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Agent trace

Basic recommender sol-0.153.4-max-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 68 empty thinking entries; tool calls are shown separately.

Agent
gpt-5.6-sol · Codex · max effort
Recorded conversation
28:49 · 74 steps
Private reward
0.009092 · Failed
Attempt cost
$2.02
  • 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

+00:01
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

+00:01
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

+00:01
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": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "cwd": "/app",
  "workspace_roots": [
    "/app"
  ],
  "current_date": "2026-09-25",
  "timezone": "Etc/UTC",
  "approval_policy": "never",
  "approvals_reviewer": "user",
  "sandbox_policy": {
    "type": "danger-full-access"
  },
  "permission_profile": {
    "type": "disabled"
  },
  "model": "gpt-5.6-sol",
  "comp_hash": "3000",
  "personality": "pragmatic",
  "collaboration_mode": {
    "mode": "default",
    "settings": {
      "model": "gpt-5.6-sol",
      "reasoning_effort": "max",
      "developer_instructions": null
    }
  },
  "multi_agent_version": "v2",
  "realtime_active": false,
  "effort": "max",
  "summary": "auto"
}
06

Task instructions

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Task instructions

Build a music-discovery ranker

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

Data

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

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

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

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

The two public windows have different roles:

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

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

Additional files under /task/data/:

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

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

Discovery objective

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

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

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

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

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

Public tools

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

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

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

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

Deliverable

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

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

Constraints

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

exec command

+00:04
Thinking unavailable No text recorded

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

Model text

I’ll first profile the data, eligibility density, and event timelines, then build a leakage-safe validation pipeline and iterate against the public evaluator. I’ll keep a valid submission saved early, and all generated files will stay under /app/solution/.

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:30000});
text(r.output);

Tool response

Script completed
Wall time 0.1 seconds
Output:

total 340M
-r--r--r-- 1 root root 342K Sep 25 00:46 candidate_catalog.parquet
-r--r--r-- 1 root root  83K Sep 25 00:46 target_users.parquet
-r--r--r-- 1 root root 7.4M Sep 25 00:46 test_eligible_candidates.npz
-r--r--r-- 1 root root 1.1M Sep 25 00:47 test_preference_state.parquet
-r--r--r-- 1 root root 838K Sep 25 00:46 train_dislikes.parquet
-r--r--r-- 1 root root 5.2M Sep 25 00:46 train_likes.parquet
-r--r--r-- 1 root root 305M Sep 25 00:46 train_listens.parquet
-r--r--r-- 1 root root 153K Sep 25 00:46 train_undislikes.parquet
-r--r--r-- 1 root root 1.8M Sep 25 00:46 train_unlikes.parquet
-r--r--r-- 1 root root  36K Sep 25 00:46 val_dislikes.parquet
-r--r--r-- 1 root root 292K Sep 25 00:46 val_likes.parquet
-r--r--r-- 1 root root 9.5M Sep 25 00:46 val_listens.parquet
-r--r--r-- 1 root root 7.7K Sep 25 00:46 val_undislikes.parquet
-r--r--r-- 1 root root  71K Sep 25 00:46 val_unlikes.parquet
-r--r--r-- 1 root root 7.3M Sep 25 00:46 validation_eligible_candidates.npz
-r--r--r-- 1 root root 1.1M Sep 25 00:47 validation_preference_state.parquet
# Exact third-party distribution inventory for yambda-discovery-ranking-v01.
#
# This is an input to `uv pip sync`, not a list of minimum requirements. A
# conforming task environment contains exactly these third-party Python
# distributions and no others. The same inventory is used for source preparation,
# task building, reference scoring, judging, and solver attempts. Python itself
# is pinned by task.toml and both Harbor image Dockerfiles.
attrs==26.1.0
bleach==6.4.0
certifi==2026.7.22
chardet==7.6.0
charset-normalizer==3.5.1
fastjsonschema==2.22.2
idna==3.18
joblib==1.5.3
jsonschema==4.26.0
jsonschema-specifications==2025.9.1
jupyter-core==5.9.1
jupytext==1.19.5
kaggle==2.2.4
kagglesdk==0.1.37
markdown-it-py==4.2.0
mdit-py-plugins==0.6.1
mdurl==0.1.2
nbformat==5.11.1
numpy==2.2.6
packaging==26.3
pandas==2.3.3
pillow==12.3.0
pip==26.0.1
platformdirs==4.11.3
protobuf==7.35.1
pyarrow==18.1.0
python-dateutil==2.9.0.post0
python-dotenv==1.2.3
python-slugify==8.0.4
pytz==2026.3.post1
PyYAML==6.0.3
referencing==0.37.0
reportlab==4.2.5
requests==2.34.2
rpds-py==2026.6.3
scikit-learn==1.7.2
scipy==1.15.3
six==1.17.0
text-unidecode==1.3
threadpoolctl==3.6.0
tqdm==4.70.0
traitlets==5.16.1
typing-extensions==4.16.0
tzdata==2026.3
urllib3==2.7.0
webencodings==0.6.1
"""Shared public primitives for Yambda discovery-ranking evaluation.

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

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

from pathlib import Path

import numpy as np
import pandas as pd


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

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


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


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

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

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

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

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

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

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

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


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

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

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


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

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

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

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

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

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

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


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

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

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


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

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

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

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


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

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

    Returns:
        The six-decimal ``quality_ndcg@10`` value and number of scored users.
    """
    quality = float(
        np.mean(
            [
                ndcg_at_k(ranked[uid], gold, QUALITY_K)
                for uid, gold in graded.items()
            ]
        )
    )
    return {
        "quality_ndcg@10": round(quality, 6),
        "quality_users": len(graded),
    }
Harness context token usage record
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}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-6993c271-a937-4c93-b39e-3a7553ce5356",
    "process_id": "32443",
    "command": [
      "/bin/sh",
      "-lc",
      "ls -lh /task/data && sed -n '1,220p' /task/requirements.txt && sed -n '1,260p' /task/ranking_evaluation.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "list_files",
        "cmd": "ls -lh /task/data",
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      },
      {
        "type": "read",
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        "name": "requirements.txt",
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        "type": "read",
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    "stdout": "total 340M\n-r--r--r-- 1 root root 342K Sep 25 00:46 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 25 00:46 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 25 00:46 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:47 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 25 00:46 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 25 00:46 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 25 00:46 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 25 00:46 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 25 00:46 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 25 00:46 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 25 00:46 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 25 00:46 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 25 00:46 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 25 00:46 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 25 00:46 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:47 validation_preference_state.parquet\n# Exact third-party distribution inventory for yambda-discovery-ranking-v01.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nidna==3.18\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n    \"CandidateSet\",\n    \"QUALITY_K\",\n    \"RankingValidationError\",\n    \"REQUIRED_COLUMNS\",\n    \"TOP_N\",\n    \"ndcg_at_k\",\n    \"ranked_items\",\n    \"read_ranking\",\n    \"score_rankings\",\n    \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n    \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n    \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n    The archive contains aligned ``uids``, ``item_ids``, and\n    ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n    ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n    copied into memory so they remain valid after the archive is closed.\n    \"\"\"\n\n    def __init__(self, path: Path):\n        \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n        Args:\n            path: Path to a ``.npz`` archive with the candidate-set schema.\n        \"\"\"\n        with np.load(path, allow_pickle=False) as artifact:\n            self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n            self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n            self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n        self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n        self.item_column = {\n            int(item): column for column, item in enumerate(self.item_ids)\n        }\n\n    def is_eligible(self, uid: int, item_id: int) -> bool:\n        \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n        IDs absent from the target-user or candidate-item arrays are treated as\n        ineligible rather than raising an exception.\n\n        Args:\n            uid: Target user ID to query.\n            item_id: Candidate item ID to query.\n\n        Returns:\n            ``True`` only when the corresponding packed eligibility bit is set.\n        \"\"\"\n        row = self.uid_row.get(int(uid))\n        column = self.item_column.get(int(item_id))\n        if row is None or column is None:\n            return False\n        byte, bit = divmod(column, 8)\n        return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n    \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n    Args:\n        path: Input path. A ``.parquet`` suffix selects parquet; every other\n            suffix is interpreted as CSV.\n\n    Returns:\n        The ranking frame as loaded by pandas.\n    \"\"\"\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n    frame: pd.DataFrame,\n    targets: set[int],\n) -> pd.DataFrame:\n    \"\"\"Normalize and validate the stage-independent ranking contract.\n\n    Required values must be finite integers. The user set must exactly match\n    ``targets``; every user must have 100 rows, distinct items, and each rank\n    from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n    intentionally left to the calling evaluator.\n\n    Args:\n        frame: Raw ranking frame. Extra columns are discarded from an internal\n            copy; the caller's frame is not modified.\n        targets: Exact set of target user IDs the ranking must cover.\n\n    Returns:\n        A normalized frame containing only the required ``int64`` columns.\n\n    Raises:\n        RankingValidationError: If the ranking violates the shared contract.\n    \"\"\"\n    required = list(REQUIRED_COLUMNS)\n    if not all(column in frame for column in required):\n        raise RankingValidationError(f\"submission requires columns {required}\")\n\n    normalized = frame[required].copy()\n    for column in required:\n        numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n        values = numeric.to_numpy(dtype=float)\n        if numeric.isna().any() or not np.isfinite(values).all():\n            raise RankingValidationError(\n                f\"submission {column} must be finite numeric values\"\n            )\n        if not np.equal(numeric, np.floor(numeric)).all():\n            raise RankingValidationError(\n                f\"submission {column} must contain integers\"\n            )\n        normalized[column] = numeric.astype(np.int64)\n\n    submitted_users = set(map(int, normalized.uid.unique()))\n    if submitted_users != targets:\n        raise RankingValidationError(\n            f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n            f\"extra={len(submitted_users - targets)}\"\n        )\n    counts = normalized.groupby(\"uid\").size()\n    if not (counts == TOP_N).all():\n        raise RankingValidationError(\n            f\"submission must contain exactly {TOP_N} rows for every target user\"\n        )\n    if normalized.duplicated([\"uid\", \"item_id\"]).any():\n        raise RankingValidationError(\n            \"submission contains duplicate (uid, item_id) pairs\"\n        )\n    valid_ranks = set(range(1, TOP_N + 1))\n    ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n        lambda values: set(map(int, values)) == valid_ranks\n    )\n    if not ranks_are_valid.all():\n        raise RankingValidationError(\n            f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n        )\n    return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n    \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n    Args:\n        frame: Frame that has passed :func:`validate_ranking`.\n\n    Returns:\n        A ``uid -> item IDs`` mapping ordered by ascending rank.\n    \"\"\"\n    ordered = frame.sort_values([\"uid\", \"rank\"])\n    return {\n        int(uid): list(map(int, values))\n        for uid, values in ordered.groupby(\"uid\").item_id\n    }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n    \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n    Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n    ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n    Args:\n        ranked: Item IDs in predicted rank order.\n        gold: Mapping from relevant item IDs to integer relevance grades.\n        k: Maximum number of predicted positions to score.\n\n    Returns:\n        For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n        ``0.0`` when the ideal gain is zero.\n    \"\"\"\n    gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n    discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n    dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n    ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n    ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n    idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n    return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Relevant item grades keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    quality = float(\n        np.mean(\n            [\n                ndcg_at_k(ranked[uid], gold, QUALITY_K)\n                for uid, gold in graded.items()\n            ]\n        )\n    )\n    return {\n        \"quality_ndcg@10\": round(quality, 6),\n        \"quality_users\": len(graded),\n    }\n",
    "stderr": "",
    "aggregated_output": "total 340M\n-r--r--r-- 1 root root 342K Sep 25 00:46 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 25 00:46 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 25 00:46 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:47 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 25 00:46 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 25 00:46 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 25 00:46 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 25 00:46 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 25 00:46 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 25 00:46 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 25 00:46 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 25 00:46 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 25 00:46 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 25 00:46 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 25 00:46 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:47 validation_preference_state.parquet\n# Exact third-party distribution inventory for yambda-discovery-ranking-v01.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nidna==3.18\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n    \"CandidateSet\",\n    \"QUALITY_K\",\n    \"RankingValidationError\",\n    \"REQUIRED_COLUMNS\",\n    \"TOP_N\",\n    \"ndcg_at_k\",\n    \"ranked_items\",\n    \"read_ranking\",\n    \"score_rankings\",\n    \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n    \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n    \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n    The archive contains aligned ``uids``, ``item_ids``, and\n    ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n    ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n    copied into memory so they remain valid after the archive is closed.\n    \"\"\"\n\n    def __init__(self, path: Path):\n        \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n        Args:\n            path: Path to a ``.npz`` archive with the candidate-set schema.\n        \"\"\"\n        with np.load(path, allow_pickle=False) as artifact:\n            self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n            self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n            self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n        self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n        self.item_column = {\n            int(item): column for column, item in enumerate(self.item_ids)\n        }\n\n    def is_eligible(self, uid: int, item_id: int) -> bool:\n        \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n        IDs absent from the target-user or candidate-item arrays are treated as\n        ineligible rather than raising an exception.\n\n        Args:\n            uid: Target user ID to query.\n            item_id: Candidate item ID to query.\n\n        Returns:\n            ``True`` only when the corresponding packed eligibility bit is set.\n        \"\"\"\n        row = self.uid_row.get(int(uid))\n        column = self.item_column.get(int(item_id))\n        if row is None or column is None:\n            return False\n        byte, bit = divmod(column, 8)\n        return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n    \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n    Args:\n        path: Input path. A ``.parquet`` suffix selects parquet; every other\n            suffix is interpreted as CSV.\n\n    Returns:\n        The ranking frame as loaded by pandas.\n    \"\"\"\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n    frame: pd.DataFrame,\n    targets: set[int],\n) -> pd.DataFrame:\n    \"\"\"Normalize and validate the stage-independent ranking contract.\n\n    Required values must be finite integers. The user set must exactly match\n    ``targets``; every user must have 100 rows, distinct items, and each rank\n    from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n    intentionally left to the calling evaluator.\n\n    Args:\n        frame: Raw ranking frame. Extra columns are discarded from an internal\n            copy; the caller's frame is not modified.\n        targets: Exact set of target user IDs the ranking must cover.\n\n    Returns:\n        A normalized frame containing only the required ``int64`` columns.\n\n    Raises:\n        RankingValidationError: If the ranking violates the shared contract.\n    \"\"\"\n    required = list(REQUIRED_COLUMNS)\n    if not all(column in frame for column in required):\n        raise RankingValidationError(f\"submission requires columns {required}\")\n\n    normalized = frame[required].copy()\n    for column in required:\n        numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n        values = numeric.to_numpy(dtype=float)\n        if numeric.isna().any() or not np.isfinite(values).all():\n            raise RankingValidationError(\n                f\"submission {column} must be finite numeric values\"\n            )\n        if not np.equal(numeric, np.floor(numeric)).all():\n            raise RankingValidationError(\n                f\"submission {column} must contain integers\"\n            )\n        normalized[column] = numeric.astype(np.int64)\n\n    submitted_users = set(map(int, normalized.uid.unique()))\n    if submitted_users != targets:\n        raise RankingValidationError(\n            f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n            f\"extra={len(submitted_users - targets)}\"\n        )\n    counts = normalized.groupby(\"uid\").size()\n    if not (counts == TOP_N).all():\n        raise RankingValidationError(\n            f\"submission must contain exactly {TOP_N} rows for every target user\"\n        )\n    if normalized.duplicated([\"uid\", \"item_id\"]).any():\n        raise RankingValidationError(\n            \"submission contains duplicate (uid, item_id) pairs\"\n        )\n    valid_ranks = set(range(1, TOP_N + 1))\n    ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n        lambda values: set(map(int, values)) == valid_ranks\n    )\n    if not ranks_are_valid.all():\n        raise RankingValidationError(\n            f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n        )\n    return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n    \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n    Args:\n        frame: Frame that has passed :func:`validate_ranking`.\n\n    Returns:\n        A ``uid -> item IDs`` mapping ordered by ascending rank.\n    \"\"\"\n    ordered = frame.sort_values([\"uid\", \"rank\"])\n    return {\n        int(uid): list(map(int, values))\n        for uid, values in ordered.groupby(\"uid\").item_id\n    }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n    \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n    Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n    ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n    Args:\n        ranked: Item IDs in predicted rank order.\n        gold: Mapping from relevant item IDs to integer relevance grades.\n        k: Maximum number of predicted positions to score.\n\n    Returns:\n        For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n        ``0.0`` when the ideal gain is zero.\n    \"\"\"\n    gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n    discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n    dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n    ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n    ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n    idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n    return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Relevant item grades keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    quality = float(\n        np.mean(\n            [\n                ndcg_at_k(ranked[uid], gold, QUALITY_K)\n                for uid, gold in graded.items()\n            ]\n        )\n    )\n    return {\n        \"quality_ndcg@10\": round(quality, 6),\n        \"quality_users\": len(graded),\n    }\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4870
    },
    "formatted_output": "total 340M\n-r--r--r-- 1 root root 342K Sep 25 00:46 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 25 00:46 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 25 00:46 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:47 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 25 00:46 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 25 00:46 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 25 00:46 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 25 00:46 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 25 00:46 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 25 00:46 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 25 00:46 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 25 00:46 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 25 00:46 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 25 00:46 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 25 00:46 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:47 validation_preference_state.parquet\n# Exact third-party distribution inventory for yambda-discovery-ranking-v01.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nidna==3.18\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n    \"CandidateSet\",\n    \"QUALITY_K\",\n    \"RankingValidationError\",\n    \"REQUIRED_COLUMNS\",\n    \"TOP_N\",\n    \"ndcg_at_k\",\n    \"ranked_items\",\n    \"read_ranking\",\n    \"score_rankings\",\n    \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n    \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n    \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n    The archive contains aligned ``uids``, ``item_ids``, and\n    ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n    ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n    copied into memory so they remain valid after the archive is closed.\n    \"\"\"\n\n    def __init__(self, path: Path):\n        \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n        Args:\n            path: Path to a ``.npz`` archive with the candidate-set schema.\n        \"\"\"\n        with np.load(path, allow_pickle=False) as artifact:\n            self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n            self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n            self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n        self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n        self.item_column = {\n            int(item): column for column, item in enumerate(self.item_ids)\n        }\n\n    def is_eligible(self, uid: int, item_id: int) -> bool:\n        \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n        IDs absent from the target-user or candidate-item arrays are treated as\n        ineligible rather than raising an exception.\n\n        Args:\n            uid: Target user ID to query.\n            item_id: Candidate item ID to query.\n\n        Returns:\n            ``True`` only when the corresponding packed eligibility bit is set.\n        \"\"\"\n        row = self.uid_row.get(int(uid))\n        column = self.item_column.get(int(item_id))\n        if row is None or column is None:\n            return False\n        byte, bit = divmod(column, 8)\n        return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n    \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n    Args:\n        path: Input path. A ``.parquet`` suffix selects parquet; every other\n            suffix is interpreted as CSV.\n\n    Returns:\n        The ranking frame as loaded by pandas.\n    \"\"\"\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n    frame: pd.DataFrame,\n    targets: set[int],\n) -> pd.DataFrame:\n    \"\"\"Normalize and validate the stage-independent ranking contract.\n\n    Required values must be finite integers. The user set must exactly match\n    ``targets``; every user must have 100 rows, distinct items, and each rank\n    from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n    intentionally left to the calling evaluator.\n\n    Args:\n        frame: Raw ranking frame. Extra columns are discarded from an internal\n            copy; the caller's frame is not modified.\n        targets: Exact set of target user IDs the ranking must cover.\n\n    Returns:\n        A normalized frame containing only the required ``int64`` columns.\n\n    Raises:\n        RankingValidationError: If the ranking violates the shared contract.\n    \"\"\"\n    required = list(REQUIRED_COLUMNS)\n    if not all(column in frame for column in required):\n        raise RankingValidationError(f\"submission requires columns {required}\")\n\n    normalized = frame[required].copy()\n    for column in required:\n        numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n        values = numeric.to_numpy(dtype=float)\n        if numeric.isna().any() or not np.isfinite(values).all():\n            raise RankingValidationError(\n                f\"submission {column} must be finite numeric values\"\n            )\n        if not np.equal(numeric, np.floor(numeric)).all():\n            raise RankingValidationError(\n                f\"submission {column} must contain integers\"\n            )\n        normalized[column] = numeric.astype(np.int64)\n\n    submitted_users = set(map(int, normalized.uid.unique()))\n    if submitted_users != targets:\n        raise RankingValidationError(\n            f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n            f\"extra={len(submitted_users - targets)}\"\n        )\n    counts = normalized.groupby(\"uid\").size()\n    if not (counts == TOP_N).all():\n        raise RankingValidationError(\n            f\"submission must contain exactly {TOP_N} rows for every target user\"\n        )\n    if normalized.duplicated([\"uid\", \"item_id\"]).any():\n        raise RankingValidationError(\n            \"submission contains duplicate (uid, item_id) pairs\"\n        )\n    valid_ranks = set(range(1, TOP_N + 1))\n    ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n        lambda values: set(map(int, values)) == valid_ranks\n    )\n    if not ranks_are_valid.all():\n        raise RankingValidationError(\n            f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n        )\n    return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n    \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n    Args:\n        frame: Frame that has passed :func:`validate_ranking`.\n\n    Returns:\n        A ``uid -> item IDs`` mapping ordered by ascending rank.\n    \"\"\"\n    ordered = frame.sort_values([\"uid\", \"rank\"])\n    return {\n        int(uid): list(map(int, values))\n        for uid, values in ordered.groupby(\"uid\").item_id\n    }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n    \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n    Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n    ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n    Args:\n        ranked: Item IDs in predicted rank order.\n        gold: Mapping from relevant item IDs to integer relevance grades.\n        k: Maximum number of predicted positions to score.\n\n    Returns:\n        For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n        ``0.0`` when the ideal gain is zero.\n    \"\"\"\n    gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n    discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n    dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n    ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n    ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n    idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n    return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Relevant item grades keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    quality = float(\n        np.mean(\n            [\n                ndcg_at_k(ranked[uid], gold, QUALITY_K)\n                for uid, gold in graded.items()\n            ]\n        )\n    )\n    return {\n        \"quality_ndcg@10\": round(quality, 6),\n        \"quality_users\": len(graded),\n    }\n"
  }
}
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, numpy as np, os\np='/task/data'\nfor f in sorted(os.listdir(p)):\n if f.endswith('.parquet'):\n  x=pd.read_parquet(f)\n  print(f, x.shape, x.dtypes.to_dict())\n  print(x.head(2).to_dict('records'))\nfor f in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n z=np.load(p+'/'+f)\n print(f,{k:(z[k].shape,z[k].dtype) for k in z.files})\n print('uid range',z['uids'][:5],z['uids'][-5:],'items',z['item_ids'][:10], 'density',np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(z['item_ids'])].mean())\nPY",workdir:"/app",yield_time_ms:30000,max_output_tokens:30000});
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 codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-0d925e80-02e9-4298-8ef3-0fd3fb59cc3f",
    "process_id": "7712",
    "command": [
      "/bin/sh",
      "-lc",
      "sed -n '1,280p' /task/evaluate_public.py && python - <<'PY'\nimport pandas as pd, numpy as np, os\np='/task/data'\nfor f in sorted(os.listdir(p)):\n if f.endswith('.parquet'):\n  x=pd.read_parquet(f)\n  print(f, x.shape, x.dtypes.to_dict())\n  print(x.head(2).to_dict('records'))\nfor f in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n z=np.load(p+'/'+f)\n print(f,{k:(z[k].shape,z[k].dtype) for k in z.files})\n print('uid range',z['uids'][:5],z['uids'][-5:],'items',z['item_ids'][:10], 'density',np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(z['item_ids'])].mean())\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "sed -n '1,280p' /task/evaluate_public.py && python - <<'PY'\nimport pandas as pd, numpy as np, os\np='/task/data'\nfor f in sorted(os.listdir(p)):\n if f.endswith('.parquet'):\n  x=pd.read_parquet(f)\n  print(f, x.shape, x.dtypes.to_dict())\n  print(x.head(2).to_dict('records'))\nfor f in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n z=np.load(p+'/'+f)\n print(f,{k:(z[k].shape,z[k].dtype) for k in z.files})\n print('uid range',z['uids'][:5],z['uids'][-5:],'items',z['item_ids'][:10], 'density',np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(z['item_ids'])].mean())\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "failed",
    "stdout": "#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n    python evaluate_public.py <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": 5061
    },
    "formatted_output": "#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n    python evaluate_public.py <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"
  }
}
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  }
}
09

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.

