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Basic recommender sol-0.153.4-medium-05

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 33 empty thinking entries; tool calls are shown separately.

Agent
gpt-5.6-sol · Codex · medium effort
Recorded conversation
11:45 · 39 steps
Private reward
0.034125 · Pass
Attempt cost
$0.90
  • 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": "01a0d61d-5267-7b60-b898-9b284e3ebc7f",
  "root_turn_id": "01a0d61d-5267-7b60-b898-9b284e3ebc7f",
  "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": "medium",
      "developer_instructions": null
    }
  },
  "multi_agent_version": "v2",
  "realtime_active": false,
  "effort": "medium",
  "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

+00:02
Thinking unavailable No text recorded

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

Model text

I’ll inspect the data shapes, schemas, and available libraries first, then build a leakage-safe validation baseline and iterate against the public evaluator. I’ll keep a valid test submission saved early and replace it only when the validation approach improves.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"ls -lh /task/data && sed -n '1,220p' /task/requirements.txt && sed -n '1,260p' /task/ranking_evaluation.py","workdir":"/app","yield_time_ms":10000,"max_output_tokens":20000});
text(r.output);

Tool response

Script completed
Wall time 0.1 seconds
Output:

total 340M
-r--r--r-- 1 root root 342K Sep 25 01:03 candidate_catalog.parquet
-r--r--r-- 1 root root  83K Sep 25 01:03 target_users.parquet
-r--r--r-- 1 root root 7.4M Sep 25 01:03 test_eligible_candidates.npz
-r--r--r-- 1 root root 1.1M Sep 25 01:04 test_preference_state.parquet
-r--r--r-- 1 root root 838K Sep 25 01:03 train_dislikes.parquet
-r--r--r-- 1 root root 5.2M Sep 25 01:03 train_likes.parquet
-r--r--r-- 1 root root 305M Sep 25 01:03 train_listens.parquet
-r--r--r-- 1 root root 153K Sep 25 01:03 train_undislikes.parquet
-r--r--r-- 1 root root 1.8M Sep 25 01:03 train_unlikes.parquet
-r--r--r-- 1 root root  36K Sep 25 01:03 val_dislikes.parquet
-r--r--r-- 1 root root 292K Sep 25 01:03 val_likes.parquet
-r--r--r-- 1 root root 9.5M Sep 25 01:03 val_listens.parquet
-r--r--r-- 1 root root 7.7K Sep 25 01:03 val_undislikes.parquet
-r--r--r-- 1 root root  71K Sep 25 01:03 val_unlikes.parquet
-r--r--r-- 1 root root 7.3M Sep 25 01:03 validation_eligible_candidates.npz
-r--r--r-- 1 root root 1.1M Sep 25 01:04 validation_preference_state.parquet
# Exact third-party distribution inventory for yambda-discovery-ranking-v01.
#
# This is an input to `uv pip sync`, not a list of minimum requirements. A
# conforming task environment contains exactly these third-party Python
# distributions and no others. The same inventory is used for source preparation,
# task building, reference scoring, judging, and solver attempts. Python itself
# is pinned by task.toml and both Harbor image Dockerfiles.
attrs==26.1.0
bleach==6.4.0
certifi==2026.7.22
chardet==7.6.0
charset-normalizer==3.5.1
fastjsonschema==2.22.2
idna==3.18
joblib==1.5.3
jsonschema==4.26.0
jsonschema-specifications==2025.9.1
jupyter-core==5.9.1
jupytext==1.19.5
kaggle==2.2.4
kagglesdk==0.1.37
markdown-it-py==4.2.0
mdit-py-plugins==0.6.1
mdurl==0.1.2
nbformat==5.11.1
numpy==2.2.6
packaging==26.3
pandas==2.3.3
pillow==12.3.0
pip==26.0.1
platformdirs==4.11.3
protobuf==7.35.1
pyarrow==18.1.0
python-dateutil==2.9.0.post0
python-dotenv==1.2.3
python-slugify==8.0.4
pytz==2026.3.post1
PyYAML==6.0.3
referencing==0.37.0
reportlab==4.2.5
requests==2.34.2
rpds-py==2026.6.3
scikit-learn==1.7.2
scipy==1.15.3
six==1.17.0
text-unidecode==1.3
threadpoolctl==3.6.0
tqdm==4.70.0
traitlets==5.16.1
typing-extensions==4.16.0
tzdata==2026.3
urllib3==2.7.0
webencodings==0.6.1
"""Shared public primitives for Yambda discovery-ranking evaluation.

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

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

from pathlib import Path

import numpy as np
import pandas as pd


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

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


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


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

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

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

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

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

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

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

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


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

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

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


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

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

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

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

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

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

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


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

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

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


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

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

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

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


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

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

    Returns:
        The six-decimal ``quality_ndcg@10`` value and number of scored users.
    """
    quality = float(
        np.mean(
            [
                ndcg_at_k(ranked[uid], gold, QUALITY_K)
                for uid, gold in graded.items()
            ]
        )
    )
    return {
        "quality_ndcg@10": round(quality, 6),
        "quality_users": len(graded),
    }
Harness context token usage record
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  "root_turn_id": "01a0d61d-5267-7b60-b898-9b284e3ebc7f",
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  "usage": {
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  },
  "thread_token_usage": {
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    "cached_input_tokens": 11136,
    "cache_write_input_tokens": 0,
    "output_tokens": 158,
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}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-fa523241-2706-485c-9eda-17a53c804614",
    "process_id": "44057",
    "command": [
      "/bin/sh",
      "-lc",
      "ls -lh /task/data && sed -n '1,220p' /task/requirements.txt && sed -n '1,260p' /task/ranking_evaluation.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "list_files",
        "cmd": "ls -lh /task/data",
        "path": "data"
      },
      {
        "type": "read",
        "cmd": "sed -n '1,220p' /task/requirements.txt",
        "name": "requirements.txt",
        "path": "/task/requirements.txt"
      },
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        "type": "read",
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      }
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    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "total 340M\n-r--r--r-- 1 root root 342K Sep 25 01:03 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 25 01:03 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 25 01:03 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 01:04 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 25 01:03 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 25 01:03 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 25 01:03 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 25 01:03 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 25 01:03 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 25 01:03 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 25 01:03 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 25 01:03 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 25 01:03 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 25 01:03 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 25 01:03 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 01:04 validation_preference_state.parquet\n# Exact third-party distribution inventory for yambda-discovery-ranking-v01.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nidna==3.18\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n    \"CandidateSet\",\n    \"QUALITY_K\",\n    \"RankingValidationError\",\n    \"REQUIRED_COLUMNS\",\n    \"TOP_N\",\n    \"ndcg_at_k\",\n    \"ranked_items\",\n    \"read_ranking\",\n    \"score_rankings\",\n    \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n    \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n    \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n    The archive contains aligned ``uids``, ``item_ids``, and\n    ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n    ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n    copied into memory so they remain valid after the archive is closed.\n    \"\"\"\n\n    def __init__(self, path: Path):\n        \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n        Args:\n            path: Path to a ``.npz`` archive with the candidate-set schema.\n        \"\"\"\n        with np.load(path, allow_pickle=False) as artifact:\n            self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n            self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n            self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n        self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n        self.item_column = {\n            int(item): column for column, item in enumerate(self.item_ids)\n        }\n\n    def is_eligible(self, uid: int, item_id: int) -> bool:\n        \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n        IDs absent from the target-user or candidate-item arrays are treated as\n        ineligible rather than raising an exception.\n\n        Args:\n            uid: Target user ID to query.\n            item_id: Candidate item ID to query.\n\n        Returns:\n            ``True`` only when the corresponding packed eligibility bit is set.\n        \"\"\"\n        row = self.uid_row.get(int(uid))\n        column = self.item_column.get(int(item_id))\n        if row is None or column is None:\n            return False\n        byte, bit = divmod(column, 8)\n        return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n    \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n    Args:\n        path: Input path. A ``.parquet`` suffix selects parquet; every other\n            suffix is interpreted as CSV.\n\n    Returns:\n        The ranking frame as loaded by pandas.\n    \"\"\"\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n    frame: pd.DataFrame,\n    targets: set[int],\n) -> pd.DataFrame:\n    \"\"\"Normalize and validate the stage-independent ranking contract.\n\n    Required values must be finite integers. The user set must exactly match\n    ``targets``; every user must have 100 rows, distinct items, and each rank\n    from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n    intentionally left to the calling evaluator.\n\n    Args:\n        frame: Raw ranking frame. Extra columns are discarded from an internal\n            copy; the caller's frame is not modified.\n        targets: Exact set of target user IDs the ranking must cover.\n\n    Returns:\n        A normalized frame containing only the required ``int64`` columns.\n\n    Raises:\n        RankingValidationError: If the ranking violates the shared contract.\n    \"\"\"\n    required = list(REQUIRED_COLUMNS)\n    if not all(column in frame for column in required):\n        raise RankingValidationError(f\"submission requires columns {required}\")\n\n    normalized = frame[required].copy()\n    for column in required:\n        numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n        values = numeric.to_numpy(dtype=float)\n        if numeric.isna().any() or not np.isfinite(values).all():\n            raise RankingValidationError(\n                f\"submission {column} must be finite numeric values\"\n            )\n        if not np.equal(numeric, np.floor(numeric)).all():\n            raise RankingValidationError(\n                f\"submission {column} must contain integers\"\n            )\n        normalized[column] = numeric.astype(np.int64)\n\n    submitted_users = set(map(int, normalized.uid.unique()))\n    if submitted_users != targets:\n        raise RankingValidationError(\n            f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n            f\"extra={len(submitted_users - targets)}\"\n        )\n    counts = normalized.groupby(\"uid\").size()\n    if not (counts == TOP_N).all():\n        raise RankingValidationError(\n            f\"submission must contain exactly {TOP_N} rows for every target user\"\n        )\n    if normalized.duplicated([\"uid\", \"item_id\"]).any():\n        raise RankingValidationError(\n            \"submission contains duplicate (uid, item_id) pairs\"\n        )\n    valid_ranks = set(range(1, TOP_N + 1))\n    ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n        lambda values: set(map(int, values)) == valid_ranks\n    )\n    if not ranks_are_valid.all():\n        raise RankingValidationError(\n            f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n        )\n    return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n    \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n    Args:\n        frame: Frame that has passed :func:`validate_ranking`.\n\n    Returns:\n        A ``uid -> item IDs`` mapping ordered by ascending rank.\n    \"\"\"\n    ordered = frame.sort_values([\"uid\", \"rank\"])\n    return {\n        int(uid): list(map(int, values))\n        for uid, values in ordered.groupby(\"uid\").item_id\n    }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n    \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n    Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n    ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n    Args:\n        ranked: Item IDs in predicted rank order.\n        gold: Mapping from relevant item IDs to integer relevance grades.\n        k: Maximum number of predicted positions to score.\n\n    Returns:\n        For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n        ``0.0`` when the ideal gain is zero.\n    \"\"\"\n    gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n    discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n    dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n    ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n    ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n    idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n    return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Relevant item grades keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    quality = float(\n        np.mean(\n            [\n                ndcg_at_k(ranked[uid], gold, QUALITY_K)\n                for uid, gold in graded.items()\n            ]\n        )\n    )\n    return {\n        \"quality_ndcg@10\": round(quality, 6),\n        \"quality_users\": len(graded),\n    }\n",
    "stderr": "",
    "aggregated_output": "total 340M\n-r--r--r-- 1 root root 342K Sep 25 01:03 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 25 01:03 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 25 01:03 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 01:04 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 25 01:03 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 25 01:03 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 25 01:03 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 25 01:03 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 25 01:03 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 25 01:03 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 25 01:03 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 25 01:03 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 25 01:03 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 25 01:03 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 25 01:03 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 01:04 validation_preference_state.parquet\n# Exact third-party distribution inventory for yambda-discovery-ranking-v01.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nidna==3.18\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n    \"CandidateSet\",\n    \"QUALITY_K\",\n    \"RankingValidationError\",\n    \"REQUIRED_COLUMNS\",\n    \"TOP_N\",\n    \"ndcg_at_k\",\n    \"ranked_items\",\n    \"read_ranking\",\n    \"score_rankings\",\n    \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n    \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n    \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n    The archive contains aligned ``uids``, ``item_ids``, and\n    ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n    ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n    copied into memory so they remain valid after the archive is closed.\n    \"\"\"\n\n    def __init__(self, path: Path):\n        \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n        Args:\n            path: Path to a ``.npz`` archive with the candidate-set schema.\n        \"\"\"\n        with np.load(path, allow_pickle=False) as artifact:\n            self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n            self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n            self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n        self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n        self.item_column = {\n            int(item): column for column, item in enumerate(self.item_ids)\n        }\n\n    def is_eligible(self, uid: int, item_id: int) -> bool:\n        \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n        IDs absent from the target-user or candidate-item arrays are treated as\n        ineligible rather than raising an exception.\n\n        Args:\n            uid: Target user ID to query.\n            item_id: Candidate item ID to query.\n\n        Returns:\n            ``True`` only when the corresponding packed eligibility bit is set.\n        \"\"\"\n        row = self.uid_row.get(int(uid))\n        column = self.item_column.get(int(item_id))\n        if row is None or column is None:\n            return False\n        byte, bit = divmod(column, 8)\n        return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n    \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n    Args:\n        path: Input path. A ``.parquet`` suffix selects parquet; every other\n            suffix is interpreted as CSV.\n\n    Returns:\n        The ranking frame as loaded by pandas.\n    \"\"\"\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n    frame: pd.DataFrame,\n    targets: set[int],\n) -> pd.DataFrame:\n    \"\"\"Normalize and validate the stage-independent ranking contract.\n\n    Required values must be finite integers. The user set must exactly match\n    ``targets``; every user must have 100 rows, distinct items, and each rank\n    from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n    intentionally left to the calling evaluator.\n\n    Args:\n        frame: Raw ranking frame. Extra columns are discarded from an internal\n            copy; the caller's frame is not modified.\n        targets: Exact set of target user IDs the ranking must cover.\n\n    Returns:\n        A normalized frame containing only the required ``int64`` columns.\n\n    Raises:\n        RankingValidationError: If the ranking violates the shared contract.\n    \"\"\"\n    required = list(REQUIRED_COLUMNS)\n    if not all(column in frame for column in required):\n        raise RankingValidationError(f\"submission requires columns {required}\")\n\n    normalized = frame[required].copy()\n    for column in required:\n        numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n        values = numeric.to_numpy(dtype=float)\n        if numeric.isna().any() or not np.isfinite(values).all():\n            raise RankingValidationError(\n                f\"submission {column} must be finite numeric values\"\n            )\n        if not np.equal(numeric, np.floor(numeric)).all():\n            raise RankingValidationError(\n                f\"submission {column} must contain integers\"\n            )\n        normalized[column] = numeric.astype(np.int64)\n\n    submitted_users = set(map(int, normalized.uid.unique()))\n    if submitted_users != targets:\n        raise RankingValidationError(\n            f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n            f\"extra={len(submitted_users - targets)}\"\n        )\n    counts = normalized.groupby(\"uid\").size()\n    if not (counts == TOP_N).all():\n        raise RankingValidationError(\n            f\"submission must contain exactly {TOP_N} rows for every target user\"\n        )\n    if normalized.duplicated([\"uid\", \"item_id\"]).any():\n        raise RankingValidationError(\n            \"submission contains duplicate (uid, item_id) pairs\"\n        )\n    valid_ranks = set(range(1, TOP_N + 1))\n    ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n        lambda values: set(map(int, values)) == valid_ranks\n    )\n    if not ranks_are_valid.all():\n        raise RankingValidationError(\n            f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n        )\n    return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n    \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n    Args:\n        frame: Frame that has passed :func:`validate_ranking`.\n\n    Returns:\n        A ``uid -> item IDs`` mapping ordered by ascending rank.\n    \"\"\"\n    ordered = frame.sort_values([\"uid\", \"rank\"])\n    return {\n        int(uid): list(map(int, values))\n        for uid, values in ordered.groupby(\"uid\").item_id\n    }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n    \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n    Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n    ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n    Args:\n        ranked: Item IDs in predicted rank order.\n        gold: Mapping from relevant item IDs to integer relevance grades.\n        k: Maximum number of predicted positions to score.\n\n    Returns:\n        For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n        ``0.0`` when the ideal gain is zero.\n    \"\"\"\n    gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n    discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n    dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n    ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n    ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n    idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n    return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Relevant item grades keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    quality = float(\n        np.mean(\n            [\n                ndcg_at_k(ranked[uid], gold, QUALITY_K)\n                for uid, gold in graded.items()\n            ]\n        )\n    )\n    return {\n        \"quality_ndcg@10\": round(quality, 6),\n        \"quality_users\": len(graded),\n    }\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 6350
    },
    "formatted_output": "total 340M\n-r--r--r-- 1 root root 342K Sep 25 01:03 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 25 01:03 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 25 01:03 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 01:04 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 25 01:03 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 25 01:03 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 25 01:03 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 25 01:03 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 25 01:03 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 25 01:03 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 25 01:03 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 25 01:03 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 25 01:03 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 25 01:03 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 25 01:03 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 01:04 validation_preference_state.parquet\n# Exact third-party distribution inventory for yambda-discovery-ranking-v01.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nidna==3.18\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n    \"CandidateSet\",\n    \"QUALITY_K\",\n    \"RankingValidationError\",\n    \"REQUIRED_COLUMNS\",\n    \"TOP_N\",\n    \"ndcg_at_k\",\n    \"ranked_items\",\n    \"read_ranking\",\n    \"score_rankings\",\n    \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n    \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n    \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n    The archive contains aligned ``uids``, ``item_ids``, and\n    ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n    ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n    copied into memory so they remain valid after the archive is closed.\n    \"\"\"\n\n    def __init__(self, path: Path):\n        \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n        Args:\n            path: Path to a ``.npz`` archive with the candidate-set schema.\n        \"\"\"\n        with np.load(path, allow_pickle=False) as artifact:\n            self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n            self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n            self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n        self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n        self.item_column = {\n            int(item): column for column, item in enumerate(self.item_ids)\n        }\n\n    def is_eligible(self, uid: int, item_id: int) -> bool:\n        \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n        IDs absent from the target-user or candidate-item arrays are treated as\n        ineligible rather than raising an exception.\n\n        Args:\n            uid: Target user ID to query.\n            item_id: Candidate item ID to query.\n\n        Returns:\n            ``True`` only when the corresponding packed eligibility bit is set.\n        \"\"\"\n        row = self.uid_row.get(int(uid))\n        column = self.item_column.get(int(item_id))\n        if row is None or column is None:\n            return False\n        byte, bit = divmod(column, 8)\n        return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n    \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n    Args:\n        path: Input path. A ``.parquet`` suffix selects parquet; every other\n            suffix is interpreted as CSV.\n\n    Returns:\n        The ranking frame as loaded by pandas.\n    \"\"\"\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n    frame: pd.DataFrame,\n    targets: set[int],\n) -> pd.DataFrame:\n    \"\"\"Normalize and validate the stage-independent ranking contract.\n\n    Required values must be finite integers. The user set must exactly match\n    ``targets``; every user must have 100 rows, distinct items, and each rank\n    from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n    intentionally left to the calling evaluator.\n\n    Args:\n        frame: Raw ranking frame. Extra columns are discarded from an internal\n            copy; the caller's frame is not modified.\n        targets: Exact set of target user IDs the ranking must cover.\n\n    Returns:\n        A normalized frame containing only the required ``int64`` columns.\n\n    Raises:\n        RankingValidationError: If the ranking violates the shared contract.\n    \"\"\"\n    required = list(REQUIRED_COLUMNS)\n    if not all(column in frame for column in required):\n        raise RankingValidationError(f\"submission requires columns {required}\")\n\n    normalized = frame[required].copy()\n    for column in required:\n        numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n        values = numeric.to_numpy(dtype=float)\n        if numeric.isna().any() or not np.isfinite(values).all():\n            raise RankingValidationError(\n                f\"submission {column} must be finite numeric values\"\n            )\n        if not np.equal(numeric, np.floor(numeric)).all():\n            raise RankingValidationError(\n                f\"submission {column} must contain integers\"\n            )\n        normalized[column] = numeric.astype(np.int64)\n\n    submitted_users = set(map(int, normalized.uid.unique()))\n    if submitted_users != targets:\n        raise RankingValidationError(\n            f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n            f\"extra={len(submitted_users - targets)}\"\n        )\n    counts = normalized.groupby(\"uid\").size()\n    if not (counts == TOP_N).all():\n        raise RankingValidationError(\n            f\"submission must contain exactly {TOP_N} rows for every target user\"\n        )\n    if normalized.duplicated([\"uid\", \"item_id\"]).any():\n        raise RankingValidationError(\n            \"submission contains duplicate (uid, item_id) pairs\"\n        )\n    valid_ranks = set(range(1, TOP_N + 1))\n    ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n        lambda values: set(map(int, values)) == valid_ranks\n    )\n    if not ranks_are_valid.all():\n        raise RankingValidationError(\n            f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n        )\n    return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n    \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n    Args:\n        frame: Frame that has passed :func:`validate_ranking`.\n\n    Returns:\n        A ``uid -> item IDs`` mapping ordered by ascending rank.\n    \"\"\"\n    ordered = frame.sort_values([\"uid\", \"rank\"])\n    return {\n        int(uid): list(map(int, values))\n        for uid, values in ordered.groupby(\"uid\").item_id\n    }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n    \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n    Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n    ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n    Args:\n        ranked: Item IDs in predicted rank order.\n        gold: Mapping from relevant item IDs to integer relevance grades.\n        k: Maximum number of predicted positions to score.\n\n    Returns:\n        For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n        ``0.0`` when the ideal gain is zero.\n    \"\"\"\n    gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n    discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n    dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n    ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n    ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n    idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n    return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Relevant item grades keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    quality = float(\n        np.mean(\n            [\n                ndcg_at_k(ranked[uid], gold, QUALITY_K)\n                for uid, gold in graded.items()\n            ]\n        )\n    )\n    return {\n        \"quality_ndcg@10\": round(quality, 6),\n        \"quality_users\": len(graded),\n    }\n"
  }
}
08

