state.parquet']:\n  d=pd.read_parquet(f)\n  print(d.head().to_string(index=False)); print(d.dtypes.to_dict()); print({c:(d[c].min(),d[c].max(),d[c].nunique()) for c in d.columns})\nfor name in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n z=np.load(p/name)\n print('\\n',name,{k:(z[k].shape,z[k].dtype) for k in z.files})\n counts=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(1)\n print('elig', np.percentile(counts,[0,1,10,50,90,99,100]), 'mean',counts.mean(), 'uids matches', np.array_equal(z['uids'],pd.read_parquet(p/'target_users.parquet').uid.values))\nPY","workdir":"/app","yield_time_ms":10000,"max_output_tokens":20000});
text(r.output);

Tool response 

Script completed
Wall time 1.7 seconds
Output:


 candidate_catalog.parquet 30000 4 1
 item_id  positive_listens  organic_positive_listens  popularity_rank
 5862961             26615                     20620                1
 6901374             25536                     17347                2
 5635052             24691                     17194                3
 3542184             24075                     17699                4
 9378983             24036                     18195                5
{'item_id': dtype('uint32'), 'positive_listens': dtype('int64'), 'organic_positive_listens': dtype('uint64'), 'popularity_rank': dtype('int32')}
{'item_id': (np.uint32(206), np.uint32(9390294), 30000), 'positive_listens': (np.int64(163), np.int64(26615), 3049), 'organic_positive_listens': (np.uint64(0), np.uint64(20620), 2019), 'popularity_rank': (np.int32(1), np.int32(30000), 30000)}

 target_users.parquet 8456 3 1
 uid  recent_positive_listens  history_positive_listens
 100                    844.0                      2604
 200                     50.0                       286
 300                    168.0                       359
 600                   1985.0                      2423
 700                   1776.0                      6114
{'uid': dtype('uint32'), 'recent_positive_listens': dtype('float64'), 'history_positive_listens': dtype('int64')}
{'uid': (np.uint32(100), np.uint32(1000000), 8456), 'recent_positive_listens': (np.float64(20.0), np.float64(19592.0), 3100), 'history_positive_listens': (np.int64(20), np.int64(26597), 4880)}

 test_preference_state.parquet 484776 3 1
 uid  item_id state
 100   410868 liked
 100   732449 liked
 100   982903 liked
 100  1730273 liked
 100  2621343 liked
{'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}
{'uid': (np.uint32(100), np.uint32(1000000), 7092), 'item_id': (np.uint32(206), np.uint32(9390294), 28863), 'state': ('disliked', 'liked', 2)}

 train_dislikes.parquet 101075 4 1

 train_likes.parquet 814831 4 1

 train_listens.parquet 43484159 6 42

 train_undislikes.parquet 19960 4 1

 train_unlikes.parquet 292665 4 1

 val_dislikes.parquet 3563 4 1

 val_likes.parquet 37795 4 1

 val_listens.parquet 1501697 6 2

 val_undislikes.parquet 606 4 1

 val_unlikes.parquet 9346 4 1

 validation_preference_state.parquet 469124 3 1
 uid  item_id state
 100   410868 liked
 100   732449 liked
 100   982903 liked
 100  1730273 liked
 100  2621343 liked
{'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}
{'uid': (np.uint32(100), np.uint32(1000000), 7038), 'item_id': (np.uint32(206), np.uint32(9390294), 28814), 'state': ('disliked', 'liked', 2)}

 validation_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}
elig [24657.  26724.2 28038.5 29242.  29852.  29979.  30000. ] mean 29067.306291390727 uids matches True

 test_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}
elig [24552.  26672.7 28001.  29220.  29835.  29975.  30000. ] mean 29041.3178807947 uids matches True
Harness context token usage record
{
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  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bb31158487d2b94441e6f98804e2",
  "usage": {
    "input_tokens": 19963,
    "cached_input_tokens": 19328,
    "cache_write_input_tokens": 0,
    "output_tokens": 375,
    "reasoning_output_tokens": 14,
    "total_tokens": 20338
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  "turn_token_usage": {
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    "cached_input_tokens": 70784,
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    "output_tokens": 1259,
    "reasoning_output_tokens": 182,
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  "thread_token_usage": {
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  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-7dc9b131-46f3-4f4a-a5cd-54818ec6f99f",
    "process_id": "78420",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, pyarrow.parquet as pq, numpy as np\nfrom pathlib import Path\np=Path('/task/data')\nfor f in sorted(p.glob('*.parquet')):\n m=pq.ParquetFile(f).metadata\n print('\\n',f.name, m.num_rows, m.num_columns, m.num_row_groups)\n if f.name in ['target_users.parquet','candidate_catalog.parquet','validation_preference_state.parquet','test_preference_state.parquet']:\n  d=pd.read_parquet(f)\n  print(d.head().to_string(index=False)); print(d.dtypes.to_dict()); print({c:(d[c].min(),d[c].max(),d[c].nunique()) for c in d.columns})\nfor name in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n z=np.load(p/name)\n print('\\n',name,{k:(z[k].shape,z[k].dtype) for k in z.files})\n counts=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(1)\n print('elig', np.percentile(counts,[0,1,10,50,90,99,100]), 'mean',counts.mean(), 'uids matches', np.array_equal(z['uids'],pd.read_parquet(p/'target_users.parquet').uid.values))\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, pyarrow.parquet as pq, numpy as np\nfrom pathlib import Path\np=Path('/task/data')\nfor f in sorted(p.glob('*.parquet')):\n m=pq.ParquetFile(f).metadata\n print('\\n',f.name, m.num_rows, m.num_columns, m.num_row_groups)\n if f.name in ['target_users.parquet','candidate_catalog.parquet','validation_preference_state.parquet','test_preference_state.parquet']:\n  d=pd.read_parquet(f)\n  print(d.head().to_string(index=False)); print(d.dtypes.to_dict()); print({c:(d[c].min(),d[c].max(),d[c].nunique()) for c in d.columns})\nfor name in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n z=np.load(p/name)\n print('\\n',name,{k:(z[k].shape,z[k].dtype) for k in z.files})\n counts=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(1)\n print('elig', np.percentile(counts,[0,1,10,50,90,99,100]), 'mean',counts.mean(), 'uids matches', np.array_equal(z['uids'],pd.read_parquet(p/'target_users.parquet').uid.values))\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "\n candidate_catalog.parquet 30000 4 1\n item_id  positive_listens  organic_positive_listens  popularity_rank\n 5862961             26615                     20620                1\n 6901374             25536                     17347                2\n 5635052             24691                     17194                3\n 3542184             24075                     17699                4\n 9378983             24036                     18195                5\n{'item_id': dtype('uint32'), 'positive_listens': dtype('int64'), 'organic_positive_listens': dtype('uint64'), 'popularity_rank': dtype('int32')}\n{'item_id': (np.uint32(206), np.uint32(9390294), 30000), 'positive_listens': (np.int64(163), np.int64(26615), 3049), 'organic_positive_listens': (np.uint64(0), np.uint64(20620), 2019), 'popularity_rank': (np.int32(1), np.int32(30000), 30000)}\n\n target_users.parquet 8456 3 1\n uid  recent_positive_listens  history_positive_listens\n 100                    844.0                      2604\n 200                     50.0                       286\n 300                    168.0                       359\n 600                   1985.0                      2423\n 700                   1776.0                      6114\n{'uid': dtype('uint32'), 'recent_positive_listens': dtype('float64'), 'history_positive_listens': dtype('int64')}\n{'uid': (np.uint32(100), np.uint32(1000000), 8456), 'recent_positive_listens': (np.float64(20.0), np.float64(19592.0), 3100), 'history_positive_listens': (np.int64(20), np.int64(26597), 4880)}\n\n test_preference_state.parquet 484776 3 1\n uid  item_id state\n 100   410868 liked\n 100   732449 liked\n 100   982903 liked\n 100  1730273 liked\n 100  2621343 liked\n{'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\n{'uid': (np.uint32(100), np.uint32(1000000), 7092), 'item_id': (np.uint32(206), np.uint32(9390294), 28863), 'state': ('disliked', 'liked', 2)}\n\n train_dislikes.parquet 101075 4 1\n\n train_likes.parquet 814831 4 1\n\n train_listens.parquet 43484159 6 42\n\n train_undislikes.parquet 19960 4 1\n\n train_unlikes.parquet 292665 4 1\n\n val_dislikes.parquet 3563 4 1\n\n val_likes.parquet 37795 4 1\n\n val_listens.parquet 1501697 6 2\n\n val_undislikes.parquet 606 4 1\n\n val_unlikes.parquet 9346 4 1\n\n validation_preference_state.parquet 469124 3 1\n uid  item_id state\n 100   410868 liked\n 100   732449 liked\n 100   982903 liked\n 100  1730273 liked\n 100  2621343 liked\n{'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\n{'uid': (np.uint32(100), np.uint32(1000000), 7038), 'item_id': (np.uint32(206), np.uint32(9390294), 28814), 'state': ('disliked', 'liked', 2)}\n\n validation_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\nelig [24657.  26724.2 28038.5 29242.  29852.  29979.  30000. ] mean 29067.306291390727 uids matches True\n\n test_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\nelig [24552.  26672.7 28001.  29220.  29835.  29975.  30000. ] mean 29041.3178807947 uids matches True\n",
    "stderr": "",
    "aggregated_output": "\n candidate_catalog.parquet 30000 4 1\n item_id  positive_listens  organic_positive_listens  popularity_rank\n 5862961             26615                     20620                1\n 6901374             25536                     17347                2\n 5635052             24691                     17194                3\n 3542184             24075                     17699                4\n 9378983             24036                     18195                5\n{'item_id': dtype('uint32'), 'positive_listens': dtype('int64'), 'organic_positive_listens': dtype('uint64'), 'popularity_rank': dtype('int32')}\n{'item_id': (np.uint32(206), np.uint32(9390294), 30000), 'positive_listens': (np.int64(163), np.int64(26615), 3049), 'organic_positive_listens': (np.uint64(0), np.uint64(20620), 2019), 'popularity_rank': (np.int32(1), np.int32(30000), 30000)}\n\n target_users.parquet 8456 3 1\n uid  recent_positive_listens  history_positive_listens\n 100                    844.0                      2604\n 200                     50.0                       286\n 300                    168.0                       359\n 600                   1985.0                      2423\n 700                   1776.0                      6114\n{'uid': dtype('uint32'), 'recent_positive_listens': dtype('float64'), 'history_positive_listens': dtype('int64')}\n{'uid': (np.uint32(100), np.uint32(1000000), 8456), 'recent_positive_listens': (np.float64(20.0), np.float64(19592.0), 3100), 'history_positive_listens': (np.int64(20), np.int64(26597), 4880)}\n\n test_preference_state.parquet 484776 3 1\n uid  item_id state\n 100   410868 liked\n 100   732449 liked\n 100   982903 liked\n 100  1730273 liked\n 100  2621343 liked\n{'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\n{'uid': (np.uint32(100), np.uint32(1000000), 7092), 'item_id': (np.uint32(206), np.uint32(9390294), 28863), 'state': ('disliked', 'liked', 2)}\n\n train_dislikes.parquet 101075 4 1\n\n train_likes.parquet 814831 4 1\n\n train_listens.parquet 43484159 6 42\n\n train_undislikes.parquet 19960 4 1\n\n train_unlikes.parquet 292665 4 1\n\n val_dislikes.parquet 3563 4 1\n\n val_likes.parquet 37795 4 1\n\n val_listens.parquet 1501697 6 2\n\n val_undislikes.parquet 606 4 1\n\n val_unlikes.parquet 9346 4 1\n\n validation_preference_state.parquet 469124 3 1\n uid  item_id state\n 100   410868 liked\n 100   732449 liked\n 100   982903 liked\n 100  1730273 liked\n 100  2621343 liked\n{'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\n{'uid': (np.uint32(100), np.uint32(1000000), 7038), 'item_id': (np.uint32(206), np.uint32(9390294), 28814), 'state': ('disliked', 'liked', 2)}\n\n validation_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\nelig [24657.  26724.2 28038.5 29242.  29852.  29979.  30000. ] mean 29067.306291390727 uids matches True\n\n test_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\nelig [24552.  26672.7 28001.  29220.  29835.  29975.  30000. ] mean 29041.3178807947 uids matches True\n",
    "exit_code": 0,
    "duration": {
      "secs": 1,
      "nanos": 509860337
    },
    "formatted_output": "\n candidate_catalog.parquet 30000 4 1\n item_id  positive_listens  organic_positive_listens  popularity_rank\n 5862961             26615                     20620                1\n 6901374             25536                     17347                2\n 5635052             24691                     17194                3\n 3542184             24075                     17699                4\n 9378983             24036                     18195                5\n{'item_id': dtype('uint32'), 'positive_listens': dtype('int64'), 'organic_positive_listens': dtype('uint64'), 'popularity_rank': dtype('int32')}\n{'item_id': (np.uint32(206), np.uint32(9390294), 30000), 'positive_listens': (np.int64(163), np.int64(26615), 3049), 'organic_positive_listens': (np.uint64(0), np.uint64(20620), 2019), 'popularity_rank': (np.int32(1), np.int32(30000), 30000)}\n\n target_users.parquet 8456 3 1\n uid  recent_positive_listens  history_positive_listens\n 100                    844.0                      2604\n 200                     50.0                       286\n 300                    168.0                       359\n 600                   1985.0                      2423\n 700                   1776.0                      6114\n{'uid': dtype('uint32'), 'recent_positive_listens': dtype('float64'), 'history_positive_listens': dtype('int64')}\n{'uid': (np.uint32(100), np.uint32(1000000), 8456), 'recent_positive_listens': (np.float64(20.0), np.float64(19592.0), 3100), 'history_positive_listens': (np.int64(20), np.int64(26597), 4880)}\n\n test_preference_state.parquet 484776 3 1\n uid  item_id state\n 100   410868 liked\n 100   732449 liked\n 100   982903 liked\n 100  1730273 liked\n 100  2621343 liked\n{'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\n{'uid': (np.uint32(100), np.uint32(1000000), 7092), 'item_id': (np.uint32(206), np.uint32(9390294), 28863), 'state': ('disliked', 'liked', 2)}\n\n train_dislikes.parquet 101075 4 1\n\n train_likes.parquet 814831 4 1\n\n train_listens.parquet 43484159 6 42\n\n train_undislikes.parquet 19960 4 1\n\n train_unlikes.parquet 292665 4 1\n\n val_dislikes.parquet 3563 4 1\n\n val_likes.parquet 37795 4 1\n\n val_listens.parquet 1501697 6 2\n\n val_undislikes.parquet 606 4 1\n\n val_unlikes.parquet 9346 4 1\n\n validation_preference_state.parquet 469124 3 1\n uid  item_id state\n 100   410868 liked\n 100   732449 liked\n 100   982903 liked\n 100  1730273 liked\n 100  2621343 liked\n{'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\n{'uid': (np.uint32(100), np.uint32(1000000), 7038), 'item_id': (np.uint32(206), np.uint32(9390294), 28814), 'state': ('disliked', 'liked', 2)}\n\n validation_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\nelig [24657.  26724.2 28038.5 29242.  29852.  29979.  30000. ] mean 29067.306291390727 uids matches True\n\n test_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\nelig [24552.  26672.7 28001.  29220.  29835.  29975.  30000. ] mean 29041.3178807947 uids matches True\n"
  }
}
12

exec command

+00:45
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"free -h; /opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq, pandas as pd, numpy as np\np='/task/data/'\nfor fam in ['listens','likes','dislikes','unlikes','undislikes']:\n for split in ['train','val']:\n  f=p+split+'_'+fam+'.parquet'\n  pf=pq.ParquetFile(f)\n  cols=['uid','timestamp','item_id']\n  t=pf.read(columns=cols)\n  d=t.to_pandas()\n  print(split,fam,'rows',len(d),'users',d.uid.nunique(),'items',d.item_id.nunique(),'time',d.timestamp.min(),d.timestamp.max(),'target users events',d.uid.isin(pd.read_parquet(p+'target_users.parquet').uid).sum())\n  del d,t\nPY","workdir":"/app","yield_time_ms":30000,"max_output_tokens":20000});
text(r.output);

Tool response 

Script completed
Wall time 2.1 seconds
Output:

/bin/sh: 1: free: not found
train listens rows 43484159 users 9238 items 845395 time 0 24786795 target users events 43222297
val listens rows 1501697 users 8196 items 191747 time 24788600 25393395 target users events 1482553
train likes rows 814831 users 8144 items 172303 time 80 24786790 target users events 690968
val likes rows 37795 users 4114 items 22902 time 24788815 25393390 target users events 33177
train dislikes rows 101075 users 5732 items 50876 time 75 24786580 target users events 84266
val dislikes rows 3563 users 955 items 3135 time 24789920 25393280 target users events 2664
train unlikes rows 292665 users 6197 items 113072 time 250 24786570 target users events 245808
val unlikes rows 9346 users 1313 items 8080 time 24789005 25393385 target users events 6297
train undislikes rows 19960 users 2769 items 14725 time 15950 24785395 target users events 16819
val undislikes rows 606 users 205 items 579 time 24810715 25393035 target users events 466
Harness context token usage record
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  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
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  "turn_token_usage": {
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  "thread_token_usage": {
    "input_tokens": 113264,
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Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-d696c0c4-86d7-46a5-8532-4b4638635206",
    "process_id": "16697",
    "command": [
      "/bin/sh",
      "-lc",
      "free -h; /opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq, pandas as pd, numpy as np\np='/task/data/'\nfor fam in ['listens','likes','dislikes','unlikes','undislikes']:\n for split in ['train','val']:\n  f=p+split+'_'+fam+'.parquet'\n  pf=pq.ParquetFile(f)\n  cols=['uid','timestamp','item_id']\n  t=pf.read(columns=cols)\n  d=t.to_pandas()\n  print(split,fam,'rows',len(d),'users',d.uid.nunique(),'items',d.item_id.nunique(),'time',d.timestamp.min(),d.timestamp.max(),'target users events',d.uid.isin(pd.read_parquet(p+'target_users.parquet').uid).sum())\n  del d,t\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "free -h; /opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq, pandas as pd, numpy as np\np='/task/data/'\nfor fam in ['listens','likes','dislikes','unlikes','undislikes']:\n for split in ['train','val']:\n  f=p+split+'_'+fam+'.parquet'\n  pf=pq.ParquetFile(f)\n  cols=['uid','timestamp','item_id']\n  t=pf.read(columns=cols)\n  d=t.to_pandas()\n  print(split,fam,'rows',len(d),'users',d.uid.nunique(),'items',d.item_id.nunique(),'time',d.timestamp.min(),d.timestamp.max(),'target users events',d.uid.isin(pd.read_parquet(p+'target_users.parquet').uid).sum())\n  del d,t\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "train listens rows 43484159 users 9238 items 845395 time 0 24786795 target users events 43222297\nval listens rows 1501697 users 8196 items 191747 time 24788600 25393395 target users events 1482553\ntrain likes rows 814831 users 8144 items 172303 time 80 24786790 target users events 690968\nval likes rows 37795 users 4114 items 22902 time 24788815 25393390 target users events 33177\ntrain dislikes rows 101075 users 5732 items 50876 time 75 24786580 target users events 84266\nval dislikes rows 3563 users 955 items 3135 time 24789920 25393280 target users events 2664\ntrain unlikes rows 292665 users 6197 items 113072 time 250 24786570 target users events 245808\nval unlikes rows 9346 users 1313 items 8080 time 24789005 25393385 target users events 6297\ntrain undislikes rows 19960 users 2769 items 14725 time 15950 24785395 target users events 16819\nval undislikes rows 606 users 205 items 579 time 24810715 25393035 target users events 466\n",
    "stderr": "",
    "aggregated_output": "train listens rows 43484159 users 9238 items 845395 time 0 24786795 target users events 43222297\nval listens rows 1501697 users 8196 items 191747 time 24788600 25393395 target users events 1482553\ntrain likes rows 814831 users 8144 items 172303 time 80 24786790 target users events 690968\nval likes rows 37795 users 4114 items 22902 time 24788815 25393390 target users events 33177\ntrain dislikes rows 101075 users 5732 items 50876 time 75 24786580 target users events 84266\nval dislikes rows 3563 users 955 items 3135 time 24789920 25393280 target users events 2664\ntrain unlikes rows 292665 users 6197 items 113072 time 250 24786570 target users events 245808\nval unlikes rows 9346 users 1313 items 8080 time 24789005 25393385 target users events 6297\ntrain undislikes rows 19960 users 2769 items 14725 time 15950 24785395 target users events 16819\nval undislikes rows 606 users 205 items 579 time 24810715 25393035 target users events 466\n",
    "exit_code": 0,
    "duration": {
      "secs": 1,
      "nanos": 919651213
    },
    "formatted_output": "train listens rows 43484159 users 9238 items 845395 time 0 24786795 target users events 43222297\nval listens rows 1501697 users 8196 items 191747 time 24788600 25393395 target users events 1482553\ntrain likes rows 814831 users 8144 items 172303 time 80 24786790 target users events 690968\nval likes rows 37795 users 4114 items 22902 time 24788815 25393390 target users events 33177\ntrain dislikes rows 101075 users 5732 items 50876 time 75 24786580 target users events 84266\nval dislikes rows 3563 users 955 items 3135 time 24789920 25393280 target users events 2664\ntrain unlikes rows 292665 users 6197 items 113072 time 250 24786570 target users events 245808\nval unlikes rows 9346 users 1313 items 8080 time 24789005 25393385 target users events 6297\ntrain undislikes rows 19960 users 2769 items 14725 time 15950 24785395 target users events 16819\nval undislikes rows 606 users 205 items 579 time 24810715 25393035 target users events 466\n"
  }
}
13

Agent message

+01:04
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

14

exec command

+01:06
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Model text

The data is unusually favorable for collaborative ranking: only 9,238 listeners generated 43.5M training plays, and 8,456 targets account for nearly all of them. I’m checking whether the future organic positives are mostly repeat affinities or genuinely unseen tracks; that determines whether to emphasize per-user history, neighborhood discovery, or global trends.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"mkdir -p /app/solution && /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np\nfrom pathlib import Path\np=Path('/task/data')\ntarget=set(pd.read_parquet(p/'target_users.parquet').uid.astype(int))\nz=np.load(p/'validation_eligible_candidates.npz'); uids=z['uids']; items=z['item_ids']; uidrow={int(x):i for i,x in enumerate(uids)}; itemcol={int(x):i for i,x in enumerate(items)}\ndef elig(u,i):\n r=uidrow.get(int(u)); c=itemcol.get(int(i)); return r is not None and c is not None and ((z['packed_eligible'][r,c//8]>>(c%8))&1)\nls=pd.read_parquet(p/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nlk=pd.read_parquet(p/'val_likes.parquet',columns=['uid','item_id','is_organic'])\nstrong=ls[(ls.uid.isin(target))&(ls.is_organic==1)&(ls.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\nlikes=lk[(lk.uid.isin(target))&(lk.is_organic==1)][['uid','item_id']].drop_duplicates()\ntruth=pd.concat([strong.assign(grade=1),likes.assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\ntruth=truth[[elig(u,i) for u,i in truth[['uid','item_id']].itertuples(index=False,name=None)]]\nprint('truth pairs/users/grades',len(truth),truth.uid.nunique(),truth.grade.value_counts().to_dict(), 'per user',truth.groupby('uid').size().describe(percentiles=[.5,.9,.99]).to_dict())\n# seen in train positive, any, explicit state\ncat=set(map(int,items)); truthkey=pd.MultiIndex.from_frame(truth[['uid','item_id']])\ntrls=pd.read_parquet(p/'train_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp'])\ntrls=trls[trls.item_id.isin(cat)&trls.uid.isin(target)]\nfor name,mask in [('any',np.ones(len(trls),bool)),('pos50',trls.played_ratio_pct>=50),('strong80',trls.played_ratio_pct>=80),('organic80',(trls.is_organic==1)&(trls.played_ratio_pct>=80)),('recent7d',trls.timestamp>=24786800-7*86400),('recent30d',trls.timestamp>=24786800-30*86400),('recent90d',trls.timestamp>=24786800-90*86400)]:\n seen=pd.MultiIndex.from_frame(trls.loc[mask,['uid','item_id']].drop_duplicates())\n print(name,'train pairs',len(seen),'truth overlap',truthkey.isin(seen).sum(), truthkey.isin(seen).mean())\nstate=pd.read_parquet(p/'validation_preference_state.parquet')\nfor s in ['liked','disliked']:\n q=pd.MultiIndex.from_frame(state[state.state==s][['uid','item_id']].drop_duplicates())\n print(s,'truth overlap',truthkey.isin(q).sum(),truthkey.isin(q).mean())\n# eligible exclusions matchup states and train distinct\nsm=state[state.uid.isin(target)&state.item_id.isin(cat)]\nprint('state cat',sm.groupby('state').size())\nprint('state eligibility',sm.groupby('state').apply(lambda x: np.mean([elig(u,i) for u,i in x[['uid','item_id']].itertuples(index=False,name=None)]),include_groups=False))\nPY","workdir":"/app","yield_time_ms":30000,"max_output_tokens":20000});
text(r.output);

Tool response 

Script completed
Wall time 30.2 seconds
Output:
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bb44b1b887d2a79bd281bf6ac77c",
  "usage": {
    "input_tokens": 22090,
    "cached_input_tokens": 19840,
    "cache_write_input_tokens": 0,
    "output_tokens": 1612,
    "reasoning_output_tokens": 637,
    "total_tokens": 23702
  },
  "turn_token_usage": {
    "input_tokens": 135354,
    "cached_input_tokens": 109696,
    "cache_write_input_tokens": 0,
    "output_tokens": 3221,
    "reasoning_output_tokens": 929,
    "total_tokens": 138575
  },
  "thread_token_usage": {
    "input_tokens": 135354,
    "cached_input_tokens": 109696,
    "cache_write_input_tokens": 0,
    "output_tokens": 3221,
    "reasoning_output_tokens": 929,
    "total_tokens": 138575
  }
}
15

exec command

+01:53
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"ps aux | rg 'python|PID' | head -20; ls -lh /app/solution","workdir":"/app","yield_time_ms":1000,"max_output_tokens":2000});
text(r.output);

Tool response 

Script completed
Wall time 0.0 seconds
Output:

/bin/sh: 1: ps: not found
total 0
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-39193b30-b38b-4376-a7fa-b4bb5327a3e1",
    "process_id": "60002",
    "command": [
      "/bin/sh",
      "-lc",
      "ps aux | rg 'python|PID' | head -20; ls -lh /app/solution"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "ps aux | rg 'python|PID' | head -20; ls -lh /app/solution"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "/bin/sh: 1: ps: not found\ntotal 0\n",
    "stderr": "",
    "aggregated_output": "/bin/sh: 1: ps: not found\ntotal 0\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3989
    },
    "formatted_output": "/bin/sh: 1: ps: not found\ntotal 0\n"
  }
}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bb80b9d087d2814bf98c6a448951",
  "usage": {
    "input_tokens": 23725,
    "cached_input_tokens": 21248,
    "cache_write_input_tokens": 0,
    "output_tokens": 130,
    "reasoning_output_tokens": 62,
    "total_tokens": 23855
  },
  "turn_token_usage": {
    "input_tokens": 159079,
    "cached_input_tokens": 130944,
    "cache_write_input_tokens": 0,
    "output_tokens": 3351,
    "reasoning_output_tokens": 991,
    "total_tokens": 162430
  },
  "thread_token_usage": {
    "input_tokens": 159079,
    "cached_input_tokens": 130944,
    "cache_write_input_tokens": 0,
    "output_tokens": 3351,
    "reasoning_output_tokens": 991,
    "total_tokens": 162430
  }
}
16

exec command

+01:58
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python -u - <<'PY'\nimport pandas as pd, numpy as np\nfrom pathlib import Path\np=Path('/task/data'); target=set(pd.read_parquet(p/'target_users.parquet').uid.astype(int)); z=np.load(p/'validation_eligible_candidates.npz'); uids=z['uids']; items=z['item_ids']; uidrow={int(x):i for i,x in enumerate(uids)}; itemcol={int(x):i for i,x in enumerate(items)}\ndef elig(u,i):\n r=uidrow.get(int(u)); c=itemcol.get(int(i)); return r is not None and c is not None and ((z['packed_eligible'][r,c//8]>>(c%8))&1)\nls=pd.read_parquet(p/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct']); lk=pd.read_parquet(p/'val_likes.parquet',columns=['uid','item_id','is_organic'])\nstrong=ls[(ls.uid.isin(target))&(ls.is_organic==1)&(ls.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); likes=lk[(lk.uid.isin(target))&(lk.is_organic==1)][['uid','item_id']].drop_duplicates(); truth=pd.concat([strong.assign(grade=1),likes.assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last'); truth=truth[[elig(u,i) for u,i in truth[['uid','item_id']].itertuples(index=False,name=None)]]\nprint('truth',len(truth),truth.uid.nunique(),truth.grade.value_counts().to_dict(),truth.groupby('uid').size().describe().to_dict(),flush=True)\nprint('reading train',flush=True)\ntrls=pd.read_parquet(p/'train_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp']); print('read',len(trls),flush=True)\ntrls=trls[trls.item_id.isin(items)&trls.uid.isin(target)]; print('filtered',len(trls),flush=True)\ntruthkey=pd.MultiIndex.from_frame(truth[['uid','item_id']])\nfor name,mask in [('any',np.ones(len(trls),bool)),('pos50',trls.played_ratio_pct>=50),('strong80',trls.played_ratio_pct>=80),('organic80',(trls.is_organic==1)&(trls.played_ratio_pct>=80)),('recent7d',trls.timestamp>=24786800-7*86400),('recent30d',trls.timestamp>=24786800-30*86400),('recent90d',trls.timestamp>=24786800-90*86400)]:\n seen=pd.MultiIndex.from_frame(trls.loc[mask,['uid','item_id']].drop_duplicates()); print(name,len(seen),truthkey.isin(seen).sum(),truthkey.isin(seen).mean(),flush=True)\nstate=pd.read_parquet(p/'validation_preference_state.parquet')\nfor s in ['liked','disliked']:\n q=pd.MultiIndex.from_frame(state[state.state==s][['uid','item_id']].drop_duplicates()); print(s,truthkey.isin(q).sum(),truthkey.isin(q).mean(),flush=True)\nsm=state[state.uid.isin(target)&state.item_id.isin(items)]; print('statecat',sm.groupby('state').size(),flush=True)\nprint('eligstate',sm.groupby('state').apply(lambda x: np.mean([elig(u,i) for u,i in x[['uid','item_id']].itertuples(index=False,name=None)]),include_groups=False),flush=True)\nPY","workdir":"/app","yield_time_ms":1000,"max_output_tokens":10000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response 

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=73954
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bb8486fc87d2900032ee39e1fff7",
  "usage": {
    "input_tokens": 23893,
    "cached_input_tokens": 21888,
    "cache_write_input_tokens": 0,
    "output_tokens": 1042,
    "reasoning_output_tokens": 164,
    "total_tokens": 24935
  },
  "turn_token_usage": {
    "input_tokens": 182972,
    "cached_input_tokens": 152832,
    "cache_write_input_tokens": 0,
    "output_tokens": 4393,
    "reasoning_output_tokens": 1155,
    "total_tokens": 187365
  },
  "thread_token_usage": {
    "input_tokens": 182972,
    "cached_input_tokens": 152832,
    "cache_write_input_tokens": 0,
    "output_tokens": 4393,
    "reasoning_output_tokens": 1155,
    "total_tokens": 187365
  }
}
17

exec command

+02:16
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":73954,"chars":"","yield_time_ms":1000,"max_output_tokens":10000});
text(r.output);

Tool response 

Script completed
Wall time 5.0 seconds
Output:
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bb99a1b487d2a9e0922b07dd1e69",
  "usage": {
    "input_tokens": 24963,
    "cached_input_tokens": 23552,
    "cache_write_input_tokens": 0,
    "output_tokens": 54,
    "reasoning_output_tokens": 7,
    "total_tokens": 25017
  },
  "turn_token_usage": {
    "input_tokens": 207935,
    "cached_input_tokens": 176384,
    "cache_write_input_tokens": 0,
    "output_tokens": 4447,
    "reasoning_output_tokens": 1162,
    "total_tokens": 212382
  },
  "thread_token_usage": {
    "input_tokens": 207935,
    "cached_input_tokens": 176384,
    "cache_write_input_tokens": 0,
    "output_tokens": 4447,
    "reasoning_output_tokens": 1162,
    "total_tokens": 212382
  }
}
18

exec command

+02:26
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":73954,"chars":"","yield_time_ms":10000,"max_output_tokens":10000});
text(r.output);

Tool response 

Script completed
Wall time 10.0 seconds
Output:
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bba0925087d284f6b70a2166c2ca",
  "usage": {
    "input_tokens": 25040,
    "cached_input_tokens": 24832,
    "cache_write_input_tokens": 0,
    "output_tokens": 61,
    "reasoning_output_tokens": 14,
    "total_tokens": 25101
  },
  "turn_token_usage": {
    "input_tokens": 232975,
    "cached_input_tokens": 201216,
    "cache_write_input_tokens": 0,
    "output_tokens": 4508,
    "reasoning_output_tokens": 1176,
    "total_tokens": 237483
  },
  "thread_token_usage": {
    "input_tokens": 232975,
    "cached_input_tokens": 201216,
    "cache_write_input_tokens": 0,
    "output_tokens": 4508,
    "reasoning_output_tokens": 1176,
    "total_tokens": 237483
  }
}
19

exec command

+02:38
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":73954,"chars":"","yield_time_ms":10000,"max_output_tokens":10000});
text(r.output);

Tool response 

Script completed
Wall time 10.0 seconds
Output:
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bbaee4a087d2adc2c44042a1a265",
  "usage": {
    "input_tokens": 25124,
    "cached_input_tokens": 23680,
    "cache_write_input_tokens": 0,
    "output_tokens": 86,
    "reasoning_output_tokens": 39,
    "total_tokens": 25210
  },
  "turn_token_usage": {
    "input_tokens": 258099,
    "cached_input_tokens": 224896,
    "cache_write_input_tokens": 0,
    "output_tokens": 4594,
    "reasoning_output_tokens": 1215,
    "total_tokens": 262693
  },
  "thread_token_usage": {
    "input_tokens": 258099,
    "cached_input_tokens": 224896,
    "cache_write_input_tokens": 0,
    "output_tokens": 4594,
    "reasoning_output_tokens": 1215,
    "total_tokens": 262693
  }
}
20

exec command

+02:51
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"for p in /proc/[0-9]*; do x=$(tr '\\000' ' ' < \"$p/cmdline\" 2>/dev/null); case \"$x\" in *python*) echo \"$p $x\";; esac; done","workdir":"/app","yield_time_ms":1000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 0.1 seconds
Output:

Warning: truncated output (original token count: 6019)
Total output lines: 436

/proc/8774 bash -c set -o pipefail; if [ -s ~/.nvm/nvm.sh ]; then . ~/.nvm/nvm.sh; fi; codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=max -- '# Build a music-discovery ranker

Build the strongest ranking model you can for the "Made for You" shelf of a
music streaming service. Per-user candidate eligibility, cutoff-specific
explicit preference state, and a temporal backtest are provided. Candidate
eligibility is platform-owned; all modeling within that candidate boundary is
in scope.

## Data

All inputs are under `/task/data/`. Training data ends at timestamp `24786800`.
A 30-minute gap precedes a seven-day public validation window. The hidden test
is the following seven-day window after another 30-minute gap.

For each event family there is a `train_*.parquet` and `val_*.parquet` file:

| Family | Meaning | Columns |
| --- | --- | --- |
| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |
| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |
| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |
| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |
| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |

`is_organic=1` means the listener sought out the track; `0` means the action
came through a recommendation surface. A listen is a conventional positive at
50% completion. The discovery-quality objective below uses the stronger 80%
threshold.

The two public windows have different roles:

1. Build and tune a validation ranker using only `train_*` as history and
   `val_*` as subsequent outcomes.
2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test
   time the validation events are observed history, not recommendable outcomes.

Do not use `val_*` events as features when measuring a validation ranking. They
may and should be used when refitting the final hidden-test ranker.

Additional files under `/task/data/`:

- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by
  training positive-listen popularity.
- `target_users.parquet`: users with at least 20 positive listens in the 90 days
  before the training cutoff, selected without future labels.
- `validation_preference_state.parquet`: active explicit state at the training
  cutoff, for validation ranking.
- `test_preference_state.parquet`: active explicit state after validation, for
  the final hidden-test ranking. State is `liked` or `disliked`; a pair may have
  both active channels.
- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:
  cutoff-correct candidate sets for every target user. Each contains aligned
  arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover
  the Boolean item mask with `np.unpackbits(packed_eligible[r],
  count=len(item_ids), bitorder="little")`.

The exact available Python distribution inventory is listed in
`/task/requirements.txt`. Future test interactions are hidden and used only by
the separate Harbor verifier.

## Discovery objective

Rank only the candidates returned for the relevant cutoff. Relevance is based
on organic behavior in the immediately following window:

- grade 3: the user organically likes the track;
- grade 1: the user organically listens to at least 80% of the track;
- grade 0: otherwise.

If both positive events occur, grade 3 wins. Ranking quality is measured with
graded NDCG@10. DCG uses gain `2^grade - 1` and discount
`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one
relevant item.

The public evaluator reports this objective for development, but its score is
not a separate acceptance stage. Final acceptance is determined on the hidden
test using the same objective.

The Yambda logs contain interactions, not impression propensities. Treat the
organic objective as a robust offline target, not as a causal estimate of
online lift.

## Public tools

`/task/evaluate_public.py` scores a validation-stage ranking against the public
window. Pass the path to a ranking with the same schema as the final
deliverable:

```sh
python /task/evaluate_public.py solution/validation.parquet
```

Stage-independent evaluation helpers are available in
`/task/ranking_evaluation.py`. The builder installs the same checked-in module
in the public agent image and private verifier image so scoring mechanics cannot
drift. The public copy contains no hidden labels, evaluator-owned data paths, or
pass thresholds.

The public evaluator uses `validation_eligible_candidates.npz`; it is not a
validator for the final artifact. No model, fitted representation, example
submission, or modeling architecture is supplied. Build and tune the best model
you can, then refit the selected approach as described above.

## Deliverable

Write `/app/solution/submission.parquet` or
`/app/solution/submission.csv`, with exactly 100 rows for every target user and
columns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties
or duplicate items. Every submitted item must be present in that user'"'"'s
`test_eligible_candidates.npz` candidate set.

Leave the code that builds the artifact and a concise
`/app/solution/README.md` in `/app/solution/`. Keep the best valid submission
saved throughout the attempt so it survives the deadline.

## Constraints

- Write deliverables and intermediate artifacts only under `/app/solution/`.
- Treat `/task/` and the installed Python environment as read-only.
- Do not invoke nested agents or attempt to access verifier files, credentials,
  other trials, or host paths.
- Do not install packages or use network access except for the model provider
  connection managed by Harbor.
- You have up to 30 minutes. Improve the model as much as possible within that
  limit. Structural validity alone is not sufficient; the final submission is
  also evaluated for ranking quality.
' 2>&1 </dev/null | tee /logs/agent/codex.txt 
/proc/9000 node /home/agent/.nvm/versions/node/v22.23.3/bin/codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=max -- # Build a music-discovery ranker

Build the strongest ranking model you can for the "Made for You" shelf of a
music streaming service. Per-user candidate eligibility, cutoff-specific
explicit preference state, and a temporal backtest are provided. Candidate
eligibility is platform-owned; all modeling within that candidate boundary is
in scope.

## Data

All inputs are under `/task/data/`. Training data ends at timestamp `24786800`.
A 30-minute gap precedes a seven-day public validation window. The hidden test
is the following seven-day window after another 30-minute gap.

For each event family there is a `train_*.parquet` and `val_*.parquet` file:

| Family | Meaning | Columns |
| --- | --- | --- |
| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |
| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |
| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |
| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |
| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |

`is_organic=1` means the listener sought out the track; `0` means the action
came through a recommendation surface. A listen is a conventional positive at
50% completion. The discovery-quality objective below uses the stronger 80%
threshold.

The two public windows have different roles:

1. Build and tune a validation ranker using only `train_*` as history and
   `val_*` as subsequent outcomes.
2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test
   time the validation events are observed history, not recommendable outcomes.

Do not use `val_*` events as features when measuring a validation ranking. They
may and should be used when refitting the final hidden-test ranker.

Additional files under `/task/data/`:

- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by
  training positive-listen popularity.
- `target_users.parquet`: users with at least 20 positive listens in the 90 days
  before the training cutoff, selected without future labels.
- `validation_preference_state.parquet`: active explicit state at the training
  cutoff, for validation ranking.
- `test_preference_state.parquet`: active explicit state after validation, for
  the final hidden-test ranking. State is `liked` or `disliked`; a pair may have
  both active channels.
- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:
  cutoff-correct candidate sets for every target user. Each contains aligned
  arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover
  the Boolean item mask with `np.unpackbits(packed_eligible[r],
  count=len(item_ids), bitorder="little")`.

The exact available Python distribution inventory is listed in
`/task/requirements.txt`. Future test interactions are hidden and used only by
the separate Harbor verifier.

## Discovery objective

Rank only the candidates returned for the relevant cutoff. Relevance is based
on organic behavior in the immediately following window:

- grade 3: the user organically likes the track;
- grade 1: the user organically listens to at least 80% of the track;
- grade 0: otherwise.

If both positive events occur, grade 3 wins. Ranking quality is measured with
graded NDCG@10. DCG uses gain `2^grade - 1` and discount
`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one
relevant item.

The public evaluator reports this objective for development, but its score is
not a separate acceptance…1019 tokens truncated…y and
   `val_*` as subsequent outcomes.
2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test
   time the validation events are observed history, not recommendable outcomes.

Do not use `val_*` events as features when measuring a validation ranking. They
may and should be used when refitting the final hidden-test ranker.

Additional files under `/task/data/`:

- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by
  training positive-listen popularity.
- `target_users.parquet`: users with at least 20 positive listens in the 90 days
  before the training cutoff, selected without future labels.
- `validation_preference_state.parquet`: active explicit state at the training
  cutoff, for validation ranking.
- `test_preference_state.parquet`: active explicit state after validation, for
  the final hidden-test ranking. State is `liked` or `disliked`; a pair may have
  both active channels.
- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:
  cutoff-correct candidate sets for every target user. Each contains aligned
  arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover
  the Boolean item mask with `np.unpackbits(packed_eligible[r],
  count=len(item_ids), bitorder="little")`.

The exact available Python distribution inventory is listed in
`/task/requirements.txt`. Future test interactions are hidden and used only by
the separate Harbor verifier.

## Discovery objective

Rank only the candidates returned for the relevant cutoff. Relevance is based
on organic behavior in the immediately following window:

- grade 3: the user organically likes the track;
- grade 1: the user organically listens to at least 80% of the track;
- grade 0: otherwise.

If both positive events occur, grade 3 wins. Ranking quality is measured with
graded NDCG@10. DCG uses gain `2^grade - 1` and discount
`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one
relevant item.

The public evaluator reports this objective for development, but its score is
not a separate acceptance stage. Final acceptance is determined on the hidden
test using the same objective.

The Yambda logs contain interactions, not impression propensities. Treat the
organic objective as a robust offline target, not as a causal estimate of
online lift.

## Public tools

`/task/evaluate_public.py` scores a validation-stage ranking against the public
window. Pass the path to a ranking with the same schema as the final
deliverable:

```sh
python /task/evaluate_public.py solution/validation.parquet
```

Stage-independent evaluation helpers are available in
`/task/ranking_evaluation.py`. The builder installs the same checked-in module
in the public agent image and private verifier image so scoring mechanics cannot
drift. The public copy contains no hidden labels, evaluator-owned data paths, or
pass thresholds.

The public evaluator uses `validation_eligible_candidates.npz`; it is not a
validator for the final artifact. No model, fitted representation, example
submission, or modeling architecture is supplied. Build and tune the best model
you can, then refit the selected approach as described above.

## Deliverable

Write `/app/solution/submission.parquet` or
`/app/solution/submission.csv`, with exactly 100 rows for every target user and
columns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties
or duplicate items. Every submitted item must be present in that user's
`test_eligible_candidates.npz` candidate set.

Leave the code that builds the artifact and a concise
`/app/solution/README.md` in `/app/solution/`. Keep the best valid submission
saved throughout the attempt so it survives the deadline.

## Constraints

- Write deliverables and intermediate artifacts only under `/app/solution/`.
- Treat `/task/` and the installed Python environment as read-only.
- Do not invoke nested agents or attempt to access verifier files, credentials,
  other trials, or host paths.
- Do not install packages or use network access except for the model provider
  connection managed by Harbor.
- You have up to 30 minutes. Improve the model as much as possible within that
  limit. Structural validity alone is not sufficient; the final submission is
  also evaluated for ranking quality.
 