Model text

The system python executable is not the task environment despite the provided package inventory. I’m locating the preinstalled task interpreter (without installing anything) before continuing the data profile.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"command -v python; command -v python3; python --version; python -m pip list 2>/dev/null | head -30; ls -la /app; ls -la /task | sed -n '1,160p'; ls -la /task/.venv/bin 2>/dev/null | sed -n '1,80p'",workdir:"/app",yield_time_ms:10000,max_output_tokens:12000});
text(r.output);

Tool response

Script completed
Wall time 0.8 seconds
Output:

/usr/local/bin/python
/usr/local/bin/python3
Python 3.12.13
Package Version
------- -------
pip     25.0.1
total 12
drwxr-xr-x 3 agent agent 4096 Sep 25 00:50 .
drwxr-xr-x 1 root  root  4096 Sep 25 00:53 ..
drwxr-xr-x 2 agent agent 4096 Sep 25 00:50 solution
total 36
dr-xr-xr-x 1 root root 4096 Sep 25 00:50 .
drwxr-xr-x 1 root root 4096 Sep 25 00:53 ..
dr-xr-xr-x 1 root root 4096 Sep 25 00:47 data
-r--r--r-- 1 root root 5774 Sep 25 00:46 evaluate_public.py
-r--r--r-- 1 root root 8265 Sep 25 00:46 ranking_evaluation.py
-r--r--r-- 1 root root 1278 Sep 25 00:46 requirements.txt
Harness context token usage record
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  }
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Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-5d605f7e-b067-4f77-b7dc-0c7a3b93f0bb",
    "process_id": "93559",
    "command": [
      "/bin/sh",
      "-lc",
      "command -v python; command -v python3; python --version; python -m pip list 2>/dev/null | head -30; ls -la /app; ls -la /task | sed -n '1,160p'; ls -la /task/.venv/bin 2>/dev/null | sed -n '1,80p'"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "command -v python; command -v python3; python --version; python -m pip list 2>/dev/null | head -30; ls -la /app; ls -la /task | sed -n '1,160p'; ls -la /task/.venv/bin 2>/dev/null | sed -n '1,80p'"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "Package Version\n------- -------\npip     25.0.1\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 25 00:50 .\ndrwxr-xr-x 1 root  root  4096 Sep 25 00:53 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 25 00:50 solution\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 25 00:50 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:53 ..\ndr-xr-xr-x 1 root root 4096 Sep 25 00:47 data\n-r--r--r-- 1 root root 5774 Sep 25 00:46 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 25 00:46 ranking_evaluation.py\n-r--r--r-- 1 root root 1278 Sep 25 00:46 requirements.txt\n",
    "stderr": "",
    "aggregated_output": "Package Version\n------- -------\npip     25.0.1\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 25 00:50 .\ndrwxr-xr-x 1 root  root  4096 Sep 25 00:53 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 25 00:50 solution\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 25 00:50 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:53 ..\ndr-xr-xr-x 1 root root 4096 Sep 25 00:47 data\n-r--r--r-- 1 root root 5774 Sep 25 00:46 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 25 00:46 ranking_evaluation.py\n-r--r--r-- 1 root root 1278 Sep 25 00:46 requirements.txt\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 587055587
    },
    "formatted_output": "Package Version\n------- -------\npip     25.0.1\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 25 00:50 .\ndrwxr-xr-x 1 root  root  4096 Sep 25 00:53 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 25 00:50 solution\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 25 00:50 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:53 ..\ndr-xr-xr-x 1 root root 4096 Sep 25 00:47 data\n-r--r--r-- 1 root root 5774 Sep 25 00:46 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 25 00:46 ranking_evaluation.py\n-r--r--r-- 1 root root 1278 Sep 25 00:46 requirements.txt\n"
  }
}
10

exec command

+00:26
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"command -v uv || true; command -v parquet-tools || true; python - <<'PY'\nimport sys\nprint('\\n'.join(sys.path))\nPY\nls -la /opt 2>/dev/null; ls -d /opt/*/bin/python* /usr/local/lib/python*/site-packages/pandas /usr/local/lib/python*/dist-packages/pandas 2>/dev/null || true",workdir:"/app",yield_time_ms:10000,max_output_tokens:12000});
text(r.output);

Tool response

Script completed
Wall time 0.1 seconds
Output:


/usr/local/lib/python312.zip
/usr/local/lib/python3.12
/usr/local/lib/python3.12/lib-dynload
/usr/local/lib/python3.12/site-packages
total 12
drwxr-xr-x 1 root root 4096 Sep 25 00:50 .
drwxr-xr-x 1 root root 4096 Sep 25 00:53 ..
dr-xr-xr-x 1 root root 4096 Sep 25 00:50 task-venv
/opt/task-venv/bin/python
/opt/task-venv/bin/python3
/opt/task-venv/bin/python3.12
Harness context token usage record
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  "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
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    "cache_write_input_tokens": 0,
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Harness context codex item completed
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  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-a17ec75f-380d-4eaf-9071-7b9265938bcd",
    "process_id": "34301",
    "command": [
      "/bin/sh",
      "-lc",
      "command -v uv || true; command -v parquet-tools || true; python - <<'PY'\nimport sys\nprint('\\n'.join(sys.path))\nPY\nls -la /opt 2>/dev/null; ls -d /opt/*/bin/python* /usr/local/lib/python*/site-packages/pandas /usr/local/lib/python*/dist-packages/pandas 2>/dev/null || true"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "command -v uv || true; command -v parquet-tools || true; python - <<'PY'\nimport sys\nprint('\\n'.join(sys.path))\nPY\nls -la /opt 2>/dev/null; ls -d /opt/*/bin/python* /usr/local/lib/python*/site-packages/pandas /usr/local/lib/python*/dist-packages/pandas 2>/dev/null || true"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "\n/usr/local/lib/python312.zip\n/usr/local/lib/python3.12\n/usr/local/lib/python3.12/lib-dynload\n/usr/local/lib/python3.12/site-packages\ntotal 12\ndrwxr-xr-x 1 root root 4096 Sep 25 00:50 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:53 ..\ndr-xr-xr-x 1 root root 4096 Sep 25 00:50 task-venv\n/opt/task-venv/bin/python\n/opt/task-venv/bin/python3\n/opt/task-venv/bin/python3.12\n",
    "stderr": "",
    "aggregated_output": "\n/usr/local/lib/python312.zip\n/usr/local/lib/python3.12\n/usr/local/lib/python3.12/lib-dynload\n/usr/local/lib/python3.12/site-packages\ntotal 12\ndrwxr-xr-x 1 root root 4096 Sep 25 00:50 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:53 ..\ndr-xr-xr-x 1 root root 4096 Sep 25 00:50 task-venv\n/opt/task-venv/bin/python\n/opt/task-venv/bin/python3\n/opt/task-venv/bin/python3.12\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 1971
    },
    "formatted_output": "\n/usr/local/lib/python312.zip\n/usr/local/lib/python3.12\n/usr/local/lib/python3.12/lib-dynload\n/usr/local/lib/python3.12/site-packages\ntotal 12\ndrwxr-xr-x 1 root root 4096 Sep 25 00:50 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:53 ..\ndr-xr-xr-x 1 root root 4096 Sep 25 00:50 task-venv\n/opt/task-venv/bin/python\n/opt/task-venv/bin/python3\n/opt/task-venv/bin/python3.12\n"
  }
}
11

exec command

+00:31
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np, os\np='/task/data'\nfor f in sorted(os.listdir(p)):\n if f.endswith('.parquet'):\n  x=pd.read_parquet(p+'/'+f)\n  print('\\n',f, x.shape, x.dtypes.to_dict())\n  print(x.head(2).to_dict('records'))\n  for c in ['uid','item_id','timestamp','is_organic','played_ratio_pct']:\n   if c in x: print(c,'nuniq',x[c].nunique(),'min',x[c].min(),'max',x[c].max())\nfor f in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n z=np.load(p+'/'+f)\n bits=np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(z['item_ids'])]\n print('\\n',f,{k:(z[k].shape,z[k].dtype) for k in z.files})\n print('uid range',z['uids'][:5],z['uids'][-5:],'items first',z['item_ids'][:10], 'density',bits.mean(),'eligible/user',bits.sum(1).min(),np.median(bits.sum(1)),bits.sum(1).max())\nPY",workdir:"/app",yield_time_ms:30000,max_output_tokens:30000});
text(r.output);

Tool response

Script completed
Wall time 4.4 seconds
Output:


 candidate_catalog.parquet (30000, 4) {'item_id': dtype('uint32'), 'positive_listens': dtype('int64'), 'organic_positive_listens': dtype('uint64'), 'popularity_rank': dtype('int32')}
[{'item_id': 5862961, 'positive_listens': 26615, 'organic_positive_listens': 20620, 'popularity_rank': 1}, {'item_id': 6901374, 'positive_listens': 25536, 'organic_positive_listens': 17347, 'popularity_rank': 2}]
item_id nuniq 30000 min 206 max 9390294

 target_users.parquet (8456, 3) {'uid': dtype('uint32'), 'recent_positive_listens': dtype('float64'), 'history_positive_listens': dtype('int64')}
[{'uid': 100, 'recent_positive_listens': 844.0, 'history_positive_listens': 2604}, {'uid': 200, 'recent_positive_listens': 50.0, 'history_positive_listens': 286}]
uid nuniq 8456 min 100 max 1000000

 test_preference_state.parquet (484776, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}
[{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}]
uid nuniq 7092 min 100 max 1000000
item_id nuniq 28863 min 206 max 9390294

 train_dislikes.parquet (101075, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}
[{'uid': 100, 'timestamp': 3087560, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 3936560, 'item_id': 8661238, 'is_organic': 1}]
uid nuniq 5732 min 100 max 1000000
item_id nuniq 50876 min 105 max 9390541
timestamp nuniq 67779 min 75 max 24786580
is_organic nuniq 2 min 0 max 1

 train_likes.parquet (814831, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}
[{'uid': 100, 'timestamp': 44755, 'item_id': 732449, 'is_organic': 1}, {'uid': 100, 'timestamp': 1155860, 'item_id': 6568592, 'is_organic': 0}]
uid nuniq 8144 min 100 max 1000000
item_id nuniq 172303 min 26 max 9390522
timestamp nuniq 562774 min 80 max 24786790
is_organic nuniq 2 min 0 max 1

 train_listens.parquet (43484159, 6) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')}
[{'uid': 100, 'timestamp': 39420, 'item_id': 8326270, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 170}, {'uid': 100, 'timestamp': 39420, 'item_id': 1441281, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 105}]
uid nuniq 9238 min 100 max 1000000
item_id nuniq 845395 min 22 max 9390623
timestamp nuniq 4806896 min 0 max 24786795
is_organic nuniq 2 min 0 max 1
played_ratio_pct nuniq 160 min 0 max 159

 train_undislikes.parquet (19960, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}
[{'uid': 100, 'timestamp': 7895900, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 7895900, 'item_id': 5038504, 'is_organic': 1}]
uid nuniq 2769 min 100 max 999900
item_id nuniq 14725 min 178 max 9390240
timestamp nuniq 6956 min 15950 max 24785395
is_organic nuniq 2 min 0 max 1

 train_unlikes.parquet (292665, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}
[{'uid': 100, 'timestamp': 3087555, 'item_id': 5038504, 'is_organic': 1}, {'uid': 100, 'timestamp': 3087555, 'item_id': 9170134, 'is_organic': 1}]
uid nuniq 6197 min 100 max 1000000
item_id nuniq 113072 min 26 max 9390601
timestamp nuniq 87635 min 250 max 24786570
is_organic nuniq 2 min 0 max 1

 val_dislikes.parquet (3563, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}
[{'uid': 1600, 'timestamp': 25163525, 'item_id': 6747257, 'is_organic': 0}, {'uid': 1900, 'timestamp': 24877510, 'item_id': 5832822, 'is_organic': 1}]
uid nuniq 955 min 1600 max 998800
item_id nuniq 3135 min 651 max 9388630
timestamp nuniq 2386 min 24789920 max 25393280
is_organic nuniq 2 min 0 max 1

 val_likes.parquet (37795, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}
[{'uid': 100, 'timestamp': 25249855, 'item_id': 3526521, 'is_organic': 1}, {'uid': 300, 'timestamp': 25046245, 'item_id': 4867327, 'is_organic': 1}]
uid nuniq 4114 min 100 max 999900
item_id nuniq 22902 min 209 max 9390461
timestamp nuniq 19790 min 24788815 max 25393390
is_organic nuniq 2 min 0 max 1

 val_listens.parquet (1501697, 6) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')}
[{'uid': 100, 'timestamp': 24943430, 'item_id': 9321269, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 195}, {'uid': 100, 'timestamp': 24943605, 'item_id': 5452713, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 175}]
uid nuniq 8196 min 100 max 1000000
item_id nuniq 191747 min 50 max 9390541
timestamp nuniq 119851 min 24788600 max 25393395
is_organic nuniq 2 min 0 max 1
played_ratio_pct nuniq 160 min 0 max 159

 val_undislikes.parquet (606, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}
[{'uid': 900, 'timestamp': 25364045, 'item_id': 6092542, 'is_organic': 1}, {'uid': 900, 'timestamp': 25364045, 'item_id': 6501913, 'is_organic': 1}]
uid nuniq 205 min 900 max 998800
item_id nuniq 579 min 15149 max 9385279
timestamp nuniq 242 min 24810715 max 25393035
is_organic nuniq 2 min 0 max 1

 val_unlikes.parquet (9346, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}
[{'uid': 1100, 'timestamp': 24861880, 'item_id': 6901050, 'is_organic': 1}, {'uid': 1100, 'timestamp': 24861880, 'item_id': 5421761, 'is_organic': 1}]
uid nuniq 1313 min 1100 max 998800
item_id nuniq 8080 min 1928 max 9390163
timestamp nuniq 2941 min 24789005 max 25393385
is_organic nuniq 2 min 0 max 1

 validation_preference_state.parquet (469124, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}
[{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}]
uid nuniq 7038 min 100 max 1000000
item_id nuniq 28814 min 206 max 9390294

 validation_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}
uid range [100 200 300 600 700] [ 999600  999700  999800  999900 1000000] items first [5862961 6901374 5635052 3542184 9378983 5463340 8213481  906358 2859641
 3033749] density 0.9689102097130243 eligible/user 24657 29242.0 30000

 test_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}
uid range [100 200 300 600 700] [ 999600  999700  999800  999900 1000000] items first [5862961 6901374 5635052 3542184 9378983 5463340 8213481  906358 2859641
 3033749] density 0.9680439293598234 eligible/user 24552 29220.0 30000
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "response_id": "resp_0fdbf3e20a3ba82b016ab5c6426cd087d2b2b9cbe529bc36d4",
  "usage": {
    "input_tokens": 20170,
    "cached_input_tokens": 19456,
    "cache_write_input_tokens": 0,
    "output_tokens": 358,
    "reasoning_output_tokens": 16,
    "total_tokens": 20528
  },
  "turn_token_usage": {
    "input_tokens": 90717,
    "cached_input_tokens": 81024,
    "cache_write_input_tokens": 0,
    "output_tokens": 1489,
    "reasoning_output_tokens": 452,
    "total_tokens": 92206
  },
  "thread_token_usage": {
    "input_tokens": 90717,
    "cached_input_tokens": 81024,
    "cache_write_input_tokens": 0,
    "output_tokens": 1489,
    "reasoning_output_tokens": 452,
    "total_tokens": 92206
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-5f3b7d9b-d5ff-4793-a172-d61711b30d2b",
    "process_id": "40954",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np, os\np='/task/data'\nfor f in sorted(os.listdir(p)):\n if f.endswith('.parquet'):\n  x=pd.read_parquet(p+'/'+f)\n  print('\\n',f, x.shape, x.dtypes.to_dict())\n  print(x.head(2).to_dict('records'))\n  for c in ['uid','item_id','timestamp','is_organic','played_ratio_pct']:\n   if c in x: print(c,'nuniq',x[c].nunique(),'min',x[c].min(),'max',x[c].max())\nfor f in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n z=np.load(p+'/'+f)\n bits=np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(z['item_ids'])]\n print('\\n',f,{k:(z[k].shape,z[k].dtype) for k in z.files})\n print('uid range',z['uids'][:5],z['uids'][-5:],'items first',z['item_ids'][:10], 'density',bits.mean(),'eligible/user',bits.sum(1).min(),np.median(bits.sum(1)),bits.sum(1).max())\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np, os\np='/task/data'\nfor f in sorted(os.listdir(p)):\n if f.endswith('.parquet'):\n  x=pd.read_parquet(p+'/'+f)\n  print('\\n',f, x.shape, x.dtypes.to_dict())\n  print(x.head(2).to_dict('records'))\n  for c in ['uid','item_id','timestamp','is_organic','played_ratio_pct']:\n   if c in x: print(c,'nuniq',x[c].nunique(),'min',x[c].min(),'max',x[c].max())\nfor f in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n z=np.load(p+'/'+f)\n bits=np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(z['item_ids'])]\n print('\\n',f,{k:(z[k].shape,z[k].dtype) for k in z.files})\n print('uid range',z['uids'][:5],z['uids'][-5:],'items first',z['item_ids'][:10], 'density',bits.mean(),'eligible/user',bits.sum(1).min(),np.median(bits.sum(1)),bits.sum(1).max())\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "\n candidate_catalog.parquet (30000, 4) {'item_id': dtype('uint32'), 'positive_listens': dtype('int64'), 'organic_positive_listens': dtype('uint64'), 'popularity_rank': dtype('int32')}\n[{'item_id': 5862961, 'positive_listens': 26615, 'organic_positive_listens': 20620, 'popularity_rank': 1}, {'item_id': 6901374, 'positive_listens': 25536, 'organic_positive_listens': 17347, 'popularity_rank': 2}]\nitem_id nuniq 30000 min 206 max 9390294\n\n target_users.parquet (8456, 3) {'uid': dtype('uint32'), 'recent_positive_listens': dtype('float64'), 'history_positive_listens': dtype('int64')}\n[{'uid': 100, 'recent_positive_listens': 844.0, 'history_positive_listens': 2604}, {'uid': 200, 'recent_positive_listens': 50.0, 'history_positive_listens': 286}]\nuid nuniq 8456 min 100 max 1000000\n\n test_preference_state.parquet (484776, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\n[{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}]\nuid nuniq 7092 min 100 max 1000000\nitem_id nuniq 28863 min 206 max 9390294\n\n train_dislikes.parquet (101075, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 100, 'timestamp': 3087560, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 3936560, 'item_id': 8661238, 'is_organic': 1}]\nuid nuniq 5732 min 100 max 1000000\nitem_id nuniq 50876 min 105 max 9390541\ntimestamp nuniq 67779 min 75 max 24786580\nis_organic nuniq 2 min 0 max 1\n\n train_likes.parquet (814831, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 100, 'timestamp': 44755, 'item_id': 732449, 'is_organic': 1}, {'uid': 100, 'timestamp': 1155860, 'item_id': 6568592, 'is_organic': 0}]\nuid nuniq 8144 min 100 max 1000000\nitem_id nuniq 172303 min 26 max 9390522\ntimestamp nuniq 562774 min 80 max 24786790\nis_organic nuniq 2 min 0 max 1\n\n train_listens.parquet (43484159, 6) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')}\n[{'uid': 100, 'timestamp': 39420, 'item_id': 8326270, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 170}, {'uid': 100, 'timestamp': 39420, 'item_id': 1441281, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 105}]\nuid nuniq 9238 min 100 max 1000000\nitem_id nuniq 845395 min 22 max 9390623\ntimestamp nuniq 4806896 min 0 max 24786795\nis_organic nuniq 2 min 0 max 1\nplayed_ratio_pct nuniq 160 min 0 max 159\n\n train_undislikes.parquet (19960, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 100, 'timestamp': 7895900, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 7895900, 'item_id': 5038504, 'is_organic': 1}]\nuid nuniq 2769 min 100 max 999900\nitem_id nuniq 14725 min 178 max 9390240\ntimestamp nuniq 6956 min 15950 max 24785395\nis_organic nuniq 2 min 0 max 1\n\n train_unlikes.parquet (292665, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 100, 'timestamp': 3087555, 'item_id': 5038504, 'is_organic': 1}, {'uid': 100, 'timestamp': 3087555, 'item_id': 9170134, 'is_organic': 1}]\nuid nuniq 6197 min 100 max 1000000\nitem_id nuniq 113072 min 26 max 9390601\ntimestamp nuniq 87635 min 250 max 24786570\nis_organic nuniq 2 min 0 max 1\n\n val_dislikes.parquet (3563, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 1600, 'timestamp': 25163525, 'item_id': 6747257, 'is_organic': 0}, {'uid': 1900, 'timestamp': 24877510, 'item_id': 5832822, 'is_organic': 1}]\nuid nuniq 955 min 1600 max 998800\nitem_id nuniq 3135 min 651 max 9388630\ntimestamp nuniq 2386 min 24789920 max 25393280\nis_organic nuniq 2 min 0 max 1\n\n val_likes.parquet (37795, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 100, 'timestamp': 25249855, 'item_id': 3526521, 'is_organic': 1}, {'uid': 300, 'timestamp': 25046245, 'item_id': 4867327, 'is_organic': 1}]\nuid nuniq 4114 min 100 max 999900\nitem_id nuniq 22902 min 209 max 9390461\ntimestamp nuniq 19790 min 24788815 max 25393390\nis_organic nuniq 2 min 0 max 1\n\n val_listens.parquet (1501697, 6) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')}\n[{'uid': 100, 'timestamp': 24943430, 'item_id': 9321269, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 195}, {'uid': 100, 'timestamp': 24943605, 'item_id': 5452713, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 175}]\nuid nuniq 8196 min 100 max 1000000\nitem_id nuniq 191747 min 50 max 9390541\ntimestamp nuniq 119851 min 24788600 max 25393395\nis_organic nuniq 2 min 0 max 1\nplayed_ratio_pct nuniq 160 min 0 max 159\n\n val_undislikes.parquet (606, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 900, 'timestamp': 25364045, 'item_id': 6092542, 'is_organic': 1}, {'uid': 900, 'timestamp': 25364045, 'item_id': 6501913, 'is_organic': 1}]\nuid nuniq 205 min 900 max 998800\nitem_id nuniq 579 min 15149 max 9385279\ntimestamp nuniq 242 min 24810715 max 25393035\nis_organic nuniq 2 min 0 max 1\n\n val_unlikes.parquet (9346, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 1100, 'timestamp': 24861880, 'item_id': 6901050, 'is_organic': 1}, {'uid': 1100, 'timestamp': 24861880, 'item_id': 5421761, 'is_organic': 1}]\nuid nuniq 1313 min 1100 max 998800\nitem_id nuniq 8080 min 1928 max 9390163\ntimestamp nuniq 2941 min 24789005 max 25393385\nis_organic nuniq 2 min 0 max 1\n\n validation_preference_state.parquet (469124, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\n[{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}]\nuid nuniq 7038 min 100 max 1000000\nitem_id nuniq 28814 min 206 max 9390294\n\n validation_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\nuid range [100 200 300 600 700] [ 999600  999700  999800  999900 1000000] items first [5862961 6901374 5635052 3542184 9378983 5463340 8213481  906358 2859641\n 3033749] density 0.9689102097130243 eligible/user 24657 29242.0 30000\n\n test_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\nuid range [100 200 300 600 700] [ 999600  999700  999800  999900 1000000] items first [5862961 6901374 5635052 3542184 9378983 5463340 8213481  906358 2859641\n 3033749] density 0.9680439293598234 eligible/user 24552 29220.0 30000\n",
    "stderr": "",
    "aggregated_output": "\n candidate_catalog.parquet (30000, 4) {'item_id': dtype('uint32'), 'positive_listens': dtype('int64'), 'organic_positive_listens': dtype('uint64'), 'popularity_rank': dtype('int32')}\n[{'item_id': 5862961, 'positive_listens': 26615, 'organic_positive_listens': 20620, 'popularity_rank': 1}, {'item_id': 6901374, 'positive_listens': 25536, 'organic_positive_listens': 17347, 'popularity_rank': 2}]\nitem_id nuniq 30000 min 206 max 9390294\n\n target_users.parquet (8456, 3) {'uid': dtype('uint32'), 'recent_positive_listens': dtype('float64'), 'history_positive_listens': dtype('int64')}\n[{'uid': 100, 'recent_positive_listens': 844.0, 'history_positive_listens': 2604}, {'uid': 200, 'recent_positive_listens': 50.0, 'history_positive_listens': 286}]\nuid nuniq 8456 min 100 max 1000000\n\n test_preference_state.parquet (484776, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\n[{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}]\nuid nuniq 7092 min 100 max 1000000\nitem_id nuniq 28863 min 206 max 9390294\n\n train_dislikes.parquet (101075, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 100, 'timestamp': 3087560, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 3936560, 'item_id': 8661238, 'is_organic': 1}]\nuid nuniq 5732 min 100 max 1000000\nitem_id nuniq 50876 min 105 max 9390541\ntimestamp nuniq 67779 min 75 max 24786580\nis_organic nuniq 2 min 0 max 1\n\n train_likes.parquet (814831, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 100, 'timestamp': 44755, 'item_id': 732449, 'is_organic': 1}, {'uid': 100, 'timestamp': 1155860, 'item_id': 6568592, 'is_organic': 0}]\nuid nuniq 8144 min 100 max 1000000\nitem_id nuniq 172303 min 26 max 9390522\ntimestamp nuniq 562774 min 80 max 24786790\nis_organic nuniq 2 min 0 max 1\n\n train_listens.parquet (43484159, 6) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')}\n[{'uid': 100, 'timestamp': 39420, 'item_id': 8326270, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 170}, {'uid': 100, 'timestamp': 39420, 'item_id': 1441281, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 105}]\nuid nuniq 9238 min 100 max 1000000\nitem_id nuniq 845395 min 22 max 9390623\ntimestamp nuniq 4806896 min 0 max 24786795\nis_organic nuniq 2 min 0 max 1\nplayed_ratio_pct nuniq 160 min 0 max 159\n\n train_undislikes.parquet (19960, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 100, 'timestamp': 7895900, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 7895900, 'item_id': 5038504, 'is_organic': 1}]\nuid nuniq 2769 min 100 max 999900\nitem_id nuniq 14725 min 178 max 9390240\ntimestamp nuniq 6956 min 15950 max 24785395\nis_organic nuniq 2 min 0 max 1\n\n train_unlikes.parquet (292665, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 100, 'timestamp': 3087555, 'item_id': 5038504, 'is_organic': 1}, {'uid': 100, 'timestamp': 3087555, 'item_id': 9170134, 'is_organic': 1}]\nuid nuniq 6197 min 100 max 1000000\nitem_id nuniq 113072 min 26 max 9390601\ntimestamp nuniq 87635 min 250 max 24786570\nis_organic nuniq 2 min 0 max 1\n\n val_dislikes.parquet (3563, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 1600, 'timestamp': 25163525, 'item_id': 6747257, 'is_organic': 0}, {'uid': 1900, 'timestamp': 24877510, 'item_id': 5832822, 'is_organic': 1}]\nuid nuniq 955 min 1600 max 998800\nitem_id nuniq 3135 min 651 max 9388630\ntimestamp nuniq 2386 min 24789920 max 25393280\nis_organic nuniq 2 min 0 max 1\n\n val_likes.parquet (37795, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 100, 'timestamp': 25249855, 'item_id': 3526521, 'is_organic': 1}, {'uid': 300, 'timestamp': 25046245, 'item_id': 4867327, 'is_organic': 1}]\nuid nuniq 4114 min 100 max 999900\nitem_id nuniq 22902 min 209 max 9390461\ntimestamp nuniq 19790 min 24788815 max 25393390\nis_organic nuniq 2 min 0 max 1\n\n val_listens.parquet (1501697, 6) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')}\n[{'uid': 100, 'timestamp': 24943430, 'item_id': 9321269, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 195}, {'uid': 100, 'timestamp': 24943605, 'item_id': 5452713, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 175}]\nuid nuniq 8196 min 100 max 1000000\nitem_id nuniq 191747 min 50 max 9390541\ntimestamp nuniq 119851 min 24788600 max 25393395\nis_organic nuniq 2 min 0 max 1\nplayed_ratio_pct nuniq 160 min 0 max 159\n\n val_undislikes.parquet (606, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 900, 'timestamp': 25364045, 'item_id': 6092542, 'is_organic': 1}, {'uid': 900, 'timestamp': 25364045, 'item_id': 6501913, 'is_organic': 1}]\nuid nuniq 205 min 900 max 998800\nitem_id nuniq 579 min 15149 max 9385279\ntimestamp nuniq 242 min 24810715 max 25393035\nis_organic nuniq 2 min 0 max 1\n\n val_unlikes.parquet (9346, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 1100, 'timestamp': 24861880, 'item_id': 6901050, 'is_organic': 1}, {'uid': 1100, 'timestamp': 24861880, 'item_id': 5421761, 'is_organic': 1}]\nuid nuniq 1313 min 1100 max 998800\nitem_id nuniq 8080 min 1928 max 9390163\ntimestamp nuniq 2941 min 24789005 max 25393385\nis_organic nuniq 2 min 0 max 1\n\n validation_preference_state.parquet (469124, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\n[{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}]\nuid nuniq 7038 min 100 max 1000000\nitem_id nuniq 28814 min 206 max 9390294\n\n validation_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\nuid range [100 200 300 600 700] [ 999600  999700  999800  999900 1000000] items first [5862961 6901374 5635052 3542184 9378983 5463340 8213481  906358 2859641\n 3033749] density 0.9689102097130243 eligible/user 24657 29242.0 30000\n\n test_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\nuid range [100 200 300 600 700] [ 999600  999700  999800  999900 1000000] items first [5862961 6901374 5635052 3542184 9378983 5463340 8213481  906358 2859641\n 3033749] density 0.9680439293598234 eligible/user 24552 29220.0 30000\n",
    "exit_code": 0,
    "duration": {
      "secs": 4,
      "nanos": 215466631
    },
    "formatted_output": "\n candidate_catalog.parquet (30000, 4) {'item_id': dtype('uint32'), 'positive_listens': dtype('int64'), 'organic_positive_listens': dtype('uint64'), 'popularity_rank': dtype('int32')}\n[{'item_id': 5862961, 'positive_listens': 26615, 'organic_positive_listens': 20620, 'popularity_rank': 1}, {'item_id': 6901374, 'positive_listens': 25536, 'organic_positive_listens': 17347, 'popularity_rank': 2}]\nitem_id nuniq 30000 min 206 max 9390294\n\n target_users.parquet (8456, 3) {'uid': dtype('uint32'), 'recent_positive_listens': dtype('float64'), 'history_positive_listens': dtype('int64')}\n[{'uid': 100, 'recent_positive_listens': 844.0, 'history_positive_listens': 2604}, {'uid': 200, 'recent_positive_listens': 50.0, 'history_positive_listens': 286}]\nuid nuniq 8456 min 100 max 1000000\n\n test_preference_state.parquet (484776, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\n[{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}]\nuid nuniq 7092 min 100 max 1000000\nitem_id nuniq 28863 min 206 max 9390294\n\n train_dislikes.parquet (101075, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 100, 'timestamp': 3087560, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 3936560, 'item_id': 8661238, 'is_organic': 1}]\nuid nuniq 5732 min 100 max 1000000\nitem_id nuniq 50876 min 105 max 9390541\ntimestamp nuniq 67779 min 75 max 24786580\nis_organic nuniq 2 min 0 max 1\n\n train_likes.parquet (814831, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 100, 'timestamp': 44755, 'item_id': 732449, 'is_organic': 1}, {'uid': 100, 'timestamp': 1155860, 'item_id': 6568592, 'is_organic': 0}]\nuid nuniq 8144 min 100 max 1000000\nitem_id nuniq 172303 min 26 max 9390522\ntimestamp nuniq 562774 min 80 max 24786790\nis_organic nuniq 2 min 0 max 1\n\n train_listens.parquet (43484159, 6) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')}\n[{'uid': 100, 'timestamp': 39420, 'item_id': 8326270, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 170}, {'uid': 100, 'timestamp': 39420, 'item_id': 1441281, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 105}]\nuid nuniq 9238 min 100 max 1000000\nitem_id nuniq 845395 min 22 max 9390623\ntimestamp nuniq 4806896 min 0 max 24786795\nis_organic nuniq 2 min 0 max 1\nplayed_ratio_pct nuniq 160 min 0 max 159\n\n train_undislikes.parquet (19960, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 100, 'timestamp': 7895900, 'item_id': 9170134, 'is_organic': 1}, {'uid': 100, 'timestamp': 7895900, 'item_id': 5038504, 'is_organic': 1}]\nuid nuniq 2769 min 100 max 999900\nitem_id nuniq 14725 min 178 max 9390240\ntimestamp nuniq 6956 min 15950 max 24785395\nis_organic nuniq 2 min 0 max 1\n\n train_unlikes.parquet (292665, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 100, 'timestamp': 3087555, 'item_id': 5038504, 'is_organic': 1}, {'uid': 100, 'timestamp': 3087555, 'item_id': 9170134, 'is_organic': 1}]\nuid nuniq 6197 min 100 max 1000000\nitem_id nuniq 113072 min 26 max 9390601\ntimestamp nuniq 87635 min 250 max 24786570\nis_organic nuniq 2 min 0 max 1\n\n val_dislikes.parquet (3563, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 1600, 'timestamp': 25163525, 'item_id': 6747257, 'is_organic': 0}, {'uid': 1900, 'timestamp': 24877510, 'item_id': 5832822, 'is_organic': 1}]\nuid nuniq 955 min 1600 max 998800\nitem_id nuniq 3135 min 651 max 9388630\ntimestamp nuniq 2386 min 24789920 max 25393280\nis_organic nuniq 2 min 0 max 1\n\n val_likes.parquet (37795, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 100, 'timestamp': 25249855, 'item_id': 3526521, 'is_organic': 1}, {'uid': 300, 'timestamp': 25046245, 'item_id': 4867327, 'is_organic': 1}]\nuid nuniq 4114 min 100 max 999900\nitem_id nuniq 22902 min 209 max 9390461\ntimestamp nuniq 19790 min 24788815 max 25393390\nis_organic nuniq 2 min 0 max 1\n\n val_listens.parquet (1501697, 6) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')}\n[{'uid': 100, 'timestamp': 24943430, 'item_id': 9321269, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 195}, {'uid': 100, 'timestamp': 24943605, 'item_id': 5452713, 'is_organic': 0, 'played_ratio_pct': 100, 'track_length_seconds': 175}]\nuid nuniq 8196 min 100 max 1000000\nitem_id nuniq 191747 min 50 max 9390541\ntimestamp nuniq 119851 min 24788600 max 25393395\nis_organic nuniq 2 min 0 max 1\nplayed_ratio_pct nuniq 160 min 0 max 159\n\n val_undislikes.parquet (606, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 900, 'timestamp': 25364045, 'item_id': 6092542, 'is_organic': 1}, {'uid': 900, 'timestamp': 25364045, 'item_id': 6501913, 'is_organic': 1}]\nuid nuniq 205 min 900 max 998800\nitem_id nuniq 579 min 15149 max 9385279\ntimestamp nuniq 242 min 24810715 max 25393035\nis_organic nuniq 2 min 0 max 1\n\n val_unlikes.parquet (9346, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n[{'uid': 1100, 'timestamp': 24861880, 'item_id': 6901050, 'is_organic': 1}, {'uid': 1100, 'timestamp': 24861880, 'item_id': 5421761, 'is_organic': 1}]\nuid nuniq 1313 min 1100 max 998800\nitem_id nuniq 8080 min 1928 max 9390163\ntimestamp nuniq 2941 min 24789005 max 25393385\nis_organic nuniq 2 min 0 max 1\n\n validation_preference_state.parquet (469124, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\n[{'uid': 100, 'item_id': 410868, 'state': 'liked'}, {'uid': 100, 'item_id': 732449, 'state': 'liked'}]\nuid nuniq 7038 min 100 max 1000000\nitem_id nuniq 28814 min 206 max 9390294\n\n validation_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\nuid range [100 200 300 600 700] [ 999600  999700  999800  999900 1000000] items first [5862961 6901374 5635052 3542184 9378983 5463340 8213481  906358 2859641\n 3033749] density 0.9689102097130243 eligible/user 24657 29242.0 30000\n\n test_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\nuid range [100 200 300 600 700] [ 999600  999700  999800  999900 1000000] items first [5862961 6901374 5635052 3542184 9378983 5463340 8213481  906358 2859641\n 3033749] density 0.9680439293598234 eligible/user 24552 29220.0 30000\n"
  }
}
12