exec command

+00:08
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:"sed -n '1,280p' /task/evaluate_public.py && python - <<'PY'\nimport pandas as pd, pyarrow.parquet as pq, numpy as np, os\nD='/task/data'\nfor f in sorted(os.listdir(D)):\n if f.endswith('.parquet'):\n  p=D+'/'+f; pf=pq.ParquetFile(p)\n  print(f, pf.metadata.num_rows, pf.schema.names)\nfor f in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n z=np.load(D+'/'+f); print(f,{k:(z[k].shape,z[k].dtype) for k in z.files},'density',np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(z['item_ids'])].mean())\nprint(pd.read_parquet(D+'/candidate_catalog.parquet').head(), pd.read_parquet(D+'/target_users.parquet').head())\nPY","workdir":"/app","yield_time_ms":10000,"max_output_tokens":20000});
text(r.output);

Tool response

Script completed
Wall time 0.1 seconds
Output:

#!/usr/bin/env python3
"""Score a validation-stage ranking against public discovery labels.

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

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

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

Usage::

    python evaluate_public.py <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()
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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  "root_turn_id": "01a0d61d-5267-7b60-b898-9b284e3ebc7f",
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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-6039074a-dc79-43bb-99f4-88ca01eadda8",
    "process_id": "88473",
    "command": [
      "/bin/sh",
      "-lc",
      "sed -n '1,280p' /task/evaluate_public.py && python - <<'PY'\nimport pandas as pd, pyarrow.parquet as pq, numpy as np, os\nD='/task/data'\nfor f in sorted(os.listdir(D)):\n if f.endswith('.parquet'):\n  p=D+'/'+f; pf=pq.ParquetFile(p)\n  print(f, pf.metadata.num_rows, pf.schema.names)\nfor f in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n z=np.load(D+'/'+f); print(f,{k:(z[k].shape,z[k].dtype) for k in z.files},'density',np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(z['item_ids'])].mean())\nprint(pd.read_parquet(D+'/candidate_catalog.parquet').head(), pd.read_parquet(D+'/target_users.parquet').head())\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "sed -n '1,280p' /task/evaluate_public.py && python - <<'PY'\nimport pandas as pd, pyarrow.parquet as pq, numpy as np, os\nD='/task/data'\nfor f in sorted(os.listdir(D)):\n if f.endswith('.parquet'):\n  p=D+'/'+f; pf=pq.ParquetFile(p)\n  print(f, pf.metadata.num_rows, pf.schema.names)\nfor f in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n z=np.load(D+'/'+f); print(f,{k:(z[k].shape,z[k].dtype) for k in z.files},'density',np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(z['item_ids'])].mean())\nprint(pd.read_parquet(D+'/candidate_catalog.parquet').head(), pd.read_parquet(D+'/target_users.parquet').head())\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "failed",
    "stdout": "#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n    python evaluate_public.py <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()\nTraceback (most recent call last):\n  File \"<stdin>\", line 1, in <module>\nModuleNotFoundError: No module named 'pandas'\n",
    "stderr": "",
    "aggregated_output": "#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n    python evaluate_public.py <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()\nTraceback (most recent call last):\n  File \"<stdin>\", line 1, in <module>\nModuleNotFoundError: No module named 'pandas'\n",
    "exit_code": 1,
    "duration": {
      "secs": 0,
      "nanos": 3800
    },
    "formatted_output": "#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n    python evaluate_public.py <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()\nTraceback (most recent call last):\n  File \"<stdin>\", line 1, in <module>\nModuleNotFoundError: No module named 'pandas'\n"
  }
}
09

exec command

+00:13
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; ls -la /app; find / -maxdepth 3 -type f -name python 2>/dev/null | head -20","workdir":"/app","yield_time_ms":10000,"max_output_tokens":2000});
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
total 12
drwxr-xr-x 3 agent agent 4096 Sep 25 01:07 .
drwxr-xr-x 1 root  root  4096 Sep 25 01:10 ..
drwxr-xr-x 2 agent agent 4096 Sep 25 01:07 solution
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    "stderr": "",
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    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3380
    },
    "formatted_output": "/usr/local/bin/python\n/usr/local/bin/python3\n/usr/bin/python3\n/bin/python3\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 25 01:07 .\ndrwxr-xr-x 1 root  root  4096 Sep 25 01:10 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 25 01:07 solution\n"
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10

exec command

+00:16
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:"python -m pip list | head -30; find / -type d -name 'pandas' 2>/dev/null | head","workdir":"/app","yield_time_ms":10000,"max_output_tokens":3000});
text(r.output);

Tool response

Script completed
Wall time 1.1 seconds
Output:

Package Version
------- -------
pip     25.0.1
/opt/task-venv/lib/python3.12/site-packages/pandas
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    "status": "completed",
    "stdout": "Package Version\n------- -------\npip     25.0.1\n/opt/task-venv/lib/python3.12/site-packages/pandas\n",
    "stderr": "",
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  }
}
11

exec command

+00:20
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 task’s packages are in the provided read-only virtual environment, so I’ve switched to that interpreter. The data is large enough that I’ll use sparse matrices and vectorized aggregation rather than expanding user–catalog pairs.

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, os\nD='/task/data'\nfor f in sorted(os.listdir(D)):\n if f.endswith('.parquet'):\n  pf=pq.ParquetFile(D+'/'+f)\n  print(f, pf.metadata.num_rows, pf.schema.names)\nfor f in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n z=np.load(D+'/'+f); bits=np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(z['item_ids'])]\n print(f,{k:(z[k].shape,str(z[k].dtype)) for k in z.files},'density',bits.mean(),'per user',bits.sum(1).min(),bits.sum(1).mean(),bits.sum(1).max())\nprint('catalog',pd.read_parquet(D+'/candidate_catalog.parquet').head(),pd.read_parquet(D+'/candidate_catalog.parquet').tail())\nprint('targets',pd.read_parquet(D+'/target_users.parquet').shape,pd.read_parquet(D+'/target_users.parquet').head())\nfor f in ['train_listens','val_listens']:\n x=pd.read_parquet(D+'/'+f+'.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\n print(f,'uids',x.uid.nunique(),'items',x.item_id.nunique(),'time',x.timestamp.min(),x.timestamp.max(),'org',x.is_organic.mean(),'strong',((x.is_organic==1)&(x.played_ratio_pct>=80)).sum())\nPY","workdir":"/app","yield_time_ms":30000,"max_output_tokens":10000});
text(r.output);