/proc/9455 /bin/sh -c mkdir -p /app/solution && /opt/task-venv/bin/python - <<'PY'
import pandas as pd, numpy as np
from pathlib import Path
p=Path('/task/data')
target=set(pd.read_parquet(p/'target_users.parquet').uid.astype(int))
z=np.load(p/'validation_eligible_candidates.npz'); uids=z['uids']; items=z['item_ids']; uidrow={int(x):i for i,x in enumerate(uids)}; itemcol={int(x):i for i,x in enumerate(items)}
def elig(u,i):
 r=uidrow.get(int(u)); c=itemcol.get(int(i)); return r is not None and c is not None and ((z['packed_eligible'][r,c//8]>>(c%8))&1)
ls=pd.read_parquet(p/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])
lk=pd.read_parquet(p/'val_likes.parquet',columns=['uid','item_id','is_organic'])
strong=ls[(ls.uid.isin(target))&(ls.is_organic==1)&(ls.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()
likes=lk[(lk.uid.isin(target))&(lk.is_organic==1)][['uid','item_id']].drop_duplicates()
truth=pd.concat([strong.assign(grade=1),likes.assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')
truth=truth[[elig(u,i) for u,i in truth[['uid','item_id']].itertuples(index=False,name=None)]]
print('truth pairs/users/grades',len(truth),truth.uid.nunique(),truth.grade.value_counts().to_dict(), 'per user',truth.groupby('uid').size().describe(percentiles=[.5,.9,.99]).to_dict())
# seen in train positive, any, explicit state
cat=set(map(int,items)); truthkey=pd.MultiIndex.from_frame(truth[['uid','item_id']])
trls=pd.read_parquet(p/'train_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp'])
trls=trls[trls.item_id.isin(cat)&trls.uid.isin(target)]
for name,mask in [('any',np.ones(len(trls),bool)),('pos50',trls.played_ratio_pct>=50),('strong80',trls.played_ratio_pct>=80),('organic80',(trls.is_organic==1)&(trls.played_ratio_pct>=80)),('recent7d',trls.timestamp>=24786800-7*86400),('recent30d',trls.timestamp>=24786800-30*86400),('recent90d',trls.timestamp>=24786800-90*86400)]:
 seen=pd.MultiIndex.from_frame(trls.loc[mask,['uid','item_id']].drop_duplicates())
 print(name,'train pairs',len(seen),'truth overlap',truthkey.isin(seen).sum(), truthkey.isin(seen).mean())
state=pd.read_parquet(p/'validation_preference_state.parquet')
for s in ['liked','disliked']:
 q=pd.MultiIndex.from_frame(state[state.state==s][['uid','item_id']].drop_duplicates())
 print(s,'truth overlap',truthkey.isin(q).sum(),truthkey.isin(q).mean())
# eligible exclusions matchup states and train distinct
sm=state[state.uid.isin(target)&state.item_id.isin(cat)]
print('state cat',sm.groupby('state').size())
print('state eligibility',sm.groupby('state').apply(lambda x: np.mean([elig(u,i) for u,i in x[['uid','item_id']].itertuples(index=False,name=None)]),include_groups=False))
PY 
/proc/9457 /opt/task-venv/bin/python - 
/proc/9493 /bin/sh -c /opt/task-venv/bin/python -u - <<'PY'
import pandas as pd, numpy as np
from pathlib import Path
p=Path('/task/data'); target=set(pd.read_parquet(p/'target_users.parquet').uid.astype(int)); z=np.load(p/'validation_eligible_candidates.npz'); uids=z['uids']; items=z['item_ids']; uidrow={int(x):i for i,x in enumerate(uids)}; itemcol={int(x):i for i,x in enumerate(items)}
def elig(u,i):
 r=uidrow.get(int(u)); c=itemcol.get(int(i)); return r is not None and c is not None and ((z['packed_eligible'][r,c//8]>>(c%8))&1)
ls=pd.read_parquet(p/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct']); lk=pd.read_parquet(p/'val_likes.parquet',columns=['uid','item_id','is_organic'])
strong=ls[(ls.uid.isin(target))&(ls.is_organic==1)&(ls.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); likes=lk[(lk.uid.isin(target))&(lk.is_organic==1)][['uid','item_id']].drop_duplicates(); truth=pd.concat([strong.assign(grade=1),likes.assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last'); truth=truth[[elig(u,i) for u,i in truth[['uid','item_id']].itertuples(index=False,name=None)]]
print('truth',len(truth),truth.uid.nunique(),truth.grade.value_counts().to_dict(),truth.groupby('uid').size().describe().to_dict(),flush=True)
print('reading train',flush=True)
trls=pd.read_parquet(p/'train_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp']); print('read',len(trls),flush=True)
trls=trls[trls.item_id.isin(items)&trls.uid.isin(target)]; print('filtered',len(trls),flush=True)
truthkey=pd.MultiIndex.from_frame(truth[['uid','item_id']])
for name,mask in [('any',np.ones(len(trls),bool)),('pos50',trls.played_ratio_pct>=50),('strong80',trls.played_ratio_pct>=80),('organic80',(trls.is_organic==1)&(trls.played_ratio_pct>=80)),('recent7d',trls.timestamp>=24786800-7*86400),('recent30d',trls.timestamp>=24786800-30*86400),('recent90d',trls.timestamp>=24786800-90*86400)]:
 seen=pd.MultiIndex.from_frame(trls.loc[mask,['uid','item_id']].drop_duplicates()); print(name,len(seen),truthkey.isin(seen).sum(),truthkey.isin(seen).mean(),flush=True)
state=pd.read_parquet(p/'validation_preference_state.parquet')
for s in ['liked','disliked']:
 q=pd.MultiIndex.from_frame(state[state.state==s][['uid','item_id']].drop_duplicates()); print(s,truthkey.isin(q).sum(),truthkey.isin(q).mean(),flush=True)
sm=state[state.uid.isin(target)&state.item_id.isin(items)]; print('statecat',sm.groupby('state').size(),flush=True)
print('eligstate',sm.groupby('state').apply(lambda x: np.mean([elig(u,i) for u,i in x[['uid','item_id']].itertuples(index=False,name=None)]),include_groups=False),flush=True)
PY 
/proc/9494 /opt/task-venv/bin/python -u - 
/proc/9530 /bin/sh -c for p in /proc/[0-9]*; do x=$(tr '' ' ' < "$p/cmdline" 2>/dev/null); case "$x" in *python*) echo "$p $x";; esac; done
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-0cd1dc42-f54f-43ac-ae39-7da70d6bfbcf",
    "process_id": "52397",
    "command": [
      "/bin/sh",
      "-lc",
      "for p in /proc/[0-9]*; do x=$(tr '\\000' ' ' < \"$p/cmdline\" 2>/dev/null); case \"$x\" in *python*) echo \"$p $x\";; esac; done"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "for p in /proc/[0-9]*; do x=$(tr '\\000' ' ' < \"$p/cmdline\" 2>/dev/null); case \"$x\" in *python*) echo \"$p $x\";; esac; done"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "/proc/8774 bash -c set -o pipefail; if [ -s ~/.nvm/nvm.sh ]; then . ~/.nvm/nvm.sh; fi; codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=max -- '# 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' 2>&1 </dev/null | tee /logs/agent/codex.txt \n/proc/9000 node /home/agent/.nvm/versions/node/v22.23.3/bin/codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=max -- # 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 \n/proc/9008 /home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex/node_modules/@openai/codex-linux-x64/vendor/x86_64-unknown-linux-musl/bin/codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=max -- # 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 \n/proc/9455 /bin/sh -c mkdir -p /app/solution && /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np\nfrom pathlib import Path\np=Path('/task/data')\ntarget=set(pd.read_parquet(p/'target_users.parquet').uid.astype(int))\nz=np.load(p/'validation_eligible_candidates.npz'); uids=z['uids']; items=z['item_ids']; uidrow={int(x):i for i,x in enumerate(uids)}; itemcol={int(x):i for i,x in enumerate(items)}\ndef elig(u,i):\n r=uidrow.get(int(u)); c=itemcol.get(int(i)); return r is not None and c is not None and ((z['packed_eligible'][r,c//8]>>(c%8))&1)\nls=pd.read_parquet(p/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nlk=pd.read_parquet(p/'val_likes.parquet',columns=['uid','item_id','is_organic'])\nstrong=ls[(ls.uid.isin(target))&(ls.is_organic==1)&(ls.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\nlikes=lk[(lk.uid.isin(target))&(lk.is_organic==1)][['uid','item_id']].drop_duplicates()\ntruth=pd.concat([strong.assign(grade=1),likes.assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\ntruth=truth[[elig(u,i) for u,i in truth[['uid','item_id']].itertuples(index=False,name=None)]]\nprint('truth pairs/users/grades',len(truth),truth.uid.nunique(),truth.grade.value_counts().to_dict(), 'per user',truth.groupby('uid').size().describe(percentiles=[.5,.9,.99]).to_dict())\n# seen in train positive, any, explicit state\ncat=set(map(int,items)); truthkey=pd.MultiIndex.from_frame(truth[['uid','item_id']])\ntrls=pd.read_parquet(p/'train_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp'])\ntrls=trls[trls.item_id.isin(cat)&trls.uid.isin(target)]\nfor name,mask in [('any',np.ones(len(trls),bool)),('pos50',trls.played_ratio_pct>=50),('strong80',trls.played_ratio_pct>=80),('organic80',(trls.is_organic==1)&(trls.played_ratio_pct>=80)),('recent7d',trls.timestamp>=24786800-7*86400),('recent30d',trls.timestamp>=24786800-30*86400),('recent90d',trls.timestamp>=24786800-90*86400)]:\n seen=pd.MultiIndex.from_frame(trls.loc[mask,['uid','item_id']].drop_duplicates())\n print(name,'train pairs',len(seen),'truth overlap',truthkey.isin(seen).sum(), truthkey.isin(seen).mean())\nstate=pd.read_parquet(p/'validation_preference_state.parquet')\nfor s in ['liked','disliked']:\n q=pd.MultiIndex.from_frame(state[state.state==s][['uid','item_id']].drop_duplicates())\n print(s,'truth overlap',truthkey.isin(q).sum(),truthkey.isin(q).mean())\n# eligible exclusions matchup states and train distinct\nsm=state[state.uid.isin(target)&state.item_id.isin(cat)]\nprint('state cat',sm.groupby('state').size())\nprint('state eligibility',sm.groupby('state').apply(lambda x: np.mean([elig(u,i) for u,i in x[['uid','item_id']].itertuples(index=False,name=None)]),include_groups=False))\nPY \n/proc/9457 /opt/task-venv/bin/python - \n/proc/9493 /bin/sh -c /opt/task-venv/bin/python -u - <<'PY'\nimport pandas as pd, numpy as np\nfrom pathlib import Path\np=Path('/task/data'); target=set(pd.read_parquet(p/'target_users.parquet').uid.astype(int)); z=np.load(p/'validation_eligible_candidates.npz'); uids=z['uids']; items=z['item_ids']; uidrow={int(x):i for i,x in enumerate(uids)}; itemcol={int(x):i for i,x in enumerate(items)}\ndef elig(u,i):\n r=uidrow.get(int(u)); c=itemcol.get(int(i)); return r is not None and c is not None and ((z['packed_eligible'][r,c//8]>>(c%8))&1)\nls=pd.read_parquet(p/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct']); lk=pd.read_parquet(p/'val_likes.parquet',columns=['uid','item_id','is_organic'])\nstrong=ls[(ls.uid.isin(target))&(ls.is_organic==1)&(ls.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); likes=lk[(lk.uid.isin(target))&(lk.is_organic==1)][['uid','item_id']].drop_duplicates(); truth=pd.concat([strong.assign(grade=1),likes.assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last'); truth=truth[[elig(u,i) for u,i in truth[['uid','item_id']].itertuples(index=False,name=None)]]\nprint('truth',len(truth),truth.uid.nunique(),truth.grade.value_counts().to_dict(),truth.groupby('uid').size().describe().to_dict(),flush=True)\nprint('reading train',flush=True)\ntrls=pd.read_parquet(p/'train_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp']); print('read',len(trls),flush=True)\ntrls=trls[trls.item_id.isin(items)&trls.uid.isin(target)]; print('filtered',len(trls),flush=True)\ntruthkey=pd.MultiIndex.from_frame(truth[['uid','item_id']])\nfor name,mask in [('any',np.ones(len(trls),bool)),('pos50',trls.played_ratio_pct>=50),('strong80',trls.played_ratio_pct>=80),('organic80',(trls.is_organic==1)&(trls.played_ratio_pct>=80)),('recent7d',trls.timestamp>=24786800-7*86400),('recent30d',trls.timestamp>=24786800-30*86400),('recent90d',trls.timestamp>=24786800-90*86400)]:\n seen=pd.MultiIndex.from_frame(trls.loc[mask,['uid','item_id']].drop_duplicates()); print(name,len(seen),truthkey.isin(seen).sum(),truthkey.isin(seen).mean(),flush=True)\nstate=pd.read_parquet(p/'validation_preference_state.parquet')\nfor s in ['liked','disliked']:\n q=pd.MultiIndex.from_frame(state[state.state==s][['uid','item_id']].drop_duplicates()); print(s,truthkey.isin(q).sum(),truthkey.isin(q).mean(),flush=True)\nsm=state[state.uid.isin(target)&state.item_id.isin(items)]; print('statecat',sm.groupby('state').size(),flush=True)\nprint('eligstate',sm.groupby('state').apply(lambda x: np.mean([elig(u,i) for u,i in x[['uid','item_id']].itertuples(index=False,name=None)]),include_groups=False),flush=True)\nPY \n/proc/9494 /opt/task-venv/bin/python -u - \n/proc/9530 /bin/sh -c for p in /proc/[0-9]*; do x=$(tr '\u0000' ' ' < \"$p/cmdline\" 2>/dev/null); case \"$x\" in *python*) echo \"$p $x\";; esac; done \n",
    "stderr": "",
    "aggregated_output": "/proc/8774 bash -c set -o pipefail; if [ -s ~/.nvm/nvm.sh ]; then . ~/.nvm/nvm.sh; fi; codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=max -- '# 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' 2>&1 </dev/null | tee /logs/agent/codex.txt \n/proc/9000 node /home/agent/.nvm/versions/node/v22.23.3/bin/codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=max -- # 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 \n/proc/9008 /home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex/node_modules/@openai/codex-linux-x64/vendor/x86_64-unknown-linux-musl/bin/codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=max -- # 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 \n/proc/9455 /bin/sh -c mkdir -p /app/solution && /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np\nfrom pathlib import Path\np=Path('/task/data')\ntarget=set(pd.read_parquet(p/'target_users.parquet').uid.astype(int))\nz=np.load(p/'validation_eligible_candidates.npz'); uids=z['uids']; items=z['item_ids']; uidrow={int(x):i for i,x in enumerate(uids)}; itemcol={int(x):i for i,x in enumerate(items)}\ndef elig(u,i):\n r=uidrow.get(int(u)); c=itemcol.get(int(i)); return r is not None and c is not None and ((z['packed_eligible'][r,c//8]>>(c%8))&1)\nls=pd.read_parquet(p/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nlk=pd.read_parquet(p/'val_likes.parquet',columns=['uid','item_id','is_organic'])\nstrong=ls[(ls.uid.isin(target))&(ls.is_organic==1)&(ls.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\nlikes=lk[(lk.uid.isin(target))&(lk.is_organic==1)][['uid','item_id']].drop_duplicates()\ntruth=pd.concat([strong.assign(grade=1),likes.assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\ntruth=truth[[elig(u,i) for u,i in truth[['uid','item_id']].itertuples(index=False,name=None)]]\nprint('truth pairs/users/grades',len(truth),truth.uid.nunique(),truth.grade.value_counts().to_dict(), 'per user',truth.groupby('uid').size().describe(percentiles=[.5,.9,.99]).to_dict())\n# seen in train positive, any, explicit state\ncat=set(map(int,items)); truthkey=pd.MultiIndex.from_frame(truth[['uid','item_id']])\ntrls=pd.read_parquet(p/'train_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp'])\ntrls=trls[trls.item_id.isin(cat)&trls.uid.isin(target)]\nfor name,mask in [('any',np.ones(len(trls),bool)),('pos50',trls.played_ratio_pct>=50),('strong80',trls.played_ratio_pct>=80),('organic80',(trls.is_organic==1)&(trls.played_ratio_pct>=80)),('recent7d',trls.timestamp>=24786800-7*86400),('recent30d',trls.timestamp>=24786800-30*86400),('recent90d',trls.timestamp>=24786800-90*86400)]:\n seen=pd.MultiIndex.from_frame(trls.loc[mask,['uid','item_id']].drop_duplicates())\n print(name,'train pairs',len(seen),'truth overlap',truthkey.isin(seen).sum(), truthkey.isin(seen).mean())\nstate=pd.read_parquet(p/'validation_preference_state.parquet')\nfor s in ['liked','disliked']:\n q=pd.MultiIndex.from_frame(state[state.state==s][['uid','item_id']].drop_duplicates())\n print(s,'truth overlap',truthkey.isin(q).sum(),truthkey.isin(q).mean())\n# eligible exclusions matchup states and train distinct\nsm=state[state.uid.isin(target)&state.item_id.isin(cat)]\nprint('state cat',sm.groupby('state').size())\nprint('state eligibility',sm.groupby('state').apply(lambda x: np.mean([elig(u,i) for u,i in x[['uid','item_id']].itertuples(index=False,name=None)]),include_groups=False))\nPY \n/proc/9457 /opt/task-venv/bin/python - \n/proc/9493 /bin/sh -c /opt/task-venv/bin/python -u - <<'PY'\nimport pandas as pd, numpy as np\nfrom pathlib import Path\np=Path('/task/data'); target=set(pd.read_parquet(p/'target_users.parquet').uid.astype(int)); z=np.load(p/'validation_eligible_candidates.npz'); uids=z['uids']; items=z['item_ids']; uidrow={int(x):i for i,x in enumerate(uids)}; itemcol={int(x):i for i,x in enumerate(items)}\ndef elig(u,i):\n r=uidrow.get(int(u)); c=itemcol.get(int(i)); return r is not None and c is not None and ((z['packed_eligible'][r,c//8]>>(c%8))&1)\nls=pd.read_parquet(p/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct']); lk=pd.read_parquet(p/'val_likes.parquet',columns=['uid','item_id','is_organic'])\nstrong=ls[(ls.uid.isin(target))&(ls.is_organic==1)&(ls.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); likes=lk[(lk.uid.isin(target))&(lk.is_organic==1)][['uid','item_id']].drop_duplicates(); truth=pd.concat([strong.assign(grade=1),likes.assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last'); truth=truth[[elig(u,i) for u,i in truth[['uid','item_id']].itertuples(index=False,name=None)]]\nprint('truth',len(truth),truth.uid.nunique(),truth.grade.value_counts().to_dict(),truth.groupby('uid').size().describe().to_dict(),flush=True)\nprint('reading train',flush=True)\ntrls=pd.read_parquet(p/'train_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp']); print('read',len(trls),flush=True)\ntrls=trls[trls.item_id.isin(items)&trls.uid.isin(target)]; print('filtered',len(trls),flush=True)\ntruthkey=pd.MultiIndex.from_frame(truth[['uid','item_id']])\nfor name,mask in [('any',np.ones(len(trls),bool)),('pos50',trls.played_ratio_pct>=50),('strong80',trls.played_ratio_pct>=80),('organic80',(trls.is_organic==1)&(trls.played_ratio_pct>=80)),('recent7d',trls.timestamp>=24786800-7*86400),('recent30d',trls.timestamp>=24786800-30*86400),('recent90d',trls.timestamp>=24786800-90*86400)]:\n seen=pd.MultiIndex.from_frame(trls.loc[mask,['uid','item_id']].drop_duplicates()); print(name,len(seen),truthkey.isin(seen).sum(),truthkey.isin(seen).mean(),flush=True)\nstate=pd.read_parquet(p/'validation_preference_state.parquet')\nfor s in ['liked','disliked']:\n q=pd.MultiIndex.from_frame(state[state.state==s][['uid','item_id']].drop_duplicates()); print(s,truthkey.isin(q).sum(),truthkey.isin(q).mean(),flush=True)\nsm=state[state.uid.isin(target)&state.item_id.isin(items)]; print('statecat',sm.groupby('state').size(),flush=True)\nprint('eligstate',sm.groupby('state').apply(lambda x: np.mean([elig(u,i) for u,i in x[['uid','item_id']].itertuples(index=False,name=None)]),include_groups=False),flush=True)\nPY \n/proc/9494 /opt/task-venv/bin/python -u - \n/proc/9530 /bin/sh -c for p in /proc/[0-9]*; do x=$(tr '\u0000' ' ' < \"$p/cmdline\" 2>/dev/null); case \"$x\" in *python*) echo \"$p $x\";; esac; done \n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3740
    },
    "formatted_output": "/proc/8774 bash -c set -o pipefail; if [ -s ~/.nvm/nvm.sh ]; then . ~/.nvm/nvm.sh; fi; codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=max -- '# 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' 2>&1 </dev/null | tee /logs/agent/codex.txt \n/proc/9000 node /home/agent/.nvm/versions/node/v22.23.3/bin/codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=max -- # 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 \n/proc/9008 /home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex/node_modules/@openai/codex-linux-x64/vendor/x86_64-unknown-linux-musl/bin/codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=max -- # 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 \n/proc/9455 /bin/sh -c mkdir -p /app/solution && /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np\nfrom pathlib import Path\np=Path('/task/data')\ntarget=set(pd.read_parquet(p/'target_users.parquet').uid.astype(int))\nz=np.load(p/'validation_eligible_candidates.npz'); uids=z['uids']; items=z['item_ids']; uidrow={int(x):i for i,x in enumerate(uids)}; itemcol={int(x):i for i,x in enumerate(items)}\ndef elig(u,i):\n r=uidrow.get(int(u)); c=itemcol.get(int(i)); return r is not None and c is not None and ((z['packed_eligible'][r,c//8]>>(c%8))&1)\nls=pd.read_parquet(p/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nlk=pd.read_parquet(p/'val_likes.parquet',columns=['uid','item_id','is_organic'])\nstrong=ls[(ls.uid.isin(target))&(ls.is_organic==1)&(ls.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\nlikes=lk[(lk.uid.isin(target))&(lk.is_organic==1)][['uid','item_id']].drop_duplicates()\ntruth=pd.concat([strong.assign(grade=1),likes.assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\ntruth=truth[[elig(u,i) for u,i in truth[['uid','item_id']].itertuples(index=False,name=None)]]\nprint('truth pairs/users/grades',len(truth),truth.uid.nunique(),truth.grade.value_counts().to_dict(), 'per user',truth.groupby('uid').size().describe(percentiles=[.5,.9,.99]).to_dict())\n# seen in train positive, any, explicit state\ncat=set(map(int,items)); truthkey=pd.MultiIndex.from_frame(truth[['uid','item_id']])\ntrls=pd.read_parquet(p/'train_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp'])\ntrls=trls[trls.item_id.isin(cat)&trls.uid.isin(target)]\nfor name,mask in [('any',np.ones(len(trls),bool)),('pos50',trls.played_ratio_pct>=50),('strong80',trls.played_ratio_pct>=80),('organic80',(trls.is_organic==1)&(trls.played_ratio_pct>=80)),('recent7d',trls.timestamp>=24786800-7*86400),('recent30d',trls.timestamp>=24786800-30*86400),('recent90d',trls.timestamp>=24786800-90*86400)]:\n seen=pd.MultiIndex.from_frame(trls.loc[mask,['uid','item_id']].drop_duplicates())\n print(name,'train pairs',len(seen),'truth overlap',truthkey.isin(seen).sum(), truthkey.isin(seen).mean())\nstate=pd.read_parquet(p/'validation_preference_state.parquet')\nfor s in ['liked','disliked']:\n q=pd.MultiIndex.from_frame(state[state.state==s][['uid','item_id']].drop_duplicates())\n print(s,'truth overlap',truthkey.isin(q).sum(),truthkey.isin(q).mean())\n# eligible exclusions matchup states and train distinct\nsm=state[state.uid.isin(target)&state.item_id.isin(cat)]\nprint('state cat',sm.groupby('state').size())\nprint('state eligibility',sm.groupby('state').apply(lambda x: np.mean([elig(u,i) for u,i in x[['uid','item_id']].itertuples(index=False,name=None)]),include_groups=False))\nPY \n/proc/9457 /opt/task-venv/bin/python - \n/proc/9493 /bin/sh -c /opt/task-venv/bin/python -u - <<'PY'\nimport pandas as pd, numpy as np\nfrom pathlib import Path\np=Path('/task/data'); target=set(pd.read_parquet(p/'target_users.parquet').uid.astype(int)); z=np.load(p/'validation_eligible_candidates.npz'); uids=z['uids']; items=z['item_ids']; uidrow={int(x):i for i,x in enumerate(uids)}; itemcol={int(x):i for i,x in enumerate(items)}\ndef elig(u,i):\n r=uidrow.get(int(u)); c=itemcol.get(int(i)); return r is not None and c is not None and ((z['packed_eligible'][r,c//8]>>(c%8))&1)\nls=pd.read_parquet(p/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct']); lk=pd.read_parquet(p/'val_likes.parquet',columns=['uid','item_id','is_organic'])\nstrong=ls[(ls.uid.isin(target))&(ls.is_organic==1)&(ls.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); likes=lk[(lk.uid.isin(target))&(lk.is_organic==1)][['uid','item_id']].drop_duplicates(); truth=pd.concat([strong.assign(grade=1),likes.assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last'); truth=truth[[elig(u,i) for u,i in truth[['uid','item_id']].itertuples(index=False,name=None)]]\nprint('truth',len(truth),truth.uid.nunique(),truth.grade.value_counts().to_dict(),truth.groupby('uid').size().describe().to_dict(),flush=True)\nprint('reading train',flush=True)\ntrls=pd.read_parquet(p/'train_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp']); print('read',len(trls),flush=True)\ntrls=trls[trls.item_id.isin(items)&trls.uid.isin(target)]; print('filtered',len(trls),flush=True)\ntruthkey=pd.MultiIndex.from_frame(truth[['uid','item_id']])\nfor name,mask in [('any',np.ones(len(trls),bool)),('pos50',trls.played_ratio_pct>=50),('strong80',trls.played_ratio_pct>=80),('organic80',(trls.is_organic==1)&(trls.played_ratio_pct>=80)),('recent7d',trls.timestamp>=24786800-7*86400),('recent30d',trls.timestamp>=24786800-30*86400),('recent90d',trls.timestamp>=24786800-90*86400)]:\n seen=pd.MultiIndex.from_frame(trls.loc[mask,['uid','item_id']].drop_duplicates()); print(name,len(seen),truthkey.isin(seen).sum(),truthkey.isin(seen).mean(),flush=True)\nstate=pd.read_parquet(p/'validation_preference_state.parquet')\nfor s in ['liked','disliked']:\n q=pd.MultiIndex.from_frame(state[state.state==s][['uid','item_id']].drop_duplicates()); print(s,truthkey.isin(q).sum(),truthkey.isin(q).mean(),flush=True)\nsm=state[state.uid.isin(target)&state.item_id.isin(items)]; print('statecat',sm.groupby('state').size(),flush=True)\nprint('eligstate',sm.groupby('state').apply(lambda x: np.mean([elig(u,i) for u,i in x[['uid','item_id']].itertuples(index=False,name=None)]),include_groups=False),flush=True)\nPY \n/proc/9494 /opt/task-venv/bin/python -u - \n/proc/9530 /bin/sh -c for p in /proc/[0-9]*; do x=$(tr '\u0000' ' ' < \"$p/cmdline\" 2>/dev/null); case \"$x\" in *python*) echo \"$p $x\";; esac; done \n"
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}
Harness context token usage record
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}
21

exec command

+02:56
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"kill 9457 9455 2>/dev/null || true","workdir":"/app","yield_time_ms":1000,"max_output_tokens":1000});
text(r.output);

Tool response 

Script completed
Wall time 0.0 seconds
Output:
Harness context token usage record
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Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-26b05dd1-055f-459b-a2b3-c0e3935e7352",
    "process_id": "12103",
    "command": [
      "/bin/sh",
      "-lc",
      "mkdir -p /app/solution && /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np\nfrom pathlib import Path\np=Path('/task/data')\ntarget=set(pd.read_parquet(p/'target_users.parquet').uid.astype(int))\nz=np.load(p/'validation_eligible_candidates.npz'); uids=z['uids']; items=z['item_ids']; uidrow={int(x):i for i,x in enumerate(uids)}; itemcol={int(x):i for i,x in enumerate(items)}\ndef elig(u,i):\n r=uidrow.get(int(u)); c=itemcol.get(int(i)); return r is not None and c is not None and ((z['packed_eligible'][r,c//8]>>(c%8))&1)\nls=pd.read_parquet(p/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nlk=pd.read_parquet(p/'val_likes.parquet',columns=['uid','item_id','is_organic'])\nstrong=ls[(ls.uid.isin(target))&(ls.is_organic==1)&(ls.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\nlikes=lk[(lk.uid.isin(target))&(lk.is_organic==1)][['uid','item_id']].drop_duplicates()\ntruth=pd.concat([strong.assign(grade=1),likes.assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\ntruth=truth[[elig(u,i) for u,i in truth[['uid','item_id']].itertuples(index=False,name=None)]]\nprint('truth pairs/users/grades',len(truth),truth.uid.nunique(),truth.grade.value_counts().to_dict(), 'per user',truth.groupby('uid').size().describe(percentiles=[.5,.9,.99]).to_dict())\n# seen in train positive, any, explicit state\ncat=set(map(int,items)); truthkey=pd.MultiIndex.from_frame(truth[['uid','item_id']])\ntrls=pd.read_parquet(p/'train_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp'])\ntrls=trls[trls.item_id.isin(cat)&trls.uid.isin(target)]\nfor name,mask in [('any',np.ones(len(trls),bool)),('pos50',trls.played_ratio_pct>=50),('strong80',trls.played_ratio_pct>=80),('organic80',(trls.is_organic==1)&(trls.played_ratio_pct>=80)),('recent7d',trls.timestamp>=24786800-7*86400),('recent30d',trls.timestamp>=24786800-30*86400),('recent90d',trls.timestamp>=24786800-90*86400)]:\n seen=pd.MultiIndex.from_frame(trls.loc[mask,['uid','item_id']].drop_duplicates())\n print(name,'train pairs',len(seen),'truth overlap',truthkey.isin(seen).sum(), truthkey.isin(seen).mean())\nstate=pd.read_parquet(p/'validation_preference_state.parquet')\nfor s in ['liked','disliked']:\n q=pd.MultiIndex.from_frame(state[state.state==s][['uid','item_id']].drop_duplicates())\n print(s,'truth overlap',truthkey.isin(q).sum(),truthkey.isin(q).mean())\n# eligible exclusions matchup states and train distinct\nsm=state[state.uid.isin(target)&state.item_id.isin(cat)]\nprint('state cat',sm.groupby('state').size())\nprint('state eligibility',sm.groupby('state').apply(lambda x: np.mean([elig(u,i) for u,i in x[['uid','item_id']].itertuples(index=False,name=None)]),include_groups=False))\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "mkdir -p /app/solution && /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np\nfrom pathlib import Path\np=Path('/task/data')\ntarget=set(pd.read_parquet(p/'target_users.parquet').uid.astype(int))\nz=np.load(p/'validation_eligible_candidates.npz'); uids=z['uids']; items=z['item_ids']; uidrow={int(x):i for i,x in enumerate(uids)}; itemcol={int(x):i for i,x in enumerate(items)}\ndef elig(u,i):\n r=uidrow.get(int(u)); c=itemcol.get(int(i)); return r is not None and c is not None and ((z['packed_eligible'][r,c//8]>>(c%8))&1)\nls=pd.read_parquet(p/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nlk=pd.read_parquet(p/'val_likes.parquet',columns=['uid','item_id','is_organic'])\nstrong=ls[(ls.uid.isin(target))&(ls.is_organic==1)&(ls.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\nlikes=lk[(lk.uid.isin(target))&(lk.is_organic==1)][['uid','item_id']].drop_duplicates()\ntruth=pd.concat([strong.assign(grade=1),likes.assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\ntruth=truth[[elig(u,i) for u,i in truth[['uid','item_id']].itertuples(index=False,name=None)]]\nprint('truth pairs/users/grades',len(truth),truth.uid.nunique(),truth.grade.value_counts().to_dict(), 'per user',truth.groupby('uid').size().describe(percentiles=[.5,.9,.99]).to_dict())\n# seen in train positive, any, explicit state\ncat=set(map(int,items)); truthkey=pd.MultiIndex.from_frame(truth[['uid','item_id']])\ntrls=pd.read_parquet(p/'train_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp'])\ntrls=trls[trls.item_id.isin(cat)&trls.uid.isin(target)]\nfor name,mask in [('any',np.ones(len(trls),bool)),('pos50',trls.played_ratio_pct>=50),('strong80',trls.played_ratio_pct>=80),('organic80',(trls.is_organic==1)&(trls.played_ratio_pct>=80)),('recent7d',trls.timestamp>=24786800-7*86400),('recent30d',trls.timestamp>=24786800-30*86400),('recent90d',trls.timestamp>=24786800-90*86400)]:\n seen=pd.MultiIndex.from_frame(trls.loc[mask,['uid','item_id']].drop_duplicates())\n print(name,'train pairs',len(seen),'truth overlap',truthkey.isin(seen).sum(), truthkey.isin(seen).mean())\nstate=pd.read_parquet(p/'validation_preference_state.parquet')\nfor s in ['liked','disliked']:\n q=pd.MultiIndex.from_frame(state[state.state==s][['uid','item_id']].drop_duplicates())\n print(s,'truth overlap',truthkey.isin(q).sum(),truthkey.isin(q).mean())\n# eligible exclusions matchup states and train distinct\nsm=state[state.uid.isin(target)&state.item_id.isin(cat)]\nprint('state cat',sm.groupby('state').size())\nprint('state eligibility',sm.groupby('state').apply(lambda x: np.mean([elig(u,i) for u,i in x[['uid','item_id']].itertuples(index=False,name=None)]),include_groups=False))\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "failed",
    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
    "exit_code": 143,
    "duration": {
      "secs": 96,
      "nanos": 528740477
    },
    "formatted_output": ""
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-fe2d5efd-0a8f-4d10-a30e-a1e3e828e5f8",
    "process_id": "30637",
    "command": [
      "/bin/sh",
      "-lc",
      "kill 9457 9455 2>/dev/null || true"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "kill 9457 9455 2>/dev/null || true"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3490
    },
    "formatted_output": ""
  }
}
22

exec command

+02:58
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":73954,"chars":"","yield_time_ms":10000,"max_output_tokens":10000});
text(r.output);

Tool response 

Script completed
Wall time 10.0 seconds
Output:
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bbc3715c87d2a81bdd13eacb017d",
  "usage": {
    "input_tokens": 30682,
    "cached_input_tokens": 30336,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 30735
  },
  "turn_token_usage": {
    "input_tokens": 344499,
    "cached_input_tokens": 305152,
    "cache_write_input_tokens": 0,
    "output_tokens": 4970,
    "reasoning_output_tokens": 1384,
    "total_tokens": 349469
  },
  "thread_token_usage": {
    "input_tokens": 344499,
    "cached_input_tokens": 305152,
    "cache_write_input_tokens": 0,
    "output_tokens": 4970,
    "reasoning_output_tokens": 1384,
    "total_tokens": 349469
  }
}
23

exec command

+03:12
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"for p in 9457 9494; do echo PROC$p; sed -n '1,30p' /proc/$p/status 2>/dev/null; done","workdir":"/app","yield_time_ms":1000,"max_output_tokens":3000});
text(r.output);

Tool response 

Script completed
Wall time 0.0 seconds
Output:

PROC9457
PROC9494
Name:	python
Umask:	0022
State:	R (running)
Tgid:	9494
Ngid:	0
Pid:	9494
PPid:	9493
TracerPid:	0
Uid:	1000	1000	1000	1000
Gid:	1000	1000	1000	1000
FDSize:	64
Groups:	1000 
NStgid:	9494
NSpid:	9494
NSpgid:	9493
NSsid:	9493
Kthread:	0
VmPeak:	 2156080 kB
VmSize:	 2141496 kB
VmLck:	       0 kB
VmPin:	       0 kB
VmHWM:	  254940 kB
VmRSS:	  244472 kB
RssAnon:	  175112 kB
RssFile:	   69360 kB
RssShmem:	       0 kB
VmData:	  703324 kB
VmStk:	     140 kB
VmExe:	       4 kB
VmLib:	  109188 kB
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-0f28aed5-e753-4143-a0a7-963b834bd3ea",
    "process_id": "35882",
    "command": [
      "/bin/sh",
      "-lc",
      "for p in 9457 9494; do echo PROC$p; sed -n '1,30p' /proc/$p/status 2>/dev/null; done"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "for p in 9457 9494; do echo PROC$p; sed -n '1,30p' /proc/$p/status 2>/dev/null; done"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "PROC9457\nPROC9494\nName:\tpython\nUmask:\t0022\nState:\tR (running)\nTgid:\t9494\nNgid:\t0\nPid:\t9494\nPPid:\t9493\nTracerPid:\t0\nUid:\t1000\t1000\t1000\t1000\nGid:\t1000\t1000\t1000\t1000\nFDSize:\t64\nGroups:\t1000 \nNStgid:\t9494\nNSpid:\t9494\nNSpgid:\t9493\nNSsid:\t9493\nKthread:\t0\nVmPeak:\t 2156080 kB\nVmSize:\t 2141496 kB\nVmLck:\t       0 kB\nVmPin:\t       0 kB\nVmHWM:\t  254940 kB\nVmRSS:\t  244472 kB\nRssAnon:\t  175112 kB\nRssFile:\t   69360 kB\nRssShmem:\t       0 kB\nVmData:\t  703324 kB\nVmStk:\t     140 kB\nVmExe:\t       4 kB\nVmLib:\t  109188 kB\n",
    "stderr": "",
    "aggregated_output": "PROC9457\nPROC9494\nName:\tpython\nUmask:\t0022\nState:\tR (running)\nTgid:\t9494\nNgid:\t0\nPid:\t9494\nPPid:\t9493\nTracerPid:\t0\nUid:\t1000\t1000\t1000\t1000\nGid:\t1000\t1000\t1000\t1000\nFDSize:\t64\nGroups:\t1000 \nNStgid:\t9494\nNSpid:\t9494\nNSpgid:\t9493\nNSsid:\t9493\nKthread:\t0\nVmPeak:\t 2156080 kB\nVmSize:\t 2141496 kB\nVmLck:\t       0 kB\nVmPin:\t       0 kB\nVmHWM:\t  254940 kB\nVmRSS:\t  244472 kB\nRssAnon:\t  175112 kB\nRssFile:\t   69360 kB\nRssShmem:\t       0 kB\nVmData:\t  703324 kB\nVmStk:\t     140 kB\nVmExe:\t       4 kB\nVmLib:\t  109188 kB\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3470
    },
    "formatted_output": "PROC9457\nPROC9494\nName:\tpython\nUmask:\t0022\nState:\tR (running)\nTgid:\t9494\nNgid:\t0\nPid:\t9494\nPPid:\t9493\nTracerPid:\t0\nUid:\t1000\t1000\t1000\t1000\nGid:\t1000\t1000\t1000\t1000\nFDSize:\t64\nGroups:\t1000 \nNStgid:\t9494\nNSpid:\t9494\nNSpgid:\t9493\nNSsid:\t9493\nKthread:\t0\nVmPeak:\t 2156080 kB\nVmSize:\t 2141496 kB\nVmLck:\t       0 kB\nVmPin:\t       0 kB\nVmHWM:\t  254940 kB\nVmRSS:\t  244472 kB\nRssAnon:\t  175112 kB\nRssFile:\t   69360 kB\nRssShmem:\t       0 kB\nVmData:\t  703324 kB\nVmStk:\t     140 kB\nVmExe:\t       4 kB\nVmLib:\t  109188 kB\n"
  }
}
Harness context token usage record
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  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bbcf8cb887d28ec04e3c82933416",
  "usage": {
    "input_tokens": 30758,
    "cached_input_tokens": 30464,
    "cache_write_input_tokens": 0,
    "output_tokens": 102,
    "reasoning_output_tokens": 19,
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  },
  "turn_token_usage": {
    "input_tokens": 375257,
    "cached_input_tokens": 335616,
    "cache_write_input_tokens": 0,
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    "reasoning_output_tokens": 1403,
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  },
  "thread_token_usage": {
    "input_tokens": 375257,
    "cached_input_tokens": 335616,
    "cache_write_input_tokens": 0,
    "output_tokens": 5072,
    "reasoning_output_tokens": 1403,
    "total_tokens": 380329
  }
}
24

exec command

+03:16
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"awk '{print $14,$15,$22}' /proc/9494/stat; sleep 1; awk '{print $14,$15,$22}' /proc/9494/stat","workdir":"/app","yield_time_ms":2000,"max_output_tokens":1000});
text(r.output);

Tool response 

Script completed
Wall time 1.0 seconds
Output:

6300 78 79045
6399 79 79045
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
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  "usage": {
    "input_tokens": 31155,
    "cached_input_tokens": 30592,
    "cache_write_input_tokens": 0,
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  },
  "turn_token_usage": {
    "input_tokens": 406412,
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    "cache_write_input_tokens": 0,
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  },
  "thread_token_usage": {
    "input_tokens": 406412,
    "cached_input_tokens": 366208,
    "cache_write_input_tokens": 0,
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    "reasoning_output_tokens": 1414,
    "total_tokens": 411579
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-69ed1926-8171-49ef-86b9-3aba38fd1cd2",
    "process_id": "29504",
    "command": [
      "/bin/sh",
      "-lc",
      "awk '{print $14,$15,$22}' /proc/9494/stat; sleep 1; awk '{print $14,$15,$22}' /proc/9494/stat"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "awk '{print $14,$15,$22}' /proc/9494/stat; sleep 1; awk '{print $14,$15,$22}' /proc/9494/stat"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "6399 79 79045\n",
    "stderr": "",
    "aggregated_output": "6399 79 79045\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 853436997
    },
    "formatted_output": "6399 79 79045\n"
  }
}
25

exec command

+03:21
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":73954,"chars":"","yield_time_ms":10000,"max_output_tokens":10000});
text(r.output);

Tool response 

Script completed
Wall time 10.0 seconds
Output:
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bbd8df9c87d2b198b925c655320a",
  "usage": {
    "input_tokens": 31289,
    "cached_input_tokens": 30976,
    "cache_write_input_tokens": 0,
    "output_tokens": 154,
    "reasoning_output_tokens": 107,
    "total_tokens": 31443
  },
  "turn_token_usage": {
    "input_tokens": 437701,
    "cached_input_tokens": 397184,
    "cache_write_input_tokens": 0,
    "output_tokens": 5321,
    "reasoning_output_tokens": 1521,
    "total_tokens": 443022
  },
  "thread_token_usage": {
    "input_tokens": 437701,
    "cached_input_tokens": 397184,
    "cache_write_input_tokens": 0,
    "output_tokens": 5321,
    "reasoning_output_tokens": 1521,
    "total_tokens": 443022
  }
}
26

exec command

+03:34
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":73954,"chars":"\u0003","yield_time_ms":1000,"max_output_tokens":3000});
text(r.output);

Tool response 

Script completed
Wall time 0.1 seconds
Output:

Traceback (most recent call last):
  File "<stdin>", line 7, in <module>
  File "<stdin>", line 5, in elig
  File "/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py", line 254, in __getitem__
    return format.read_array(bytes,
           ^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py", line 851, in read_array
    data = _read_bytes(fp, read_size, "array data")
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py", line 986, in _read_bytes
    r = fp.read(size - len(data))
        ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/zipfile/__init__.py", line 1008, in read
    data = self._read1(n)
           ^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/zipfile/__init__.py", line 1084, in _read1
    data = self._decompressor.decompress(data, n)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
KeyboardInterrupt
Harness context token usage record
{
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  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
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  },
  "turn_token_usage": {
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  },
  "thread_token_usage": {
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    "cache_write_input_tokens": 0,
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  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-62ff6f92-3fe7-4429-8903-e3de8e33053e",
    "process_id": "73954",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python -u - <<'PY'\nimport pandas as pd, numpy as np\nfrom pathlib import Path\np=Path('/task/data'); target=set(pd.read_parquet(p/'target_users.parquet').uid.astype(int)); z=np.load(p/'validation_eligible_candidates.npz'); uids=z['uids']; items=z['item_ids']; uidrow={int(x):i for i,x in enumerate(uids)}; itemcol={int(x):i for i,x in enumerate(items)}\ndef elig(u,i):\n r=uidrow.get(int(u)); c=itemcol.get(int(i)); return r is not None and c is not None and ((z['packed_eligible'][r,c//8]>>(c%8))&1)\nls=pd.read_parquet(p/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct']); lk=pd.read_parquet(p/'val_likes.parquet',columns=['uid','item_id','is_organic'])\nstrong=ls[(ls.uid.isin(target))&(ls.is_organic==1)&(ls.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); likes=lk[(lk.uid.isin(target))&(lk.is_organic==1)][['uid','item_id']].drop_duplicates(); truth=pd.concat([strong.assign(grade=1),likes.assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last'); truth=truth[[elig(u,i) for u,i in truth[['uid','item_id']].itertuples(index=False,name=None)]]\nprint('truth',len(truth),truth.uid.nunique(),truth.grade.value_counts().to_dict(),truth.groupby('uid').size().describe().to_dict(),flush=True)\nprint('reading train',flush=True)\ntrls=pd.read_parquet(p/'train_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp']); print('read',len(trls),flush=True)\ntrls=trls[trls.item_id.isin(items)&trls.uid.isin(target)]; print('filtered',len(trls),flush=True)\ntruthkey=pd.MultiIndex.from_frame(truth[['uid','item_id']])\nfor name,mask in [('any',np.ones(len(trls),bool)),('pos50',trls.played_ratio_pct>=50),('strong80',trls.played_ratio_pct>=80),('organic80',(trls.is_organic==1)&(trls.played_ratio_pct>=80)),('recent7d',trls.timestamp>=24786800-7*86400),('recent30d',trls.timestamp>=24786800-30*86400),('recent90d',trls.timestamp>=24786800-90*86400)]:\n seen=pd.MultiIndex.from_frame(trls.loc[mask,['uid','item_id']].drop_duplicates()); print(name,len(seen),truthkey.isin(seen).sum(),truthkey.isin(seen).mean(),flush=True)\nstate=pd.read_parquet(p/'validation_preference_state.parquet')\nfor s in ['liked','disliked']:\n q=pd.MultiIndex.from_frame(state[state.state==s][['uid','item_id']].drop_duplicates()); print(s,truthkey.isin(q).sum(),truthkey.isin(q).mean(),flush=True)\nsm=state[state.uid.isin(target)&state.item_id.isin(items)]; print('statecat',sm.groupby('state').size(),flush=True)\nprint('eligstate',sm.groupby('state').apply(lambda x: np.mean([elig(u,i) for u,i in x[['uid','item_id']].itertuples(index=False,name=None)]),include_groups=False),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\nfrom pathlib import Path\np=Path('/task/data'); target=set(pd.read_parquet(p/'target_users.parquet').uid.astype(int)); z=np.load(p/'validation_eligible_candidates.npz'); uids=z['uids']; items=z['item_ids']; uidrow={int(x):i for i,x in enumerate(uids)}; itemcol={int(x):i for i,x in enumerate(items)}\ndef elig(u,i):\n r=uidrow.get(int(u)); c=itemcol.get(int(i)); return r is not None and c is not None and ((z['packed_eligible'][r,c//8]>>(c%8))&1)\nls=pd.read_parquet(p/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct']); lk=pd.read_parquet(p/'val_likes.parquet',columns=['uid','item_id','is_organic'])\nstrong=ls[(ls.uid.isin(target))&(ls.is_organic==1)&(ls.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); likes=lk[(lk.uid.isin(target))&(lk.is_organic==1)][['uid','item_id']].drop_duplicates(); truth=pd.concat([strong.assign(grade=1),likes.assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last'); truth=truth[[elig(u,i) for u,i in truth[['uid','item_id']].itertuples(index=False,name=None)]]\nprint('truth',len(truth),truth.uid.nunique(),truth.grade.value_counts().to_dict(),truth.groupby('uid').size().describe().to_dict(),flush=True)\nprint('reading train',flush=True)\ntrls=pd.read_parquet(p/'train_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp']); print('read',len(trls),flush=True)\ntrls=trls[trls.item_id.isin(items)&trls.uid.isin(target)]; print('filtered',len(trls),flush=True)\ntruthkey=pd.MultiIndex.from_frame(truth[['uid','item_id']])\nfor name,mask in [('any',np.ones(len(trls),bool)),('pos50',trls.played_ratio_pct>=50),('strong80',trls.played_ratio_pct>=80),('organic80',(trls.is_organic==1)&(trls.played_ratio_pct>=80)),('recent7d',trls.timestamp>=24786800-7*86400),('recent30d',trls.timestamp>=24786800-30*86400),('recent90d',trls.timestamp>=24786800-90*86400)]:\n seen=pd.MultiIndex.from_frame(trls.loc[mask,['uid','item_id']].drop_duplicates()); print(name,len(seen),truthkey.isin(seen).sum(),truthkey.isin(seen).mean(),flush=True)\nstate=pd.read_parquet(p/'validation_preference_state.parquet')\nfor s in ['liked','disliked']:\n q=pd.MultiIndex.from_frame(state[state.state==s][['uid','item_id']].drop_duplicates()); print(s,truthkey.isin(q).sum(),truthkey.isin(q).mean(),flush=True)\nsm=state[state.uid.isin(target)&state.item_id.isin(items)]; print('statecat',sm.groupby('state').size(),flush=True)\nprint('eligstate',sm.groupby('state').apply(lambda x: np.mean([elig(u,i) for u,i in x[['uid','item_id']].itertuples(index=False,name=None)]),include_groups=False),flush=True)\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "failed",
    "stdout": "Traceback (most recent call last):\n  File \"<stdin>\", line 7, in <module>\n  File \"<stdin>\", line 5, in elig\n  File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py\", line 254, in __getitem__\n    return format.read_array(bytes,\n           ^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 851, in read_array\n    data = _read_bytes(fp, read_size, \"array data\")\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 986, in _read_bytes\n    r = fp.read(size - len(data))\n        ^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1008, in read\n    data = self._read1(n)\n           ^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1084, in _read1\n    data = self._decompressor.decompress(data, n)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nKeyboardInterrupt\n",
    "stderr": "",
    "aggregated_output": "Traceback (most recent call last):\n  File \"<stdin>\", line 7, in <module>\n  File \"<stdin>\", line 5, in elig\n  File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py\", line 254, in __getitem__\n    return format.read_array(bytes,\n           ^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 851, in read_array\n    data = _read_bytes(fp, read_size, \"array data\")\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 986, in _read_bytes\n    r = fp.read(size - len(data))\n        ^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1008, in read\n    data = self._read1(n)\n           ^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1084, in _read1\n    data = self._decompressor.decompress(data, n)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nKeyboardInterrupt\n",
    "exit_code": 130,
    "duration": {
      "secs": 80,
      "nanos": 519025907
    },
    "formatted_output": "Traceback (most recent call last):\n  File \"<stdin>\", line 7, in <module>\n  File \"<stdin>\", line 5, in elig\n  File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py\", line 254, in __getitem__\n    return format.read_array(bytes,\n           ^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 851, in read_array\n    data = _read_bytes(fp, read_size, \"array data\")\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 986, in _read_bytes\n    r = fp.read(size - len(data))\n        ^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1008, in read\n    data = self._read1(n)\n           ^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1084, in _read1\n    data = self._decompressor.decompress(data, n)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nKeyboardInterrupt\n"
  }
}
27

exec command

+03:37
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python -u - <<'PY'\nimport pandas as pd, numpy as np\nfrom pathlib import Path\np=Path('/task/data'); targets=pd.read_parquet(p/'target_users.parquet').uid.astype(int); target=set(targets)\nwith np.load(p/'validation_eligible_candidates.npz') as z: uids=z['uids'].copy(); items=z['item_ids'].copy(); packed=z['packed_eligible'].copy()\nuidrow=pd.Series(np.arange(len(uids)),index=uids.astype(np.int64)); itemcol=pd.Series(np.arange(len(items)),index=items.astype(np.int64))\nls=pd.read_parquet(p/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct']); lk=pd.read_parquet(p/'val_likes.parquet',columns=['uid','item_id','is_organic'])\nstrong=ls[(ls.uid.isin(target))&(ls.is_organic==1)&(ls.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); likes=lk[(lk.uid.isin(target))&(lk.is_organic==1)][['uid','item_id']].drop_duplicates(); truth=pd.concat([strong.assign(grade=1),likes.assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last'); print('raw truth',len(truth),flush=True)\nr=uidrow.reindex(truth.uid.astype('int64')).values; c=itemcol.reindex(truth.item_id.astype('int64')).values; ok=~(np.isnan(r)|np.isnan(c)); ri=r[ok].astype(int); ci=c[ok].astype(int); ix=np.flatnonzero(ok); ok[ix]=((packed[ri,ci//8]>>(ci%8))&1).astype(bool); truth=truth[ok]\nprint('truth',len(truth),truth.uid.nunique(),truth.grade.value_counts().to_dict(),truth.groupby('uid').size().describe(percentiles=[.5,.9,.99]).to_dict(),flush=True)\nprint('reading train',flush=True); trls=pd.read_parquet(p/'train_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp']); print('read',len(trls),flush=True); trls=trls[trls.item_id.isin(items)&trls.uid.isin(target)]; print('filtered',len(trls),flush=True)\ntruthkey=pd.MultiIndex.from_frame(truth[['uid','item_id']])\nfor name,mask in [('any',np.ones(len(trls),bool)),('pos50',trls.played_ratio_pct>=50),('strong80',trls.played_ratio_pct>=80),('organic80',(trls.is_organic==1)&(trls.played_ratio_pct>=80)),('recent7d',trls.timestamp>=24786800-7*86400),('recent30d',trls.timestamp>=24786800-30*86400),('recent90d',trls.timestamp>=24786800-90*86400)]:\n seen=pd.MultiIndex.from_frame(trls.loc[mask,['uid','item_id']].drop_duplicates()); print(name,len(seen),truthkey.isin(seen).sum(),truthkey.isin(seen).mean(),flush=True)\nstate=pd.read_parquet(p/'validation_preference_state.parquet')\nfor s in ['liked','disliked']:\n q=pd.MultiIndex.from_frame(state[state.state==s][['uid','item_id']].drop_duplicates()); print(s,truthkey.isin(q).sum(),truthkey.isin(q).mean(),flush=True)\nsm=state[state.uid.isin(target)&state.item_id.isin(items)].copy(); rr=uidrow.reindex(sm.uid.astype('int64')).values.astype(int); cc=itemcol.reindex(sm.item_id.astype('int64')).values.astype(int); sm['eligible']=((packed[rr,cc//8]>>(cc%8))&1).astype(bool); print(sm.groupby('state').eligible.agg(['size','mean']),flush=True)\nPY","workdir":"/app","yield_time_ms":1000,"max_output_tokens":10000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response 

Script completed
Wall time 1.2 seconds
Output:

raw truth 266887
truth 37769 4263 {1: 33352, 3: 4417} {'count': 4263.0, 'mean': 8.859723199624677, 'std': 15.77344377431561, 'min': 1.0, '50%': 4.0, '90%': 21.0, '99%': 63.0, 'max': 442.0}
reading train

SESSION_ID=2419
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bbe9476487d2b8bfee5256f9a54a",
  "usage": {
    "input_tokens": 31873,
    "cached_input_tokens": 31232,
    "cache_write_input_tokens": 0,
    "output_tokens": 998,
    "reasoning_output_tokens": 50,
    "total_tokens": 32871
  },
  "turn_token_usage": {
    "input_tokens": 501040,
    "cached_input_tokens": 459520,
    "cache_write_input_tokens": 0,
    "output_tokens": 6422,
    "reasoning_output_tokens": 1622,
    "total_tokens": 507462
  },
  "thread_token_usage": {
    "input_tokens": 501040,
    "cached_input_tokens": 459520,
    "cache_write_input_tokens": 0,
    "output_tokens": 6422,
    "reasoning_output_tokens": 1622,
    "total_tokens": 507462
  }
}
28

exec command

+03:56
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":2419,"chars":"","yield_time_ms":10000,"max_output_tokens":10000});
text(r.output);

Tool response 

Script completed
Wall time 10.0 seconds
Output:

read 43484159
filtered 31062540
any 7868664 0 0.0
pos50 5514505 0 0.0
strong80 5290619 0 0.0
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bbfd4a5887d293d7877c462de031",
  "usage": {
    "input_tokens": 33005,
    "cached_input_tokens": 31744,
    "cache_write_input_tokens": 0,
    "output_tokens": 55,
    "reasoning_output_tokens": 8,
    "total_tokens": 33060
  },
  "turn_token_usage": {
    "input_tokens": 534045,
    "cached_input_tokens": 491264,
    "cache_write_input_tokens": 0,
    "output_tokens": 6477,
    "reasoning_output_tokens": 1630,
    "total_tokens": 540522
  },
  "thread_token_usage": {
    "input_tokens": 534045,
    "cached_input_tokens": 491264,
    "cache_write_input_tokens": 0,
    "output_tokens": 6477,
    "reasoning_output_tokens": 1630,
    "total_tokens": 540522
  }
}
29

exec command

+04:10
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-b2ac34a6-4a95-4d1a-9578-2f56c16f5437",
    "process_id": "2419",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python -u - <<'PY'\nimport pandas as pd, numpy as np\nfrom pathlib import Path\np=Path('/task/data'); targets=pd.read_parquet(p/'target_users.parquet').uid.astype(int); target=set(targets)\nwith np.load(p/'validation_eligible_candidates.npz') as z: uids=z['uids'].copy(); items=z['item_ids'].copy(); packed=z['packed_eligible'].copy()\nuidrow=pd.Series(np.arange(len(uids)),index=uids.astype(np.int64)); itemcol=pd.Series(np.arange(len(items)),index=items.astype(np.int64))\nls=pd.read_parquet(p/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct']); lk=pd.read_parquet(p/'val_likes.parquet',columns=['uid','item_id','is_organic'])\nstrong=ls[(ls.uid.isin(target))&(ls.is_organic==1)&(ls.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); likes=lk[(lk.uid.isin(target))&(lk.is_organic==1)][['uid','item_id']].drop_duplicates(); truth=pd.concat([strong.assign(grade=1),likes.assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last'); print('raw truth',len(truth),flush=True)\nr=uidrow.reindex(truth.uid.astype('int64')).values; c=itemcol.reindex(truth.item_id.astype('int64')).values; ok=~(np.isnan(r)|np.isnan(c)); ri=r[ok].astype(int); ci=c[ok].astype(int); ix=np.flatnonzero(ok); ok[ix]=((packed[ri,ci//8]>>(ci%8))&1).astype(bool); truth=truth[ok]\nprint('truth',len(truth),truth.uid.nunique(),truth.grade.value_counts().to_dict(),truth.groupby('uid').size().describe(percentiles=[.5,.9,.99]).to_dict(),flush=True)\nprint('reading train',flush=True); trls=pd.read_parquet(p/'train_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp']); print('read',len(trls),flush=True); trls=trls[trls.item_id.isin(items)&trls.uid.isin(target)]; print('filtered',len(trls),flush=True)\ntruthkey=pd.MultiIndex.from_frame(truth[['uid','item_id']])\nfor name,mask in [('any',np.ones(len(trls),bool)),('pos50',trls.played_ratio_pct>=50),('strong80',trls.played_ratio_pct>=80),('organic80',(trls.is_organic==1)&(trls.played_ratio_pct>=80)),('recent7d',trls.timestamp>=24786800-7*86400),('recent30d',trls.timestamp>=24786800-30*86400),('recent90d',trls.timestamp>=24786800-90*86400)]:\n seen=pd.MultiIndex.from_frame(trls.loc[mask,['uid','item_id']].drop_duplicates()); print(name,len(seen),truthkey.isin(seen).sum(),truthkey.isin(seen).mean(),flush=True)\nstate=pd.read_parquet(p/'validation_preference_state.parquet')\nfor s in ['liked','disliked']:\n q=pd.MultiIndex.from_frame(state[state.state==s][['uid','item_id']].drop_duplicates()); print(s,truthkey.isin(q).sum(),truthkey.isin(q).mean(),flush=True)\nsm=state[state.uid.isin(target)&state.item_id.isin(items)].copy(); rr=uidrow.reindex(sm.uid.astype('int64')).values.astype(int); cc=itemcol.reindex(sm.item_id.astype('int64')).values.astype(int); sm['eligible']=((packed[rr,cc//8]>>(cc%8))&1).astype(bool); print(sm.groupby('state').eligible.agg(['size','mean']),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\nfrom pathlib import Path\np=Path('/task/data'); targets=pd.read_parquet(p/'target_users.parquet').uid.astype(int); target=set(targets)\nwith np.load(p/'validation_eligible_candidates.npz') as z: uids=z['uids'].copy(); items=z['item_ids'].copy(); packed=z['packed_eligible'].copy()\nuidrow=pd.Series(np.arange(len(uids)),index=uids.astype(np.int64)); itemcol=pd.Series(np.arange(len(items)),index=items.astype(np.int64))\nls=pd.read_parquet(p/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct']); lk=pd.read_parquet(p/'val_likes.parquet',columns=['uid','item_id','is_organic'])\nstrong=ls[(ls.uid.isin(target))&(ls.is_organic==1)&(ls.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); likes=lk[(lk.uid.isin(target))&(lk.is_organic==1)][['uid','item_id']].drop_duplicates(); truth=pd.concat([strong.assign(grade=1),likes.assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last'); print('raw truth',len(truth),flush=True)\nr=uidrow.reindex(truth.uid.astype('int64')).values; c=itemcol.reindex(truth.item_id.astype('int64')).values; ok=~(np.isnan(r)|np.isnan(c)); ri=r[ok].astype(int); ci=c[ok].astype(int); ix=np.flatnonzero(ok); ok[ix]=((packed[ri,ci//8]>>(ci%8))&1).astype(bool); truth=truth[ok]\nprint('truth',len(truth),truth.uid.nunique(),truth.grade.value_counts().to_dict(),truth.groupby('uid').size().describe(percentiles=[.5,.9,.99]).to_dict(),flush=True)\nprint('reading train',flush=True); trls=pd.read_parquet(p/'train_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp']); print('read',len(trls),flush=True); trls=trls[trls.item_id.isin(items)&trls.uid.isin(target)]; print('filtered',len(trls),flush=True)\ntruthkey=pd.MultiIndex.from_frame(truth[['uid','item_id']])\nfor name,mask in [('any',np.ones(len(trls),bool)),('pos50',trls.played_ratio_pct>=50),('strong80',trls.played_ratio_pct>=80),('organic80',(trls.is_organic==1)&(trls.played_ratio_pct>=80)),('recent7d',trls.timestamp>=24786800-7*86400),('recent30d',trls.timestamp>=24786800-30*86400),('recent90d',trls.timestamp>=24786800-90*86400)]:\n seen=pd.MultiIndex.from_frame(trls.loc[mask,['uid','item_id']].drop_duplicates()); print(name,len(seen),truthkey.isin(seen).sum(),truthkey.isin(seen).mean(),flush=True)\nstate=pd.read_parquet(p/'validation_preference_state.parquet')\nfor s in ['liked','disliked']:\n q=pd.MultiIndex.from_frame(state[state.state==s][['uid','item_id']].drop_duplicates()); print(s,truthkey.isin(q).sum(),truthkey.isin(q).mean(),flush=True)\nsm=state[state.uid.isin(target)&state.item_id.isin(items)].copy(); rr=uidrow.reindex(sm.uid.astype('int64')).values.astype(int); cc=itemcol.reindex(sm.item_id.astype('int64')).values.astype(int); sm['eligible']=((packed[rr,cc//8]>>(cc%8))&1).astype(bool); print(sm.groupby('state').eligible.agg(['size','mean']),flush=True)\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "raw truth 266887\ntruth 37769 4263 {1: 33352, 3: 4417} {'count': 4263.0, 'mean': 8.859723199624677, 'std': 15.77344377431561, 'min': 1.0, '50%': 4.0, '90%': 21.0, '99%': 63.0, 'max': 442.0}\nreading train\nread 43484159\nfiltered 31062540\nany 7868664 0 0.0\npos50 5514505 0 0.0\nstrong80 5290619 0 0.0\norganic80 1935328 0 0.0\nrecent7d 715074 0 0.0\nrecent30d 2149455 0 0.0\nrecent90d 4306717 0 0.0\nliked 0 0.0\ndisliked 0 0.0\n            size  mean\nstate                 \ndisliked   50689   0.0\nliked     418435   0.0\n",
    "stderr": "",
    "aggregated_output": "raw truth 266887\ntruth 37769 4263 {1: 33352, 3: 4417} {'count': 4263.0, 'mean': 8.859723199624677, 'std': 15.77344377431561, 'min': 1.0, '50%': 4.0, '90%': 21.0, '99%': 63.0, 'max': 442.0}\nreading train\nread 43484159\nfiltered 31062540\nany 7868664 0 0.0\npos50 5514505 0 0.0\nstrong80 5290619 0 0.0\norganic80 1935328 0 0.0\nrecent7d 715074 0 0.0\nrecent30d 2149455 0 0.0\nrecent90d 4306717 0 0.0\nliked 0 0.0\ndisliked 0 0.0\n            size  mean\nstate                 \ndisliked   50689   0.0\nliked     418435   0.0\n",
    "exit_code": 0,
    "duration": {
      "secs": 16,
      "nanos": 106814689
    },
    "formatted_output": "raw truth 266887\ntruth 37769 4263 {1: 33352, 3: 4417} {'count': 4263.0, 'mean': 8.859723199624677, 'std': 15.77344377431561, 'min': 1.0, '50%': 4.0, '90%': 21.0, '99%': 63.0, 'max': 442.0}\nreading train\nread 43484159\nfiltered 31062540\nany 7868664 0 0.0\npos50 5514505 0 0.0\nstrong80 5290619 0 0.0\norganic80 1935328 0 0.0\nrecent7d 715074 0 0.0\nrecent30d 2149455 0 0.0\nrecent90d 4306717 0 0.0\nliked 0 0.0\ndisliked 0 0.0\n            size  mean\nstate                 \ndisliked   50689   0.0\nliked     418435   0.0\n"
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":2419,"chars":"","yield_time_ms":10000,"max_output_tokens":10000});
text(r.output);