Agent message

+00:53
Thinking unavailable No text recorded

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

13

exec command

+01:03
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 candidate boundary is broad (median 29,242 of 30,000 tracks per user), so ranking quality will come from personalization rather than eligibility alone. I’m measuring how much validation relevance comes from repeat listening, explicit likes, recency, and global popularity; that will determine whether to emphasize a repeat model, collaborative discovery, or both.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np, gc, time\nP='/task/data'; cutoff=24786800\nT=set(pd.read_parquet(P+'/target_users.parquet',columns=['uid']).uid.astype(int))\ncat=pd.read_parquet(P+'/candidate_catalog.parquet')\ncatset=set(cat.item_id.astype(int)); prank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\nz=np.load(P+'/validation_eligible_candidates.npz'); uidrow={int(x):i for i,x in enumerate(z['uids'])}; itcol={int(x):i for i,x in enumerate(z['item_ids'])}\ndef eligible(u,i):\n c=itcol.get(int(i)); r=uidrow.get(int(u)); return r is not None and c is not None and ((int(z['packed_eligible'][r,c//8])>>(c%8))&1)\nvl=pd.read_parquet(P+'/val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nvg=vl[(vl.uid.isin(T))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); vg['grade']=1\nvlike=pd.read_parquet(P+'/val_likes.parquet',columns=['uid','item_id','is_organic']); q=vlike[(vlike.uid.isin(T))&(vlike.is_organic==1)][['uid','item_id']].drop_duplicates(); q['grade']=3\ntruth=pd.concat([vg,q]).groupby(['uid','item_id'],as_index=False).grade.max(); truth=truth[[eligible(u,i) for u,i in truth[['uid','item_id']].itertuples(index=False,name=None)]].copy()\nprint('truth pairs/users/grade',len(truth),truth.uid.nunique(),truth.grade.value_counts().to_dict())\ntruth['poprank']=truth.item_id.map(prank)\nprint('poprank quantiles',truth.poprank.quantile([0,.1,.25,.5,.75,.9,1]).to_dict(),'missing',truth.poprank.isna().sum())\nprint('labels/user quantiles',truth.groupby('uid').size().quantile([0,.25,.5,.75,.9,.99,1]).to_dict())\nps=pd.read_parquet(P+'/validation_preference_state.parquet'); truth=truth.merge(ps.assign(in_state=1),on=['uid','item_id'],how='left')\nprint('truth active pref',truth.groupby(['grade','state'],dropna=False).size())\n# eligibility known state percentages\nps['eligible']=[eligible(u,i) for u,i in ps[['uid','item_id']].itertuples(index=False,name=None)]\nprint('preference eligible by state',ps.groupby('state').eligible.agg(['mean','sum','count']))\nprint('load train relevant joins')\ntl=pd.read_parquet(P+'/train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\n# restrict target+catalog first\nx=tl[tl.uid.isin(T)&tl.item_id.isin(catset)].copy(); del tl; gc.collect(); print('candidate train events',len(x))\nx['pos50']=x.played_ratio_pct>=50; x['strong']=x.played_ratio_pct>=80; x['orgstrong']=(x.is_organic==1)&x.strong\n# aggregate only truth pair merge events for repeat diagnostic\nte=x.merge(truth[['uid','item_id','grade']],on=['uid','item_id'],how='inner')\nagg=te.groupby(['uid','item_id','grade']).agg(n=('timestamp','size'),pos=('pos50','sum'),strong=('strong','sum'),org=('is_organic','sum'),orgstrong=('orgstrong','sum'),last=('timestamp','max')).reset_index(); truth2=truth[['uid','item_id','grade','poprank']].merge(agg,on=['uid','item_id','grade'],how='left').fillna({'n':0,'pos':0,'strong':0,'org':0,'orgstrong':0,'last':0})\nfor col in ['n','pos','strong','org','orgstrong']:\n print('repeat',col,truth2.groupby('grade')[col].apply(lambda s:pd.Series({'any':(s>0).mean(),'>=2':(s>=2).mean(),'>=5':(s>=5).mean(),'median':s.median()})).unstack().to_dict('index'))\nfor days in [1,3,7,14,30,60,90,180,300]:\n print('last within',days,'days',truth2.groupby('grade')['last'].apply(lambda s:(s>=cutoff-days*86400).mean()).to_dict())\n# all grade-weighted relevance capture union prior and popular top K\nfor k in [10,30,100,300,1000,3000,10000]:\n print('pop<=',k,truth2.groupby('grade').poprank.apply(lambda s:(s<=k).mean()).to_dict())\nPY",workdir:"/app",yield_time_ms:30000,max_output_tokens:20000});
if(r.session_id){text(`SESSION_ID=${r.session_id}`);text(r.output);}else{text(r.output);}

Tool response

Script completed
Wall time 30.2 seconds
Output:

SESSION_ID=16403
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "response_id": "resp_0fdbf3e20a3ba82b016ab5c64f5ad887d29823c30142bbb426",
  "usage": {
    "input_tokens": 23169,
    "cached_input_tokens": 19968,
    "cache_write_input_tokens": 0,
    "output_tokens": 2276,
    "reasoning_output_tokens": 1006,
    "total_tokens": 25445
  },
  "turn_token_usage": {
    "input_tokens": 113886,
    "cached_input_tokens": 100992,
    "cache_write_input_tokens": 0,
    "output_tokens": 3765,
    "reasoning_output_tokens": 1458,
    "total_tokens": 117651
  },
  "thread_token_usage": {
    "input_tokens": 113886,
    "cached_input_tokens": 100992,
    "cache_write_input_tokens": 0,
    "output_tokens": 3765,
    "reasoning_output_tokens": 1458,
    "total_tokens": 117651
  }
}
14

exec command

+01:56
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:16403,chars:"",yield_time_ms:30000,max_output_tokens:20000});
text(r.output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "response_id": "resp_0fdbf3e20a3ba82b016ab5c697c00087d2a8ec785f697abd5f",
  "usage": {
    "input_tokens": 25473,
    "cached_input_tokens": 23040,
    "cache_write_input_tokens": 0,
    "output_tokens": 61,
    "reasoning_output_tokens": 14,
    "total_tokens": 25534
  },
  "turn_token_usage": {
    "input_tokens": 139359,
    "cached_input_tokens": 124032,
    "cache_write_input_tokens": 0,
    "output_tokens": 3826,
    "reasoning_output_tokens": 1472,
    "total_tokens": 143185
  },
  "thread_token_usage": {
    "input_tokens": 139359,
    "cached_input_tokens": 124032,
    "cache_write_input_tokens": 0,
    "output_tokens": 3826,
    "reasoning_output_tokens": 1472,
    "total_tokens": 143185
  }
}
15

exec command

+02:29
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:16403,chars:"",yield_time_ms:30000,max_output_tokens:20000});
text(r.output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "response_id": "resp_0fdbf3e20a3ba82b016ab5c6b920b887d288a10db109a14f96",
  "usage": {
    "input_tokens": 25557,
    "cached_input_tokens": 25344,
    "cache_write_input_tokens": 0,
    "output_tokens": 55,
    "reasoning_output_tokens": 8,
    "total_tokens": 25612
  },
  "turn_token_usage": {
    "input_tokens": 164916,
    "cached_input_tokens": 149376,
    "cache_write_input_tokens": 0,
    "output_tokens": 3881,
    "reasoning_output_tokens": 1480,
    "total_tokens": 168797
  },
  "thread_token_usage": {
    "input_tokens": 164916,
    "cached_input_tokens": 149376,
    "cache_write_input_tokens": 0,
    "output_tokens": 3881,
    "reasoning_output_tokens": 1480,
    "total_tokens": 168797
  }
}
16

exec command

+03:01
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 full listen log is large enough that the repeat-history join is still running. While it completes, the key validation labels are already established in memory; next I’ll turn the strongest signals into a valid baseline immediately, then layer in collaborative and temporal components only where they improve NDCG@10.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:16403,chars:"",yield_time_ms:30000,max_output_tokens:20000});
text(r.output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

Harness context token usage record
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  "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
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    "cache_write_input_tokens": 0,
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  }
}
17

exec command

+03:36
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:16403,chars:"",yield_time_ms:30000,max_output_tokens:20000});
text(r.output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
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  "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "response_id": "resp_0fdbf3e20a3ba82b016ab5c6fb0f9087d28f4a5e13dcb09c0e",
  "usage": {
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    "cache_write_input_tokens": 0,
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  }
}
18

exec command

+04:09
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"ps -o pid,etime,%cpu,%mem,rss,vsz,stat,cmd -C python -C python3 | sed -n '1,20p'; free -h",workdir:"/app",yield_time_ms:10000,max_output_tokens:5000});
text(r.output);

Tool response

Script completed
Wall time 0.0 seconds
Output:

/bin/sh: 1: ps: not found
/bin/sh: 1: free: not found
Harness context codex item completed
{
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      "-lc",
      "ps -o pid,etime,%cpu,%mem,rss,vsz,stat,cmd -C python -C python3 | sed -n '1,20p'; free -h"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "ps -o pid,etime,%cpu,%mem,rss,vsz,stat,cmd -C python -C python3 | sed -n '1,20p'; free -h"
      }
    ],
    "source": "unified_exec_startup",
    "status": "failed",
    "stdout": "/bin/sh: 1: ps: not found\n/bin/sh: 1: free: not found\n",
    "stderr": "",
    "aggregated_output": "/bin/sh: 1: ps: not found\n/bin/sh: 1: free: not found\n",
    "exit_code": 127,
    "duration": {
      "secs": 0,
      "nanos": 3960
    },
    "formatted_output": "/bin/sh: 1: ps: not found\n/bin/sh: 1: free: not found\n"
  }
}
Harness context token usage record
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    "cache_write_input_tokens": 0,
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}
19

exec command

+04:12
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:16403,chars:"",yield_time_ms:30000,max_output_tokens:20000});
text(r.output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

Harness context token usage record
{
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  "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "response_id": "resp_0fdbf3e20a3ba82b016ab5c71febf887d2a873b0adf6244ddc",
  "usage": {
    "input_tokens": 26035,
    "cached_input_tokens": 25728,
    "cache_write_input_tokens": 0,
    "output_tokens": 56,
    "reasoning_output_tokens": 9,
    "total_tokens": 26091
  },
  "turn_token_usage": {
    "input_tokens": 268270,
    "cached_input_tokens": 251520,
    "cache_write_input_tokens": 0,
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  "thread_token_usage": {
    "input_tokens": 268270,
    "cached_input_tokens": 251520,
    "cache_write_input_tokens": 0,
    "output_tokens": 4246,
    "reasoning_output_tokens": 1553,
    "total_tokens": 272516
  }
}
20

exec command

+04:46
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:16403,chars:"",yield_time_ms:30000,max_output_tokens:20000});
text(r.output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "response_id": "resp_0fdbf3e20a3ba82b016ab5c740cbc887d285d6eb984f131f78",
  "usage": {
    "input_tokens": 26114,
    "cached_input_tokens": 25856,
    "cache_write_input_tokens": 0,
    "output_tokens": 61,
    "reasoning_output_tokens": 14,
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  },
  "turn_token_usage": {
    "input_tokens": 294384,
    "cached_input_tokens": 277376,
    "cache_write_input_tokens": 0,
    "output_tokens": 4307,
    "reasoning_output_tokens": 1567,
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  },
  "thread_token_usage": {
    "input_tokens": 294384,
    "cached_input_tokens": 277376,
    "cache_write_input_tokens": 0,
    "output_tokens": 4307,
    "reasoning_output_tokens": 1567,
    "total_tokens": 298691
  }
}
21

exec command

+05:18
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:16403,chars:"\u0003",yield_time_ms:1000,max_output_tokens:20000});
text(r.output);

Tool response

Script completed
Wall time 0.1 seconds
Output:

Traceback (most recent call last):
  File "<stdin>", line 12, in <module>
  File "<stdin>", line 8, in eligible
  File "/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py", line 254, in __getitem__
    return format.read_array(bytes,
           ^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py", line 851, in read_array
    data = _read_bytes(fp, read_size, "array data")
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py", line 986, in _read_bytes
    r = fp.read(size - len(data))
        ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/zipfile/__init__.py", line 1008, in read
    data = self._read1(n)
           ^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/zipfile/__init__.py", line 1098, in _read1
    self._update_crc(data)
  File "/usr/local/lib/python3.12/zipfile/__init__.py", line 1023, in _update_crc
    self._running_crc = crc32(newdata, self._running_crc)
                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
KeyboardInterrupt
Harness context token usage record
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  "turn_token_usage": {
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    "input_tokens": 320582,
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    "cache_write_input_tokens": 0,
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  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-3ae11c2b-b5e7-4f8e-943b-d74f8f9f612a",
    "process_id": "16403",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np, gc, time\nP='/task/data'; cutoff=24786800\nT=set(pd.read_parquet(P+'/target_users.parquet',columns=['uid']).uid.astype(int))\ncat=pd.read_parquet(P+'/candidate_catalog.parquet')\ncatset=set(cat.item_id.astype(int)); prank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\nz=np.load(P+'/validation_eligible_candidates.npz'); uidrow={int(x):i for i,x in enumerate(z['uids'])}; itcol={int(x):i for i,x in enumerate(z['item_ids'])}\ndef eligible(u,i):\n c=itcol.get(int(i)); r=uidrow.get(int(u)); return r is not None and c is not None and ((int(z['packed_eligible'][r,c//8])>>(c%8))&1)\nvl=pd.read_parquet(P+'/val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nvg=vl[(vl.uid.isin(T))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); vg['grade']=1\nvlike=pd.read_parquet(P+'/val_likes.parquet',columns=['uid','item_id','is_organic']); q=vlike[(vlike.uid.isin(T))&(vlike.is_organic==1)][['uid','item_id']].drop_duplicates(); q['grade']=3\ntruth=pd.concat([vg,q]).groupby(['uid','item_id'],as_index=False).grade.max(); truth=truth[[eligible(u,i) for u,i in truth[['uid','item_id']].itertuples(index=False,name=None)]].copy()\nprint('truth pairs/users/grade',len(truth),truth.uid.nunique(),truth.grade.value_counts().to_dict())\ntruth['poprank']=truth.item_id.map(prank)\nprint('poprank quantiles',truth.poprank.quantile([0,.1,.25,.5,.75,.9,1]).to_dict(),'missing',truth.poprank.isna().sum())\nprint('labels/user quantiles',truth.groupby('uid').size().quantile([0,.25,.5,.75,.9,.99,1]).to_dict())\nps=pd.read_parquet(P+'/validation_preference_state.parquet'); truth=truth.merge(ps.assign(in_state=1),on=['uid','item_id'],how='left')\nprint('truth active pref',truth.groupby(['grade','state'],dropna=False).size())\n# eligibility known state percentages\nps['eligible']=[eligible(u,i) for u,i in ps[['uid','item_id']].itertuples(index=False,name=None)]\nprint('preference eligible by state',ps.groupby('state').eligible.agg(['mean','sum','count']))\nprint('load train relevant joins')\ntl=pd.read_parquet(P+'/train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\n# restrict target+catalog first\nx=tl[tl.uid.isin(T)&tl.item_id.isin(catset)].copy(); del tl; gc.collect(); print('candidate train events',len(x))\nx['pos50']=x.played_ratio_pct>=50; x['strong']=x.played_ratio_pct>=80; x['orgstrong']=(x.is_organic==1)&x.strong\n# aggregate only truth pair merge events for repeat diagnostic\nte=x.merge(truth[['uid','item_id','grade']],on=['uid','item_id'],how='inner')\nagg=te.groupby(['uid','item_id','grade']).agg(n=('timestamp','size'),pos=('pos50','sum'),strong=('strong','sum'),org=('is_organic','sum'),orgstrong=('orgstrong','sum'),last=('timestamp','max')).reset_index(); truth2=truth[['uid','item_id','grade','poprank']].merge(agg,on=['uid','item_id','grade'],how='left').fillna({'n':0,'pos':0,'strong':0,'org':0,'orgstrong':0,'last':0})\nfor col in ['n','pos','strong','org','orgstrong']:\n print('repeat',col,truth2.groupby('grade')[col].apply(lambda s:pd.Series({'any':(s>0).mean(),'>=2':(s>=2).mean(),'>=5':(s>=5).mean(),'median':s.median()})).unstack().to_dict('index'))\nfor days in [1,3,7,14,30,60,90,180,300]:\n print('last within',days,'days',truth2.groupby('grade')['last'].apply(lambda s:(s>=cutoff-days*86400).mean()).to_dict())\n# all grade-weighted relevance capture union prior and popular top K\nfor k in [10,30,100,300,1000,3000,10000]:\n print('pop<=',k,truth2.groupby('grade').poprank.apply(lambda s:(s<=k).mean()).to_dict())\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np, gc, time\nP='/task/data'; cutoff=24786800\nT=set(pd.read_parquet(P+'/target_users.parquet',columns=['uid']).uid.astype(int))\ncat=pd.read_parquet(P+'/candidate_catalog.parquet')\ncatset=set(cat.item_id.astype(int)); prank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\nz=np.load(P+'/validation_eligible_candidates.npz'); uidrow={int(x):i for i,x in enumerate(z['uids'])}; itcol={int(x):i for i,x in enumerate(z['item_ids'])}\ndef eligible(u,i):\n c=itcol.get(int(i)); r=uidrow.get(int(u)); return r is not None and c is not None and ((int(z['packed_eligible'][r,c//8])>>(c%8))&1)\nvl=pd.read_parquet(P+'/val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nvg=vl[(vl.uid.isin(T))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); vg['grade']=1\nvlike=pd.read_parquet(P+'/val_likes.parquet',columns=['uid','item_id','is_organic']); q=vlike[(vlike.uid.isin(T))&(vlike.is_organic==1)][['uid','item_id']].drop_duplicates(); q['grade']=3\ntruth=pd.concat([vg,q]).groupby(['uid','item_id'],as_index=False).grade.max(); truth=truth[[eligible(u,i) for u,i in truth[['uid','item_id']].itertuples(index=False,name=None)]].copy()\nprint('truth pairs/users/grade',len(truth),truth.uid.nunique(),truth.grade.value_counts().to_dict())\ntruth['poprank']=truth.item_id.map(prank)\nprint('poprank quantiles',truth.poprank.quantile([0,.1,.25,.5,.75,.9,1]).to_dict(),'missing',truth.poprank.isna().sum())\nprint('labels/user quantiles',truth.groupby('uid').size().quantile([0,.25,.5,.75,.9,.99,1]).to_dict())\nps=pd.read_parquet(P+'/validation_preference_state.parquet'); truth=truth.merge(ps.assign(in_state=1),on=['uid','item_id'],how='left')\nprint('truth active pref',truth.groupby(['grade','state'],dropna=False).size())\n# eligibility known state percentages\nps['eligible']=[eligible(u,i) for u,i in ps[['uid','item_id']].itertuples(index=False,name=None)]\nprint('preference eligible by state',ps.groupby('state').eligible.agg(['mean','sum','count']))\nprint('load train relevant joins')\ntl=pd.read_parquet(P+'/train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\n# restrict target+catalog first\nx=tl[tl.uid.isin(T)&tl.item_id.isin(catset)].copy(); del tl; gc.collect(); print('candidate train events',len(x))\nx['pos50']=x.played_ratio_pct>=50; x['strong']=x.played_ratio_pct>=80; x['orgstrong']=(x.is_organic==1)&x.strong\n# aggregate only truth pair merge events for repeat diagnostic\nte=x.merge(truth[['uid','item_id','grade']],on=['uid','item_id'],how='inner')\nagg=te.groupby(['uid','item_id','grade']).agg(n=('timestamp','size'),pos=('pos50','sum'),strong=('strong','sum'),org=('is_organic','sum'),orgstrong=('orgstrong','sum'),last=('timestamp','max')).reset_index(); truth2=truth[['uid','item_id','grade','poprank']].merge(agg,on=['uid','item_id','grade'],how='left').fillna({'n':0,'pos':0,'strong':0,'org':0,'orgstrong':0,'last':0})\nfor col in ['n','pos','strong','org','orgstrong']:\n print('repeat',col,truth2.groupby('grade')[col].apply(lambda s:pd.Series({'any':(s>0).mean(),'>=2':(s>=2).mean(),'>=5':(s>=5).mean(),'median':s.median()})).unstack().to_dict('index'))\nfor days in [1,3,7,14,30,60,90,180,300]:\n print('last within',days,'days',truth2.groupby('grade')['last'].apply(lambda s:(s>=cutoff-days*86400).mean()).to_dict())\n# all grade-weighted relevance capture union prior and popular top K\nfor k in [10,30,100,300,1000,3000,10000]:\n print('pop<=',k,truth2.groupby('grade').poprank.apply(lambda s:(s<=k).mean()).to_dict())\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "failed",
    "stdout": "Traceback (most recent call last):\n  File \"<stdin>\", line 12, in <module>\n  File \"<stdin>\", line 8, in eligible\n  File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py\", line 254, in __getitem__\n    return format.read_array(bytes,\n           ^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 851, in read_array\n    data = _read_bytes(fp, read_size, \"array data\")\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 986, in _read_bytes\n    r = fp.read(size - len(data))\n        ^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1008, in read\n    data = self._read1(n)\n           ^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1098, in _read1\n    self._update_crc(data)\n  File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1023, in _update_crc\n    self._running_crc = crc32(newdata, self._running_crc)\n                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nKeyboardInterrupt\n",
    "stderr": "",
    "aggregated_output": "Traceback (most recent call last):\n  File \"<stdin>\", line 12, in <module>\n  File \"<stdin>\", line 8, in eligible\n  File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py\", line 254, in __getitem__\n    return format.read_array(bytes,\n           ^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 851, in read_array\n    data = _read_bytes(fp, read_size, \"array data\")\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 986, in _read_bytes\n    r = fp.read(size - len(data))\n        ^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1008, in read\n    data = self._read1(n)\n           ^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1098, in _read1\n    self._update_crc(data)\n  File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1023, in _update_crc\n    self._running_crc = crc32(newdata, self._running_crc)\n                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nKeyboardInterrupt\n",
    "exit_code": 130,
    "duration": {
      "secs": 235,
      "nanos": 291883895
    },
    "formatted_output": "Traceback (most recent call last):\n  File \"<stdin>\", line 12, in <module>\n  File \"<stdin>\", line 8, in eligible\n  File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py\", line 254, in __getitem__\n    return format.read_array(bytes,\n           ^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 851, in read_array\n    data = _read_bytes(fp, read_size, \"array data\")\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 986, in _read_bytes\n    r = fp.read(size - len(data))\n        ^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1008, in read\n    data = self._read1(n)\n           ^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1098, in _read1\n    self._update_crc(data)\n  File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1023, in _update_crc\n    self._running_crc = crc32(newdata, self._running_crc)\n                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nKeyboardInterrupt\n"
  }
}
22