Tool response

Script completed
Wall time 3.0 seconds
Output:

candidate_catalog.parquet 30000 ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']
target_users.parquet 8456 ['uid', 'recent_positive_listens', 'history_positive_listens']
test_preference_state.parquet 484776 ['uid', 'item_id', 'state']
train_dislikes.parquet 101075 ['uid', 'timestamp', 'item_id', 'is_organic']
train_likes.parquet 814831 ['uid', 'timestamp', 'item_id', 'is_organic']
train_listens.parquet 43484159 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']
train_undislikes.parquet 19960 ['uid', 'timestamp', 'item_id', 'is_organic']
train_unlikes.parquet 292665 ['uid', 'timestamp', 'item_id', 'is_organic']
val_dislikes.parquet 3563 ['uid', 'timestamp', 'item_id', 'is_organic']
val_likes.parquet 37795 ['uid', 'timestamp', 'item_id', 'is_organic']
val_listens.parquet 1501697 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']
val_undislikes.parquet 606 ['uid', 'timestamp', 'item_id', 'is_organic']
val_unlikes.parquet 9346 ['uid', 'timestamp', 'item_id', 'is_organic']
validation_preference_state.parquet 469124 ['uid', 'item_id', 'state']
validation_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')} density 0.9689102097130243 per user 24657 29067.306291390727 30000
test_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')} density 0.9680439293598234 per user 24552 29041.3178807947 30000
catalog    item_id  positive_listens  organic_positive_listens  popularity_rank
0  5862961             26615                     20620                1
1  6901374             25536                     17347                2
2  5635052             24691                     17194                3
3  3542184             24075                     17699                4
4  9378983             24036                     18195                5        item_id  positive_listens  organic_positive_listens  popularity_rank
29995  1275692               163                        81            29996
29996  1280011               163                        76            29997
29997  1309246               163                       138            29998
29998  1634958               163                        46            29999
29999  1670524               163                        73            30000
targets (8456, 3)    uid  recent_positive_listens  history_positive_listens
0  100                    844.0                      2604
1  200                     50.0                       286
2  300                    168.0                       359
3  600                   1985.0                      2423
4  700                   1776.0                      6114
train_listens uids 9238 items 845395 time 0 24786795 org 0.5169151828370419 strong 12177583
val_listens uids 8196 items 191747 time 24788600 25393395 org 0.517072352145606 strong 426145
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      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, pyarrow.parquet as pq, numpy as np, os\nD='/task/data'\nfor f in sorted(os.listdir(D)):\n if f.endswith('.parquet'):\n  pf=pq.ParquetFile(D+'/'+f)\n  print(f, pf.metadata.num_rows, pf.schema.names)\nfor f in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n z=np.load(D+'/'+f); bits=np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(z['item_ids'])]\n print(f,{k:(z[k].shape,str(z[k].dtype)) for k in z.files},'density',bits.mean(),'per user',bits.sum(1).min(),bits.sum(1).mean(),bits.sum(1).max())\nprint('catalog',pd.read_parquet(D+'/candidate_catalog.parquet').head(),pd.read_parquet(D+'/candidate_catalog.parquet').tail())\nprint('targets',pd.read_parquet(D+'/target_users.parquet').shape,pd.read_parquet(D+'/target_users.parquet').head())\nfor f in ['train_listens','val_listens']:\n x=pd.read_parquet(D+'/'+f+'.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\n print(f,'uids',x.uid.nunique(),'items',x.item_id.nunique(),'time',x.timestamp.min(),x.timestamp.max(),'org',x.is_organic.mean(),'strong',((x.is_organic==1)&(x.played_ratio_pct>=80)).sum())\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, os\nD='/task/data'\nfor f in sorted(os.listdir(D)):\n if f.endswith('.parquet'):\n  pf=pq.ParquetFile(D+'/'+f)\n  print(f, pf.metadata.num_rows, pf.schema.names)\nfor f in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n z=np.load(D+'/'+f); bits=np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(z['item_ids'])]\n print(f,{k:(z[k].shape,str(z[k].dtype)) for k in z.files},'density',bits.mean(),'per user',bits.sum(1).min(),bits.sum(1).mean(),bits.sum(1).max())\nprint('catalog',pd.read_parquet(D+'/candidate_catalog.parquet').head(),pd.read_parquet(D+'/candidate_catalog.parquet').tail())\nprint('targets',pd.read_parquet(D+'/target_users.parquet').shape,pd.read_parquet(D+'/target_users.parquet').head())\nfor f in ['train_listens','val_listens']:\n x=pd.read_parquet(D+'/'+f+'.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\n print(f,'uids',x.uid.nunique(),'items',x.item_id.nunique(),'time',x.timestamp.min(),x.timestamp.max(),'org',x.is_organic.mean(),'strong',((x.is_organic==1)&(x.played_ratio_pct>=80)).sum())\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "candidate_catalog.parquet 30000 ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\ntarget_users.parquet 8456 ['uid', 'recent_positive_listens', 'history_positive_listens']\ntest_preference_state.parquet 484776 ['uid', 'item_id', 'state']\ntrain_dislikes.parquet 101075 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_likes.parquet 814831 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_listens.parquet 43484159 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\ntrain_undislikes.parquet 19960 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_unlikes.parquet 292665 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_dislikes.parquet 3563 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_likes.parquet 37795 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_listens.parquet 1501697 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\nval_undislikes.parquet 606 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_unlikes.parquet 9346 ['uid', 'timestamp', 'item_id', 'is_organic']\nvalidation_preference_state.parquet 469124 ['uid', 'item_id', 'state']\nvalidation_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')} density 0.9689102097130243 per user 24657 29067.306291390727 30000\ntest_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')} density 0.9680439293598234 per user 24552 29041.3178807947 30000\ncatalog    item_id  positive_listens  organic_positive_listens  popularity_rank\n0  5862961             26615                     20620                1\n1  6901374             25536                     17347                2\n2  5635052             24691                     17194                3\n3  3542184             24075                     17699                4\n4  9378983             24036                     18195                5        item_id  positive_listens  organic_positive_listens  popularity_rank\n29995  1275692               163                        81            29996\n29996  1280011               163                        76            29997\n29997  1309246               163                       138            29998\n29998  1634958               163                        46            29999\n29999  1670524               163                        73            30000\ntargets (8456, 3)    uid  recent_positive_listens  history_positive_listens\n0  100                    844.0                      2604\n1  200                     50.0                       286\n2  300                    168.0                       359\n3  600                   1985.0                      2423\n4  700                   1776.0                      6114\ntrain_listens uids 9238 items 845395 time 0 24786795 org 0.5169151828370419 strong 12177583\nval_listens uids 8196 items 191747 time 24788600 25393395 org 0.517072352145606 strong 426145\n",
    "stderr": "",
    "aggregated_output": "candidate_catalog.parquet 30000 ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\ntarget_users.parquet 8456 ['uid', 'recent_positive_listens', 'history_positive_listens']\ntest_preference_state.parquet 484776 ['uid', 'item_id', 'state']\ntrain_dislikes.parquet 101075 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_likes.parquet 814831 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_listens.parquet 43484159 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\ntrain_undislikes.parquet 19960 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_unlikes.parquet 292665 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_dislikes.parquet 3563 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_likes.parquet 37795 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_listens.parquet 1501697 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\nval_undislikes.parquet 606 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_unlikes.parquet 9346 ['uid', 'timestamp', 'item_id', 'is_organic']\nvalidation_preference_state.parquet 469124 ['uid', 'item_id', 'state']\nvalidation_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')} density 0.9689102097130243 per user 24657 29067.306291390727 30000\ntest_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')} density 0.9680439293598234 per user 24552 29041.3178807947 30000\ncatalog    item_id  positive_listens  organic_positive_listens  popularity_rank\n0  5862961             26615                     20620                1\n1  6901374             25536                     17347                2\n2  5635052             24691                     17194                3\n3  3542184             24075                     17699                4\n4  9378983             24036                     18195                5        item_id  positive_listens  organic_positive_listens  popularity_rank\n29995  1275692               163                        81            29996\n29996  1280011               163                        76            29997\n29997  1309246               163                       138            29998\n29998  1634958               163                        46            29999\n29999  1670524               163                        73            30000\ntargets (8456, 3)    uid  recent_positive_listens  history_positive_listens\n0  100                    844.0                      2604\n1  200                     50.0                       286\n2  300                    168.0                       359\n3  600                   1985.0                      2423\n4  700                   1776.0                      6114\ntrain_listens uids 9238 items 845395 time 0 24786795 org 0.5169151828370419 strong 12177583\nval_listens uids 8196 items 191747 time 24788600 25393395 org 0.517072352145606 strong 426145\n",
    "exit_code": 0,
    "duration": {
      "secs": 2,
      "nanos": 825245219
    },
    "formatted_output": "candidate_catalog.parquet 30000 ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\ntarget_users.parquet 8456 ['uid', 'recent_positive_listens', 'history_positive_listens']\ntest_preference_state.parquet 484776 ['uid', 'item_id', 'state']\ntrain_dislikes.parquet 101075 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_likes.parquet 814831 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_listens.parquet 43484159 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\ntrain_undislikes.parquet 19960 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_unlikes.parquet 292665 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_dislikes.parquet 3563 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_likes.parquet 37795 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_listens.parquet 1501697 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\nval_undislikes.parquet 606 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_unlikes.parquet 9346 ['uid', 'timestamp', 'item_id', 'is_organic']\nvalidation_preference_state.parquet 469124 ['uid', 'item_id', 'state']\nvalidation_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')} density 0.9689102097130243 per user 24657 29067.306291390727 30000\ntest_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')} density 0.9680439293598234 per user 24552 29041.3178807947 30000\ncatalog    item_id  positive_listens  organic_positive_listens  popularity_rank\n0  5862961             26615                     20620                1\n1  6901374             25536                     17347                2\n2  5635052             24691                     17194                3\n3  3542184             24075                     17699                4\n4  9378983             24036                     18195                5        item_id  positive_listens  organic_positive_listens  popularity_rank\n29995  1275692               163                        81            29996\n29996  1280011               163                        76            29997\n29997  1309246               163                       138            29998\n29998  1634958               163                        46            29999\n29999  1670524               163                        73            30000\ntargets (8456, 3)    uid  recent_positive_listens  history_positive_listens\n0  100                    844.0                      2604\n1  200                     50.0                       286\n2  300                    168.0                       359\n3  600                   1985.0                      2423\n4  700                   1776.0                      6114\ntrain_listens uids 9238 items 845395 time 0 24786795 org 0.5169151828370419 strong 12177583\nval_listens uids 8196 items 191747 time 24788600 25393395 org 0.517072352145606 strong 426145\n"
  }
}
12

exec command

+00:38
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.exec_command({cmd:"free -h","workdir":"/app","yield_time_ms":10000,"max_output_tokens":1000});
text(r.output);

Tool response

Script completed
Wall time 0.0 seconds
Output:

/bin/sh: 1: free: not found
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-519360f8-1235-430d-8832-40d86782777e",
    "process_id": "44595",
    "command": [
      "/bin/sh",
      "-lc",
      "free -h"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "free -h"
      }
    ],
    "source": "unified_exec_startup",
    "status": "failed",
    "stdout": "/bin/sh: 1: free: not found\n",
    "stderr": "",
    "aggregated_output": "/bin/sh: 1: free: not found\n",
    "exit_code": 127,
    "duration": {
      "secs": 0,
      "nanos": 6330
    },
    "formatted_output": "/bin/sh: 1: free: not found\n"
  }
}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d61d-5259-7ea0-97f9-6b8e97d0f227",
  "turn_id": "01a0d61d-5267-7b60-b898-9b284e3ebc7f",
  "session_id": "01a0d61d-5259-7ea0-97f9-6b8e97d0f227",
  "root_turn_id": "01a0d61d-5267-7b60-b898-9b284e3ebc7f",
  "response_id": "resp_0e3b5c2d311aeb04016ab5ca30845887d280b34a3d8009489a",
  "usage": {
    "input_tokens": 20800,
    "cached_input_tokens": 19200,
    "cache_write_input_tokens": 0,
    "output_tokens": 418,
    "reasoning_output_tokens": 368,
    "total_tokens": 21218
  },
  "turn_token_usage": {
    "input_tokens": 110377,
    "cached_input_tokens": 99840,
    "cache_write_input_tokens": 0,
    "output_tokens": 1558,
    "reasoning_output_tokens": 498,
    "total_tokens": 111935
  },
  "thread_token_usage": {
    "input_tokens": 110377,
    "cached_input_tokens": 99840,
    "cache_write_input_tokens": 0,
    "output_tokens": 1558,
    "reasoning_output_tokens": 498,
    "total_tokens": 111935
  }
}
13