Tool response 

Script completed
Wall time 0.0 seconds
Output:

organic80 1935328 0 0.0
recent7d 715074 0 0.0
recent30d 2149455 0 0.0
recent90d 4306717 0 0.0
liked 0 0.0
disliked 0 0.0
            size  mean
state                 
disliked   50689   0.0
liked     418435   0.0
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30

exec command

+04:16
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const patch = "*** Begin Patch\n*** Add File: /app/solution/build_ranker.py\n+#!/usr/bin/env python3\n+\"\"\"Build validation and final Made-for-You rankings.\n+\n+The implementation is intentionally self contained and reads only the published\n+task inputs.  Run it with the task virtual environment, for example:\n+\n+    /opt/task-venv/bin/python solution/build_ranker.py\n+\"\"\"\n+from __future__ import annotations\n+\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+\n+\n+def load_candidates(stage: str):\n+    with np.load(DATA / f\"{stage}_eligible_candidates.npz\") as z:\n+        return z[\"uids\"].copy(), z[\"item_ids\"].copy(), z[\"packed_eligible\"].copy()\n+\n+\n+def write_global(stage: str, item_order: np.ndarray, path: Path) -> None:\n+    \"\"\"Write the first 100 eligible items from a shared item ordering.\"\"\"\n+    uids, item_ids, packed = load_candidates(stage)\n+    col = {int(item): j for j, item in enumerate(item_ids)}\n+    order_cols = np.asarray([col[int(item)] for item in item_order], dtype=np.int32)\n+    rows = []\n+    for r, uid in enumerate(uids):\n+        selected = []\n+        for c in order_cols:\n+            if (int(packed[r, c // 8]) >> (c % 8)) & 1:\n+                selected.append(int(item_ids[c]))\n+                if len(selected) == 100:\n+                    break\n+        rows.extend((int(uid), item, rank) for rank, item in enumerate(selected, 1))\n+    pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]).to_parquet(path, index=False)\n+\n+\n+def main() -> None:\n+    OUT.mkdir(exist_ok=True)\n+    catalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n+    order = catalog.sort_values(\"popularity_rank\").item_id.to_numpy()\n+    write_global(\"validation\", order, OUT / \"validation.parquet\")\n+    write_global(\"test\", order, OUT / \"submission.parquet\")\n+\n+\n+if __name__ == \"__main__\":\n+    main()\n*** Add File: /app/solution/README.md\n+# Made-for-You ranker\n+\n+`build_ranker.py` builds the validation and final rankings from the published\n+event history and cutoff-specific eligibility archives. Run with:\n+\n+```sh\n+/opt/task-venv/bin/python /app/solution/build_ranker.py\n+```\n+\n+The output schema is `uid, item_id, rank`, with exactly 100 cutoff-eligible\n+items per target user.\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response 

Script completed
Wall time 0.0 seconds
Output:

{}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-88bae5b4-98bf-4171-9289-43d2b242130d",
    "changes": {
      "/app/solution/README.md": {
        "type": "add",
        "content": "# Made-for-You ranker\n\n`build_ranker.py` builds the validation and final rankings from the published\nevent history and cutoff-specific eligibility archives. Run with:\n\n```sh\n/opt/task-venv/bin/python /app/solution/build_ranker.py\n```\n\nThe output schema is `uid, item_id, rank`, with exactly 100 cutoff-eligible\nitems per target user.\n"
      },
      "/app/solution/build_ranker.py": {
        "type": "add",
        "content": "#!/usr/bin/env python3\n\"\"\"Build validation and final Made-for-You rankings.\n\nThe implementation is intentionally self contained and reads only the published\ntask inputs.  Run it with the task virtual environment, for example:\n\n    /opt/task-venv/bin/python solution/build_ranker.py\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\nDATA = Path(\"/task/data\")\nOUT = Path(\"/app/solution\")\n\n\ndef load_candidates(stage: str):\n    with np.load(DATA / f\"{stage}_eligible_candidates.npz\") as z:\n        return z[\"uids\"].copy(), z[\"item_ids\"].copy(), z[\"packed_eligible\"].copy()\n\n\ndef write_global(stage: str, item_order: np.ndarray, path: Path) -> None:\n    \"\"\"Write the first 100 eligible items from a shared item ordering.\"\"\"\n    uids, item_ids, packed = load_candidates(stage)\n    col = {int(item): j for j, item in enumerate(item_ids)}\n    order_cols = np.asarray([col[int(item)] for item in item_order], dtype=np.int32)\n    rows = []\n    for r, uid in enumerate(uids):\n        selected = []\n        for c in order_cols:\n            if (int(packed[r, c // 8]) >> (c % 8)) & 1:\n                selected.append(int(item_ids[c]))\n                if len(selected) == 100:\n                    break\n        rows.extend((int(uid), item, rank) for rank, item in enumerate(selected, 1))\n    pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]).to_parquet(path, index=False)\n\n\ndef main() -> None:\n    OUT.mkdir(exist_ok=True)\n    catalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n    order = catalog.sort_values(\"popularity_rank\").item_id.to_numpy()\n    write_global(\"validation\", order, OUT / \"validation.parquet\")\n    write_global(\"test\", order, OUT / \"submission.parquet\")\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": ""
  }
}
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31

exec command

+04:31
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python /app/solution/build_ranker.py && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet && ls -lh /app/solution","workdir":"/app","yield_time_ms":1000,"max_output_tokens":5000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response 

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=61887
Harness context token usage record
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  }
}
32

exec command

+04:35
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":61887,"chars":"","yield_time_ms":10000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 3.4 seconds
Output:

{
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.01071,
    "quality_users": 4263
  }
}
total 1.5M
-rw-r--r-- 1 agent agent  334 Sep 25 00:11 README.md
-rw-r--r-- 1 agent agent 1.8K Sep 25 00:11 build_ranker.py
-rw-r--r-- 1 agent agent 719K Sep 25 00:11 submission.parquet
-rw-r--r-- 1 agent agent 712K Sep 25 00:11 validation.parquet
Harness context token usage record
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Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-830d1345-7be3-4d60-9faa-f2648363bf7a",
    "process_id": "61887",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python /app/solution/build_ranker.py && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet && ls -lh /app/solution"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python /app/solution/build_ranker.py && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet && ls -lh /app/solution"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\ntotal 1.5M\n-rw-r--r-- 1 agent agent  334 Sep 25 00:11 README.md\n-rw-r--r-- 1 agent agent 1.8K Sep 25 00:11 build_ranker.py\n-rw-r--r-- 1 agent agent 719K Sep 25 00:11 submission.parquet\n-rw-r--r-- 1 agent agent 712K Sep 25 00:11 validation.parquet\n",
    "stderr": "",
    "aggregated_output": "{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\ntotal 1.5M\n-rw-r--r-- 1 agent agent  334 Sep 25 00:11 README.md\n-rw-r--r-- 1 agent agent 1.8K Sep 25 00:11 build_ranker.py\n-rw-r--r-- 1 agent agent 719K Sep 25 00:11 submission.parquet\n-rw-r--r-- 1 agent agent 712K Sep 25 00:11 validation.parquet\n",
    "exit_code": 0,
    "duration": {
      "secs": 6,
      "nanos": 171653001
    },
    "formatted_output": "{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\ntotal 1.5M\n-rw-r--r-- 1 agent agent  334 Sep 25 00:11 README.md\n-rw-r--r-- 1 agent agent 1.8K Sep 25 00:11 build_ranker.py\n-rw-r--r-- 1 agent agent 719K Sep 25 00:11 submission.parquet\n-rw-r--r-- 1 agent agent 712K Sep 25 00:11 validation.parquet\n"
  }
}
33

Agent message

+04:56
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

34

Agent message

+05:11
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

35

exec command

+05:15
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const patch = "*** Begin Patch\n*** Add File: /app/solution/explore_global.py\n+from pathlib import Path\n+import numpy as np\n+import pandas as pd\n+\n+DATA=Path('/task/data'); OUT=Path('/app/solution')\n+\n+def load(stage):\n+    with np.load(DATA/f'{stage}_eligible_candidates.npz') as z:\n+        return z['uids'].copy(),z['item_ids'].copy(),z['packed_eligible'].copy()\n+\n+def write(order,name):\n+    uids,items,packed=load('validation'); idx=pd.Index(items); cols=idx.get_indexer(order).astype(np.int32); cols=cols[cols>=0]\n+    out=[]\n+    for r,u in enumerate(uids):\n+        take=[]\n+        for c in cols:\n+            if (int(packed[r,c//8])>>(c%8))&1:\n+                take.append(int(items[c]))\n+                if len(take)==100: break\n+        out.extend((int(u),x,k+1) for k,x in enumerate(take))\n+    pd.DataFrame(out,columns=['uid','item_id','rank']).to_parquet(OUT/f'val_{name}.parquet',index=False)\n+\n+cat=pd.read_parquet(DATA/'candidate_catalog.parquet'); items=cat.item_id.to_numpy(); base=cat.set_index('item_id')\n+scores={\n+ 'catpos':base.positive_listens,\n+ 'catorg':base.organic_positive_listens,\n+ 'catratio':base.organic_positive_listens/(base.positive_listens+20),\n+}\n+d=pd.read_parquet(DATA/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\n+d=d[d.item_id.isin(items)]\n+cut=24786800\n+for days in [7,14,30,60,90,180,1000]:\n+ q=d[(d.timestamp>=cut-days*86400)&(d.played_ratio_pct>=80)]\n+ for org in [False,True]:\n+  qq=q[q.is_organic==1] if org else q\n+  tag='o' if org else 'a'\n+  scores[f'cnt{tag}{days}']=qq.groupby('item_id').size().reindex(items,fill_value=0)\n+  scores[f'usr{tag}{days}']=qq.groupby('item_id').uid.nunique().reindex(items,fill_value=0)\n+del d\n+for name,s in scores.items():\n+ s=s.reindex(items).fillna(0)\n+ # Stable catalog popularity is the tie breaker.\n+ key=pd.DataFrame({'item_id':items,'s':s.to_numpy(),'pop':-cat.popularity_rank.to_numpy()})\n+ order=key.sort_values(['s','pop'],ascending=False).item_id.to_numpy()\n+ write(order,name)\n+ print(name,flush=True)\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response 

Script completed
Wall time 0.0 seconds
Output:

{}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-c237fde4-549f-4428-9a33-4d30ccea36e5",
    "changes": {
      "/app/solution/explore_global.py": {
        "type": "add",
        "content": "from pathlib import Path\nimport numpy as np\nimport pandas as pd\n\nDATA=Path('/task/data'); OUT=Path('/app/solution')\n\ndef load(stage):\n    with np.load(DATA/f'{stage}_eligible_candidates.npz') as z:\n        return z['uids'].copy(),z['item_ids'].copy(),z['packed_eligible'].copy()\n\ndef write(order,name):\n    uids,items,packed=load('validation'); idx=pd.Index(items); cols=idx.get_indexer(order).astype(np.int32); cols=cols[cols>=0]\n    out=[]\n    for r,u in enumerate(uids):\n        take=[]\n        for c in cols:\n            if (int(packed[r,c//8])>>(c%8))&1:\n                take.append(int(items[c]))\n                if len(take)==100: break\n        out.extend((int(u),x,k+1) for k,x in enumerate(take))\n    pd.DataFrame(out,columns=['uid','item_id','rank']).to_parquet(OUT/f'val_{name}.parquet',index=False)\n\ncat=pd.read_parquet(DATA/'candidate_catalog.parquet'); items=cat.item_id.to_numpy(); base=cat.set_index('item_id')\nscores={\n 'catpos':base.positive_listens,\n 'catorg':base.organic_positive_listens,\n 'catratio':base.organic_positive_listens/(base.positive_listens+20),\n}\nd=pd.read_parquet(DATA/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\nd=d[d.item_id.isin(items)]\ncut=24786800\nfor days in [7,14,30,60,90,180,1000]:\n q=d[(d.timestamp>=cut-days*86400)&(d.played_ratio_pct>=80)]\n for org in [False,True]:\n  qq=q[q.is_organic==1] if org else q\n  tag='o' if org else 'a'\n  scores[f'cnt{tag}{days}']=qq.groupby('item_id').size().reindex(items,fill_value=0)\n  scores[f'usr{tag}{days}']=qq.groupby('item_id').uid.nunique().reindex(items,fill_value=0)\ndel d\nfor name,s in scores.items():\n s=s.reindex(items).fillna(0)\n # Stable catalog popularity is the tie breaker.\n key=pd.DataFrame({'item_id':items,'s':s.to_numpy(),'pop':-cat.popularity_rank.to_numpy()})\n order=key.sort_values(['s','pop'],ascending=False).item_id.to_numpy()\n write(order,name)\n print(name,flush=True)\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/explore_global.py\n",
    "stderr": ""
  }
}
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36

exec command

+05:23
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python -u /app/solution/explore_global.py","workdir":"/app","yield_time_ms":1000,"max_output_tokens":3000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response 

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=30583
Harness context token usage record
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  "turn_token_usage": {
    "input_tokens": 740712,
    "cached_input_tokens": 693120,
    "cache_write_input_tokens": 0,
    "output_tokens": 9657,
    "reasoning_output_tokens": 3102,
    "total_tokens": 750369
  },
  "thread_token_usage": {
    "input_tokens": 740712,
    "cached_input_tokens": 693120,
    "cache_write_input_tokens": 0,
    "output_tokens": 9657,
    "reasoning_output_tokens": 3102,
    "total_tokens": 750369
  }
}
37

exec command

+05:29
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":30583,"chars":"","yield_time_ms":10000,"max_output_tokens":3000});
text(r.output);

Tool response 

Script completed
Wall time 10.0 seconds
Output:

catpos
catorg
catratio
cnta7
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bc586bac87d28c7698987f90f077",
  "usage": {
    "input_tokens": 36714,
    "cached_input_tokens": 36480,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 36767
  },
  "turn_token_usage": {
    "input_tokens": 777426,
    "cached_input_tokens": 729600,
    "cache_write_input_tokens": 0,
    "output_tokens": 9710,
    "reasoning_output_tokens": 3108,
    "total_tokens": 787136
  },
  "thread_token_usage": {
    "input_tokens": 777426,
    "cached_input_tokens": 729600,
    "cache_write_input_tokens": 0,
    "output_tokens": 9710,
    "reasoning_output_tokens": 3108,
    "total_tokens": 787136
  }
}
38

exec command

+05:40
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":30583,"chars":"","yield_time_ms":10000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 10.0 seconds
Output:

usra7
cnto7
usro7
cnta14
usra14
cnto14
usro14
cnta30
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bc65d3cc87d2b4bb5540e5fa3683",
  "usage": {
    "input_tokens": 36804,
    "cached_input_tokens": 36480,
    "cache_write_input_tokens": 0,
    "output_tokens": 69,
    "reasoning_output_tokens": 22,
    "total_tokens": 36873
  },
  "turn_token_usage": {
    "input_tokens": 814230,
    "cached_input_tokens": 766080,
    "cache_write_input_tokens": 0,
    "output_tokens": 9779,
    "reasoning_output_tokens": 3130,
    "total_tokens": 824009
  },
  "thread_token_usage": {
    "input_tokens": 814230,
    "cached_input_tokens": 766080,
    "cache_write_input_tokens": 0,
    "output_tokens": 9779,
    "reasoning_output_tokens": 3130,
    "total_tokens": 824009
  }
}
39

exec command

+05:53
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":30583,"chars":"","yield_time_ms":10000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 10.0 seconds
Output:

usra30
cnto30
usro30
cnta60
usra60
cnto60
usro60
cnta90
usra90
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bc71d78887d2b7cf558b65b88820",
  "usage": {
    "input_tokens": 36928,
    "cached_input_tokens": 36608,
    "cache_write_input_tokens": 0,
    "output_tokens": 65,
    "reasoning_output_tokens": 18,
    "total_tokens": 36993
  },
  "turn_token_usage": {
    "input_tokens": 851158,
    "cached_input_tokens": 802688,
    "cache_write_input_tokens": 0,
    "output_tokens": 9844,
    "reasoning_output_tokens": 3148,
    "total_tokens": 861002
  },
  "thread_token_usage": {
    "input_tokens": 851158,
    "cached_input_tokens": 802688,
    "cache_write_input_tokens": 0,
    "output_tokens": 9844,
    "reasoning_output_tokens": 3148,
    "total_tokens": 861002
  }
}
40

exec command

+06:06
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":30583,"chars":"","yield_time_ms":10000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 10.0 seconds
Output:

cnto90
usro90
cnta180
usra180
cnto180
usro180
cnta1000
usra1000
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bc7ec05487d299b9a6e1e95e3014",
  "usage": {
    "input_tokens": 37052,
    "cached_input_tokens": 36736,
    "cache_write_input_tokens": 0,
    "output_tokens": 75,
    "reasoning_output_tokens": 28,
    "total_tokens": 37127
  },
  "turn_token_usage": {
    "input_tokens": 888210,
    "cached_input_tokens": 839424,
    "cache_write_input_tokens": 0,
    "output_tokens": 9919,
    "reasoning_output_tokens": 3176,
    "total_tokens": 898129
  },
  "thread_token_usage": {
    "input_tokens": 888210,
    "cached_input_tokens": 839424,
    "cache_write_input_tokens": 0,
    "output_tokens": 9919,
    "reasoning_output_tokens": 3176,
    "total_tokens": 898129
  }
}
41

exec command

+06:18
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-f2539e0c-e9b4-460d-a388-7150f667dfd9",
    "process_id": "30583",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python -u /app/solution/explore_global.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python -u /app/solution/explore_global.py"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "catpos\ncatorg\ncatratio\ncnta7\nusra7\ncnto7\nusro7\ncnta14\nusra14\ncnto14\nusro14\ncnta30\nusra30\ncnto30\nusro30\ncnta60\nusra60\ncnto60\nusro60\ncnta90\nusra90\ncnto90\nusro90\ncnta180\nusra180\ncnto180\nusro180\ncnta1000\nusra1000\ncnto1000\nusro1000\n",
    "stderr": "",
    "aggregated_output": "catpos\ncatorg\ncatratio\ncnta7\nusra7\ncnto7\nusro7\ncnta14\nusra14\ncnto14\nusro14\ncnta30\nusra30\ncnto30\nusro30\ncnta60\nusra60\ncnto60\nusro60\ncnta90\nusra90\ncnto90\nusro90\ncnta180\nusra180\ncnto180\nusro180\ncnta1000\nusra1000\ncnto1000\nusro1000\n",
    "exit_code": 0,
    "duration": {
      "secs": 53,
      "nanos": 395393786
    },
    "formatted_output": "catpos\ncatorg\ncatratio\ncnta7\nusra7\ncnto7\nusro7\ncnta14\nusra14\ncnto14\nusro14\ncnta30\nusra30\ncnto30\nusro30\ncnta60\nusra60\ncnto60\nusro60\ncnta90\nusra90\ncnto90\nusro90\ncnta180\nusra180\ncnto180\nusro180\ncnta1000\nusra1000\ncnto1000\nusro1000\n"
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":30583,"chars":"","yield_time_ms":10000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 0.0 seconds
Output:

cnto1000
usro1000
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bc8aed3487d29d22a36cc5b0a060",
  "usage": {
    "input_tokens": 37184,
    "cached_input_tokens": 36864,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 37237
  },
  "turn_token_usage": {
    "input_tokens": 925394,
    "cached_input_tokens": 876288,
    "cache_write_input_tokens": 0,
    "output_tokens": 9972,
    "reasoning_output_tokens": 3182,
    "total_tokens": 935366
  },
  "thread_token_usage": {
    "input_tokens": 925394,
    "cached_input_tokens": 876288,
    "cache_write_input_tokens": 0,
    "output_tokens": 9972,
    "reasoning_output_tokens": 3182,
    "total_tokens": 935366
  }
}
42

exec command

+06:21
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python - <<'PY'\nimport sys,glob\nsys.path.insert(0,'/task')\nimport evaluate_public as e\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nfrom pathlib import Path\nt=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int)); c=CandidateSet(Path('/task/data/validation_eligible_candidates.npz')); g=e.validation_truth(c,t)\nres=[]\nfor f in glob.glob('/app/solution/val_*.parquet'):\n try: res.append((e.score(e.load_submission(Path(f),c,t),g)['quality_ndcg@10'],Path(f).stem))\n except Exception as x: print(x,f)\nfor x in sorted(res,reverse=True): print(x)\nPY","workdir":"/app","yield_time_ms":1000,"max_output_tokens":10000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response 

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=52652
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bc8d862487d2b3b1b2dfb962d7e2",
  "usage": {
    "input_tokens": 37270,
    "cached_input_tokens": 36992,
    "cache_write_input_tokens": 0,
    "output_tokens": 271,
    "reasoning_output_tokens": 35,
    "total_tokens": 37541
  },
  "turn_token_usage": {
    "input_tokens": 962664,
    "cached_input_tokens": 913280,
    "cache_write_input_tokens": 0,
    "output_tokens": 10243,
    "reasoning_output_tokens": 3217,
    "total_tokens": 972907
  },
  "thread_token_usage": {
    "input_tokens": 962664,
    "cached_input_tokens": 913280,
    "cache_write_input_tokens": 0,
    "output_tokens": 10243,
    "reasoning_output_tokens": 3217,
    "total_tokens": 972907
  }
}
43

exec command

+06:28
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":52652,"chars":"","yield_time_ms":10000,"max_output_tokens":10000});
text(r.output);

Tool response 

Script completed
Wall time 10.0 seconds
Output:
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bc95555087d2bab62ff0a877d006",
  "usage": {
    "input_tokens": 37569,
    "cached_input_tokens": 37120,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 37622
  },
  "turn_token_usage": {
    "input_tokens": 1000233,
    "cached_input_tokens": 950400,
    "cache_write_input_tokens": 0,
    "output_tokens": 10296,
    "reasoning_output_tokens": 3223,
    "total_tokens": 1010529
  },
  "thread_token_usage": {
    "input_tokens": 1000233,
    "cached_input_tokens": 950400,
    "cache_write_input_tokens": 0,
    "output_tokens": 10296,
    "reasoning_output_tokens": 3223,
    "total_tokens": 1010529
  }
}
44

exec command

+06:40
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":52652,"chars":"","yield_time_ms":10000,"max_output_tokens":10000});
text(r.output);

Tool response 

Script completed
Wall time 10.0 seconds
Output:
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bca0fcdc87d298cdd219767a1b64",
  "usage": {
    "input_tokens": 37645,
    "cached_input_tokens": 37376,
    "cache_write_input_tokens": 0,
    "output_tokens": 100,
    "reasoning_output_tokens": 53,
    "total_tokens": 37745
  },
  "turn_token_usage": {
    "input_tokens": 1037878,
    "cached_input_tokens": 987776,
    "cache_write_input_tokens": 0,
    "output_tokens": 10396,
    "reasoning_output_tokens": 3276,
    "total_tokens": 1048274
  },
  "thread_token_usage": {
    "input_tokens": 1037878,
    "cached_input_tokens": 987776,
    "cache_write_input_tokens": 0,
    "output_tokens": 10396,
    "reasoning_output_tokens": 3276,
    "total_tokens": 1048274
  }
}
45

exec command

+06:52
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":52652,"chars":"","yield_time_ms":10000,"max_output_tokens":10000});
text(r.output);

Tool response 

Script completed
Wall time 10.0 seconds
Output:
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bcad977887d2891510b0708ec927",
  "usage": {
    "input_tokens": 37768,
    "cached_input_tokens": 37504,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 37821
  },
  "turn_token_usage": {
    "input_tokens": 1075646,
    "cached_input_tokens": 1025280,
    "cache_write_input_tokens": 0,
    "output_tokens": 10449,
    "reasoning_output_tokens": 3282,
    "total_tokens": 1086095
  },
  "thread_token_usage": {
    "input_tokens": 1075646,
    "cached_input_tokens": 1025280,
    "cache_write_input_tokens": 0,
    "output_tokens": 10449,
    "reasoning_output_tokens": 3282,
    "total_tokens": 1086095
  }
}
46

exec command

+07:04
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":52652,"chars":"\u0003","yield_time_ms":1000,"max_output_tokens":2000});
text(r.output);

Tool response 

Script completed
Wall time 0.1 seconds
Output:

Traceback (most recent call last):
  File "<stdin>", line 10, in <module>
  File "/task/evaluate_public.py", line 114, in load_submission
    if not all(
           ^^^^
  File "/task/evaluate_public.py", line 115, in <genexpr>
    eligibility.is_eligible(uid, item)
  File "/task/ranking_evaluation.py", line 85, in is_eligible
    return bool((int(self.packed[row, byte]) >> bit) & 1)
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^
KeyboardInterrupt
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-c6b115d3-45a0-4019-b67a-6a59ebd8b10a",
    "process_id": "52652",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nimport sys,glob\nsys.path.insert(0,'/task')\nimport evaluate_public as e\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nfrom pathlib import Path\nt=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int)); c=CandidateSet(Path('/task/data/validation_eligible_candidates.npz')); g=e.validation_truth(c,t)\nres=[]\nfor f in glob.glob('/app/solution/val_*.parquet'):\n try: res.append((e.score(e.load_submission(Path(f),c,t),g)['quality_ndcg@10'],Path(f).stem))\n except Exception as x: print(x,f)\nfor x in sorted(res,reverse=True): print(x)\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport sys,glob\nsys.path.insert(0,'/task')\nimport evaluate_public as e\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nfrom pathlib import Path\nt=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int)); c=CandidateSet(Path('/task/data/validation_eligible_candidates.npz')); g=e.validation_truth(c,t)\nres=[]\nfor f in glob.glob('/app/solution/val_*.parquet'):\n try: res.append((e.score(e.load_submission(Path(f),c,t),g)['quality_ndcg@10'],Path(f).stem))\n except Exception as x: print(x,f)\nfor x in sorted(res,reverse=True): print(x)\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "failed",
    "stdout": "Traceback (most recent call last):\n  File \"<stdin>\", line 10, in <module>\n  File \"/task/evaluate_public.py\", line 114, in load_submission\n    if not all(\n           ^^^^\n  File \"/task/evaluate_public.py\", line 115, in <genexpr>\n    eligibility.is_eligible(uid, item)\n  File \"/task/ranking_evaluation.py\", line 85, in is_eligible\n    return bool((int(self.packed[row, byte]) >> bit) & 1)\n                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^\nKeyboardInterrupt\n",
    "stderr": "",
    "aggregated_output": "Traceback (most recent call last):\n  File \"<stdin>\", line 10, in <module>\n  File \"/task/evaluate_public.py\", line 114, in load_submission\n    if not all(\n           ^^^^\n  File \"/task/evaluate_public.py\", line 115, in <genexpr>\n    eligibility.is_eligible(uid, item)\n  File \"/task/ranking_evaluation.py\", line 85, in is_eligible\n    return bool((int(self.packed[row, byte]) >> bit) & 1)\n                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^\nKeyboardInterrupt\n",
    "exit_code": 130,
    "duration": {
      "secs": 39,
      "nanos": 155084294
    },
    "formatted_output": "Traceback (most recent call last):\n  File \"<stdin>\", line 10, in <module>\n  File \"/task/evaluate_public.py\", line 114, in load_submission\n    if not all(\n           ^^^^\n  File \"/task/evaluate_public.py\", line 115, in <genexpr>\n    eligibility.is_eligible(uid, item)\n  File \"/task/ranking_evaluation.py\", line 85, in is_eligible\n    return bool((int(self.packed[row, byte]) >> bit) & 1)\n                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^\nKeyboardInterrupt\n"
  }
}
Harness context token usage record
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47

exec command

+07:07
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python -u - <<'PY'\nimport sys,glob\nsys.path.insert(0,'/task'); import evaluate_public as e\nfrom ranking_evaluation import CandidateSet,score_rankings\nimport pandas as pd\nfrom pathlib import Path\nt=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int)); c=CandidateSet(Path('/task/data/validation_eligible_candidates.npz')); g=e.validation_truth(c,t); print('truth ready',len(g),flush=True)\nres=[]\nfor f in glob.glob('/app/solution/val_*.parquet'):\n d=pd.read_parquet(f); d=d[d['rank']<=10].sort_values(['uid','rank']); ranked={int(u):list(map(int,x.item_id)) for u,x in d.groupby('uid')}; res.append((score_rankings(ranked,g)['quality_ndcg@10'],Path(f).stem))\nfor x in sorted(res,reverse=True): print(x)\nPY","workdir":"/app","yield_time_ms":1000,"max_output_tokens":10000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response 

Script completed
Wall time 1.2 seconds
Output:

truth ready 4263

SESSION_ID=91687
Harness context token usage record
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48

exec command

+07:15
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":91687,"chars":"","yield_time_ms":10000,"max_output_tokens":10000});
text(r.output);

Tool response 

Script completed
Wall time 7.9 seconds
Output:

(0.030286, 'val_usro7')
(0.030107, 'val_cnta7')
(0.028131, 'val_cnto7')
(0.025481, 'val_usro14')
(0.022946, 'val_usra7')
(0.022878, 'val_cnto14')
(0.022238, 'val_cnta14')
(0.01919, 'val_usra14')
(0.018928, 'val_usro30')
(0.017371, 'val_usro60')
(0.016986, 'val_usra30')
(0.016953, 'val_cnta60')
(0.016688, 'val_cnta30')
(0.01598, 'val_cnta90')
(0.015848, 'val_usro90')
(0.015822, 'val_cnto90')
(0.015802, 'val_cnto60')
(0.014975, 'val_usra60')
(0.013992, 'val_usra90')
(0.013314, 'val_cnto30')
(0.012821, 'val_usro180')
(0.01204, 'val_cnta180')
(0.011908, 'val_cnto180')
(0.011516, 'val_usra180')
(0.011277, 'val_usro1000')
(0.010994, 'val_usra1000')
(0.010727, 'val_cnta1000')
(0.01071, 'val_catpos')
(0.010577, 'val_catorg')
(0.010551, 'val_cnto1000')
(0.00058, 'val_catratio')
Harness context token usage record
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  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
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Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-c5adffa8-7ac1-4db2-bcf8-1f4869f02e0e",
    "process_id": "91687",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python -u - <<'PY'\nimport sys,glob\nsys.path.insert(0,'/task'); import evaluate_public as e\nfrom ranking_evaluation import CandidateSet,score_rankings\nimport pandas as pd\nfrom pathlib import Path\nt=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int)); c=CandidateSet(Path('/task/data/validation_eligible_candidates.npz')); g=e.validation_truth(c,t); print('truth ready',len(g),flush=True)\nres=[]\nfor f in glob.glob('/app/solution/val_*.parquet'):\n d=pd.read_parquet(f); d=d[d['rank']<=10].sort_values(['uid','rank']); ranked={int(u):list(map(int,x.item_id)) for u,x in d.groupby('uid')}; res.append((score_rankings(ranked,g)['quality_ndcg@10'],Path(f).stem))\nfor x in sorted(res,reverse=True): print(x)\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport sys,glob\nsys.path.insert(0,'/task'); import evaluate_public as e\nfrom ranking_evaluation import CandidateSet,score_rankings\nimport pandas as pd\nfrom pathlib import Path\nt=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int)); c=CandidateSet(Path('/task/data/validation_eligible_candidates.npz')); g=e.validation_truth(c,t); print('truth ready',len(g),flush=True)\nres=[]\nfor f in glob.glob('/app/solution/val_*.parquet'):\n d=pd.read_parquet(f); d=d[d['rank']<=10].sort_values(['uid','rank']); ranked={int(u):list(map(int,x.item_id)) for u,x in d.groupby('uid')}; res.append((score_rankings(ranked,g)['quality_ndcg@10'],Path(f).stem))\nfor x in sorted(res,reverse=True): print(x)\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "truth ready 4263\n(0.030286, 'val_usro7')\n(0.030107, 'val_cnta7')\n(0.028131, 'val_cnto7')\n(0.025481, 'val_usro14')\n(0.022946, 'val_usra7')\n(0.022878, 'val_cnto14')\n(0.022238, 'val_cnta14')\n(0.01919, 'val_usra14')\n(0.018928, 'val_usro30')\n(0.017371, 'val_usro60')\n(0.016986, 'val_usra30')\n(0.016953, 'val_cnta60')\n(0.016688, 'val_cnta30')\n(0.01598, 'val_cnta90')\n(0.015848, 'val_usro90')\n(0.015822, 'val_cnto90')\n(0.015802, 'val_cnto60')\n(0.014975, 'val_usra60')\n(0.013992, 'val_usra90')\n(0.013314, 'val_cnto30')\n(0.012821, 'val_usro180')\n(0.01204, 'val_cnta180')\n(0.011908, 'val_cnto180')\n(0.011516, 'val_usra180')\n(0.011277, 'val_usro1000')\n(0.010994, 'val_usra1000')\n(0.010727, 'val_cnta1000')\n(0.01071, 'val_catpos')\n(0.010577, 'val_catorg')\n(0.010551, 'val_cnto1000')\n(0.00058, 'val_catratio')\n",
    "stderr": "",
    "aggregated_output": "truth ready 4263\n(0.030286, 'val_usro7')\n(0.030107, 'val_cnta7')\n(0.028131, 'val_cnto7')\n(0.025481, 'val_usro14')\n(0.022946, 'val_usra7')\n(0.022878, 'val_cnto14')\n(0.022238, 'val_cnta14')\n(0.01919, 'val_usra14')\n(0.018928, 'val_usro30')\n(0.017371, 'val_usro60')\n(0.016986, 'val_usra30')\n(0.016953, 'val_cnta60')\n(0.016688, 'val_cnta30')\n(0.01598, 'val_cnta90')\n(0.015848, 'val_usro90')\n(0.015822, 'val_cnto90')\n(0.015802, 'val_cnto60')\n(0.014975, 'val_usra60')\n(0.013992, 'val_usra90')\n(0.013314, 'val_cnto30')\n(0.012821, 'val_usro180')\n(0.01204, 'val_cnta180')\n(0.011908, 'val_cnto180')\n(0.011516, 'val_usra180')\n(0.011277, 'val_usro1000')\n(0.010994, 'val_usra1000')\n(0.010727, 'val_cnta1000')\n(0.01071, 'val_catpos')\n(0.010577, 'val_catorg')\n(0.010551, 'val_cnto1000')\n(0.00058, 'val_catratio')\n",
    "exit_code": 0,
    "duration": {
      "secs": 11,
      "nanos": 651051090
    },
    "formatted_output": "truth ready 4263\n(0.030286, 'val_usro7')\n(0.030107, 'val_cnta7')\n(0.028131, 'val_cnto7')\n(0.025481, 'val_usro14')\n(0.022946, 'val_usra7')\n(0.022878, 'val_cnto14')\n(0.022238, 'val_cnta14')\n(0.01919, 'val_usra14')\n(0.018928, 'val_usro30')\n(0.017371, 'val_usro60')\n(0.016986, 'val_usra30')\n(0.016953, 'val_cnta60')\n(0.016688, 'val_cnta30')\n(0.01598, 'val_cnta90')\n(0.015848, 'val_usro90')\n(0.015822, 'val_cnto90')\n(0.015802, 'val_cnto60')\n(0.014975, 'val_usra60')\n(0.013992, 'val_usra90')\n(0.013314, 'val_cnto30')\n(0.012821, 'val_usro180')\n(0.01204, 'val_cnta180')\n(0.011908, 'val_cnto180')\n(0.011516, 'val_usra180')\n(0.011277, 'val_usro1000')\n(0.010994, 'val_usra1000')\n(0.010727, 'val_cnta1000')\n(0.01071, 'val_catpos')\n(0.010577, 'val_catorg')\n(0.010551, 'val_cnto1000')\n(0.00058, 'val_catratio')\n"
  }
}
49

Agent message

+07:35
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

50

Agent message

+07:45
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

51

Agent message

+07:54
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

52

exec command

+08:02
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Model text

The eligibility service excludes every track a user previously played or explicitly rated, so repeat-history heuristics are correctly useless here. The strongest simple signal is momentum: unique organic listeners in the last 7 days scores 0.0303 NDCG@10, almost 3× the catalog-popularity baseline. I’m now layering user-neighborhood taste similarity over that time-local trend.

Tool callexecTool call Returned

Tool call · Input 

const patch = "*** Begin Patch\n*** Add File: /app/solution/knn_experiment.py\n+\"\"\"Leakage-safe neighborhood experiments for the public cutoff.\"\"\"\n+from pathlib import Path\n+import numpy as np\n+import pandas as pd\n+from scipy import sparse\n+from sklearn.preprocessing import normalize\n+\n+DATA=Path('/task/data'); OUT=Path('/app/solution'); CUT=24786800\n+\n+def load_candidates():\n+    with np.load(DATA/'validation_eligible_candidates.npz') as z:\n+        return z['uids'].copy(),z['item_ids'].copy(),z['packed_eligible'].copy()\n+\n+uids,items,packed=load_candidates(); uid_index=pd.Index(uids)\n+print('read',flush=True)\n+d=pd.read_parquet(DATA/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\n+# Strong organic plays form a clean taste profile; a six-month window retains\n+# stable taste while avoiding very stale activity.\n+d=d[(d.is_organic==1)&(d.played_ratio_pct>=80)&(d.timestamp>=CUT-180*86400)]\n+print('selected events',len(d),flush=True)\n+\n+# All 9,238 users help define neighborhoods.  Targets are a subset.\n+all_uids=np.sort(d.uid.unique()); user_index=pd.Index(all_uids)\n+ur=user_index.get_indexer(d.uid).astype(np.int32)\n+codes,unique_items=pd.factorize(d.item_id,sort=False); ic=codes.astype(np.int32)\n+# Binary user-track incidence, with inverse-user-frequency weighting.\n+X=sparse.coo_matrix((np.ones(len(d),np.float32),(ur,ic)),shape=(len(all_uids),len(unique_items))).tocsr()\n+X.data[:]=1; X.eliminate_zeros()\n+df=np.diff(X.tocsc().indptr).astype(np.float32)\n+idf=np.log1p(len(all_uids)/(1+df)).astype(np.float32)\n+X=X.multiply(idf).tocsr(); normalize(X,norm='l2',axis=1,copy=False)\n+print('profile',X.shape,X.nnz,flush=True)\n+\n+# Find 250 exact cosine neighbors in small blocks without retaining a dense\n+# all-user similarity matrix.\n+target_rows=user_index.get_indexer(uids)\n+KMAX=250; neigh=np.empty((len(uids),KMAX),np.int32); sims=np.empty((len(uids),KMAX),np.float32)\n+for a in range(0,len(uids),128):\n+    b=min(a+128,len(uids)); ss=(X[target_rows[a:b]]@X.T).toarray()\n+    ss[np.arange(b-a),target_rows[a:b]]=-1\n+    ix=np.argpartition(ss,-KMAX,axis=1)[:,-KMAX:]\n+    vv=np.take_along_axis(ss,ix,axis=1); oo=np.argsort(vv,axis=1)[:,::-1]\n+    neigh[a:b]=np.take_along_axis(ix,oo,axis=1); sims[a:b]=np.take_along_axis(vv,oo,axis=1)\n+    if a%1024==0: print('neighbors',a,flush=True)\n+del X\n+\n+# Recent candidate engagement matrix for neighbors.  Binary organic audience is\n+# robust to a handful of very heavy listeners.\n+col_index=pd.Index(items); cc=col_index.get_indexer(d.item_id); recent=(d.timestamp>=CUT-7*86400)&(cc>=0)\n+Y=sparse.coo_matrix((np.ones(recent.sum(),np.float32),(ur[recent],cc[recent])),shape=(len(all_uids),len(items))).tocsr(); Y.data[:]=1; Y.eliminate_zeros()\n+global_users=np.asarray((Y>0).sum(axis=0)).ravel().astype(np.float32)\n+global_score=np.log1p(global_users); global_score/=global_score.max()\n+global_order=np.lexsort((np.arange(len(items)),-global_score))\n+print('recent matrix',Y.nnz,flush=True)\n+\n+def rank_config(k,power,alpha,name):\n+    rows=[]\n+    for r,uid in enumerate(uids):\n+        ns=neigh[r,:k]; sw=np.maximum(sims[r,:k],0)**power\n+        score=np.asarray(sw@Y[ns]).ravel()\n+        if score.max()>0: score/=score.max()\n+        score += alpha*global_score\n+        eligible=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool)\n+        score[~eligible]=-np.inf\n+        top=np.argpartition(score,-100)[-100:]\n+        top=top[np.argsort(score[top],kind='stable')[::-1]]\n+        rows.extend((int(uid),int(items[c]),rank) for rank,c in enumerate(top,1))\n+    pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(OUT/f'val_knn_{name}.parquet',index=False)\n+    print('wrote',name,flush=True)\n+\n+for k in [20,50,100,250]:\n+    for power in [1,2]:\n+        rank_config(k,power,0.02,f'k{k}_p{power}_a02')\n+for alpha in [0,0.005,0.05,0.1,0.2]:\n+    rank_config(100,2,alpha,f'k100_p2_a{str(alpha).replace(\".\",\"\")}')\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response 

Script completed
Wall time 0.0 seconds
Output:

{}
Harness context codex item completed
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  "item": {
    "type": "FileChange",
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    "changes": {
      "/app/solution/knn_experiment.py": {
        "type": "add",
        "content": "\"\"\"Leakage-safe neighborhood experiments for the public cutoff.\"\"\"\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.preprocessing import normalize\n\nDATA=Path('/task/data'); OUT=Path('/app/solution'); CUT=24786800\n\ndef load_candidates():\n    with np.load(DATA/'validation_eligible_candidates.npz') as z:\n        return z['uids'].copy(),z['item_ids'].copy(),z['packed_eligible'].copy()\n\nuids,items,packed=load_candidates(); uid_index=pd.Index(uids)\nprint('read',flush=True)\nd=pd.read_parquet(DATA/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\n# Strong organic plays form a clean taste profile; a six-month window retains\n# stable taste while avoiding very stale activity.\nd=d[(d.is_organic==1)&(d.played_ratio_pct>=80)&(d.timestamp>=CUT-180*86400)]\nprint('selected events',len(d),flush=True)\n\n# All 9,238 users help define neighborhoods.  Targets are a subset.\nall_uids=np.sort(d.uid.unique()); user_index=pd.Index(all_uids)\nur=user_index.get_indexer(d.uid).astype(np.int32)\ncodes,unique_items=pd.factorize(d.item_id,sort=False); ic=codes.astype(np.int32)\n# Binary user-track incidence, with inverse-user-frequency weighting.\nX=sparse.coo_matrix((np.ones(len(d),np.float32),(ur,ic)),shape=(len(all_uids),len(unique_items))).tocsr()\nX.data[:]=1; X.eliminate_zeros()\ndf=np.diff(X.tocsc().indptr).astype(np.float32)\nidf=np.log1p(len(all_uids)/(1+df)).astype(np.float32)\nX=X.multiply(idf).tocsr(); normalize(X,norm='l2',axis=1,copy=False)\nprint('profile',X.shape,X.nnz,flush=True)\n\n# Find 250 exact cosine neighbors in small blocks without retaining a dense\n# all-user similarity matrix.\ntarget_rows=user_index.get_indexer(uids)\nKMAX=250; neigh=np.empty((len(uids),KMAX),np.int32); sims=np.empty((len(uids),KMAX),np.float32)\nfor a in range(0,len(uids),128):\n    b=min(a+128,len(uids)); ss=(X[target_rows[a:b]]@X.T).toarray()\n    ss[np.arange(b-a),target_rows[a:b]]=-1\n    ix=np.argpartition(ss,-KMAX,axis=1)[:,-KMAX:]\n    vv=np.take_along_axis(ss,ix,axis=1); oo=np.argsort(vv,axis=1)[:,::-1]\n    neigh[a:b]=np.take_along_axis(ix,oo,axis=1); sims[a:b]=np.take_along_axis(vv,oo,axis=1)\n    if a%1024==0: print('neighbors',a,flush=True)\ndel X\n\n# Recent candidate engagement matrix for neighbors.  Binary organic audience is\n# robust to a handful of very heavy listeners.\ncol_index=pd.Index(items); cc=col_index.get_indexer(d.item_id); recent=(d.timestamp>=CUT-7*86400)&(cc>=0)\nY=sparse.coo_matrix((np.ones(recent.sum(),np.float32),(ur[recent],cc[recent])),shape=(len(all_uids),len(items))).tocsr(); Y.data[:]=1; Y.eliminate_zeros()\nglobal_users=np.asarray((Y>0).sum(axis=0)).ravel().astype(np.float32)\nglobal_score=np.log1p(global_users); global_score/=global_score.max()\nglobal_order=np.lexsort((np.arange(len(items)),-global_score))\nprint('recent matrix',Y.nnz,flush=True)\n\ndef rank_config(k,power,alpha,name):\n    rows=[]\n    for r,uid in enumerate(uids):\n        ns=neigh[r,:k]; sw=np.maximum(sims[r,:k],0)**power\n        score=np.asarray(sw@Y[ns]).ravel()\n        if score.max()>0: score/=score.max()\n        score += alpha*global_score\n        eligible=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool)\n        score[~eligible]=-np.inf\n        top=np.argpartition(score,-100)[-100:]\n        top=top[np.argsort(score[top],kind='stable')[::-1]]\n        rows.extend((int(uid),int(items[c]),rank) for rank,c in enumerate(top,1))\n    pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(OUT/f'val_knn_{name}.parquet',index=False)\n    print('wrote',name,flush=True)\n\nfor k in [20,50,100,250]:\n    for power in [1,2]:\n        rank_config(k,power,0.02,f'k{k}_p{power}_a02')\nfor alpha in [0,0.005,0.05,0.1,0.2]:\n    rank_config(100,2,alpha,f'k100_p2_a{str(alpha).replace(\".\",\"\")}')\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/knn_experiment.py\n",
    "stderr": ""
  }
}
Harness context token usage record
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  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
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    "cache_write_input_tokens": 0,
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  }
}
53

exec command

+08:29
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"OMP_NUM_THREADS=4 OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python -u /app/solution/knn_experiment.py","workdir":"/app","yield_time_ms":1000,"max_output_tokens":5000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response 

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=36197
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bd0d729487d28626177fbdea49cf",
  "usage": {
    "input_tokens": 42277,
    "cached_input_tokens": 38656,
    "cache_write_input_tokens": 0,
    "output_tokens": 149,
    "reasoning_output_tokens": 55,
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  },
  "turn_token_usage": {
    "input_tokens": 1271154,
    "cached_input_tokens": 1215360,
    "cache_write_input_tokens": 0,
    "output_tokens": 14429,
    "reasoning_output_tokens": 5398,
    "total_tokens": 1285583
  },
  "thread_token_usage": {
    "input_tokens": 1271154,
    "cached_input_tokens": 1215360,
    "cache_write_input_tokens": 0,
    "output_tokens": 14429,
    "reasoning_output_tokens": 5398,
    "total_tokens": 1285583
  }
}
54

exec command

+08:33
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":36197,"chars":"","yield_time_ms":10000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 10.0 seconds
Output:

read
selected events 8566731
profile (8548, 366713) 2431725
neighbors 0
neighbors 1024
neighbors 2048
neighbors 3072
neighbors 4096
neighbors 5120
neighbors 6144
neighbors 7168
neighbors 8192
recent matrix 173200
wrote k20_p1_a02
wrote k20_p2_a02
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
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    "cache_write_input_tokens": 0,
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    "input_tokens": 1313608,
    "cached_input_tokens": 1257472,
    "cache_write_input_tokens": 0,
    "output_tokens": 14482,
    "reasoning_output_tokens": 5404,
    "total_tokens": 1328090
  }
}
55

exec command

+08:46
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":36197,"chars":"","yield_time_ms":10000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 10.0 seconds
Output:

wrote k50_p1_a02
wrote k50_p2_a02
wrote k100_p1_a02
wrote k100_p2_a02
wrote k250_p1_a02
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bd1dc3fc87d2aa2ff9cbc8cd8172",
  "usage": {
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    "cached_input_tokens": 42240,
    "cache_write_input_tokens": 0,
    "output_tokens": 157,
    "reasoning_output_tokens": 110,
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  },
  "turn_token_usage": {
    "input_tokens": 1356229,
    "cached_input_tokens": 1299712,
    "cache_write_input_tokens": 0,
    "output_tokens": 14639,
    "reasoning_output_tokens": 5514,
    "total_tokens": 1370868
  },
  "thread_token_usage": {
    "input_tokens": 1356229,
    "cached_input_tokens": 1299712,
    "cache_write_input_tokens": 0,
    "output_tokens": 14639,
    "reasoning_output_tokens": 5514,
    "total_tokens": 1370868
  }
}
56

exec command

+08:58
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":36197,"chars":"","yield_time_ms":10000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 10.0 seconds
Output:

wrote k250_p2_a02
wrote k100_p2_a0
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bd2b7c1087d28d6c109746dd2937",
  "usage": {
    "input_tokens": 42846,
    "cached_input_tokens": 42496,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 42899
  },
  "turn_token_usage": {
    "input_tokens": 1399075,
    "cached_input_tokens": 1342208,
    "cache_write_input_tokens": 0,
    "output_tokens": 14692,
    "reasoning_output_tokens": 5520,
    "total_tokens": 1413767
  },
  "thread_token_usage": {
    "input_tokens": 1399075,
    "cached_input_tokens": 1342208,
    "cache_write_input_tokens": 0,
    "output_tokens": 14692,
    "reasoning_output_tokens": 5520,
    "total_tokens": 1413767
  }
}
57

exec command

+09:10
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":36197,"chars":"","yield_time_ms":10000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 9.9 seconds
Output:

wrote k100_p2_a0005
wrote k100_p2_a005
wrote k100_p2_a01
wrote k100_p2_a02
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bd37318087d282648731389b1d7a",
  "usage": {
    "input_tokens": 42940,
    "cached_input_tokens": 42624,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 42993
  },
  "turn_token_usage": {
    "input_tokens": 1442015,
    "cached_input_tokens": 1384832,
    "cache_write_input_tokens": 0,
    "output_tokens": 14745,
    "reasoning_output_tokens": 5526,
    "total_tokens": 1456760
  },
  "thread_token_usage": {
    "input_tokens": 1442015,
    "cached_input_tokens": 1384832,
    "cache_write_input_tokens": 0,
    "output_tokens": 14745,
    "reasoning_output_tokens": 5526,
    "total_tokens": 1456760
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-82c4ba27-405e-4109-a99a-5c9921527921",
    "process_id": "36197",
    "command": [
      "/bin/sh",
      "-lc",
      "OMP_NUM_THREADS=4 OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python -u /app/solution/knn_experiment.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "OMP_NUM_THREADS=4 OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python -u /app/solution/knn_experiment.py"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "read\nselected events 8566731\nprofile (8548, 366713) 2431725\nneighbors 0\nneighbors 1024\nneighbors 2048\nneighbors 3072\nneighbors 4096\nneighbors 5120\nneighbors 6144\nneighbors 7168\nneighbors 8192\nrecent matrix 173200\nwrote k20_p1_a02\nwrote k20_p2_a02\nwrote k50_p1_a02\nwrote k50_p2_a02\nwrote k100_p1_a02\nwrote k100_p2_a02\nwrote k250_p1_a02\nwrote k250_p2_a02\nwrote k100_p2_a0\nwrote k100_p2_a0005\nwrote k100_p2_a005\nwrote k100_p2_a01\nwrote k100_p2_a02\n",
    "stderr": "",
    "aggregated_output": "read\nselected events 8566731\nprofile (8548, 366713) 2431725\nneighbors 0\nneighbors 1024\nneighbors 2048\nneighbors 3072\nneighbors 4096\nneighbors 5120\nneighbors 6144\nneighbors 7168\nneighbors 8192\nrecent matrix 173200\nwrote k20_p1_a02\nwrote k20_p2_a02\nwrote k50_p1_a02\nwrote k50_p2_a02\nwrote k100_p1_a02\nwrote k100_p2_a02\nwrote k250_p1_a02\nwrote k250_p2_a02\nwrote k100_p2_a0\nwrote k100_p2_a0005\nwrote k100_p2_a005\nwrote k100_p2_a01\nwrote k100_p2_a02\n",
    "exit_code": 0,
    "duration": {
      "secs": 49,
      "nanos": 703437311
    },
    "formatted_output": "read\nselected events 8566731\nprofile (8548, 366713) 2431725\nneighbors 0\nneighbors 1024\nneighbors 2048\nneighbors 3072\nneighbors 4096\nneighbors 5120\nneighbors 6144\nneighbors 7168\nneighbors 8192\nrecent matrix 173200\nwrote k20_p1_a02\nwrote k20_p2_a02\nwrote k50_p1_a02\nwrote k50_p2_a02\nwrote k100_p1_a02\nwrote k100_p2_a02\nwrote k250_p1_a02\nwrote k250_p2_a02\nwrote k100_p2_a0\nwrote k100_p2_a0005\nwrote k100_p2_a005\nwrote k100_p2_a01\nwrote k100_p2_a02\n"
  }
}
58

exec command

+09:22
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python -u - <<'PY'\nimport sys,glob\nsys.path.insert(0,'/task'); import evaluate_public as e\nfrom ranking_evaluation import CandidateSet,score_rankings\nimport pandas as pd\nfrom pathlib import Path\nt=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int)); c=CandidateSet(Path('/task/data/validation_eligible_candidates.npz')); g=e.validation_truth(c,t)\nres=[]\nfor f in glob.glob('/app/solution/val_knn*.parquet'):\n d=pd.read_parquet(f); d=d[d['rank']<=10].sort_values(['uid','rank']); ranked={int(u):list(map(int,x.item_id)) for u,x in d.groupby('uid')}; res.append((score_rankings(ranked,g)['quality_ndcg@10'],Path(f).stem))\nfor x in sorted(res,reverse=True): print(x)\nPY","workdir":"/app","yield_time_ms":1000,"max_output_tokens":10000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response 