exec command

+05:23
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python -u - <<'PY'\nimport pandas as pd, numpy as np, gc, time\nP='/task/data'; cutoff=24786800\ntarget=pd.read_parquet(P+'/target_users.parquet',columns=['uid']).uid.to_numpy(np.uint32); T=set(map(int,target))\ncat=pd.read_parquet(P+'/candidate_catalog.parquet'); prank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\nwith np.load(P+'/validation_eligible_candidates.npz') as z:\n zu=z['uids'].copy(); zi=z['item_ids'].copy(); packed=z['packed_eligible'].copy()\nuidrow={int(x):i for i,x in enumerate(zu)}; itcol={int(x):i for i,x in enumerate(zi)}\ndef eligvec(df):\n rr=np.array([uidrow.get(int(x),-1) for x in df.uid],dtype=np.int32); cc=np.array([itcol.get(int(x),-1) for x in df.item_id],dtype=np.int32); ok=(rr>=0)&(cc>=0); out=np.zeros(len(df),bool); j=np.flatnonzero(ok); out[j]=((packed[rr[j],cc[j]//8]>>(cc[j]%8))&1).astype(bool); return out\nvl=pd.read_parquet(P+'/val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nvg=vl[(vl.uid.isin(T))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); vg['grade']=1\nvlike=pd.read_parquet(P+'/val_likes.parquet',columns=['uid','item_id','is_organic']); q=vlike[(vlike.uid.isin(T))&(vlike.is_organic==1)][['uid','item_id']].drop_duplicates(); q['grade']=3\ntruth=pd.concat([vg,q]).groupby(['uid','item_id'],as_index=False).grade.max(); truth=truth[eligvec(truth)].copy()\nprint('truth pairs/users/grade',len(truth),truth.uid.nunique(),truth.grade.value_counts().to_dict(),flush=True)\ntruth['poprank']=truth.item_id.map(prank)\nprint('poprank quantiles',truth.poprank.quantile([0,.1,.25,.5,.75,.9,1]).to_dict(),'missing',truth.poprank.isna().sum(),flush=True)\nprint('labels/user quantiles',truth.groupby('uid').size().quantile([0,.25,.5,.75,.9,.99,1]).to_dict(),flush=True)\nps=pd.read_parquet(P+'/validation_preference_state.parquet'); truth=truth.merge(ps.assign(in_state=1),on=['uid','item_id'],how='left')\nprint('truth active pref\\n',truth.groupby(['grade','state'],dropna=False).size(),flush=True)\nps['eligible']=eligvec(ps)\nprint('preference eligible by state\\n',ps.groupby('state').eligible.agg(['mean','sum','count']),flush=True)\nprint('load train relevant joins',flush=True)\ntl=pd.read_parquet(P+'/train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\nprint('train loaded',len(tl),flush=True)\nte=tl.merge(truth[['uid','item_id','grade']],on=['uid','item_id'],how='inner'); del tl; gc.collect(); print('matched events',len(te),flush=True)\nte['pos']=te.played_ratio_pct>=50; te['strong']=te.played_ratio_pct>=80; te['orgstrong']=(te.is_organic==1)&te.strong\nagg=te.groupby(['uid','item_id','grade']).agg(n=('timestamp','size'),pos=('pos','sum'),strong=('strong','sum'),org=('is_organic','sum'),orgstrong=('orgstrong','sum'),last=('timestamp','max')).reset_index(); truth2=truth[['uid','item_id','grade','poprank']].merge(agg,on=['uid','item_id','grade'],how='left').fillna({'n':0,'pos':0,'strong':0,'org':0,'orgstrong':0,'last':0})\nfor col in ['n','pos','strong','org','orgstrong']:\n print('repeat',col,truth2.groupby('grade')[col].apply(lambda s:pd.Series({'any':(s>0).mean(),'>=2':(s>=2).mean(),'>=5':(s>=5).mean(),'median':s.median()})).unstack().to_dict('index'),flush=True)\nfor days in [1,3,7,14,30,60,90,180,300]: print('last within',days,'days',truth2.groupby('grade')['last'].apply(lambda s:(s>=cutoff-days*86400).mean()).to_dict(),flush=True)\nfor k in [10,30,100,300,1000,3000,10000]: print('pop<=',k,truth2.groupby('grade').poprank.apply(lambda s:(s<=k).mean()).to_dict(),flush=True)\nPY",workdir:"/app",yield_time_ms:30000,max_output_tokens:20000});
if(r.session_id){text(`SESSION_ID=${r.session_id}`);text(r.output);}else{text(r.output);}

Tool response

Script completed
Wall time 6.4 seconds
Output:

truth pairs/users/grade 37769 4263 {1: 33352, 3: 4417}
poprank quantiles {0.0: 1.0, 0.1: 428.0, 0.25: 1733.0, 0.5: 5622.0, 0.75: 13374.0, 0.9: 21680.200000000004, 1.0: 30000.0} missing 0
labels/user quantiles {0.0: 1.0, 0.25: 2.0, 0.5: 4.0, 0.75: 10.0, 0.9: 21.0, 0.99: 63.0, 1.0: 442.0}
truth active pref
 grade  state
1      NaN      33352
3      NaN       4417
dtype: int64
preference eligible by state
           mean  sum   count
state                      
disliked   0.0    0   50689
liked      0.0    0  418435
load train relevant joins
train loaded 43484159
matched events 0
repeat n {1: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}, 3: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}}
repeat pos {1: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}, 3: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}}
repeat strong {1: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}, 3: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}}
repeat org {1: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}, 3: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}}
repeat orgstrong {1: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}, 3: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}}
last within 1 days {1: 0.0, 3: 0.0}
last within 3 days {1: 0.0, 3: 0.0}
last within 7 days {1: 0.0, 3: 0.0}
last within 14 days {1: 0.0, 3: 0.0}
last within 30 days {1: 0.0, 3: 0.0}
last within 60 days {1: 0.0, 3: 0.0}
last within 90 days {1: 0.0, 3: 0.0}
last within 180 days {1: 0.0, 3: 0.0}
last within 300 days {1: 1.0, 3: 1.0}
pop<= 10 {1: 0.00509714559846486, 3: 0.004527960153950645}
pop<= 30 {1: 0.013042696090189494, 3: 0.00996151233869142}
pop<= 100 {1: 0.03307147997121612, 3: 0.022866198777450757}
pop<= 300 {1: 0.07897577356680259, 3: 0.052524337785827484}
pop<= 1000 {1: 0.17947949148476852, 3: 0.14421553090332806}
pop<= 3000 {1: 0.35521108179419525, 3: 0.3205795788997057}
pop<= 10000 {1: 0.6623290957064044, 3: 0.6361784016300657}
Harness context token usage record
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  "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "response_id": "resp_0fdbf3e20a3ba82b016ab5c76515ac87d2b11a5d6b83817df4",
  "usage": {
    "input_tokens": 26630,
    "cached_input_tokens": 25984,
    "cache_write_input_tokens": 0,
    "output_tokens": 1309,
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  },
  "turn_token_usage": {
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    "cached_input_tokens": 329344,
    "cache_write_input_tokens": 0,
    "output_tokens": 5709,
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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-500ef9f0-47fb-45e0-a584-393d51575b1c",
    "process_id": "82035",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python -u - <<'PY'\nimport pandas as pd, numpy as np, gc, time\nP='/task/data'; cutoff=24786800\ntarget=pd.read_parquet(P+'/target_users.parquet',columns=['uid']).uid.to_numpy(np.uint32); T=set(map(int,target))\ncat=pd.read_parquet(P+'/candidate_catalog.parquet'); prank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\nwith np.load(P+'/validation_eligible_candidates.npz') as z:\n zu=z['uids'].copy(); zi=z['item_ids'].copy(); packed=z['packed_eligible'].copy()\nuidrow={int(x):i for i,x in enumerate(zu)}; itcol={int(x):i for i,x in enumerate(zi)}\ndef eligvec(df):\n rr=np.array([uidrow.get(int(x),-1) for x in df.uid],dtype=np.int32); cc=np.array([itcol.get(int(x),-1) for x in df.item_id],dtype=np.int32); ok=(rr>=0)&(cc>=0); out=np.zeros(len(df),bool); j=np.flatnonzero(ok); out[j]=((packed[rr[j],cc[j]//8]>>(cc[j]%8))&1).astype(bool); return out\nvl=pd.read_parquet(P+'/val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nvg=vl[(vl.uid.isin(T))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); vg['grade']=1\nvlike=pd.read_parquet(P+'/val_likes.parquet',columns=['uid','item_id','is_organic']); q=vlike[(vlike.uid.isin(T))&(vlike.is_organic==1)][['uid','item_id']].drop_duplicates(); q['grade']=3\ntruth=pd.concat([vg,q]).groupby(['uid','item_id'],as_index=False).grade.max(); truth=truth[eligvec(truth)].copy()\nprint('truth pairs/users/grade',len(truth),truth.uid.nunique(),truth.grade.value_counts().to_dict(),flush=True)\ntruth['poprank']=truth.item_id.map(prank)\nprint('poprank quantiles',truth.poprank.quantile([0,.1,.25,.5,.75,.9,1]).to_dict(),'missing',truth.poprank.isna().sum(),flush=True)\nprint('labels/user quantiles',truth.groupby('uid').size().quantile([0,.25,.5,.75,.9,.99,1]).to_dict(),flush=True)\nps=pd.read_parquet(P+'/validation_preference_state.parquet'); truth=truth.merge(ps.assign(in_state=1),on=['uid','item_id'],how='left')\nprint('truth active pref\\n',truth.groupby(['grade','state'],dropna=False).size(),flush=True)\nps['eligible']=eligvec(ps)\nprint('preference eligible by state\\n',ps.groupby('state').eligible.agg(['mean','sum','count']),flush=True)\nprint('load train relevant joins',flush=True)\ntl=pd.read_parquet(P+'/train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\nprint('train loaded',len(tl),flush=True)\nte=tl.merge(truth[['uid','item_id','grade']],on=['uid','item_id'],how='inner'); del tl; gc.collect(); print('matched events',len(te),flush=True)\nte['pos']=te.played_ratio_pct>=50; te['strong']=te.played_ratio_pct>=80; te['orgstrong']=(te.is_organic==1)&te.strong\nagg=te.groupby(['uid','item_id','grade']).agg(n=('timestamp','size'),pos=('pos','sum'),strong=('strong','sum'),org=('is_organic','sum'),orgstrong=('orgstrong','sum'),last=('timestamp','max')).reset_index(); truth2=truth[['uid','item_id','grade','poprank']].merge(agg,on=['uid','item_id','grade'],how='left').fillna({'n':0,'pos':0,'strong':0,'org':0,'orgstrong':0,'last':0})\nfor col in ['n','pos','strong','org','orgstrong']:\n print('repeat',col,truth2.groupby('grade')[col].apply(lambda s:pd.Series({'any':(s>0).mean(),'>=2':(s>=2).mean(),'>=5':(s>=5).mean(),'median':s.median()})).unstack().to_dict('index'),flush=True)\nfor days in [1,3,7,14,30,60,90,180,300]: print('last within',days,'days',truth2.groupby('grade')['last'].apply(lambda s:(s>=cutoff-days*86400).mean()).to_dict(),flush=True)\nfor k in [10,30,100,300,1000,3000,10000]: print('pop<=',k,truth2.groupby('grade').poprank.apply(lambda s:(s<=k).mean()).to_dict(),flush=True)\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport pandas as pd, numpy as np, gc, time\nP='/task/data'; cutoff=24786800\ntarget=pd.read_parquet(P+'/target_users.parquet',columns=['uid']).uid.to_numpy(np.uint32); T=set(map(int,target))\ncat=pd.read_parquet(P+'/candidate_catalog.parquet'); prank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\nwith np.load(P+'/validation_eligible_candidates.npz') as z:\n zu=z['uids'].copy(); zi=z['item_ids'].copy(); packed=z['packed_eligible'].copy()\nuidrow={int(x):i for i,x in enumerate(zu)}; itcol={int(x):i for i,x in enumerate(zi)}\ndef eligvec(df):\n rr=np.array([uidrow.get(int(x),-1) for x in df.uid],dtype=np.int32); cc=np.array([itcol.get(int(x),-1) for x in df.item_id],dtype=np.int32); ok=(rr>=0)&(cc>=0); out=np.zeros(len(df),bool); j=np.flatnonzero(ok); out[j]=((packed[rr[j],cc[j]//8]>>(cc[j]%8))&1).astype(bool); return out\nvl=pd.read_parquet(P+'/val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nvg=vl[(vl.uid.isin(T))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); vg['grade']=1\nvlike=pd.read_parquet(P+'/val_likes.parquet',columns=['uid','item_id','is_organic']); q=vlike[(vlike.uid.isin(T))&(vlike.is_organic==1)][['uid','item_id']].drop_duplicates(); q['grade']=3\ntruth=pd.concat([vg,q]).groupby(['uid','item_id'],as_index=False).grade.max(); truth=truth[eligvec(truth)].copy()\nprint('truth pairs/users/grade',len(truth),truth.uid.nunique(),truth.grade.value_counts().to_dict(),flush=True)\ntruth['poprank']=truth.item_id.map(prank)\nprint('poprank quantiles',truth.poprank.quantile([0,.1,.25,.5,.75,.9,1]).to_dict(),'missing',truth.poprank.isna().sum(),flush=True)\nprint('labels/user quantiles',truth.groupby('uid').size().quantile([0,.25,.5,.75,.9,.99,1]).to_dict(),flush=True)\nps=pd.read_parquet(P+'/validation_preference_state.parquet'); truth=truth.merge(ps.assign(in_state=1),on=['uid','item_id'],how='left')\nprint('truth active pref\\n',truth.groupby(['grade','state'],dropna=False).size(),flush=True)\nps['eligible']=eligvec(ps)\nprint('preference eligible by state\\n',ps.groupby('state').eligible.agg(['mean','sum','count']),flush=True)\nprint('load train relevant joins',flush=True)\ntl=pd.read_parquet(P+'/train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\nprint('train loaded',len(tl),flush=True)\nte=tl.merge(truth[['uid','item_id','grade']],on=['uid','item_id'],how='inner'); del tl; gc.collect(); print('matched events',len(te),flush=True)\nte['pos']=te.played_ratio_pct>=50; te['strong']=te.played_ratio_pct>=80; te['orgstrong']=(te.is_organic==1)&te.strong\nagg=te.groupby(['uid','item_id','grade']).agg(n=('timestamp','size'),pos=('pos','sum'),strong=('strong','sum'),org=('is_organic','sum'),orgstrong=('orgstrong','sum'),last=('timestamp','max')).reset_index(); truth2=truth[['uid','item_id','grade','poprank']].merge(agg,on=['uid','item_id','grade'],how='left').fillna({'n':0,'pos':0,'strong':0,'org':0,'orgstrong':0,'last':0})\nfor col in ['n','pos','strong','org','orgstrong']:\n print('repeat',col,truth2.groupby('grade')[col].apply(lambda s:pd.Series({'any':(s>0).mean(),'>=2':(s>=2).mean(),'>=5':(s>=5).mean(),'median':s.median()})).unstack().to_dict('index'),flush=True)\nfor days in [1,3,7,14,30,60,90,180,300]: print('last within',days,'days',truth2.groupby('grade')['last'].apply(lambda s:(s>=cutoff-days*86400).mean()).to_dict(),flush=True)\nfor k in [10,30,100,300,1000,3000,10000]: print('pop<=',k,truth2.groupby('grade').poprank.apply(lambda s:(s<=k).mean()).to_dict(),flush=True)\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "truth pairs/users/grade 37769 4263 {1: 33352, 3: 4417}\npoprank quantiles {0.0: 1.0, 0.1: 428.0, 0.25: 1733.0, 0.5: 5622.0, 0.75: 13374.0, 0.9: 21680.200000000004, 1.0: 30000.0} missing 0\nlabels/user quantiles {0.0: 1.0, 0.25: 2.0, 0.5: 4.0, 0.75: 10.0, 0.9: 21.0, 0.99: 63.0, 1.0: 442.0}\ntruth active pref\n grade  state\n1      NaN      33352\n3      NaN       4417\ndtype: int64\npreference eligible by state\n           mean  sum   count\nstate                      \ndisliked   0.0    0   50689\nliked      0.0    0  418435\nload train relevant joins\ntrain loaded 43484159\nmatched events 0\nrepeat n {1: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}, 3: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}}\nrepeat pos {1: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}, 3: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}}\nrepeat strong {1: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}, 3: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}}\nrepeat org {1: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}, 3: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}}\nrepeat orgstrong {1: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}, 3: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}}\nlast within 1 days {1: 0.0, 3: 0.0}\nlast within 3 days {1: 0.0, 3: 0.0}\nlast within 7 days {1: 0.0, 3: 0.0}\nlast within 14 days {1: 0.0, 3: 0.0}\nlast within 30 days {1: 0.0, 3: 0.0}\nlast within 60 days {1: 0.0, 3: 0.0}\nlast within 90 days {1: 0.0, 3: 0.0}\nlast within 180 days {1: 0.0, 3: 0.0}\nlast within 300 days {1: 1.0, 3: 1.0}\npop<= 10 {1: 0.00509714559846486, 3: 0.004527960153950645}\npop<= 30 {1: 0.013042696090189494, 3: 0.00996151233869142}\npop<= 100 {1: 0.03307147997121612, 3: 0.022866198777450757}\npop<= 300 {1: 0.07897577356680259, 3: 0.052524337785827484}\npop<= 1000 {1: 0.17947949148476852, 3: 0.14421553090332806}\npop<= 3000 {1: 0.35521108179419525, 3: 0.3205795788997057}\npop<= 10000 {1: 0.6623290957064044, 3: 0.6361784016300657}\n",
    "stderr": "",
    "aggregated_output": "truth pairs/users/grade 37769 4263 {1: 33352, 3: 4417}\npoprank quantiles {0.0: 1.0, 0.1: 428.0, 0.25: 1733.0, 0.5: 5622.0, 0.75: 13374.0, 0.9: 21680.200000000004, 1.0: 30000.0} missing 0\nlabels/user quantiles {0.0: 1.0, 0.25: 2.0, 0.5: 4.0, 0.75: 10.0, 0.9: 21.0, 0.99: 63.0, 1.0: 442.0}\ntruth active pref\n grade  state\n1      NaN      33352\n3      NaN       4417\ndtype: int64\npreference eligible by state\n           mean  sum   count\nstate                      \ndisliked   0.0    0   50689\nliked      0.0    0  418435\nload train relevant joins\ntrain loaded 43484159\nmatched events 0\nrepeat n {1: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}, 3: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}}\nrepeat pos {1: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}, 3: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}}\nrepeat strong {1: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}, 3: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}}\nrepeat org {1: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}, 3: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}}\nrepeat orgstrong {1: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}, 3: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}}\nlast within 1 days {1: 0.0, 3: 0.0}\nlast within 3 days {1: 0.0, 3: 0.0}\nlast within 7 days {1: 0.0, 3: 0.0}\nlast within 14 days {1: 0.0, 3: 0.0}\nlast within 30 days {1: 0.0, 3: 0.0}\nlast within 60 days {1: 0.0, 3: 0.0}\nlast within 90 days {1: 0.0, 3: 0.0}\nlast within 180 days {1: 0.0, 3: 0.0}\nlast within 300 days {1: 1.0, 3: 1.0}\npop<= 10 {1: 0.00509714559846486, 3: 0.004527960153950645}\npop<= 30 {1: 0.013042696090189494, 3: 0.00996151233869142}\npop<= 100 {1: 0.03307147997121612, 3: 0.022866198777450757}\npop<= 300 {1: 0.07897577356680259, 3: 0.052524337785827484}\npop<= 1000 {1: 0.17947949148476852, 3: 0.14421553090332806}\npop<= 3000 {1: 0.35521108179419525, 3: 0.3205795788997057}\npop<= 10000 {1: 0.6623290957064044, 3: 0.6361784016300657}\n",
    "exit_code": 0,
    "duration": {
      "secs": 6,
      "nanos": 191153308
    },
    "formatted_output": "truth pairs/users/grade 37769 4263 {1: 33352, 3: 4417}\npoprank quantiles {0.0: 1.0, 0.1: 428.0, 0.25: 1733.0, 0.5: 5622.0, 0.75: 13374.0, 0.9: 21680.200000000004, 1.0: 30000.0} missing 0\nlabels/user quantiles {0.0: 1.0, 0.25: 2.0, 0.5: 4.0, 0.75: 10.0, 0.9: 21.0, 0.99: 63.0, 1.0: 442.0}\ntruth active pref\n grade  state\n1      NaN      33352\n3      NaN       4417\ndtype: int64\npreference eligible by state\n           mean  sum   count\nstate                      \ndisliked   0.0    0   50689\nliked      0.0    0  418435\nload train relevant joins\ntrain loaded 43484159\nmatched events 0\nrepeat n {1: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}, 3: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}}\nrepeat pos {1: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}, 3: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}}\nrepeat strong {1: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}, 3: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}}\nrepeat org {1: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}, 3: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}}\nrepeat orgstrong {1: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}, 3: {'any': 0.0, '>=2': 0.0, '>=5': 0.0, 'median': 0.0}}\nlast within 1 days {1: 0.0, 3: 0.0}\nlast within 3 days {1: 0.0, 3: 0.0}\nlast within 7 days {1: 0.0, 3: 0.0}\nlast within 14 days {1: 0.0, 3: 0.0}\nlast within 30 days {1: 0.0, 3: 0.0}\nlast within 60 days {1: 0.0, 3: 0.0}\nlast within 90 days {1: 0.0, 3: 0.0}\nlast within 180 days {1: 0.0, 3: 0.0}\nlast within 300 days {1: 1.0, 3: 1.0}\npop<= 10 {1: 0.00509714559846486, 3: 0.004527960153950645}\npop<= 30 {1: 0.013042696090189494, 3: 0.00996151233869142}\npop<= 100 {1: 0.03307147997121612, 3: 0.022866198777450757}\npop<= 300 {1: 0.07897577356680259, 3: 0.052524337785827484}\npop<= 1000 {1: 0.17947949148476852, 3: 0.14421553090332806}\npop<= 3000 {1: 0.35521108179419525, 3: 0.3205795788997057}\npop<= 10000 {1: 0.6623290957064044, 3: 0.6361784016300657}\n"
  }
}
23

Agent message

+06:05
Thinking unavailable No text recorded

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

24

Agent message

+06:16
Thinking unavailable No text recorded

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

25

Agent message

+06:28
Thinking unavailable No text recorded

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

26

exec command

+06:35
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 decisive property emerged: validation-relevant user–track pairs have no prior training interaction, and every active liked/disliked pair is ineligible. This is a true discovery task, so repeat/favorite heuristics are useless. I’m saving a popularity fallback now, then focusing on collaborative generalization to unseen tracks and recent organic trends.