exec command

+00:43
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*** Add File: /app/solution/build.py\n+#!/usr/bin/env python3\n+\"\"\"Build leakage-safe validation and final discovery rankings.\"\"\"\n+from pathlib import Path\n+import argparse\n+import numpy as np\n+import pandas as pd\n+\n+D = Path('/task/data')\n+OUT = Path('/app/solution')\n+CUTOFF = 24786800\n+\n+\n+def load_elig(stage):\n+    z = np.load(D / f'{stage}_eligible_candidates.npz')\n+    uids = z['uids'].astype(np.int64)\n+    items = z['item_ids'].astype(np.int64)\n+    bits = np.unpackbits(z['packed_eligible'], axis=1, bitorder='little')[:, :len(items)].astype(bool)\n+    return uids, items, bits\n+\n+\n+def build(stage='validation', variant=0):\n+    final = stage == 'test'\n+    uids, items, eligible = load_elig(stage)\n+    uidpos = pd.Series(np.arange(len(uids), dtype=np.int32), index=uids)\n+    itempos = pd.Series(np.arange(len(items), dtype=np.int32), index=items)\n+    files = ['train_listens'] + (['val_listens'] if final else [])\n+    frames=[]\n+    for fn in files:\n+        x=pd.read_parquet(D/f'{fn}.parquet', columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\n+        x=x[x.uid.isin(uids) & x.item_id.isin(items)]\n+        x['positive']=(x.played_ratio_pct>=50).astype(np.int8)\n+        x['strong']=(x.played_ratio_pct>=80).astype(np.int8)\n+        frames.append(x)\n+    x=pd.concat(frames,ignore_index=True)\n+    cutoff = int(x.timestamp.max()) + 5\n+    # One row per observed user-catalog pair. Organic strong behavior is closest to target.\n+    x['orgstrong']=((x.is_organic==1)&(x.strong==1)).astype(np.int8)\n+    x['orgpos']=((x.is_organic==1)&(x.positive==1)).astype(np.int8)\n+    x['recstrong']=x.orgstrong*np.exp(-(cutoff-x.timestamp)/2_592_000.0)\n+    x['recpos']=x.positive*np.exp(-(cutoff-x.timestamp)/2_592_000.0)\n+    g=x.groupby(['uid','item_id'],sort=False).agg(\n+        n=('positive','sum'), strong=('strong','sum'), org=('orgpos','sum'),\n+        orgstrong=('orgstrong','sum'), rec=('recstrong','sum'), recall=('recpos','sum'),\n+        last=('timestamp','max'), lastorg=('is_organic','max')).reset_index()\n+    del x, frames\n+    # Explicit cutoff state is supplied and is safer than reconstructing it.\n+    pref=pd.read_parquet(D/f'{stage}_preference_state.parquet')\n+    pref=pref[pref.uid.isin(uids)&pref.item_id.isin(items)]\n+    liked=set(zip(pref.loc[pref.state.eq('liked'),'uid'],pref.loc[pref.state.eq('liked'),'item_id']))\n+    disliked=set(zip(pref.loc[pref.state.eq('disliked'),'uid'],pref.loc[pref.state.eq('disliked'),'item_id']))\n+    pairs=list(zip(g.uid,g.item_id))\n+    g['liked']=np.fromiter((p in liked for p in pairs),bool,len(g))\n+    g['disliked']=np.fromiter((p in disliked for p in pairs),bool,len(g))\n+    age=(cutoff-g['last'])/86400.0\n+    # Variants are deliberately compact for quick public tuning.\n+    if variant==0:\n+        score=3.2*np.log1p(g.orgstrong)+1.0*np.log1p(g.strong)+2.0*g.rec+0.5*g.recall+3.0*g.liked-8*g.disliked-0.012*age\n+    elif variant==1:\n+        score=2.2*np.log1p(g.orgstrong)+0.5*np.log1p(g.strong)+4.0*g.rec+0.8*g.recall+4.5*g.liked-10*g.disliked-0.020*age\n+    elif variant==2:\n+        score=4.0*np.log1p(g.orgstrong)+1.0*np.log1p(g.org)+1.2*g.rec+0.3*g.recall+5.0*g.liked-10*g.disliked-0.006*age\n+    else:\n+        score=2.0*np.log1p(g.orgstrong)+0.5*np.log1p(g.strong)+2.5*g.rec+0.4*g.recall+7.0*g.liked-10*g.disliked-0.01*age\n+    g['score']=score\n+    # Catalog order is a strong and stable fallback; organic popularity breaks sparse-history ties.\n+    cat=pd.read_parquet(D/'candidate_catalog.parquet').set_index('item_id').loc[items]\n+    pop=np.log1p(cat.organic_positive_listens.to_numpy())\n+    pop=(pop-pop.min())/(pop.max()-pop.min())\n+    rows=[]\n+    grouped={int(uid): q for uid,q in g.groupby('uid',sort=False)}\n+    for r,uid in enumerate(uids):\n+        mask=eligible[r]\n+        idx=np.flatnonzero(mask)\n+        # fallback score tiny enough to preserve personal candidates\n+        base=0.03*pop[idx]\n+        q=grouped.get(int(uid))\n+        if q is not None:\n+            qi=itempos.loc[q.item_id].to_numpy()\n+            ok=mask[qi]\n+            qi=qi[ok]; qs=q.score.to_numpy()[ok]\n+            # Put personalized values into eligible candidate vector.\n+            pos=np.searchsorted(idx,qi)\n+            valid=(pos<len(idx))&(idx[np.minimum(pos,len(idx)-1)]==qi)\n+            base[pos[valid]]=qs[valid]+0.03*pop[qi[valid]]\n+        take=np.argpartition(-base,99)[:100]\n+        take=take[np.argsort(-base[take],kind='stable')]\n+        chosen=items[idx[take]]\n+        rows.append(pd.DataFrame({'uid':uid,'item_id':chosen,'rank':np.arange(1,101,dtype=np.int64)}))\n+    out=pd.concat(rows,ignore_index=True).astype({'uid':'int64','item_id':'int64','rank':'int64'})\n+    path=OUT/('submission.parquet' if final else f'validation_v{variant}.parquet')\n+    out.to_parquet(path,index=False)\n+    print(path, len(g), len(liked), len(disliked))\n+\n+\n+if __name__=='__main__':\n+    ap=argparse.ArgumentParser(); ap.add_argument('--stage',choices=['validation','test'],default='validation'); ap.add_argument('--variant',type=int,default=0)\n+    a=ap.parse_args(); build(a.stage,a.variant)\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-21752a7e-c16e-4322-9faa-de330445fa0a",
    "changes": {
      "/app/solution/build.py": {
        "type": "add",
        "content": "#!/usr/bin/env python3\n\"\"\"Build leakage-safe validation and final discovery rankings.\"\"\"\nfrom pathlib import Path\nimport argparse\nimport numpy as np\nimport pandas as pd\n\nD = Path('/task/data')\nOUT = Path('/app/solution')\nCUTOFF = 24786800\n\n\ndef load_elig(stage):\n    z = np.load(D / f'{stage}_eligible_candidates.npz')\n    uids = z['uids'].astype(np.int64)\n    items = z['item_ids'].astype(np.int64)\n    bits = np.unpackbits(z['packed_eligible'], axis=1, bitorder='little')[:, :len(items)].astype(bool)\n    return uids, items, bits\n\n\ndef build(stage='validation', variant=0):\n    final = stage == 'test'\n    uids, items, eligible = load_elig(stage)\n    uidpos = pd.Series(np.arange(len(uids), dtype=np.int32), index=uids)\n    itempos = pd.Series(np.arange(len(items), dtype=np.int32), index=items)\n    files = ['train_listens'] + (['val_listens'] if final else [])\n    frames=[]\n    for fn in files:\n        x=pd.read_parquet(D/f'{fn}.parquet', columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\n        x=x[x.uid.isin(uids) & x.item_id.isin(items)]\n        x['positive']=(x.played_ratio_pct>=50).astype(np.int8)\n        x['strong']=(x.played_ratio_pct>=80).astype(np.int8)\n        frames.append(x)\n    x=pd.concat(frames,ignore_index=True)\n    cutoff = int(x.timestamp.max()) + 5\n    # One row per observed user-catalog pair. Organic strong behavior is closest to target.\n    x['orgstrong']=((x.is_organic==1)&(x.strong==1)).astype(np.int8)\n    x['orgpos']=((x.is_organic==1)&(x.positive==1)).astype(np.int8)\n    x['recstrong']=x.orgstrong*np.exp(-(cutoff-x.timestamp)/2_592_000.0)\n    x['recpos']=x.positive*np.exp(-(cutoff-x.timestamp)/2_592_000.0)\n    g=x.groupby(['uid','item_id'],sort=False).agg(\n        n=('positive','sum'), strong=('strong','sum'), org=('orgpos','sum'),\n        orgstrong=('orgstrong','sum'), rec=('recstrong','sum'), recall=('recpos','sum'),\n        last=('timestamp','max'), lastorg=('is_organic','max')).reset_index()\n    del x, frames\n    # Explicit cutoff state is supplied and is safer than reconstructing it.\n    pref=pd.read_parquet(D/f'{stage}_preference_state.parquet')\n    pref=pref[pref.uid.isin(uids)&pref.item_id.isin(items)]\n    liked=set(zip(pref.loc[pref.state.eq('liked'),'uid'],pref.loc[pref.state.eq('liked'),'item_id']))\n    disliked=set(zip(pref.loc[pref.state.eq('disliked'),'uid'],pref.loc[pref.state.eq('disliked'),'item_id']))\n    pairs=list(zip(g.uid,g.item_id))\n    g['liked']=np.fromiter((p in liked for p in pairs),bool,len(g))\n    g['disliked']=np.fromiter((p in disliked for p in pairs),bool,len(g))\n    age=(cutoff-g['last'])/86400.0\n    # Variants are deliberately compact for quick public tuning.\n    if variant==0:\n        score=3.2*np.log1p(g.orgstrong)+1.0*np.log1p(g.strong)+2.0*g.rec+0.5*g.recall+3.0*g.liked-8*g.disliked-0.012*age\n    elif variant==1:\n        score=2.2*np.log1p(g.orgstrong)+0.5*np.log1p(g.strong)+4.0*g.rec+0.8*g.recall+4.5*g.liked-10*g.disliked-0.020*age\n    elif variant==2:\n        score=4.0*np.log1p(g.orgstrong)+1.0*np.log1p(g.org)+1.2*g.rec+0.3*g.recall+5.0*g.liked-10*g.disliked-0.006*age\n    else:\n        score=2.0*np.log1p(g.orgstrong)+0.5*np.log1p(g.strong)+2.5*g.rec+0.4*g.recall+7.0*g.liked-10*g.disliked-0.01*age\n    g['score']=score\n    # Catalog order is a strong and stable fallback; organic popularity breaks sparse-history ties.\n    cat=pd.read_parquet(D/'candidate_catalog.parquet').set_index('item_id').loc[items]\n    pop=np.log1p(cat.organic_positive_listens.to_numpy())\n    pop=(pop-pop.min())/(pop.max()-pop.min())\n    rows=[]\n    grouped={int(uid): q for uid,q in g.groupby('uid',sort=False)}\n    for r,uid in enumerate(uids):\n        mask=eligible[r]\n        idx=np.flatnonzero(mask)\n        # fallback score tiny enough to preserve personal candidates\n        base=0.03*pop[idx]\n        q=grouped.get(int(uid))\n        if q is not None:\n            qi=itempos.loc[q.item_id].to_numpy()\n            ok=mask[qi]\n            qi=qi[ok]; qs=q.score.to_numpy()[ok]\n            # Put personalized values into eligible candidate vector.\n            pos=np.searchsorted(idx,qi)\n            valid=(pos<len(idx))&(idx[np.minimum(pos,len(idx)-1)]==qi)\n            base[pos[valid]]=qs[valid]+0.03*pop[qi[valid]]\n        take=np.argpartition(-base,99)[:100]\n        take=take[np.argsort(-base[take],kind='stable')]\n        chosen=items[idx[take]]\n        rows.append(pd.DataFrame({'uid':uid,'item_id':chosen,'rank':np.arange(1,101,dtype=np.int64)}))\n    out=pd.concat(rows,ignore_index=True).astype({'uid':'int64','item_id':'int64','rank':'int64'})\n    path=OUT/('submission.parquet' if final else f'validation_v{variant}.parquet')\n    out.to_parquet(path,index=False)\n    print(path, len(g), len(liked), len(disliked))\n\n\nif __name__=='__main__':\n    ap=argparse.ArgumentParser(); ap.add_argument('--stage',choices=['validation','test'],default='validation'); ap.add_argument('--variant',type=int,default=0)\n    a=ap.parse_args(); build(a.stage,a.variant)\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/build.py\n",
    "stderr": ""
  }
}
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14

exec command

+01:14
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:"ls -l solution/build.py && /opt/task-venv/bin/python solution/build.py --stage validation --variant 0","workdir":"/app","yield_time_ms":30000,"max_output_tokens":3000});
text(r.output);

Tool response

Script completed
Wall time 20.4 seconds
Output:

-rw-r--r-- 1 agent agent 4974 Sep 25 01:11 solution/build.py
/opt/task-venv/lib/python3.12/site-packages/pandas/core/arraylike.py:399: RuntimeWarning: overflow encountered in exp
  result = getattr(ufunc, method)(*inputs, **kwargs)
/opt/task-venv/lib/python3.12/site-packages/pandas/core/arraylike.py:399: RuntimeWarning: overflow encountered in exp
  result = getattr(ufunc, method)(*inputs, **kwargs)
/app/solution/validation_v0.parquet 7868664 418435 50689
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  }
}
15

exec command

+01:38
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 patch = "*** Begin Patch\n*** Update File: /app/solution/build.py\n@@\n         x=pd.read_parquet(D/f'{fn}.parquet', columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\n         x=x[x.uid.isin(uids) & x.item_id.isin(items)]\n+        x['timestamp']=x.timestamp.astype(np.int64)\n@@\n     pref=pd.read_parquet(D/f'{stage}_preference_state.parquet')\n     pref=pref[pref.uid.isin(uids)&pref.item_id.isin(items)]\n@@\n     disliked=set(zip(pref.loc[pref.state.eq('disliked'),'uid'],pref.loc[pref.state.eq('disliked'),'item_id']))\n+    # Preserve explicitly liked candidates even in the unusual case with no catalog listen row.\n+    missing=pref.loc[pref.state.eq('liked'),['uid','item_id']].drop_duplicates().merge(\n+        g[['uid','item_id']],on=['uid','item_id'],how='left',indicator=True)\n+    missing=missing[missing._merge.eq('left_only')][['uid','item_id']]\n+    if len(missing):\n+        for c in ['n','strong','org','orgstrong','rec','recall','last','lastorg']:\n+            missing[c]=0\n+        g=pd.concat([g,missing],ignore_index=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-fa5f4a91-ae8e-49a9-ac54-d1f74e6c6866",
    "changes": {
      "/app/solution/build.py": {
        "type": "update",
        "unified_diff": "@@ -30,2 +30,3 @@\n         x=x[x.uid.isin(uids) & x.item_id.isin(items)]\n+        x['timestamp']=x.timestamp.astype(np.int64)\n         x['positive']=(x.played_ratio_pct>=50).astype(np.int8)\n@@ -50,2 +51,10 @@\n     disliked=set(zip(pref.loc[pref.state.eq('disliked'),'uid'],pref.loc[pref.state.eq('disliked'),'item_id']))\n+    # Preserve explicitly liked candidates even in the unusual case with no catalog listen row.\n+    missing=pref.loc[pref.state.eq('liked'),['uid','item_id']].drop_duplicates().merge(\n+        g[['uid','item_id']],on=['uid','item_id'],how='left',indicator=True)\n+    missing=missing[missing._merge.eq('left_only')][['uid','item_id']]\n+    if len(missing):\n+        for c in ['n','strong','org','orgstrong','rec','recall','last','lastorg']:\n+            missing[c]=0\n+        g=pd.concat([g,missing],ignore_index=True)\n     pairs=list(zip(g.uid,g.item_id))\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/build.py\n",
    "stderr": ""
  }
}
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16

exec command

+01:46
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.exec_command({cmd:"/opt/task-venv/bin/python solution/build.py --stage validation --variant 0 && /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_v0.parquet","workdir":"/app","yield_time_ms":30000,"max_output_tokens":3000});
text(r.output);

Tool response

Script completed
Wall time 24.5 seconds
Output:

/app/solution/validation_v0.parquet 7875832 418435 50689
{
  "model": "solution/validation_v0.parquet",
  "scores": {
    "quality_ndcg@10": 0.010577,
    "quality_users": 4263
  }
}
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}
17

exec command

+02:20
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 first personalized frequency/recency model is valid but scores only 0.010577 NDCG@10. That suggests the next-week organic targets are not well served by simply replaying each user’s most frequent tracks. I’m testing global popularity/trend and latent collaborative signals next; the explicit state will remain a feature, not an automatic top-rank rule.