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=50606
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bd42d34c87d2b62e3349931c067d",
  "usage": {
    "input_tokens": 43053,
    "cached_input_tokens": 42752,
    "cache_write_input_tokens": 0,
    "output_tokens": 280,
    "reasoning_output_tokens": 12,
    "total_tokens": 43333
  },
  "turn_token_usage": {
    "input_tokens": 1485068,
    "cached_input_tokens": 1427584,
    "cache_write_input_tokens": 0,
    "output_tokens": 15025,
    "reasoning_output_tokens": 5538,
    "total_tokens": 1500093
  },
  "thread_token_usage": {
    "input_tokens": 1485068,
    "cached_input_tokens": 1427584,
    "cache_write_input_tokens": 0,
    "output_tokens": 15025,
    "reasoning_output_tokens": 5538,
    "total_tokens": 1500093
  }
}
59

exec command

+09:28
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":50606,"chars":"","yield_time_ms":10000,"max_output_tokens":10000});
text(r.output);

Tool response 

Script completed
Wall time 2.6 seconds
Output:

(0.045057, 'val_knn_k250_p1_a02')
(0.04308, 'val_knn_k100_p1_a02')
(0.042631, 'val_knn_k250_p2_a02')
(0.039496, 'val_knn_k100_p2_a02')
(0.039083, 'val_knn_k100_p2_a01')
(0.038687, 'val_knn_k100_p2_a005')
(0.038565, 'val_knn_k100_p2_a0')
(0.038499, 'val_knn_k100_p2_a0005')
(0.036548, 'val_knn_k50_p1_a02')
(0.033413, 'val_knn_k50_p2_a02')
(0.028401, 'val_knn_k20_p1_a02')
(0.025988, 'val_knn_k20_p2_a02')
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bd4a154887d285f917fb995afa56",
  "usage": {
    "input_tokens": 43361,
    "cached_input_tokens": 42880,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 43414
  },
  "turn_token_usage": {
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    "cache_write_input_tokens": 0,
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  },
  "thread_token_usage": {
    "input_tokens": 1528429,
    "cached_input_tokens": 1470464,
    "cache_write_input_tokens": 0,
    "output_tokens": 15078,
    "reasoning_output_tokens": 5544,
    "total_tokens": 1543507
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-076cdaf9-ec64-4aa0-b051-b9d4d3540a76",
    "process_id": "50606",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python -u - <<'PY'\nimport sys,glob\nsys.path.insert(0,'/task'); import evaluate_public as e\nfrom ranking_evaluation import CandidateSet,score_rankings\nimport pandas as pd\nfrom pathlib import Path\nt=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int)); c=CandidateSet(Path('/task/data/validation_eligible_candidates.npz')); g=e.validation_truth(c,t)\nres=[]\nfor f in glob.glob('/app/solution/val_knn*.parquet'):\n d=pd.read_parquet(f); d=d[d['rank']<=10].sort_values(['uid','rank']); ranked={int(u):list(map(int,x.item_id)) for u,x in d.groupby('uid')}; res.append((score_rankings(ranked,g)['quality_ndcg@10'],Path(f).stem))\nfor x in sorted(res,reverse=True): print(x)\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport sys,glob\nsys.path.insert(0,'/task'); import evaluate_public as e\nfrom ranking_evaluation import CandidateSet,score_rankings\nimport pandas as pd\nfrom pathlib import Path\nt=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int)); c=CandidateSet(Path('/task/data/validation_eligible_candidates.npz')); g=e.validation_truth(c,t)\nres=[]\nfor f in glob.glob('/app/solution/val_knn*.parquet'):\n d=pd.read_parquet(f); d=d[d['rank']<=10].sort_values(['uid','rank']); ranked={int(u):list(map(int,x.item_id)) for u,x in d.groupby('uid')}; res.append((score_rankings(ranked,g)['quality_ndcg@10'],Path(f).stem))\nfor x in sorted(res,reverse=True): print(x)\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "(0.045057, 'val_knn_k250_p1_a02')\n(0.04308, 'val_knn_k100_p1_a02')\n(0.042631, 'val_knn_k250_p2_a02')\n(0.039496, 'val_knn_k100_p2_a02')\n(0.039083, 'val_knn_k100_p2_a01')\n(0.038687, 'val_knn_k100_p2_a005')\n(0.038565, 'val_knn_k100_p2_a0')\n(0.038499, 'val_knn_k100_p2_a0005')\n(0.036548, 'val_knn_k50_p1_a02')\n(0.033413, 'val_knn_k50_p2_a02')\n(0.028401, 'val_knn_k20_p1_a02')\n(0.025988, 'val_knn_k20_p2_a02')\n",
    "stderr": "",
    "aggregated_output": "(0.045057, 'val_knn_k250_p1_a02')\n(0.04308, 'val_knn_k100_p1_a02')\n(0.042631, 'val_knn_k250_p2_a02')\n(0.039496, 'val_knn_k100_p2_a02')\n(0.039083, 'val_knn_k100_p2_a01')\n(0.038687, 'val_knn_k100_p2_a005')\n(0.038565, 'val_knn_k100_p2_a0')\n(0.038499, 'val_knn_k100_p2_a0005')\n(0.036548, 'val_knn_k50_p1_a02')\n(0.033413, 'val_knn_k50_p2_a02')\n(0.028401, 'val_knn_k20_p1_a02')\n(0.025988, 'val_knn_k20_p2_a02')\n",
    "exit_code": 0,
    "duration": {
      "secs": 5,
      "nanos": 350594798
    },
    "formatted_output": "(0.045057, 'val_knn_k250_p1_a02')\n(0.04308, 'val_knn_k100_p1_a02')\n(0.042631, 'val_knn_k250_p2_a02')\n(0.039496, 'val_knn_k100_p2_a02')\n(0.039083, 'val_knn_k100_p2_a01')\n(0.038687, 'val_knn_k100_p2_a005')\n(0.038565, 'val_knn_k100_p2_a0')\n(0.038499, 'val_knn_k100_p2_a0005')\n(0.036548, 'val_knn_k50_p1_a02')\n(0.033413, 'val_knn_k50_p2_a02')\n(0.028401, 'val_knn_k20_p1_a02')\n(0.025988, 'val_knn_k20_p2_a02')\n"
  }
}
60

Agent message

+09:42
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

61

Agent message

+09:51
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

62

Agent message

+10:01
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

63

exec command

+10:04
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const patch = "*** Begin Patch\n*** Add File: /app/solution/knn_experiment2.py\n+\"\"\"Second-stage neighborhood and signal-blending experiments.\"\"\"\n+from pathlib import Path\n+import numpy as np\n+import pandas as pd\n+from scipy import sparse\n+from sklearn.preprocessing import normalize\n+\n+DATA=Path('/task/data'); OUT=Path('/app/solution'); CUT=24786800\n+with np.load(DATA/'validation_eligible_candidates.npz') as z:\n+    uids=z['uids'].copy(); items=z['item_ids'].copy(); packed=z['packed_eligible'].copy()\n+cat_index=pd.Index(items)\n+\n+print('read listens',flush=True)\n+raw=pd.read_parquet(DATA/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\n+all_uids=np.sort(raw.uid.unique()); user_index=pd.Index(all_uids); target_rows=user_index.get_indexer(uids)\n+ur_all=user_index.get_indexer(raw.uid).astype(np.int32); cc_all=cat_index.get_indexer(raw.item_id).astype(np.int32)\n+\n+def candidate_matrix(mask, mode='binary'):\n+    rr=ur_all[mask]; cc=cc_all[mask]\n+    y=sparse.coo_matrix((np.ones(mask.sum(),np.float32),(rr,cc)),shape=(len(all_uids),len(items))).tocsr()\n+    if mode=='binary': y.data[:]=1\n+    elif mode=='logcount': y.data=np.log1p(y.data)\n+    return y\n+\n+strong=raw.played_ratio_pct>=80\n+o7=(raw.is_organic==1)&strong&(raw.timestamp>=CUT-7*86400)&(cc_all>=0)\n+o14=(raw.is_organic==1)&strong&(raw.timestamp>=CUT-14*86400)&(cc_all>=0)\n+a7=strong&(raw.timestamp>=CUT-7*86400)&(cc_all>=0)\n+Ys={\n+ 'o7b':candidate_matrix(o7,'binary'),\n+ 'o7c':candidate_matrix(o7,'logcount'),\n+ 'o14b':candidate_matrix(o14,'binary'),\n+ 'a7b':candidate_matrix(a7,'binary'),\n+ 'a7c':candidate_matrix(a7,'logcount'),\n+}\n+print('candidate matrices',{k:v.nnz for k,v in Ys.items()},flush=True)\n+\n+# Active explicit likes provide a long-term high-confidence neighbor signal.\n+state=pd.read_parquet(DATA/'validation_preference_state.parquet')\n+state=state[(state.state=='liked')&state.item_id.isin(items)&state.uid.isin(all_uids)]\n+sr=user_index.get_indexer(state.uid); sc=cat_index.get_indexer(state.item_id)\n+Ys['likes']=sparse.coo_matrix((np.ones(len(state),np.float32),(sr,sc)),shape=(len(all_uids),len(items))).tocsr(); Ys['likes'].data[:]=1\n+\n+# Profile source kept broad; matrices below choose horizons/weighting.\n+prof=raw[(raw.is_organic==1)&strong&(raw.timestamp>=CUT-180*86400)][['uid','timestamp','item_id']]\n+pr=user_index.get_indexer(prof.uid).astype(np.int32)\n+pc,profile_items=pd.factorize(prof.item_id,sort=False); pc=pc.astype(np.int32)\n+del raw,ur_all,cc_all\n+\n+def make_profile(days,count_weight):\n+    mask=prof.timestamp.to_numpy()>=CUT-days*86400\n+    x=sparse.coo_matrix((np.ones(mask.sum(),np.float32),(pr[mask],pc[mask])),shape=(len(all_uids),len(profile_items))).tocsr()\n+    if count_weight: x.data=np.log1p(x.data)\n+    else: x.data[:]=1\n+    df=np.diff(x.tocsc().indptr).astype(np.float32)\n+    idf=np.log1p(len(all_uids)/(1+df)).astype(np.float32)\n+    x=x.multiply(idf).tocsr(); normalize(x,norm='l2',axis=1,copy=False)\n+    return x\n+\n+def neighbors(x,kmax=1000):\n+    ni=np.empty((len(uids),kmax),np.int32); nv=np.empty((len(uids),kmax),np.float32)\n+    for a in range(0,len(uids),128):\n+        b=min(a+128,len(uids)); ss=(x[target_rows[a:b]]@x.T).toarray(); ss[np.arange(b-a),target_rows[a:b]]=-1\n+        ix=np.argpartition(ss,-kmax,axis=1)[:,-kmax:]; vv=np.take_along_axis(ss,ix,axis=1); oo=np.argsort(vv,axis=1)[:,::-1]\n+        ni[a:b]=np.take_along_axis(ix,oo,axis=1); nv[a:b]=np.take_along_axis(vv,oo,axis=1)\n+    return ni,nv\n+\n+global_score=np.log1p(np.asarray((Ys['o7b']>0).sum(axis=0)).ravel().astype(np.float32)); global_score/=global_score.max()\n+\n+def rank_config(ni,nv,k,power,parts,alpha,name):\n+    rows=[]\n+    for r,uid in enumerate(uids):\n+        ns=ni[r,:k]; sw=np.maximum(nv[r,:k],0)**power\n+        score=np.zeros(len(items),np.float32)\n+        for yn,coef in parts:\n+            z=np.asarray(sw@Ys[yn][ns]).ravel()\n+            if z.max()>0: z/=z.max()\n+            score += coef*z\n+        if score.max()>0: score/=score.max()\n+        score += alpha*global_score\n+        eligible=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool); score[~eligible]=-np.inf\n+        top=np.argpartition(score,-100)[-100:]; top=top[np.argsort(score[top],kind='stable')[::-1]]\n+        rows.extend((int(uid),int(items[c]),rank) for rank,c in enumerate(top,1))\n+    pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(OUT/f'val_k2_{name}.parquet',index=False)\n+    print('wrote',name,flush=True)\n+\n+for days,cw,tag in [(180,False,'b180'),(180,True,'c180'),(90,False,'b90'),(30,False,'b30')]:\n+    print('profile',tag,flush=True); x=make_profile(days,cw); ni,nv=neighbors(x); del x\n+    if tag=='b180':\n+        rank_config(ni,nv,500,1,[('o7b',1)],.02,tag+'_k500')\n+        rank_config(ni,nv,1000,1,[('o7b',1)],.02,tag+'_k1000')\n+        rank_config(ni,nv,500,.5,[('o7b',1)],.02,tag+'_k500p05')\n+        rank_config(ni,nv,500,1,[('o7c',1)],.02,tag+'_o7c')\n+        rank_config(ni,nv,500,1,[('o14b',1)],.02,tag+'_o14b')\n+        rank_config(ni,nv,500,1,[('a7b',1)],.02,tag+'_a7b')\n+        rank_config(ni,nv,500,1,[('a7c',1)],.02,tag+'_a7c')\n+        rank_config(ni,nv,500,1,[('o7b',1),('likes',.25)],.02,tag+'_likes25')\n+        rank_config(ni,nv,500,1,[('o7b',1),('likes',.1)],.02,tag+'_likes10')\n+    else:\n+        rank_config(ni,nv,500,1,[('o7b',1)],.02,tag+'_k500')\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response 

Script completed
Wall time 0.0 seconds
Output:

{}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-de734226-8ffb-4f2b-bae3-cd69bd981c96",
    "changes": {
      "/app/solution/knn_experiment2.py": {
        "type": "add",
        "content": "\"\"\"Second-stage neighborhood and signal-blending experiments.\"\"\"\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.preprocessing import normalize\n\nDATA=Path('/task/data'); OUT=Path('/app/solution'); CUT=24786800\nwith np.load(DATA/'validation_eligible_candidates.npz') as z:\n    uids=z['uids'].copy(); items=z['item_ids'].copy(); packed=z['packed_eligible'].copy()\ncat_index=pd.Index(items)\n\nprint('read listens',flush=True)\nraw=pd.read_parquet(DATA/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\nall_uids=np.sort(raw.uid.unique()); user_index=pd.Index(all_uids); target_rows=user_index.get_indexer(uids)\nur_all=user_index.get_indexer(raw.uid).astype(np.int32); cc_all=cat_index.get_indexer(raw.item_id).astype(np.int32)\n\ndef candidate_matrix(mask, mode='binary'):\n    rr=ur_all[mask]; cc=cc_all[mask]\n    y=sparse.coo_matrix((np.ones(mask.sum(),np.float32),(rr,cc)),shape=(len(all_uids),len(items))).tocsr()\n    if mode=='binary': y.data[:]=1\n    elif mode=='logcount': y.data=np.log1p(y.data)\n    return y\n\nstrong=raw.played_ratio_pct>=80\no7=(raw.is_organic==1)&strong&(raw.timestamp>=CUT-7*86400)&(cc_all>=0)\no14=(raw.is_organic==1)&strong&(raw.timestamp>=CUT-14*86400)&(cc_all>=0)\na7=strong&(raw.timestamp>=CUT-7*86400)&(cc_all>=0)\nYs={\n 'o7b':candidate_matrix(o7,'binary'),\n 'o7c':candidate_matrix(o7,'logcount'),\n 'o14b':candidate_matrix(o14,'binary'),\n 'a7b':candidate_matrix(a7,'binary'),\n 'a7c':candidate_matrix(a7,'logcount'),\n}\nprint('candidate matrices',{k:v.nnz for k,v in Ys.items()},flush=True)\n\n# Active explicit likes provide a long-term high-confidence neighbor signal.\nstate=pd.read_parquet(DATA/'validation_preference_state.parquet')\nstate=state[(state.state=='liked')&state.item_id.isin(items)&state.uid.isin(all_uids)]\nsr=user_index.get_indexer(state.uid); sc=cat_index.get_indexer(state.item_id)\nYs['likes']=sparse.coo_matrix((np.ones(len(state),np.float32),(sr,sc)),shape=(len(all_uids),len(items))).tocsr(); Ys['likes'].data[:]=1\n\n# Profile source kept broad; matrices below choose horizons/weighting.\nprof=raw[(raw.is_organic==1)&strong&(raw.timestamp>=CUT-180*86400)][['uid','timestamp','item_id']]\npr=user_index.get_indexer(prof.uid).astype(np.int32)\npc,profile_items=pd.factorize(prof.item_id,sort=False); pc=pc.astype(np.int32)\ndel raw,ur_all,cc_all\n\ndef make_profile(days,count_weight):\n    mask=prof.timestamp.to_numpy()>=CUT-days*86400\n    x=sparse.coo_matrix((np.ones(mask.sum(),np.float32),(pr[mask],pc[mask])),shape=(len(all_uids),len(profile_items))).tocsr()\n    if count_weight: x.data=np.log1p(x.data)\n    else: x.data[:]=1\n    df=np.diff(x.tocsc().indptr).astype(np.float32)\n    idf=np.log1p(len(all_uids)/(1+df)).astype(np.float32)\n    x=x.multiply(idf).tocsr(); normalize(x,norm='l2',axis=1,copy=False)\n    return x\n\ndef neighbors(x,kmax=1000):\n    ni=np.empty((len(uids),kmax),np.int32); nv=np.empty((len(uids),kmax),np.float32)\n    for a in range(0,len(uids),128):\n        b=min(a+128,len(uids)); ss=(x[target_rows[a:b]]@x.T).toarray(); ss[np.arange(b-a),target_rows[a:b]]=-1\n        ix=np.argpartition(ss,-kmax,axis=1)[:,-kmax:]; vv=np.take_along_axis(ss,ix,axis=1); oo=np.argsort(vv,axis=1)[:,::-1]\n        ni[a:b]=np.take_along_axis(ix,oo,axis=1); nv[a:b]=np.take_along_axis(vv,oo,axis=1)\n    return ni,nv\n\nglobal_score=np.log1p(np.asarray((Ys['o7b']>0).sum(axis=0)).ravel().astype(np.float32)); global_score/=global_score.max()\n\ndef rank_config(ni,nv,k,power,parts,alpha,name):\n    rows=[]\n    for r,uid in enumerate(uids):\n        ns=ni[r,:k]; sw=np.maximum(nv[r,:k],0)**power\n        score=np.zeros(len(items),np.float32)\n        for yn,coef in parts:\n            z=np.asarray(sw@Ys[yn][ns]).ravel()\n            if z.max()>0: z/=z.max()\n            score += coef*z\n        if score.max()>0: score/=score.max()\n        score += alpha*global_score\n        eligible=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool); score[~eligible]=-np.inf\n        top=np.argpartition(score,-100)[-100:]; top=top[np.argsort(score[top],kind='stable')[::-1]]\n        rows.extend((int(uid),int(items[c]),rank) for rank,c in enumerate(top,1))\n    pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(OUT/f'val_k2_{name}.parquet',index=False)\n    print('wrote',name,flush=True)\n\nfor days,cw,tag in [(180,False,'b180'),(180,True,'c180'),(90,False,'b90'),(30,False,'b30')]:\n    print('profile',tag,flush=True); x=make_profile(days,cw); ni,nv=neighbors(x); del x\n    if tag=='b180':\n        rank_config(ni,nv,500,1,[('o7b',1)],.02,tag+'_k500')\n        rank_config(ni,nv,1000,1,[('o7b',1)],.02,tag+'_k1000')\n        rank_config(ni,nv,500,.5,[('o7b',1)],.02,tag+'_k500p05')\n        rank_config(ni,nv,500,1,[('o7c',1)],.02,tag+'_o7c')\n        rank_config(ni,nv,500,1,[('o14b',1)],.02,tag+'_o14b')\n        rank_config(ni,nv,500,1,[('a7b',1)],.02,tag+'_a7b')\n        rank_config(ni,nv,500,1,[('a7c',1)],.02,tag+'_a7c')\n        rank_config(ni,nv,500,1,[('o7b',1),('likes',.25)],.02,tag+'_likes25')\n        rank_config(ni,nv,500,1,[('o7b',1),('likes',.1)],.02,tag+'_likes10')\n    else:\n        rank_config(ni,nv,500,1,[('o7b',1)],.02,tag+'_k500')\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/knn_experiment2.py\n",
    "stderr": ""
  }
}
Harness context token usage record
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    "cache_write_input_tokens": 0,
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    "total_tokens": 1590778
  }
}
64

exec command

+10:39
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"OMP_NUM_THREADS=4 OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python -u /app/solution/knn_experiment2.py","workdir":"/app","yield_time_ms":1000,"max_output_tokens":5000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response 

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=39647
Harness context token usage record
{
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  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
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    "reasoning_output_tokens": 6,
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    "reasoning_output_tokens": 7259,
    "total_tokens": 1638174
  }
}
65

exec command

+10:46
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":39647,"chars":"","yield_time_ms":10000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 10.0 seconds
Output:

read listens
candidate matrices {'o7b': 173200, 'o7c': 173200, 'o14b': 293468, 'a7b': 459591, 'a7c': 459591}
profile b180
wrote b180_k500
wrote b180_k1000
Harness context token usage record
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  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bd947b8487d2b15bd9825ae1ab7b",
  "usage": {
    "input_tokens": 47424,
    "cached_input_tokens": 47104,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 47477
  },
  "turn_token_usage": {
    "input_tokens": 1666790,
    "cached_input_tokens": 1604224,
    "cache_write_input_tokens": 0,
    "output_tokens": 18861,
    "reasoning_output_tokens": 7265,
    "total_tokens": 1685651
  },
  "thread_token_usage": {
    "input_tokens": 1666790,
    "cached_input_tokens": 1604224,
    "cache_write_input_tokens": 0,
    "output_tokens": 18861,
    "reasoning_output_tokens": 7265,
    "total_tokens": 1685651
  }
}
66

exec command

+10:57
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":39647,"chars":"","yield_time_ms":10000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 10.0 seconds
Output:

wrote b180_k500p05
wrote b180_o7c
wrote b180_o14b
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bda2819487d2baf4ae5b1f0d24c1",
  "usage": {
    "input_tokens": 47569,
    "cached_input_tokens": 47232,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 47622
  },
  "turn_token_usage": {
    "input_tokens": 1714359,
    "cached_input_tokens": 1651456,
    "cache_write_input_tokens": 0,
    "output_tokens": 18914,
    "reasoning_output_tokens": 7271,
    "total_tokens": 1733273
  },
  "thread_token_usage": {
    "input_tokens": 1714359,
    "cached_input_tokens": 1651456,
    "cache_write_input_tokens": 0,
    "output_tokens": 18914,
    "reasoning_output_tokens": 7271,
    "total_tokens": 1733273
  }
}
67

exec command

+11:09
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":39647,"chars":"","yield_time_ms":10000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 10.0 seconds
Output:

wrote b180_a7b
wrote b180_a7c
wrote b180_likes25
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bdaeb8bc87d2bb5796ca87490b8a",
  "usage": {
    "input_tokens": 47670,
    "cached_input_tokens": 47360,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 47723
  },
  "turn_token_usage": {
    "input_tokens": 1762029,
    "cached_input_tokens": 1698816,
    "cache_write_input_tokens": 0,
    "output_tokens": 18967,
    "reasoning_output_tokens": 7277,
    "total_tokens": 1780996
  },
  "thread_token_usage": {
    "input_tokens": 1762029,
    "cached_input_tokens": 1698816,
    "cache_write_input_tokens": 0,
    "output_tokens": 18967,
    "reasoning_output_tokens": 7277,
    "total_tokens": 1780996
  }
}
68

exec command

+11:21
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":39647,"chars":"","yield_time_ms":10000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 10.0 seconds
Output:

wrote b180_likes10
profile c180
wrote c180_k500
profile b90
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bdba668887d291f1c34076bf7ccd",
  "usage": {
    "input_tokens": 47770,
    "cached_input_tokens": 47488,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 47823
  },
  "turn_token_usage": {
    "input_tokens": 1809799,
    "cached_input_tokens": 1746304,
    "cache_write_input_tokens": 0,
    "output_tokens": 19020,
    "reasoning_output_tokens": 7283,
    "total_tokens": 1828819
  },
  "thread_token_usage": {
    "input_tokens": 1809799,
    "cached_input_tokens": 1746304,
    "cache_write_input_tokens": 0,
    "output_tokens": 19020,
    "reasoning_output_tokens": 7283,
    "total_tokens": 1828819
  }
}
69

exec command

+11:33
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":39647,"chars":"","yield_time_ms":10000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 8.2 seconds
Output:

wrote b90_k500
profile b30
wrote b30_k500
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bdc6491c87d283ae60734c389a2a",
  "usage": {
    "input_tokens": 47869,
    "cached_input_tokens": 47616,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 47922
  },
  "turn_token_usage": {
    "input_tokens": 1857668,
    "cached_input_tokens": 1793920,
    "cache_write_input_tokens": 0,
    "output_tokens": 19073,
    "reasoning_output_tokens": 7289,
    "total_tokens": 1876741
  },
  "thread_token_usage": {
    "input_tokens": 1857668,
    "cached_input_tokens": 1793920,
    "cache_write_input_tokens": 0,
    "output_tokens": 19073,
    "reasoning_output_tokens": 7289,
    "total_tokens": 1876741
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-797d0315-a0b0-44bc-a85c-7cdb784beb8a",
    "process_id": "39647",
    "command": [
      "/bin/sh",
      "-lc",
      "OMP_NUM_THREADS=4 OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python -u /app/solution/knn_experiment2.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "OMP_NUM_THREADS=4 OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python -u /app/solution/knn_experiment2.py"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "read listens\ncandidate matrices {'o7b': 173200, 'o7c': 173200, 'o14b': 293468, 'a7b': 459591, 'a7c': 459591}\nprofile b180\nwrote b180_k500\nwrote b180_k1000\nwrote b180_k500p05\nwrote b180_o7c\nwrote b180_o14b\nwrote b180_a7b\nwrote b180_a7c\nwrote b180_likes25\nwrote b180_likes10\nprofile c180\nwrote c180_k500\nprofile b90\nwrote b90_k500\nprofile b30\nwrote b30_k500\n",
    "stderr": "",
    "aggregated_output": "read listens\ncandidate matrices {'o7b': 173200, 'o7c': 173200, 'o14b': 293468, 'a7b': 459591, 'a7c': 459591}\nprofile b180\nwrote b180_k500\nwrote b180_k1000\nwrote b180_k500p05\nwrote b180_o7c\nwrote b180_o14b\nwrote b180_a7b\nwrote b180_a7c\nwrote b180_likes25\nwrote b180_likes10\nprofile c180\nwrote c180_k500\nprofile b90\nwrote b90_k500\nprofile b30\nwrote b30_k500\n",
    "exit_code": 0,
    "duration": {
      "secs": 61,
      "nanos": 119182077
    },
    "formatted_output": "read listens\ncandidate matrices {'o7b': 173200, 'o7c': 173200, 'o14b': 293468, 'a7b': 459591, 'a7c': 459591}\nprofile b180\nwrote b180_k500\nwrote b180_k1000\nwrote b180_k500p05\nwrote b180_o7c\nwrote b180_o14b\nwrote b180_a7b\nwrote b180_a7c\nwrote b180_likes25\nwrote b180_likes10\nprofile c180\nwrote c180_k500\nprofile b90\nwrote b90_k500\nprofile b30\nwrote b30_k500\n"
  }
}
70

exec command

+11:45
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python -u - <<'PY'\nimport sys,glob\nsys.path.insert(0,'/task'); import evaluate_public as e\nfrom ranking_evaluation import CandidateSet,score_rankings\nimport pandas as pd\nfrom pathlib import Path\nt=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int)); c=CandidateSet(Path('/task/data/validation_eligible_candidates.npz')); g=e.validation_truth(c,t); res=[]\nfor f in glob.glob('/app/solution/val_k2*.parquet'):\n d=pd.read_parquet(f); d=d[d['rank']<=10].sort_values(['uid','rank']); ranked={int(u):list(map(int,x.item_id)) for u,x in d.groupby('uid')}; res.append((score_rankings(ranked,g)['quality_ndcg@10'],Path(f).stem))\nfor x in sorted(res,reverse=True): print(x)\nPY","workdir":"/app","yield_time_ms":1000,"max_output_tokens":10000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response 

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=90974
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bdd0dc1487d29f1a25ce90d0d839",
  "usage": {
    "input_tokens": 47963,
    "cached_input_tokens": 47744,
    "cache_write_input_tokens": 0,
    "output_tokens": 273,
    "reasoning_output_tokens": 6,
    "total_tokens": 48236
  },
  "turn_token_usage": {
    "input_tokens": 1905631,
    "cached_input_tokens": 1841664,
    "cache_write_input_tokens": 0,
    "output_tokens": 19346,
    "reasoning_output_tokens": 7295,
    "total_tokens": 1924977
  },
  "thread_token_usage": {
    "input_tokens": 1905631,
    "cached_input_tokens": 1841664,
    "cache_write_input_tokens": 0,
    "output_tokens": 19346,
    "reasoning_output_tokens": 7295,
    "total_tokens": 1924977
  }
}
71

exec command

+11:52
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":90974,"chars":"","yield_time_ms":10000,"max_output_tokens":10000});
text(r.output);

Tool response 

Script completed
Wall time 2.2 seconds
Output:

(0.048252, 'val_k2_b180_a7c')
(0.048215, 'val_k2_b180_o7c')
(0.046428, 'val_k2_b180_k500')
(0.04579, 'val_k2_b180_k500p05')
(0.045787, 'val_k2_b180_likes10')
(0.045667, 'val_k2_b90_k500')
(0.045634, 'val_k2_c180_k500')
(0.045493, 'val_k2_b180_k1000')
(0.044676, 'val_k2_b180_a7b')
(0.04388, 'val_k2_b180_likes25')
(0.043592, 'val_k2_b30_k500')
(0.040666, 'val_k2_b180_o14b')
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bdd9930c87d28d4155e69cd21b5f",
  "usage": {
    "input_tokens": 48264,
    "cached_input_tokens": 47744,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 48317
  },
  "turn_token_usage": {
    "input_tokens": 1953895,
    "cached_input_tokens": 1889408,
    "cache_write_input_tokens": 0,
    "output_tokens": 19399,
    "reasoning_output_tokens": 7301,
    "total_tokens": 1973294
  },
  "thread_token_usage": {
    "input_tokens": 1953895,
    "cached_input_tokens": 1889408,
    "cache_write_input_tokens": 0,
    "output_tokens": 19399,
    "reasoning_output_tokens": 7301,
    "total_tokens": 1973294
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-fd595689-2279-4052-9904-8211bd1ff5df",
    "process_id": "90974",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python -u - <<'PY'\nimport sys,glob\nsys.path.insert(0,'/task'); import evaluate_public as e\nfrom ranking_evaluation import CandidateSet,score_rankings\nimport pandas as pd\nfrom pathlib import Path\nt=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int)); c=CandidateSet(Path('/task/data/validation_eligible_candidates.npz')); g=e.validation_truth(c,t); res=[]\nfor f in glob.glob('/app/solution/val_k2*.parquet'):\n d=pd.read_parquet(f); d=d[d['rank']<=10].sort_values(['uid','rank']); ranked={int(u):list(map(int,x.item_id)) for u,x in d.groupby('uid')}; res.append((score_rankings(ranked,g)['quality_ndcg@10'],Path(f).stem))\nfor x in sorted(res,reverse=True): print(x)\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport sys,glob\nsys.path.insert(0,'/task'); import evaluate_public as e\nfrom ranking_evaluation import CandidateSet,score_rankings\nimport pandas as pd\nfrom pathlib import Path\nt=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int)); c=CandidateSet(Path('/task/data/validation_eligible_candidates.npz')); g=e.validation_truth(c,t); res=[]\nfor f in glob.glob('/app/solution/val_k2*.parquet'):\n d=pd.read_parquet(f); d=d[d['rank']<=10].sort_values(['uid','rank']); ranked={int(u):list(map(int,x.item_id)) for u,x in d.groupby('uid')}; res.append((score_rankings(ranked,g)['quality_ndcg@10'],Path(f).stem))\nfor x in sorted(res,reverse=True): print(x)\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "(0.048252, 'val_k2_b180_a7c')\n(0.048215, 'val_k2_b180_o7c')\n(0.046428, 'val_k2_b180_k500')\n(0.04579, 'val_k2_b180_k500p05')\n(0.045787, 'val_k2_b180_likes10')\n(0.045667, 'val_k2_b90_k500')\n(0.045634, 'val_k2_c180_k500')\n(0.045493, 'val_k2_b180_k1000')\n(0.044676, 'val_k2_b180_a7b')\n(0.04388, 'val_k2_b180_likes25')\n(0.043592, 'val_k2_b30_k500')\n(0.040666, 'val_k2_b180_o14b')\n",
    "stderr": "",
    "aggregated_output": "(0.048252, 'val_k2_b180_a7c')\n(0.048215, 'val_k2_b180_o7c')\n(0.046428, 'val_k2_b180_k500')\n(0.04579, 'val_k2_b180_k500p05')\n(0.045787, 'val_k2_b180_likes10')\n(0.045667, 'val_k2_b90_k500')\n(0.045634, 'val_k2_c180_k500')\n(0.045493, 'val_k2_b180_k1000')\n(0.044676, 'val_k2_b180_a7b')\n(0.04388, 'val_k2_b180_likes25')\n(0.043592, 'val_k2_b30_k500')\n(0.040666, 'val_k2_b180_o14b')\n",
    "exit_code": 0,
    "duration": {
      "secs": 5,
      "nanos": 341595107
    },
    "formatted_output": "(0.048252, 'val_k2_b180_a7c')\n(0.048215, 'val_k2_b180_o7c')\n(0.046428, 'val_k2_b180_k500')\n(0.04579, 'val_k2_b180_k500p05')\n(0.045787, 'val_k2_b180_likes10')\n(0.045667, 'val_k2_b90_k500')\n(0.045634, 'val_k2_c180_k500')\n(0.045493, 'val_k2_b180_k1000')\n(0.044676, 'val_k2_b180_a7b')\n(0.04388, 'val_k2_b180_likes25')\n(0.043592, 'val_k2_b30_k500')\n(0.040666, 'val_k2_b180_o14b')\n"
  }
}
72

exec command

+12:01
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np,sys\nfrom pathlib import Path\n# vector truth leakage solely analysis\np=Path('/task/data'); target=pd.read_parquet(p/'target_users.parquet').uid\nwith np.load(p/'validation_eligible_candidates.npz') as z: uids=z['uids'].copy(); items=z['item_ids'].copy(); packed=z['packed_eligible'].copy()\nui=pd.Series(np.arange(len(uids)),index=uids.astype('int64')); ci=pd.Series(np.arange(len(items)),index=items.astype('int64'))\nls=pd.read_parquet(p/'val_listens.parquet'); lk=pd.read_parquet(p/'val_likes.parquet'); a=ls[(ls.uid.isin(target))&(ls.is_organic==1)&(ls.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates().assign(grade=1); b=lk[(lk.uid.isin(target))&(lk.is_organic==1)][['uid','item_id']].drop_duplicates().assign(grade=3); g=pd.concat([a,b]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last'); r=ui.reindex(g.uid.astype('int64')).values;c=ci.reindex(g.item_id.astype('int64')).values;ok=~(np.isnan(r)|np.isnan(c)); ii=np.flatnonzero(ok);ok[ii]=((packed[r[ok].astype(int),c[ok].astype(int)//8]>>(c[ok].astype(int)%8))&1)>0;g=g[ok]\nfor f in ['/app/solution/val_usro7.parquet','/app/solution/val_k2_b180_a7c.parquet','/app/solution/val_k2_b180_o7c.parquet','/app/solution/val_knn_k250_p1_a02.parquet']:\n d=pd.read_parquet(f); q=g.merge(d[d['rank']<=10],on=['uid','item_id'],how='left');print(Path(f).stem,'hits',q['rank'].notna().sum(),'users hit',q[q['rank'].notna()].uid.nunique());print(q.groupby('grade')['rank'].agg(n='size',hits=lambda x:x.notna().sum(),mean='mean'))\nPY","workdir":"/app","yield_time_ms":1000,"max_output_tokens":5000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response 

Script completed
Wall time 1.1 seconds
Output:

val_usro7 hits 899 users hit 473
           n  hits      mean
grade                       
1      33352   803  4.748443
3       4417    96  4.718750
val_k2_b180_a7c hits 1321 users hit 717
           n  hits      mean
grade                       
1      33352  1143  4.529309
3       4417   178  3.971910
val_k2_b180_o7c hits 1341 users hit 709
           n  hits      mean
grade                       
1      33352  1170  4.650427
3       4417   171  4.175439
val_knn_k250_p1_a02 hits 1279 users hit 679
           n  hits      mean
grade                       
1      33352  1129  4.657219
3       4417   150  4.086667
Harness context token usage record
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Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-d7ed14ec-ba53-4f5e-83b7-b5ef7f41bd83",
    "process_id": "65756",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np,sys\nfrom pathlib import Path\n# vector truth leakage solely analysis\np=Path('/task/data'); target=pd.read_parquet(p/'target_users.parquet').uid\nwith np.load(p/'validation_eligible_candidates.npz') as z: uids=z['uids'].copy(); items=z['item_ids'].copy(); packed=z['packed_eligible'].copy()\nui=pd.Series(np.arange(len(uids)),index=uids.astype('int64')); ci=pd.Series(np.arange(len(items)),index=items.astype('int64'))\nls=pd.read_parquet(p/'val_listens.parquet'); lk=pd.read_parquet(p/'val_likes.parquet'); a=ls[(ls.uid.isin(target))&(ls.is_organic==1)&(ls.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates().assign(grade=1); b=lk[(lk.uid.isin(target))&(lk.is_organic==1)][['uid','item_id']].drop_duplicates().assign(grade=3); g=pd.concat([a,b]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last'); r=ui.reindex(g.uid.astype('int64')).values;c=ci.reindex(g.item_id.astype('int64')).values;ok=~(np.isnan(r)|np.isnan(c)); ii=np.flatnonzero(ok);ok[ii]=((packed[r[ok].astype(int),c[ok].astype(int)//8]>>(c[ok].astype(int)%8))&1)>0;g=g[ok]\nfor f in ['/app/solution/val_usro7.parquet','/app/solution/val_k2_b180_a7c.parquet','/app/solution/val_k2_b180_o7c.parquet','/app/solution/val_knn_k250_p1_a02.parquet']:\n d=pd.read_parquet(f); q=g.merge(d[d['rank']<=10],on=['uid','item_id'],how='left');print(Path(f).stem,'hits',q['rank'].notna().sum(),'users hit',q[q['rank'].notna()].uid.nunique());print(q.groupby('grade')['rank'].agg(n='size',hits=lambda x:x.notna().sum(),mean='mean'))\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np,sys\nfrom pathlib import Path\n# vector truth leakage solely analysis\np=Path('/task/data'); target=pd.read_parquet(p/'target_users.parquet').uid\nwith np.load(p/'validation_eligible_candidates.npz') as z: uids=z['uids'].copy(); items=z['item_ids'].copy(); packed=z['packed_eligible'].copy()\nui=pd.Series(np.arange(len(uids)),index=uids.astype('int64')); ci=pd.Series(np.arange(len(items)),index=items.astype('int64'))\nls=pd.read_parquet(p/'val_listens.parquet'); lk=pd.read_parquet(p/'val_likes.parquet'); a=ls[(ls.uid.isin(target))&(ls.is_organic==1)&(ls.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates().assign(grade=1); b=lk[(lk.uid.isin(target))&(lk.is_organic==1)][['uid','item_id']].drop_duplicates().assign(grade=3); g=pd.concat([a,b]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last'); r=ui.reindex(g.uid.astype('int64')).values;c=ci.reindex(g.item_id.astype('int64')).values;ok=~(np.isnan(r)|np.isnan(c)); ii=np.flatnonzero(ok);ok[ii]=((packed[r[ok].astype(int),c[ok].astype(int)//8]>>(c[ok].astype(int)%8))&1)>0;g=g[ok]\nfor f in ['/app/solution/val_usro7.parquet','/app/solution/val_k2_b180_a7c.parquet','/app/solution/val_k2_b180_o7c.parquet','/app/solution/val_knn_k250_p1_a02.parquet']:\n d=pd.read_parquet(f); q=g.merge(d[d['rank']<=10],on=['uid','item_id'],how='left');print(Path(f).stem,'hits',q['rank'].notna().sum(),'users hit',q[q['rank'].notna()].uid.nunique());print(q.groupby('grade')['rank'].agg(n='size',hits=lambda x:x.notna().sum(),mean='mean'))\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "val_usro7 hits 899 users hit 473\n           n  hits      mean\ngrade                       \n1      33352   803  4.748443\n3       4417    96  4.718750\nval_k2_b180_a7c hits 1321 users hit 717\n           n  hits      mean\ngrade                       \n1      33352  1143  4.529309\n3       4417   178  3.971910\nval_k2_b180_o7c hits 1341 users hit 709\n           n  hits      mean\ngrade                       \n1      33352  1170  4.650427\n3       4417   171  4.175439\nval_knn_k250_p1_a02 hits 1279 users hit 679\n           n  hits      mean\ngrade                       \n1      33352  1129  4.657219\n3       4417   150  4.086667\n",
    "stderr": "",
    "aggregated_output": "val_usro7 hits 899 users hit 473\n           n  hits      mean\ngrade                       \n1      33352   803  4.748443\n3       4417    96  4.718750\nval_k2_b180_a7c hits 1321 users hit 717\n           n  hits      mean\ngrade                       \n1      33352  1143  4.529309\n3       4417   178  3.971910\nval_k2_b180_o7c hits 1341 users hit 709\n           n  hits      mean\ngrade                       \n1      33352  1170  4.650427\n3       4417   171  4.175439\nval_knn_k250_p1_a02 hits 1279 users hit 679\n           n  hits      mean\ngrade                       \n1      33352  1129  4.657219\n3       4417   150  4.086667\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 911142504
    },
    "formatted_output": "val_usro7 hits 899 users hit 473\n           n  hits      mean\ngrade                       \n1      33352   803  4.748443\n3       4417    96  4.718750\nval_k2_b180_a7c hits 1321 users hit 717\n           n  hits      mean\ngrade                       \n1      33352  1143  4.529309\n3       4417   178  3.971910\nval_k2_b180_o7c hits 1341 users hit 709\n           n  hits      mean\ngrade                       \n1      33352  1170  4.650427\n3       4417   171  4.175439\nval_knn_k250_p1_a02 hits 1279 users hit 679\n           n  hits      mean\ngrade                       \n1      33352  1129  4.657219\n3       4417   150  4.086667\n"
  }
}
73

Agent message

+12:22
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

74

Agent message

+12:33
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

75

exec command

+12:41
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const patch = "*** Begin Patch\n*** Add File: /app/solution/tune_knn.py\n+\"\"\"Small public-window grid search; every feature is training-cutoff only.\"\"\"\n+from pathlib import Path\n+import sys\n+import numpy as np\n+import pandas as pd\n+from scipy import sparse\n+from sklearn.preprocessing import normalize\n+sys.path.insert(0,'/task')\n+from ranking_evaluation import CandidateSet\n+import evaluate_public\n+\n+D=Path('/task/data'); CUT=24786800\n+with np.load(D/'validation_eligible_candidates.npz') as z:\n+ uids=z['uids'].copy(); items=z['item_ids'].copy(); packed=z['packed_eligible'].copy()\n+ui=pd.Index(uids); ci=pd.Index(items)\n+raw=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\n+all_uids=np.sort(raw.uid.unique()); ux=pd.Index(all_uids); ur=ux.get_indexer(raw.uid).astype(np.int32); cc=ci.get_indexer(raw.item_id).astype(np.int32)\n+strong=raw.played_ratio_pct>=80\n+def ymatrix(mask,counts):\n+ y=sparse.coo_matrix((np.ones(mask.sum(),np.float32),(ur[mask],cc[mask])),shape=(len(all_uids),len(items))).tocsr()\n+ y.data=np.log1p(y.data) if counts else np.ones_like(y.data)\n+ return y\n+o7=(raw.is_organic==1)&strong&(raw.timestamp>=CUT-7*86400)&(cc>=0); a7=strong&(raw.timestamp>=CUT-7*86400)&(cc>=0)\n+Yo=ymatrix(o7,True); Ya=ymatrix(a7,True); deg=np.asarray(Ya.sum(axis=0)).ravel().astype(np.float32)\n+glob=np.log1p(np.asarray((Yo>0).sum(axis=0)).ravel().astype(np.float32)); glob/=glob.max()\n+prof=raw[(raw.is_organic==1)&strong&(raw.timestamp>=CUT-180*86400)]; pr=ux.get_indexer(prof.uid); pc,_=pd.factorize(prof.item_id,sort=False)\n+x=sparse.coo_matrix((np.ones(len(prof),np.float32),(pr,pc)),shape=(len(all_uids),pc.max()+1)).tocsr(); x.data[:]=1\n+df=np.diff(x.tocsc().indptr).astype(np.float32); x=x.multiply(np.log1p(len(all_uids)/(1+df))).tocsr(); normalize(x,axis=1,copy=False)\n+tr=ux.get_indexer(uids); K=1000; ni=np.empty((len(uids),K),np.int32); nv=np.empty((len(uids),K),np.float32)\n+for a in range(0,len(uids),128):\n+ b=min(a+128,len(uids)); ss=(x[tr[a:b]]@x.T).toarray();ss[np.arange(b-a),tr[a:b]]=-1;ix=np.argpartition(ss,-K,axis=1)[:,-K:];vv=np.take_along_axis(ss,ix,axis=1);oo=np.argsort(vv,axis=1)[:,::-1];ni[a:b]=np.take_along_axis(ix,oo,axis=1);nv[a:b]=np.take_along_axis(vv,oo,axis=1)\n+print('features ready',flush=True)\n+targets=set(map(int,uids)); cs=CandidateSet(D/'validation_eligible_candidates.npz'); truth=evaluate_public.validation_truth(cs,targets)\n+discount=1/np.log2(np.arange(2,12));\n+def ndcg(top,gold):\n+ gains=np.array([gold.get(int(items[c]),0) for c in top]); ideal=np.array(sorted(gold.values(),reverse=True)[:10]); return ((2**gains-1)*discount).sum()/((2**ideal-1)*discount[:len(ideal)]).sum()\n+configs=[]\n+for k in [250,400,500,700,1000]:\n+ for beta in [0,.2,.4,.6]:\n+  for alpha in [.02,.05,.1,.2,.4]: configs.append((k,beta,alpha,'a'))\n+for mix in [.25,.5,.75]:\n+ for alpha in [.05,.1,.2]: configs.append((500,0,alpha,f'm{mix}'))\n+sums=np.zeros(len(configs)); n=0\n+for r,uid in enumerate(uids):\n+ gold=truth.get(int(uid));\n+ if not gold: continue\n+ n+=1; elig=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool)\n+ cache={}\n+ for j,(k,beta,alpha,typ) in enumerate(configs):\n+  key=(k,typ)\n+  if key not in cache:\n+   sw=np.maximum(nv[r,:k],0); ns=ni[r,:k]\n+   za=np.asarray(sw@Ya[ns]).ravel()\n+   if typ=='a': z=za\n+   else:\n+    mix=float(typ[1:]); zo=np.asarray(sw@Yo[ns]).ravel(); z=(1-mix)*za+mix*zo\n+   cache[key]=z\n+  z=cache[key].copy()\n+  if beta: z/=np.maximum(deg,0.1)**beta\n+  if z.max(): z/=z.max()\n+  z += alpha*glob; z[~elig]=-np.inf\n+  top=np.argpartition(z,-10)[-10:]; top=top[np.argsort(z[top])[::-1]]; sums[j]+=ndcg(top,gold)\n+ if n%500==0: print('users',n,flush=True)\n+for s,c in sorted(zip(sums/n,configs),reverse=True): print(round(s,6),c)\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response 

Script completed
Wall time 0.0 seconds
Output:

{}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-ee03146c-6a59-4356-af60-339a0de52494",
    "changes": {
      "/app/solution/tune_knn.py": {
        "type": "add",
        "content": "\"\"\"Small public-window grid search; every feature is training-cutoff only.\"\"\"\nfrom pathlib import Path\nimport sys\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.preprocessing import normalize\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\nD=Path('/task/data'); CUT=24786800\nwith np.load(D/'validation_eligible_candidates.npz') as z:\n uids=z['uids'].copy(); items=z['item_ids'].copy(); packed=z['packed_eligible'].copy()\nui=pd.Index(uids); ci=pd.Index(items)\nraw=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\nall_uids=np.sort(raw.uid.unique()); ux=pd.Index(all_uids); ur=ux.get_indexer(raw.uid).astype(np.int32); cc=ci.get_indexer(raw.item_id).astype(np.int32)\nstrong=raw.played_ratio_pct>=80\ndef ymatrix(mask,counts):\n y=sparse.coo_matrix((np.ones(mask.sum(),np.float32),(ur[mask],cc[mask])),shape=(len(all_uids),len(items))).tocsr()\n y.data=np.log1p(y.data) if counts else np.ones_like(y.data)\n return y\no7=(raw.is_organic==1)&strong&(raw.timestamp>=CUT-7*86400)&(cc>=0); a7=strong&(raw.timestamp>=CUT-7*86400)&(cc>=0)\nYo=ymatrix(o7,True); Ya=ymatrix(a7,True); deg=np.asarray(Ya.sum(axis=0)).ravel().astype(np.float32)\nglob=np.log1p(np.asarray((Yo>0).sum(axis=0)).ravel().astype(np.float32)); glob/=glob.max()\nprof=raw[(raw.is_organic==1)&strong&(raw.timestamp>=CUT-180*86400)]; pr=ux.get_indexer(prof.uid); pc,_=pd.factorize(prof.item_id,sort=False)\nx=sparse.coo_matrix((np.ones(len(prof),np.float32),(pr,pc)),shape=(len(all_uids),pc.max()+1)).tocsr(); x.data[:]=1\ndf=np.diff(x.tocsc().indptr).astype(np.float32); x=x.multiply(np.log1p(len(all_uids)/(1+df))).tocsr(); normalize(x,axis=1,copy=False)\ntr=ux.get_indexer(uids); K=1000; ni=np.empty((len(uids),K),np.int32); nv=np.empty((len(uids),K),np.float32)\nfor a in range(0,len(uids),128):\n b=min(a+128,len(uids)); ss=(x[tr[a:b]]@x.T).toarray();ss[np.arange(b-a),tr[a:b]]=-1;ix=np.argpartition(ss,-K,axis=1)[:,-K:];vv=np.take_along_axis(ss,ix,axis=1);oo=np.argsort(vv,axis=1)[:,::-1];ni[a:b]=np.take_along_axis(ix,oo,axis=1);nv[a:b]=np.take_along_axis(vv,oo,axis=1)\nprint('features ready',flush=True)\ntargets=set(map(int,uids)); cs=CandidateSet(D/'validation_eligible_candidates.npz'); truth=evaluate_public.validation_truth(cs,targets)\ndiscount=1/np.log2(np.arange(2,12));\ndef ndcg(top,gold):\n gains=np.array([gold.get(int(items[c]),0) for c in top]); ideal=np.array(sorted(gold.values(),reverse=True)[:10]); return ((2**gains-1)*discount).sum()/((2**ideal-1)*discount[:len(ideal)]).sum()\nconfigs=[]\nfor k in [250,400,500,700,1000]:\n for beta in [0,.2,.4,.6]:\n  for alpha in [.02,.05,.1,.2,.4]: configs.append((k,beta,alpha,'a'))\nfor mix in [.25,.5,.75]:\n for alpha in [.05,.1,.2]: configs.append((500,0,alpha,f'm{mix}'))\nsums=np.zeros(len(configs)); n=0\nfor r,uid in enumerate(uids):\n gold=truth.get(int(uid));\n if not gold: continue\n n+=1; elig=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool)\n cache={}\n for j,(k,beta,alpha,typ) in enumerate(configs):\n  key=(k,typ)\n  if key not in cache:\n   sw=np.maximum(nv[r,:k],0); ns=ni[r,:k]\n   za=np.asarray(sw@Ya[ns]).ravel()\n   if typ=='a': z=za\n   else:\n    mix=float(typ[1:]); zo=np.asarray(sw@Yo[ns]).ravel(); z=(1-mix)*za+mix*zo\n   cache[key]=z\n  z=cache[key].copy()\n  if beta: z/=np.maximum(deg,0.1)**beta\n  if z.max(): z/=z.max()\n  z += alpha*glob; z[~elig]=-np.inf\n  top=np.argpartition(z,-10)[-10:]; top=top[np.argsort(z[top])[::-1]]; sums[j]+=ndcg(top,gold)\n if n%500==0: print('users',n,flush=True)\nfor s,c in sorted(zip(sums/n,configs),reverse=True): print(round(s,6),c)\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/tune_knn.py\n",
    "stderr": ""
  }
}
Harness context token usage record
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}
76

exec command

+13:09
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const patch = "*** Begin Patch\n*** Update File: /app/solution/tune_knn.py\n@@\n-for k in [250,400,500,700,1000]:\n- for beta in [0,.2,.4,.6]:\n-  for alpha in [.02,.05,.1,.2,.4]: configs.append((k,beta,alpha,'a'))\n+for k in [250,500,1000]:\n+ for beta in [0,.3]:\n+  for alpha in [.05,.1,.2]: configs.append((k,beta,alpha,'a'))\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response 

Script completed
Wall time 0.0 seconds
Output:

{}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-cca2840c-e2ff-4626-a343-db7277d2a1bd",
    "changes": {
      "/app/solution/tune_knn.py": {
        "type": "update",
        "unified_diff": "@@ -37,5 +37,5 @@\n configs=[]\n-for k in [250,400,500,700,1000]:\n- for beta in [0,.2,.4,.6]:\n-  for alpha in [.02,.05,.1,.2,.4]: configs.append((k,beta,alpha,'a'))\n+for k in [250,500,1000]:\n+ for beta in [0,.3]:\n+  for alpha in [.05,.1,.2]: configs.append((k,beta,alpha,'a'))\n for mix in [.25,.5,.75]:\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/tune_knn.py\n",
    "stderr": ""
  }
}
Harness context token usage record
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}
77

exec command

+13:14
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"OMP_NUM_THREADS=4 OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python -u /app/solution/tune_knn.py","workdir":"/app","yield_time_ms":1000,"max_output_tokens":5000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response 

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=37227
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78

exec command

+13:17
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":37227,"chars":"","yield_time_ms":10000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 10.0 seconds
Output:

features ready
users 500
Harness context token usage record
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79

exec command

+13:29
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":37227,"chars":"","yield_time_ms":10000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 10.0 seconds
Output:

users 1000
users 1500
users 2000
Harness context token usage record
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80

exec command

+13:42
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":37227,"chars":"","yield_time_ms":10000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 10.0 seconds
Output:

users 2500
users 3000
users 3500
Harness context token usage record
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81

exec command

+13:54
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":37227,"chars":"","yield_time_ms":10000,"max_output_tokens":10000});
text(r.output);

Tool response 

Script completed
Wall time 0.4 seconds
Output:

users 4000
0.050487 (1000, 0.3, 0.05, 'a')
0.050423 (500, 0.3, 0.2, 'a')
0.050411 (500, 0.3, 0.1, 'a')
0.05041 (500, 0.3, 0.05, 'a')
0.050339 (1000, 0.3, 0.1, 'a')
0.049993 (1000, 0.3, 0.2, 'a')
0.048526 (250, 0, 0.05, 'a')
0.048472 (500, 0, 0.05, 'm0.25')
0.048351 (250, 0, 0.1, 'a')
0.048327 (500, 0, 0.05, 'm0.5')
0.048296 (500, 0, 0.05, 'a')
0.048259 (500, 0, 0.1, 'm0.5')
0.048242 (500, 0, 0.1, 'a')
0.048188 (500, 0, 0.1, 'm0.25')
0.048079 (500, 0, 0.2, 'm0.25')
0.048061 (500, 0, 0.1, 'm0.75')
0.048013 (500, 0, 0.2, 'm0.5')
0.048004 (500, 0, 0.2, 'a')
0.047983 (500, 0, 0.05, 'm0.75')
0.047952 (250, 0, 0.2, 'a')
0.047892 (500, 0, 0.2, 'm0.75')
0.047383 (250, 0.3, 0.2, 'a')
0.046856 (250, 0.3, 0.1, 'a')
0.046782 (250, 0.3, 0.05, 'a')
0.046025 (1000, 0, 0.05, 'a')
0.045837 (1000, 0, 0.1, 'a')
0.045668 (1000, 0, 0.2, 'a')
Harness context token usage record
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Harness context codex item completed
{
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  "item": {
    "type": "CommandExecution",
    "id": "exec-bd08dec1-fabe-47c7-8313-b6f6f741dca0",
    "process_id": "37227",
    "command": [
      "/bin/sh",
      "-lc",
      "OMP_NUM_THREADS=4 OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python -u /app/solution/tune_knn.py"
    ],
    "cwd": "file:///app",
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      {
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        "cmd": "OMP_NUM_THREADS=4 OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python -u /app/solution/tune_knn.py"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "features ready\nusers 500\nusers 1000\nusers 1500\nusers 2000\nusers 2500\nusers 3000\nusers 3500\nusers 4000\n0.050487 (1000, 0.3, 0.05, 'a')\n0.050423 (500, 0.3, 0.2, 'a')\n0.050411 (500, 0.3, 0.1, 'a')\n0.05041 (500, 0.3, 0.05, 'a')\n0.050339 (1000, 0.3, 0.1, 'a')\n0.049993 (1000, 0.3, 0.2, 'a')\n0.048526 (250, 0, 0.05, 'a')\n0.048472 (500, 0, 0.05, 'm0.25')\n0.048351 (250, 0, 0.1, 'a')\n0.048327 (500, 0, 0.05, 'm0.5')\n0.048296 (500, 0, 0.05, 'a')\n0.048259 (500, 0, 0.1, 'm0.5')\n0.048242 (500, 0, 0.1, 'a')\n0.048188 (500, 0, 0.1, 'm0.25')\n0.048079 (500, 0, 0.2, 'm0.25')\n0.048061 (500, 0, 0.1, 'm0.75')\n0.048013 (500, 0, 0.2, 'm0.5')\n0.048004 (500, 0, 0.2, 'a')\n0.047983 (500, 0, 0.05, 'm0.75')\n0.047952 (250, 0, 0.2, 'a')\n0.047892 (500, 0, 0.2, 'm0.75')\n0.047383 (250, 0.3, 0.2, 'a')\n0.046856 (250, 0.3, 0.1, 'a')\n0.046782 (250, 0.3, 0.05, 'a')\n0.046025 (1000, 0, 0.05, 'a')\n0.045837 (1000, 0, 0.1, 'a')\n0.045668 (1000, 0, 0.2, 'a')\n",
    "stderr": "",
    "aggregated_output": "features ready\nusers 500\nusers 1000\nusers 1500\nusers 2000\nusers 2500\nusers 3000\nusers 3500\nusers 4000\n0.050487 (1000, 0.3, 0.05, 'a')\n0.050423 (500, 0.3, 0.2, 'a')\n0.050411 (500, 0.3, 0.1, 'a')\n0.05041 (500, 0.3, 0.05, 'a')\n0.050339 (1000, 0.3, 0.1, 'a')\n0.049993 (1000, 0.3, 0.2, 'a')\n0.048526 (250, 0, 0.05, 'a')\n0.048472 (500, 0, 0.05, 'm0.25')\n0.048351 (250, 0, 0.1, 'a')\n0.048327 (500, 0, 0.05, 'm0.5')\n0.048296 (500, 0, 0.05, 'a')\n0.048259 (500, 0, 0.1, 'm0.5')\n0.048242 (500, 0, 0.1, 'a')\n0.048188 (500, 0, 0.1, 'm0.25')\n0.048079 (500, 0, 0.2, 'm0.25')\n0.048061 (500, 0, 0.1, 'm0.75')\n0.048013 (500, 0, 0.2, 'm0.5')\n0.048004 (500, 0, 0.2, 'a')\n0.047983 (500, 0, 0.05, 'm0.75')\n0.047952 (250, 0, 0.2, 'a')\n0.047892 (500, 0, 0.2, 'm0.75')\n0.047383 (250, 0.3, 0.2, 'a')\n0.046856 (250, 0.3, 0.1, 'a')\n0.046782 (250, 0.3, 0.05, 'a')\n0.046025 (1000, 0, 0.05, 'a')\n0.045837 (1000, 0, 0.1, 'a')\n0.045668 (1000, 0, 0.2, 'a')\n",
    "exit_code": 0,
    "duration": {
      "secs": 39,
      "nanos": 779154809
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    "formatted_output": "features ready\nusers 500\nusers 1000\nusers 1500\nusers 2000\nusers 2500\nusers 3000\nusers 3500\nusers 4000\n0.050487 (1000, 0.3, 0.05, 'a')\n0.050423 (500, 0.3, 0.2, 'a')\n0.050411 (500, 0.3, 0.1, 'a')\n0.05041 (500, 0.3, 0.05, 'a')\n0.050339 (1000, 0.3, 0.1, 'a')\n0.049993 (1000, 0.3, 0.2, 'a')\n0.048526 (250, 0, 0.05, 'a')\n0.048472 (500, 0, 0.05, 'm0.25')\n0.048351 (250, 0, 0.1, 'a')\n0.048327 (500, 0, 0.05, 'm0.5')\n0.048296 (500, 0, 0.05, 'a')\n0.048259 (500, 0, 0.1, 'm0.5')\n0.048242 (500, 0, 0.1, 'a')\n0.048188 (500, 0, 0.1, 'm0.25')\n0.048079 (500, 0, 0.2, 'm0.25')\n0.048061 (500, 0, 0.1, 'm0.75')\n0.048013 (500, 0, 0.2, 'm0.5')\n0.048004 (500, 0, 0.2, 'a')\n0.047983 (500, 0, 0.05, 'm0.75')\n0.047952 (250, 0, 0.2, 'a')\n0.047892 (500, 0, 0.2, 'm0.75')\n0.047383 (250, 0.3, 0.2, 'a')\n0.046856 (250, 0.3, 0.1, 'a')\n0.046782 (250, 0.3, 0.05, 'a')\n0.046025 (1000, 0, 0.05, 'a')\n0.045837 (1000, 0, 0.1, 'a')\n0.045668 (1000, 0, 0.2, 'a')\n"
  }
}
82

Agent message

+14:05
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

83

exec command

+14:08
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Model text

Neighborhood CF now reaches about 0.0505 after correcting for each track’s recent exposure volume; the best setting uses 1,000 taste neighbors and a mild popularity fallback. I’m testing a complementary low-rank model next—its errors should differ enough that either it wins outright or improves an ensemble.

Tool callexecTool call Returned

Tool call · Input 

const patch = "*** Begin Patch\n*** Add File: /app/solution/svd_experiment.py\n+\"\"\"PureSVD collaborative-filtering experiment at the public cutoff.\"\"\"\n+from pathlib import Path\n+import numpy as np\n+import pandas as pd\n+from scipy import sparse\n+from sklearn.decomposition import TruncatedSVD\n+\n+D=Path('/task/data'); O=Path('/app/solution'); CUT=24786800\n+with np.load(D/'validation_eligible_candidates.npz') as z:\n+ uids=z['uids'].copy(); items=z['item_ids'].copy(); packed=z['packed_eligible'].copy()\n+ci=pd.Index(items)\n+d=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\n+all_uids=np.sort(d.uid.unique()); ui=pd.Index(all_uids); ur=ui.get_indexer(d.uid).astype(np.int32); cc=ci.get_indexer(d.item_id).astype(np.int32)\n+strong=(d.played_ratio_pct>=80)&(d.timestamp>=CUT-180*86400)&(cc>=0)\n+# Keep recommendation exposure but make organic plays substantially stronger.\n+vals=np.where(d.is_organic.to_numpy()[strong]==1,3.0,1.0).astype(np.float32)\n+R=sparse.coo_matrix((vals,(ur[strong],cc[strong])),shape=(len(all_uids),len(items))).tocsr(); R.data=np.log1p(R.data)\n+# Damp ubiquitous tracks during factor learning.\n+idf=np.log1p(len(all_uids)/(1+np.diff(R.tocsc().indptr))).astype(np.float32)\n+R=R.multiply(idf**0.5).tocsr()\n+print('matrix',R.shape,R.nnz,flush=True)\n+svd=TruncatedSVD(n_components=160,n_iter=7,random_state=42)\n+U=svd.fit_transform(R).astype(np.float32); V=svd.components_.astype(np.float32)\n+print('fit',svd.explained_variance_ratio_.sum(),flush=True)\n+# Best global trend signal from the preceding week.\n+recent=(d.is_organic==1)&(d.played_ratio_pct>=80)&(d.timestamp>=CUT-7*86400)&(cc>=0)\n+G=sparse.coo_matrix((np.ones(recent.sum(),np.float32),(ur[recent],cc[recent])),shape=R.shape).tocsr();G.data[:]=1\n+glob=np.log1p(np.asarray(G.sum(axis=0)).ravel());glob/=glob.max()\n+tr=ui.get_indexer(uids)\n+for alpha in [.02,.05,.1,.2,.4]:\n+ rows=[]\n+ for a in range(0,len(uids),128):\n+  b=min(a+128,len(uids)); scores=U[tr[a:b]]@V\n+  mx=np.maximum(scores.max(axis=1,keepdims=True),1e-6); scores=scores/mx+alpha*glob\n+  for j,r in enumerate(range(a,b)):\n+   elig=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool); scores[j,~elig]=-np.inf\n+   top=np.argpartition(scores[j],-100)[-100:];top=top[np.argsort(scores[j,top])[::-1]]\n+   rows.extend((int(uids[r]),int(items[c]),rank) for rank,c in enumerate(top,1))\n+ pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/f'val_svd_a{str(alpha).replace(\".\",\"\")}.parquet',index=False)\n+ print('wrote',alpha,flush=True)\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response 

Script completed
Wall time 0.0 seconds
Output:

{}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-73e422c6-ee3d-4b40-8b49-476ad20f84f6",
    "changes": {
      "/app/solution/svd_experiment.py": {
        "type": "add",
        "content": "\"\"\"PureSVD collaborative-filtering experiment at the public cutoff.\"\"\"\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\n\nD=Path('/task/data'); O=Path('/app/solution'); CUT=24786800\nwith np.load(D/'validation_eligible_candidates.npz') as z:\n uids=z['uids'].copy(); items=z['item_ids'].copy(); packed=z['packed_eligible'].copy()\nci=pd.Index(items)\nd=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\nall_uids=np.sort(d.uid.unique()); ui=pd.Index(all_uids); ur=ui.get_indexer(d.uid).astype(np.int32); cc=ci.get_indexer(d.item_id).astype(np.int32)\nstrong=(d.played_ratio_pct>=80)&(d.timestamp>=CUT-180*86400)&(cc>=0)\n# Keep recommendation exposure but make organic plays substantially stronger.\nvals=np.where(d.is_organic.to_numpy()[strong]==1,3.0,1.0).astype(np.float32)\nR=sparse.coo_matrix((vals,(ur[strong],cc[strong])),shape=(len(all_uids),len(items))).tocsr(); R.data=np.log1p(R.data)\n# Damp ubiquitous tracks during factor learning.\nidf=np.log1p(len(all_uids)/(1+np.diff(R.tocsc().indptr))).astype(np.float32)\nR=R.multiply(idf**0.5).tocsr()\nprint('matrix',R.shape,R.nnz,flush=True)\nsvd=TruncatedSVD(n_components=160,n_iter=7,random_state=42)\nU=svd.fit_transform(R).astype(np.float32); V=svd.components_.astype(np.float32)\nprint('fit',svd.explained_variance_ratio_.sum(),flush=True)\n# Best global trend signal from the preceding week.\nrecent=(d.is_organic==1)&(d.played_ratio_pct>=80)&(d.timestamp>=CUT-7*86400)&(cc>=0)\nG=sparse.coo_matrix((np.ones(recent.sum(),np.float32),(ur[recent],cc[recent])),shape=R.shape).tocsr();G.data[:]=1\nglob=np.log1p(np.asarray(G.sum(axis=0)).ravel());glob/=glob.max()\ntr=ui.get_indexer(uids)\nfor alpha in [.02,.05,.1,.2,.4]:\n rows=[]\n for a in range(0,len(uids),128):\n  b=min(a+128,len(uids)); scores=U[tr[a:b]]@V\n  mx=np.maximum(scores.max(axis=1,keepdims=True),1e-6); scores=scores/mx+alpha*glob\n  for j,r in enumerate(range(a,b)):\n   elig=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool); scores[j,~elig]=-np.inf\n   top=np.argpartition(scores[j],-100)[-100:];top=top[np.argsort(scores[j,top])[::-1]]\n   rows.extend((int(uids[r]),int(items[c]),rank) for rank,c in enumerate(top,1))\n pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/f'val_svd_a{str(alpha).replace(\".\",\"\")}.parquet',index=False)\n print('wrote',alpha,flush=True)\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/svd_experiment.py\n",
    "stderr": ""
  }
}
Harness context token usage record
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  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5be5599d087d2977f070ba8039bb5",
  "usage": {
    "input_tokens": 53785,
    "cached_input_tokens": 52992,
    "cache_write_input_tokens": 0,
    "output_tokens": 1599,
    "reasoning_output_tokens": 659,
    "total_tokens": 55384
  },
  "turn_token_usage": {
    "input_tokens": 2423128,
    "cached_input_tokens": 2351616,
    "cache_write_input_tokens": 0,
    "output_tokens": 25261,
    "reasoning_output_tokens": 9836,
    "total_tokens": 2448389
  },
  "thread_token_usage": {
    "input_tokens": 2423128,
    "cached_input_tokens": 2351616,
    "cache_write_input_tokens": 0,
    "output_tokens": 25261,
    "reasoning_output_tokens": 9836,
    "total_tokens": 2448389
  }
}
84

exec command

+14:28
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"OMP_NUM_THREADS=4 OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python -u /app/solution/svd_experiment.py","workdir":"/app","yield_time_ms":1000,"max_output_tokens":5000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response 

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=94671
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5be73753887d294cb4d0d878ceb4b",
  "usage": {
    "input_tokens": 55408,
    "cached_input_tokens": 53632,
    "cache_write_input_tokens": 0,
    "output_tokens": 99,
    "reasoning_output_tokens": 6,
    "total_tokens": 55507
  },
  "turn_token_usage": {
    "input_tokens": 2478536,
    "cached_input_tokens": 2405248,
    "cache_write_input_tokens": 0,
    "output_tokens": 25360,
    "reasoning_output_tokens": 9842,
    "total_tokens": 2503896
  },
  "thread_token_usage": {
    "input_tokens": 2478536,
    "cached_input_tokens": 2405248,
    "cache_write_input_tokens": 0,
    "output_tokens": 25360,
    "reasoning_output_tokens": 9842,
    "total_tokens": 2503896
  }
}
85

exec command

+14:32
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":94671,"chars":"","yield_time_ms":10000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 10.0 seconds
Output:

matrix (9238, 30000) 4191840
fit 0.31272697
wrote 0.02
wrote 0.05
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5be7920d087d2a585353fa858bfcd",
  "usage": {
    "input_tokens": 55535,
    "cached_input_tokens": 55168,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 55588
  },
  "turn_token_usage": {
    "input_tokens": 2534071,
    "cached_input_tokens": 2460416,
    "cache_write_input_tokens": 0,
    "output_tokens": 25413,
    "reasoning_output_tokens": 9848,
    "total_tokens": 2559484
  },
  "thread_token_usage": {
    "input_tokens": 2534071,
    "cached_input_tokens": 2460416,
    "cache_write_input_tokens": 0,
    "output_tokens": 25413,
    "reasoning_output_tokens": 9848,
    "total_tokens": 2559484
  }
}
86

exec command

+14:45
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":94671,"chars":"","yield_time_ms":10000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 2.7 seconds
Output:

wrote 0.1
wrote 0.2
wrote 0.4
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5be85075c87d2bd7c4bab3d7a3a29",
  "usage": {
    "input_tokens": 55647,
    "cached_input_tokens": 55296,
    "cache_write_input_tokens": 0,
    "output_tokens": 55,
    "reasoning_output_tokens": 8,
    "total_tokens": 55702
  },
  "turn_token_usage": {
    "input_tokens": 2589718,
    "cached_input_tokens": 2515712,
    "cache_write_input_tokens": 0,
    "output_tokens": 25468,
    "reasoning_output_tokens": 9856,
    "total_tokens": 2615186
  },
  "thread_token_usage": {
    "input_tokens": 2589718,
    "cached_input_tokens": 2515712,
    "cache_write_input_tokens": 0,
    "output_tokens": 25468,
    "reasoning_output_tokens": 9856,
    "total_tokens": 2615186
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-edeb59fc-2200-4a23-aa0b-a9b4a9c38091",
    "process_id": "94671",
    "command": [
      "/bin/sh",
      "-lc",
      "OMP_NUM_THREADS=4 OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python -u /app/solution/svd_experiment.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "OMP_NUM_THREADS=4 OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python -u /app/solution/svd_experiment.py"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "matrix (9238, 30000) 4191840\nfit 0.31272697\nwrote 0.02\nwrote 0.05\nwrote 0.1\nwrote 0.2\nwrote 0.4\n",
    "stderr": "",
    "aggregated_output": "matrix (9238, 30000) 4191840\nfit 0.31272697\nwrote 0.02\nwrote 0.05\nwrote 0.1\nwrote 0.2\nwrote 0.4\n",
    "exit_code": 0,
    "duration": {
      "secs": 19,
      "nanos": 343086077
    },
    "formatted_output": "matrix (9238, 30000) 4191840\nfit 0.31272697\nwrote 0.02\nwrote 0.05\nwrote 0.1\nwrote 0.2\nwrote 0.4\n"
  }
}
87

exec command

+14:50
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python -u - <<'PY'\nimport sys,glob\nsys.path.insert(0,'/task'); import evaluate_public as e\nfrom ranking_evaluation import CandidateSet,score_rankings\nimport pandas as pd\nfrom pathlib import Path\nt=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int)); c=CandidateSet(Path('/task/data/validation_eligible_candidates.npz')); g=e.validation_truth(c,t);res=[]\nfor f in glob.glob('/app/solution/val_svd*.parquet'):\n d=pd.read_parquet(f);d=d[d['rank']<=10].sort_values(['uid','rank']);r={int(u):list(map(int,x.item_id)) for u,x in d.groupby('uid')};res.append((score_rankings(r,g)['quality_ndcg@10'],Path(f).stem))\nprint(*sorted(res,reverse=True),sep='\\n')\nPY","workdir":"/app","yield_time_ms":1000,"max_output_tokens":5000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response 

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=67241
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5be8b714887d2aa51f6bc8e4e0ac6",
  "usage": {
    "input_tokens": 55746,
    "cached_input_tokens": 55424,
    "cache_write_input_tokens": 0,
    "output_tokens": 274,
    "reasoning_output_tokens": 6,
    "total_tokens": 56020
  },
  "turn_token_usage": {
    "input_tokens": 2645464,
    "cached_input_tokens": 2571136,
    "cache_write_input_tokens": 0,
    "output_tokens": 25742,
    "reasoning_output_tokens": 9862,
    "total_tokens": 2671206
  },
  "thread_token_usage": {
    "input_tokens": 2645464,
    "cached_input_tokens": 2571136,
    "cache_write_input_tokens": 0,
    "output_tokens": 25742,
    "reasoning_output_tokens": 9862,
    "total_tokens": 2671206
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-b8f4aca3-b183-4cd5-9de6-49202a941a35",
    "process_id": "67241",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python -u - <<'PY'\nimport sys,glob\nsys.path.insert(0,'/task'); import evaluate_public as e\nfrom ranking_evaluation import CandidateSet,score_rankings\nimport pandas as pd\nfrom pathlib import Path\nt=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int)); c=CandidateSet(Path('/task/data/validation_eligible_candidates.npz')); g=e.validation_truth(c,t);res=[]\nfor f in glob.glob('/app/solution/val_svd*.parquet'):\n d=pd.read_parquet(f);d=d[d['rank']<=10].sort_values(['uid','rank']);r={int(u):list(map(int,x.item_id)) for u,x in d.groupby('uid')};res.append((score_rankings(r,g)['quality_ndcg@10'],Path(f).stem))\nprint(*sorted(res,reverse=True),sep='\\n')\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport sys,glob\nsys.path.insert(0,'/task'); import evaluate_public as e\nfrom ranking_evaluation import CandidateSet,score_rankings\nimport pandas as pd\nfrom pathlib import Path\nt=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int)); c=CandidateSet(Path('/task/data/validation_eligible_candidates.npz')); g=e.validation_truth(c,t);res=[]\nfor f in glob.glob('/app/solution/val_svd*.parquet'):\n d=pd.read_parquet(f);d=d[d['rank']<=10].sort_values(['uid','rank']);r={int(u):list(map(int,x.item_id)) for u,x in d.groupby('uid')};res.append((score_rankings(r,g)['quality_ndcg@10'],Path(f).stem))\nprint(*sorted(res,reverse=True),sep='\\n')\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "(0.030117, 'val_svd_a04')\n(0.027332, 'val_svd_a02')\n(0.026039, 'val_svd_a01')\n(0.025507, 'val_svd_a005')\n(0.024945, 'val_svd_a002')\n",
    "stderr": "",
    "aggregated_output": "(0.030117, 'val_svd_a04')\n(0.027332, 'val_svd_a02')\n(0.026039, 'val_svd_a01')\n(0.025507, 'val_svd_a005')\n(0.024945, 'val_svd_a002')\n",
    "exit_code": 0,
    "duration": {
      "secs": 2,
      "nanos": 937707001
    },
    "formatted_output": "(0.030117, 'val_svd_a04')\n(0.027332, 'val_svd_a02')\n(0.026039, 'val_svd_a01')\n(0.025507, 'val_svd_a005')\n(0.024945, 'val_svd_a002')\n"
  }
}
88

exec command

+14:58
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":67241,"chars":"","yield_time_ms":10000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 0.0 seconds
Output:

(0.030117, 'val_svd_a04')
(0.027332, 'val_svd_a02')
(0.026039, 'val_svd_a01')
(0.025507, 'val_svd_a005')
(0.024945, 'val_svd_a002')
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5be92607087d2841f2110b63f9ced",
  "usage": {
    "input_tokens": 56048,
    "cached_input_tokens": 55552,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 56101
  },
  "turn_token_usage": {
    "input_tokens": 2701512,
    "cached_input_tokens": 2626688,
    "cache_write_input_tokens": 0,
    "output_tokens": 25795,
    "reasoning_output_tokens": 9868,
    "total_tokens": 2727307
  },
  "thread_token_usage": {
    "input_tokens": 2701512,
    "cached_input_tokens": 2626688,
    "cache_write_input_tokens": 0,
    "output_tokens": 25795,
    "reasoning_output_tokens": 9868,
    "total_tokens": 2727307
  }
}
89

Agent message

+15:11
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

90

exec command

+15:15
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const patch = "*** Begin Patch\n*** Add File: /app/solution/likes_profile_experiment.py\n+\"\"\"Test explicit-like emphasis in the taste-neighborhood profile.\"\"\"\n+from pathlib import Path\n+import numpy as np\n+import pandas as pd\n+from scipy import sparse\n+from sklearn.preprocessing import normalize\n+D=Path('/task/data');O=Path('/app/solution');CUT=24786800\n+with np.load(D/'validation_eligible_candidates.npz') as z: uids=z['uids'].copy();items=z['item_ids'].copy();packed=z['packed_eligible'].copy()\n+ci=pd.Index(items);d=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']);au=np.sort(d.uid.unique());ui=pd.Index(au);ur=ui.get_indexer(d.uid).astype(np.int32);cc=ci.get_indexer(d.item_id).astype(np.int32)\n+strong=d.played_ratio_pct>=80;a7=strong&(d.timestamp>=CUT-7*86400)&(cc>=0)\n+Ya=sparse.coo_matrix((np.ones(a7.sum(),np.float32),(ur[a7],cc[a7])),shape=(len(au),len(items))).tocsr();Ya.data=np.log1p(Ya.data);deg=np.asarray(Ya.sum(axis=0)).ravel().astype(np.float32)\n+o7=(d.is_organic==1)&strong&(d.timestamp>=CUT-7*86400)&(cc>=0);G=sparse.coo_matrix((np.ones(o7.sum(),np.float32),(ur[o7],cc[o7])),shape=Ya.shape).tocsr();G.data[:]=1;glob=np.log1p(np.asarray(G.sum(axis=0)).ravel());glob/=glob.max()\n+p=d[(d.is_organic==1)&strong&(d.timestamp>=CUT-180*86400)][['uid','item_id']]\n+state=pd.read_parquet(D/'validation_preference_state.parquet');state=state[(state.state=='liked')&state.uid.isin(au)][['uid','item_id']]\n+both=np.concatenate([p.item_id.to_numpy(),state.item_id.to_numpy()]);codes,uniq=pd.factorize(both,sort=False);pc=codes[:len(p)];lc=codes[len(p):];pr=ui.get_indexer(p.uid);lr=ui.get_indexer(state.uid);shape=(len(au),len(uniq))\n+B=sparse.coo_matrix((np.ones(len(p),np.float32),(pr,pc)),shape=shape).tocsr();B.data[:]=1\n+L=sparse.coo_matrix((np.ones(len(state),np.float32),(lr,lc)),shape=shape).tocsr();L.data[:]=1\n+df=np.diff(((B+L)>0).tocsc().indptr).astype(np.float32);idf=np.log1p(len(au)/(1+df)).astype(np.float32);tr=ui.get_indexer(uids)\n+def run(q):\n+ X=(B+q*L).multiply(idf).tocsr();normalize(X,axis=1,copy=False);K=1000;ni=np.empty((len(uids),K),np.int32);nv=np.empty((len(uids),K),np.float32)\n+ for a in range(0,len(uids),128):\n+  b=min(a+128,len(uids));ss=(X[tr[a:b]]@X.T).toarray();ss[np.arange(b-a),tr[a:b]]=-1;ix=np.argpartition(ss,-K,axis=1)[:,-K:];vv=np.take_along_axis(ss,ix,axis=1);oo=np.argsort(vv,axis=1)[:,::-1];ni[a:b]=np.take_along_axis(ix,oo,axis=1);nv[a:b]=np.take_along_axis(vv,oo,axis=1)\n+ rows=[]\n+ for r,u in enumerate(uids):\n+  z=np.asarray(np.maximum(nv[r],0)@Ya[ni[r]]).ravel()/np.maximum(deg,.1)**.3\n+  if z.max():z/=z.max()\n+  z+=.05*glob;elig=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool);z[~elig]=-np.inf;top=np.argpartition(z,-100)[-100:];top=top[np.argsort(z[top])[::-1]];rows.extend((int(u),int(items[c]),rank) for rank,c in enumerate(top,1))\n+ pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/f'val_likeprof_q{str(q).replace(\".\",\"\")}.parquet',index=False);print('wrote',q,flush=True)\n+for q in [.5,1,2,4]:run(q)\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response 

Script completed
Wall time 0.0 seconds
Output:

{}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-26659361-8323-4b36-8133-7353e027affc",
    "changes": {
      "/app/solution/likes_profile_experiment.py": {
        "type": "add",
        "content": "\"\"\"Test explicit-like emphasis in the taste-neighborhood profile.\"\"\"\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.preprocessing import normalize\nD=Path('/task/data');O=Path('/app/solution');CUT=24786800\nwith np.load(D/'validation_eligible_candidates.npz') as z: uids=z['uids'].copy();items=z['item_ids'].copy();packed=z['packed_eligible'].copy()\nci=pd.Index(items);d=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']);au=np.sort(d.uid.unique());ui=pd.Index(au);ur=ui.get_indexer(d.uid).astype(np.int32);cc=ci.get_indexer(d.item_id).astype(np.int32)\nstrong=d.played_ratio_pct>=80;a7=strong&(d.timestamp>=CUT-7*86400)&(cc>=0)\nYa=sparse.coo_matrix((np.ones(a7.sum(),np.float32),(ur[a7],cc[a7])),shape=(len(au),len(items))).tocsr();Ya.data=np.log1p(Ya.data);deg=np.asarray(Ya.sum(axis=0)).ravel().astype(np.float32)\no7=(d.is_organic==1)&strong&(d.timestamp>=CUT-7*86400)&(cc>=0);G=sparse.coo_matrix((np.ones(o7.sum(),np.float32),(ur[o7],cc[o7])),shape=Ya.shape).tocsr();G.data[:]=1;glob=np.log1p(np.asarray(G.sum(axis=0)).ravel());glob/=glob.max()\np=d[(d.is_organic==1)&strong&(d.timestamp>=CUT-180*86400)][['uid','item_id']]\nstate=pd.read_parquet(D/'validation_preference_state.parquet');state=state[(state.state=='liked')&state.uid.isin(au)][['uid','item_id']]\nboth=np.concatenate([p.item_id.to_numpy(),state.item_id.to_numpy()]);codes,uniq=pd.factorize(both,sort=False);pc=codes[:len(p)];lc=codes[len(p):];pr=ui.get_indexer(p.uid);lr=ui.get_indexer(state.uid);shape=(len(au),len(uniq))\nB=sparse.coo_matrix((np.ones(len(p),np.float32),(pr,pc)),shape=shape).tocsr();B.data[:]=1\nL=sparse.coo_matrix((np.ones(len(state),np.float32),(lr,lc)),shape=shape).tocsr();L.data[:]=1\ndf=np.diff(((B+L)>0).tocsc().indptr).astype(np.float32);idf=np.log1p(len(au)/(1+df)).astype(np.float32);tr=ui.get_indexer(uids)\ndef run(q):\n X=(B+q*L).multiply(idf).tocsr();normalize(X,axis=1,copy=False);K=1000;ni=np.empty((len(uids),K),np.int32);nv=np.empty((len(uids),K),np.float32)\n for a in range(0,len(uids),128):\n  b=min(a+128,len(uids));ss=(X[tr[a:b]]@X.T).toarray();ss[np.arange(b-a),tr[a:b]]=-1;ix=np.argpartition(ss,-K,axis=1)[:,-K:];vv=np.take_along_axis(ss,ix,axis=1);oo=np.argsort(vv,axis=1)[:,::-1];ni[a:b]=np.take_along_axis(ix,oo,axis=1);nv[a:b]=np.take_along_axis(vv,oo,axis=1)\n rows=[]\n for r,u in enumerate(uids):\n  z=np.asarray(np.maximum(nv[r],0)@Ya[ni[r]]).ravel()/np.maximum(deg,.1)**.3\n  if z.max():z/=z.max()\n  z+=.05*glob;elig=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool);z[~elig]=-np.inf;top=np.argpartition(z,-100)[-100:];top=top[np.argsort(z[top])[::-1]];rows.extend((int(u),int(items[c]),rank) for rank,c in enumerate(top,1))\n pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/f'val_likeprof_q{str(q).replace(\".\",\"\")}.parquet',index=False);print('wrote',q,flush=True)\nfor q in [.5,1,2,4]:run(q)\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/likes_profile_experiment.py\n",
    "stderr": ""
  }
}
Harness context token usage record
{
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  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
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    "cache_write_input_tokens": 0,
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  "turn_token_usage": {
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  }
}
91

exec command

+15:35
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"OMP_NUM_THREADS=4 OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python -u /app/solution/likes_profile_experiment.py","workdir":"/app","yield_time_ms":1000,"max_output_tokens":3000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response 

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=76329
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5beb820f887d2825c16f980cc722f",
  "usage": {
    "input_tokens": 58058,
    "cached_input_tokens": 56064,
    "cache_write_input_tokens": 0,
    "output_tokens": 100,
    "reasoning_output_tokens": 6,
    "total_tokens": 58158
  },
  "turn_token_usage": {
    "input_tokens": 2815759,
    "cached_input_tokens": 2738560,
    "cache_write_input_tokens": 0,
    "output_tokens": 27740,
    "reasoning_output_tokens": 10610,
    "total_tokens": 2843499
  },
  "thread_token_usage": {
    "input_tokens": 2815759,
    "cached_input_tokens": 2738560,
    "cache_write_input_tokens": 0,
    "output_tokens": 27740,
    "reasoning_output_tokens": 10610,
    "total_tokens": 2843499
  }
}
92

exec command

+15:39
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":76329,"chars":"","yield_time_ms":10000,"max_output_tokens":3000});
text(r.output);

Tool response 

Script completed
Wall time 10.0 seconds
Output:
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bebc832087d2be9156d9a9e651c6",
  "usage": {
    "input_tokens": 58186,
    "cached_input_tokens": 57856,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 58239
  },
  "turn_token_usage": {
    "input_tokens": 2873945,
    "cached_input_tokens": 2796416,
    "cache_write_input_tokens": 0,
    "output_tokens": 27793,
    "reasoning_output_tokens": 10616,
    "total_tokens": 2901738
  },
  "thread_token_usage": {
    "input_tokens": 2873945,
    "cached_input_tokens": 2796416,
    "cache_write_input_tokens": 0,
    "output_tokens": 27793,
    "reasoning_output_tokens": 10616,
    "total_tokens": 2901738
  }
}
93

exec command

+15:51
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":76329,"chars":"","yield_time_ms":10000,"max_output_tokens":3000});
text(r.output);

Tool response 

Script completed
Wall time 10.0 seconds
Output:

wrote 0.5
wrote 1
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bec8607087d28da9fc8652dd04e3",
  "usage": {
    "input_tokens": 58262,
    "cached_input_tokens": 57984,
    "cache_write_input_tokens": 0,
    "output_tokens": 60,
    "reasoning_output_tokens": 13,
    "total_tokens": 58322
  },
  "turn_token_usage": {
    "input_tokens": 2932207,
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    "cache_write_input_tokens": 0,
    "output_tokens": 27853,
    "reasoning_output_tokens": 10629,
    "total_tokens": 2960060
  },
  "thread_token_usage": {
    "input_tokens": 2932207,
    "cached_input_tokens": 2854400,
    "cache_write_input_tokens": 0,
    "output_tokens": 27853,
    "reasoning_output_tokens": 10629,
    "total_tokens": 2960060
  }
}
94

exec command

+16:03
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":76329,"chars":"","yield_time_ms":10000,"max_output_tokens":3000});
text(r.output);

Tool response 

Script completed
Wall time 10.0 seconds
Output:

wrote 2
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bed4718487d28316523ed1047dba",
  "usage": {
    "input_tokens": 58357,
    "cached_input_tokens": 58112,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 58410
  },
  "turn_token_usage": {
    "input_tokens": 2990564,
    "cached_input_tokens": 2912512,
    "cache_write_input_tokens": 0,
    "output_tokens": 27906,
    "reasoning_output_tokens": 10635,
    "total_tokens": 3018470
  },
  "thread_token_usage": {
    "input_tokens": 2990564,
    "cached_input_tokens": 2912512,
    "cache_write_input_tokens": 0,
    "output_tokens": 27906,
    "reasoning_output_tokens": 10635,
    "total_tokens": 3018470
  }
}
95

exec command

+16:15
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":76329,"chars":"","yield_time_ms":10000,"max_output_tokens":3000});
text(r.output);

Tool response 

Script completed
Wall time 2.9 seconds
Output:

wrote 4
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bee0622087d295757f626643a02a",
  "usage": {
    "input_tokens": 58438,
    "cached_input_tokens": 58240,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 58491
  },
  "turn_token_usage": {
    "input_tokens": 3049002,
    "cached_input_tokens": 2970752,
    "cache_write_input_tokens": 0,
    "output_tokens": 27959,
    "reasoning_output_tokens": 10641,
    "total_tokens": 3076961
  },
  "thread_token_usage": {
    "input_tokens": 3049002,
    "cached_input_tokens": 2970752,
    "cache_write_input_tokens": 0,
    "output_tokens": 27959,
    "reasoning_output_tokens": 10641,
    "total_tokens": 3076961
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-5c70cb98-7332-48f8-9fc9-a9333cdae462",
    "process_id": "76329",
    "command": [
      "/bin/sh",
      "-lc",
      "OMP_NUM_THREADS=4 OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python -u /app/solution/likes_profile_experiment.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "OMP_NUM_THREADS=4 OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python -u /app/solution/likes_profile_experiment.py"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "wrote 0.5\nwrote 1\nwrote 2\nwrote 4\n",
    "stderr": "",
    "aggregated_output": "wrote 0.5\nwrote 1\nwrote 2\nwrote 4\n",
    "exit_code": 0,
    "duration": {
      "secs": 41,
      "nanos": 974802404
    },
    "formatted_output": "wrote 0.5\nwrote 1\nwrote 2\nwrote 4\n"
  }
}
96

exec command

+16:21
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python -u - <<'PY'\nimport sys,glob\nsys.path.insert(0,'/task');import evaluate_public as e\nfrom ranking_evaluation import *\nimport pandas as pd\nfrom pathlib import Path\nt=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int));c=CandidateSet(Path('/task/data/validation_eligible_candidates.npz'));g=e.validation_truth(c,t);res=[]\nfor f in glob.glob('/app/solution/val_likeprof*.parquet'):\n d=pd.read_parquet(f);d=d[d['rank']<=10].sort_values(['uid','rank']);r={int(u):list(map(int,x.item_id)) for u,x in d.groupby('uid')};res.append((score_rankings(r,g)['quality_ndcg@10'],Path(f).stem))\nprint(*sorted(res,reverse=True),sep='\\n')\nPY","workdir":"/app","yield_time_ms":1000,"max_output_tokens":3000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response 

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=3502
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bee5a95c87d2b22f92aa31ec7afc",
  "usage": {
    "input_tokens": 58519,
    "cached_input_tokens": 58240,
    "cache_write_input_tokens": 0,
    "output_tokens": 270,
    "reasoning_output_tokens": 6,
    "total_tokens": 58789
  },
  "turn_token_usage": {
    "input_tokens": 3107521,
    "cached_input_tokens": 3028992,
    "cache_write_input_tokens": 0,
    "output_tokens": 28229,
    "reasoning_output_tokens": 10647,
    "total_tokens": 3135750
  },
  "thread_token_usage": {
    "input_tokens": 3107521,
    "cached_input_tokens": 3028992,
    "cache_write_input_tokens": 0,
    "output_tokens": 28229,
    "reasoning_output_tokens": 10647,
    "total_tokens": 3135750
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-7f402da1-ee55-4f75-8b92-5add96171efb",
    "process_id": "3502",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python -u - <<'PY'\nimport sys,glob\nsys.path.insert(0,'/task');import evaluate_public as e\nfrom ranking_evaluation import *\nimport pandas as pd\nfrom pathlib import Path\nt=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int));c=CandidateSet(Path('/task/data/validation_eligible_candidates.npz'));g=e.validation_truth(c,t);res=[]\nfor f in glob.glob('/app/solution/val_likeprof*.parquet'):\n d=pd.read_parquet(f);d=d[d['rank']<=10].sort_values(['uid','rank']);r={int(u):list(map(int,x.item_id)) for u,x in d.groupby('uid')};res.append((score_rankings(r,g)['quality_ndcg@10'],Path(f).stem))\nprint(*sorted(res,reverse=True),sep='\\n')\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport sys,glob\nsys.path.insert(0,'/task');import evaluate_public as e\nfrom ranking_evaluation import *\nimport pandas as pd\nfrom pathlib import Path\nt=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int));c=CandidateSet(Path('/task/data/validation_eligible_candidates.npz'));g=e.validation_truth(c,t);res=[]\nfor f in glob.glob('/app/solution/val_likeprof*.parquet'):\n d=pd.read_parquet(f);d=d[d['rank']<=10].sort_values(['uid','rank']);r={int(u):list(map(int,x.item_id)) for u,x in d.groupby('uid')};res.append((score_rankings(r,g)['quality_ndcg@10'],Path(f).stem))\nprint(*sorted(res,reverse=True),sep='\\n')\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "(0.052637, 'val_likeprof_q2')\n(0.052498, 'val_likeprof_q1')\n(0.05238, 'val_likeprof_q4')\n(0.051879, 'val_likeprof_q05')\n",
    "stderr": "",
    "aggregated_output": "(0.052637, 'val_likeprof_q2')\n(0.052498, 'val_likeprof_q1')\n(0.05238, 'val_likeprof_q4')\n(0.051879, 'val_likeprof_q05')\n",
    "exit_code": 0,
    "duration": {
      "secs": 2,
      "nanos": 494315995
    },
    "formatted_output": "(0.052637, 'val_likeprof_q2')\n(0.052498, 'val_likeprof_q1')\n(0.05238, 'val_likeprof_q4')\n(0.051879, 'val_likeprof_q05')\n"
  }
}
97

exec command

+16:29
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":3502,"chars":"","yield_time_ms":10000,"max_output_tokens":3000});
text(r.output);

Tool response 

Script completed
Wall time 0.0 seconds
Output:

(0.052637, 'val_likeprof_q2')
(0.052498, 'val_likeprof_q1')
(0.05238, 'val_likeprof_q4')
(0.051879, 'val_likeprof_q05')
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5beed612487d2b74bb48f3a2f1437",
  "usage": {
    "input_tokens": 58817,
    "cached_input_tokens": 58368,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 58870
  },
  "turn_token_usage": {
    "input_tokens": 3166338,
    "cached_input_tokens": 3087360,
    "cache_write_input_tokens": 0,
    "output_tokens": 28282,
    "reasoning_output_tokens": 10653,
    "total_tokens": 3194620
  },
  "thread_token_usage": {
    "input_tokens": 3166338,
    "cached_input_tokens": 3087360,
    "cache_write_input_tokens": 0,
    "output_tokens": 28282,
    "reasoning_output_tokens": 10653,
    "total_tokens": 3194620
  }
}
98

exec command

+16:36
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd\nD=Path('/task/data');O=Path('/app/solution');cut=24786800\nwith np.load(D/'validation_eligible_candidates.npz') as z:uids=z['uids'].copy();items=z['item_ids'].copy();packed=z['packed_eligible'].copy()\nci=pd.Index(items); l=pd.read_parquet(D/'train_likes.parquet'); l=l[(l.is_organic==1)&l.item_id.isin(items)];\n# organic strong listener prevalence\nd=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']);d=d[(d.is_organic==1)&(d.played_ratio_pct>=80)&(d.timestamp>=cut-7*86400)&d.item_id.isin(items)];g=d.groupby('item_id').uid.nunique().reindex(items,fill_value=0).to_numpy();g=np.log1p(g);g=g/g.max()\nfor days in [7,14,30]:\n q=l[l.timestamp>=cut-days*86400]; s=q.groupby('item_id').size().reindex(items,fill_value=0).to_numpy();s=np.log1p(s);s=s/(s.max() or 1)\n for a in [1,.5,.2]:\n  score=a*s+(1-a)*g;order=np.argsort(score,kind='stable')[::-1];rows=[]\n  for r,u in enumerate(uids):\n   take=[]\n   for c in order:\n    if (int(packed[r,c//8])>>(c%8))&1:take.append(c)\n    if len(take)==100:break\n   rows.extend((int(u),int(items[c]),k+1) for k,c in enumerate(take))\n  name=f'val_glike_d{days}_a{str(a).replace(\".\",\"\")}.parquet';pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/name,index=False);print(name,flush=True)\nPY","workdir":"/app","yield_time_ms":1000,"max_output_tokens":3000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response 

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=67641
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bef03ac887d2b6dadd706decfc13",
  "usage": {
    "input_tokens": 58945,
    "cached_input_tokens": 58624,
    "cache_write_input_tokens": 0,
    "output_tokens": 794,
    "reasoning_output_tokens": 254,
    "total_tokens": 59739
  },
  "turn_token_usage": {
    "input_tokens": 3225283,
    "cached_input_tokens": 3145984,
    "cache_write_input_tokens": 0,
    "output_tokens": 29076,
    "reasoning_output_tokens": 10907,
    "total_tokens": 3254359
  },
  "thread_token_usage": {
    "input_tokens": 3225283,
    "cached_input_tokens": 3145984,
    "cache_write_input_tokens": 0,
    "output_tokens": 29076,
    "reasoning_output_tokens": 10907,
    "total_tokens": 3254359
  }
}
99

exec command

+16:47
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":67641,"chars":"","yield_time_ms":10000,"max_output_tokens":3000});
text(r.output);

Tool response 

Script completed
Wall time 10.0 seconds
Output:

val_glike_d7_a1.parquet
val_glike_d7_a05.parquet
val_glike_d7_a02.parquet
val_glike_d14_a1.parquet
val_glike_d14_a05.parquet
val_glike_d14_a02.parquet
val_glike_d30_a1.parquet
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bf00c9bc87d2aca7a14de5163582",
  "usage": {
    "input_tokens": 59767,
    "cached_input_tokens": 58752,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 59820
  },
  "turn_token_usage": {
    "input_tokens": 3285050,
    "cached_input_tokens": 3204736,
    "cache_write_input_tokens": 0,
    "output_tokens": 29129,
    "reasoning_output_tokens": 10913,
    "total_tokens": 3314179
  },
  "thread_token_usage": {
    "input_tokens": 3285050,
    "cached_input_tokens": 3204736,
    "cache_write_input_tokens": 0,
    "output_tokens": 29129,
    "reasoning_output_tokens": 10913,
    "total_tokens": 3314179
  }
}
100

exec command

+17:00
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-212287bc-b9c9-4713-94f6-ee3a0f307a82",
    "process_id": "67641",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd\nD=Path('/task/data');O=Path('/app/solution');cut=24786800\nwith np.load(D/'validation_eligible_candidates.npz') as z:uids=z['uids'].copy();items=z['item_ids'].copy();packed=z['packed_eligible'].copy()\nci=pd.Index(items); l=pd.read_parquet(D/'train_likes.parquet'); l=l[(l.is_organic==1)&l.item_id.isin(items)];\n# organic strong listener prevalence\nd=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']);d=d[(d.is_organic==1)&(d.played_ratio_pct>=80)&(d.timestamp>=cut-7*86400)&d.item_id.isin(items)];g=d.groupby('item_id').uid.nunique().reindex(items,fill_value=0).to_numpy();g=np.log1p(g);g=g/g.max()\nfor days in [7,14,30]:\n q=l[l.timestamp>=cut-days*86400]; s=q.groupby('item_id').size().reindex(items,fill_value=0).to_numpy();s=np.log1p(s);s=s/(s.max() or 1)\n for a in [1,.5,.2]:\n  score=a*s+(1-a)*g;order=np.argsort(score,kind='stable')[::-1];rows=[]\n  for r,u in enumerate(uids):\n   take=[]\n   for c in order:\n    if (int(packed[r,c//8])>>(c%8))&1:take.append(c)\n    if len(take)==100:break\n   rows.extend((int(u),int(items[c]),k+1) for k,c in enumerate(take))\n  name=f'val_glike_d{days}_a{str(a).replace(\".\",\"\")}.parquet';pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/name,index=False);print(name,flush=True)\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nfrom pathlib import Path\nimport numpy as np,pandas as pd\nD=Path('/task/data');O=Path('/app/solution');cut=24786800\nwith np.load(D/'validation_eligible_candidates.npz') as z:uids=z['uids'].copy();items=z['item_ids'].copy();packed=z['packed_eligible'].copy()\nci=pd.Index(items); l=pd.read_parquet(D/'train_likes.parquet'); l=l[(l.is_organic==1)&l.item_id.isin(items)];\n# organic strong listener prevalence\nd=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']);d=d[(d.is_organic==1)&(d.played_ratio_pct>=80)&(d.timestamp>=cut-7*86400)&d.item_id.isin(items)];g=d.groupby('item_id').uid.nunique().reindex(items,fill_value=0).to_numpy();g=np.log1p(g);g=g/g.max()\nfor days in [7,14,30]:\n q=l[l.timestamp>=cut-days*86400]; s=q.groupby('item_id').size().reindex(items,fill_value=0).to_numpy();s=np.log1p(s);s=s/(s.max() or 1)\n for a in [1,.5,.2]:\n  score=a*s+(1-a)*g;order=np.argsort(score,kind='stable')[::-1];rows=[]\n  for r,u in enumerate(uids):\n   take=[]\n   for c in order:\n    if (int(packed[r,c//8])>>(c%8))&1:take.append(c)\n    if len(take)==100:break\n   rows.extend((int(u),int(items[c]),k+1) for k,c in enumerate(take))\n  name=f'val_glike_d{days}_a{str(a).replace(\".\",\"\")}.parquet';pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/name,index=False);print(name,flush=True)\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "val_glike_d7_a1.parquet\nval_glike_d7_a05.parquet\nval_glike_d7_a02.parquet\nval_glike_d14_a1.parquet\nval_glike_d14_a05.parquet\nval_glike_d14_a02.parquet\nval_glike_d30_a1.parquet\nval_glike_d30_a05.parquet\nval_glike_d30_a02.parquet\n",
    "stderr": "",
    "aggregated_output": "val_glike_d7_a1.parquet\nval_glike_d7_a05.parquet\nval_glike_d7_a02.parquet\nval_glike_d14_a1.parquet\nval_glike_d14_a05.parquet\nval_glike_d14_a02.parquet\nval_glike_d30_a1.parquet\nval_glike_d30_a05.parquet\nval_glike_d30_a02.parquet\n",
    "exit_code": 0,
    "duration": {
      "secs": 15,
      "nanos": 316864471
    },
    "formatted_output": "val_glike_d7_a1.parquet\nval_glike_d7_a05.parquet\nval_glike_d7_a02.parquet\nval_glike_d14_a1.parquet\nval_glike_d14_a05.parquet\nval_glike_d14_a02.parquet\nval_glike_d30_a1.parquet\nval_glike_d30_a05.parquet\nval_glike_d30_a02.parquet\n"
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":67641,"chars":"","yield_time_ms":10000,"max_output_tokens":3000});
text(r.output);

Tool response 

Script completed
Wall time 0.0 seconds
Output:

val_glike_d30_a05.parquet
val_glike_d30_a02.parquet
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bf0cddcc87d2a8310afa87987ab6",
  "usage": {
    "input_tokens": 59913,
    "cached_input_tokens": 59648,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 59966
  },
  "turn_token_usage": {
    "input_tokens": 3344963,
    "cached_input_tokens": 3264384,
    "cache_write_input_tokens": 0,
    "output_tokens": 29182,
    "reasoning_output_tokens": 10919,
    "total_tokens": 3374145
  },
  "thread_token_usage": {
    "input_tokens": 3344963,
    "cached_input_tokens": 3264384,
    "cache_write_input_tokens": 0,
    "output_tokens": 29182,
    "reasoning_output_tokens": 10919,
    "total_tokens": 3374145
  }
}
101

exec command

+17:04
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python -u - <<'PY'\nimport sys,glob;sys.path.insert(0,'/task');import evaluate_public as e\nfrom ranking_evaluation import *\nimport pandas as pd\nfrom pathlib import Path\nt=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int));c=CandidateSet(Path('/task/data/validation_eligible_candidates.npz'));g=e.validation_truth(c,t);res=[]\nfor f in glob.glob('/app/solution/val_glike*.parquet'):\n d=pd.read_parquet(f);d=d[d['rank']<=10].sort_values(['uid','rank']);r={int(u):list(map(int,x.item_id)) for u,x in d.groupby('uid')};res.append((score_rankings(r,g)['quality_ndcg@10'],Path(f).stem))\nprint(*sorted(res,reverse=True),sep='\\n')\nPY","workdir":"/app","yield_time_ms":1000,"max_output_tokens":3000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response 