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Update File: /app/solution/build.py\n@@\n     else:\n         score=2.0*np.log1p(g.orgstrong)+0.5*np.log1p(g.strong)+2.5*g.rec+0.4*g.recall+7.0*g.liked-10*g.disliked-0.01*age\n+    if variant==9:\n+        score=pd.Series(-1.0,index=g.index)\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-faa74ee3-6be0-4969-9781-d0ed95cb8f39",
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        "unified_diff": "@@ -72,2 +72,4 @@\n         score=2.0*np.log1p(g.orgstrong)+0.5*np.log1p(g.strong)+2.5*g.rec+0.4*g.recall+7.0*g.liked-10*g.disliked-0.01*age\n+    if variant==9:\n+        score=pd.Series(-1.0,index=g.index)\n     g['score']=score\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/build.py\n",
    "stderr": ""
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python solution/build.py --stage validation --variant 9 && /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_v9.parquet","workdir":"/app","yield_time_ms":30000,"max_output_tokens":2000});
text(r.output);

Tool response

Script completed
Wall time 24.5 seconds
Output:

/app/solution/validation_v9.parquet 7875832 418435 50689
{
  "model": "solution/validation_v9.parquet",
  "scores": {
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}
18

exec command

+02:54
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.exec_command({cmd:"/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd\nx=pd.read_parquet('solution/validation_v0.parquet'); y=pd.read_parquet('solution/validation_v9.parquet')\nprint((x.item_id==y.item_id).mean());print(x.head(15));print(y.head(15))\nPY","workdir":"/app","yield_time_ms":10000,"max_output_tokens":2000});
text(r.output);

Tool response

Script completed
Wall time 0.8 seconds
Output:

1.0
    uid  item_id  rank
0   100  5862961     1
1   100  3542184     2
2   100  6901374     3
3   100  5635052     4
4   100   906358     5
5   100  8213481     6
6   100  2859641     7
7   100  3033749     8
8   100  5436843     9
9   100  2651314    10
10  100  5252571    11
11  100  5927996    12
12  100  4341029    13
13  100  7537006    14
14  100  1846252    15
    uid  item_id  rank
0   100  5862961     1
1   100  3542184     2
2   100  6901374     3
3   100  5635052     4
4   100   906358     5
5   100  8213481     6
6   100  2859641     7
7   100  3033749     8
8   100  5436843     9
9   100  2651314    10
10  100  5252571    11
11  100  5927996    12
12  100  4341029    13
13  100  7537006    14
14  100  1846252    15
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Harness context codex item completed
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    "stdout": "1.0\n    uid  item_id  rank\n0   100  5862961     1\n1   100  3542184     2\n2   100  6901374     3\n3   100  5635052     4\n4   100   906358     5\n5   100  8213481     6\n6   100  2859641     7\n7   100  3033749     8\n8   100  5436843     9\n9   100  2651314    10\n10  100  5252571    11\n11  100  5927996    12\n12  100  4341029    13\n13  100  7537006    14\n14  100  1846252    15\n    uid  item_id  rank\n0   100  5862961     1\n1   100  3542184     2\n2   100  6901374     3\n3   100  5635052     4\n4   100   906358     5\n5   100  8213481     6\n6   100  2859641     7\n7   100  3033749     8\n8   100  5436843     9\n9   100  2651314    10\n10  100  5252571    11\n11  100  5927996    12\n12  100  4341029    13\n13  100  7537006    14\n14  100  1846252    15\n",
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    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 620277972
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  }
}
19

Agent message

+03:08
Thinking unavailable No text recorded

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

20

exec command

+03:15
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/latent.py\n+#!/usr/bin/env python3\n+\"\"\"PureSVD collaborative ranker over cutoff-correct positive listening history.\"\"\"\n+from pathlib import Path\n+import argparse\n+import numpy as np\n+import pandas as pd\n+from scipy.sparse import coo_matrix\n+from sklearn.decomposition import TruncatedSVD\n+\n+D=Path('/task/data'); O=Path('/app/solution')\n+\n+def run(stage, components=96):\n+    final=stage=='test'\n+    z=np.load(D/f'{stage}_eligible_candidates.npz')\n+    tu=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64)\n+    elig=np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(items)].astype(bool)\n+    ip=pd.Series(np.arange(len(items),dtype=np.int32),index=items)\n+    fs=['train_listens']+(['val_listens'] if final else [])\n+    aa=[]\n+    for f in fs:\n+        x=pd.read_parquet(D/f'{f}.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\n+        x=x[x.item_id.isin(items)&(x.played_ratio_pct>=50)]\n+        x['w']=1.0 + 1.5*((x.is_organic==1)&(x.played_ratio_pct>=80)) + .35*(x.played_ratio_pct>=80)\n+        aa.append(x[['uid','item_id','w']])\n+    x=pd.concat(aa,ignore_index=True)\n+    g=x.groupby(['uid','item_id'],sort=False).w.sum().reset_index()\n+    users=np.sort(g.uid.unique()); up=pd.Series(np.arange(len(users),dtype=np.int32),index=users)\n+    rr=up.loc[g.uid].to_numpy(); cc=ip.loc[g.item_id].to_numpy()\n+    # Saturation prevents a few habitual tracks from determining the latent space.\n+    vv=np.log1p(g.w.to_numpy()).astype(np.float32)\n+    X=coo_matrix((vv,(rr,cc)),shape=(len(users),len(items))).tocsr()\n+    # IDF downweights universal hits; popularity is blended explicitly at ranking time.\n+    df=np.diff(X.tocsc().indptr)\n+    idf=np.log((len(users)+1)/(df+30)).clip(.15,4.0).astype(np.float32)\n+    X=X.multiply(idf).tocsr()\n+    svd=TruncatedSVD(n_components=components,n_iter=5,random_state=1977)\n+    U=svd.fit_transform(X).astype(np.float32)\n+    V=svd.components_.astype(np.float32)\n+    print('matrix',X.shape,X.nnz,'variance',svd.explained_variance_ratio_.sum())\n+    ur=up.loc[tu].to_numpy()\n+    cat=pd.read_parquet(D/'candidate_catalog.parquet').set_index('item_id').loc[items]\n+    pop=np.log1p(cat.organic_positive_listens.to_numpy()).astype(np.float32)\n+    pop=(pop-pop.mean())/pop.std()\n+    # Multiple blends cost little and enable public selection without refitting.\n+    alphas=[0.0,.05,.12,.25,.5]\n+    outputs=[[] for _ in alphas]\n+    for start in range(0,len(tu),256):\n+        stop=min(start+256,len(tu))\n+        s=U[ur[start:stop]]@V\n+        # standardize latent scores user-wise so blend weights are interpretable\n+        s=(s-s.mean(1,keepdims=True))/(s.std(1,keepdims=True)+1e-6)\n+        s[~elig[start:stop]]=-1e9\n+        for oi,a in enumerate(alphas):\n+            q=s+a*pop[None,:]\n+            ix=np.argpartition(-q,99,axis=1)[:,:100]\n+            vals=np.take_along_axis(q,ix,axis=1)\n+            order=np.argsort(-vals,axis=1,kind='stable')\n+            ix=np.take_along_axis(ix,order,axis=1)\n+            for j,row in enumerate(ix):\n+                outputs[oi].append(pd.DataFrame({'uid':int(tu[start+j]),'item_id':items[row],'rank':np.arange(1,101)}))\n+    for a,parts in zip(alphas,outputs):\n+        out=pd.concat(parts,ignore_index=True).astype(np.int64)\n+        name='submission.parquet' if final and a==.12 else f'{stage}_svd_{components}_a{a}.parquet'\n+        out.to_parquet(O/name,index=False)\n+        print(name)\n+\n+if __name__=='__main__':\n+    p=argparse.ArgumentParser();p.add_argument('--stage',default='validation',choices=['validation','test']);p.add_argument('--components',type=int,default=96)\n+    a=p.parse_args();run(a.stage,a.components)\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-47a4d0fa-c9df-46a1-badc-9d69b384d068",
    "changes": {
      "/app/solution/latent.py": {
        "type": "add",
        "content": "#!/usr/bin/env python3\n\"\"\"PureSVD collaborative ranker over cutoff-correct positive listening history.\"\"\"\nfrom pathlib import Path\nimport argparse\nimport numpy as np\nimport pandas as pd\nfrom scipy.sparse import coo_matrix\nfrom sklearn.decomposition import TruncatedSVD\n\nD=Path('/task/data'); O=Path('/app/solution')\n\ndef run(stage, components=96):\n    final=stage=='test'\n    z=np.load(D/f'{stage}_eligible_candidates.npz')\n    tu=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64)\n    elig=np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(items)].astype(bool)\n    ip=pd.Series(np.arange(len(items),dtype=np.int32),index=items)\n    fs=['train_listens']+(['val_listens'] if final else [])\n    aa=[]\n    for f in fs:\n        x=pd.read_parquet(D/f'{f}.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\n        x=x[x.item_id.isin(items)&(x.played_ratio_pct>=50)]\n        x['w']=1.0 + 1.5*((x.is_organic==1)&(x.played_ratio_pct>=80)) + .35*(x.played_ratio_pct>=80)\n        aa.append(x[['uid','item_id','w']])\n    x=pd.concat(aa,ignore_index=True)\n    g=x.groupby(['uid','item_id'],sort=False).w.sum().reset_index()\n    users=np.sort(g.uid.unique()); up=pd.Series(np.arange(len(users),dtype=np.int32),index=users)\n    rr=up.loc[g.uid].to_numpy(); cc=ip.loc[g.item_id].to_numpy()\n    # Saturation prevents a few habitual tracks from determining the latent space.\n    vv=np.log1p(g.w.to_numpy()).astype(np.float32)\n    X=coo_matrix((vv,(rr,cc)),shape=(len(users),len(items))).tocsr()\n    # IDF downweights universal hits; popularity is blended explicitly at ranking time.\n    df=np.diff(X.tocsc().indptr)\n    idf=np.log((len(users)+1)/(df+30)).clip(.15,4.0).astype(np.float32)\n    X=X.multiply(idf).tocsr()\n    svd=TruncatedSVD(n_components=components,n_iter=5,random_state=1977)\n    U=svd.fit_transform(X).astype(np.float32)\n    V=svd.components_.astype(np.float32)\n    print('matrix',X.shape,X.nnz,'variance',svd.explained_variance_ratio_.sum())\n    ur=up.loc[tu].to_numpy()\n    cat=pd.read_parquet(D/'candidate_catalog.parquet').set_index('item_id').loc[items]\n    pop=np.log1p(cat.organic_positive_listens.to_numpy()).astype(np.float32)\n    pop=(pop-pop.mean())/pop.std()\n    # Multiple blends cost little and enable public selection without refitting.\n    alphas=[0.0,.05,.12,.25,.5]\n    outputs=[[] for _ in alphas]\n    for start in range(0,len(tu),256):\n        stop=min(start+256,len(tu))\n        s=U[ur[start:stop]]@V\n        # standardize latent scores user-wise so blend weights are interpretable\n        s=(s-s.mean(1,keepdims=True))/(s.std(1,keepdims=True)+1e-6)\n        s[~elig[start:stop]]=-1e9\n        for oi,a in enumerate(alphas):\n            q=s+a*pop[None,:]\n            ix=np.argpartition(-q,99,axis=1)[:,:100]\n            vals=np.take_along_axis(q,ix,axis=1)\n            order=np.argsort(-vals,axis=1,kind='stable')\n            ix=np.take_along_axis(ix,order,axis=1)\n            for j,row in enumerate(ix):\n                outputs[oi].append(pd.DataFrame({'uid':int(tu[start+j]),'item_id':items[row],'rank':np.arange(1,101)}))\n    for a,parts in zip(alphas,outputs):\n        out=pd.concat(parts,ignore_index=True).astype(np.int64)\n        name='submission.parquet' if final and a==.12 else f'{stage}_svd_{components}_a{a}.parquet'\n        out.to_parquet(O/name,index=False)\n        print(name)\n\nif __name__=='__main__':\n    p=argparse.ArgumentParser();p.add_argument('--stage',default='validation',choices=['validation','test']);p.add_argument('--components',type=int,default=96)\n    a=p.parse_args();run(a.stage,a.components)\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/latent.py\n",
    "stderr": ""
  }
}
Harness context token usage record
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21

exec command

+03:39
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:"/opt/task-venv/bin/python solution/latent.py --stage validation --components 96","workdir":"/app","yield_time_ms":30000,"max_output_tokens":3000});
text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 7.7 seconds
Output:

Traceback (most recent call last):
  File "/app/solution/latent.py", line 69, in <module>
matrix (9135, 30000) 5564926 variance 0.28869814
    a=p.parse_args();run(a.stage,a.components)
                     ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/app/solution/latent.py", line 40, in run
    ur=up.loc[tu].to_numpy()
       ~~~~~~^^^^
  File "/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexing.py", line 1192, in __getitem__
    return self._getitem_axis(maybe_callable, axis=axis)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexing.py", line 1421, in _getitem_axis
    return self._getitem_iterable(key, axis=axis)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexing.py", line 1361, in _getitem_iterable
    keyarr, indexer = self._get_listlike_indexer(key, axis)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexing.py", line 1559, in _get_listlike_indexer
    keyarr, indexer = ax._get_indexer_strict(key, axis_name)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexes/base.py", line 6212, in _get_indexer_strict
    self._raise_if_missing(keyarr, indexer, axis_name)
  File "/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexes/base.py", line 6264, in _raise_if_missing
    raise KeyError(f"{not_found} not in index")
KeyError: '[121300, 122000, 213900, 319800, 523800, 772200, 862800] not in index'
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Harness context codex item completed
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    "type": "CommandExecution",
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      "/bin/sh",
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        "cmd": "/opt/task-venv/bin/python solution/latent.py --stage validation --components 96"
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    "status": "failed",
    "stdout": "Traceback (most recent call last):\n  File \"/app/solution/latent.py\", line 69, in <module>\nmatrix (9135, 30000) 5564926 variance 0.28869814\n    a=p.parse_args();run(a.stage,a.components)\n                     ^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/app/solution/latent.py\", line 40, in run\n    ur=up.loc[tu].to_numpy()\n       ~~~~~~^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexing.py\", line 1192, in __getitem__\n    return self._getitem_axis(maybe_callable, axis=axis)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexing.py\", line 1421, in _getitem_axis\n    return self._getitem_iterable(key, axis=axis)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexing.py\", line 1361, in _getitem_iterable\n    keyarr, indexer = self._get_listlike_indexer(key, axis)\n                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexing.py\", line 1559, in _get_listlike_indexer\n    keyarr, indexer = ax._get_indexer_strict(key, axis_name)\n                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexes/base.py\", line 6212, in _get_indexer_strict\n    self._raise_if_missing(keyarr, indexer, axis_name)\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexes/base.py\", line 6264, in _raise_if_missing\n    raise KeyError(f\"{not_found} not in index\")\nKeyError: '[121300, 122000, 213900, 319800, 523800, 772200, 862800] not in index'\n",
    "stderr": "",
    "aggregated_output": "Traceback (most recent call last):\n  File \"/app/solution/latent.py\", line 69, in <module>\nmatrix (9135, 30000) 5564926 variance 0.28869814\n    a=p.parse_args();run(a.stage,a.components)\n                     ^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/app/solution/latent.py\", line 40, in run\n    ur=up.loc[tu].to_numpy()\n       ~~~~~~^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexing.py\", line 1192, in __getitem__\n    return self._getitem_axis(maybe_callable, axis=axis)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexing.py\", line 1421, in _getitem_axis\n    return self._getitem_iterable(key, axis=axis)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexing.py\", line 1361, in _getitem_iterable\n    keyarr, indexer = self._get_listlike_indexer(key, axis)\n                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexing.py\", line 1559, in _get_listlike_indexer\n    keyarr, indexer = ax._get_indexer_strict(key, axis_name)\n                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexes/base.py\", line 6212, in _get_indexer_strict\n    self._raise_if_missing(keyarr, indexer, axis_name)\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexes/base.py\", line 6264, in _raise_if_missing\n    raise KeyError(f\"{not_found} not in index\")\nKeyError: '[121300, 122000, 213900, 319800, 523800, 772200, 862800] not in index'\n",
    "exit_code": 1,
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      "secs": 7,
      "nanos": 569829206
    },
    "formatted_output": "Traceback (most recent call last):\n  File \"/app/solution/latent.py\", line 69, in <module>\nmatrix (9135, 30000) 5564926 variance 0.28869814\n    a=p.parse_args();run(a.stage,a.components)\n                     ^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/app/solution/latent.py\", line 40, in run\n    ur=up.loc[tu].to_numpy()\n       ~~~~~~^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexing.py\", line 1192, in __getitem__\n    return self._getitem_axis(maybe_callable, axis=axis)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexing.py\", line 1421, in _getitem_axis\n    return self._getitem_iterable(key, axis=axis)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexing.py\", line 1361, in _getitem_iterable\n    keyarr, indexer = self._get_listlike_indexer(key, axis)\n                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexing.py\", line 1559, in _get_listlike_indexer\n    keyarr, indexer = ax._get_indexer_strict(key, axis_name)\n                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexes/base.py\", line 6212, in _get_indexer_strict\n    self._raise_if_missing(keyarr, indexer, axis_name)\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/core/indexes/base.py\", line 6264, in _raise_if_missing\n    raise KeyError(f\"{not_found} not in index\")\nKeyError: '[121300, 122000, 213900, 319800, 523800, 772200, 862800] not in index'\n"
  }
}
22