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=37852
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bf0f61b487d29dc3b6cd2d1433dc",
  "usage": {
    "input_tokens": 60009,
    "cached_input_tokens": 59776,
    "cache_write_input_tokens": 0,
    "output_tokens": 270,
    "reasoning_output_tokens": 6,
    "total_tokens": 60279
  },
  "turn_token_usage": {
    "input_tokens": 3404972,
    "cached_input_tokens": 3324160,
    "cache_write_input_tokens": 0,
    "output_tokens": 29452,
    "reasoning_output_tokens": 10925,
    "total_tokens": 3434424
  },
  "thread_token_usage": {
    "input_tokens": 3404972,
    "cached_input_tokens": 3324160,
    "cache_write_input_tokens": 0,
    "output_tokens": 29452,
    "reasoning_output_tokens": 10925,
    "total_tokens": 3434424
  }
}
102

exec command

+17:13
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-178ca95e-8993-4a83-84d9-5051a20cb273",
    "process_id": "37852",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python -u - <<'PY'\nimport sys,glob;sys.path.insert(0,'/task');import evaluate_public as e\nfrom ranking_evaluation import *\nimport pandas as pd\nfrom pathlib import Path\nt=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int));c=CandidateSet(Path('/task/data/validation_eligible_candidates.npz'));g=e.validation_truth(c,t);res=[]\nfor f in glob.glob('/app/solution/val_glike*.parquet'):\n d=pd.read_parquet(f);d=d[d['rank']<=10].sort_values(['uid','rank']);r={int(u):list(map(int,x.item_id)) for u,x in d.groupby('uid')};res.append((score_rankings(r,g)['quality_ndcg@10'],Path(f).stem))\nprint(*sorted(res,reverse=True),sep='\\n')\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport sys,glob;sys.path.insert(0,'/task');import evaluate_public as e\nfrom ranking_evaluation import *\nimport pandas as pd\nfrom pathlib import Path\nt=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int));c=CandidateSet(Path('/task/data/validation_eligible_candidates.npz'));g=e.validation_truth(c,t);res=[]\nfor f in glob.glob('/app/solution/val_glike*.parquet'):\n d=pd.read_parquet(f);d=d[d['rank']<=10].sort_values(['uid','rank']);r={int(u):list(map(int,x.item_id)) for u,x in d.groupby('uid')};res.append((score_rankings(r,g)['quality_ndcg@10'],Path(f).stem))\nprint(*sorted(res,reverse=True),sep='\\n')\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "(0.032434, 'val_glike_d7_a02')\n(0.032356, 'val_glike_d14_a05')\n(0.032146, 'val_glike_d14_a02')\n(0.031584, 'val_glike_d7_a05')\n(0.030804, 'val_glike_d30_a02')\n(0.029279, 'val_glike_d7_a1')\n(0.027174, 'val_glike_d30_a05')\n(0.024206, 'val_glike_d14_a1')\n(0.018435, 'val_glike_d30_a1')\n",
    "stderr": "",
    "aggregated_output": "(0.032434, 'val_glike_d7_a02')\n(0.032356, 'val_glike_d14_a05')\n(0.032146, 'val_glike_d14_a02')\n(0.031584, 'val_glike_d7_a05')\n(0.030804, 'val_glike_d30_a02')\n(0.029279, 'val_glike_d7_a1')\n(0.027174, 'val_glike_d30_a05')\n(0.024206, 'val_glike_d14_a1')\n(0.018435, 'val_glike_d30_a1')\n",
    "exit_code": 0,
    "duration": {
      "secs": 4,
      "nanos": 377428776
    },
    "formatted_output": "(0.032434, 'val_glike_d7_a02')\n(0.032356, 'val_glike_d14_a05')\n(0.032146, 'val_glike_d14_a02')\n(0.031584, 'val_glike_d7_a05')\n(0.030804, 'val_glike_d30_a02')\n(0.029279, 'val_glike_d7_a1')\n(0.027174, 'val_glike_d30_a05')\n(0.024206, 'val_glike_d14_a1')\n(0.018435, 'val_glike_d30_a1')\n"
  }
}
Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":37852,"chars":"","yield_time_ms":10000,"max_output_tokens":3000});
text(r.output);

Tool response 

Script completed
Wall time 0.0 seconds
Output:

(0.032434, 'val_glike_d7_a02')
(0.032356, 'val_glike_d14_a05')
(0.032146, 'val_glike_d14_a02')
(0.031584, 'val_glike_d7_a05')
(0.030804, 'val_glike_d30_a02')
(0.029279, 'val_glike_d7_a1')
(0.027174, 'val_glike_d30_a05')
(0.024206, 'val_glike_d14_a1')
(0.018435, 'val_glike_d30_a1')
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bf1897b887d288b1a8bd1e73402a",
  "usage": {
    "input_tokens": 60307,
    "cached_input_tokens": 59776,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 60360
  },
  "turn_token_usage": {
    "input_tokens": 3465279,
    "cached_input_tokens": 3383936,
    "cache_write_input_tokens": 0,
    "output_tokens": 29505,
    "reasoning_output_tokens": 10931,
    "total_tokens": 3494784
  },
  "thread_token_usage": {
    "input_tokens": 3465279,
    "cached_input_tokens": 3383936,
    "cache_write_input_tokens": 0,
    "output_tokens": 29505,
    "reasoning_output_tokens": 10931,
    "total_tokens": 3494784
  }
}
103

Agent message

+17:26
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

104

exec command

+17:26
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np,sys,itertools\nfrom pathlib import Path\nsys.path.insert(0,'/task');from ranking_evaluation import *;import evaluate_public as e\nt=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int));c=CandidateSet(Path('/task/data/validation_eligible_candidates.npz'));g=e.validation_truth(c,t)\nfiles={'q2':'/app/solution/val_likeprof_q2.parquet','q1':'/app/solution/val_likeprof_q1.parquet','o7':'/app/solution/val_k2_b180_o7c.parquet','a7':'/app/solution/val_k2_b180_a7c.parquet','glob':'/app/solution/val_glike_d7_a02.parquet'}\nR={}\nfor n,f in files.items():\n d=pd.read_parquet(f).sort_values(['uid','rank']);R[n]={int(u):list(map(int,x.item_id)) for u,x in d.groupby('uid')}\nfor other in ['q1','o7','a7','glob']:\n for cc in [0,10,50,100]:\n  for w in [.1,.25,.5,1]:\n   rr={}\n   for u in t:\n    a=R['q2'][u];b=R[other][u];scores={x:1/(i+1+cc) for i,x in enumerate(a)}\n    for i,x in enumerate(b):scores[x]=scores.get(x,0)+w/(i+1+cc)\n    rr[u]=sorted(scores,key=scores.get,reverse=True)[:10]\n   print(other,cc,w,score_rankings(rr,g)['quality_ndcg@10'])\nPY","workdir":"/app","yield_time_ms":1000,"max_output_tokens":10000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response 

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=19047
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bf1ca47087d2ac52f77dc64daa1e",
  "usage": {
    "input_tokens": 60518,
    "cached_input_tokens": 60160,
    "cache_write_input_tokens": 0,
    "output_tokens": 1016,
    "reasoning_output_tokens": 552,
    "total_tokens": 61534
  },
  "turn_token_usage": {
    "input_tokens": 3525797,
    "cached_input_tokens": 3444096,
    "cache_write_input_tokens": 0,
    "output_tokens": 30521,
    "reasoning_output_tokens": 11483,
    "total_tokens": 3556318
  },
  "thread_token_usage": {
    "input_tokens": 3525797,
    "cached_input_tokens": 3444096,
    "cache_write_input_tokens": 0,
    "output_tokens": 30521,
    "reasoning_output_tokens": 11483,
    "total_tokens": 3556318
  }
}
105

exec command

+17:38
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":19047,"chars":"","yield_time_ms":10000,"max_output_tokens":10000});
text(r.output);

Tool response 

Script completed
Wall time 10.0 seconds
Output:
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bf31f86487d2963637c0ea660462",
  "usage": {
    "input_tokens": 61562,
    "cached_input_tokens": 60288,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 61615
  },
  "turn_token_usage": {
    "input_tokens": 3587359,
    "cached_input_tokens": 3504384,
    "cache_write_input_tokens": 0,
    "output_tokens": 30574,
    "reasoning_output_tokens": 11489,
    "total_tokens": 3617933
  },
  "thread_token_usage": {
    "input_tokens": 3587359,
    "cached_input_tokens": 3504384,
    "cache_write_input_tokens": 0,
    "output_tokens": 30574,
    "reasoning_output_tokens": 11489,
    "total_tokens": 3617933
  }
}
106

exec command

+17:51
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":19047,"chars":"","yield_time_ms":10000,"max_output_tokens":10000});
text(r.output);

Tool response 

Script completed
Wall time 10.0 seconds
Output:
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bf3fc3a887d2899e5766397128e4",
  "usage": {
    "input_tokens": 61638,
    "cached_input_tokens": 61440,
    "cache_write_input_tokens": 0,
    "output_tokens": 78,
    "reasoning_output_tokens": 31,
    "total_tokens": 61716
  },
  "turn_token_usage": {
    "input_tokens": 3648997,
    "cached_input_tokens": 3565824,
    "cache_write_input_tokens": 0,
    "output_tokens": 30652,
    "reasoning_output_tokens": 11520,
    "total_tokens": 3679649
  },
  "thread_token_usage": {
    "input_tokens": 3648997,
    "cached_input_tokens": 3565824,
    "cache_write_input_tokens": 0,
    "output_tokens": 30652,
    "reasoning_output_tokens": 11520,
    "total_tokens": 3679649
  }
}
107

exec command

+18:04
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":19047,"chars":"","yield_time_ms":10000,"max_output_tokens":10000});
text(r.output);

Tool response 

Script completed
Wall time 1.2 seconds
Output:

q1 0 0.1 0.052633
q1 0 0.25 0.052672
q1 0 0.5 0.052772
q1 0 1 0.052838
q1 10 0.1 0.052638
q1 10 0.25 0.052641
q1 10 0.5 0.05278
q1 10 1 0.052713
q1 50 0.1 0.052705
q1 50 0.25 0.0526
q1 50 0.5 0.052768
q1 50 1 0.052592
q1 100 0.1 0.052692
q1 100 0.25 0.052641
q1 100 0.5 0.052771
q1 100 1 0.052592
o7 0 0.1 0.053029
o7 0 0.25 0.052933
o7 0 0.5 0.052364
o7 0 1 0.051719
o7 10 0.1 0.052756
o7 10 0.25 0.052592
o7 10 0.5 0.052182
o7 10 1 0.051221
o7 50 0.1 0.052599
o7 50 0.25 0.052496
o7 50 0.5 0.051453
o7 50 1 0.050692
o7 100 0.1 0.052683
o7 100 0.25 0.051924
o7 100 0.5 0.051299
o7 100 1 0.050577
a7 0 0.1 0.052718
a7 0 0.25 0.052636
a7 0 0.5 0.052555
a7 0 1 0.051527
a7 10 0.1 0.052666
a7 10 0.25 0.052561
a7 10 0.5 0.052165
a7 10 1 0.05076
a7 50 0.1 0.05275
a7 50 0.25 0.052332
a7 50 0.5 0.051427
a7 50 1 0.050557
a7 100 0.1 0.052545
a7 100 0.25 0.05208
a7 100 0.5 0.051373
a7 100 1 0.050507
glob 0 0.1 0.053365
glob 0 0.25 0.05299
glob 0 0.5 0.052237
glob 0 1 0.049079
glob 10 0.1 0.052748
glob 10 0.25 0.051745
glob 10 0.5 0.050917
glob 10 1 0.047557
glob 50 0.1 0.051168
glob 50 0.25 0.04943
glob 50 0.5 0.046527
glob 50 1 0.044836
glob 100 0.1 0.049926
glob 100 0.25 0.048172
glob 100 0.5 0.045543
glob 100 1 0.044125
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5bf4cb02887d2939a703f2ca90d01",
  "usage": {
    "input_tokens": 61739,
    "cached_input_tokens": 61440,
    "cache_write_input_tokens": 0,
    "output_tokens": 57,
    "reasoning_output_tokens": 10,
    "total_tokens": 61796
  },
  "turn_token_usage": {
    "input_tokens": 3710736,
    "cached_input_tokens": 3627264,
    "cache_write_input_tokens": 0,
    "output_tokens": 30709,
    "reasoning_output_tokens": 11530,
    "total_tokens": 3741445
  },
  "thread_token_usage": {
    "input_tokens": 3710736,
    "cached_input_tokens": 3627264,
    "cache_write_input_tokens": 0,
    "output_tokens": 30709,
    "reasoning_output_tokens": 11530,
    "total_tokens": 3741445
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-8dc40624-7124-4516-a203-75b27ab863c4",
    "process_id": "19047",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np,sys,itertools\nfrom pathlib import Path\nsys.path.insert(0,'/task');from ranking_evaluation import *;import evaluate_public as e\nt=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int));c=CandidateSet(Path('/task/data/validation_eligible_candidates.npz'));g=e.validation_truth(c,t)\nfiles={'q2':'/app/solution/val_likeprof_q2.parquet','q1':'/app/solution/val_likeprof_q1.parquet','o7':'/app/solution/val_k2_b180_o7c.parquet','a7':'/app/solution/val_k2_b180_a7c.parquet','glob':'/app/solution/val_glike_d7_a02.parquet'}\nR={}\nfor n,f in files.items():\n d=pd.read_parquet(f).sort_values(['uid','rank']);R[n]={int(u):list(map(int,x.item_id)) for u,x in d.groupby('uid')}\nfor other in ['q1','o7','a7','glob']:\n for cc in [0,10,50,100]:\n  for w in [.1,.25,.5,1]:\n   rr={}\n   for u in t:\n    a=R['q2'][u];b=R[other][u];scores={x:1/(i+1+cc) for i,x in enumerate(a)}\n    for i,x in enumerate(b):scores[x]=scores.get(x,0)+w/(i+1+cc)\n    rr[u]=sorted(scores,key=scores.get,reverse=True)[:10]\n   print(other,cc,w,score_rankings(rr,g)['quality_ndcg@10'])\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np,sys,itertools\nfrom pathlib import Path\nsys.path.insert(0,'/task');from ranking_evaluation import *;import evaluate_public as e\nt=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int));c=CandidateSet(Path('/task/data/validation_eligible_candidates.npz'));g=e.validation_truth(c,t)\nfiles={'q2':'/app/solution/val_likeprof_q2.parquet','q1':'/app/solution/val_likeprof_q1.parquet','o7':'/app/solution/val_k2_b180_o7c.parquet','a7':'/app/solution/val_k2_b180_a7c.parquet','glob':'/app/solution/val_glike_d7_a02.parquet'}\nR={}\nfor n,f in files.items():\n d=pd.read_parquet(f).sort_values(['uid','rank']);R[n]={int(u):list(map(int,x.item_id)) for u,x in d.groupby('uid')}\nfor other in ['q1','o7','a7','glob']:\n for cc in [0,10,50,100]:\n  for w in [.1,.25,.5,1]:\n   rr={}\n   for u in t:\n    a=R['q2'][u];b=R[other][u];scores={x:1/(i+1+cc) for i,x in enumerate(a)}\n    for i,x in enumerate(b):scores[x]=scores.get(x,0)+w/(i+1+cc)\n    rr[u]=sorted(scores,key=scores.get,reverse=True)[:10]\n   print(other,cc,w,score_rankings(rr,g)['quality_ndcg@10'])\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "q1 0 0.1 0.052633\nq1 0 0.25 0.052672\nq1 0 0.5 0.052772\nq1 0 1 0.052838\nq1 10 0.1 0.052638\nq1 10 0.25 0.052641\nq1 10 0.5 0.05278\nq1 10 1 0.052713\nq1 50 0.1 0.052705\nq1 50 0.25 0.0526\nq1 50 0.5 0.052768\nq1 50 1 0.052592\nq1 100 0.1 0.052692\nq1 100 0.25 0.052641\nq1 100 0.5 0.052771\nq1 100 1 0.052592\no7 0 0.1 0.053029\no7 0 0.25 0.052933\no7 0 0.5 0.052364\no7 0 1 0.051719\no7 10 0.1 0.052756\no7 10 0.25 0.052592\no7 10 0.5 0.052182\no7 10 1 0.051221\no7 50 0.1 0.052599\no7 50 0.25 0.052496\no7 50 0.5 0.051453\no7 50 1 0.050692\no7 100 0.1 0.052683\no7 100 0.25 0.051924\no7 100 0.5 0.051299\no7 100 1 0.050577\na7 0 0.1 0.052718\na7 0 0.25 0.052636\na7 0 0.5 0.052555\na7 0 1 0.051527\na7 10 0.1 0.052666\na7 10 0.25 0.052561\na7 10 0.5 0.052165\na7 10 1 0.05076\na7 50 0.1 0.05275\na7 50 0.25 0.052332\na7 50 0.5 0.051427\na7 50 1 0.050557\na7 100 0.1 0.052545\na7 100 0.25 0.05208\na7 100 0.5 0.051373\na7 100 1 0.050507\nglob 0 0.1 0.053365\nglob 0 0.25 0.05299\nglob 0 0.5 0.052237\nglob 0 1 0.049079\nglob 10 0.1 0.052748\nglob 10 0.25 0.051745\nglob 10 0.5 0.050917\nglob 10 1 0.047557\nglob 50 0.1 0.051168\nglob 50 0.25 0.04943\nglob 50 0.5 0.046527\nglob 50 1 0.044836\nglob 100 0.1 0.049926\nglob 100 0.25 0.048172\nglob 100 0.5 0.045543\nglob 100 1 0.044125\n",
    "stderr": "",
    "aggregated_output": "q1 0 0.1 0.052633\nq1 0 0.25 0.052672\nq1 0 0.5 0.052772\nq1 0 1 0.052838\nq1 10 0.1 0.052638\nq1 10 0.25 0.052641\nq1 10 0.5 0.05278\nq1 10 1 0.052713\nq1 50 0.1 0.052705\nq1 50 0.25 0.0526\nq1 50 0.5 0.052768\nq1 50 1 0.052592\nq1 100 0.1 0.052692\nq1 100 0.25 0.052641\nq1 100 0.5 0.052771\nq1 100 1 0.052592\no7 0 0.1 0.053029\no7 0 0.25 0.052933\no7 0 0.5 0.052364\no7 0 1 0.051719\no7 10 0.1 0.052756\no7 10 0.25 0.052592\no7 10 0.5 0.052182\no7 10 1 0.051221\no7 50 0.1 0.052599\no7 50 0.25 0.052496\no7 50 0.5 0.051453\no7 50 1 0.050692\no7 100 0.1 0.052683\no7 100 0.25 0.051924\no7 100 0.5 0.051299\no7 100 1 0.050577\na7 0 0.1 0.052718\na7 0 0.25 0.052636\na7 0 0.5 0.052555\na7 0 1 0.051527\na7 10 0.1 0.052666\na7 10 0.25 0.052561\na7 10 0.5 0.052165\na7 10 1 0.05076\na7 50 0.1 0.05275\na7 50 0.25 0.052332\na7 50 0.5 0.051427\na7 50 1 0.050557\na7 100 0.1 0.052545\na7 100 0.25 0.05208\na7 100 0.5 0.051373\na7 100 1 0.050507\nglob 0 0.1 0.053365\nglob 0 0.25 0.05299\nglob 0 0.5 0.052237\nglob 0 1 0.049079\nglob 10 0.1 0.052748\nglob 10 0.25 0.051745\nglob 10 0.5 0.050917\nglob 10 1 0.047557\nglob 50 0.1 0.051168\nglob 50 0.25 0.04943\nglob 50 0.5 0.046527\nglob 50 1 0.044836\nglob 100 0.1 0.049926\nglob 100 0.25 0.048172\nglob 100 0.5 0.045543\nglob 100 1 0.044125\n",
    "exit_code": 0,
    "duration": {
      "secs": 31,
      "nanos": 902922247
    },
    "formatted_output": "q1 0 0.1 0.052633\nq1 0 0.25 0.052672\nq1 0 0.5 0.052772\nq1 0 1 0.052838\nq1 10 0.1 0.052638\nq1 10 0.25 0.052641\nq1 10 0.5 0.05278\nq1 10 1 0.052713\nq1 50 0.1 0.052705\nq1 50 0.25 0.0526\nq1 50 0.5 0.052768\nq1 50 1 0.052592\nq1 100 0.1 0.052692\nq1 100 0.25 0.052641\nq1 100 0.5 0.052771\nq1 100 1 0.052592\no7 0 0.1 0.053029\no7 0 0.25 0.052933\no7 0 0.5 0.052364\no7 0 1 0.051719\no7 10 0.1 0.052756\no7 10 0.25 0.052592\no7 10 0.5 0.052182\no7 10 1 0.051221\no7 50 0.1 0.052599\no7 50 0.25 0.052496\no7 50 0.5 0.051453\no7 50 1 0.050692\no7 100 0.1 0.052683\no7 100 0.25 0.051924\no7 100 0.5 0.051299\no7 100 1 0.050577\na7 0 0.1 0.052718\na7 0 0.25 0.052636\na7 0 0.5 0.052555\na7 0 1 0.051527\na7 10 0.1 0.052666\na7 10 0.25 0.052561\na7 10 0.5 0.052165\na7 10 1 0.05076\na7 50 0.1 0.05275\na7 50 0.25 0.052332\na7 50 0.5 0.051427\na7 50 1 0.050557\na7 100 0.1 0.052545\na7 100 0.25 0.05208\na7 100 0.5 0.051373\na7 100 1 0.050507\nglob 0 0.1 0.053365\nglob 0 0.25 0.05299\nglob 0 0.5 0.052237\nglob 0 1 0.049079\nglob 10 0.1 0.052748\nglob 10 0.25 0.051745\nglob 10 0.5 0.050917\nglob 10 1 0.047557\nglob 50 0.1 0.051168\nglob 50 0.25 0.04943\nglob 50 0.5 0.046527\nglob 50 1 0.044836\nglob 100 0.1 0.049926\nglob 100 0.25 0.048172\nglob 100 0.5 0.045543\nglob 100 1 0.044125\n"
  }
}
108

exec command

+18:15
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const patch = "*** Begin Patch\n*** Add File: /app/solution/tune_like_model.py\n+\"\"\"Tune a few robust neighborhood hyperparameters on the public window.\"\"\"\n+from pathlib import Path\n+import sys\n+import numpy as np\n+import pandas as pd\n+from scipy import sparse\n+from sklearn.preprocessing import normalize\n+sys.path.insert(0,'/task')\n+from ranking_evaluation import CandidateSet\n+import evaluate_public\n+D=Path('/task/data');CUT=24786800\n+with np.load(D/'validation_eligible_candidates.npz') as z:uids=z['uids'].copy();items=z['item_ids'].copy();packed=z['packed_eligible'].copy()\n+ci=pd.Index(items);d=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']);au=np.sort(d.uid.unique());ui=pd.Index(au);ur=ui.get_indexer(d.uid).astype(np.int32);cc=ci.get_indexer(d.item_id).astype(np.int32);strong=d.played_ratio_pct>=80\n+def mat(mask,log=True):\n+ y=sparse.coo_matrix((np.ones(mask.sum(),np.float32),(ur[mask],cc[mask])),shape=(len(au),len(items))).tocsr();y.data=np.log1p(y.data) if log else np.ones_like(y.data);return y\n+a7=strong&(d.timestamp>=CUT-7*86400)&(cc>=0);o7=(d.is_organic==1)&a7;Ya=mat(a7);Yo=mat(o7);deg=np.asarray(Ya.sum(axis=0)).ravel().astype(np.float32)\n+globo=np.log1p(np.asarray((Yo>0).sum(axis=0)).ravel().astype(np.float32));globo/=globo.max()\n+likes=pd.read_parquet(D/'train_likes.parquet');l7=(likes.is_organic==1)&(likes.timestamp>=CUT-7*86400)&likes.item_id.isin(items)&likes.uid.isin(au);ld=likes[l7];lr=ui.get_indexer(ld.uid);lc=ci.get_indexer(ld.item_id);Yl=sparse.coo_matrix((np.ones(len(ld),np.float32),(lr,lc)),shape=Ya.shape).tocsr();Yl.data=np.log1p(Yl.data)\n+gl=np.log1p(np.asarray((Yl>0).sum(axis=0)).ravel().astype(np.float32));gl/=max(gl.max(),1);globm=.8*globo+.2*gl;globm/=globm.max()\n+# q=2 active-like emphasis in the user taste profile.\n+p=d[(d.is_organic==1)&strong&(d.timestamp>=CUT-180*86400)][['uid','item_id']];state=pd.read_parquet(D/'validation_preference_state.parquet');state=state[(state.state=='liked')&state.uid.isin(au)][['uid','item_id']];both=np.concatenate([p.item_id.to_numpy(),state.item_id.to_numpy()]);codes,_=pd.factorize(both,sort=False);pc=codes[:len(p)];lc2=codes[len(p):];B=sparse.coo_matrix((np.ones(len(p),np.float32),(ui.get_indexer(p.uid),pc)),shape=(len(au),codes.max()+1)).tocsr();B.data[:]=1;L=sparse.coo_matrix((np.ones(len(state),np.float32),(ui.get_indexer(state.uid),lc2)),shape=B.shape).tocsr();L.data[:]=1;X=B+2*L;idf=np.log1p(len(au)/(1+np.diff((X>0).tocsc().indptr))).astype(np.float32);X=X.multiply(idf).tocsr();normalize(X,axis=1,copy=False);tr=ui.get_indexer(uids)\n+K=1500;ni=np.empty((len(uids),K),np.int32);nv=np.empty((len(uids),K),np.float32)\n+for a in range(0,len(uids),128):\n+ b=min(a+128,len(uids));ss=(X[tr[a:b]]@X.T).toarray();ss[np.arange(b-a),tr[a:b]]=-1;ix=np.argpartition(ss,-K,axis=1)[:,-K:];vv=np.take_along_axis(ss,ix,axis=1);oo=np.argsort(vv,axis=1)[:,::-1];ni[a:b]=np.take_along_axis(ix,oo,axis=1);nv[a:b]=np.take_along_axis(vv,oo,axis=1)\n+print('features ready',flush=True)\n+cs=CandidateSet(D/'validation_eligible_candidates.npz');truth=evaluate_public.validation_truth(cs,set(map(int,uids)));disc=1/np.log2(np.arange(2,12))\n+configs=[]\n+for beta in [.15,.25,.3,.35,.45]:\n+ for alpha in [.02,.05,.1,.2]:\n+  configs.append((1000,beta,alpha,0,1,'m'))\n+for k in [500,750,1250,1500]:configs.append((k,.3,.05,0,1,'m'))\n+for pwr in [.7,.85,1.15,1.3]:configs.append((1000,.3,.05,0,pwr,'m'))\n+for lc3 in [.05,.1,.2,.4]:configs.append((1000,.3,.05,lc3,1,'m'))\n+configs += [(1000,.3,a,0,1,'o') for a in [.02,.05,.1,.2]]\n+sums=np.zeros(len(configs));n=0\n+for r,u in enumerate(uids):\n+ gold=truth.get(int(u));\n+ if not gold:continue\n+ n+=1;elig=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool);cache={}\n+ for j,(k,beta,alpha,lcoef,pwr,gt) in enumerate(configs):\n+  key=(k,pwr,lcoef)\n+  if key not in cache:\n+   sw=np.maximum(nv[r,:k],0)**pwr;ns=ni[r,:k];z=np.asarray(sw@Ya[ns]).ravel()\n+   if lcoef:\n+    zl=np.asarray(sw@Yl[ns]).ravel();\n+    if zl.max():zl/=zl.max()\n+    if z.max():z/=z.max()\n+    z=z+lcoef*zl\n+   cache[key]=z\n+  z=cache[key].copy()/np.maximum(deg,.1)**beta\n+  if z.max():z/=z.max()\n+  z+=alpha*(globm if gt=='m' else globo);z[~elig]=-np.inf;top=np.argpartition(z,-10)[-10:];top=top[np.argsort(z[top])[::-1]];gains=np.array([gold.get(int(items[c]),0) for c in top]);ideal=np.array(sorted(gold.values(),reverse=True)[:10]);sums[j]+=((2**gains-1)*disc).sum()/((2**ideal-1)*disc[:len(ideal)]).sum()\n+ if n%500==0:print(n,flush=True)\n+for s,c in sorted(zip(sums/n,configs),reverse=True):print(round(s,6),c)\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response 

Script completed
Wall time 0.0 seconds
Output:

{}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-978b3de1-82ce-43b4-a90f-756507a14642",
    "changes": {
      "/app/solution/tune_like_model.py": {
        "type": "add",
        "content": "\"\"\"Tune a few robust neighborhood hyperparameters on the public window.\"\"\"\nfrom pathlib import Path\nimport sys\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.preprocessing import normalize\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\nD=Path('/task/data');CUT=24786800\nwith np.load(D/'validation_eligible_candidates.npz') as z:uids=z['uids'].copy();items=z['item_ids'].copy();packed=z['packed_eligible'].copy()\nci=pd.Index(items);d=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']);au=np.sort(d.uid.unique());ui=pd.Index(au);ur=ui.get_indexer(d.uid).astype(np.int32);cc=ci.get_indexer(d.item_id).astype(np.int32);strong=d.played_ratio_pct>=80\ndef mat(mask,log=True):\n y=sparse.coo_matrix((np.ones(mask.sum(),np.float32),(ur[mask],cc[mask])),shape=(len(au),len(items))).tocsr();y.data=np.log1p(y.data) if log else np.ones_like(y.data);return y\na7=strong&(d.timestamp>=CUT-7*86400)&(cc>=0);o7=(d.is_organic==1)&a7;Ya=mat(a7);Yo=mat(o7);deg=np.asarray(Ya.sum(axis=0)).ravel().astype(np.float32)\nglobo=np.log1p(np.asarray((Yo>0).sum(axis=0)).ravel().astype(np.float32));globo/=globo.max()\nlikes=pd.read_parquet(D/'train_likes.parquet');l7=(likes.is_organic==1)&(likes.timestamp>=CUT-7*86400)&likes.item_id.isin(items)&likes.uid.isin(au);ld=likes[l7];lr=ui.get_indexer(ld.uid);lc=ci.get_indexer(ld.item_id);Yl=sparse.coo_matrix((np.ones(len(ld),np.float32),(lr,lc)),shape=Ya.shape).tocsr();Yl.data=np.log1p(Yl.data)\ngl=np.log1p(np.asarray((Yl>0).sum(axis=0)).ravel().astype(np.float32));gl/=max(gl.max(),1);globm=.8*globo+.2*gl;globm/=globm.max()\n# q=2 active-like emphasis in the user taste profile.\np=d[(d.is_organic==1)&strong&(d.timestamp>=CUT-180*86400)][['uid','item_id']];state=pd.read_parquet(D/'validation_preference_state.parquet');state=state[(state.state=='liked')&state.uid.isin(au)][['uid','item_id']];both=np.concatenate([p.item_id.to_numpy(),state.item_id.to_numpy()]);codes,_=pd.factorize(both,sort=False);pc=codes[:len(p)];lc2=codes[len(p):];B=sparse.coo_matrix((np.ones(len(p),np.float32),(ui.get_indexer(p.uid),pc)),shape=(len(au),codes.max()+1)).tocsr();B.data[:]=1;L=sparse.coo_matrix((np.ones(len(state),np.float32),(ui.get_indexer(state.uid),lc2)),shape=B.shape).tocsr();L.data[:]=1;X=B+2*L;idf=np.log1p(len(au)/(1+np.diff((X>0).tocsc().indptr))).astype(np.float32);X=X.multiply(idf).tocsr();normalize(X,axis=1,copy=False);tr=ui.get_indexer(uids)\nK=1500;ni=np.empty((len(uids),K),np.int32);nv=np.empty((len(uids),K),np.float32)\nfor a in range(0,len(uids),128):\n b=min(a+128,len(uids));ss=(X[tr[a:b]]@X.T).toarray();ss[np.arange(b-a),tr[a:b]]=-1;ix=np.argpartition(ss,-K,axis=1)[:,-K:];vv=np.take_along_axis(ss,ix,axis=1);oo=np.argsort(vv,axis=1)[:,::-1];ni[a:b]=np.take_along_axis(ix,oo,axis=1);nv[a:b]=np.take_along_axis(vv,oo,axis=1)\nprint('features ready',flush=True)\ncs=CandidateSet(D/'validation_eligible_candidates.npz');truth=evaluate_public.validation_truth(cs,set(map(int,uids)));disc=1/np.log2(np.arange(2,12))\nconfigs=[]\nfor beta in [.15,.25,.3,.35,.45]:\n for alpha in [.02,.05,.1,.2]:\n  configs.append((1000,beta,alpha,0,1,'m'))\nfor k in [500,750,1250,1500]:configs.append((k,.3,.05,0,1,'m'))\nfor pwr in [.7,.85,1.15,1.3]:configs.append((1000,.3,.05,0,pwr,'m'))\nfor lc3 in [.05,.1,.2,.4]:configs.append((1000,.3,.05,lc3,1,'m'))\nconfigs += [(1000,.3,a,0,1,'o') for a in [.02,.05,.1,.2]]\nsums=np.zeros(len(configs));n=0\nfor r,u in enumerate(uids):\n gold=truth.get(int(u));\n if not gold:continue\n n+=1;elig=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool);cache={}\n for j,(k,beta,alpha,lcoef,pwr,gt) in enumerate(configs):\n  key=(k,pwr,lcoef)\n  if key not in cache:\n   sw=np.maximum(nv[r,:k],0)**pwr;ns=ni[r,:k];z=np.asarray(sw@Ya[ns]).ravel()\n   if lcoef:\n    zl=np.asarray(sw@Yl[ns]).ravel();\n    if zl.max():zl/=zl.max()\n    if z.max():z/=z.max()\n    z=z+lcoef*zl\n   cache[key]=z\n  z=cache[key].copy()/np.maximum(deg,.1)**beta\n  if z.max():z/=z.max()\n  z+=alpha*(globm if gt=='m' else globo);z[~elig]=-np.inf;top=np.argpartition(z,-10)[-10:];top=top[np.argsort(z[top])[::-1]];gains=np.array([gold.get(int(items[c]),0) for c in top]);ideal=np.array(sorted(gold.values(),reverse=True)[:10]);sums[j]+=((2**gains-1)*disc).sum()/((2**ideal-1)*disc[:len(ideal)]).sum()\n if n%500==0:print(n,flush=True)\nfor s,c in sorted(zip(sums/n,configs),reverse=True):print(round(s,6),c)\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/tune_like_model.py\n",
    "stderr": ""
  }
}
Harness context token usage record
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109

exec command

+18:50
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"OMP_NUM_THREADS=4 OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python -u /app/solution/tune_like_model.py","workdir":"/app","yield_time_ms":1000,"max_output_tokens":5000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response 

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=58576
Harness context token usage record
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  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
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  }
}
110

exec command

+18:53
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":58576,"chars":"","yield_time_ms":10000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 10.0 seconds
Output:

features ready
Harness context token usage record
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111

exec command

+19:10
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":58576,"chars":"","yield_time_ms":10000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 10.0 seconds
Output:

500
1000
Harness context token usage record
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}
112

exec command

+19:22
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":58576,"chars":"","yield_time_ms":10000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 10.0 seconds
Output:

1500
Harness context token usage record
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  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
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}
113

exec command

+19:34
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":58576,"chars":"","yield_time_ms":10000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 10.0 seconds
Output:

2000
2500
Harness context token usage record
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  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
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    "cache_write_input_tokens": 0,
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  }
}
114

exec command

+19:47
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":58576,"chars":"","yield_time_ms":10000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 10.0 seconds
Output:

3000
Harness context token usage record
{
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115

exec command

+20:00
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":58576,"chars":"","yield_time_ms":10000,"max_output_tokens":10000});
text(r.output);

Tool response 

Script completed
Wall time 10.0 seconds
Output:

3500
Harness context token usage record
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116

exec command

+20:12
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":58576,"chars":"","yield_time_ms":10000,"max_output_tokens":10000});
text(r.output);

Tool response 

Script completed
Wall time 3.7 seconds
Output:

4000
0.053581 (1000, 0.3, 0.05, 0.1, 1, 'm')
0.05352 (1000, 0.3, 0.05, 0.05, 1, 'm')
0.053096 (1000, 0.35, 0.05, 0, 1, 'm')
0.052983 (1000, 0.35, 0.02, 0, 1, 'm')
0.052961 (1000, 0.35, 0.1, 0, 1, 'm')
0.05291 (750, 0.3, 0.05, 0, 1, 'm')
0.052839 (1000, 0.3, 0.1, 0, 1, 'm')
0.052776 (1000, 0.3, 0.05, 0, 0.85, 'm')
0.052768 (1000, 0.3, 0.05, 0, 1.15, 'm')
0.052753 (1000, 0.35, 0.2, 0, 1, 'm')
0.05275 (1000, 0.3, 0.05, 0, 1, 'm')
0.052722 (1000, 0.3, 0.1, 0, 1, 'o')
0.05272 (1000, 0.3, 0.2, 0, 1, 'm')
0.052647 (1000, 0.3, 0.05, 0, 1.3, 'm')
0.052637 (1000, 0.3, 0.05, 0, 1, 'o')
0.052613 (1000, 0.25, 0.02, 0, 1, 'm')
0.052612 (1000, 0.3, 0.02, 0, 1, 'm')
0.052598 (1000, 0.3, 0.02, 0, 1, 'o')
0.052566 (1000, 0.45, 0.2, 0, 1, 'm')
0.05254 (1000, 0.25, 0.05, 0, 1, 'm')
0.052393 (1250, 0.3, 0.05, 0, 1, 'm')
0.052341 (1000, 0.3, 0.2, 0, 1, 'o')
0.052341 (1000, 0.25, 0.1, 0, 1, 'm')
0.052282 (500, 0.3, 0.05, 0, 1, 'm')
0.052267 (1000, 0.3, 0.05, 0, 0.7, 'm')
0.05189 (1000, 0.3, 0.05, 0.2, 1, 'm')
0.051876 (1000, 0.45, 0.05, 0, 1, 'm')
0.051873 (1000, 0.45, 0.1, 0, 1, 'm')
0.051602 (1000, 0.45, 0.02, 0, 1, 'm')
0.051502 (1000, 0.25, 0.2, 0, 1, 'm')
0.051201 (1500, 0.3, 0.05, 0, 1, 'm')
0.050419 (1000, 0.15, 0.02, 0, 1, 'm')
0.050293 (1000, 0.15, 0.05, 0, 1, 'm')
0.050152 (1000, 0.15, 0.1, 0, 1, 'm')
0.049856 (1000, 0.15, 0.2, 0, 1, 'm')
0.047409 (1000, 0.3, 0.05, 0.4, 1, 'm')
Harness context token usage record
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Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-ff5660e8-8439-4095-b9a6-8a8eaac4a78d",
    "process_id": "58576",
    "command": [
      "/bin/sh",
      "-lc",
      "OMP_NUM_THREADS=4 OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python -u /app/solution/tune_like_model.py"
    ],
    "cwd": "file:///app",
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      {
        "type": "unknown",
        "cmd": "OMP_NUM_THREADS=4 OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python -u /app/solution/tune_like_model.py"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "features ready\n500\n1000\n1500\n2000\n2500\n3000\n3500\n4000\n0.053581 (1000, 0.3, 0.05, 0.1, 1, 'm')\n0.05352 (1000, 0.3, 0.05, 0.05, 1, 'm')\n0.053096 (1000, 0.35, 0.05, 0, 1, 'm')\n0.052983 (1000, 0.35, 0.02, 0, 1, 'm')\n0.052961 (1000, 0.35, 0.1, 0, 1, 'm')\n0.05291 (750, 0.3, 0.05, 0, 1, 'm')\n0.052839 (1000, 0.3, 0.1, 0, 1, 'm')\n0.052776 (1000, 0.3, 0.05, 0, 0.85, 'm')\n0.052768 (1000, 0.3, 0.05, 0, 1.15, 'm')\n0.052753 (1000, 0.35, 0.2, 0, 1, 'm')\n0.05275 (1000, 0.3, 0.05, 0, 1, 'm')\n0.052722 (1000, 0.3, 0.1, 0, 1, 'o')\n0.05272 (1000, 0.3, 0.2, 0, 1, 'm')\n0.052647 (1000, 0.3, 0.05, 0, 1.3, 'm')\n0.052637 (1000, 0.3, 0.05, 0, 1, 'o')\n0.052613 (1000, 0.25, 0.02, 0, 1, 'm')\n0.052612 (1000, 0.3, 0.02, 0, 1, 'm')\n0.052598 (1000, 0.3, 0.02, 0, 1, 'o')\n0.052566 (1000, 0.45, 0.2, 0, 1, 'm')\n0.05254 (1000, 0.25, 0.05, 0, 1, 'm')\n0.052393 (1250, 0.3, 0.05, 0, 1, 'm')\n0.052341 (1000, 0.3, 0.2, 0, 1, 'o')\n0.052341 (1000, 0.25, 0.1, 0, 1, 'm')\n0.052282 (500, 0.3, 0.05, 0, 1, 'm')\n0.052267 (1000, 0.3, 0.05, 0, 0.7, 'm')\n0.05189 (1000, 0.3, 0.05, 0.2, 1, 'm')\n0.051876 (1000, 0.45, 0.05, 0, 1, 'm')\n0.051873 (1000, 0.45, 0.1, 0, 1, 'm')\n0.051602 (1000, 0.45, 0.02, 0, 1, 'm')\n0.051502 (1000, 0.25, 0.2, 0, 1, 'm')\n0.051201 (1500, 0.3, 0.05, 0, 1, 'm')\n0.050419 (1000, 0.15, 0.02, 0, 1, 'm')\n0.050293 (1000, 0.15, 0.05, 0, 1, 'm')\n0.050152 (1000, 0.15, 0.1, 0, 1, 'm')\n0.049856 (1000, 0.15, 0.2, 0, 1, 'm')\n0.047409 (1000, 0.3, 0.05, 0.4, 1, 'm')\n",
    "stderr": "",
    "aggregated_output": "features ready\n500\n1000\n1500\n2000\n2500\n3000\n3500\n4000\n0.053581 (1000, 0.3, 0.05, 0.1, 1, 'm')\n0.05352 (1000, 0.3, 0.05, 0.05, 1, 'm')\n0.053096 (1000, 0.35, 0.05, 0, 1, 'm')\n0.052983 (1000, 0.35, 0.02, 0, 1, 'm')\n0.052961 (1000, 0.35, 0.1, 0, 1, 'm')\n0.05291 (750, 0.3, 0.05, 0, 1, 'm')\n0.052839 (1000, 0.3, 0.1, 0, 1, 'm')\n0.052776 (1000, 0.3, 0.05, 0, 0.85, 'm')\n0.052768 (1000, 0.3, 0.05, 0, 1.15, 'm')\n0.052753 (1000, 0.35, 0.2, 0, 1, 'm')\n0.05275 (1000, 0.3, 0.05, 0, 1, 'm')\n0.052722 (1000, 0.3, 0.1, 0, 1, 'o')\n0.05272 (1000, 0.3, 0.2, 0, 1, 'm')\n0.052647 (1000, 0.3, 0.05, 0, 1.3, 'm')\n0.052637 (1000, 0.3, 0.05, 0, 1, 'o')\n0.052613 (1000, 0.25, 0.02, 0, 1, 'm')\n0.052612 (1000, 0.3, 0.02, 0, 1, 'm')\n0.052598 (1000, 0.3, 0.02, 0, 1, 'o')\n0.052566 (1000, 0.45, 0.2, 0, 1, 'm')\n0.05254 (1000, 0.25, 0.05, 0, 1, 'm')\n0.052393 (1250, 0.3, 0.05, 0, 1, 'm')\n0.052341 (1000, 0.3, 0.2, 0, 1, 'o')\n0.052341 (1000, 0.25, 0.1, 0, 1, 'm')\n0.052282 (500, 0.3, 0.05, 0, 1, 'm')\n0.052267 (1000, 0.3, 0.05, 0, 0.7, 'm')\n0.05189 (1000, 0.3, 0.05, 0.2, 1, 'm')\n0.051876 (1000, 0.45, 0.05, 0, 1, 'm')\n0.051873 (1000, 0.45, 0.1, 0, 1, 'm')\n0.051602 (1000, 0.45, 0.02, 0, 1, 'm')\n0.051502 (1000, 0.25, 0.2, 0, 1, 'm')\n0.051201 (1500, 0.3, 0.05, 0, 1, 'm')\n0.050419 (1000, 0.15, 0.02, 0, 1, 'm')\n0.050293 (1000, 0.15, 0.05, 0, 1, 'm')\n0.050152 (1000, 0.15, 0.1, 0, 1, 'm')\n0.049856 (1000, 0.15, 0.2, 0, 1, 'm')\n0.047409 (1000, 0.3, 0.05, 0.4, 1, 'm')\n",
    "exit_code": 0,
    "duration": {
      "secs": 85,
      "nanos": 208823794
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    "formatted_output": "features ready\n500\n1000\n1500\n2000\n2500\n3000\n3500\n4000\n0.053581 (1000, 0.3, 0.05, 0.1, 1, 'm')\n0.05352 (1000, 0.3, 0.05, 0.05, 1, 'm')\n0.053096 (1000, 0.35, 0.05, 0, 1, 'm')\n0.052983 (1000, 0.35, 0.02, 0, 1, 'm')\n0.052961 (1000, 0.35, 0.1, 0, 1, 'm')\n0.05291 (750, 0.3, 0.05, 0, 1, 'm')\n0.052839 (1000, 0.3, 0.1, 0, 1, 'm')\n0.052776 (1000, 0.3, 0.05, 0, 0.85, 'm')\n0.052768 (1000, 0.3, 0.05, 0, 1.15, 'm')\n0.052753 (1000, 0.35, 0.2, 0, 1, 'm')\n0.05275 (1000, 0.3, 0.05, 0, 1, 'm')\n0.052722 (1000, 0.3, 0.1, 0, 1, 'o')\n0.05272 (1000, 0.3, 0.2, 0, 1, 'm')\n0.052647 (1000, 0.3, 0.05, 0, 1.3, 'm')\n0.052637 (1000, 0.3, 0.05, 0, 1, 'o')\n0.052613 (1000, 0.25, 0.02, 0, 1, 'm')\n0.052612 (1000, 0.3, 0.02, 0, 1, 'm')\n0.052598 (1000, 0.3, 0.02, 0, 1, 'o')\n0.052566 (1000, 0.45, 0.2, 0, 1, 'm')\n0.05254 (1000, 0.25, 0.05, 0, 1, 'm')\n0.052393 (1250, 0.3, 0.05, 0, 1, 'm')\n0.052341 (1000, 0.3, 0.2, 0, 1, 'o')\n0.052341 (1000, 0.25, 0.1, 0, 1, 'm')\n0.052282 (500, 0.3, 0.05, 0, 1, 'm')\n0.052267 (1000, 0.3, 0.05, 0, 0.7, 'm')\n0.05189 (1000, 0.3, 0.05, 0.2, 1, 'm')\n0.051876 (1000, 0.45, 0.05, 0, 1, 'm')\n0.051873 (1000, 0.45, 0.1, 0, 1, 'm')\n0.051602 (1000, 0.45, 0.02, 0, 1, 'm')\n0.051502 (1000, 0.25, 0.2, 0, 1, 'm')\n0.051201 (1500, 0.3, 0.05, 0, 1, 'm')\n0.050419 (1000, 0.15, 0.02, 0, 1, 'm')\n0.050293 (1000, 0.15, 0.05, 0, 1, 'm')\n0.050152 (1000, 0.15, 0.1, 0, 1, 'm')\n0.049856 (1000, 0.15, 0.2, 0, 1, 'm')\n0.047409 (1000, 0.3, 0.05, 0.4, 1, 'm')\n"
  }
}
117

Agent message

+20:28
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

118

Agent message

+20:37
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

119

exec command

+20:43
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Model text

The low-rank model did not help, while emphasizing active likes in the user-similarity profile did. The selected validation configuration scores 0.0536 NDCG@10 (about 5× the catalog baseline). I’m now refitting that exact architecture on training plus validation history, using the test-cutoff preference state and eligibility mask.

Tool callexecTool call Returned

Tool call · Input 

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 leakage-safe validation and hidden-test rankings.\n+\n+The model is a temporally local, user-neighborhood collaborative filter.  User\n+similarity comes from six months of strong organic plays plus active explicit\n+likes. Candidate evidence is restricted to the final seven history days and\n+the supplied eligibility service is applied after scoring.\n+\"\"\"\n+from __future__ import annotations\n+\n+import argparse\n+import gc\n+import os\n+from pathlib import Path\n+\n+import numpy as np\n+import pandas as pd\n+from scipy import sparse\n+from sklearn.preprocessing import normalize\n+\n+\n+DATA = Path(\"/task/data\")\n+OUT = Path(\"/app/solution\")\n+TRAIN_CUTOFF = 24_786_800\n+WINDOW = 7 * 86_400\n+GAP = 1_800\n+TEST_CUTOFF = TRAIN_CUTOFF + GAP + WINDOW\n+PROFILE_DAYS = 180\n+NEIGHBORS = 1_000\n+\n+\n+def load_events(family: str, include_validation: bool) -> pd.DataFrame:\n+    columns = (\n+        [\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"]\n+        if family == \"listens\"\n+        else [\"uid\", \"timestamp\", \"item_id\", \"is_organic\"]\n+    )\n+    frames = [pd.read_parquet(DATA / f\"train_{family}.parquet\", columns=columns)]\n+    if include_validation:\n+        frames.append(pd.read_parquet(DATA / f\"val_{family}.parquet\", columns=columns))\n+    return pd.concat(frames, ignore_index=True) if len(frames) == 2 else frames[0]\n+\n+\n+def candidate_archive(stage: str) -> tuple[np.ndarray, np.ndarray, np.ndarray]:\n+    with np.load(DATA / f\"{stage}_eligible_candidates.npz\") as z:\n+        return z[\"uids\"].copy(), z[\"item_ids\"].copy(), z[\"packed_eligible\"].copy()\n+\n+\n+def interaction_matrix(\n+    user_rows: np.ndarray,\n+    item_cols: np.ndarray,\n+    mask: np.ndarray,\n+    shape: tuple[int, int],\n+) -> sparse.csr_matrix:\n+    \"\"\"Log-count user/item events into CSR format.\"\"\"\n+    matrix = sparse.coo_matrix(\n+        (\n+            np.ones(int(mask.sum()), dtype=np.float32),\n+            (user_rows[mask], item_cols[mask]),\n+        ),\n+        shape=shape,\n+    ).tocsr()\n+    matrix.data = np.log1p(matrix.data)\n+    return matrix\n+\n+\n+def build(stage: str) -> Path:\n+    include_validation = stage == \"test\"\n+    cutoff = TEST_CUTOFF if include_validation else TRAIN_CUTOFF\n+    state_name = \"test_preference_state.parquet\" if include_validation else \"validation_preference_state.parquet\"\n+    output_name = \"submission.parquet\" if include_validation else \"validation.parquet\"\n+\n+    target_uids, item_ids, packed = candidate_archive(stage)\n+    item_index = pd.Index(item_ids)\n+    listens = load_events(\"listens\", include_validation)\n+    all_uids = np.sort(listens.uid.unique())\n+    user_index = pd.Index(all_uids)\n+    user_rows = user_index.get_indexer(listens.uid).astype(np.int32)\n+    item_cols = item_index.get_indexer(listens.item_id).astype(np.int32)\n+    strong = listens.played_ratio_pct.to_numpy() >= 80\n+    recent = listens.timestamp.to_numpy() >= cutoff - WINDOW\n+    in_catalog = item_cols >= 0\n+    shape = (len(all_uids), len(item_ids))\n+\n+    # Candidate evidence: repeated strong plays carry signal, including plays\n+    # initiated on recommendation surfaces. Organic activity separately forms\n+    # the less biased global momentum prior.\n+    all_recent_mask = strong & recent & in_catalog\n+    organic_recent_mask = (\n+        all_recent_mask & (listens.is_organic.to_numpy() == 1)\n+    )\n+    candidate_evidence = interaction_matrix(\n+        user_rows, item_cols, all_recent_mask, shape\n+    )\n+    degree = np.asarray(candidate_evidence.sum(axis=0)).ravel().astype(np.float32)\n+    organic_recent = interaction_matrix(\n+        user_rows, item_cols, organic_recent_mask, shape\n+    )\n+    organic_recent.data[:] = 1.0\n+    organic_global = np.log1p(\n+        np.asarray(organic_recent.sum(axis=0)).ravel().astype(np.float32)\n+    )\n+    if organic_global.max() > 0:\n+        organic_global /= organic_global.max()\n+\n+    # Recent organic likes are a sparse, high-value secondary signal.\n+    likes = load_events(\"likes\", include_validation)\n+    like_mask = (\n+        (likes.is_organic.to_numpy() == 1)\n+        & (likes.timestamp.to_numpy() >= cutoff - WINDOW)\n+        & likes.item_id.isin(item_ids).to_numpy()\n+        & likes.uid.isin(all_uids).to_numpy()\n+    )\n+    recent_likes = likes.loc[like_mask]\n+    like_rows = user_index.get_indexer(recent_likes.uid).astype(np.int32)\n+    like_cols = item_index.get_indexer(recent_likes.item_id).astype(np.int32)\n+    like_evidence = sparse.coo_matrix(\n+        (np.ones(len(recent_likes), np.float32), (like_rows, like_cols)),\n+        shape=shape,\n+    ).tocsr()\n+    like_evidence.data = np.log1p(like_evidence.data)\n+    like_global = np.log1p(\n+        np.asarray((like_evidence > 0).sum(axis=0)).ravel().astype(np.float32)\n+    )\n+    if like_global.max() > 0:\n+        like_global /= like_global.max()\n+    global_score = 0.8 * organic_global + 0.2 * like_global\n+    if global_score.max() > 0:\n+        global_score /= global_score.max()\n+\n+    # User profiles use all tracks, not just retrieval-catalog tracks. This\n+    # preserves rare shared interests that are especially informative for\n+    # nearest-neighbor discovery.\n+    profile_mask = (\n+        strong\n+        & (listens.is_organic.to_numpy() == 1)\n+        & (listens.timestamp.to_numpy() >= cutoff - PROFILE_DAYS * 86_400)\n+    )\n+    profile_events = listens.loc[profile_mask, [\"uid\", \"item_id\"]]\n+    active = pd.read_parquet(DATA / state_name)\n+    active = active[\n+        (active.state == \"liked\") & active.uid.isin(all_uids)\n+    ][[\"uid\", \"item_id\"]]\n+    combined_items = np.concatenate(\n+        [profile_events.item_id.to_numpy(), active.item_id.to_numpy()]\n+    )\n+    item_codes, unique_profile_items = pd.factorize(combined_items, sort=False)\n+    play_codes = item_codes[: len(profile_events)]\n+    like_codes = item_codes[len(profile_events) :]\n+    profile_shape = (len(all_uids), len(unique_profile_items))\n+    plays = sparse.coo_matrix(\n+        (\n+            np.ones(len(profile_events), np.float32),\n+            (user_index.get_indexer(profile_events.uid), play_codes),\n+        ),\n+        shape=profile_shape,\n+    ).tocsr()\n+    plays.data[:] = 1.0\n+    active_likes = sparse.coo_matrix(\n+        (\n+            np.ones(len(active), np.float32),\n+            (user_index.get_indexer(active.uid), like_codes),\n+        ),\n+        shape=profile_shape,\n+    ).tocsr()\n+    active_likes.data[:] = 1.0\n+    profiles = plays + 2.0 * active_likes\n+    document_frequency = np.diff((profiles > 0).tocsc().indptr).astype(np.float32)\n+    inverse_frequency = np.log1p(\n+        len(all_uids) / (1.0 + document_frequency)\n+    ).astype(np.float32)\n+    profiles = profiles.multiply(inverse_frequency).tocsr()\n+    normalize(profiles, norm=\"l2\", axis=1, copy=False)\n+\n+    # Exact cosine neighbors are inexpensive for only ~9k users. Work in\n+    # blocks so the dense similarity scratch array remains small.\n+    target_rows = user_index.get_indexer(target_uids)\n+    neighbor_ids = np.empty((len(target_uids), NEIGHBORS), dtype=np.int32)\n+    similarities = np.empty((len(target_uids), NEIGHBORS), dtype=np.float32)\n+    for start in range(0, len(target_uids), 128):\n+        stop = min(start + 128, len(target_uids))\n+        sim = (profiles[target_rows[start:stop]] @ profiles.T).toarray()\n+        sim[np.arange(stop - start), target_rows[start:stop]] = -1.0\n+        idx = np.argpartition(sim, -NEIGHBORS, axis=1)[:, -NEIGHBORS:]\n+        values = np.take_along_axis(sim, idx, axis=1)\n+        order = np.argsort(values, axis=1)[:, ::-1]\n+        neighbor_ids[start:stop] = np.take_along_axis(idx, order, axis=1)\n+        similarities[start:stop] = np.take_along_axis(values, order, axis=1)\n+\n+    del listens, likes, profiles, plays, active_likes, profile_events, active\n+    gc.collect()\n+\n+    rows: list[tuple[int, int, int]] = []\n+    degree_penalty = np.maximum(degree, 0.1) ** 0.3\n+    for row, uid in enumerate(target_uids):\n+        neighbor = neighbor_ids[row]\n+        weight = np.maximum(similarities[row], 0.0)\n+        score = np.asarray(weight @ candidate_evidence[neighbor]).ravel()\n+        # Calibrate the sparse like component per user before blending.\n+        like_score = np.asarray(weight @ like_evidence[neighbor]).ravel()\n+        if score.max() > 0:\n+            score /= score.max()\n+        if like_score.max() > 0:\n+            like_score /= like_score.max()\n+        score = (score + 0.1 * like_score) / degree_penalty\n+        if score.max() > 0:\n+            score /= score.max()\n+        score += 0.05 * global_score\n+\n+        eligible = np.unpackbits(\n+            packed[row], count=len(item_ids), bitorder=\"little\"\n+        ).astype(bool)\n+        score[~eligible] = -np.inf\n+        top = np.argpartition(score, -100)[-100:]\n+        top = top[np.argsort(score[top], kind=\"stable\")[::-1]]\n+        rows.extend(\n+            (int(uid), int(item_ids[col]), rank)\n+            for rank, col in enumerate(top, 1)\n+        )\n+\n+    result = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n+    temporary = OUT / f\".{output_name}.tmp.parquet\"\n+    destination = OUT / output_name\n+    result.to_parquet(temporary, index=False)\n+    os.replace(temporary, destination)\n+    print(f\"wrote {destination} ({len(result):,} rows)\", flush=True)\n+    return destination\n+\n+\n+def main() -> None:\n+    parser = argparse.ArgumentParser()\n+    parser.add_argument(\n+        \"--stage\", choices=(\"validation\", \"test\", \"both\"), default=\"both\"\n+    )\n+    args = parser.parse_args()\n+    OUT.mkdir(parents=True, exist_ok=True)\n+    if args.stage in (\"validation\", \"both\"):\n+        build(\"validation\")\n+    if args.stage in (\"test\", \"both\"):\n+        build(\"test\")\n+\n+\n+if __name__ == \"__main__\":\n+    main()\n*** Delete File: /app/solution/README.md\n*** Add File: /app/solution/README.md\n+# Made-for-You discovery ranker\n+\n+`build_ranker.py` builds both the public-backtest ranking and final hidden-test\n+submission. It uses exact user-neighborhood collaborative filtering, rare-track\n+weighting, six months of strong organic taste history, active explicit likes,\n+and seven-day momentum. All scoring is performed strictly inside the supplied\n+per-user candidate boundary.\n+\n+Run:\n+\n+```sh\n+OMP_NUM_THREADS=4 OPENBLAS_NUM_THREADS=4 \\\n+  /opt/task-venv/bin/python /app/solution/build_ranker.py\n+```\n+\n+Use `--stage validation` or `--stage test` to build just one cutoff. The final\n+artifact is `submission.parquet` with columns `uid`, `item_id`, and `rank`.\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response 

Script failed
Wall time 0.0 seconds
Output:

Script error:
apply_patch verification failed: invalid patch: multiple operations target /app/solution/build_ranker.py
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120

exec command

+21:43
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const patch = "*** Begin Patch\n*** Delete File: /app/solution/build_ranker.py\n*** Delete File: /app/solution/README.md\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response 

Script completed
Wall time 0.0 seconds
Output:

{}
Harness context codex item completed
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      "/app/solution/README.md": {
        "type": "delete",
        "content": "# Made-for-You ranker\n\n`build_ranker.py` builds the validation and final rankings from the published\nevent history and cutoff-specific eligibility archives. Run with:\n\n```sh\n/opt/task-venv/bin/python /app/solution/build_ranker.py\n```\n\nThe output schema is `uid, item_id, rank`, with exactly 100 cutoff-eligible\nitems per target user.\n"
      },
      "/app/solution/build_ranker.py": {
        "type": "delete",
        "content": "#!/usr/bin/env python3\n\"\"\"Build validation and final Made-for-You rankings.\n\nThe implementation is intentionally self contained and reads only the published\ntask inputs.  Run it with the task virtual environment, for example:\n\n    /opt/task-venv/bin/python solution/build_ranker.py\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\nDATA = Path(\"/task/data\")\nOUT = Path(\"/app/solution\")\n\n\ndef load_candidates(stage: str):\n    with np.load(DATA / f\"{stage}_eligible_candidates.npz\") as z:\n        return z[\"uids\"].copy(), z[\"item_ids\"].copy(), z[\"packed_eligible\"].copy()\n\n\ndef write_global(stage: str, item_order: np.ndarray, path: Path) -> None:\n    \"\"\"Write the first 100 eligible items from a shared item ordering.\"\"\"\n    uids, item_ids, packed = load_candidates(stage)\n    col = {int(item): j for j, item in enumerate(item_ids)}\n    order_cols = np.asarray([col[int(item)] for item in item_order], dtype=np.int32)\n    rows = []\n    for r, uid in enumerate(uids):\n        selected = []\n        for c in order_cols:\n            if (int(packed[r, c // 8]) >> (c % 8)) & 1:\n                selected.append(int(item_ids[c]))\n                if len(selected) == 100:\n                    break\n        rows.extend((int(uid), item, rank) for rank, item in enumerate(selected, 1))\n    pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]).to_parquet(path, index=False)\n\n\ndef main() -> None:\n    OUT.mkdir(exist_ok=True)\n    catalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n    order = catalog.sort_values(\"popularity_rank\").item_id.to_numpy()\n    write_global(\"validation\", order, OUT / \"validation.parquet\")\n    write_global(\"test\", order, OUT / \"submission.parquet\")\n\n\nif __name__ == \"__main__\":\n    main()\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nD /app/solution/build_ranker.py\nD /app/solution/README.md\n",
    "stderr": ""
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121

exec command

+21:46
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const patch = "*** Begin Patch\n*** Add File: /app/solution/build_ranker.py\n+#!/usr/bin/env python3\n+\"\"\"Build the leakage-safe validation and hidden-test rankings.\n+\n+The model is a temporally local, user-neighborhood collaborative filter. User\n+similarity comes from six months of strong organic plays plus active explicit\n+likes. Candidate evidence is restricted to the final seven history days and\n+the supplied eligibility service is applied after scoring.\n+\"\"\"\n+from __future__ import annotations\n+\n+import argparse\n+import gc\n+import os\n+from pathlib import Path\n+\n+import numpy as np\n+import pandas as pd\n+from scipy import sparse\n+from sklearn.preprocessing import normalize\n+\n+\n+DATA = Path(\"/task/data\")\n+OUT = Path(\"/app/solution\")\n+TRAIN_CUTOFF = 24_786_800\n+WINDOW = 7 * 86_400\n+GAP = 1_800\n+TEST_CUTOFF = TRAIN_CUTOFF + GAP + WINDOW\n+PROFILE_DAYS = 180\n+NEIGHBORS = 1_000\n+\n+\n+def load_events(family: str, include_validation: bool) -> pd.DataFrame:\n+    columns = (\n+        [\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"]\n+        if family == \"listens\"\n+        else [\"uid\", \"timestamp\", \"item_id\", \"is_organic\"]\n+    )\n+    frames = [pd.read_parquet(DATA / f\"train_{family}.parquet\", columns=columns)]\n+    if include_validation:\n+        frames.append(pd.read_parquet(DATA / f\"val_{family}.parquet\", columns=columns))\n+    return pd.concat(frames, ignore_index=True) if len(frames) == 2 else frames[0]\n+\n+\n+def candidate_archive(stage: str) -> tuple[np.ndarray, np.ndarray, np.ndarray]:\n+    with np.load(DATA / f\"{stage}_eligible_candidates.npz\") as z:\n+        return z[\"uids\"].copy(), z[\"item_ids\"].copy(), z[\"packed_eligible\"].copy()\n+\n+\n+def interaction_matrix(\n+    user_rows: np.ndarray,\n+    item_cols: np.ndarray,\n+    mask: np.ndarray,\n+    shape: tuple[int, int],\n+) -> sparse.csr_matrix:\n+    \"\"\"Log-count user/item events into CSR format.\"\"\"\n+    matrix = sparse.coo_matrix(\n+        (\n+            np.ones(int(mask.sum()), dtype=np.float32),\n+            (user_rows[mask], item_cols[mask]),\n+        ),\n+        shape=shape,\n+    ).tocsr()\n+    matrix.data = np.log1p(matrix.data)\n+    return matrix\n+\n+\n+def build(stage: str) -> Path:\n+    include_validation = stage == \"test\"\n+    cutoff = TEST_CUTOFF if include_validation else TRAIN_CUTOFF\n+    state_name = (\n+        \"test_preference_state.parquet\"\n+        if include_validation\n+        else \"validation_preference_state.parquet\"\n+    )\n+    output_name = \"submission.parquet\" if include_validation else \"validation.parquet\"\n+\n+    target_uids, item_ids, packed = candidate_archive(stage)\n+    item_index = pd.Index(item_ids)\n+    listens = load_events(\"listens\", include_validation)\n+    all_uids = np.sort(listens.uid.unique())\n+    user_index = pd.Index(all_uids)\n+    user_rows = user_index.get_indexer(listens.uid).astype(np.int32)\n+    item_cols = item_index.get_indexer(listens.item_id).astype(np.int32)\n+    strong = listens.played_ratio_pct.to_numpy() >= 80\n+    recent = listens.timestamp.to_numpy() >= cutoff - WINDOW\n+    in_catalog = item_cols >= 0\n+    shape = (len(all_uids), len(item_ids))\n+\n+    # Repeated strong plays carry signal, including plays initiated on\n+    # recommendation surfaces. Organic activity separately forms the global\n+    # momentum prior.\n+    all_recent_mask = strong & recent & in_catalog\n+    organic_recent_mask = all_recent_mask & (listens.is_organic.to_numpy() == 1)\n+    candidate_evidence = interaction_matrix(\n+        user_rows, item_cols, all_recent_mask, shape\n+    )\n+    degree = np.asarray(candidate_evidence.sum(axis=0)).ravel().astype(np.float32)\n+    organic_recent = interaction_matrix(\n+        user_rows, item_cols, organic_recent_mask, shape\n+    )\n+    organic_recent.data[:] = 1.0\n+    organic_global = np.log1p(\n+        np.asarray(organic_recent.sum(axis=0)).ravel().astype(np.float32)\n+    )\n+    if organic_global.max() > 0:\n+        organic_global /= organic_global.max()\n+\n+    # Recent organic likes are a sparse, high-value secondary signal.\n+    likes = load_events(\"likes\", include_validation)\n+    like_mask = (\n+        (likes.is_organic.to_numpy() == 1)\n+        & (likes.timestamp.to_numpy() >= cutoff - WINDOW)\n+        & likes.item_id.isin(item_ids).to_numpy()\n+        & likes.uid.isin(all_uids).to_numpy()\n+    )\n+    recent_likes = likes.loc[like_mask]\n+    like_rows = user_index.get_indexer(recent_likes.uid).astype(np.int32)\n+    like_cols = item_index.get_indexer(recent_likes.item_id).astype(np.int32)\n+    like_evidence = sparse.coo_matrix(\n+        (np.ones(len(recent_likes), np.float32), (like_rows, like_cols)),\n+        shape=shape,\n+    ).tocsr()\n+    like_evidence.data = np.log1p(like_evidence.data)\n+    like_global = np.log1p(\n+        np.asarray((like_evidence > 0).sum(axis=0)).ravel().astype(np.float32)\n+    )\n+    if like_global.max() > 0:\n+        like_global /= like_global.max()\n+    global_score = 0.8 * organic_global + 0.2 * like_global\n+    if global_score.max() > 0:\n+        global_score /= global_score.max()\n+\n+    # Profiles use all tracks, not just catalog tracks, preserving rare shared\n+    # interests that are especially informative for discovery.\n+    profile_mask = (\n+        strong\n+        & (listens.is_organic.to_numpy() == 1)\n+        & (listens.timestamp.to_numpy() >= cutoff - PROFILE_DAYS * 86_400)\n+    )\n+    profile_events = listens.loc[profile_mask, [\"uid\", \"item_id\"]]\n+    active = pd.read_parquet(DATA / state_name)\n+    active = active[\n+        (active.state == \"liked\") & active.uid.isin(all_uids)\n+    ][[\"uid\", \"item_id\"]]\n+    combined_items = np.concatenate(\n+        [profile_events.item_id.to_numpy(), active.item_id.to_numpy()]\n+    )\n+    item_codes, unique_profile_items = pd.factorize(combined_items, sort=False)\n+    play_codes = item_codes[: len(profile_events)]\n+    like_codes = item_codes[len(profile_events) :]\n+    profile_shape = (len(all_uids), len(unique_profile_items))\n+    plays = sparse.coo_matrix(\n+        (\n+            np.ones(len(profile_events), np.float32),\n+            (user_index.get_indexer(profile_events.uid), play_codes),\n+        ),\n+        shape=profile_shape,\n+    ).tocsr()\n+    plays.data[:] = 1.0\n+    active_likes = sparse.coo_matrix(\n+        (\n+            np.ones(len(active), np.float32),\n+            (user_index.get_indexer(active.uid), like_codes),\n+        ),\n+        shape=profile_shape,\n+    ).tocsr()\n+    active_likes.data[:] = 1.0\n+    profiles = plays + 2.0 * active_likes\n+    document_frequency = np.diff((profiles > 0).tocsc().indptr).astype(np.float32)\n+    inverse_frequency = np.log1p(\n+        len(all_uids) / (1.0 + document_frequency)\n+    ).astype(np.float32)\n+    profiles = profiles.multiply(inverse_frequency).tocsr()\n+    normalize(profiles, norm=\"l2\", axis=1, copy=False)\n+\n+    # Exact cosine neighbors are inexpensive for only ~9k users. Blocking\n+    # bounds the dense similarity scratch array.\n+    target_rows = user_index.get_indexer(target_uids)\n+    neighbor_ids = np.empty((len(target_uids), NEIGHBORS), dtype=np.int32)\n+    similarities = np.empty((len(target_uids), NEIGHBORS), dtype=np.float32)\n+    for start in range(0, len(target_uids), 128):\n+        stop = min(start + 128, len(target_uids))\n+        sim = (profiles[target_rows[start:stop]] @ profiles.T).toarray()\n+        sim[np.arange(stop - start), target_rows[start:stop]] = -1.0\n+        idx = np.argpartition(sim, -NEIGHBORS, axis=1)[:, -NEIGHBORS:]\n+        values = np.take_along_axis(sim, idx, axis=1)\n+        order = np.argsort(values, axis=1)[:, ::-1]\n+        neighbor_ids[start:stop] = np.take_along_axis(idx, order, axis=1)\n+        similarities[start:stop] = np.take_along_axis(values, order, axis=1)\n+\n+    del listens, likes, profiles, plays, active_likes, profile_events, active\n+    gc.collect()\n+\n+    rows: list[tuple[int, int, int]] = []\n+    degree_penalty = np.maximum(degree, 0.1) ** 0.3\n+    for row, uid in enumerate(target_uids):\n+        neighbor = neighbor_ids[row]\n+        weight = np.maximum(similarities[row], 0.0)\n+        score = np.asarray(weight @ candidate_evidence[neighbor]).ravel()\n+        like_score = np.asarray(weight @ like_evidence[neighbor]).ravel()\n+        if score.max() > 0:\n+            score /= score.max()\n+        if like_score.max() > 0:\n+            like_score /= like_score.max()\n+        score = (score + 0.1 * like_score) / degree_penalty\n+        if score.max() > 0:\n+            score /= score.max()\n+        score += 0.05 * global_score\n+\n+        eligible = np.unpackbits(\n+            packed[row], count=len(item_ids), bitorder=\"little\"\n+        ).astype(bool)\n+        score[~eligible] = -np.inf\n+        top = np.argpartition(score, -100)[-100:]\n+        top = top[np.argsort(score[top], kind=\"stable\")[::-1]]\n+        rows.extend(\n+            (int(uid), int(item_ids[col]), rank)\n+            for rank, col in enumerate(top, 1)\n+        )\n+\n+    result = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n+    temporary = OUT / f\".{output_name}.tmp.parquet\"\n+    destination = OUT / output_name\n+    result.to_parquet(temporary, index=False)\n+    os.replace(temporary, destination)\n+    print(f\"wrote {destination} ({len(result):,} rows)\", flush=True)\n+    return destination\n+\n+\n+def main() -> None:\n+    parser = argparse.ArgumentParser()\n+    parser.add_argument(\n+        \"--stage\", choices=(\"validation\", \"test\", \"both\"), default=\"both\"\n+    )\n+    args = parser.parse_args()\n+    OUT.mkdir(parents=True, exist_ok=True)\n+    if args.stage in (\"validation\", \"both\"):\n+        build(\"validation\")\n+    if args.stage in (\"test\", \"both\"):\n+        build(\"test\")\n+\n+\n+if __name__ == \"__main__\":\n+    main()\n*** Add File: /app/solution/README.md\n+# Made-for-You discovery ranker\n+\n+`build_ranker.py` builds both the public-backtest ranking and final hidden-test\n+submission. It uses exact user-neighborhood collaborative filtering, rare-track\n+weighting, six months of strong organic taste history, active explicit likes,\n+and seven-day momentum. All scoring is performed strictly inside the supplied\n+per-user candidate boundary.\n+\n+Run:\n+\n+```sh\n+OMP_NUM_THREADS=4 OPENBLAS_NUM_THREADS=4 \\\n+  /opt/task-venv/bin/python /app/solution/build_ranker.py\n+```\n+\n+Use `--stage validation` or `--stage test` to build just one cutoff. The final\n+artifact is `submission.parquet` with columns `uid`, `item_id`, and `rank`.\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response 

Script completed
Wall time 0.0 seconds
Output:

{}
Harness context codex item completed
{
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  "item": {
    "type": "FileChange",
    "id": "exec-91016f7e-c9b4-4873-bfaa-24bdc571c143",
    "changes": {
      "/app/solution/README.md": {
        "type": "add",
        "content": "# Made-for-You discovery ranker\n\n`build_ranker.py` builds both the public-backtest ranking and final hidden-test\nsubmission. It uses exact user-neighborhood collaborative filtering, rare-track\nweighting, six months of strong organic taste history, active explicit likes,\nand seven-day momentum. All scoring is performed strictly inside the supplied\nper-user candidate boundary.\n\nRun:\n\n```sh\nOMP_NUM_THREADS=4 OPENBLAS_NUM_THREADS=4 \\\n  /opt/task-venv/bin/python /app/solution/build_ranker.py\n```\n\nUse `--stage validation` or `--stage test` to build just one cutoff. The final\nartifact is `submission.parquet` with columns `uid`, `item_id`, and `rank`.\n"
      },
      "/app/solution/build_ranker.py": {
        "type": "add",
        "content": "#!/usr/bin/env python3\n\"\"\"Build the leakage-safe validation and hidden-test rankings.\n\nThe model is a temporally local, user-neighborhood collaborative filter. User\nsimilarity comes from six months of strong organic plays plus active explicit\nlikes. Candidate evidence is restricted to the final seven history days and\nthe supplied eligibility service is applied after scoring.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport gc\nimport os\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.preprocessing import normalize\n\n\nDATA = Path(\"/task/data\")\nOUT = Path(\"/app/solution\")\nTRAIN_CUTOFF = 24_786_800\nWINDOW = 7 * 86_400\nGAP = 1_800\nTEST_CUTOFF = TRAIN_CUTOFF + GAP + WINDOW\nPROFILE_DAYS = 180\nNEIGHBORS = 1_000\n\n\ndef load_events(family: str, include_validation: bool) -> pd.DataFrame:\n    columns = (\n        [\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"]\n        if family == \"listens\"\n        else [\"uid\", \"timestamp\", \"item_id\", \"is_organic\"]\n    )\n    frames = [pd.read_parquet(DATA / f\"train_{family}.parquet\", columns=columns)]\n    if include_validation:\n        frames.append(pd.read_parquet(DATA / f\"val_{family}.parquet\", columns=columns))\n    return pd.concat(frames, ignore_index=True) if len(frames) == 2 else frames[0]\n\n\ndef candidate_archive(stage: str) -> tuple[np.ndarray, np.ndarray, np.ndarray]:\n    with np.load(DATA / f\"{stage}_eligible_candidates.npz\") as z:\n        return z[\"uids\"].copy(), z[\"item_ids\"].copy(), z[\"packed_eligible\"].copy()\n\n\ndef interaction_matrix(\n    user_rows: np.ndarray,\n    item_cols: np.ndarray,\n    mask: np.ndarray,\n    shape: tuple[int, int],\n) -> sparse.csr_matrix:\n    \"\"\"Log-count user/item events into CSR format.\"\"\"\n    matrix = sparse.coo_matrix(\n        (\n            np.ones(int(mask.sum()), dtype=np.float32),\n            (user_rows[mask], item_cols[mask]),\n        ),\n        shape=shape,\n    ).tocsr()\n    matrix.data = np.log1p(matrix.data)\n    return matrix\n\n\ndef build(stage: str) -> Path:\n    include_validation = stage == \"test\"\n    cutoff = TEST_CUTOFF if include_validation else TRAIN_CUTOFF\n    state_name = (\n        \"test_preference_state.parquet\"\n        if include_validation\n        else \"validation_preference_state.parquet\"\n    )\n    output_name = \"submission.parquet\" if include_validation else \"validation.parquet\"\n\n    target_uids, item_ids, packed = candidate_archive(stage)\n    item_index = pd.Index(item_ids)\n    listens = load_events(\"listens\", include_validation)\n    all_uids = np.sort(listens.uid.unique())\n    user_index = pd.Index(all_uids)\n    user_rows = user_index.get_indexer(listens.uid).astype(np.int32)\n    item_cols = item_index.get_indexer(listens.item_id).astype(np.int32)\n    strong = listens.played_ratio_pct.to_numpy() >= 80\n    recent = listens.timestamp.to_numpy() >= cutoff - WINDOW\n    in_catalog = item_cols >= 0\n    shape = (len(all_uids), len(item_ids))\n\n    # Repeated strong plays carry signal, including plays initiated on\n    # recommendation surfaces. Organic activity separately forms the global\n    # momentum prior.\n    all_recent_mask = strong & recent & in_catalog\n    organic_recent_mask = all_recent_mask & (listens.is_organic.to_numpy() == 1)\n    candidate_evidence = interaction_matrix(\n        user_rows, item_cols, all_recent_mask, shape\n    )\n    degree = np.asarray(candidate_evidence.sum(axis=0)).ravel().astype(np.float32)\n    organic_recent = interaction_matrix(\n        user_rows, item_cols, organic_recent_mask, shape\n    )\n    organic_recent.data[:] = 1.0\n    organic_global = np.log1p(\n        np.asarray(organic_recent.sum(axis=0)).ravel().astype(np.float32)\n    )\n    if organic_global.max() > 0:\n        organic_global /= organic_global.max()\n\n    # Recent organic likes are a sparse, high-value secondary signal.\n    likes = load_events(\"likes\", include_validation)\n    like_mask = (\n        (likes.is_organic.to_numpy() == 1)\n        & (likes.timestamp.to_numpy() >= cutoff - WINDOW)\n        & likes.item_id.isin(item_ids).to_numpy()\n        & likes.uid.isin(all_uids).to_numpy()\n    )\n    recent_likes = likes.loc[like_mask]\n    like_rows = user_index.get_indexer(recent_likes.uid).astype(np.int32)\n    like_cols = item_index.get_indexer(recent_likes.item_id).astype(np.int32)\n    like_evidence = sparse.coo_matrix(\n        (np.ones(len(recent_likes), np.float32), (like_rows, like_cols)),\n        shape=shape,\n    ).tocsr()\n    like_evidence.data = np.log1p(like_evidence.data)\n    like_global = np.log1p(\n        np.asarray((like_evidence > 0).sum(axis=0)).ravel().astype(np.float32)\n    )\n    if like_global.max() > 0:\n        like_global /= like_global.max()\n    global_score = 0.8 * organic_global + 0.2 * like_global\n    if global_score.max() > 0:\n        global_score /= global_score.max()\n\n    # Profiles use all tracks, not just catalog tracks, preserving rare shared\n    # interests that are especially informative for discovery.\n    profile_mask = (\n        strong\n        & (listens.is_organic.to_numpy() == 1)\n        & (listens.timestamp.to_numpy() >= cutoff - PROFILE_DAYS * 86_400)\n    )\n    profile_events = listens.loc[profile_mask, [\"uid\", \"item_id\"]]\n    active = pd.read_parquet(DATA / state_name)\n    active = active[\n        (active.state == \"liked\") & active.uid.isin(all_uids)\n    ][[\"uid\", \"item_id\"]]\n    combined_items = np.concatenate(\n        [profile_events.item_id.to_numpy(), active.item_id.to_numpy()]\n    )\n    item_codes, unique_profile_items = pd.factorize(combined_items, sort=False)\n    play_codes = item_codes[: len(profile_events)]\n    like_codes = item_codes[len(profile_events) :]\n    profile_shape = (len(all_uids), len(unique_profile_items))\n    plays = sparse.coo_matrix(\n        (\n            np.ones(len(profile_events), np.float32),\n            (user_index.get_indexer(profile_events.uid), play_codes),\n        ),\n        shape=profile_shape,\n    ).tocsr()\n    plays.data[:] = 1.0\n    active_likes = sparse.coo_matrix(\n        (\n            np.ones(len(active), np.float32),\n            (user_index.get_indexer(active.uid), like_codes),\n        ),\n        shape=profile_shape,\n    ).tocsr()\n    active_likes.data[:] = 1.0\n    profiles = plays + 2.0 * active_likes\n    document_frequency = np.diff((profiles > 0).tocsc().indptr).astype(np.float32)\n    inverse_frequency = np.log1p(\n        len(all_uids) / (1.0 + document_frequency)\n    ).astype(np.float32)\n    profiles = profiles.multiply(inverse_frequency).tocsr()\n    normalize(profiles, norm=\"l2\", axis=1, copy=False)\n\n    # Exact cosine neighbors are inexpensive for only ~9k users. Blocking\n    # bounds the dense similarity scratch array.\n    target_rows = user_index.get_indexer(target_uids)\n    neighbor_ids = np.empty((len(target_uids), NEIGHBORS), dtype=np.int32)\n    similarities = np.empty((len(target_uids), NEIGHBORS), dtype=np.float32)\n    for start in range(0, len(target_uids), 128):\n        stop = min(start + 128, len(target_uids))\n        sim = (profiles[target_rows[start:stop]] @ profiles.T).toarray()\n        sim[np.arange(stop - start), target_rows[start:stop]] = -1.0\n        idx = np.argpartition(sim, -NEIGHBORS, axis=1)[:, -NEIGHBORS:]\n        values = np.take_along_axis(sim, idx, axis=1)\n        order = np.argsort(values, axis=1)[:, ::-1]\n        neighbor_ids[start:stop] = np.take_along_axis(idx, order, axis=1)\n        similarities[start:stop] = np.take_along_axis(values, order, axis=1)\n\n    del listens, likes, profiles, plays, active_likes, profile_events, active\n    gc.collect()\n\n    rows: list[tuple[int, int, int]] = []\n    degree_penalty = np.maximum(degree, 0.1) ** 0.3\n    for row, uid in enumerate(target_uids):\n        neighbor = neighbor_ids[row]\n        weight = np.maximum(similarities[row], 0.0)\n        score = np.asarray(weight @ candidate_evidence[neighbor]).ravel()\n        like_score = np.asarray(weight @ like_evidence[neighbor]).ravel()\n        if score.max() > 0:\n            score /= score.max()\n        if like_score.max() > 0:\n            like_score /= like_score.max()\n        score = (score + 0.1 * like_score) / degree_penalty\n        if score.max() > 0:\n            score /= score.max()\n        score += 0.05 * global_score\n\n        eligible = np.unpackbits(\n            packed[row], count=len(item_ids), bitorder=\"little\"\n        ).astype(bool)\n        score[~eligible] = -np.inf\n        top = np.argpartition(score, -100)[-100:]\n        top = top[np.argsort(score[top], kind=\"stable\")[::-1]]\n        rows.extend(\n            (int(uid), int(item_ids[col]), rank)\n            for rank, col in enumerate(top, 1)\n        )\n\n    result = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n    temporary = OUT / f\".{output_name}.tmp.parquet\"\n    destination = OUT / output_name\n    result.to_parquet(temporary, index=False)\n    os.replace(temporary, destination)\n    print(f\"wrote {destination} ({len(result):,} rows)\", flush=True)\n    return destination\n\n\ndef main() -> None:\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\n        \"--stage\", choices=(\"validation\", \"test\", \"both\"), default=\"both\"\n    )\n    args = parser.parse_args()\n    OUT.mkdir(parents=True, exist_ok=True)\n    if args.stage in (\"validation\", \"both\"):\n        build(\"validation\")\n    if args.stage in (\"test\", \"both\"):\n        build(\"test\")\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": ""
  }
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122

exec command

+22:44
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"OMP_NUM_THREADS=4 OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python -u /app/solution/build_ranker.py --stage validation","workdir":"/app","yield_time_ms":1000,"max_output_tokens":5000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response 

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=1937
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5c064f72487d2bfd2c4f949e991fb",
  "usage": {
    "input_tokens": 74392,
    "cached_input_tokens": 71040,
    "cache_write_input_tokens": 0,
    "output_tokens": 139,
    "reasoning_output_tokens": 44,
    "total_tokens": 74531
  },
  "turn_token_usage": {
    "input_tokens": 4577532,
    "cached_input_tokens": 4478976,
    "cache_write_input_tokens": 0,
    "output_tokens": 41255,
    "reasoning_output_tokens": 13422,
    "total_tokens": 4618787
  },
  "thread_token_usage": {
    "input_tokens": 4577532,
    "cached_input_tokens": 4478976,
    "cache_write_input_tokens": 0,
    "output_tokens": 41255,
    "reasoning_output_tokens": 13422,
    "total_tokens": 4618787
  }
}
123

exec command

+22:48
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":1937,"chars":"","yield_time_ms":10000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 9.3 seconds
Output:

wrote /app/solution/validation.parquet (845,600 rows)
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5c06997b487d28bf2e4cc1dbb64fe",
  "usage": {
    "input_tokens": 74559,
    "cached_input_tokens": 74240,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 74612
  },
  "turn_token_usage": {
    "input_tokens": 4652091,
    "cached_input_tokens": 4553216,
    "cache_write_input_tokens": 0,
    "output_tokens": 41308,
    "reasoning_output_tokens": 13428,
    "total_tokens": 4693399
  },
  "thread_token_usage": {
    "input_tokens": 4652091,
    "cached_input_tokens": 4553216,
    "cache_write_input_tokens": 0,
    "output_tokens": 41308,
    "reasoning_output_tokens": 13428,
    "total_tokens": 4693399
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-643d72c7-cb2d-4d11-aff2-a2e8d6dc9ff3",
    "process_id": "1937",
    "command": [
      "/bin/sh",
      "-lc",
      "OMP_NUM_THREADS=4 OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python -u /app/solution/build_ranker.py --stage validation"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "OMP_NUM_THREADS=4 OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python -u /app/solution/build_ranker.py --stage validation"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "wrote /app/solution/validation.parquet (845,600 rows)\n",
    "stderr": "",
    "aggregated_output": "wrote /app/solution/validation.parquet (845,600 rows)\n",
    "exit_code": 0,
    "duration": {
      "secs": 12,
      "nanos": 324301269
    },
    "formatted_output": "wrote /app/solution/validation.parquet (845,600 rows)\n"
  }
}
124

exec command

+23:00
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet","workdir":"/app","yield_time_ms":1000,"max_output_tokens":3000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response 

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=16155
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5c074e49c87d2859a11ab0d193561",
  "usage": {
    "input_tokens": 74651,
    "cached_input_tokens": 74368,
    "cache_write_input_tokens": 0,
    "output_tokens": 96,
    "reasoning_output_tokens": 12,
    "total_tokens": 74747
  },
  "turn_token_usage": {
    "input_tokens": 4726742,
    "cached_input_tokens": 4627584,
    "cache_write_input_tokens": 0,
    "output_tokens": 41404,
    "reasoning_output_tokens": 13440,
    "total_tokens": 4768146
  },
  "thread_token_usage": {
    "input_tokens": 4726742,
    "cached_input_tokens": 4627584,
    "cache_write_input_tokens": 0,
    "output_tokens": 41404,
    "reasoning_output_tokens": 13440,
    "total_tokens": 4768146
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-305e89fa-7cf2-4334-9ec9-6318c7597c17",
    "process_id": "16155",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.053581,\n    \"quality_users\": 4263\n  }\n}\n",
    "stderr": "",
    "aggregated_output": "{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.053581,\n    \"quality_users\": 4263\n  }\n}\n",
    "exit_code": 0,
    "duration": {
      "secs": 2,
      "nanos": 410552171
    },
    "formatted_output": "{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.053581,\n    \"quality_users\": 4263\n  }\n}\n"
  }
}
125

exec command

+23:06
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":16155,"chars":"","yield_time_ms":10000,"max_output_tokens":3000});
text(r.output);

Tool response 

Script completed
Wall time 0.0 seconds
Output:

{
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.053581,
    "quality_users": 4263
  }
}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5c079294087d288df99e840eb907f",
  "usage": {
    "input_tokens": 74775,
    "cached_input_tokens": 74496,
    "cache_write_input_tokens": 0,
    "output_tokens": 57,
    "reasoning_output_tokens": 10,
    "total_tokens": 74832
  },
  "turn_token_usage": {
    "input_tokens": 4801517,
    "cached_input_tokens": 4702080,
    "cache_write_input_tokens": 0,
    "output_tokens": 41461,
    "reasoning_output_tokens": 13450,
    "total_tokens": 4842978
  },
  "thread_token_usage": {
    "input_tokens": 4801517,
    "cached_input_tokens": 4702080,
    "cache_write_input_tokens": 0,
    "output_tokens": 41461,
    "reasoning_output_tokens": 13450,
    "total_tokens": 4842978
  }
}
126

exec command

+23:11
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"OMP_NUM_THREADS=4 OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python -u /app/solution/build_ranker.py --stage test","workdir":"/app","yield_time_ms":1000,"max_output_tokens":5000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response 

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=83010
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5c07ffff087d29f1352de35b12ff8",
  "usage": {
    "input_tokens": 74900,
    "cached_input_tokens": 74624,
    "cache_write_input_tokens": 0,
    "output_tokens": 106,
    "reasoning_output_tokens": 11,
    "total_tokens": 75006
  },
  "turn_token_usage": {
    "input_tokens": 4876417,
    "cached_input_tokens": 4776704,
    "cache_write_input_tokens": 0,
    "output_tokens": 41567,
    "reasoning_output_tokens": 13461,
    "total_tokens": 4917984
  },
  "thread_token_usage": {
    "input_tokens": 4876417,
    "cached_input_tokens": 4776704,
    "cache_write_input_tokens": 0,
    "output_tokens": 41567,
    "reasoning_output_tokens": 13461,
    "total_tokens": 4917984
  }
}
127

exec command

+23:15
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":83010,"chars":"","yield_time_ms":10000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 9.4 seconds
Output:

wrote /app/solution/submission.parquet (845,600 rows)
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5c084a4a887d290394dc748e37910",
  "usage": {
    "input_tokens": 75034,
    "cached_input_tokens": 74752,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 75087
  },
  "turn_token_usage": {
    "input_tokens": 4951451,
    "cached_input_tokens": 4851456,
    "cache_write_input_tokens": 0,
    "output_tokens": 41620,
    "reasoning_output_tokens": 13467,
    "total_tokens": 4993071
  },
  "thread_token_usage": {
    "input_tokens": 4951451,
    "cached_input_tokens": 4851456,
    "cache_write_input_tokens": 0,
    "output_tokens": 41620,
    "reasoning_output_tokens": 13467,
    "total_tokens": 4993071
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-c10dd46e-5381-47e4-9589-300f15a91d90",
    "process_id": "83010",
    "command": [
      "/bin/sh",
      "-lc",
      "OMP_NUM_THREADS=4 OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python -u /app/solution/build_ranker.py --stage test"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "OMP_NUM_THREADS=4 OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python -u /app/solution/build_ranker.py --stage test"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "wrote /app/solution/submission.parquet (845,600 rows)\n",
    "stderr": "",
    "aggregated_output": "wrote /app/solution/submission.parquet (845,600 rows)\n",
    "exit_code": 0,
    "duration": {
      "secs": 12,
      "nanos": 430413926
    },
    "formatted_output": "wrote /app/solution/submission.parquet (845,600 rows)\n"
  }
}
128

exec command

+23:31
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python - <<'PY'\nimport sys\nfrom pathlib import Path\nimport numpy as np,pandas as pd\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import validate_ranking\nD=Path('/task/data'); f=Path('/app/solution/submission.parquet'); d=pd.read_parquet(f)\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); n=validate_ranking(d,targets)\nwith np.load(D/'test_eligible_candidates.npz') as z: u=z['uids'].copy(); items=z['item_ids'].copy(); packed=z['packed_eligible'].copy()\nui=pd.Series(np.arange(len(u)),index=u.astype(np.int64)); ci=pd.Series(np.arange(len(items)),index=items.astype(np.int64));r=ui.reindex(n.uid).to_numpy();c=ci.reindex(n.item_id).to_numpy();known=~(np.isnan(r)|np.isnan(c));ok=np.zeros(len(n),bool);rr=r[known].astype(int);cc=c[known].astype(int);ok[np.flatnonzero(known)]=((packed[rr,cc//8]>>(cc%8))&1).astype(bool)\nprint({'rows':len(n),'users':n.uid.nunique(),'items':n.item_id.nunique(),'all_known':bool(known.all()),'all_eligible':bool(ok.all()),'bytes':f.stat().st_size})\nprint(n.head(12).to_string(index=False))\nPY","workdir":"/app","yield_time_ms":1000,"max_output_tokens":5000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response 

Script completed
Wall time 1.2 seconds
Output:

{'rows': 845600, 'users': 8456, 'items': 16745, 'all_known': True, 'all_eligible': True, 'bytes': 1717619}
 uid  item_id  rank
 100  8250570     1
 100  4948220     2
 100  3972057     3
 100  5488609     4
 100  6511826     5
 100  7958357     6
 100  8460142     7
 100  2736766     8
 100  8256194     9
 100  2621516    10
 100  9164132    11
 100  6043575    12

SESSION_ID=18317
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5c090055887d2bc975f7dc81140f9",
  "usage": {
    "input_tokens": 75126,
    "cached_input_tokens": 74880,
    "cache_write_input_tokens": 0,
    "output_tokens": 585,
    "reasoning_output_tokens": 193,
    "total_tokens": 75711
  },
  "turn_token_usage": {
    "input_tokens": 5026577,
    "cached_input_tokens": 4926336,
    "cache_write_input_tokens": 0,
    "output_tokens": 42205,
    "reasoning_output_tokens": 13660,
    "total_tokens": 5068782
  },
  "thread_token_usage": {
    "input_tokens": 5026577,
    "cached_input_tokens": 4926336,
    "cache_write_input_tokens": 0,
    "output_tokens": 42205,
    "reasoning_output_tokens": 13660,
    "total_tokens": 5068782
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-2e4b2811-4ba3-437d-8251-437768d19f79",
    "process_id": "18317",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nimport sys\nfrom pathlib import Path\nimport numpy as np,pandas as pd\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import validate_ranking\nD=Path('/task/data'); f=Path('/app/solution/submission.parquet'); d=pd.read_parquet(f)\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); n=validate_ranking(d,targets)\nwith np.load(D/'test_eligible_candidates.npz') as z: u=z['uids'].copy(); items=z['item_ids'].copy(); packed=z['packed_eligible'].copy()\nui=pd.Series(np.arange(len(u)),index=u.astype(np.int64)); ci=pd.Series(np.arange(len(items)),index=items.astype(np.int64));r=ui.reindex(n.uid).to_numpy();c=ci.reindex(n.item_id).to_numpy();known=~(np.isnan(r)|np.isnan(c));ok=np.zeros(len(n),bool);rr=r[known].astype(int);cc=c[known].astype(int);ok[np.flatnonzero(known)]=((packed[rr,cc//8]>>(cc%8))&1).astype(bool)\nprint({'rows':len(n),'users':n.uid.nunique(),'items':n.item_id.nunique(),'all_known':bool(known.all()),'all_eligible':bool(ok.all()),'bytes':f.stat().st_size})\nprint(n.head(12).to_string(index=False))\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport sys\nfrom pathlib import Path\nimport numpy as np,pandas as pd\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import validate_ranking\nD=Path('/task/data'); f=Path('/app/solution/submission.parquet'); d=pd.read_parquet(f)\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)); n=validate_ranking(d,targets)\nwith np.load(D/'test_eligible_candidates.npz') as z: u=z['uids'].copy(); items=z['item_ids'].copy(); packed=z['packed_eligible'].copy()\nui=pd.Series(np.arange(len(u)),index=u.astype(np.int64)); ci=pd.Series(np.arange(len(items)),index=items.astype(np.int64));r=ui.reindex(n.uid).to_numpy();c=ci.reindex(n.item_id).to_numpy();known=~(np.isnan(r)|np.isnan(c));ok=np.zeros(len(n),bool);rr=r[known].astype(int);cc=c[known].astype(int);ok[np.flatnonzero(known)]=((packed[rr,cc//8]>>(cc%8))&1).astype(bool)\nprint({'rows':len(n),'users':n.uid.nunique(),'items':n.item_id.nunique(),'all_known':bool(known.all()),'all_eligible':bool(ok.all()),'bytes':f.stat().st_size})\nprint(n.head(12).to_string(index=False))\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{'rows': 845600, 'users': 8456, 'items': 16745, 'all_known': True, 'all_eligible': True, 'bytes': 1717619}\n uid  item_id  rank\n 100  8250570     1\n 100  4948220     2\n 100  3972057     3\n 100  5488609     4\n 100  6511826     5\n 100  7958357     6\n 100  8460142     7\n 100  2736766     8\n 100  8256194     9\n 100  2621516    10\n 100  9164132    11\n 100  6043575    12\n",
    "stderr": "",
    "aggregated_output": "{'rows': 845600, 'users': 8456, 'items': 16745, 'all_known': True, 'all_eligible': True, 'bytes': 1717619}\n uid  item_id  rank\n 100  8250570     1\n 100  4948220     2\n 100  3972057     3\n 100  5488609     4\n 100  6511826     5\n 100  7958357     6\n 100  8460142     7\n 100  2736766     8\n 100  8256194     9\n 100  2621516    10\n 100  9164132    11\n 100  6043575    12\n",
    "exit_code": 0,
    "duration": {
      "secs": 1,
      "nanos": 46673624
    },
    "formatted_output": "{'rows': 845600, 'users': 8456, 'items': 16745, 'all_known': True, 'all_eligible': True, 'bytes': 1717619}\n uid  item_id  rank\n 100  8250570     1\n 100  4948220     2\n 100  3972057     3\n 100  5488609     4\n 100  6511826     5\n 100  7958357     6\n 100  8460142     7\n 100  2736766     8\n 100  8256194     9\n 100  2621516    10\n 100  9164132    11\n 100  6043575    12\n"
  }
}
129

exec command

+23:40
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":18317,"chars":"","yield_time_ms":1000,"max_output_tokens":1000});
text(r.output);

Tool response 

Script completed
Wall time 0.0 seconds
Output:
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5c09d48d887d2967d9c908bb88351",
  "usage": {
    "input_tokens": 75920,
    "cached_input_tokens": 75008,
    "cache_write_input_tokens": 0,
    "output_tokens": 72,
    "reasoning_output_tokens": 25,
    "total_tokens": 75992
  },
  "turn_token_usage": {
    "input_tokens": 5102497,
    "cached_input_tokens": 5001344,
    "cache_write_input_tokens": 0,
    "output_tokens": 42277,
    "reasoning_output_tokens": 13685,
    "total_tokens": 5144774
  },
  "thread_token_usage": {
    "input_tokens": 5102497,
    "cached_input_tokens": 5001344,
    "cache_write_input_tokens": 0,
    "output_tokens": 42277,
    "reasoning_output_tokens": 13685,
    "total_tokens": 5144774
  }
}
130

Agent message

+23:52
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

131

Agent message

+23:53
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Model text

The final refit is complete. It contains 845,600 rows (100 for each of 8,456 users), every rank is unique and integral, and a vectorized check confirms every submitted track is in that user’s test-time eligibility set. The rebuilt validation artifact reproduces 0.053581 NDCG@10.

132

Agent message

+24:04
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

133

exec command

+24:08
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python -u - <<'PY'\nimport sys\nsys.path.insert(0,'/task');import evaluate_public as e\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nimport pandas as pd,numpy as np\nfrom pathlib import Path\nD=Path('/task/data');cut=24786800\nwith np.load(D/'validation_eligible_candidates.npz') as z:uids=z['uids'].copy();items=z['item_ids'].copy();packed=z['packed_eligible'].copy()\ncs=CandidateSet(D/'validation_eligible_candidates.npz');truth=e.validation_truth(cs,set(map(int,uids)));ci=pd.Index(items)\nd=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']);d=d[(d.played_ratio_pct>=80)&d.item_id.isin(items)];l=pd.read_parquet(D/'train_likes.parquet');l=l[(l.is_organic==1)&l.item_id.isin(items)]\nfor days in [1,2,3,4,5,6,7,9,11,14]:\n q=d[d.timestamp>=cut-days*86400]; a=np.log1p(q.groupby('item_id').size().reindex(items,fill_value=0).to_numpy());o=np.log1p(q[q.is_organic==1].groupby('item_id').uid.nunique().reindex(items,fill_value=0).to_numpy());ll=np.log1p(l[l.timestamp>=cut-days*86400].groupby('item_id').size().reindex(items,fill_value=0).to_numpy());\n for x in [a,o,.8*o/(o.max() or 1)+.2*ll/(ll.max() or 1)]:\n  order=np.argsort(x,kind='stable')[::-1];vals=[]\n  for r,u in enumerate(uids):\n   gold=truth.get(int(u));\n   if gold:\n    top=[]\n    for c in order:\n     if (int(packed[r,c//8])>>(c%8))&1:top.append(int(items[c]))\n     if len(top)==10:break\n    vals.append(ndcg_at_k(top,gold,10))\n  print(days,round(float(np.mean(vals)),6),end=' ')\n print(flush=True)\nPY","workdir":"/app","yield_time_ms":1000,"max_output_tokens":5000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response 

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=20117
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5c09fd70c87d288a53ff6bf96f8f6",
  "usage": {
    "input_tokens": 76015,
    "cached_input_tokens": 75776,
    "cache_write_input_tokens": 0,
    "output_tokens": 1986,
    "reasoning_output_tokens": 1327,
    "total_tokens": 78001
  },
  "turn_token_usage": {
    "input_tokens": 5178512,
    "cached_input_tokens": 5077120,
    "cache_write_input_tokens": 0,
    "output_tokens": 44263,
    "reasoning_output_tokens": 15012,
    "total_tokens": 5222775
  },
  "thread_token_usage": {
    "input_tokens": 5178512,
    "cached_input_tokens": 5077120,
    "cache_write_input_tokens": 0,
    "output_tokens": 44263,
    "reasoning_output_tokens": 15012,
    "total_tokens": 5222775
  }
}
134

exec command

+24:22
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":20117,"chars":"","yield_time_ms":10000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 3.0 seconds
Output:

1 0.017664 1 0.032918 1 0.0341 
2 0.020449 2 0.03263 2 0.033376 
3 0.023152 3 0.031536 3 0.034421 
4 0.024084 4 0.031885 4 0.033707 
5 0.024764 5 0.030694 5 0.03275 
6 0.028513 6 0.030456 6 0.032596 
7 0.030107 7 0.030362 7 0.032434 
9 0.027452 9 0.028511 9 0.032265 
11 0.024846 11 0.027826 11 0.030474 
14 0.022239 14 0.025466 14 0.028664
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5c0c651a087d2af63f241c89ed6c3",
  "usage": {
    "input_tokens": 78029,
    "cached_input_tokens": 75776,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 78082
  },
  "turn_token_usage": {
    "input_tokens": 5256541,
    "cached_input_tokens": 5152896,
    "cache_write_input_tokens": 0,
    "output_tokens": 44316,
    "reasoning_output_tokens": 15018,
    "total_tokens": 5300857
  },
  "thread_token_usage": {
    "input_tokens": 5256541,
    "cached_input_tokens": 5152896,
    "cache_write_input_tokens": 0,
    "output_tokens": 44316,
    "reasoning_output_tokens": 15018,
    "total_tokens": 5300857
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-71e5ef40-5da0-4f58-ac14-7162b43b24a2",
    "process_id": "20117",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python -u - <<'PY'\nimport sys\nsys.path.insert(0,'/task');import evaluate_public as e\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nimport pandas as pd,numpy as np\nfrom pathlib import Path\nD=Path('/task/data');cut=24786800\nwith np.load(D/'validation_eligible_candidates.npz') as z:uids=z['uids'].copy();items=z['item_ids'].copy();packed=z['packed_eligible'].copy()\ncs=CandidateSet(D/'validation_eligible_candidates.npz');truth=e.validation_truth(cs,set(map(int,uids)));ci=pd.Index(items)\nd=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']);d=d[(d.played_ratio_pct>=80)&d.item_id.isin(items)];l=pd.read_parquet(D/'train_likes.parquet');l=l[(l.is_organic==1)&l.item_id.isin(items)]\nfor days in [1,2,3,4,5,6,7,9,11,14]:\n q=d[d.timestamp>=cut-days*86400]; a=np.log1p(q.groupby('item_id').size().reindex(items,fill_value=0).to_numpy());o=np.log1p(q[q.is_organic==1].groupby('item_id').uid.nunique().reindex(items,fill_value=0).to_numpy());ll=np.log1p(l[l.timestamp>=cut-days*86400].groupby('item_id').size().reindex(items,fill_value=0).to_numpy());\n for x in [a,o,.8*o/(o.max() or 1)+.2*ll/(ll.max() or 1)]:\n  order=np.argsort(x,kind='stable')[::-1];vals=[]\n  for r,u in enumerate(uids):\n   gold=truth.get(int(u));\n   if gold:\n    top=[]\n    for c in order:\n     if (int(packed[r,c//8])>>(c%8))&1:top.append(int(items[c]))\n     if len(top)==10:break\n    vals.append(ndcg_at_k(top,gold,10))\n  print(days,round(float(np.mean(vals)),6),end=' ')\n print(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');import evaluate_public as e\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nimport pandas as pd,numpy as np\nfrom pathlib import Path\nD=Path('/task/data');cut=24786800\nwith np.load(D/'validation_eligible_candidates.npz') as z:uids=z['uids'].copy();items=z['item_ids'].copy();packed=z['packed_eligible'].copy()\ncs=CandidateSet(D/'validation_eligible_candidates.npz');truth=e.validation_truth(cs,set(map(int,uids)));ci=pd.Index(items)\nd=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']);d=d[(d.played_ratio_pct>=80)&d.item_id.isin(items)];l=pd.read_parquet(D/'train_likes.parquet');l=l[(l.is_organic==1)&l.item_id.isin(items)]\nfor days in [1,2,3,4,5,6,7,9,11,14]:\n q=d[d.timestamp>=cut-days*86400]; a=np.log1p(q.groupby('item_id').size().reindex(items,fill_value=0).to_numpy());o=np.log1p(q[q.is_organic==1].groupby('item_id').uid.nunique().reindex(items,fill_value=0).to_numpy());ll=np.log1p(l[l.timestamp>=cut-days*86400].groupby('item_id').size().reindex(items,fill_value=0).to_numpy());\n for x in [a,o,.8*o/(o.max() or 1)+.2*ll/(ll.max() or 1)]:\n  order=np.argsort(x,kind='stable')[::-1];vals=[]\n  for r,u in enumerate(uids):\n   gold=truth.get(int(u));\n   if gold:\n    top=[]\n    for c in order:\n     if (int(packed[r,c//8])>>(c%8))&1:top.append(int(items[c]))\n     if len(top)==10:break\n    vals.append(ndcg_at_k(top,gold,10))\n  print(days,round(float(np.mean(vals)),6),end=' ')\n print(flush=True)\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "1 0.017664 1 0.032918 1 0.0341 \n2 0.020449 2 0.03263 2 0.033376 \n3 0.023152 3 0.031536 3 0.034421 \n4 0.024084 4 0.031885 4 0.033707 \n5 0.024764 5 0.030694 5 0.03275 \n6 0.028513 6 0.030456 6 0.032596 \n7 0.030107 7 0.030362 7 0.032434 \n9 0.027452 9 0.028511 9 0.032265 \n11 0.024846 11 0.027826 11 0.030474 \n14 0.022239 14 0.025466 14 0.028664 \n",
    "stderr": "",
    "aggregated_output": "1 0.017664 1 0.032918 1 0.0341 \n2 0.020449 2 0.03263 2 0.033376 \n3 0.023152 3 0.031536 3 0.034421 \n4 0.024084 4 0.031885 4 0.033707 \n5 0.024764 5 0.030694 5 0.03275 \n6 0.028513 6 0.030456 6 0.032596 \n7 0.030107 7 0.030362 7 0.032434 \n9 0.027452 9 0.028511 9 0.032265 \n11 0.024846 11 0.027826 11 0.030474 \n14 0.022239 14 0.025466 14 0.028664 \n",
    "exit_code": 0,
    "duration": {
      "secs": 7,
      "nanos": 455837025
    },
    "formatted_output": "1 0.017664 1 0.032918 1 0.0341 \n2 0.020449 2 0.03263 2 0.033376 \n3 0.023152 3 0.031536 3 0.034421 \n4 0.024084 4 0.031885 4 0.033707 \n5 0.024764 5 0.030694 5 0.03275 \n6 0.028513 6 0.030456 6 0.032596 \n7 0.030107 7 0.030362 7 0.032434 \n9 0.027452 9 0.028511 9 0.032265 \n11 0.024846 11 0.027826 11 0.030474 \n14 0.022239 14 0.025466 14 0.028664 \n"
  }
}
135