exec command

+03:50
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 patch = "*** Begin Patch\n*** Update File: /app/solution/latent.py\n@@\n-    ur=up.loc[tu].to_numpy()\n+    ur=up.reindex(tu).fillna(-1).astype(np.int32).to_numpy()\n+    target_U=np.zeros((len(tu),components),dtype=np.float32)\n+    known=ur>=0; target_U[known]=U[ur[known]]\n@@\n-        s=U[ur[start:stop]]@V\n+        s=target_U[start:stop]@V\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-5b903b5f-1c26-49ac-a25f-86b707f77532",
    "changes": {
      "/app/solution/latent.py": {
        "type": "update",
        "unified_diff": "@@ -39,3 +39,5 @@\n     print('matrix',X.shape,X.nnz,'variance',svd.explained_variance_ratio_.sum())\n-    ur=up.loc[tu].to_numpy()\n+    ur=up.reindex(tu).fillna(-1).astype(np.int32).to_numpy()\n+    target_U=np.zeros((len(tu),components),dtype=np.float32)\n+    known=ur>=0; target_U[known]=U[ur[known]]\n     cat=pd.read_parquet(D/'candidate_catalog.parquet').set_index('item_id').loc[items]\n@@ -48,3 +50,3 @@\n         stop=min(start+256,len(tu))\n-        s=U[ur[start:stop]]@V\n+        s=target_U[start:stop]@V\n         # standardize latent scores user-wise so blend weights are interpretable\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/latent.py\n",
    "stderr": ""
  }
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python solution/latent.py --stage validation --components 96","workdir":"/app","yield_time_ms":30000,"max_output_tokens":3000});
text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 21.3 seconds
Output:

matrix (9135, 30000) 5564926 variance 0.28869814
validation_svd_96_a0.0.parquet
validation_svd_96_a0.05.parquet
validation_svd_96_a0.12.parquet
validation_svd_96_a0.25.parquet
validation_svd_96_a0.5.parquet
Harness context token usage record
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    "stdout": "matrix (9135, 30000) 5564926 variance 0.28869814\nvalidation_svd_96_a0.0.parquet\nvalidation_svd_96_a0.05.parquet\nvalidation_svd_96_a0.12.parquet\nvalidation_svd_96_a0.25.parquet\nvalidation_svd_96_a0.5.parquet\n",
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}
23

exec command

+04:19
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.exec_command({cmd:"for f in solution/validation_svd_96_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done","workdir":"/app","yield_time_ms":30000,"max_output_tokens":5000});
text(r.output);

Tool response

Script completed
Wall time 12.9 seconds
Output:

{
  "model": "solution/validation_svd_96_a0.0.parquet",
  "scores": {
    "quality_ndcg@10": 0.018265,
    "quality_users": 4263
  }
}
{
  "model": "solution/validation_svd_96_a0.05.parquet",
  "scores": {
    "quality_ndcg@10": 0.018348,
    "quality_users": 4263
  }
}
{
  "model": "solution/validation_svd_96_a0.12.parquet",
  "scores": {
    "quality_ndcg@10": 0.018661,
    "quality_users": 4263
  }
}
{
  "model": "solution/validation_svd_96_a0.25.parquet",
  "scores": {
    "quality_ndcg@10": 0.01892,
    "quality_users": 4263
  }
}
{
  "model": "solution/validation_svd_96_a0.5.parquet",
  "scores": {
    "quality_ndcg@10": 0.019665,
    "quality_users": 4263
  }
}
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    "exit_code": 0,
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  }
}
24

exec command

+04:45
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 collaborative model lifts validation to 0.019665, nearly doubling the popularity baseline. The improvement is still increasing as I blend in popularity, so I’m tuning that blend and adding two signals aligned with the metric: prior likes and recent organic-strong catalog momentum.

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Update File: /app/solution/latent.py\n@@\n-        x=pd.read_parquet(D/f'{f}.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\n+        x=pd.read_parquet(D/f'{f}.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\n@@\n-        aa.append(x[['uid','item_id','w']])\n+        aa.append(x[['uid','timestamp','item_id','w','is_organic','played_ratio_pct']])\n     x=pd.concat(aa,ignore_index=True)\n+    cutoff=int(x.timestamp.max())+1\n+    recent=x[(x.timestamp>=cutoff-30*86400)&(x.is_organic==1)&(x.played_ratio_pct>=80)].groupby('item_id').size()\n     g=x.groupby(['uid','item_id'],sort=False).w.sum().reset_index()\n+    # Explicit positives strengthen preference geometry (future labels weight likes heavily).\n+    lf=['train_likes']+(['val_likes'] if final else [])\n+    likes=pd.concat([pd.read_parquet(D/f'{f}.parquet',columns=['uid','item_id','is_organic']) for f in lf],ignore_index=True)\n+    likes=likes[likes.item_id.isin(items)]\n+    likes['w']=2.0+2.0*likes.is_organic\n+    lg=likes.groupby(['uid','item_id'],sort=False).w.sum().reset_index()\n+    g=pd.concat([g,lg],ignore_index=True).groupby(['uid','item_id'],sort=False).w.sum().reset_index()\n@@\n     pop=np.log1p(cat.organic_positive_listens.to_numpy()).astype(np.float32)\n     pop=(pop-pop.mean())/pop.std()\n+    momentum=np.log1p(recent.reindex(items,fill_value=0).to_numpy()).astype(np.float32)\n+    momentum=(momentum-momentum.mean())/(momentum.std()+1e-6)\n+    pop=.45*pop+.55*momentum\n@@\n-    alphas=[0.0,.05,.12,.25,.5]\n+    alphas=[.25,.5,.75,1.0,1.5]\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-6866dd35-30a3-4f2a-a05b-51e02de8e675",
    "changes": {
      "/app/solution/latent.py": {
        "type": "update",
        "unified_diff": "@@ -20,8 +20,17 @@\n     for f in fs:\n-        x=pd.read_parquet(D/f'{f}.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\n+        x=pd.read_parquet(D/f'{f}.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\n         x=x[x.item_id.isin(items)&(x.played_ratio_pct>=50)]\n         x['w']=1.0 + 1.5*((x.is_organic==1)&(x.played_ratio_pct>=80)) + .35*(x.played_ratio_pct>=80)\n-        aa.append(x[['uid','item_id','w']])\n+        aa.append(x[['uid','timestamp','item_id','w','is_organic','played_ratio_pct']])\n     x=pd.concat(aa,ignore_index=True)\n+    cutoff=int(x.timestamp.max())+1\n+    recent=x[(x.timestamp>=cutoff-30*86400)&(x.is_organic==1)&(x.played_ratio_pct>=80)].groupby('item_id').size()\n     g=x.groupby(['uid','item_id'],sort=False).w.sum().reset_index()\n+    # Explicit positives strengthen preference geometry (future labels weight likes heavily).\n+    lf=['train_likes']+(['val_likes'] if final else [])\n+    likes=pd.concat([pd.read_parquet(D/f'{f}.parquet',columns=['uid','item_id','is_organic']) for f in lf],ignore_index=True)\n+    likes=likes[likes.item_id.isin(items)]\n+    likes['w']=2.0+2.0*likes.is_organic\n+    lg=likes.groupby(['uid','item_id'],sort=False).w.sum().reset_index()\n+    g=pd.concat([g,lg],ignore_index=True).groupby(['uid','item_id'],sort=False).w.sum().reset_index()\n     users=np.sort(g.uid.unique()); up=pd.Series(np.arange(len(users),dtype=np.int32),index=users)\n@@ -45,4 +54,7 @@\n     pop=(pop-pop.mean())/pop.std()\n+    momentum=np.log1p(recent.reindex(items,fill_value=0).to_numpy()).astype(np.float32)\n+    momentum=(momentum-momentum.mean())/(momentum.std()+1e-6)\n+    pop=.45*pop+.55*momentum\n     # Multiple blends cost little and enable public selection without refitting.\n-    alphas=[0.0,.05,.12,.25,.5]\n+    alphas=[.25,.5,.75,1.0,1.5]\n     outputs=[[] for _ in alphas]\n",
        "move_path": null
      }
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    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/latent.py\n",
    "stderr": ""
  }
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25

exec command

+04:57
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 solution/latent.py --stage validation --components 192","workdir":"/app","yield_time_ms":30000,"max_output_tokens":3000});
text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 24.6 seconds
Output:

matrix (9910, 30000) 5687487 variance 0.3514017
validation_svd_192_a0.25.parquet
validation_svd_192_a0.5.parquet
validation_svd_192_a0.75.parquet
validation_svd_192_a1.0.parquet
validation_svd_192_a1.5.parquet
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26

exec command

+05:25
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 f in solution/validation_svd_192_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done","workdir":"/app","yield_time_ms":30000,"max_output_tokens":5000});
text(r.output);

Tool response

Script completed
Wall time 12.9 seconds
Output:

{
  "model": "solution/validation_svd_192_a0.25.parquet",
  "scores": {
    "quality_ndcg@10": 0.020904,
    "quality_users": 4263
  }
}
{
  "model": "solution/validation_svd_192_a0.5.parquet",
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    "quality_ndcg@10": 0.021714,
    "quality_users": 4263
  }
}
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  "model": "solution/validation_svd_192_a0.75.parquet",
  "scores": {
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    "quality_users": 4263
  }
}
{
  "model": "solution/validation_svd_192_a1.0.parquet",
  "scores": {
    "quality_ndcg@10": 0.023179,
    "quality_users": 4263
  }
}
{
  "model": "solution/validation_svd_192_a1.5.parquet",
  "scores": {
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    "quality_users": 4263
  }
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}
27

exec command

+05:42
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 patch = "*** Begin Patch\n*** Update File: /app/solution/latent.py\n@@\n-    alphas=[.25,.5,.75,1.0,1.5]\n+    alphas=[1.5,2.0,3.0,5.0,10.0]\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-792384d3-5aa3-478d-afa8-171b58d6db1e",
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        "type": "update",
        "unified_diff": "@@ -58,3 +58,3 @@\n     # Multiple blends cost little and enable public selection without refitting.\n-    alphas=[.25,.5,.75,1.0,1.5]\n+    alphas=[1.5,2.0,3.0,5.0,10.0]\n     outputs=[[] for _ in alphas]\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/latent.py\n",
    "stderr": ""
  }
}
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python solution/latent.py --stage validation --components 256","workdir":"/app","yield_time_ms":30000,"max_output_tokens":3000});
text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 26.1 seconds
Output:

matrix (9910, 30000) 5687487 variance 0.38356566
validation_svd_256_a1.5.parquet
validation_svd_256_a2.0.parquet
validation_svd_256_a3.0.parquet
validation_svd_256_a5.0.parquet
validation_svd_256_a10.0.parquet
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"for f in solution/validation_svd_256_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done","workdir":"/app","yield_time_ms":30000,"max_output_tokens":5000});
text(r.output);

Tool response

Script completed
Wall time 12.7 seconds
Output:

{
  "model": "solution/validation_svd_256_a1.5.parquet",
  "scores": {
    "quality_ndcg@10": 0.025239,
    "quality_users": 4263
  }
}
{
  "model": "solution/validation_svd_256_a10.0.parquet",
  "scores": {
    "quality_ndcg@10": 0.017392,
    "quality_users": 4263
  }
}
{
  "model": "solution/validation_svd_256_a2.0.parquet",
  "scores": {
    "quality_ndcg@10": 0.025807,
    "quality_users": 4263
  }
}
{
  "model": "solution/validation_svd_256_a3.0.parquet",
  "scores": {
    "quality_ndcg@10": 0.024902,
    "quality_users": 4263
  }
}
{
  "model": "solution/validation_svd_256_a5.0.parquet",
  "scores": {
    "quality_ndcg@10": 0.021708,
    "quality_users": 4263
  }
}
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  }
}
28

exec command

+06:31
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 patch = "*** Begin Patch\n*** Add File: /app/solution/globaltest.py\n+from pathlib import Path\n+import numpy as np, pandas as pd\n+D=Path('/task/data');O=Path('/app/solution')\n+z=np.load(D/'validation_eligible_candidates.npz'); us=z['uids'].astype('int64'); it=z['item_ids'].astype('int64')\n+el=np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(it)].astype(bool)\n+x=pd.read_parquet(D/'train_listens.parquet',columns=['timestamp','item_id','is_organic','played_ratio_pct'])\n+x=x[x.item_id.isin(it)&(x.is_organic==1)&(x.played_ratio_pct>=80)]; end=24786800\n+cat=pd.read_parquet(D/'candidate_catalog.parquet').set_index('item_id').loc[it]\n+signals={'all':np.log1p(cat.organic_positive_listens.to_numpy())}\n+for d in [3,7,14,30,60,120]: signals[str(d)]=np.log1p(x[x.timestamp>=end-d*86400].groupby('item_id').size().reindex(it,fill_value=0).to_numpy())\n+for name,s in signals.items():\n+ rows=[]\n+ for r,u in enumerate(us):\n+  q=s.copy();q[~el[r]]=-1e9; ix=np.argpartition(-q,99)[:100];ix=ix[np.argsort(-q[ix],kind='stable')]\n+  rows.append(pd.DataFrame({'uid':u,'item_id':it[ix],'rank':np.arange(1,101)}))\n+ pd.concat(rows).astype('int64').to_parquet(O/f'global_{name}.parquet',index=False)\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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    "id": "exec-d7b2466d-6e17-4d8d-972f-62a4e3b6fbed",
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      "/app/solution/globaltest.py": {
        "type": "add",
        "content": "from pathlib import Path\nimport numpy as np, pandas as pd\nD=Path('/task/data');O=Path('/app/solution')\nz=np.load(D/'validation_eligible_candidates.npz'); us=z['uids'].astype('int64'); it=z['item_ids'].astype('int64')\nel=np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(it)].astype(bool)\nx=pd.read_parquet(D/'train_listens.parquet',columns=['timestamp','item_id','is_organic','played_ratio_pct'])\nx=x[x.item_id.isin(it)&(x.is_organic==1)&(x.played_ratio_pct>=80)]; end=24786800\ncat=pd.read_parquet(D/'candidate_catalog.parquet').set_index('item_id').loc[it]\nsignals={'all':np.log1p(cat.organic_positive_listens.to_numpy())}\nfor d in [3,7,14,30,60,120]: signals[str(d)]=np.log1p(x[x.timestamp>=end-d*86400].groupby('item_id').size().reindex(it,fill_value=0).to_numpy())\nfor name,s in signals.items():\n rows=[]\n for r,u in enumerate(us):\n  q=s.copy();q[~el[r]]=-1e9; ix=np.argpartition(-q,99)[:100];ix=ix[np.argsort(-q[ix],kind='stable')]\n  rows.append(pd.DataFrame({'uid':u,'item_id':it[ix],'rank':np.arange(1,101)}))\n pd.concat(rows).astype('int64').to_parquet(O/f'global_{name}.parquet',index=False)\n"
      }
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    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/globaltest.py\n",
    "stderr": ""
  }
}
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python solution/globaltest.py && for f in solution/global_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done","workdir":"/app","yield_time_ms":30000,"max_output_tokens":7000});
text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 30.2 seconds
Output:

{
  "model": "solution/global_120.parquet",
  "scores": {
    "quality_ndcg@10": 0.014185,
    "quality_users": 4263
  }
}
{
  "model": "solution/global_14.parquet",
  "scores": {
    "quality_ndcg@10": 0.022878,
    "quality_users": 4263
  }
}
{
  "model": "solution/global_3.parquet",
  "scores": {
    "quality_ndcg@10": 0.025784,
    "quality_users": 4263
  }
}
{
  "model": "solution/global_30.parquet",
  "scores": {
    "quality_ndcg@10": 0.013313,
    "quality_users": 4263
  }
}

SESSION_ID=65737
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29

exec command

+07:13
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:65737,chars:"",yield_time_ms:30000,max_output_tokens:5000});
text(r.output);

Tool response

Script completed
Wall time 3.3 seconds
Output:

{
  "model": "solution/global_60.parquet",
  "scores": {
    "quality_ndcg@10": 0.015826,
    "quality_users": 4263
  }
}
{
  "model": "solution/global_7.parquet",
  "scores": {
    "quality_ndcg@10": 0.028131,
    "quality_users": 4263
  }
}
{
  "model": "solution/global_all.parquet",
  "scores": {
    "quality_ndcg@10": 0.010577,
    "quality_users": 4263
  }
}
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    "stderr": "",
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  }
}
30

exec command

+07:19
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 key temporal effect emerged: catalog popularity from only the last 7 training days scores 0.028131 by itself, while 30-day and lifetime popularity are substantially worse. I’m replacing the broad momentum feature with this cutoff-local signal and blending it with the collaborative model; this should also transfer naturally when the final cutoff advances by one week.

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Update File: /app/solution/latent.py\n@@\n-    recent=x[(x.timestamp>=cutoff-30*86400)&(x.is_organic==1)&(x.played_ratio_pct>=80)].groupby('item_id').size()\n+    recent=x[(x.timestamp>=cutoff-7*86400)&(x.is_organic==1)&(x.played_ratio_pct>=80)].groupby('item_id').size()\n@@\n-    pop=.45*pop+.55*momentum\n+    pop=.10*pop+.90*momentum\n@@\n-    alphas=[1.5,2.0,3.0,5.0,10.0]\n+    alphas=[.5,1.0,1.5,2.0,3.0]\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-25a056c7-9e6d-490f-9cbc-86d8b14b08cd",
    "changes": {
      "/app/solution/latent.py": {
        "type": "update",
        "unified_diff": "@@ -26,3 +26,3 @@\n     cutoff=int(x.timestamp.max())+1\n-    recent=x[(x.timestamp>=cutoff-30*86400)&(x.is_organic==1)&(x.played_ratio_pct>=80)].groupby('item_id').size()\n+    recent=x[(x.timestamp>=cutoff-7*86400)&(x.is_organic==1)&(x.played_ratio_pct>=80)].groupby('item_id').size()\n     g=x.groupby(['uid','item_id'],sort=False).w.sum().reset_index()\n@@ -56,5 +56,5 @@\n     momentum=(momentum-momentum.mean())/(momentum.std()+1e-6)\n-    pop=.45*pop+.55*momentum\n+    pop=.10*pop+.90*momentum\n     # Multiple blends cost little and enable public selection without refitting.\n-    alphas=[1.5,2.0,3.0,5.0,10.0]\n+    alphas=[.5,1.0,1.5,2.0,3.0]\n     outputs=[[] for _ in alphas]\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/latent.py\n",
    "stderr": ""
  }
}
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python solution/latent.py --stage validation --components 256 && for f in solution/validation_svd_256_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done","workdir":"/app","yield_time_ms":30000,"max_output_tokens":7000});
text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 30.2 seconds
Output:

matrix (9910, 30000) 5687487 variance 0.38356566
validation_svd_256_a0.5.parquet
validation_svd_256_a1.0.parquet
validation_svd_256_a1.5.parquet
validation_svd_256_a2.0.parquet
validation_svd_256_a3.0.parquet
{
  "model": "solution/validation_svd_256_a0.5.parquet",
  "scores": {
    "quality_ndcg@10": 0.02298,
    "quality_users": 4263
  }
}

SESSION_ID=47832
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31

exec command

+07:59
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:47832,chars:"",yield_time_ms:30000,max_output_tokens:6000});
text(r.output);

Tool response

Script completed
Wall time 11.6 seconds
Output:

{
  "model": "solution/validation_svd_256_a1.0.parquet",
  "scores": {
    "quality_ndcg@10": 0.026658,
    "quality_users": 4263
  }
}
{
  "model": "solution/validation_svd_256_a1.5.parquet",
  "scores": {
    "quality_ndcg@10": 0.030491,
    "quality_users": 4263
  }
}
{
  "model": "solution/validation_svd_256_a10.0.parquet",
  "scores": {
    "quality_ndcg@10": 0.017392,
    "quality_users": 4263
  }
}
{
  "model": "solution/validation_svd_256_a2.0.parquet",
  "scores": {
    "quality_ndcg@10": 0.033796,
    "quality_users": 4263
  }
}
{
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  "scores": {
    "quality_ndcg@10": 0.036732,
    "quality_users": 4263
  }
}
{
  "model": "solution/validation_svd_256_a5.0.parquet",
  "scores": {
    "quality_ndcg@10": 0.021708,
    "quality_users": 4263
  }
}
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Harness context codex item completed
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}
32

exec command

+08:15
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*** Update File: /app/solution/latent.py\n@@\n-    alphas=[.5,1.0,1.5,2.0,3.0]\n+    alphas=[3.0,4.0,5.0,7.0,10.0]\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-948b1ea6-e1c8-4765-a435-a2717eac469e",
    "changes": {
      "/app/solution/latent.py": {
        "type": "update",
        "unified_diff": "@@ -58,3 +58,3 @@\n     # Multiple blends cost little and enable public selection without refitting.\n-    alphas=[.5,1.0,1.5,2.0,3.0]\n+    alphas=[3.0,4.0,5.0,7.0,10.0]\n     outputs=[[] for _ in alphas]\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/latent.py\n",
    "stderr": ""
  }
}
Harness context token usage record
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python solution/latent.py --stage validation --components 256","workdir":"/app","yield_time_ms":30000,"max_output_tokens":2000});
text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 25.8 seconds
Output:

matrix (9910, 30000) 5687487 variance 0.38356566
validation_svd_256_a3.0.parquet
validation_svd_256_a4.0.parquet
validation_svd_256_a5.0.parquet
validation_svd_256_a7.0.parquet
validation_svd_256_a10.0.parquet
Harness context token usage record
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Harness context codex item completed
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"for f in solution/validation_svd_256_a3.0.parquet solution/validation_svd_256_a4.0.parquet solution/validation_svd_256_a5.0.parquet solution/validation_svd_256_a7.0.parquet solution/validation_svd_256_a10.0.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done","workdir":"/app","yield_time_ms":30000,"max_output_tokens":6000});
text(r.output);

Tool response

Script completed
Wall time 12.7 seconds
Output:

{
  "model": "solution/validation_svd_256_a3.0.parquet",
  "scores": {
    "quality_ndcg@10": 0.036732,
    "quality_users": 4263
  }
}
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    "quality_ndcg@10": 0.037394,
    "quality_users": 4263
  }
}
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  "model": "solution/validation_svd_256_a5.0.parquet",
  "scores": {
    "quality_ndcg@10": 0.036901,
    "quality_users": 4263
  }
}
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  "scores": {
    "quality_ndcg@10": 0.035291,
    "quality_users": 4263
  }
}
{
  "model": "solution/validation_svd_256_a10.0.parquet",
  "scores": {
    "quality_ndcg@10": 0.034866,
    "quality_users": 4263
  }
}
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    "stderr": "",
    "aggregated_output": "{\n  \"model\": \"solution/validation_svd_256_a3.0.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.036732,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/validation_svd_256_a4.0.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.037394,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/validation_svd_256_a5.0.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.036901,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/validation_svd_256_a7.0.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.035291,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/validation_svd_256_a10.0.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.034866,\n    \"quality_users\": 4263\n  }\n}\n",
    "exit_code": 0,
    "duration": {
      "secs": 12,
      "nanos": 513159067
    },
    "formatted_output": "{\n  \"model\": \"solution/validation_svd_256_a3.0.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.036732,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/validation_svd_256_a4.0.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.037394,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/validation_svd_256_a5.0.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.036901,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/validation_svd_256_a7.0.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.035291,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/validation_svd_256_a10.0.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.034866,\n    \"quality_users\": 4263\n  }\n}\n"
  }
}
33

exec command

+09:07
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 patch = "*** Begin Patch\n*** Update File: /app/solution/globaltest.py\n@@\n for d in [3,7,14,30,60,120]: signals[str(d)]=np.log1p(x[x.timestamp>=end-d*86400].groupby('item_id').size().reindex(it,fill_value=0).to_numpy())\n+lk=pd.read_parquet(D/'train_likes.parquet',columns=['timestamp','item_id','is_organic'])\n+lk=lk[lk.item_id.isin(it)&(lk.is_organic==1)&(lk.timestamp>=end-7*86400)]\n+ls=np.log1p(lk.groupby('item_id').size().reindex(it,fill_value=0).to_numpy())\n+signals['like7']=ls\n+def z(v): return (v-v.mean())/(v.std()+1e-9)\n+signals['combo7']=.7*z(signals['7'])+.3*z(ls)\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-3765c403-f8b3-4120-905f-069bcde458f4",
    "changes": {
      "/app/solution/globaltest.py": {
        "type": "update",
        "unified_diff": "@@ -10,2 +10,8 @@\n for d in [3,7,14,30,60,120]: signals[str(d)]=np.log1p(x[x.timestamp>=end-d*86400].groupby('item_id').size().reindex(it,fill_value=0).to_numpy())\n+lk=pd.read_parquet(D/'train_likes.parquet',columns=['timestamp','item_id','is_organic'])\n+lk=lk[lk.item_id.isin(it)&(lk.is_organic==1)&(lk.timestamp>=end-7*86400)]\n+ls=np.log1p(lk.groupby('item_id').size().reindex(it,fill_value=0).to_numpy())\n+signals['like7']=ls\n+def z(v): return (v-v.mean())/(v.std()+1e-9)\n+signals['combo7']=.7*z(signals['7'])+.3*z(ls)\n for name,s in signals.items():\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/globaltest.py\n",
    "stderr": ""
  }
}
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python solution/globaltest.py && /opt/task-venv/bin/python /task/evaluate_public.py solution/global_like7.parquet && /opt/task-venv/bin/python /task/evaluate_public.py solution/global_combo7.parquet","workdir":"/app","yield_time_ms":30000,"max_output_tokens":3000});
text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 26.8 seconds
Output:

{
  "model": "solution/global_like7.parquet",
  "scores": {
    "quality_ndcg@10": 0.02927,
    "quality_users": 4263
  }
}
{
  "model": "solution/global_combo7.parquet",
  "scores": {
    "quality_ndcg@10": 0.031407,
    "quality_users": 4263
  }
}
Harness context token usage record
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Harness context codex item completed
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    "stdout": "{\n  \"model\": \"solution/global_like7.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.02927,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/global_combo7.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.031407,\n    \"quality_users\": 4263\n  }\n}\n",
    "stderr": "",
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  }
}
34

exec command

+09:43
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*** Update File: /app/solution/latent.py\n@@\n-    likes=pd.concat([pd.read_parquet(D/f'{f}.parquet',columns=['uid','item_id','is_organic']) for f in lf],ignore_index=True)\n+    likes=pd.concat([pd.read_parquet(D/f'{f}.parquet',columns=['uid','timestamp','item_id','is_organic']) for f in lf],ignore_index=True)\n     likes=likes[likes.item_id.isin(items)]\n+    like_recent=likes[(likes.timestamp>=cutoff-7*86400)&(likes.is_organic==1)].groupby('item_id').size()\n@@\n     momentum=np.log1p(recent.reindex(items,fill_value=0).to_numpy()).astype(np.float32)\n     momentum=(momentum-momentum.mean())/(momentum.std()+1e-6)\n-    pop=.10*pop+.90*momentum\n+    likepop=np.log1p(like_recent.reindex(items,fill_value=0).to_numpy()).astype(np.float32)\n+    likepop=(likepop-likepop.mean())/(likepop.std()+1e-6)\n+    pop=.10*pop+.63*momentum+.27*likepop\n@@\n-    alphas=[3.0,4.0,5.0,7.0,10.0]\n+    alphas=[3.0,3.5,4.0,4.5,5.0]\n@@\n-        name='submission.parquet' if final and a==.12 else f'{stage}_svd_{components}_a{a}.parquet'\n+        name='submission.parquet' if final and a==4.0 else f'{stage}_svd_{components}_a{a}.parquet'\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-9e9847d4-59bc-4923-8821-e335aed7bdfd",
    "changes": {
      "/app/solution/latent.py": {
        "type": "update",
        "unified_diff": "@@ -30,4 +30,5 @@\n     lf=['train_likes']+(['val_likes'] if final else [])\n-    likes=pd.concat([pd.read_parquet(D/f'{f}.parquet',columns=['uid','item_id','is_organic']) for f in lf],ignore_index=True)\n+    likes=pd.concat([pd.read_parquet(D/f'{f}.parquet',columns=['uid','timestamp','item_id','is_organic']) for f in lf],ignore_index=True)\n     likes=likes[likes.item_id.isin(items)]\n+    like_recent=likes[(likes.timestamp>=cutoff-7*86400)&(likes.is_organic==1)].groupby('item_id').size()\n     likes['w']=2.0+2.0*likes.is_organic\n@@ -56,5 +57,7 @@\n     momentum=(momentum-momentum.mean())/(momentum.std()+1e-6)\n-    pop=.10*pop+.90*momentum\n+    likepop=np.log1p(like_recent.reindex(items,fill_value=0).to_numpy()).astype(np.float32)\n+    likepop=(likepop-likepop.mean())/(likepop.std()+1e-6)\n+    pop=.10*pop+.63*momentum+.27*likepop\n     # Multiple blends cost little and enable public selection without refitting.\n-    alphas=[3.0,4.0,5.0,7.0,10.0]\n+    alphas=[3.0,3.5,4.0,4.5,5.0]\n     outputs=[[] for _ in alphas]\n@@ -76,3 +79,3 @@\n         out=pd.concat(parts,ignore_index=True).astype(np.int64)\n-        name='submission.parquet' if final and a==.12 else f'{stage}_svd_{components}_a{a}.parquet'\n+        name='submission.parquet' if final and a==4.0 else f'{stage}_svd_{components}_a{a}.parquet'\n         out.to_parquet(O/name,index=False)\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/latent.py\n",
    "stderr": ""
  }
}
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python solution/latent.py --stage validation --components 256 && for f in solution/validation_svd_256_a3.0.parquet solution/validation_svd_256_a3.5.parquet solution/validation_svd_256_a4.0.parquet solution/validation_svd_256_a4.5.parquet solution/validation_svd_256_a5.0.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done","workdir":"/app","yield_time_ms":30000,"max_output_tokens":6000});
text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 30.2 seconds
Output:

matrix (9910, 30000) 5687487 variance 0.38356566
validation_svd_256_a3.0.parquet
validation_svd_256_a3.5.parquet
validation_svd_256_a4.0.parquet
validation_svd_256_a4.5.parquet
validation_svd_256_a5.0.parquet
{
  "model": "solution/validation_svd_256_a3.0.parquet",
  "scores": {
    "quality_ndcg@10": 0.041006,
    "quality_users": 4263
  }
}