Agent message

+24:37
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

136

exec command

+24:45
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const patch = "*** Begin Patch\n*** Update File: /app/solution/build_ranker.py\n@@\n WINDOW = 7 * 86_400\n+GLOBAL_WINDOW = 3 * 86_400\n@@\n-    organic_recent = interaction_matrix(\n-        user_rows, item_cols, organic_recent_mask, shape\n+    organic_momentum_mask = (\n+        strong\n+        & (listens.is_organic.to_numpy() == 1)\n+        & (listens.timestamp.to_numpy() >= cutoff - GLOBAL_WINDOW)\n+        & in_catalog\n+    )\n+    organic_recent = interaction_matrix(\n+        user_rows, item_cols, organic_momentum_mask, shape\n@@\n-    like_global = np.log1p(\n-        np.asarray((like_evidence > 0).sum(axis=0)).ravel().astype(np.float32)\n-    )\n+    global_like_mask = (\n+        (likes.is_organic.to_numpy() == 1)\n+        & (likes.timestamp.to_numpy() >= cutoff - GLOBAL_WINDOW)\n+        & likes.item_id.isin(item_ids).to_numpy()\n+        & likes.uid.isin(all_uids).to_numpy()\n+    )\n+    global_likes = likes.loc[global_like_mask]\n+    global_like_rows = user_index.get_indexer(global_likes.uid).astype(np.int32)\n+    global_like_cols = item_index.get_indexer(global_likes.item_id).astype(np.int32)\n+    global_like_evidence = sparse.coo_matrix(\n+        (\n+            np.ones(len(global_likes), np.float32),\n+            (global_like_rows, global_like_cols),\n+        ),\n+        shape=shape,\n+    ).tocsr()\n+    global_like_evidence.data[:] = 1.0\n+    like_global = np.log1p(\n+        np.asarray(global_like_evidence.sum(axis=0)).ravel().astype(np.float32)\n+    )\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response 

Script completed
Wall time 0.0 seconds
Output:

{}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-dd5abded-c850-4631-8081-7082c6c79141",
    "changes": {
      "/app/solution/build_ranker.py": {
        "type": "update",
        "unified_diff": "@@ -25,2 +25,3 @@\n WINDOW = 7 * 86_400\n+GLOBAL_WINDOW = 3 * 86_400\n GAP = 1_800\n@@ -97,4 +98,10 @@\n     degree = np.asarray(candidate_evidence.sum(axis=0)).ravel().astype(np.float32)\n+    organic_momentum_mask = (\n+        strong\n+        & (listens.is_organic.to_numpy() == 1)\n+        & (listens.timestamp.to_numpy() >= cutoff - GLOBAL_WINDOW)\n+        & in_catalog\n+    )\n     organic_recent = interaction_matrix(\n-        user_rows, item_cols, organic_recent_mask, shape\n+        user_rows, item_cols, organic_momentum_mask, shape\n     )\n@@ -123,4 +130,21 @@\n     like_evidence.data = np.log1p(like_evidence.data)\n+    global_like_mask = (\n+        (likes.is_organic.to_numpy() == 1)\n+        & (likes.timestamp.to_numpy() >= cutoff - GLOBAL_WINDOW)\n+        & likes.item_id.isin(item_ids).to_numpy()\n+        & likes.uid.isin(all_uids).to_numpy()\n+    )\n+    global_likes = likes.loc[global_like_mask]\n+    global_like_rows = user_index.get_indexer(global_likes.uid).astype(np.int32)\n+    global_like_cols = item_index.get_indexer(global_likes.item_id).astype(np.int32)\n+    global_like_evidence = sparse.coo_matrix(\n+        (\n+            np.ones(len(global_likes), np.float32),\n+            (global_like_rows, global_like_cols),\n+        ),\n+        shape=shape,\n+    ).tocsr()\n+    global_like_evidence.data[:] = 1.0\n     like_global = np.log1p(\n-        np.asarray((like_evidence > 0).sum(axis=0)).ravel().astype(np.float32)\n+        np.asarray(global_like_evidence.sum(axis=0)).ravel().astype(np.float32)\n     )\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/build_ranker.py\n",
    "stderr": ""
  }
}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5c0cc807887d2998507ac58e8a893",
  "usage": {
    "input_tokens": 78315,
    "cached_input_tokens": 77824,
    "cache_write_input_tokens": 0,
    "output_tokens": 1418,
    "reasoning_output_tokens": 946,
    "total_tokens": 79733
  },
  "turn_token_usage": {
    "input_tokens": 5334856,
    "cached_input_tokens": 5230720,
    "cache_write_input_tokens": 0,
    "output_tokens": 45734,
    "reasoning_output_tokens": 15964,
    "total_tokens": 5380590
  },
  "thread_token_usage": {
    "input_tokens": 5334856,
    "cached_input_tokens": 5230720,
    "cache_write_input_tokens": 0,
    "output_tokens": 45734,
    "reasoning_output_tokens": 15964,
    "total_tokens": 5380590
  }
}
137

exec command

+24:55
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"OMP_NUM_THREADS=4 OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python -u /app/solution/build_ranker.py --stage validation && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet","workdir":"/app","yield_time_ms":1000,"max_output_tokens":3000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response 

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=69225
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5c0e7ea2887d2bd3bd0391d260483",
  "usage": {
    "input_tokens": 79757,
    "cached_input_tokens": 78080,
    "cache_write_input_tokens": 0,
    "output_tokens": 149,
    "reasoning_output_tokens": 31,
    "total_tokens": 79906
  },
  "turn_token_usage": {
    "input_tokens": 5414613,
    "cached_input_tokens": 5308800,
    "cache_write_input_tokens": 0,
    "output_tokens": 45883,
    "reasoning_output_tokens": 15995,
    "total_tokens": 5460496
  },
  "thread_token_usage": {
    "input_tokens": 5414613,
    "cached_input_tokens": 5308800,
    "cache_write_input_tokens": 0,
    "output_tokens": 45883,
    "reasoning_output_tokens": 15995,
    "total_tokens": 5460496
  }
}
138

exec command

+25:02
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":69225,"chars":"","yield_time_ms":10000,"max_output_tokens":3000});
text(r.output);

Tool response 

Script completed
Wall time 9.7 seconds
Output:

wrote /app/solution/validation.parquet (845,600 rows)
{
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.053455,
    "quality_users": 4263
  }
}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5c0ef30d887d2b0a5ef160f0006b6",
  "usage": {
    "input_tokens": 79934,
    "cached_input_tokens": 79616,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 79987
  },
  "turn_token_usage": {
    "input_tokens": 5494547,
    "cached_input_tokens": 5388416,
    "cache_write_input_tokens": 0,
    "output_tokens": 45936,
    "reasoning_output_tokens": 16001,
    "total_tokens": 5540483
  },
  "thread_token_usage": {
    "input_tokens": 5494547,
    "cached_input_tokens": 5388416,
    "cache_write_input_tokens": 0,
    "output_tokens": 45936,
    "reasoning_output_tokens": 16001,
    "total_tokens": 5540483
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-31ef3a8d-3a10-4ca1-b6aa-16463eda7b73",
    "process_id": "69225",
    "command": [
      "/bin/sh",
      "-lc",
      "OMP_NUM_THREADS=4 OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python -u /app/solution/build_ranker.py --stage validation && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "OMP_NUM_THREADS=4 OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python -u /app/solution/build_ranker.py --stage validation && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "wrote /app/solution/validation.parquet (845,600 rows)\n{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.053455,\n    \"quality_users\": 4263\n  }\n}\n",
    "stderr": "",
    "aggregated_output": "wrote /app/solution/validation.parquet (845,600 rows)\n{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.053455,\n    \"quality_users\": 4263\n  }\n}\n",
    "exit_code": 0,
    "duration": {
      "secs": 15,
      "nanos": 18680112
    },
    "formatted_output": "wrote /app/solution/validation.parquet (845,600 rows)\n{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.053455,\n    \"quality_users\": 4263\n  }\n}\n"
  }
}
139

exec command

+25:19
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const patch = "*** Begin Patch\n*** Update File: /app/solution/build_ranker.py\n@@\n-GLOBAL_WINDOW = 3 * 86_400\n@@\n-    organic_momentum_mask = (\n-        strong\n-        & (listens.is_organic.to_numpy() == 1)\n-        & (listens.timestamp.to_numpy() >= cutoff - GLOBAL_WINDOW)\n-        & in_catalog\n-    )\n     organic_recent = interaction_matrix(\n-        user_rows, item_cols, organic_momentum_mask, shape\n+        user_rows, item_cols, organic_recent_mask, shape\n@@\n-    global_like_mask = (\n-        (likes.is_organic.to_numpy() == 1)\n-        & (likes.timestamp.to_numpy() >= cutoff - GLOBAL_WINDOW)\n-        & likes.item_id.isin(item_ids).to_numpy()\n-        & likes.uid.isin(all_uids).to_numpy()\n-    )\n-    global_likes = likes.loc[global_like_mask]\n-    global_like_rows = user_index.get_indexer(global_likes.uid).astype(np.int32)\n-    global_like_cols = item_index.get_indexer(global_likes.item_id).astype(np.int32)\n-    global_like_evidence = sparse.coo_matrix(\n-        (\n-            np.ones(len(global_likes), np.float32),\n-            (global_like_rows, global_like_cols),\n-        ),\n-        shape=shape,\n-    ).tocsr()\n-    global_like_evidence.data[:] = 1.0\n     like_global = np.log1p(\n-        np.asarray(global_like_evidence.sum(axis=0)).ravel().astype(np.float32)\n+        np.asarray((like_evidence > 0).sum(axis=0)).ravel().astype(np.float32)\n     )\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response 

Script completed
Wall time 0.0 seconds
Output:

{}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-cbdd4f43-3ea3-4b86-b9f0-6ad8ae91b986",
    "changes": {
      "/app/solution/build_ranker.py": {
        "type": "update",
        "unified_diff": "@@ -25,3 +25,2 @@\n WINDOW = 7 * 86_400\n-GLOBAL_WINDOW = 3 * 86_400\n GAP = 1_800\n@@ -98,10 +97,4 @@\n     degree = np.asarray(candidate_evidence.sum(axis=0)).ravel().astype(np.float32)\n-    organic_momentum_mask = (\n-        strong\n-        & (listens.is_organic.to_numpy() == 1)\n-        & (listens.timestamp.to_numpy() >= cutoff - GLOBAL_WINDOW)\n-        & in_catalog\n-    )\n     organic_recent = interaction_matrix(\n-        user_rows, item_cols, organic_momentum_mask, shape\n+        user_rows, item_cols, organic_recent_mask, shape\n     )\n@@ -130,21 +123,4 @@\n     like_evidence.data = np.log1p(like_evidence.data)\n-    global_like_mask = (\n-        (likes.is_organic.to_numpy() == 1)\n-        & (likes.timestamp.to_numpy() >= cutoff - GLOBAL_WINDOW)\n-        & likes.item_id.isin(item_ids).to_numpy()\n-        & likes.uid.isin(all_uids).to_numpy()\n-    )\n-    global_likes = likes.loc[global_like_mask]\n-    global_like_rows = user_index.get_indexer(global_likes.uid).astype(np.int32)\n-    global_like_cols = item_index.get_indexer(global_likes.item_id).astype(np.int32)\n-    global_like_evidence = sparse.coo_matrix(\n-        (\n-            np.ones(len(global_likes), np.float32),\n-            (global_like_rows, global_like_cols),\n-        ),\n-        shape=shape,\n-    ).tocsr()\n-    global_like_evidence.data[:] = 1.0\n     like_global = np.log1p(\n-        np.asarray(global_like_evidence.sum(axis=0)).ravel().astype(np.float32)\n+        np.asarray((like_evidence > 0).sum(axis=0)).ravel().astype(np.float32)\n     )\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/build_ranker.py\n",
    "stderr": ""
  }
}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5c0fb3b5887d2ad1c8e64c773f56f",
  "usage": {
    "input_tokens": 80071,
    "cached_input_tokens": 79744,
    "cache_write_input_tokens": 0,
    "output_tokens": 564,
    "reasoning_output_tokens": 130,
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  },
  "turn_token_usage": {
    "input_tokens": 5574618,
    "cached_input_tokens": 5468160,
    "cache_write_input_tokens": 0,
    "output_tokens": 46500,
    "reasoning_output_tokens": 16131,
    "total_tokens": 5621118
  },
  "thread_token_usage": {
    "input_tokens": 5574618,
    "cached_input_tokens": 5468160,
    "cache_write_input_tokens": 0,
    "output_tokens": 46500,
    "reasoning_output_tokens": 16131,
    "total_tokens": 5621118
  }
}
140

Agent message

+25:37
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

141

exec command

+25:41
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"OMP_NUM_THREADS=4 OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python -u /app/solution/build_ranker.py --stage validation","workdir":"/app","yield_time_ms":1000,"max_output_tokens":2000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response 

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=10050
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5c109087887d28ff6cb51ff13279f",
  "usage": {
    "input_tokens": 80659,
    "cached_input_tokens": 79872,
    "cache_write_input_tokens": 0,
    "output_tokens": 816,
    "reasoning_output_tokens": 721,
    "total_tokens": 81475
  },
  "turn_token_usage": {
    "input_tokens": 5655277,
    "cached_input_tokens": 5548032,
    "cache_write_input_tokens": 0,
    "output_tokens": 47316,
    "reasoning_output_tokens": 16852,
    "total_tokens": 5702593
  },
  "thread_token_usage": {
    "input_tokens": 5655277,
    "cached_input_tokens": 5548032,
    "cache_write_input_tokens": 0,
    "output_tokens": 47316,
    "reasoning_output_tokens": 16852,
    "total_tokens": 5702593
  }
}
142

exec command

+25:45
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":10050,"chars":"","yield_time_ms":10000,"max_output_tokens":2000});
text(r.output);

Tool response 

Script completed
Wall time 9.0 seconds
Output:

wrote /app/solution/validation.parquet (845,600 rows)
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5c119e49487d2b34de99c217e91b9",
  "usage": {
    "input_tokens": 81503,
    "cached_input_tokens": 80512,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 81556
  },
  "turn_token_usage": {
    "input_tokens": 5736780,
    "cached_input_tokens": 5628544,
    "cache_write_input_tokens": 0,
    "output_tokens": 47369,
    "reasoning_output_tokens": 16858,
    "total_tokens": 5784149
  },
  "thread_token_usage": {
    "input_tokens": 5736780,
    "cached_input_tokens": 5628544,
    "cache_write_input_tokens": 0,
    "output_tokens": 47369,
    "reasoning_output_tokens": 16858,
    "total_tokens": 5784149
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-fa03b9f9-42d4-4794-bf74-c91f74029756",
    "process_id": "10050",
    "command": [
      "/bin/sh",
      "-lc",
      "OMP_NUM_THREADS=4 OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python -u /app/solution/build_ranker.py --stage validation"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "OMP_NUM_THREADS=4 OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python -u /app/solution/build_ranker.py --stage validation"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "wrote /app/solution/validation.parquet (845,600 rows)\n",
    "stderr": "",
    "aggregated_output": "wrote /app/solution/validation.parquet (845,600 rows)\n",
    "exit_code": 0,
    "duration": {
      "secs": 12,
      "nanos": 421927055
    },
    "formatted_output": "wrote /app/solution/validation.parquet (845,600 rows)\n"
  }
}
143

exec command

+25:57
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python -u - <<'PY'\nimport pandas as pd,sys\nsys.path.insert(0,'/task');import evaluate_public as e\nfrom ranking_evaluation import *\nfrom pathlib import Path\nfiles={'best':'/app/solution/validation.parquet','q1':'/app/solution/val_likeprof_q1.parquet','o7':'/app/solution/val_k2_b180_o7c.parquet','glob':'/app/solution/val_glike_d7_a02.parquet'};R={}\nfor n,f in files.items():\n d=pd.read_parquet(f).sort_values(['uid','rank']);R[n]={int(u):list(map(int,x.item_id)) for u,x in d.groupby('uid')}\nt=set(R['best']);c=CandidateSet(Path('/task/data/validation_eligible_candidates.npz'));g=e.validation_truth(c,t)\nprint('base',score_rankings(R['best'],g))\nfor other in ['q1','o7','glob']:\n for cc in [0,5,10,25]:\n  for w in [.03,.05,.1,.15,.25]:\n   rr={}\n   for u,a in R['best'].items():\n    s={x:1/(i+1+cc) for i,x in enumerate(a)}\n    for i,x in enumerate(R[other][u]):s[x]=s.get(x,0)+w/(i+1+cc)\n    rr[u]=sorted(s,key=s.get,reverse=True)[:10]\n   print(other,cc,w,score_rankings(rr,g)['quality_ndcg@10'],flush=True)\nPY","workdir":"/app","yield_time_ms":1000,"max_output_tokens":10000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response 

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=1143
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5c125415887d2b104fdfd7d0b1a54",
  "usage": {
    "input_tokens": 81595,
    "cached_input_tokens": 81280,
    "cache_write_input_tokens": 0,
    "output_tokens": 448,
    "reasoning_output_tokens": 18,
    "total_tokens": 82043
  },
  "turn_token_usage": {
    "input_tokens": 5818375,
    "cached_input_tokens": 5709824,
    "cache_write_input_tokens": 0,
    "output_tokens": 47817,
    "reasoning_output_tokens": 16876,
    "total_tokens": 5866192
  },
  "thread_token_usage": {
    "input_tokens": 5818375,
    "cached_input_tokens": 5709824,
    "cache_write_input_tokens": 0,
    "output_tokens": 47817,
    "reasoning_output_tokens": 16876,
    "total_tokens": 5866192
  }
}
144

exec command

+26:07
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":1143,"chars":"","yield_time_ms":10000,"max_output_tokens":10000});
text(r.output);

Tool response 

Script completed
Wall time 10.0 seconds
Output:

base {'quality_ndcg@10': 0.053581, 'quality_users': 4263}
q1 0 0.03 0.053581
q1 0 0.05 0.053591
q1 0 0.1 0.053602
q1 0 0.15 0.053619
q1 0 0.25 0.053573
q1 5 0.03 0.053581
q1 5 0.05 0.05359
q1 5 0.1 0.0536
q1 5 0.15 0.053691
q1 5 0.25 0.053528
q1 10 0.03 0.053581
q1 10 0.05 0.053591
q1 10 0.1 0.05363
q1 10 0.15 0.053688
q1 10 0.25 0.053502
q1 25 0.03 0.053591
q1 25 0.05 0.053613
q1 25 0.1 0.053659
q1 25 0.15 0.0537
q1 25 0.25 0.053526
o7 0 0.03 0.05355
o7 0 0.05 0.053715
o7 0 0.1 0.053658
o7 0 0.15 0.053545
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
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  },
  "thread_token_usage": {
    "input_tokens": 5900446,
    "cached_input_tokens": 5791232,
    "cache_write_input_tokens": 0,
    "output_tokens": 47880,
    "reasoning_output_tokens": 16892,
    "total_tokens": 5948326
  }
}
145

exec command

+26:19
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":1143,"chars":"","yield_time_ms":10000,"max_output_tokens":10000});
text(r.output);

Tool response 

Script completed
Wall time 10.0 seconds
Output:

o7 0 0.25 0.053475
o7 5 0.03 0.053596
o7 5 0.05 0.053564
o7 5 0.1 0.053505
o7 5 0.15 0.053561
o7 5 0.25 0.053299
o7 10 0.03 0.053596
o7 10 0.05 0.053566
o7 10 0.1 0.053441
o7 10 0.15 0.053385
o7 10 0.25 0.053201
o7 25 0.03 0.05354
o7 25 0.05 0.05349
o7 25 0.1 0.05318
o7 25 0.15 0.053312
o7 25 0.25 0.053167
glob 0 0.03 0.053793
glob 0 0.05 0.053801
glob 0 0.1 0.054258
glob 0 0.15 0.054042
glob 0 0.25 0.053925
glob 5 0.03 0.053649
glob 5 0.05 0.05356
glob 5 0.1 0.053561
glob 5 0.15 0.053436
glob 5 0.25 0.053187
Harness context token usage record
{
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  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5c13c79c887d280815a02253d6be1",
  "usage": {
    "input_tokens": 82515,
    "cached_input_tokens": 81920,
    "cache_write_input_tokens": 0,
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    "reasoning_output_tokens": 6,
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  },
  "turn_token_usage": {
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    "total_tokens": 6030894
  },
  "thread_token_usage": {
    "input_tokens": 5982961,
    "cached_input_tokens": 5873152,
    "cache_write_input_tokens": 0,
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    "reasoning_output_tokens": 16898,
    "total_tokens": 6030894
  }
}
146

exec command

+26:31
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":1143,"chars":"","yield_time_ms":10000,"max_output_tokens":5000});
text(r.output);

Tool response 

Script completed
Wall time 2.7 seconds
Output:

glob 10 0.03 0.053581
glob 10 0.05 0.053563
glob 10 0.1 0.053354
glob 10 0.15 0.052971
glob 10 0.25 0.052543
glob 25 0.03 0.053453
glob 25 0.05 0.053114
glob 25 0.1 0.052341
glob 25 0.15 0.05191
glob 25 0.25 0.051224
Harness context token usage record
{
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  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5c1488f8087d29c3a30a90decbf5b",
  "usage": {
    "input_tokens": 82945,
    "cached_input_tokens": 82304,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 82998
  },
  "turn_token_usage": {
    "input_tokens": 6065906,
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    "cache_write_input_tokens": 0,
    "output_tokens": 47986,
    "reasoning_output_tokens": 16904,
    "total_tokens": 6113892
  },
  "thread_token_usage": {
    "input_tokens": 6065906,
    "cached_input_tokens": 5955456,
    "cache_write_input_tokens": 0,
    "output_tokens": 47986,
    "reasoning_output_tokens": 16904,
    "total_tokens": 6113892
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-43975bab-6c89-4a9b-b4aa-647c44ffa2dc",
    "process_id": "1143",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python -u - <<'PY'\nimport pandas as pd,sys\nsys.path.insert(0,'/task');import evaluate_public as e\nfrom ranking_evaluation import *\nfrom pathlib import Path\nfiles={'best':'/app/solution/validation.parquet','q1':'/app/solution/val_likeprof_q1.parquet','o7':'/app/solution/val_k2_b180_o7c.parquet','glob':'/app/solution/val_glike_d7_a02.parquet'};R={}\nfor n,f in files.items():\n d=pd.read_parquet(f).sort_values(['uid','rank']);R[n]={int(u):list(map(int,x.item_id)) for u,x in d.groupby('uid')}\nt=set(R['best']);c=CandidateSet(Path('/task/data/validation_eligible_candidates.npz'));g=e.validation_truth(c,t)\nprint('base',score_rankings(R['best'],g))\nfor other in ['q1','o7','glob']:\n for cc in [0,5,10,25]:\n  for w in [.03,.05,.1,.15,.25]:\n   rr={}\n   for u,a in R['best'].items():\n    s={x:1/(i+1+cc) for i,x in enumerate(a)}\n    for i,x in enumerate(R[other][u]):s[x]=s.get(x,0)+w/(i+1+cc)\n    rr[u]=sorted(s,key=s.get,reverse=True)[:10]\n   print(other,cc,w,score_rankings(rr,g)['quality_ndcg@10'],flush=True)\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport pandas as pd,sys\nsys.path.insert(0,'/task');import evaluate_public as e\nfrom ranking_evaluation import *\nfrom pathlib import Path\nfiles={'best':'/app/solution/validation.parquet','q1':'/app/solution/val_likeprof_q1.parquet','o7':'/app/solution/val_k2_b180_o7c.parquet','glob':'/app/solution/val_glike_d7_a02.parquet'};R={}\nfor n,f in files.items():\n d=pd.read_parquet(f).sort_values(['uid','rank']);R[n]={int(u):list(map(int,x.item_id)) for u,x in d.groupby('uid')}\nt=set(R['best']);c=CandidateSet(Path('/task/data/validation_eligible_candidates.npz'));g=e.validation_truth(c,t)\nprint('base',score_rankings(R['best'],g))\nfor other in ['q1','o7','glob']:\n for cc in [0,5,10,25]:\n  for w in [.03,.05,.1,.15,.25]:\n   rr={}\n   for u,a in R['best'].items():\n    s={x:1/(i+1+cc) for i,x in enumerate(a)}\n    for i,x in enumerate(R[other][u]):s[x]=s.get(x,0)+w/(i+1+cc)\n    rr[u]=sorted(s,key=s.get,reverse=True)[:10]\n   print(other,cc,w,score_rankings(rr,g)['quality_ndcg@10'],flush=True)\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "base {'quality_ndcg@10': 0.053581, 'quality_users': 4263}\nq1 0 0.03 0.053581\nq1 0 0.05 0.053591\nq1 0 0.1 0.053602\nq1 0 0.15 0.053619\nq1 0 0.25 0.053573\nq1 5 0.03 0.053581\nq1 5 0.05 0.05359\nq1 5 0.1 0.0536\nq1 5 0.15 0.053691\nq1 5 0.25 0.053528\nq1 10 0.03 0.053581\nq1 10 0.05 0.053591\nq1 10 0.1 0.05363\nq1 10 0.15 0.053688\nq1 10 0.25 0.053502\nq1 25 0.03 0.053591\nq1 25 0.05 0.053613\nq1 25 0.1 0.053659\nq1 25 0.15 0.0537\nq1 25 0.25 0.053526\no7 0 0.03 0.05355\no7 0 0.05 0.053715\no7 0 0.1 0.053658\no7 0 0.15 0.053545\no7 0 0.25 0.053475\no7 5 0.03 0.053596\no7 5 0.05 0.053564\no7 5 0.1 0.053505\no7 5 0.15 0.053561\no7 5 0.25 0.053299\no7 10 0.03 0.053596\no7 10 0.05 0.053566\no7 10 0.1 0.053441\no7 10 0.15 0.053385\no7 10 0.25 0.053201\no7 25 0.03 0.05354\no7 25 0.05 0.05349\no7 25 0.1 0.05318\no7 25 0.15 0.053312\no7 25 0.25 0.053167\nglob 0 0.03 0.053793\nglob 0 0.05 0.053801\nglob 0 0.1 0.054258\nglob 0 0.15 0.054042\nglob 0 0.25 0.053925\nglob 5 0.03 0.053649\nglob 5 0.05 0.05356\nglob 5 0.1 0.053561\nglob 5 0.15 0.053436\nglob 5 0.25 0.053187\nglob 10 0.03 0.053581\nglob 10 0.05 0.053563\nglob 10 0.1 0.053354\nglob 10 0.15 0.052971\nglob 10 0.25 0.052543\nglob 25 0.03 0.053453\nglob 25 0.05 0.053114\nglob 25 0.1 0.052341\nglob 25 0.15 0.05191\nglob 25 0.25 0.051224\n",
    "stderr": "",
    "aggregated_output": "base {'quality_ndcg@10': 0.053581, 'quality_users': 4263}\nq1 0 0.03 0.053581\nq1 0 0.05 0.053591\nq1 0 0.1 0.053602\nq1 0 0.15 0.053619\nq1 0 0.25 0.053573\nq1 5 0.03 0.053581\nq1 5 0.05 0.05359\nq1 5 0.1 0.0536\nq1 5 0.15 0.053691\nq1 5 0.25 0.053528\nq1 10 0.03 0.053581\nq1 10 0.05 0.053591\nq1 10 0.1 0.05363\nq1 10 0.15 0.053688\nq1 10 0.25 0.053502\nq1 25 0.03 0.053591\nq1 25 0.05 0.053613\nq1 25 0.1 0.053659\nq1 25 0.15 0.0537\nq1 25 0.25 0.053526\no7 0 0.03 0.05355\no7 0 0.05 0.053715\no7 0 0.1 0.053658\no7 0 0.15 0.053545\no7 0 0.25 0.053475\no7 5 0.03 0.053596\no7 5 0.05 0.053564\no7 5 0.1 0.053505\no7 5 0.15 0.053561\no7 5 0.25 0.053299\no7 10 0.03 0.053596\no7 10 0.05 0.053566\no7 10 0.1 0.053441\no7 10 0.15 0.053385\no7 10 0.25 0.053201\no7 25 0.03 0.05354\no7 25 0.05 0.05349\no7 25 0.1 0.05318\no7 25 0.15 0.053312\no7 25 0.25 0.053167\nglob 0 0.03 0.053793\nglob 0 0.05 0.053801\nglob 0 0.1 0.054258\nglob 0 0.15 0.054042\nglob 0 0.25 0.053925\nglob 5 0.03 0.053649\nglob 5 0.05 0.05356\nglob 5 0.1 0.053561\nglob 5 0.15 0.053436\nglob 5 0.25 0.053187\nglob 10 0.03 0.053581\nglob 10 0.05 0.053563\nglob 10 0.1 0.053354\nglob 10 0.15 0.052971\nglob 10 0.25 0.052543\nglob 25 0.03 0.053453\nglob 25 0.05 0.053114\nglob 25 0.1 0.052341\nglob 25 0.15 0.05191\nglob 25 0.25 0.051224\n",
    "exit_code": 0,
    "duration": {
      "secs": 30,
      "nanos": 136598853
    },
    "formatted_output": "base {'quality_ndcg@10': 0.053581, 'quality_users': 4263}\nq1 0 0.03 0.053581\nq1 0 0.05 0.053591\nq1 0 0.1 0.053602\nq1 0 0.15 0.053619\nq1 0 0.25 0.053573\nq1 5 0.03 0.053581\nq1 5 0.05 0.05359\nq1 5 0.1 0.0536\nq1 5 0.15 0.053691\nq1 5 0.25 0.053528\nq1 10 0.03 0.053581\nq1 10 0.05 0.053591\nq1 10 0.1 0.05363\nq1 10 0.15 0.053688\nq1 10 0.25 0.053502\nq1 25 0.03 0.053591\nq1 25 0.05 0.053613\nq1 25 0.1 0.053659\nq1 25 0.15 0.0537\nq1 25 0.25 0.053526\no7 0 0.03 0.05355\no7 0 0.05 0.053715\no7 0 0.1 0.053658\no7 0 0.15 0.053545\no7 0 0.25 0.053475\no7 5 0.03 0.053596\no7 5 0.05 0.053564\no7 5 0.1 0.053505\no7 5 0.15 0.053561\no7 5 0.25 0.053299\no7 10 0.03 0.053596\no7 10 0.05 0.053566\no7 10 0.1 0.053441\no7 10 0.15 0.053385\no7 10 0.25 0.053201\no7 25 0.03 0.05354\no7 25 0.05 0.05349\no7 25 0.1 0.05318\no7 25 0.15 0.053312\no7 25 0.25 0.053167\nglob 0 0.03 0.053793\nglob 0 0.05 0.053801\nglob 0 0.1 0.054258\nglob 0 0.15 0.054042\nglob 0 0.25 0.053925\nglob 5 0.03 0.053649\nglob 5 0.05 0.05356\nglob 5 0.1 0.053561\nglob 5 0.15 0.053436\nglob 5 0.25 0.053187\nglob 10 0.03 0.053581\nglob 10 0.05 0.053563\nglob 10 0.1 0.053354\nglob 10 0.15 0.052971\nglob 10 0.25 0.052543\nglob 25 0.03 0.053453\nglob 25 0.05 0.053114\nglob 25 0.1 0.052341\nglob 25 0.15 0.05191\nglob 25 0.25 0.051224\n"
  }
}
147

Agent message

+26:45
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

148

exec command

+26:53
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Model text

A final rank-level diversity check found a small but consistent gain from fusing the personalized list with the independently ranked seven-day momentum list. I’m applying that conservative blend (it raises public NDCG from 0.053581 to roughly 0.0543) and rebuilding the test artifact once more.

Tool callexecTool call Returned

Tool call · Input 

const patch = "*** Begin Patch\n*** Update File: /app/solution/build_ranker.py\n@@\n     rows: list[tuple[int, int, int]] = []\n     degree_penalty = np.maximum(degree, 0.1) ** 0.3\n+    global_order = np.argsort(global_score, kind=\"stable\")[::-1]\n     for row, uid in enumerate(target_uids):\n@@\n-        top = np.argpartition(score, -100)[-100:]\n-        top = top[np.argsort(score[top], kind=\"stable\")[::-1]]\n+        model_top = np.argpartition(score, -100)[-100:]\n+        model_top = model_top[\n+            np.argsort(score[model_top], kind=\"stable\")[::-1]\n+        ]\n+\n+        # A light reciprocal-rank fusion with the independent momentum list\n+        # improves robustness for brand-new globally accelerating tracks.\n+        global_top: list[int] = []\n+        for col in global_order:\n+            if eligible[col]:\n+                global_top.append(int(col))\n+                if len(global_top) == 100:\n+                    break\n+        fused = {\n+            int(col): 1.0 / rank\n+            for rank, col in enumerate(model_top, 1)\n+        }\n+        for rank, col in enumerate(global_top, 1):\n+            fused[col] = fused.get(col, 0.0) + 0.1 / rank\n+        top = sorted(fused, key=fused.get, reverse=True)[:100]\n         rows.extend(\n             (int(uid), int(item_ids[col]), rank)\n             for rank, col in enumerate(top, 1)\n*** Update File: /app/solution/README.md\n@@\n-weighting, six months of strong organic taste history, active explicit likes,\n-and seven-day momentum. All scoring is performed strictly inside the supplied\n-per-user candidate boundary.\n+weighting, six months of strong organic taste history, active explicit likes,\n+seven-day momentum, and a light reciprocal-rank fusion with the independent\n+momentum list. All scoring is performed strictly inside the supplied per-user\n+candidate boundary.\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response 

Script completed
Wall time 0.0 seconds
Output:

{}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-5fed2ca8-e769-43d9-a6e8-41d9406e4d79",
    "changes": {
      "/app/solution/build_ranker.py": {
        "type": "update",
        "unified_diff": "@@ -195,2 +195,3 @@\n     degree_penalty = np.maximum(degree, 0.1) ** 0.3\n+    global_order = np.argsort(global_score, kind=\"stable\")[::-1]\n     for row, uid in enumerate(target_uids):\n@@ -213,4 +214,22 @@\n         score[~eligible] = -np.inf\n-        top = np.argpartition(score, -100)[-100:]\n-        top = top[np.argsort(score[top], kind=\"stable\")[::-1]]\n+        model_top = np.argpartition(score, -100)[-100:]\n+        model_top = model_top[\n+            np.argsort(score[model_top], kind=\"stable\")[::-1]\n+        ]\n+\n+        # A light reciprocal-rank fusion with the independent momentum list\n+        # improves robustness for brand-new globally accelerating tracks.\n+        global_top: list[int] = []\n+        for col in global_order:\n+            if eligible[col]:\n+                global_top.append(int(col))\n+                if len(global_top) == 100:\n+                    break\n+        fused = {\n+            int(col): 1.0 / rank\n+            for rank, col in enumerate(model_top, 1)\n+        }\n+        for rank, col in enumerate(global_top, 1):\n+            fused[col] = fused.get(col, 0.0) + 0.1 / rank\n+        top = sorted(fused, key=fused.get, reverse=True)[:100]\n         rows.extend(\n",
        "move_path": null
      },
      "/app/solution/README.md": {
        "type": "update",
        "unified_diff": "@@ -5,4 +5,5 @@\n weighting, six months of strong organic taste history, active explicit likes,\n-and seven-day momentum. All scoring is performed strictly inside the supplied\n-per-user candidate boundary.\n+seven-day momentum, and a light reciprocal-rank fusion with the independent\n+momentum list. All scoring is performed strictly inside the supplied per-user\n+candidate boundary.\n \n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/build_ranker.py\nM /app/solution/README.md\n",
    "stderr": ""
  }
}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5c14d3e1087d2a875593782bc202d",
  "usage": {
    "input_tokens": 83151,
    "cached_input_tokens": 82816,
    "cache_write_input_tokens": 0,
    "output_tokens": 1573,
    "reasoning_output_tokens": 978,
    "total_tokens": 84724
  },
  "turn_token_usage": {
    "input_tokens": 6149057,
    "cached_input_tokens": 6038272,
    "cache_write_input_tokens": 0,
    "output_tokens": 49559,
    "reasoning_output_tokens": 17882,
    "total_tokens": 6198616
  },
  "thread_token_usage": {
    "input_tokens": 6149057,
    "cached_input_tokens": 6038272,
    "cache_write_input_tokens": 0,
    "output_tokens": 49559,
    "reasoning_output_tokens": 17882,
    "total_tokens": 6198616
  }
}
149

exec command

+27:06
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"OMP_NUM_THREADS=4 OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python -u /app/solution/build_ranker.py --stage validation && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet","workdir":"/app","yield_time_ms":1000,"max_output_tokens":3000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response 

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=82842
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5c16ae89c87d2a4aa9e6e78aefdbb",
  "usage": {
    "input_tokens": 84748,
    "cached_input_tokens": 82944,
    "cache_write_input_tokens": 0,
    "output_tokens": 133,
    "reasoning_output_tokens": 15,
    "total_tokens": 84881
  },
  "turn_token_usage": {
    "input_tokens": 6233805,
    "cached_input_tokens": 6121216,
    "cache_write_input_tokens": 0,
    "output_tokens": 49692,
    "reasoning_output_tokens": 17897,
    "total_tokens": 6283497
  },
  "thread_token_usage": {
    "input_tokens": 6233805,
    "cached_input_tokens": 6121216,
    "cache_write_input_tokens": 0,
    "output_tokens": 49692,
    "reasoning_output_tokens": 17897,
    "total_tokens": 6283497
  }
}
150

exec command

+27:11
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":82842,"chars":"","yield_time_ms":10000,"max_output_tokens":3000});
text(r.output);

Tool response 

Script completed
Wall time 10.0 seconds
Output:

wrote /app/solution/validation.parquet (845,600 rows)
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5c16f762c87d2b42ad672d88122e1",
  "usage": {
    "input_tokens": 84909,
    "cached_input_tokens": 84608,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 84962
  },
  "turn_token_usage": {
    "input_tokens": 6318714,
    "cached_input_tokens": 6205824,
    "cache_write_input_tokens": 0,
    "output_tokens": 49745,
    "reasoning_output_tokens": 17903,
    "total_tokens": 6368459
  },
  "thread_token_usage": {
    "input_tokens": 6318714,
    "cached_input_tokens": 6205824,
    "cache_write_input_tokens": 0,
    "output_tokens": 49745,
    "reasoning_output_tokens": 17903,
    "total_tokens": 6368459
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-07bd2ae2-ea68-4cd7-a1e3-04f547b92b8a",
    "process_id": "82842",
    "command": [
      "/bin/sh",
      "-lc",
      "OMP_NUM_THREADS=4 OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python -u /app/solution/build_ranker.py --stage validation && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "OMP_NUM_THREADS=4 OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python -u /app/solution/build_ranker.py --stage validation && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "wrote /app/solution/validation.parquet (845,600 rows)\n{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.054285,\n    \"quality_users\": 4263\n  }\n}\n",
    "stderr": "",
    "aggregated_output": "wrote /app/solution/validation.parquet (845,600 rows)\n{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.054285,\n    \"quality_users\": 4263\n  }\n}\n",
    "exit_code": 0,
    "duration": {
      "secs": 15,
      "nanos": 612686878
    },
    "formatted_output": "wrote /app/solution/validation.parquet (845,600 rows)\n{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.054285,\n    \"quality_users\": 4263\n  }\n}\n"
  }
}
151

exec command

+27:27
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":82842,"chars":"","yield_time_ms":10000,"max_output_tokens":3000});
text(r.output);

Tool response 

Script completed
Wall time 0.0 seconds
Output:

{
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.054285,
    "quality_users": 4263
  }
}
Harness context token usage record
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  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5c17cb9e487d2b7034bdab4bf8ef4",
  "usage": {
    "input_tokens": 85001,
    "cached_input_tokens": 84736,
    "cache_write_input_tokens": 0,
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    "reasoning_output_tokens": 13,
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  },
  "turn_token_usage": {
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    "cache_write_input_tokens": 0,
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  },
  "thread_token_usage": {
    "input_tokens": 6403715,
    "cached_input_tokens": 6290560,
    "cache_write_input_tokens": 0,
    "output_tokens": 49805,
    "reasoning_output_tokens": 17916,
    "total_tokens": 6453520
  }
}
152

exec command

+27:28
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"OMP_NUM_THREADS=4 OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python -u /app/solution/build_ranker.py --stage test","workdir":"/app","yield_time_ms":1000,"max_output_tokens":3000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response 

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=81952
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5c181a98c87d2a0402f0ca35f5057",
  "usage": {
    "input_tokens": 85129,
    "cached_input_tokens": 84864,
    "cache_write_input_tokens": 0,
    "output_tokens": 112,
    "reasoning_output_tokens": 17,
    "total_tokens": 85241
  },
  "turn_token_usage": {
    "input_tokens": 6488844,
    "cached_input_tokens": 6375424,
    "cache_write_input_tokens": 0,
    "output_tokens": 49917,
    "reasoning_output_tokens": 17933,
    "total_tokens": 6538761
  },
  "thread_token_usage": {
    "input_tokens": 6488844,
    "cached_input_tokens": 6375424,
    "cache_write_input_tokens": 0,
    "output_tokens": 49917,
    "reasoning_output_tokens": 17933,
    "total_tokens": 6538761
  }
}
153

exec command

+27:35
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":81952,"chars":"","yield_time_ms":10000,"max_output_tokens":3000});
text(r.output);

Tool response 

Script completed
Wall time 8.1 seconds
Output:

wrote /app/solution/submission.parquet (845,600 rows)
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5c185c12487d289b40819ccffb1cf",
  "usage": {
    "input_tokens": 85269,
    "cached_input_tokens": 84992,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 85322
  },
  "turn_token_usage": {
    "input_tokens": 6574113,
    "cached_input_tokens": 6460416,
    "cache_write_input_tokens": 0,
    "output_tokens": 49970,
    "reasoning_output_tokens": 17939,
    "total_tokens": 6624083
  },
  "thread_token_usage": {
    "input_tokens": 6574113,
    "cached_input_tokens": 6460416,
    "cache_write_input_tokens": 0,
    "output_tokens": 49970,
    "reasoning_output_tokens": 17939,
    "total_tokens": 6624083
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-4cb78d36-06c1-4f5b-94a7-ebc54fa80518",
    "process_id": "81952",
    "command": [
      "/bin/sh",
      "-lc",
      "OMP_NUM_THREADS=4 OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python -u /app/solution/build_ranker.py --stage test"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "OMP_NUM_THREADS=4 OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python -u /app/solution/build_ranker.py --stage test"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "wrote /app/solution/submission.parquet (845,600 rows)\n",
    "stderr": "",
    "aggregated_output": "wrote /app/solution/submission.parquet (845,600 rows)\n",
    "exit_code": 0,
    "duration": {
      "secs": 13,
      "nanos": 295415990
    },
    "formatted_output": "wrote /app/solution/submission.parquet (845,600 rows)\n"
  }
}
154

exec command

+27:45
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.exec_command({"cmd":"/opt/task-venv/bin/python - <<'PY'\nimport sys\nfrom pathlib import Path\nimport numpy as np,pandas as pd\nsys.path.insert(0,'/task');from ranking_evaluation import validate_ranking\nD=Path('/task/data');F=Path('/app/solution/submission.parquet');d=validate_ranking(pd.read_parquet(F),set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)))\nwith np.load(D/'test_eligible_candidates.npz') as z:u=z['uids'].copy();it=z['item_ids'].copy();p=z['packed_eligible'].copy()\nui=pd.Series(np.arange(len(u)),index=u.astype('int64'));ii=pd.Series(np.arange(len(it)),index=it.astype('int64'));r=ui.reindex(d.uid).to_numpy();c=ii.reindex(d.item_id).to_numpy();known=~(np.isnan(r)|np.isnan(c));ok=np.zeros(len(d),bool);rr=r[known].astype(int);cc=c[known].astype(int);ok[np.flatnonzero(known)]=((p[rr,cc//8]>>(cc%8))&1).astype(bool)\nprint(f'rows={len(d)} users={d.uid.nunique()} all_known={known.all()} all_eligible={ok.all()} size={F.stat().st_size}')\nPY\nsha256sum /app/solution/submission.parquet /app/solution/build_ranker.py\nls -lh /app/solution/submission.parquet /app/solution/validation.parquet /app/solution/build_ranker.py /app/solution/README.md","workdir":"/app","yield_time_ms":1000,"max_output_tokens":3000});
text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response 

Script completed
Wall time 1.2 seconds
Output:


SESSION_ID=32686
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5c1920d5487d29526ce5f09af2151",
  "usage": {
    "input_tokens": 85361,
    "cached_input_tokens": 85120,
    "cache_write_input_tokens": 0,
    "output_tokens": 420,
    "reasoning_output_tokens": 8,
    "total_tokens": 85781
  },
  "turn_token_usage": {
    "input_tokens": 6659474,
    "cached_input_tokens": 6545536,
    "cache_write_input_tokens": 0,
    "output_tokens": 50390,
    "reasoning_output_tokens": 17947,
    "total_tokens": 6709864
  },
  "thread_token_usage": {
    "input_tokens": 6659474,
    "cached_input_tokens": 6545536,
    "cache_write_input_tokens": 0,
    "output_tokens": 50390,
    "reasoning_output_tokens": 17947,
    "total_tokens": 6709864
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-acb5909c-c7c7-4263-ac92-854e749ae2cb",
    "process_id": "32686",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nimport sys\nfrom pathlib import Path\nimport numpy as np,pandas as pd\nsys.path.insert(0,'/task');from ranking_evaluation import validate_ranking\nD=Path('/task/data');F=Path('/app/solution/submission.parquet');d=validate_ranking(pd.read_parquet(F),set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)))\nwith np.load(D/'test_eligible_candidates.npz') as z:u=z['uids'].copy();it=z['item_ids'].copy();p=z['packed_eligible'].copy()\nui=pd.Series(np.arange(len(u)),index=u.astype('int64'));ii=pd.Series(np.arange(len(it)),index=it.astype('int64'));r=ui.reindex(d.uid).to_numpy();c=ii.reindex(d.item_id).to_numpy();known=~(np.isnan(r)|np.isnan(c));ok=np.zeros(len(d),bool);rr=r[known].astype(int);cc=c[known].astype(int);ok[np.flatnonzero(known)]=((p[rr,cc//8]>>(cc%8))&1).astype(bool)\nprint(f'rows={len(d)} users={d.uid.nunique()} all_known={known.all()} all_eligible={ok.all()} size={F.stat().st_size}')\nPY\nsha256sum /app/solution/submission.parquet /app/solution/build_ranker.py\nls -lh /app/solution/submission.parquet /app/solution/validation.parquet /app/solution/build_ranker.py /app/solution/README.md"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport sys\nfrom pathlib import Path\nimport numpy as np,pandas as pd\nsys.path.insert(0,'/task');from ranking_evaluation import validate_ranking\nD=Path('/task/data');F=Path('/app/solution/submission.parquet');d=validate_ranking(pd.read_parquet(F),set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)))\nwith np.load(D/'test_eligible_candidates.npz') as z:u=z['uids'].copy();it=z['item_ids'].copy();p=z['packed_eligible'].copy()\nui=pd.Series(np.arange(len(u)),index=u.astype('int64'));ii=pd.Series(np.arange(len(it)),index=it.astype('int64'));r=ui.reindex(d.uid).to_numpy();c=ii.reindex(d.item_id).to_numpy();known=~(np.isnan(r)|np.isnan(c));ok=np.zeros(len(d),bool);rr=r[known].astype(int);cc=c[known].astype(int);ok[np.flatnonzero(known)]=((p[rr,cc//8]>>(cc%8))&1).astype(bool)\nprint(f'rows={len(d)} users={d.uid.nunique()} all_known={known.all()} all_eligible={ok.all()} size={F.stat().st_size}')\nPY\nsha256sum /app/solution/submission.parquet /app/solution/build_ranker.py\nls -lh /app/solution/submission.parquet /app/solution/validation.parquet /app/solution/build_ranker.py /app/solution/README.md"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "rows=845600 users=8456 all_known=True all_eligible=True size=1679550\ne11585378c7679c3c8be38a59729db9cf5c1e85cad4c666ef13baf0bfd0df36e  /app/solution/submission.parquet\ncbb8828d6c5ccf994d30363e8af5afe3c99b2f7e33410d66e8f641b3402eec47  /app/solution/build_ranker.py\n-rw-r--r-- 1 agent agent  719 Sep 25 00:33 /app/solution/README.md\n-rw-r--r-- 1 agent agent 9.9K Sep 25 00:33 /app/solution/build_ranker.py\n-rw-r--r-- 1 agent agent 1.7M Sep 25 00:34 /app/solution/submission.parquet\n-rw-r--r-- 1 agent agent 1.7M Sep 25 00:34 /app/solution/validation.parquet\n",
    "stderr": "",
    "aggregated_output": "rows=845600 users=8456 all_known=True all_eligible=True size=1679550\ne11585378c7679c3c8be38a59729db9cf5c1e85cad4c666ef13baf0bfd0df36e  /app/solution/submission.parquet\ncbb8828d6c5ccf994d30363e8af5afe3c99b2f7e33410d66e8f641b3402eec47  /app/solution/build_ranker.py\n-rw-r--r-- 1 agent agent  719 Sep 25 00:33 /app/solution/README.md\n-rw-r--r-- 1 agent agent 9.9K Sep 25 00:33 /app/solution/build_ranker.py\n-rw-r--r-- 1 agent agent 1.7M Sep 25 00:34 /app/solution/submission.parquet\n-rw-r--r-- 1 agent agent 1.7M Sep 25 00:34 /app/solution/validation.parquet\n",
    "exit_code": 0,
    "duration": {
      "secs": 1,
      "nanos": 74508658
    },
    "formatted_output": "rows=845600 users=8456 all_known=True all_eligible=True size=1679550\ne11585378c7679c3c8be38a59729db9cf5c1e85cad4c666ef13baf0bfd0df36e  /app/solution/submission.parquet\ncbb8828d6c5ccf994d30363e8af5afe3c99b2f7e33410d66e8f641b3402eec47  /app/solution/build_ranker.py\n-rw-r--r-- 1 agent agent  719 Sep 25 00:33 /app/solution/README.md\n-rw-r--r-- 1 agent agent 9.9K Sep 25 00:33 /app/solution/build_ranker.py\n-rw-r--r-- 1 agent agent 1.7M Sep 25 00:34 /app/solution/submission.parquet\n-rw-r--r-- 1 agent agent 1.7M Sep 25 00:34 /app/solution/validation.parquet\n"
  }
}
155

exec command

+27:59
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Tool callexecTool call Returned

Tool call · Input 

const r = await tools.write_stdin({"session_id":32686,"chars":"","yield_time_ms":10000,"max_output_tokens":3000});
text(r.output);

Tool response 

Script completed
Wall time 0.0 seconds
Output:

rows=845600 users=8456 all_known=True all_eligible=True size=1679550
e11585378c7679c3c8be38a59729db9cf5c1e85cad4c666ef13baf0bfd0df36e  /app/solution/submission.parquet
cbb8828d6c5ccf994d30363e8af5afe3c99b2f7e33410d66e8f641b3402eec47  /app/solution/build_ranker.py
-rw-r--r-- 1 agent agent  719 Sep 25 00:33 /app/solution/README.md
-rw-r--r-- 1 agent agent 9.9K Sep 25 00:33 /app/solution/build_ranker.py
-rw-r--r-- 1 agent agent 1.7M Sep 25 00:34 /app/solution/submission.parquet
-rw-r--r-- 1 agent agent 1.7M Sep 25 00:34 /app/solution/validation.parquet
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5c19bbb9887d2b61bdb6f19f4220f",
  "usage": {
    "input_tokens": 85809,
    "cached_input_tokens": 85120,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 85862
  },
  "turn_token_usage": {
    "input_tokens": 6745283,
    "cached_input_tokens": 6630656,
    "cache_write_input_tokens": 0,
    "output_tokens": 50443,
    "reasoning_output_tokens": 17953,
    "total_tokens": 6795726
  },
  "thread_token_usage": {
    "input_tokens": 6745283,
    "cached_input_tokens": 6630656,
    "cache_write_input_tokens": 0,
    "output_tokens": 50443,
    "reasoning_output_tokens": 17953,
    "total_tokens": 6795726
  }
}
156

Final response

+28:02
Thinking unavailable No text recorded

The archive records a thinking event with empty text. No readable thinking is available for this event.

Model text

Built and validated the music-discovery ranker.

  • Final submission: submission.parquet
  • Build code: build_ranker.py
  • Documentation: README.md
  • Public validation NDCG@10: 0.054285
  • Verified: 845,600 rows, 8,456 users, valid ranks, unique items, and 100% test-candidate eligibility.
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "session_id": "01a0d5e2-bdcd-7c62-a5c9-83ef171f1502",
  "root_turn_id": "01a0d5e2-bddb-7310-85f5-53d4ddfa701b",
  "response_id": "resp_00368acb562197f4016ab5c1a2999487d2ba3e4ebb499140ac",
  "usage": {
    "input_tokens": 86126,
    "cached_input_tokens": 85632,
    "cache_write_input_tokens": 0,
    "output_tokens": 155,
    "reasoning_output_tokens": 42,
    "total_tokens": 86281
  },
  "turn_token_usage": {
    "input_tokens": 6831409,
    "cached_input_tokens": 6716288,
    "cache_write_input_tokens": 0,
    "output_tokens": 50598,
    "reasoning_output_tokens": 17995,
    "total_tokens": 6882007
  },
  "thread_token_usage": {
    "input_tokens": 6831409,
    "cached_input_tokens": 6716288,
    "cache_write_input_tokens": 0,
    "output_tokens": 50598,
    "reasoning_output_tokens": 17995,
    "total_tokens": 6882007
  }
}
About this recording

The full recorded conversation: task prompt, agent messages, thinking text when available, tool inputs and outputs, and harness context. Empty thinking entries are labeled unavailable, not reconstructed from tool calls. Disclosures change the view; they do not shorten the content. Any truncation already present in tool output is preserved.

Steps follow the recorded model messages and reasoning events. Tool results appear with their originating call. Elapsed time starts with the first recorded event and excludes verification.

Attempt
sol-max-0-153-4-basic-r04-20260924T221413Z
Recorded model
gpt-5.6-sol
Harness version
0.153.4
Session
01a0d5e2-bdcd-7c62-a5c9-83ef171f1502
Source SHA-256
129cff398fd9a5281879240cd66753c6e60a4f0760d6ba25c2efdecb1a831ff2

Session bookkeeping and encrypted reasoning or signatures are excluded from the reading export. System instructions present in this archive are included above.

Download textDownload JSON