SESSION_ID=90106
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35

exec command

+10:26
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:90106,chars:"",yield_time_ms:30000,max_output_tokens:5000});
text(r.output);

Tool response

Script completed
Wall time 6.4 seconds
Output:

{
  "model": "solution/validation_svd_256_a3.5.parquet",
  "scores": {
    "quality_ndcg@10": 0.039895,
    "quality_users": 4263
  }
}
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}
{
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}
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Harness context codex item completed
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    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "matrix (9910, 30000) 5687487 variance 0.38356566\nvalidation_svd_256_a3.0.parquet\nvalidation_svd_256_a3.5.parquet\nvalidation_svd_256_a4.0.parquet\nvalidation_svd_256_a4.5.parquet\nvalidation_svd_256_a5.0.parquet\n{\n  \"model\": \"solution/validation_svd_256_a3.0.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.041006,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/validation_svd_256_a3.5.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.039895,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/validation_svd_256_a4.0.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.039233,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/validation_svd_256_a4.5.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.038842,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/validation_svd_256_a5.0.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.038314,\n    \"quality_users\": 4263\n  }\n}\n",
    "stderr": "",
    "aggregated_output": "matrix (9910, 30000) 5687487 variance 0.38356566\nvalidation_svd_256_a3.0.parquet\nvalidation_svd_256_a3.5.parquet\nvalidation_svd_256_a4.0.parquet\nvalidation_svd_256_a4.5.parquet\nvalidation_svd_256_a5.0.parquet\n{\n  \"model\": \"solution/validation_svd_256_a3.0.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.041006,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/validation_svd_256_a3.5.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.039895,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/validation_svd_256_a4.0.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.039233,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/validation_svd_256_a4.5.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.038842,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/validation_svd_256_a5.0.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.038314,\n    \"quality_users\": 4263\n  }\n}\n",
    "exit_code": 0,
    "duration": {
      "secs": 38,
      "nanos": 316662657
    },
    "formatted_output": "matrix (9910, 30000) 5687487 variance 0.38356566\nvalidation_svd_256_a3.0.parquet\nvalidation_svd_256_a3.5.parquet\nvalidation_svd_256_a4.0.parquet\nvalidation_svd_256_a4.5.parquet\nvalidation_svd_256_a5.0.parquet\n{\n  \"model\": \"solution/validation_svd_256_a3.0.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.041006,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/validation_svd_256_a3.5.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.039895,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/validation_svd_256_a4.0.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.039233,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/validation_svd_256_a4.5.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.038842,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/validation_svd_256_a5.0.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.038314,\n    \"quality_users\": 4263\n  }\n}\n"
  }
}
36

exec command

+10:37
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 like-aware seven-day momentum blend is the strongest validation result so far: 0.041006 NDCG@10 at blend weight 3.0. I’m locking that configuration, refitting on training plus validation history, and then running an independent schema and test-eligibility check on the final artifact.

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Update File: /app/solution/latent.py\n@@\n-        name='submission.parquet' if final and a==4.0 else f'{stage}_svd_{components}_a{a}.parquet'\n+        name='submission.parquet' if final and a==3.0 else f'{stage}_svd_{components}_a{a}.parquet'\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-2ec326eb-732e-4fbb-8273-a9c23d34a2a6",
    "changes": {
      "/app/solution/latent.py": {
        "type": "update",
        "unified_diff": "@@ -79,3 +79,3 @@\n         out=pd.concat(parts,ignore_index=True).astype(np.int64)\n-        name='submission.parquet' if final and a==4.0 else f'{stage}_svd_{components}_a{a}.parquet'\n+        name='submission.parquet' if final and a==3.0 else f'{stage}_svd_{components}_a{a}.parquet'\n         out.to_parquet(O/name,index=False)\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/latent.py\n",
    "stderr": ""
  }
}
Harness context token usage record
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Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python solution/latent.py --stage test --components 256","workdir":"/app","yield_time_ms":30000,"max_output_tokens":3000});
text(r.output); if(r.session_id) text(`SESSION_ID=${r.session_id}`);

Tool response

Script completed
Wall time 27.2 seconds
Output:

matrix (9924, 30000) 5850135 variance 0.38377437
submission.parquet
test_svd_256_a3.5.parquet
test_svd_256_a4.0.parquet
test_svd_256_a4.5.parquet
test_svd_256_a5.0.parquet
Harness context token usage record
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Harness context codex item completed
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  "item": {
    "type": "CommandExecution",
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      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python solution/latent.py --stage test --components 256"
    ],
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    "parsed_cmd": [
      {
        "type": "unknown",
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      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "matrix (9924, 30000) 5850135 variance 0.38377437\nsubmission.parquet\ntest_svd_256_a3.5.parquet\ntest_svd_256_a4.0.parquet\ntest_svd_256_a4.5.parquet\ntest_svd_256_a5.0.parquet\n",
    "stderr": "",
    "aggregated_output": "matrix (9924, 30000) 5850135 variance 0.38377437\nsubmission.parquet\ntest_svd_256_a3.5.parquet\ntest_svd_256_a4.0.parquet\ntest_svd_256_a4.5.parquet\ntest_svd_256_a5.0.parquet\n",
    "exit_code": 0,
    "duration": {
      "secs": 27,
      "nanos": 82583817
    },
    "formatted_output": "matrix (9924, 30000) 5850135 variance 0.38377437\nsubmission.parquet\ntest_svd_256_a3.5.parquet\ntest_svd_256_a4.0.parquet\ntest_svd_256_a4.5.parquet\ntest_svd_256_a5.0.parquet\n"
  }
}
37

exec command

+11:13
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/README.md\n+# Music discovery ranker\n+\n+`submission.parquet` is the final test-cutoff ranking (845,600 rows: 100 for\n+each of 8,456 users).\n+\n+The selected model is an implicit-feedback PureSVD ranker with 256 latent\n+components. Its interaction weights emphasize organic listens completed to at\n+least 80%, incorporate explicit likes, saturate repeat counts, and apply item\n+IDF. Candidate scores blend the personalized latent reconstruction with\n+seven-day organic strong-listen and organic-like momentum (plus a small\n+long-run popularity stabilizer). All scores are masked by the supplied\n+per-user cutoff eligibility before top-100 selection.\n+\n+Model selection used training history only and reached public validation\n+NDCG@10 **0.041006**. The final artifact refits the same configuration on\n+training plus validation history and uses `test_eligible_candidates.npz`.\n+\n+Rebuild with the provided environment:\n+\n+```sh\n+/opt/task-venv/bin/python /app/solution/latent.py --stage test --components 256\n+```\n+\n+`build.py` and `globaltest.py` record the earlier personalized-history and\n+temporal-baseline experiments; `latent.py` is the final builder.\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-7c759d1f-6f26-4f62-9a21-2b4f34542732",
    "changes": {
      "/app/solution/README.md": {
        "type": "add",
        "content": "# Music discovery ranker\n\n`submission.parquet` is the final test-cutoff ranking (845,600 rows: 100 for\neach of 8,456 users).\n\nThe selected model is an implicit-feedback PureSVD ranker with 256 latent\ncomponents. Its interaction weights emphasize organic listens completed to at\nleast 80%, incorporate explicit likes, saturate repeat counts, and apply item\nIDF. Candidate scores blend the personalized latent reconstruction with\nseven-day organic strong-listen and organic-like momentum (plus a small\nlong-run popularity stabilizer). All scores are masked by the supplied\nper-user cutoff eligibility before top-100 selection.\n\nModel selection used training history only and reached public validation\nNDCG@10 **0.041006**. The final artifact refits the same configuration on\ntraining plus validation history and uses `test_eligible_candidates.npz`.\n\nRebuild with the provided environment:\n\n```sh\n/opt/task-venv/bin/python /app/solution/latent.py --stage test --components 256\n```\n\n`build.py` and `globaltest.py` record the earlier personalized-history and\ntemporal-baseline experiments; `latent.py` is the final builder.\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/README.md\n",
    "stderr": ""
  }
}
Harness context token usage record
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}
Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np\nfrom ranking_evaluation import validate_ranking, CandidateSet\nD=Path('/task/data'); p=Path('/app/solution/submission.parquet')\nx=pd.read_parquet(p); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\ny=validate_ranking(x,targets); c=CandidateSet(D/'test_eligible_candidates.npz')\nbad=sum(not c.is_eligible(u,i) for u,i in y[['uid','item_id']].itertuples(index=False,name=None))\nprint({'rows':len(y),'users':y.uid.nunique(),'bad_eligibility':bad,'columns':list(x.columns),'size_bytes':p.stat().st_size})\nprint(y.head(10).to_string(index=False))\nPY","workdir":"/app","yield_time_ms":30000,"max_output_tokens":3000});
text(r.output);

Tool response

Script completed
Wall time 1.9 seconds
Output:

{'rows': 845600, 'users': 8456, 'bad_eligibility': 0, 'columns': ['uid', 'item_id', 'rank'], 'size_bytes': 1526073}
 uid  item_id  rank
 100  8250570     1
 100  1938919     2
 100  2621516     3
 100  3542184     4
 100  2055777     5
 100  8823268     6
 100  4948220     7
 100  5565024     8
 100  5327382     9
 100  5862961    10
Harness context token usage record
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Harness context codex item completed
{
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  "item": {
    "type": "CommandExecution",
    "id": "exec-3f4b2472-72bc-43f3-b676-a1f1f5653d3b",
    "process_id": "33451",
    "command": [
      "/bin/sh",
      "-lc",
      "PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np\nfrom ranking_evaluation import validate_ranking, CandidateSet\nD=Path('/task/data'); p=Path('/app/solution/submission.parquet')\nx=pd.read_parquet(p); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\ny=validate_ranking(x,targets); c=CandidateSet(D/'test_eligible_candidates.npz')\nbad=sum(not c.is_eligible(u,i) for u,i in y[['uid','item_id']].itertuples(index=False,name=None))\nprint({'rows':len(y),'users':y.uid.nunique(),'bad_eligibility':bad,'columns':list(x.columns),'size_bytes':p.stat().st_size})\nprint(y.head(10).to_string(index=False))\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "PYTHONPATH=/task /opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pandas as pd, numpy as np\nfrom ranking_evaluation import validate_ranking, CandidateSet\nD=Path('/task/data'); p=Path('/app/solution/submission.parquet')\nx=pd.read_parquet(p); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\ny=validate_ranking(x,targets); c=CandidateSet(D/'test_eligible_candidates.npz')\nbad=sum(not c.is_eligible(u,i) for u,i in y[['uid','item_id']].itertuples(index=False,name=None))\nprint({'rows':len(y),'users':y.uid.nunique(),'bad_eligibility':bad,'columns':list(x.columns),'size_bytes':p.stat().st_size})\nprint(y.head(10).to_string(index=False))\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{'rows': 845600, 'users': 8456, 'bad_eligibility': 0, 'columns': ['uid', 'item_id', 'rank'], 'size_bytes': 1526073}\n uid  item_id  rank\n 100  8250570     1\n 100  1938919     2\n 100  2621516     3\n 100  3542184     4\n 100  2055777     5\n 100  8823268     6\n 100  4948220     7\n 100  5565024     8\n 100  5327382     9\n 100  5862961    10\n",
    "stderr": "",
    "aggregated_output": "{'rows': 845600, 'users': 8456, 'bad_eligibility': 0, 'columns': ['uid', 'item_id', 'rank'], 'size_bytes': 1526073}\n uid  item_id  rank\n 100  8250570     1\n 100  1938919     2\n 100  2621516     3\n 100  3542184     4\n 100  2055777     5\n 100  8823268     6\n 100  4948220     7\n 100  5565024     8\n 100  5327382     9\n 100  5862961    10\n",
    "exit_code": 0,
    "duration": {
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      "nanos": 677738058
    },
    "formatted_output": "{'rows': 845600, 'users': 8456, 'bad_eligibility': 0, 'columns': ['uid', 'item_id', 'rank'], 'size_bytes': 1526073}\n uid  item_id  rank\n 100  8250570     1\n 100  1938919     2\n 100  2621516     3\n 100  3542184     4\n 100  2055777     5\n 100  8823268     6\n 100  4948220     7\n 100  5565024     8\n 100  5327382     9\n 100  5862961    10\n"
  }
}
38

Agent message

+11:37
Thinking unavailable No text recorded

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

39

Final response

+11:45
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 final ranker.

  • Submission: submission.parquet
  • Builder: latent.py
  • Documentation: README.md
  • Public validation NDCG@10: 0.041006
  • Final artifact: 845,600 rows across 8,456 users
  • Eligibility violations: 0
  • Schema, ranks, uniqueness, and per-user row counts all validated successfully.
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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-medium-0-153-4-basic-r05-20260924T221413Z
Recorded model
gpt-5.6-sol
Harness version
0.153.4
Session
01a0d61d-5259-7ea0-97f9-6b8e97d0f227
Source SHA-256
a82750c79658a4d13b06348b59bdcd41eac39c511b4bf46c95d3c75f2191bc1e

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