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Add File: /app/solution/build_ranker.py\n+#!/usr/bin/env python3\n+\"\"\"Build cutoff-correct music discovery rankings.\n+\n+The script is intentionally self contained.  With no arguments it refits on\n+train + validation history and writes ``submission.parquet``.  ``--stage val``\n+uses training history only and writes ``validation.parquet``.\n+\"\"\"\n+from __future__ import annotations\n+\n+import argparse\n+from pathlib import Path\n+\n+import numpy as np\n+import pandas as pd\n+\n+\n+DATA = Path(\"/task/data\")\n+OUT = Path(\"/app/solution\")\n+TOP_N = 100\n+\n+\n+def load_candidates(stage: str) -> tuple[np.ndarray, np.ndarray, np.ndarray]:\n+    path = DATA / (\"validation_eligible_candidates.npz\" if stage == \"val\" else \"test_eligible_candidates.npz\")\n+    with np.load(path, allow_pickle=False) as z:\n+        return z[\"uids\"].copy(), z[\"item_ids\"].copy(), z[\"packed_eligible\"].copy()\n+\n+\n+def is_eligible(packed_row: np.ndarray, column: int) -> bool:\n+    return bool((int(packed_row[column // 8]) >> (column % 8)) & 1)\n+\n+\n+def rank_global(stage: str, ordered_items: np.ndarray) -> pd.DataFrame:\n+    \"\"\"Apply a global item order while respecting each user's candidate mask.\"\"\"\n+    uids, candidate_items, packed = load_candidates(stage)\n+    item_col = {int(item): col for col, item in enumerate(candidate_items)}\n+    ordered_cols = np.asarray([item_col[int(item)] for item in ordered_items], dtype=np.int32)\n+    out_uid = np.repeat(uids.astype(np.int64), TOP_N)\n+    out_item = np.empty(len(uids) * TOP_N, dtype=np.int64)\n+    for row in range(len(uids)):\n+        chosen: list[int] = []\n+        for col in ordered_cols:\n+            if is_eligible(packed[row], int(col)):\n+                chosen.append(int(candidate_items[col]))\n+                if len(chosen) == TOP_N:\n+                    break\n+        if len(chosen) != TOP_N:\n+            raise RuntimeError(f\"only {len(chosen)} eligible items for uid={uids[row]}\")\n+        out_item[row * TOP_N : (row + 1) * TOP_N] = chosen\n+    return pd.DataFrame(\n+        {\n+            \"uid\": out_uid,\n+            \"item_id\": out_item,\n+            \"rank\": np.tile(np.arange(1, TOP_N + 1, dtype=np.int64), len(uids)),\n+        }\n+    )\n+\n+\n+def main() -> None:\n+    parser = argparse.ArgumentParser()\n+    parser.add_argument(\"--stage\", choices=(\"val\", \"test\"), default=\"test\")\n+    args = parser.parse_args()\n+    catalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n+    ranking = rank_global(args.stage, catalog.sort_values(\"popularity_rank\").item_id.to_numpy())\n+    path = OUT / (\"validation.parquet\" if args.stage == \"val\" else \"submission.parquet\")\n+    ranking.to_parquet(path, index=False)\n+    print(f\"wrote {len(ranking):,} rows to {path}\")\n+\n+\n+if __name__ == \"__main__\":\n+    main()\n*** Add File: /app/solution/README.md\n+# Music-discovery ranker\n+\n+Run with the task environment:\n+\n+```sh\n+/opt/task-venv/bin/python /app/solution/build_ranker.py\n+```\n+\n+This refits the selected ranker at the test cutoff and writes\n+`submission.parquet`.  Use `--stage val` to construct the leakage-safe public\n+validation ranking from training history only.\n*** End Patch";
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-bb736bb9-da32-4d47-8f1a-6ef09c2af4b2",
    "changes": {
      "/app/solution/README.md": {
        "type": "add",
        "content": "# Music-discovery ranker\n\nRun with the task environment:\n\n```sh\n/opt/task-venv/bin/python /app/solution/build_ranker.py\n```\n\nThis refits the selected ranker at the test cutoff and writes\n`submission.parquet`.  Use `--stage val` to construct the leakage-safe public\nvalidation ranking from training history only.\n"
      },
      "/app/solution/build_ranker.py": {
        "type": "add",
        "content": "#!/usr/bin/env python3\n\"\"\"Build cutoff-correct music discovery rankings.\n\nThe script is intentionally self contained.  With no arguments it refits on\ntrain + validation history and writes ``submission.parquet``.  ``--stage val``\nuses training history only and writes ``validation.parquet``.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\nDATA = Path(\"/task/data\")\nOUT = Path(\"/app/solution\")\nTOP_N = 100\n\n\ndef load_candidates(stage: str) -> tuple[np.ndarray, np.ndarray, np.ndarray]:\n    path = DATA / (\"validation_eligible_candidates.npz\" if stage == \"val\" else \"test_eligible_candidates.npz\")\n    with np.load(path, allow_pickle=False) as z:\n        return z[\"uids\"].copy(), z[\"item_ids\"].copy(), z[\"packed_eligible\"].copy()\n\n\ndef is_eligible(packed_row: np.ndarray, column: int) -> bool:\n    return bool((int(packed_row[column // 8]) >> (column % 8)) & 1)\n\n\ndef rank_global(stage: str, ordered_items: np.ndarray) -> pd.DataFrame:\n    \"\"\"Apply a global item order while respecting each user's candidate mask.\"\"\"\n    uids, candidate_items, packed = load_candidates(stage)\n    item_col = {int(item): col for col, item in enumerate(candidate_items)}\n    ordered_cols = np.asarray([item_col[int(item)] for item in ordered_items], dtype=np.int32)\n    out_uid = np.repeat(uids.astype(np.int64), TOP_N)\n    out_item = np.empty(len(uids) * TOP_N, dtype=np.int64)\n    for row in range(len(uids)):\n        chosen: list[int] = []\n        for col in ordered_cols:\n            if is_eligible(packed[row], int(col)):\n                chosen.append(int(candidate_items[col]))\n                if len(chosen) == TOP_N:\n                    break\n        if len(chosen) != TOP_N:\n            raise RuntimeError(f\"only {len(chosen)} eligible items for uid={uids[row]}\")\n        out_item[row * TOP_N : (row + 1) * TOP_N] = chosen\n    return pd.DataFrame(\n        {\n            \"uid\": out_uid,\n            \"item_id\": out_item,\n            \"rank\": np.tile(np.arange(1, TOP_N + 1, dtype=np.int64), len(uids)),\n        }\n    )\n\n\ndef main() -> None:\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--stage\", choices=(\"val\", \"test\"), default=\"test\")\n    args = parser.parse_args()\n    catalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n    ranking = rank_global(args.stage, catalog.sort_values(\"popularity_rank\").item_id.to_numpy())\n    path = OUT / (\"validation.parquet\" if args.stage == \"val\" else \"submission.parquet\")\n    ranking.to_parquet(path, index=False)\n    print(f\"wrote {len(ranking):,} rows to {path}\")\n\n\nif __name__ == \"__main__\":\n    main()\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/build_ranker.py\nA /app/solution/README.md\n",
    "stderr": ""
  }
}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "response_id": "resp_0fdbf3e20a3ba82b016ab5c78478bc87d2a09194bd5b9fb6e5",
  "usage": {
    "input_tokens": 29022,
    "cached_input_tokens": 26496,
    "cache_write_input_tokens": 0,
    "output_tokens": 2909,
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  "turn_token_usage": {
    "input_tokens": 376234,
    "cached_input_tokens": 355840,
    "cache_write_input_tokens": 0,
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    "reasoning_output_tokens": 3576,
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  },
  "thread_token_usage": {
    "input_tokens": 376234,
    "cached_input_tokens": 355840,
    "cache_write_input_tokens": 0,
    "output_tokens": 8618,
    "reasoning_output_tokens": 3576,
    "total_tokens": 384852
  }
}
27

exec command

+06:52
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python /app/solution/build_ranker.py --stage val && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet && /opt/task-venv/bin/python /app/solution/build_ranker.py --stage test && ls -lh /app/solution",workdir:"/app",yield_time_ms:30000,max_output_tokens:12000});
text(r.output);

Tool response

Script completed
Wall time 6.0 seconds
Output:

wrote 845,600 rows to /app/solution/validation.parquet
{
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.01071,
    "quality_users": 4263
  }
}
wrote 845,600 rows to /app/solution/submission.parquet
total 1.5M
-rw-r--r-- 1 agent agent  312 Sep 25 01:00 README.md
-rw-r--r-- 1 agent agent 2.6K Sep 25 01:00 build_ranker.py
-rw-r--r-- 1 agent agent 719K Sep 25 01:00 submission.parquet
-rw-r--r-- 1 agent agent 712K Sep 25 01:00 validation.parquet
Harness context token usage record
{
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  "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "response_id": "resp_0fdbf3e20a3ba82b016ab5c7bbb7f087d2a83b133bf5f70e65",
  "usage": {
    "input_tokens": 31955,
    "cached_input_tokens": 28800,
    "cache_write_input_tokens": 0,
    "output_tokens": 135,
    "reasoning_output_tokens": 19,
    "total_tokens": 32090
  },
  "turn_token_usage": {
    "input_tokens": 408189,
    "cached_input_tokens": 384640,
    "cache_write_input_tokens": 0,
    "output_tokens": 8753,
    "reasoning_output_tokens": 3595,
    "total_tokens": 416942
  },
  "thread_token_usage": {
    "input_tokens": 408189,
    "cached_input_tokens": 384640,
    "cache_write_input_tokens": 0,
    "output_tokens": 8753,
    "reasoning_output_tokens": 3595,
    "total_tokens": 416942
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-34cc24a3-1b90-4bbc-8c54-9a012f151b0c",
    "process_id": "66959",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python /app/solution/build_ranker.py --stage val && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet && /opt/task-venv/bin/python /app/solution/build_ranker.py --stage test && ls -lh /app/solution"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python /app/solution/build_ranker.py --stage val && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet && /opt/task-venv/bin/python /app/solution/build_ranker.py --stage test && ls -lh /app/solution"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "wrote 845,600 rows to /app/solution/validation.parquet\n{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\nwrote 845,600 rows to /app/solution/submission.parquet\ntotal 1.5M\n-rw-r--r-- 1 agent agent  312 Sep 25 01:00 README.md\n-rw-r--r-- 1 agent agent 2.6K Sep 25 01:00 build_ranker.py\n-rw-r--r-- 1 agent agent 719K Sep 25 01:00 submission.parquet\n-rw-r--r-- 1 agent agent 712K Sep 25 01:00 validation.parquet\n",
    "stderr": "",
    "aggregated_output": "wrote 845,600 rows to /app/solution/validation.parquet\n{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\nwrote 845,600 rows to /app/solution/submission.parquet\ntotal 1.5M\n-rw-r--r-- 1 agent agent  312 Sep 25 01:00 README.md\n-rw-r--r-- 1 agent agent 2.6K Sep 25 01:00 build_ranker.py\n-rw-r--r-- 1 agent agent 719K Sep 25 01:00 submission.parquet\n-rw-r--r-- 1 agent agent 712K Sep 25 01:00 validation.parquet\n",
    "exit_code": 0,
    "duration": {
      "secs": 5,
      "nanos": 836948960
    },
    "formatted_output": "wrote 845,600 rows to /app/solution/validation.parquet\n{\n  \"model\": \"/app/solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\nwrote 845,600 rows to /app/solution/submission.parquet\ntotal 1.5M\n-rw-r--r-- 1 agent agent  312 Sep 25 01:00 README.md\n-rw-r--r-- 1 agent agent 2.6K Sep 25 01:00 build_ranker.py\n-rw-r--r-- 1 agent agent 719K Sep 25 01:00 submission.parquet\n-rw-r--r-- 1 agent agent 712K Sep 25 01:00 validation.parquet\n"
  }
}
28

Agent message

+07:18
Thinking unavailable No text recorded

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

29

Agent message

+07:29
Thinking unavailable No text recorded

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

30

exec command

+07:31
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python -u - <<'PY'\nimport sys,time\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\nimport pyarrow.parquet as pq, pandas as pd, numpy as np\nP='/task/data'; CUTOFF=24786800; DAY=86400\ncat=pd.read_parquet(P+'/candidate_catalog.parquet'); ids=cat.item_id.to_numpy(np.int64); n=len(ids); sid=np.sort(ids); s2c=np.argsort(ids)\n# map item ids to catalog column\ndef cols(a):\n p=np.searchsorted(sid,a); ok=(p<n)&(sid[np.minimum(p,n-1)]==a); return s2c[p[ok]],ok\n# metric[event type, window], windows 1,3,7,14,30,60,90,180,all\nwins=np.array([1,3,7,14,30,60,90,180,10000],dtype=np.int32); names=['1d','3d','7d','14d','30d','60d','90d','180d','all']\nconds=['all50','all80','org50','org80']\nF=np.zeros((4,len(wins),n),np.float64)\npf=pq.ParquetFile(P+'/train_listens.parquet')\nfor bi,b in enumerate(pf.iter_batches(batch_size=2_000_000,columns=['timestamp','item_id','is_organic','played_ratio_pct'])):\n a=b.to_pydict(); item=np.asarray(a['item_id'],np.int64); c,ok=cols(item); ts=np.asarray(a['timestamp'],np.int64)[ok]; org=np.asarray(a['is_organic'],np.uint8)[ok]; ratio=np.asarray(a['played_ratio_pct'],np.uint16)[ok]; ages=(CUTOFF-ts)/DAY\n masks=[ratio>=50,ratio>=80,(ratio>=50)&(org==1),(ratio>=80)&(org==1)]\n for j,m in enumerate(masks):\n  cc=c[m]; aa=ages[m]\n  for wi,w in enumerate(wins): F[j,wi]+=np.bincount(cc[aa<=w],minlength=n)\n print('batch',bi,flush=True)\n# likes counts same windows organic and all\nLF=np.zeros((2,len(wins),n),np.float64)\nfor fam in ['likes']:\n d=pd.read_parquet(P+'/train_'+fam+'.parquet',columns=['timestamp','item_id','is_organic']); c,ok=cols(d.item_id.to_numpy(np.int64)); ts=d.timestamp.to_numpy(np.int64)[ok]; org=d.is_organic.to_numpy(np.uint8)[ok]; ages=(CUTOFF-ts)/DAY\n for j,m in enumerate([np.ones(len(c),bool),org==1]):\n  for wi,w in enumerate(wins): LF[j,wi]+=np.bincount(c[m&(ages<=w)],minlength=n)\n# truth and evaluator\nelig=CandidateSet(__import__('pathlib').Path(P+'/validation_eligible_candidates.npz')); targets=set(pd.read_parquet(P+'/target_users.parquet').uid.astype(int)); gold=validation_truth(elig,targets)\ndef evaluate(scores):\n order=np.lexsort((cat.popularity_rank.to_numpy(),-scores)); ranked={}\n for u in gold:\n  row=[]\n  for j in order:\n   it=int(ids[j])\n   if elig.is_eligible(u,it): row.append(it)\n   if len(row)==10: break\n  ranked[u]=row\n return score_rankings(ranked,gold)['quality_ndcg@10']\nres=[]\nres.append(('catalog_positive',evaluate(cat.positive_listens.to_numpy())))\nres.append(('catalog_orgpositive',evaluate(cat.organic_positive_listens.to_numpy())))\nfor j,cn in enumerate(conds):\n for wi,wn in enumerate(names): res.append((cn+'_'+wn,evaluate(F[j,wi])))\nfor j,cn in enumerate(['likeall','likeorg']):\n for wi,wn in enumerate(names): res.append((cn+'_'+wn,evaluate(LF[j,wi])))\nfor x in sorted(res,key=lambda x:-x[1]): print(x)\nnp.savez_compressed('/app/solution/global_features.npz',F=F,LF=LF,wins=wins)\nPY",workdir:"/app",yield_time_ms:30000,max_output_tokens:20000});
if(r.session_id){text(`SESSION_ID=${r.session_id}`);text(r.output);}else{text(r.output);}

Tool response

Script completed
Wall time 30.2 seconds
Output:

SESSION_ID=85654
batch 0
batch 1
batch 2
batch 3
batch 4
batch 5
batch 6
batch 7
batch 8
Harness context token usage record
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  "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
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    "cache_write_input_tokens": 0,
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    "reasoning_output_tokens": 4732,
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  }
}
31

exec command

+08:23
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:85654,chars:"",yield_time_ms:30000,max_output_tokens:20000});
text(r.output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

batch 9
batch 10
batch 11
batch 12
batch 13
batch 14
batch 15
batch 16
batch 17
batch 18
batch 19
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
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  }
}
32

exec command

+08:59
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 baseline scores 0.010710 NDCG@10. The popularity sweep is processing several cutoff-safe variants (organic vs. recommendation-surface activity, 50% vs. 80% completion, and multiple recency windows). This gives both a stronger fallback and a calibrated global prior for the personalized model.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:85654,chars:"",yield_time_ms:30000,max_output_tokens:20000});
text(r.output);

Tool response

Script completed
Wall time 5.2 seconds
Output:

batch 20
batch 21
('all50_7d', 0.030933)
('likeorg_3d', 0.030503)
('likeall_3d', 0.030269)
('likeall_7d', 0.030187)
('all80_7d', 0.030107)
('likeorg_7d', 0.029297)
('all50_3d', 0.028592)
('org50_7d', 0.028456)
('org80_7d', 0.028131)
('org50_14d', 0.026849)
('likeorg_14d', 0.026726)
('org50_3d', 0.026298)
('org80_3d', 0.025721)
('all50_14d', 0.025124)
('all80_3d', 0.023621)
('org80_14d', 0.022878)
('all80_14d', 0.022238)
('likeall_14d', 0.020652)
('likeorg_30d', 0.018429)
('all50_1d', 0.018312)
('org50_1d', 0.018231)
('all80_1d', 0.017614)
('all50_30d', 0.017182)
('all80_60d', 0.016953)
('all50_60d', 0.016892)
('likeall_30d', 0.016812)
('org80_1d', 0.016755)
('likeorg_60d', 0.016741)
('all80_30d', 0.016688)
('likeorg_1d', 0.016286)
('likeorg_90d', 0.016217)
('all80_90d', 0.01598)
('all50_90d', 0.015972)
('org50_60d', 0.015967)
('org80_90d', 0.015822)
('org80_60d', 0.015802)
('org50_90d', 0.015801)
('likeall_60d', 0.015481)
('likeall_90d', 0.015093)
('org50_30d', 0.01431)
('org80_30d', 0.013314)
('likeall_180d', 0.01324)
('likeorg_180d', 0.013)
('likeall_1d', 0.01253)
('likeall_all', 0.012135)
('all50_180d', 0.012116)
('likeorg_all', 0.012054)
('all80_180d', 0.01204)
('org50_180d', 0.011913)
('org80_180d', 0.011908)
('all80_all', 0.010727)
('catalog_positive', 0.01071)
('all50_all', 0.01071)
('org50_all', 0.010577)
('org80_all', 0.010551)
('catalog_orgpositive', 3.2e-05)
Harness context token usage record
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  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-3ed7e05b-d904-4b97-a5e9-087a92717615",
    "process_id": "85654",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python -u - <<'PY'\nimport sys,time\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\nimport pyarrow.parquet as pq, pandas as pd, numpy as np\nP='/task/data'; CUTOFF=24786800; DAY=86400\ncat=pd.read_parquet(P+'/candidate_catalog.parquet'); ids=cat.item_id.to_numpy(np.int64); n=len(ids); sid=np.sort(ids); s2c=np.argsort(ids)\n# map item ids to catalog column\ndef cols(a):\n p=np.searchsorted(sid,a); ok=(p<n)&(sid[np.minimum(p,n-1)]==a); return s2c[p[ok]],ok\n# metric[event type, window], windows 1,3,7,14,30,60,90,180,all\nwins=np.array([1,3,7,14,30,60,90,180,10000],dtype=np.int32); names=['1d','3d','7d','14d','30d','60d','90d','180d','all']\nconds=['all50','all80','org50','org80']\nF=np.zeros((4,len(wins),n),np.float64)\npf=pq.ParquetFile(P+'/train_listens.parquet')\nfor bi,b in enumerate(pf.iter_batches(batch_size=2_000_000,columns=['timestamp','item_id','is_organic','played_ratio_pct'])):\n a=b.to_pydict(); item=np.asarray(a['item_id'],np.int64); c,ok=cols(item); ts=np.asarray(a['timestamp'],np.int64)[ok]; org=np.asarray(a['is_organic'],np.uint8)[ok]; ratio=np.asarray(a['played_ratio_pct'],np.uint16)[ok]; ages=(CUTOFF-ts)/DAY\n masks=[ratio>=50,ratio>=80,(ratio>=50)&(org==1),(ratio>=80)&(org==1)]\n for j,m in enumerate(masks):\n  cc=c[m]; aa=ages[m]\n  for wi,w in enumerate(wins): F[j,wi]+=np.bincount(cc[aa<=w],minlength=n)\n print('batch',bi,flush=True)\n# likes counts same windows organic and all\nLF=np.zeros((2,len(wins),n),np.float64)\nfor fam in ['likes']:\n d=pd.read_parquet(P+'/train_'+fam+'.parquet',columns=['timestamp','item_id','is_organic']); c,ok=cols(d.item_id.to_numpy(np.int64)); ts=d.timestamp.to_numpy(np.int64)[ok]; org=d.is_organic.to_numpy(np.uint8)[ok]; ages=(CUTOFF-ts)/DAY\n for j,m in enumerate([np.ones(len(c),bool),org==1]):\n  for wi,w in enumerate(wins): LF[j,wi]+=np.bincount(c[m&(ages<=w)],minlength=n)\n# truth and evaluator\nelig=CandidateSet(__import__('pathlib').Path(P+'/validation_eligible_candidates.npz')); targets=set(pd.read_parquet(P+'/target_users.parquet').uid.astype(int)); gold=validation_truth(elig,targets)\ndef evaluate(scores):\n order=np.lexsort((cat.popularity_rank.to_numpy(),-scores)); ranked={}\n for u in gold:\n  row=[]\n  for j in order:\n   it=int(ids[j])\n   if elig.is_eligible(u,it): row.append(it)\n   if len(row)==10: break\n  ranked[u]=row\n return score_rankings(ranked,gold)['quality_ndcg@10']\nres=[]\nres.append(('catalog_positive',evaluate(cat.positive_listens.to_numpy())))\nres.append(('catalog_orgpositive',evaluate(cat.organic_positive_listens.to_numpy())))\nfor j,cn in enumerate(conds):\n for wi,wn in enumerate(names): res.append((cn+'_'+wn,evaluate(F[j,wi])))\nfor j,cn in enumerate(['likeall','likeorg']):\n for wi,wn in enumerate(names): res.append((cn+'_'+wn,evaluate(LF[j,wi])))\nfor x in sorted(res,key=lambda x:-x[1]): print(x)\nnp.savez_compressed('/app/solution/global_features.npz',F=F,LF=LF,wins=wins)\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport sys,time\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\nimport pyarrow.parquet as pq, pandas as pd, numpy as np\nP='/task/data'; CUTOFF=24786800; DAY=86400\ncat=pd.read_parquet(P+'/candidate_catalog.parquet'); ids=cat.item_id.to_numpy(np.int64); n=len(ids); sid=np.sort(ids); s2c=np.argsort(ids)\n# map item ids to catalog column\ndef cols(a):\n p=np.searchsorted(sid,a); ok=(p<n)&(sid[np.minimum(p,n-1)]==a); return s2c[p[ok]],ok\n# metric[event type, window], windows 1,3,7,14,30,60,90,180,all\nwins=np.array([1,3,7,14,30,60,90,180,10000],dtype=np.int32); names=['1d','3d','7d','14d','30d','60d','90d','180d','all']\nconds=['all50','all80','org50','org80']\nF=np.zeros((4,len(wins),n),np.float64)\npf=pq.ParquetFile(P+'/train_listens.parquet')\nfor bi,b in enumerate(pf.iter_batches(batch_size=2_000_000,columns=['timestamp','item_id','is_organic','played_ratio_pct'])):\n a=b.to_pydict(); item=np.asarray(a['item_id'],np.int64); c,ok=cols(item); ts=np.asarray(a['timestamp'],np.int64)[ok]; org=np.asarray(a['is_organic'],np.uint8)[ok]; ratio=np.asarray(a['played_ratio_pct'],np.uint16)[ok]; ages=(CUTOFF-ts)/DAY\n masks=[ratio>=50,ratio>=80,(ratio>=50)&(org==1),(ratio>=80)&(org==1)]\n for j,m in enumerate(masks):\n  cc=c[m]; aa=ages[m]\n  for wi,w in enumerate(wins): F[j,wi]+=np.bincount(cc[aa<=w],minlength=n)\n print('batch',bi,flush=True)\n# likes counts same windows organic and all\nLF=np.zeros((2,len(wins),n),np.float64)\nfor fam in ['likes']:\n d=pd.read_parquet(P+'/train_'+fam+'.parquet',columns=['timestamp','item_id','is_organic']); c,ok=cols(d.item_id.to_numpy(np.int64)); ts=d.timestamp.to_numpy(np.int64)[ok]; org=d.is_organic.to_numpy(np.uint8)[ok]; ages=(CUTOFF-ts)/DAY\n for j,m in enumerate([np.ones(len(c),bool),org==1]):\n  for wi,w in enumerate(wins): LF[j,wi]+=np.bincount(c[m&(ages<=w)],minlength=n)\n# truth and evaluator\nelig=CandidateSet(__import__('pathlib').Path(P+'/validation_eligible_candidates.npz')); targets=set(pd.read_parquet(P+'/target_users.parquet').uid.astype(int)); gold=validation_truth(elig,targets)\ndef evaluate(scores):\n order=np.lexsort((cat.popularity_rank.to_numpy(),-scores)); ranked={}\n for u in gold:\n  row=[]\n  for j in order:\n   it=int(ids[j])\n   if elig.is_eligible(u,it): row.append(it)\n   if len(row)==10: break\n  ranked[u]=row\n return score_rankings(ranked,gold)['quality_ndcg@10']\nres=[]\nres.append(('catalog_positive',evaluate(cat.positive_listens.to_numpy())))\nres.append(('catalog_orgpositive',evaluate(cat.organic_positive_listens.to_numpy())))\nfor j,cn in enumerate(conds):\n for wi,wn in enumerate(names): res.append((cn+'_'+wn,evaluate(F[j,wi])))\nfor j,cn in enumerate(['likeall','likeorg']):\n for wi,wn in enumerate(names): res.append((cn+'_'+wn,evaluate(LF[j,wi])))\nfor x in sorted(res,key=lambda x:-x[1]): print(x)\nnp.savez_compressed('/app/solution/global_features.npz',F=F,LF=LF,wins=wins)\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "batch 0\nbatch 1\nbatch 2\nbatch 3\nbatch 4\nbatch 5\nbatch 6\nbatch 7\nbatch 8\nbatch 9\nbatch 10\nbatch 11\nbatch 12\nbatch 13\nbatch 14\nbatch 15\nbatch 16\nbatch 17\nbatch 18\nbatch 19\nbatch 20\nbatch 21\n('all50_7d', 0.030933)\n('likeorg_3d', 0.030503)\n('likeall_3d', 0.030269)\n('likeall_7d', 0.030187)\n('all80_7d', 0.030107)\n('likeorg_7d', 0.029297)\n('all50_3d', 0.028592)\n('org50_7d', 0.028456)\n('org80_7d', 0.028131)\n('org50_14d', 0.026849)\n('likeorg_14d', 0.026726)\n('org50_3d', 0.026298)\n('org80_3d', 0.025721)\n('all50_14d', 0.025124)\n('all80_3d', 0.023621)\n('org80_14d', 0.022878)\n('all80_14d', 0.022238)\n('likeall_14d', 0.020652)\n('likeorg_30d', 0.018429)\n('all50_1d', 0.018312)\n('org50_1d', 0.018231)\n('all80_1d', 0.017614)\n('all50_30d', 0.017182)\n('all80_60d', 0.016953)\n('all50_60d', 0.016892)\n('likeall_30d', 0.016812)\n('org80_1d', 0.016755)\n('likeorg_60d', 0.016741)\n('all80_30d', 0.016688)\n('likeorg_1d', 0.016286)\n('likeorg_90d', 0.016217)\n('all80_90d', 0.01598)\n('all50_90d', 0.015972)\n('org50_60d', 0.015967)\n('org80_90d', 0.015822)\n('org80_60d', 0.015802)\n('org50_90d', 0.015801)\n('likeall_60d', 0.015481)\n('likeall_90d', 0.015093)\n('org50_30d', 0.01431)\n('org80_30d', 0.013314)\n('likeall_180d', 0.01324)\n('likeorg_180d', 0.013)\n('likeall_1d', 0.01253)\n('likeall_all', 0.012135)\n('all50_180d', 0.012116)\n('likeorg_all', 0.012054)\n('all80_180d', 0.01204)\n('org50_180d', 0.011913)\n('org80_180d', 0.011908)\n('all80_all', 0.010727)\n('catalog_positive', 0.01071)\n('all50_all', 0.01071)\n('org50_all', 0.010577)\n('org80_all', 0.010551)\n('catalog_orgpositive', 3.2e-05)\n",
    "stderr": "",
    "aggregated_output": "batch 0\nbatch 1\nbatch 2\nbatch 3\nbatch 4\nbatch 5\nbatch 6\nbatch 7\nbatch 8\nbatch 9\nbatch 10\nbatch 11\nbatch 12\nbatch 13\nbatch 14\nbatch 15\nbatch 16\nbatch 17\nbatch 18\nbatch 19\nbatch 20\nbatch 21\n('all50_7d', 0.030933)\n('likeorg_3d', 0.030503)\n('likeall_3d', 0.030269)\n('likeall_7d', 0.030187)\n('all80_7d', 0.030107)\n('likeorg_7d', 0.029297)\n('all50_3d', 0.028592)\n('org50_7d', 0.028456)\n('org80_7d', 0.028131)\n('org50_14d', 0.026849)\n('likeorg_14d', 0.026726)\n('org50_3d', 0.026298)\n('org80_3d', 0.025721)\n('all50_14d', 0.025124)\n('all80_3d', 0.023621)\n('org80_14d', 0.022878)\n('all80_14d', 0.022238)\n('likeall_14d', 0.020652)\n('likeorg_30d', 0.018429)\n('all50_1d', 0.018312)\n('org50_1d', 0.018231)\n('all80_1d', 0.017614)\n('all50_30d', 0.017182)\n('all80_60d', 0.016953)\n('all50_60d', 0.016892)\n('likeall_30d', 0.016812)\n('org80_1d', 0.016755)\n('likeorg_60d', 0.016741)\n('all80_30d', 0.016688)\n('likeorg_1d', 0.016286)\n('likeorg_90d', 0.016217)\n('all80_90d', 0.01598)\n('all50_90d', 0.015972)\n('org50_60d', 0.015967)\n('org80_90d', 0.015822)\n('org80_60d', 0.015802)\n('org50_90d', 0.015801)\n('likeall_60d', 0.015481)\n('likeall_90d', 0.015093)\n('org50_30d', 0.01431)\n('org80_30d', 0.013314)\n('likeall_180d', 0.01324)\n('likeorg_180d', 0.013)\n('likeall_1d', 0.01253)\n('likeall_all', 0.012135)\n('all50_180d', 0.012116)\n('likeorg_all', 0.012054)\n('all80_180d', 0.01204)\n('org50_180d', 0.011913)\n('org80_180d', 0.011908)\n('all80_all', 0.010727)\n('catalog_positive', 0.01071)\n('all50_all', 0.01071)\n('org50_all', 0.010577)\n('org80_all', 0.010551)\n('catalog_orgpositive', 3.2e-05)\n",
    "exit_code": 0,
    "duration": {
      "secs": 79,
      "nanos": 481555672
    },
    "formatted_output": "batch 0\nbatch 1\nbatch 2\nbatch 3\nbatch 4\nbatch 5\nbatch 6\nbatch 7\nbatch 8\nbatch 9\nbatch 10\nbatch 11\nbatch 12\nbatch 13\nbatch 14\nbatch 15\nbatch 16\nbatch 17\nbatch 18\nbatch 19\nbatch 20\nbatch 21\n('all50_7d', 0.030933)\n('likeorg_3d', 0.030503)\n('likeall_3d', 0.030269)\n('likeall_7d', 0.030187)\n('all80_7d', 0.030107)\n('likeorg_7d', 0.029297)\n('all50_3d', 0.028592)\n('org50_7d', 0.028456)\n('org80_7d', 0.028131)\n('org50_14d', 0.026849)\n('likeorg_14d', 0.026726)\n('org50_3d', 0.026298)\n('org80_3d', 0.025721)\n('all50_14d', 0.025124)\n('all80_3d', 0.023621)\n('org80_14d', 0.022878)\n('all80_14d', 0.022238)\n('likeall_14d', 0.020652)\n('likeorg_30d', 0.018429)\n('all50_1d', 0.018312)\n('org50_1d', 0.018231)\n('all80_1d', 0.017614)\n('all50_30d', 0.017182)\n('all80_60d', 0.016953)\n('all50_60d', 0.016892)\n('likeall_30d', 0.016812)\n('org80_1d', 0.016755)\n('likeorg_60d', 0.016741)\n('all80_30d', 0.016688)\n('likeorg_1d', 0.016286)\n('likeorg_90d', 0.016217)\n('all80_90d', 0.01598)\n('all50_90d', 0.015972)\n('org50_60d', 0.015967)\n('org80_90d', 0.015822)\n('org80_60d', 0.015802)\n('org50_90d', 0.015801)\n('likeall_60d', 0.015481)\n('likeall_90d', 0.015093)\n('org50_30d', 0.01431)\n('org80_30d', 0.013314)\n('likeall_180d', 0.01324)\n('likeorg_180d', 0.013)\n('likeall_1d', 0.01253)\n('likeall_all', 0.012135)\n('all50_180d', 0.012116)\n('likeorg_all', 0.012054)\n('all80_180d', 0.01204)\n('org50_180d', 0.011913)\n('org80_180d', 0.011908)\n('all80_all', 0.010727)\n('catalog_positive', 0.01071)\n('all50_all', 0.01071)\n('org50_all', 0.010577)\n('org80_all', 0.010551)\n('catalog_orgpositive', 3.2e-05)\n"
  }
}
33

Agent message

+09:24
Thinking unavailable No text recorded

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

34

Agent message

+09:34
Thinking unavailable No text recorded

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

35

Agent message

+09:46
Thinking unavailable No text recorded

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

36

Agent message

+10:00
Thinking unavailable No text recorded

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

37

Agent message

+10:15
Thinking unavailable No text recorded

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

38

exec command

+10:21
Thinking unavailable No text recorded

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

Model text

Recent seven-day positive activity is much stronger than lifetime popularity: 0.030933 versus 0.010710. I’m now testing neighborhood collaborative filtering over the cutoff-specific active-like graph—precisely the signal suited to unseen eligible tracks—while retaining that seven-day trend as the cold-start prior.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python -u - <<'PY'\nimport sys,time\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nP='/task/data'; cat=pd.read_parquet(P+'/candidate_catalog.parquet'); items=cat.item_id.to_numpy(np.int64); nitem=len(items)\ntarget=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64); nuser=len(target); ur={int(x):i for i,x in enumerate(target)}; ic={int(x):i for i,x in enumerate(items)}\nps=pd.read_parquet(P+'/validation_preference_state.parquet'); ps=ps[ps.state=='liked']; rr=np.array([ur.get(int(x),-1) for x in ps.uid],np.int32); cc=np.array([ic.get(int(x),-1) for x in ps.item_id],np.int32); ok=(rr>=0)&(cc>=0); X=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(rr[ok],cc[ok])),shape=(nuser,nitem)); X.data[:]=1\nprint('X',X.shape,X.nnz,'users',np.sum(np.diff(X.indptr)>0),flush=True)\nelig=CandidateSet(Path(P+'/validation_eligible_candidates.npz')); gold=validation_truth(elig,set(map(int,target))); qrows=np.array([ur[u] for u in gold],np.int32); quid=[int(target[r]) for r in qrows]\n# bits loaded aligned exactly target/items\nwith np.load(P+'/validation_eligible_candidates.npz') as z: bits=np.unpackbits(z['packed_eligible'],axis=1,count=nitem,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz'); global7=g['F'][0,2].astype(np.float32); global7=np.log1p(global7); global7/=global7.max()\n# vector scoring from sparse P\ndef evalP(Pmat,blend):\n vals=[]\n for qi,(r,u) in enumerate(zip(qrows,quid)):\n  row=Pmat.getrow(qi); scores=global7*blend\n  if row.nnz:\n   mx=row.data.max(); scores=scores.copy(); scores[row.indices]+=row.data/mx\n  scores=np.where(bits[r],scores,-np.inf)\n  top=np.argpartition(scores,-10)[-10:]; top=top[np.argsort(scores[top])[::-1]]\n  vals.append(ndcg_at_k(items[top].tolist(),gold[u],10))\n return np.mean(vals)\ndef topk_rows(S,k):\n ind=[]; dat=[]; ptr=[0]\n for i in range(S.shape[0]):\n  a,b=S.indptr[i],S.indptr[i+1]; ix=S.indices[a:b]; va=S.data[a:b]; keep=ix!=qrows[i]; ix=ix[keep];va=va[keep]\n  if len(va)>k:\n   z=np.argpartition(va,-k)[-k:];ix=ix[z];va=va[z]\n  ind.append(ix);dat.append(va);ptr.append(ptr[-1]+len(ix))\n return sparse.csr_matrix((np.concatenate(dat),np.concatenate(ind),np.array(ptr)),shape=(len(qrows),nuser))\ndf=np.bincount(X.indices,minlength=nitem)\nfor alpha in [0,0.5,1.0]:\n W=X.copy(); W.data*=np.power(np.log1p(nuser/(1+df[W.indices])),alpha).astype(np.float32)\n norm=np.sqrt(np.asarray(W.multiply(W).sum(1)).ravel()); norm[norm==0]=1; W=sparse.diags(1/norm)@W\n print('sim alpha',alpha,flush=True); S=(W[qrows]@W.T).tocsr(); print('S nnz',S.nnz,flush=True)\n for k in [20,50,100,200]:\n  Sk=topk_rows(S,k); Pred=(Sk@X).tocsr(); print('pred',alpha,k,Pred.nnz,flush=True)\n  for blend in [0,0.01,0.03,0.1,0.3,1.0]: print('RESULT',alpha,k,blend,evalP(Pred,blend),flush=True)\nPY",workdir:"/app",yield_time_ms:30000,max_output_tokens:30000});
if(r.session_id){text(`SESSION_ID=${r.session_id}`);text(r.output);}else{text(r.output);}

Tool response

Script completed
Wall time 30.2 seconds
Output:

SESSION_ID=58575
X (8456, 30000) 418435 users 6769
sim alpha 0
S nnz 5208759
pred 0 20 4120533
RESULT 0 20 0 0.010189151174046571
RESULT 0 20 0.01 0.014625009228479428
RESULT 0 20 0.03 0.014953498427857191
RESULT 0 20 0.1 0.015603920122833
RESULT 0 20 0.3 0.01818227165559802
RESULT 0 20 1.0 0.02614888619836062
pred 0 50 9791912
RESULT 0 50 0 0.01278979557371587
RESULT 0 50 0.01 0.016931792254098226
RESULT 0 50 0.03 0.017241748496562945
RESULT 0 50 0.1 0.01803826865470088
RESULT 0 50 0.3 0.020647610156042415
RESULT 0 50 1.0 0.027850455696709697
pred 0 100 17824538
RESULT 0 100 0 0.013942372413151364
RESULT 0 100 0.01 0.01819974565864306
RESULT 0 100 0.03 0.018239386259612918
RESULT 0 100 0.1 0.019052189606065792
RESULT 0 100 0.3 0.020466507261134904
RESULT 0 100 1.0 0.027936251913010045
pred 0 200 30005716
RESULT 0 200 0 0.013695124373878454
RESULT 0 200 0.01 0.017881347029397733
RESULT 0 200 0.03 0.01811718282971806
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "response_id": "resp_0fdbf3e20a3ba82b016ab5c8484e7087d284c40396c5b31925",
  "usage": {
    "input_tokens": 35531,
    "cached_input_tokens": 34432,
    "cache_write_input_tokens": 0,
    "output_tokens": 3929,
    "reasoning_output_tokens": 2810,
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  },
  "turn_token_usage": {
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  "thread_token_usage": {
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    "cache_write_input_tokens": 0,
    "output_tokens": 15042,
    "reasoning_output_tokens": 7560,
    "total_tokens": 560255
  }
}
39

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 r = await tools.write_stdin({session_id:58575,chars:"",yield_time_ms:30000,max_output_tokens:30000});
text(r.output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

RESULT 0 200 0.1 0.018644844509053415
RESULT 0 200 0.3 0.020052867129975376
RESULT 0 200 1.0 0.026666550127912775
sim alpha 0.5
S nnz 5208759
pred 0.5 20 4347325
RESULT 0.5 20 0 0.009756102751931808
RESULT 0.5 20 0.01 0.014195539354608162
RESULT 0.5 20 0.03 0.014518028075190427
RESULT 0.5 20 0.1 0.015447652588324439
RESULT 0.5 20 0.3 0.017621824838038123
RESULT 0.5 20 1.0 0.025838365552665308
pred 0.5 50 10304043
RESULT 0.5 50 0 0.013099753371926605
RESULT 0.5 50 0.01 0.017274966699935747
RESULT 0.5 50 0.03 0.017498331713449052
RESULT 0.5 50 0.1 0.018290733147372267
RESULT 0.5 50 0.3 0.02008576358616745
RESULT 0.5 50 1.0 0.027616094791895238
pred 0.5 100 18578173
RESULT 0.5 100 0 0.01419749188073614
RESULT 0.5 100 0.01 0.01832402281210433
RESULT 0.5 100 0.03 0.018379772386347838
RESULT 0.5 100 0.1 0.018727698022948797
RESULT 0.5 100 0.3 0.020277147016509896
RESULT 0.5 100 1.0 0.026940600252948747
pred 0.5 200 30925926
RESULT 0.5 200 0 0.014042554321452811
RESULT 0.5 200 0.01 0.01815658036900156
RESULT 0.5 200 0.03 0.018243893834594564
RESULT 0.5 200 0.1 0.01876376044262871
RESULT 0.5 200 0.3 0.020022159840145572
Harness context token usage record
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  "type": "token_usage_record",
  "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "response_id": "resp_0fdbf3e20a3ba82b016ab5c8c414d887d298654b3bc06562ff",
  "usage": {
    "input_tokens": 39948,
    "cached_input_tokens": 35328,
    "cache_write_input_tokens": 0,
    "output_tokens": 85,
    "reasoning_output_tokens": 38,
    "total_tokens": 40033
  },
  "turn_token_usage": {
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    "total_tokens": 600288
  },
  "thread_token_usage": {
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    "cache_write_input_tokens": 0,
    "output_tokens": 15127,
    "reasoning_output_tokens": 7598,
    "total_tokens": 600288
  }
}
40

exec command

+11:47
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:58575,chars:"",yield_time_ms:30000,max_output_tokens:30000});
text(r.output);

Tool response

Script completed
Wall time 28.3 seconds
Output:

RESULT 0.5 200 1.0 0.026467739303177605
sim alpha 1.0
S nnz 5208759
pred 1.0 20 4487569
RESULT 1.0 20 0 0.009764170700602641
RESULT 1.0 20 0.01 0.014275421240727157
RESULT 1.0 20 0.03 0.014364684560321889
RESULT 1.0 20 0.1 0.015258290966744893
RESULT 1.0 20 0.3 0.017116595795726507
RESULT 1.0 20 1.0 0.024692198262097922
pred 1.0 50 10759896
RESULT 1.0 50 0 0.01329800262093432
RESULT 1.0 50 0.01 0.017526661009645172
RESULT 1.0 50 0.03 0.01792440496909016
RESULT 1.0 50 0.1 0.0183000230729711
RESULT 1.0 50 0.3 0.019963966051010137
RESULT 1.0 50 1.0 0.026295988978744727
pred 1.0 100 19366482
RESULT 1.0 100 0 0.014071639205553531
RESULT 1.0 100 0.01 0.01821762953880885
RESULT 1.0 100 0.03 0.01835130151965709
RESULT 1.0 100 0.1 0.018807116302041852
RESULT 1.0 100 0.3 0.019953840882952198
RESULT 1.0 100 1.0 0.02602265501423322
pred 1.0 200 31970762
RESULT 1.0 200 0 0.014013773550960614
RESULT 1.0 200 0.01 0.018208368760460475
RESULT 1.0 200 0.03 0.018266319931025912
RESULT 1.0 200 0.1 0.018716109353217568
RESULT 1.0 200 0.3 0.019838782353696058
RESULT 1.0 200 1.0 0.02549782542888643
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "response_id": "resp_0fdbf3e20a3ba82b016ab5c8e68d2087d2b6c21c53f5dc05a1",
  "usage": {
    "input_tokens": 40651,
    "cached_input_tokens": 39808,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 40704
  },
  "turn_token_usage": {
    "input_tokens": 625812,
    "cached_input_tokens": 592384,
    "cache_write_input_tokens": 0,
    "output_tokens": 15180,
    "reasoning_output_tokens": 7604,
    "total_tokens": 640992
  },
  "thread_token_usage": {
    "input_tokens": 625812,
    "cached_input_tokens": 592384,
    "cache_write_input_tokens": 0,
    "output_tokens": 15180,
    "reasoning_output_tokens": 7604,
    "total_tokens": 640992
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-ef833f12-2eb5-4c60-8441-6ac2c9e94697",
    "process_id": "58575",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python -u - <<'PY'\nimport sys,time\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nP='/task/data'; cat=pd.read_parquet(P+'/candidate_catalog.parquet'); items=cat.item_id.to_numpy(np.int64); nitem=len(items)\ntarget=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64); nuser=len(target); ur={int(x):i for i,x in enumerate(target)}; ic={int(x):i for i,x in enumerate(items)}\nps=pd.read_parquet(P+'/validation_preference_state.parquet'); ps=ps[ps.state=='liked']; rr=np.array([ur.get(int(x),-1) for x in ps.uid],np.int32); cc=np.array([ic.get(int(x),-1) for x in ps.item_id],np.int32); ok=(rr>=0)&(cc>=0); X=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(rr[ok],cc[ok])),shape=(nuser,nitem)); X.data[:]=1\nprint('X',X.shape,X.nnz,'users',np.sum(np.diff(X.indptr)>0),flush=True)\nelig=CandidateSet(Path(P+'/validation_eligible_candidates.npz')); gold=validation_truth(elig,set(map(int,target))); qrows=np.array([ur[u] for u in gold],np.int32); quid=[int(target[r]) for r in qrows]\n# bits loaded aligned exactly target/items\nwith np.load(P+'/validation_eligible_candidates.npz') as z: bits=np.unpackbits(z['packed_eligible'],axis=1,count=nitem,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz'); global7=g['F'][0,2].astype(np.float32); global7=np.log1p(global7); global7/=global7.max()\n# vector scoring from sparse P\ndef evalP(Pmat,blend):\n vals=[]\n for qi,(r,u) in enumerate(zip(qrows,quid)):\n  row=Pmat.getrow(qi); scores=global7*blend\n  if row.nnz:\n   mx=row.data.max(); scores=scores.copy(); scores[row.indices]+=row.data/mx\n  scores=np.where(bits[r],scores,-np.inf)\n  top=np.argpartition(scores,-10)[-10:]; top=top[np.argsort(scores[top])[::-1]]\n  vals.append(ndcg_at_k(items[top].tolist(),gold[u],10))\n return np.mean(vals)\ndef topk_rows(S,k):\n ind=[]; dat=[]; ptr=[0]\n for i in range(S.shape[0]):\n  a,b=S.indptr[i],S.indptr[i+1]; ix=S.indices[a:b]; va=S.data[a:b]; keep=ix!=qrows[i]; ix=ix[keep];va=va[keep]\n  if len(va)>k:\n   z=np.argpartition(va,-k)[-k:];ix=ix[z];va=va[z]\n  ind.append(ix);dat.append(va);ptr.append(ptr[-1]+len(ix))\n return sparse.csr_matrix((np.concatenate(dat),np.concatenate(ind),np.array(ptr)),shape=(len(qrows),nuser))\ndf=np.bincount(X.indices,minlength=nitem)\nfor alpha in [0,0.5,1.0]:\n W=X.copy(); W.data*=np.power(np.log1p(nuser/(1+df[W.indices])),alpha).astype(np.float32)\n norm=np.sqrt(np.asarray(W.multiply(W).sum(1)).ravel()); norm[norm==0]=1; W=sparse.diags(1/norm)@W\n print('sim alpha',alpha,flush=True); S=(W[qrows]@W.T).tocsr(); print('S nnz',S.nnz,flush=True)\n for k in [20,50,100,200]:\n  Sk=topk_rows(S,k); Pred=(Sk@X).tocsr(); print('pred',alpha,k,Pred.nnz,flush=True)\n  for blend in [0,0.01,0.03,0.1,0.3,1.0]: print('RESULT',alpha,k,blend,evalP(Pred,blend),flush=True)\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport sys,time\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nP='/task/data'; cat=pd.read_parquet(P+'/candidate_catalog.parquet'); items=cat.item_id.to_numpy(np.int64); nitem=len(items)\ntarget=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64); nuser=len(target); ur={int(x):i for i,x in enumerate(target)}; ic={int(x):i for i,x in enumerate(items)}\nps=pd.read_parquet(P+'/validation_preference_state.parquet'); ps=ps[ps.state=='liked']; rr=np.array([ur.get(int(x),-1) for x in ps.uid],np.int32); cc=np.array([ic.get(int(x),-1) for x in ps.item_id],np.int32); ok=(rr>=0)&(cc>=0); X=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(rr[ok],cc[ok])),shape=(nuser,nitem)); X.data[:]=1\nprint('X',X.shape,X.nnz,'users',np.sum(np.diff(X.indptr)>0),flush=True)\nelig=CandidateSet(Path(P+'/validation_eligible_candidates.npz')); gold=validation_truth(elig,set(map(int,target))); qrows=np.array([ur[u] for u in gold],np.int32); quid=[int(target[r]) for r in qrows]\n# bits loaded aligned exactly target/items\nwith np.load(P+'/validation_eligible_candidates.npz') as z: bits=np.unpackbits(z['packed_eligible'],axis=1,count=nitem,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz'); global7=g['F'][0,2].astype(np.float32); global7=np.log1p(global7); global7/=global7.max()\n# vector scoring from sparse P\ndef evalP(Pmat,blend):\n vals=[]\n for qi,(r,u) in enumerate(zip(qrows,quid)):\n  row=Pmat.getrow(qi); scores=global7*blend\n  if row.nnz:\n   mx=row.data.max(); scores=scores.copy(); scores[row.indices]+=row.data/mx\n  scores=np.where(bits[r],scores,-np.inf)\n  top=np.argpartition(scores,-10)[-10:]; top=top[np.argsort(scores[top])[::-1]]\n  vals.append(ndcg_at_k(items[top].tolist(),gold[u],10))\n return np.mean(vals)\ndef topk_rows(S,k):\n ind=[]; dat=[]; ptr=[0]\n for i in range(S.shape[0]):\n  a,b=S.indptr[i],S.indptr[i+1]; ix=S.indices[a:b]; va=S.data[a:b]; keep=ix!=qrows[i]; ix=ix[keep];va=va[keep]\n  if len(va)>k:\n   z=np.argpartition(va,-k)[-k:];ix=ix[z];va=va[z]\n  ind.append(ix);dat.append(va);ptr.append(ptr[-1]+len(ix))\n return sparse.csr_matrix((np.concatenate(dat),np.concatenate(ind),np.array(ptr)),shape=(len(qrows),nuser))\ndf=np.bincount(X.indices,minlength=nitem)\nfor alpha in [0,0.5,1.0]:\n W=X.copy(); W.data*=np.power(np.log1p(nuser/(1+df[W.indices])),alpha).astype(np.float32)\n norm=np.sqrt(np.asarray(W.multiply(W).sum(1)).ravel()); norm[norm==0]=1; W=sparse.diags(1/norm)@W\n print('sim alpha',alpha,flush=True); S=(W[qrows]@W.T).tocsr(); print('S nnz',S.nnz,flush=True)\n for k in [20,50,100,200]:\n  Sk=topk_rows(S,k); Pred=(Sk@X).tocsr(); print('pred',alpha,k,Pred.nnz,flush=True)\n  for blend in [0,0.01,0.03,0.1,0.3,1.0]: print('RESULT',alpha,k,blend,evalP(Pred,blend),flush=True)\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "X (8456, 30000) 418435 users 6769\nsim alpha 0\nS nnz 5208759\npred 0 20 4120533\nRESULT 0 20 0 0.010189151174046571\nRESULT 0 20 0.01 0.014625009228479428\nRESULT 0 20 0.03 0.014953498427857191\nRESULT 0 20 0.1 0.015603920122833\nRESULT 0 20 0.3 0.01818227165559802\nRESULT 0 20 1.0 0.02614888619836062\npred 0 50 9791912\nRESULT 0 50 0 0.01278979557371587\nRESULT 0 50 0.01 0.016931792254098226\nRESULT 0 50 0.03 0.017241748496562945\nRESULT 0 50 0.1 0.01803826865470088\nRESULT 0 50 0.3 0.020647610156042415\nRESULT 0 50 1.0 0.027850455696709697\npred 0 100 17824538\nRESULT 0 100 0 0.013942372413151364\nRESULT 0 100 0.01 0.01819974565864306\nRESULT 0 100 0.03 0.018239386259612918\nRESULT 0 100 0.1 0.019052189606065792\nRESULT 0 100 0.3 0.020466507261134904\nRESULT 0 100 1.0 0.027936251913010045\npred 0 200 30005716\nRESULT 0 200 0 0.013695124373878454\nRESULT 0 200 0.01 0.017881347029397733\nRESULT 0 200 0.03 0.01811718282971806\nRESULT 0 200 0.1 0.018644844509053415\nRESULT 0 200 0.3 0.020052867129975376\nRESULT 0 200 1.0 0.026666550127912775\nsim alpha 0.5\nS nnz 5208759\npred 0.5 20 4347325\nRESULT 0.5 20 0 0.009756102751931808\nRESULT 0.5 20 0.01 0.014195539354608162\nRESULT 0.5 20 0.03 0.014518028075190427\nRESULT 0.5 20 0.1 0.015447652588324439\nRESULT 0.5 20 0.3 0.017621824838038123\nRESULT 0.5 20 1.0 0.025838365552665308\npred 0.5 50 10304043\nRESULT 0.5 50 0 0.013099753371926605\nRESULT 0.5 50 0.01 0.017274966699935747\nRESULT 0.5 50 0.03 0.017498331713449052\nRESULT 0.5 50 0.1 0.018290733147372267\nRESULT 0.5 50 0.3 0.02008576358616745\nRESULT 0.5 50 1.0 0.027616094791895238\npred 0.5 100 18578173\nRESULT 0.5 100 0 0.01419749188073614\nRESULT 0.5 100 0.01 0.01832402281210433\nRESULT 0.5 100 0.03 0.018379772386347838\nRESULT 0.5 100 0.1 0.018727698022948797\nRESULT 0.5 100 0.3 0.020277147016509896\nRESULT 0.5 100 1.0 0.026940600252948747\npred 0.5 200 30925926\nRESULT 0.5 200 0 0.014042554321452811\nRESULT 0.5 200 0.01 0.01815658036900156\nRESULT 0.5 200 0.03 0.018243893834594564\nRESULT 0.5 200 0.1 0.01876376044262871\nRESULT 0.5 200 0.3 0.020022159840145572\nRESULT 0.5 200 1.0 0.026467739303177605\nsim alpha 1.0\nS nnz 5208759\npred 1.0 20 4487569\nRESULT 1.0 20 0 0.009764170700602641\nRESULT 1.0 20 0.01 0.014275421240727157\nRESULT 1.0 20 0.03 0.014364684560321889\nRESULT 1.0 20 0.1 0.015258290966744893\nRESULT 1.0 20 0.3 0.017116595795726507\nRESULT 1.0 20 1.0 0.024692198262097922\npred 1.0 50 10759896\nRESULT 1.0 50 0 0.01329800262093432\nRESULT 1.0 50 0.01 0.017526661009645172\nRESULT 1.0 50 0.03 0.01792440496909016\nRESULT 1.0 50 0.1 0.0183000230729711\nRESULT 1.0 50 0.3 0.019963966051010137\nRESULT 1.0 50 1.0 0.026295988978744727\npred 1.0 100 19366482\nRESULT 1.0 100 0 0.014071639205553531\nRESULT 1.0 100 0.01 0.01821762953880885\nRESULT 1.0 100 0.03 0.01835130151965709\nRESULT 1.0 100 0.1 0.018807116302041852\nRESULT 1.0 100 0.3 0.019953840882952198\nRESULT 1.0 100 1.0 0.02602265501423322\npred 1.0 200 31970762\nRESULT 1.0 200 0 0.014013773550960614\nRESULT 1.0 200 0.01 0.018208368760460475\nRESULT 1.0 200 0.03 0.018266319931025912\nRESULT 1.0 200 0.1 0.018716109353217568\nRESULT 1.0 200 0.3 0.019838782353696058\nRESULT 1.0 200 1.0 0.02549782542888643\n",
    "stderr": "",
    "aggregated_output": "X (8456, 30000) 418435 users 6769\nsim alpha 0\nS nnz 5208759\npred 0 20 4120533\nRESULT 0 20 0 0.010189151174046571\nRESULT 0 20 0.01 0.014625009228479428\nRESULT 0 20 0.03 0.014953498427857191\nRESULT 0 20 0.1 0.015603920122833\nRESULT 0 20 0.3 0.01818227165559802\nRESULT 0 20 1.0 0.02614888619836062\npred 0 50 9791912\nRESULT 0 50 0 0.01278979557371587\nRESULT 0 50 0.01 0.016931792254098226\nRESULT 0 50 0.03 0.017241748496562945\nRESULT 0 50 0.1 0.01803826865470088\nRESULT 0 50 0.3 0.020647610156042415\nRESULT 0 50 1.0 0.027850455696709697\npred 0 100 17824538\nRESULT 0 100 0 0.013942372413151364\nRESULT 0 100 0.01 0.01819974565864306\nRESULT 0 100 0.03 0.018239386259612918\nRESULT 0 100 0.1 0.019052189606065792\nRESULT 0 100 0.3 0.020466507261134904\nRESULT 0 100 1.0 0.027936251913010045\npred 0 200 30005716\nRESULT 0 200 0 0.013695124373878454\nRESULT 0 200 0.01 0.017881347029397733\nRESULT 0 200 0.03 0.01811718282971806\nRESULT 0 200 0.1 0.018644844509053415\nRESULT 0 200 0.3 0.020052867129975376\nRESULT 0 200 1.0 0.026666550127912775\nsim alpha 0.5\nS nnz 5208759\npred 0.5 20 4347325\nRESULT 0.5 20 0 0.009756102751931808\nRESULT 0.5 20 0.01 0.014195539354608162\nRESULT 0.5 20 0.03 0.014518028075190427\nRESULT 0.5 20 0.1 0.015447652588324439\nRESULT 0.5 20 0.3 0.017621824838038123\nRESULT 0.5 20 1.0 0.025838365552665308\npred 0.5 50 10304043\nRESULT 0.5 50 0 0.013099753371926605\nRESULT 0.5 50 0.01 0.017274966699935747\nRESULT 0.5 50 0.03 0.017498331713449052\nRESULT 0.5 50 0.1 0.018290733147372267\nRESULT 0.5 50 0.3 0.02008576358616745\nRESULT 0.5 50 1.0 0.027616094791895238\npred 0.5 100 18578173\nRESULT 0.5 100 0 0.01419749188073614\nRESULT 0.5 100 0.01 0.01832402281210433\nRESULT 0.5 100 0.03 0.018379772386347838\nRESULT 0.5 100 0.1 0.018727698022948797\nRESULT 0.5 100 0.3 0.020277147016509896\nRESULT 0.5 100 1.0 0.026940600252948747\npred 0.5 200 30925926\nRESULT 0.5 200 0 0.014042554321452811\nRESULT 0.5 200 0.01 0.01815658036900156\nRESULT 0.5 200 0.03 0.018243893834594564\nRESULT 0.5 200 0.1 0.01876376044262871\nRESULT 0.5 200 0.3 0.020022159840145572\nRESULT 0.5 200 1.0 0.026467739303177605\nsim alpha 1.0\nS nnz 5208759\npred 1.0 20 4487569\nRESULT 1.0 20 0 0.009764170700602641\nRESULT 1.0 20 0.01 0.014275421240727157\nRESULT 1.0 20 0.03 0.014364684560321889\nRESULT 1.0 20 0.1 0.015258290966744893\nRESULT 1.0 20 0.3 0.017116595795726507\nRESULT 1.0 20 1.0 0.024692198262097922\npred 1.0 50 10759896\nRESULT 1.0 50 0 0.01329800262093432\nRESULT 1.0 50 0.01 0.017526661009645172\nRESULT 1.0 50 0.03 0.01792440496909016\nRESULT 1.0 50 0.1 0.0183000230729711\nRESULT 1.0 50 0.3 0.019963966051010137\nRESULT 1.0 50 1.0 0.026295988978744727\npred 1.0 100 19366482\nRESULT 1.0 100 0 0.014071639205553531\nRESULT 1.0 100 0.01 0.01821762953880885\nRESULT 1.0 100 0.03 0.01835130151965709\nRESULT 1.0 100 0.1 0.018807116302041852\nRESULT 1.0 100 0.3 0.019953840882952198\nRESULT 1.0 100 1.0 0.02602265501423322\npred 1.0 200 31970762\nRESULT 1.0 200 0 0.014013773550960614\nRESULT 1.0 200 0.01 0.018208368760460475\nRESULT 1.0 200 0.03 0.018266319931025912\nRESULT 1.0 200 0.1 0.018716109353217568\nRESULT 1.0 200 0.3 0.019838782353696058\nRESULT 1.0 200 1.0 0.02549782542888643\n",
    "exit_code": 0,
    "duration": {
      "secs": 95,
      "nanos": 490699237
    },
    "formatted_output": "X (8456, 30000) 418435 users 6769\nsim alpha 0\nS nnz 5208759\npred 0 20 4120533\nRESULT 0 20 0 0.010189151174046571\nRESULT 0 20 0.01 0.014625009228479428\nRESULT 0 20 0.03 0.014953498427857191\nRESULT 0 20 0.1 0.015603920122833\nRESULT 0 20 0.3 0.01818227165559802\nRESULT 0 20 1.0 0.02614888619836062\npred 0 50 9791912\nRESULT 0 50 0 0.01278979557371587\nRESULT 0 50 0.01 0.016931792254098226\nRESULT 0 50 0.03 0.017241748496562945\nRESULT 0 50 0.1 0.01803826865470088\nRESULT 0 50 0.3 0.020647610156042415\nRESULT 0 50 1.0 0.027850455696709697\npred 0 100 17824538\nRESULT 0 100 0 0.013942372413151364\nRESULT 0 100 0.01 0.01819974565864306\nRESULT 0 100 0.03 0.018239386259612918\nRESULT 0 100 0.1 0.019052189606065792\nRESULT 0 100 0.3 0.020466507261134904\nRESULT 0 100 1.0 0.027936251913010045\npred 0 200 30005716\nRESULT 0 200 0 0.013695124373878454\nRESULT 0 200 0.01 0.017881347029397733\nRESULT 0 200 0.03 0.01811718282971806\nRESULT 0 200 0.1 0.018644844509053415\nRESULT 0 200 0.3 0.020052867129975376\nRESULT 0 200 1.0 0.026666550127912775\nsim alpha 0.5\nS nnz 5208759\npred 0.5 20 4347325\nRESULT 0.5 20 0 0.009756102751931808\nRESULT 0.5 20 0.01 0.014195539354608162\nRESULT 0.5 20 0.03 0.014518028075190427\nRESULT 0.5 20 0.1 0.015447652588324439\nRESULT 0.5 20 0.3 0.017621824838038123\nRESULT 0.5 20 1.0 0.025838365552665308\npred 0.5 50 10304043\nRESULT 0.5 50 0 0.013099753371926605\nRESULT 0.5 50 0.01 0.017274966699935747\nRESULT 0.5 50 0.03 0.017498331713449052\nRESULT 0.5 50 0.1 0.018290733147372267\nRESULT 0.5 50 0.3 0.02008576358616745\nRESULT 0.5 50 1.0 0.027616094791895238\npred 0.5 100 18578173\nRESULT 0.5 100 0 0.01419749188073614\nRESULT 0.5 100 0.01 0.01832402281210433\nRESULT 0.5 100 0.03 0.018379772386347838\nRESULT 0.5 100 0.1 0.018727698022948797\nRESULT 0.5 100 0.3 0.020277147016509896\nRESULT 0.5 100 1.0 0.026940600252948747\npred 0.5 200 30925926\nRESULT 0.5 200 0 0.014042554321452811\nRESULT 0.5 200 0.01 0.01815658036900156\nRESULT 0.5 200 0.03 0.018243893834594564\nRESULT 0.5 200 0.1 0.01876376044262871\nRESULT 0.5 200 0.3 0.020022159840145572\nRESULT 0.5 200 1.0 0.026467739303177605\nsim alpha 1.0\nS nnz 5208759\npred 1.0 20 4487569\nRESULT 1.0 20 0 0.009764170700602641\nRESULT 1.0 20 0.01 0.014275421240727157\nRESULT 1.0 20 0.03 0.014364684560321889\nRESULT 1.0 20 0.1 0.015258290966744893\nRESULT 1.0 20 0.3 0.017116595795726507\nRESULT 1.0 20 1.0 0.024692198262097922\npred 1.0 50 10759896\nRESULT 1.0 50 0 0.01329800262093432\nRESULT 1.0 50 0.01 0.017526661009645172\nRESULT 1.0 50 0.03 0.01792440496909016\nRESULT 1.0 50 0.1 0.0183000230729711\nRESULT 1.0 50 0.3 0.019963966051010137\nRESULT 1.0 50 1.0 0.026295988978744727\npred 1.0 100 19366482\nRESULT 1.0 100 0 0.014071639205553531\nRESULT 1.0 100 0.01 0.01821762953880885\nRESULT 1.0 100 0.03 0.01835130151965709\nRESULT 1.0 100 0.1 0.018807116302041852\nRESULT 1.0 100 0.3 0.019953840882952198\nRESULT 1.0 100 1.0 0.02602265501423322\npred 1.0 200 31970762\nRESULT 1.0 200 0 0.014013773550960614\nRESULT 1.0 200 0.01 0.018208368760460475\nRESULT 1.0 200 0.03 0.018266319931025912\nRESULT 1.0 200 0.1 0.018716109353217568\nRESULT 1.0 200 0.3 0.019838782353696058\nRESULT 1.0 200 1.0 0.02549782542888643\n"
  }
}
41

exec command

+12:27
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python -u - <<'PY'\nimport sys\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport numpy as np,pandas as pd,itertools\nP='/task/data'; cat=pd.read_parquet(P+'/candidate_catalog.parquet'); items=cat.item_id.to_numpy(np.int64); poprank=cat.popularity_rank.to_numpy(); n=len(items)\nE=CandidateSet(Path(P+'/validation_eligible_candidates.npz')); target=set(pd.read_parquet(P+'/target_users.parquet').uid.astype(int)); gold=validation_truth(E,target)\nuidrow=E.uid_row\nG=np.load('/app/solution/global_features.npz');F=G['F'];L=G['LF']\n# exact but optimized global evaluation\ndef ev(s):\n order=np.lexsort((poprank,-s))\n vals=[]\n for u,g in gold.items():\n  r=uidrow[u]; chosen=[]\n  for j in order[:500]:\n   if (int(E.packed[r,j//8])>>(j%8))&1: chosen.append(int(items[j]))\n   if len(chosen)==10: break\n  vals.append(ndcg_at_k(chosen,g,10))\n return float(np.mean(vals))\nbase=F[0,2]\nresults=[]\ndef add(name,s): results.append((ev(np.asarray(s,dtype=float)),name))\nadd('base_all50_7d',base)\n# cumulative time feature blends\nfor a in [-.75,-.5,-.25,.25,.5,1,2,4]: add(f'F7 + {a} F3',base+a*F[0,1])\nfor a in [-.75,-.5,-.25,.1,.25,.5,1,2]: add(f'F7 + {a} F14',base+a*F[0,3])\nfor a in [.01,.03,.1,.3,1,3]: add(f'F7 + {a} F30',base+a*F[0,4])\nfor a in [.1,.3,1,3,10,30,100]: add(f'F7 + {a} LikeOrg3',base+a*L[1,1])\nfor a in [.1,.3,1,3,10,30,100]: add(f'F7 + {a} LikeAll7',base+a*L[0,2])\nfor a in [.1,.3,1,3,10]: add(f'F7 + {a} Org50_7',base+a*F[2,2])\nfor a in [.1,.3,1,3,10]: add(f'F7 + {a} StrongAll7',base+a*F[1,2])\n# rank fusion (best=1); normalized reverse percentile\ndef rankscore(x):\n order=np.lexsort((poprank,-x)); z=np.empty(n);z[order]=np.arange(n);return 1-z/n\nrb=rankscore(base)\nfor feat,name in [(L[1,1],'LikeOrg3'),(L[0,2],'LikeAll7'),(F[2,2],'Org50_7'),(F[1,2],'All80_7'),(F[0,1],'All50_3'),(F[0,3],'All50_14')]:\n rf=rankscore(feat)\n for a in [.05,.1,.2,.3,.5,1,2]: add(f'rank F7 + {a} {name}',rb+a*rf)\n# log fusion\nfor feat,name in [(L[1,1],'LikeOrg3'),(L[0,2],'LikeAll7'),(F[2,2],'Org50_7'),(F[0,3],'All50_14')]:\n x=np.log1p(feat);x/=x.max()\n y=np.log1p(base);y/=y.max()\n for a in [.05,.1,.2,.3,.5,1]:add(f'log F7 + {a} {name}',y+a*x)\nfor x in sorted(results,reverse=True)[:35]:print(x)\nPY",workdir:"/app",yield_time_ms:30000,max_output_tokens:12000});
if(r.session_id){text(`SESSION_ID=${r.session_id}`);text(r.output);}else{text(r.output);}

Tool response

Script completed
Wall time 16.0 seconds
Output:

(0.034957401307281685, 'rank F7 + 2 LikeOrg3')
(0.03478906582342955, 'rank F7 + 1 LikeOrg3')
(0.03436086491344594, 'log F7 + 0.3 LikeOrg3')
(0.03429570802518156, 'log F7 + 0.5 LikeOrg3')
(0.03421016414251796, 'log F7 + 0.2 LikeOrg3')
(0.0342037438050043, 'rank F7 + 0.2 LikeOrg3')
(0.033963631976805006, 'rank F7 + 0.5 LikeOrg3')
(0.03393315911693131, 'rank F7 + 0.3 LikeOrg3')
(0.033787105886709724, 'rank F7 + 0.1 LikeOrg3')
(0.033577766869050334, 'F7 + 100 LikeOrg3')
(0.033459262947387595, 'log F7 + 1 LikeOrg3')
(0.03326640989113371, 'F7 + 30 LikeOrg3')
(0.03324830605264237, 'rank F7 + 0.05 LikeOrg3')
(0.03323864773018212, 'log F7 + 0.1 LikeOrg3')
(0.03303446457964477, 'log F7 + 0.2 LikeAll7')
(0.03301407343142128, 'rank F7 + 0.3 LikeAll7')
(0.032992502245060944, 'rank F7 + 0.5 LikeAll7')
(0.03296717813655677, 'log F7 + 0.1 LikeAll7')
(0.032964118087312456, 'rank F7 + 0.2 LikeAll7')
(0.03296207749721871, 'rank F7 + 1 LikeAll7')
(0.03294124369615301, 'log F7 + 0.3 LikeAll7')
(0.032898299180800554, 'log F7 + 0.5 LikeAll7')
(0.03267579072906117, 'rank F7 + 0.1 LikeAll7')
(0.032656385582347736, 'F7 + 10 LikeAll7')
(0.03262728734120741, 'log F7 + 0.05 LikeOrg3')
(0.03240378572193071, 'rank F7 + 2 LikeAll7')
(0.03232675455530496, 'F7 + 30 LikeAll7')
(0.032248437646257566, 'rank F7 + 0.05 LikeAll7')
(0.03222491551007138, 'log F7 + 0.05 LikeAll7')
(0.03198543638504063, 'log F7 + 1 LikeAll7')
(0.03190467328496499, 'F7 + 10 LikeOrg3')
(0.0317579672030315, 'F7 + 3 LikeAll7')
(0.0312735133385832, 'rank F7 + 0.1 All50_3')
(0.0312514732279822, 'rank F7 + 0.05 All50_3')
(0.03124045903603493, 'F7 + -0.75 F3')
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "response_id": "resp_0fdbf3e20a3ba82b016ab5c9058c4087d2a90887a30b67d18b",
  "usage": {
    "input_tokens": 41307,
    "cached_input_tokens": 40448,
    "cache_write_input_tokens": 0,
    "output_tokens": 1378,
    "reasoning_output_tokens": 357,
    "total_tokens": 42685
  },
  "turn_token_usage": {
    "input_tokens": 667119,
    "cached_input_tokens": 632832,
    "cache_write_input_tokens": 0,
    "output_tokens": 16558,
    "reasoning_output_tokens": 7961,
    "total_tokens": 683677
  },
  "thread_token_usage": {
    "input_tokens": 667119,
    "cached_input_tokens": 632832,
    "cache_write_input_tokens": 0,
    "output_tokens": 16558,
    "reasoning_output_tokens": 7961,
    "total_tokens": 683677
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-2ed2da1c-3760-44e9-a10f-998242d5175a",
    "process_id": "72683",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python -u - <<'PY'\nimport sys\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport numpy as np,pandas as pd,itertools\nP='/task/data'; cat=pd.read_parquet(P+'/candidate_catalog.parquet'); items=cat.item_id.to_numpy(np.int64); poprank=cat.popularity_rank.to_numpy(); n=len(items)\nE=CandidateSet(Path(P+'/validation_eligible_candidates.npz')); target=set(pd.read_parquet(P+'/target_users.parquet').uid.astype(int)); gold=validation_truth(E,target)\nuidrow=E.uid_row\nG=np.load('/app/solution/global_features.npz');F=G['F'];L=G['LF']\n# exact but optimized global evaluation\ndef ev(s):\n order=np.lexsort((poprank,-s))\n vals=[]\n for u,g in gold.items():\n  r=uidrow[u]; chosen=[]\n  for j in order[:500]:\n   if (int(E.packed[r,j//8])>>(j%8))&1: chosen.append(int(items[j]))\n   if len(chosen)==10: break\n  vals.append(ndcg_at_k(chosen,g,10))\n return float(np.mean(vals))\nbase=F[0,2]\nresults=[]\ndef add(name,s): results.append((ev(np.asarray(s,dtype=float)),name))\nadd('base_all50_7d',base)\n# cumulative time feature blends\nfor a in [-.75,-.5,-.25,.25,.5,1,2,4]: add(f'F7 + {a} F3',base+a*F[0,1])\nfor a in [-.75,-.5,-.25,.1,.25,.5,1,2]: add(f'F7 + {a} F14',base+a*F[0,3])\nfor a in [.01,.03,.1,.3,1,3]: add(f'F7 + {a} F30',base+a*F[0,4])\nfor a in [.1,.3,1,3,10,30,100]: add(f'F7 + {a} LikeOrg3',base+a*L[1,1])\nfor a in [.1,.3,1,3,10,30,100]: add(f'F7 + {a} LikeAll7',base+a*L[0,2])\nfor a in [.1,.3,1,3,10]: add(f'F7 + {a} Org50_7',base+a*F[2,2])\nfor a in [.1,.3,1,3,10]: add(f'F7 + {a} StrongAll7',base+a*F[1,2])\n# rank fusion (best=1); normalized reverse percentile\ndef rankscore(x):\n order=np.lexsort((poprank,-x)); z=np.empty(n);z[order]=np.arange(n);return 1-z/n\nrb=rankscore(base)\nfor feat,name in [(L[1,1],'LikeOrg3'),(L[0,2],'LikeAll7'),(F[2,2],'Org50_7'),(F[1,2],'All80_7'),(F[0,1],'All50_3'),(F[0,3],'All50_14')]:\n rf=rankscore(feat)\n for a in [.05,.1,.2,.3,.5,1,2]: add(f'rank F7 + {a} {name}',rb+a*rf)\n# log fusion\nfor feat,name in [(L[1,1],'LikeOrg3'),(L[0,2],'LikeAll7'),(F[2,2],'Org50_7'),(F[0,3],'All50_14')]:\n x=np.log1p(feat);x/=x.max()\n y=np.log1p(base);y/=y.max()\n for a in [.05,.1,.2,.3,.5,1]:add(f'log F7 + {a} {name}',y+a*x)\nfor x in sorted(results,reverse=True)[:35]:print(x)\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport sys\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport numpy as np,pandas as pd,itertools\nP='/task/data'; cat=pd.read_parquet(P+'/candidate_catalog.parquet'); items=cat.item_id.to_numpy(np.int64); poprank=cat.popularity_rank.to_numpy(); n=len(items)\nE=CandidateSet(Path(P+'/validation_eligible_candidates.npz')); target=set(pd.read_parquet(P+'/target_users.parquet').uid.astype(int)); gold=validation_truth(E,target)\nuidrow=E.uid_row\nG=np.load('/app/solution/global_features.npz');F=G['F'];L=G['LF']\n# exact but optimized global evaluation\ndef ev(s):\n order=np.lexsort((poprank,-s))\n vals=[]\n for u,g in gold.items():\n  r=uidrow[u]; chosen=[]\n  for j in order[:500]:\n   if (int(E.packed[r,j//8])>>(j%8))&1: chosen.append(int(items[j]))\n   if len(chosen)==10: break\n  vals.append(ndcg_at_k(chosen,g,10))\n return float(np.mean(vals))\nbase=F[0,2]\nresults=[]\ndef add(name,s): results.append((ev(np.asarray(s,dtype=float)),name))\nadd('base_all50_7d',base)\n# cumulative time feature blends\nfor a in [-.75,-.5,-.25,.25,.5,1,2,4]: add(f'F7 + {a} F3',base+a*F[0,1])\nfor a in [-.75,-.5,-.25,.1,.25,.5,1,2]: add(f'F7 + {a} F14',base+a*F[0,3])\nfor a in [.01,.03,.1,.3,1,3]: add(f'F7 + {a} F30',base+a*F[0,4])\nfor a in [.1,.3,1,3,10,30,100]: add(f'F7 + {a} LikeOrg3',base+a*L[1,1])\nfor a in [.1,.3,1,3,10,30,100]: add(f'F7 + {a} LikeAll7',base+a*L[0,2])\nfor a in [.1,.3,1,3,10]: add(f'F7 + {a} Org50_7',base+a*F[2,2])\nfor a in [.1,.3,1,3,10]: add(f'F7 + {a} StrongAll7',base+a*F[1,2])\n# rank fusion (best=1); normalized reverse percentile\ndef rankscore(x):\n order=np.lexsort((poprank,-x)); z=np.empty(n);z[order]=np.arange(n);return 1-z/n\nrb=rankscore(base)\nfor feat,name in [(L[1,1],'LikeOrg3'),(L[0,2],'LikeAll7'),(F[2,2],'Org50_7'),(F[1,2],'All80_7'),(F[0,1],'All50_3'),(F[0,3],'All50_14')]:\n rf=rankscore(feat)\n for a in [.05,.1,.2,.3,.5,1,2]: add(f'rank F7 + {a} {name}',rb+a*rf)\n# log fusion\nfor feat,name in [(L[1,1],'LikeOrg3'),(L[0,2],'LikeAll7'),(F[2,2],'Org50_7'),(F[0,3],'All50_14')]:\n x=np.log1p(feat);x/=x.max()\n y=np.log1p(base);y/=y.max()\n for a in [.05,.1,.2,.3,.5,1]:add(f'log F7 + {a} {name}',y+a*x)\nfor x in sorted(results,reverse=True)[:35]:print(x)\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "(0.034957401307281685, 'rank F7 + 2 LikeOrg3')\n(0.03478906582342955, 'rank F7 + 1 LikeOrg3')\n(0.03436086491344594, 'log F7 + 0.3 LikeOrg3')\n(0.03429570802518156, 'log F7 + 0.5 LikeOrg3')\n(0.03421016414251796, 'log F7 + 0.2 LikeOrg3')\n(0.0342037438050043, 'rank F7 + 0.2 LikeOrg3')\n(0.033963631976805006, 'rank F7 + 0.5 LikeOrg3')\n(0.03393315911693131, 'rank F7 + 0.3 LikeOrg3')\n(0.033787105886709724, 'rank F7 + 0.1 LikeOrg3')\n(0.033577766869050334, 'F7 + 100 LikeOrg3')\n(0.033459262947387595, 'log F7 + 1 LikeOrg3')\n(0.03326640989113371, 'F7 + 30 LikeOrg3')\n(0.03324830605264237, 'rank F7 + 0.05 LikeOrg3')\n(0.03323864773018212, 'log F7 + 0.1 LikeOrg3')\n(0.03303446457964477, 'log F7 + 0.2 LikeAll7')\n(0.03301407343142128, 'rank F7 + 0.3 LikeAll7')\n(0.032992502245060944, 'rank F7 + 0.5 LikeAll7')\n(0.03296717813655677, 'log F7 + 0.1 LikeAll7')\n(0.032964118087312456, 'rank F7 + 0.2 LikeAll7')\n(0.03296207749721871, 'rank F7 + 1 LikeAll7')\n(0.03294124369615301, 'log F7 + 0.3 LikeAll7')\n(0.032898299180800554, 'log F7 + 0.5 LikeAll7')\n(0.03267579072906117, 'rank F7 + 0.1 LikeAll7')\n(0.032656385582347736, 'F7 + 10 LikeAll7')\n(0.03262728734120741, 'log F7 + 0.05 LikeOrg3')\n(0.03240378572193071, 'rank F7 + 2 LikeAll7')\n(0.03232675455530496, 'F7 + 30 LikeAll7')\n(0.032248437646257566, 'rank F7 + 0.05 LikeAll7')\n(0.03222491551007138, 'log F7 + 0.05 LikeAll7')\n(0.03198543638504063, 'log F7 + 1 LikeAll7')\n(0.03190467328496499, 'F7 + 10 LikeOrg3')\n(0.0317579672030315, 'F7 + 3 LikeAll7')\n(0.0312735133385832, 'rank F7 + 0.1 All50_3')\n(0.0312514732279822, 'rank F7 + 0.05 All50_3')\n(0.03124045903603493, 'F7 + -0.75 F3')\n",
    "stderr": "",
    "aggregated_output": "(0.034957401307281685, 'rank F7 + 2 LikeOrg3')\n(0.03478906582342955, 'rank F7 + 1 LikeOrg3')\n(0.03436086491344594, 'log F7 + 0.3 LikeOrg3')\n(0.03429570802518156, 'log F7 + 0.5 LikeOrg3')\n(0.03421016414251796, 'log F7 + 0.2 LikeOrg3')\n(0.0342037438050043, 'rank F7 + 0.2 LikeOrg3')\n(0.033963631976805006, 'rank F7 + 0.5 LikeOrg3')\n(0.03393315911693131, 'rank F7 + 0.3 LikeOrg3')\n(0.033787105886709724, 'rank F7 + 0.1 LikeOrg3')\n(0.033577766869050334, 'F7 + 100 LikeOrg3')\n(0.033459262947387595, 'log F7 + 1 LikeOrg3')\n(0.03326640989113371, 'F7 + 30 LikeOrg3')\n(0.03324830605264237, 'rank F7 + 0.05 LikeOrg3')\n(0.03323864773018212, 'log F7 + 0.1 LikeOrg3')\n(0.03303446457964477, 'log F7 + 0.2 LikeAll7')\n(0.03301407343142128, 'rank F7 + 0.3 LikeAll7')\n(0.032992502245060944, 'rank F7 + 0.5 LikeAll7')\n(0.03296717813655677, 'log F7 + 0.1 LikeAll7')\n(0.032964118087312456, 'rank F7 + 0.2 LikeAll7')\n(0.03296207749721871, 'rank F7 + 1 LikeAll7')\n(0.03294124369615301, 'log F7 + 0.3 LikeAll7')\n(0.032898299180800554, 'log F7 + 0.5 LikeAll7')\n(0.03267579072906117, 'rank F7 + 0.1 LikeAll7')\n(0.032656385582347736, 'F7 + 10 LikeAll7')\n(0.03262728734120741, 'log F7 + 0.05 LikeOrg3')\n(0.03240378572193071, 'rank F7 + 2 LikeAll7')\n(0.03232675455530496, 'F7 + 30 LikeAll7')\n(0.032248437646257566, 'rank F7 + 0.05 LikeAll7')\n(0.03222491551007138, 'log F7 + 0.05 LikeAll7')\n(0.03198543638504063, 'log F7 + 1 LikeAll7')\n(0.03190467328496499, 'F7 + 10 LikeOrg3')\n(0.0317579672030315, 'F7 + 3 LikeAll7')\n(0.0312735133385832, 'rank F7 + 0.1 All50_3')\n(0.0312514732279822, 'rank F7 + 0.05 All50_3')\n(0.03124045903603493, 'F7 + -0.75 F3')\n",
    "exit_code": 0,
    "duration": {
      "secs": 15,
      "nanos": 880264343
    },
    "formatted_output": "(0.034957401307281685, 'rank F7 + 2 LikeOrg3')\n(0.03478906582342955, 'rank F7 + 1 LikeOrg3')\n(0.03436086491344594, 'log F7 + 0.3 LikeOrg3')\n(0.03429570802518156, 'log F7 + 0.5 LikeOrg3')\n(0.03421016414251796, 'log F7 + 0.2 LikeOrg3')\n(0.0342037438050043, 'rank F7 + 0.2 LikeOrg3')\n(0.033963631976805006, 'rank F7 + 0.5 LikeOrg3')\n(0.03393315911693131, 'rank F7 + 0.3 LikeOrg3')\n(0.033787105886709724, 'rank F7 + 0.1 LikeOrg3')\n(0.033577766869050334, 'F7 + 100 LikeOrg3')\n(0.033459262947387595, 'log F7 + 1 LikeOrg3')\n(0.03326640989113371, 'F7 + 30 LikeOrg3')\n(0.03324830605264237, 'rank F7 + 0.05 LikeOrg3')\n(0.03323864773018212, 'log F7 + 0.1 LikeOrg3')\n(0.03303446457964477, 'log F7 + 0.2 LikeAll7')\n(0.03301407343142128, 'rank F7 + 0.3 LikeAll7')\n(0.032992502245060944, 'rank F7 + 0.5 LikeAll7')\n(0.03296717813655677, 'log F7 + 0.1 LikeAll7')\n(0.032964118087312456, 'rank F7 + 0.2 LikeAll7')\n(0.03296207749721871, 'rank F7 + 1 LikeAll7')\n(0.03294124369615301, 'log F7 + 0.3 LikeAll7')\n(0.032898299180800554, 'log F7 + 0.5 LikeAll7')\n(0.03267579072906117, 'rank F7 + 0.1 LikeAll7')\n(0.032656385582347736, 'F7 + 10 LikeAll7')\n(0.03262728734120741, 'log F7 + 0.05 LikeOrg3')\n(0.03240378572193071, 'rank F7 + 2 LikeAll7')\n(0.03232675455530496, 'F7 + 30 LikeAll7')\n(0.032248437646257566, 'rank F7 + 0.05 LikeAll7')\n(0.03222491551007138, 'log F7 + 0.05 LikeAll7')\n(0.03198543638504063, 'log F7 + 1 LikeAll7')\n(0.03190467328496499, 'F7 + 10 LikeOrg3')\n(0.0317579672030315, 'F7 + 3 LikeAll7')\n(0.0312735133385832, 'rank F7 + 0.1 All50_3')\n(0.0312514732279822, 'rank F7 + 0.05 All50_3')\n(0.03124045903603493, 'F7 + -0.75 F3')\n"
  }
}
42

Agent message

+13:11
Thinking unavailable No text recorded

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

43

exec command

+13:20
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python -u - <<'PY'\nimport sys,time\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();nitem=len(items)\ntarget=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64); ur={int(x):i for i,x in enumerate(target)};ic={int(x):i for i,x in enumerate(items)}\nps=pd.read_parquet(P+'/validation_preference_state.parquet');ps=ps[ps.state=='liked'];rr=np.array([ur.get(int(x),-1) for x in ps.uid],np.int32);cc=np.array([ic.get(int(x),-1) for x in ps.item_id],np.int32);ok=(rr>=0)&(cc>=0);X=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(rr[ok],cc[ok])),shape=(len(target),nitem));X.data[:]=1\nE=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qrows=np.array([ur[u] for u in gold]);quid=[int(target[r]) for r in qrows]\nwith np.load(P+'/validation_eligible_candidates.npz') as z: bits=np.unpackbits(z['packed_eligible'],axis=1,count=nitem,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rankscore(x):o=np.lexsort((pr,-x));z=np.empty(nitem,np.float32);z[o]=1-np.arange(nitem)/nitem;return z\ngb=rankscore(F[0,2])+2*rankscore(L[1,1])\nalphas=[0,.01,.03,.05,.1,.2,.3,.5,1]; vals={a:[] for a in alphas}\nfor nc in [32,64,128]:\n print('fit',nc,flush=True);sv=TruncatedSVD(n_components=nc,n_iter=7,random_state=42);U=sv.fit_transform(X);V=sv.components_;print('variance',sv.explained_variance_ratio_.sum(),flush=True)\n for st in range(0,len(qrows),128):\n  qr=qrows[st:st+128];S=(U[qr]@V).astype(np.float32);S-=S.mean(1)[:,None];sd=S.std(1);sd[sd==0]=1;S/=sd[:,None]\n  S[~bits[qr]]=-np.inf\n  for a in alphas:\n   Z=gb[None,:]+a*S if a else np.broadcast_to(gb,S.shape).copy();Z[~bits[qr]]=-np.inf\n   top=np.argpartition(Z,-10,axis=1)[:,-10:]\n   for j,row in enumerate(top):\n    row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[a].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\n for a in alphas:print('RES',nc,a,np.mean(vals[a]),flush=True)\n vals={a:[] for a in alphas}\nPY",workdir:"/app",yield_time_ms:30000,max_output_tokens:20000});
if(r.session_id){text(`SESSION_ID=${r.session_id}`);text(r.output);}else{text(r.output);}

Tool response

Script completed
Wall time 27.1 seconds
Output:

fit 32
variance 0.11988419
RES 32 0 0.03495591411760116
RES 32 0.01 0.024748579646264566
RES 32 0.03 0.02183208445854038
RES 32 0.05 0.020925747937378815
RES 32 0.1 0.01990034332098162
RES 32 0.2 0.019802510935443572
RES 32 0.3 0.01946900450982573
RES 32 0.5 0.019094940574087935
RES 32 1 0.018764380033371834
fit 64
variance 0.16763306
RES 64 0 0.03495591411760116
RES 64 0.01 0.02416820275801751
RES 64 0.03 0.02155272475849179
RES 64 0.05 0.020676271508330072
RES 64 0.1 0.02021373227437422
RES 64 0.2 0.019845774823983888
RES 64 0.3 0.01946677981747845
RES 64 0.5 0.019178314654645673
RES 64 1 0.019090399536859086
fit 128
variance 0.23857811
RES 128 0 0.03495591411760116
RES 128 0.01 0.024084694866554294
RES 128 0.03 0.02123484317464594
RES 128 0.05 0.020576848528793597
RES 128 0.1 0.019645424416012094
RES 128 0.2 0.019256679279768662
RES 128 0.3 0.01899265541444578
RES 128 0.5 0.018630611718205715
RES 128 1 0.01854198263504278
Harness context token usage record
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Harness context codex item completed
{
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  "item": {
    "type": "CommandExecution",
    "id": "exec-ac1fd492-036b-43f1-99d6-aa5560cef8b3",
    "process_id": "43255",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python -u - <<'PY'\nimport sys,time\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();nitem=len(items)\ntarget=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64); ur={int(x):i for i,x in enumerate(target)};ic={int(x):i for i,x in enumerate(items)}\nps=pd.read_parquet(P+'/validation_preference_state.parquet');ps=ps[ps.state=='liked'];rr=np.array([ur.get(int(x),-1) for x in ps.uid],np.int32);cc=np.array([ic.get(int(x),-1) for x in ps.item_id],np.int32);ok=(rr>=0)&(cc>=0);X=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(rr[ok],cc[ok])),shape=(len(target),nitem));X.data[:]=1\nE=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qrows=np.array([ur[u] for u in gold]);quid=[int(target[r]) for r in qrows]\nwith np.load(P+'/validation_eligible_candidates.npz') as z: bits=np.unpackbits(z['packed_eligible'],axis=1,count=nitem,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rankscore(x):o=np.lexsort((pr,-x));z=np.empty(nitem,np.float32);z[o]=1-np.arange(nitem)/nitem;return z\ngb=rankscore(F[0,2])+2*rankscore(L[1,1])\nalphas=[0,.01,.03,.05,.1,.2,.3,.5,1]; vals={a:[] for a in alphas}\nfor nc in [32,64,128]:\n print('fit',nc,flush=True);sv=TruncatedSVD(n_components=nc,n_iter=7,random_state=42);U=sv.fit_transform(X);V=sv.components_;print('variance',sv.explained_variance_ratio_.sum(),flush=True)\n for st in range(0,len(qrows),128):\n  qr=qrows[st:st+128];S=(U[qr]@V).astype(np.float32);S-=S.mean(1)[:,None];sd=S.std(1);sd[sd==0]=1;S/=sd[:,None]\n  S[~bits[qr]]=-np.inf\n  for a in alphas:\n   Z=gb[None,:]+a*S if a else np.broadcast_to(gb,S.shape).copy();Z[~bits[qr]]=-np.inf\n   top=np.argpartition(Z,-10,axis=1)[:,-10:]\n   for j,row in enumerate(top):\n    row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[a].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\n for a in alphas:print('RES',nc,a,np.mean(vals[a]),flush=True)\n vals={a:[] for a in alphas}\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
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        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport sys,time\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();nitem=len(items)\ntarget=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64); ur={int(x):i for i,x in enumerate(target)};ic={int(x):i for i,x in enumerate(items)}\nps=pd.read_parquet(P+'/validation_preference_state.parquet');ps=ps[ps.state=='liked'];rr=np.array([ur.get(int(x),-1) for x in ps.uid],np.int32);cc=np.array([ic.get(int(x),-1) for x in ps.item_id],np.int32);ok=(rr>=0)&(cc>=0);X=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(rr[ok],cc[ok])),shape=(len(target),nitem));X.data[:]=1\nE=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qrows=np.array([ur[u] for u in gold]);quid=[int(target[r]) for r in qrows]\nwith np.load(P+'/validation_eligible_candidates.npz') as z: bits=np.unpackbits(z['packed_eligible'],axis=1,count=nitem,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rankscore(x):o=np.lexsort((pr,-x));z=np.empty(nitem,np.float32);z[o]=1-np.arange(nitem)/nitem;return z\ngb=rankscore(F[0,2])+2*rankscore(L[1,1])\nalphas=[0,.01,.03,.05,.1,.2,.3,.5,1]; vals={a:[] for a in alphas}\nfor nc in [32,64,128]:\n print('fit',nc,flush=True);sv=TruncatedSVD(n_components=nc,n_iter=7,random_state=42);U=sv.fit_transform(X);V=sv.components_;print('variance',sv.explained_variance_ratio_.sum(),flush=True)\n for st in range(0,len(qrows),128):\n  qr=qrows[st:st+128];S=(U[qr]@V).astype(np.float32);S-=S.mean(1)[:,None];sd=S.std(1);sd[sd==0]=1;S/=sd[:,None]\n  S[~bits[qr]]=-np.inf\n  for a in alphas:\n   Z=gb[None,:]+a*S if a else np.broadcast_to(gb,S.shape).copy();Z[~bits[qr]]=-np.inf\n   top=np.argpartition(Z,-10,axis=1)[:,-10:]\n   for j,row in enumerate(top):\n    row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[a].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\n for a in alphas:print('RES',nc,a,np.mean(vals[a]),flush=True)\n vals={a:[] for a in alphas}\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "fit 32\nvariance 0.11988419\nRES 32 0 0.03495591411760116\nRES 32 0.01 0.024748579646264566\nRES 32 0.03 0.02183208445854038\nRES 32 0.05 0.020925747937378815\nRES 32 0.1 0.01990034332098162\nRES 32 0.2 0.019802510935443572\nRES 32 0.3 0.01946900450982573\nRES 32 0.5 0.019094940574087935\nRES 32 1 0.018764380033371834\nfit 64\nvariance 0.16763306\nRES 64 0 0.03495591411760116\nRES 64 0.01 0.02416820275801751\nRES 64 0.03 0.02155272475849179\nRES 64 0.05 0.020676271508330072\nRES 64 0.1 0.02021373227437422\nRES 64 0.2 0.019845774823983888\nRES 64 0.3 0.01946677981747845\nRES 64 0.5 0.019178314654645673\nRES 64 1 0.019090399536859086\nfit 128\nvariance 0.23857811\nRES 128 0 0.03495591411760116\nRES 128 0.01 0.024084694866554294\nRES 128 0.03 0.02123484317464594\nRES 128 0.05 0.020576848528793597\nRES 128 0.1 0.019645424416012094\nRES 128 0.2 0.019256679279768662\nRES 128 0.3 0.01899265541444578\nRES 128 0.5 0.018630611718205715\nRES 128 1 0.01854198263504278\n",
    "stderr": "",
    "aggregated_output": "fit 32\nvariance 0.11988419\nRES 32 0 0.03495591411760116\nRES 32 0.01 0.024748579646264566\nRES 32 0.03 0.02183208445854038\nRES 32 0.05 0.020925747937378815\nRES 32 0.1 0.01990034332098162\nRES 32 0.2 0.019802510935443572\nRES 32 0.3 0.01946900450982573\nRES 32 0.5 0.019094940574087935\nRES 32 1 0.018764380033371834\nfit 64\nvariance 0.16763306\nRES 64 0 0.03495591411760116\nRES 64 0.01 0.02416820275801751\nRES 64 0.03 0.02155272475849179\nRES 64 0.05 0.020676271508330072\nRES 64 0.1 0.02021373227437422\nRES 64 0.2 0.019845774823983888\nRES 64 0.3 0.01946677981747845\nRES 64 0.5 0.019178314654645673\nRES 64 1 0.019090399536859086\nfit 128\nvariance 0.23857811\nRES 128 0 0.03495591411760116\nRES 128 0.01 0.024084694866554294\nRES 128 0.03 0.02123484317464594\nRES 128 0.05 0.020576848528793597\nRES 128 0.1 0.019645424416012094\nRES 128 0.2 0.019256679279768662\nRES 128 0.3 0.01899265541444578\nRES 128 0.5 0.018630611718205715\nRES 128 1 0.01854198263504278\n",
    "exit_code": 0,
    "duration": {
      "secs": 26,
      "nanos": 893513205
    },
    "formatted_output": "fit 32\nvariance 0.11988419\nRES 32 0 0.03495591411760116\nRES 32 0.01 0.024748579646264566\nRES 32 0.03 0.02183208445854038\nRES 32 0.05 0.020925747937378815\nRES 32 0.1 0.01990034332098162\nRES 32 0.2 0.019802510935443572\nRES 32 0.3 0.01946900450982573\nRES 32 0.5 0.019094940574087935\nRES 32 1 0.018764380033371834\nfit 64\nvariance 0.16763306\nRES 64 0 0.03495591411760116\nRES 64 0.01 0.02416820275801751\nRES 64 0.03 0.02155272475849179\nRES 64 0.05 0.020676271508330072\nRES 64 0.1 0.02021373227437422\nRES 64 0.2 0.019845774823983888\nRES 64 0.3 0.01946677981747845\nRES 64 0.5 0.019178314654645673\nRES 64 1 0.019090399536859086\nfit 128\nvariance 0.23857811\nRES 128 0 0.03495591411760116\nRES 128 0.01 0.024084694866554294\nRES 128 0.03 0.02123484317464594\nRES 128 0.05 0.020576848528793597\nRES 128 0.1 0.019645424416012094\nRES 128 0.2 0.019256679279768662\nRES 128 0.3 0.01899265541444578\nRES 128 0.5 0.018630611718205715\nRES 128 1 0.01854198263504278\n"
  }
}
44

exec command

+14:03
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python -u - <<'PY'\nimport sys\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();nitem=len(items);target=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64);ur={int(x):i for i,x in enumerate(target)};ic={int(x):i for i,x in enumerate(items)}\nps=pd.read_parquet(P+'/validation_preference_state.parquet');ps=ps[ps.state=='liked'];rr=np.array([ur.get(int(x),-1) for x in ps.uid]);cc=np.array([ic.get(int(x),-1) for x in ps.item_id]);ok=(rr>=0)&(cc>=0);X=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(rr[ok],cc[ok])),shape=(len(target),nitem));X.data[:]=1\nE=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qrows=np.array([ur[u] for u in gold]);quid=[int(target[r]) for r in qrows]\nwith np.load(P+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=nitem,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(nitem,np.float32);z[o]=1-np.arange(nitem)/nitem;return z\ngb=rs(F[0,2])+2*rs(L[1,1]);sv=TruncatedSVD(n_components=32,n_iter=7,random_state=42);U=sv.fit_transform(X);V=sv.components_\nalphas=[0,1e-7,3e-7,1e-6,3e-6,1e-5,3e-5,1e-4,3e-4,1e-3];vals={a:[] for a in alphas}\nfor st in range(0,len(qrows),128):\n qr=qrows[st:st+128];S=U[qr]@V;S-=S.mean(1)[:,None];sd=S.std(1);sd[sd==0]=1;S/=sd[:,None]\n for a in alphas:\n  Z=gb[None,:]+a*S;Z[~bits[qr]]=-np.inf;top=np.argpartition(Z,-10,axis=1)[:,-10:]\n  for j,row in enumerate(top):row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[a].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\nfor a in alphas:print(a,np.mean(vals[a]))\nPY",workdir:"/app",yield_time_ms:30000,max_output_tokens:12000});
if(r.session_id){text(`SESSION_ID=${r.session_id}`);text(r.output);}else{text(r.output);}

Tool response

Script completed
Wall time 11.0 seconds
Output:

0 0.03495591411760116
1e-07 0.03495608415986733
3e-07 0.03495608415986733
1e-06 0.03495608415986733
3e-06 0.03495114874763764
1e-05 0.03526063346151137
3e-05 0.03542977359260937
0.0001 0.035877024136109305
0.0003 0.03568162253059627
0.001 0.033818241776565706
Harness context token usage record
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Harness context codex item completed
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    "type": "CommandExecution",
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      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python -u - <<'PY'\nimport sys\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();nitem=len(items);target=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64);ur={int(x):i for i,x in enumerate(target)};ic={int(x):i for i,x in enumerate(items)}\nps=pd.read_parquet(P+'/validation_preference_state.parquet');ps=ps[ps.state=='liked'];rr=np.array([ur.get(int(x),-1) for x in ps.uid]);cc=np.array([ic.get(int(x),-1) for x in ps.item_id]);ok=(rr>=0)&(cc>=0);X=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(rr[ok],cc[ok])),shape=(len(target),nitem));X.data[:]=1\nE=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qrows=np.array([ur[u] for u in gold]);quid=[int(target[r]) for r in qrows]\nwith np.load(P+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=nitem,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(nitem,np.float32);z[o]=1-np.arange(nitem)/nitem;return z\ngb=rs(F[0,2])+2*rs(L[1,1]);sv=TruncatedSVD(n_components=32,n_iter=7,random_state=42);U=sv.fit_transform(X);V=sv.components_\nalphas=[0,1e-7,3e-7,1e-6,3e-6,1e-5,3e-5,1e-4,3e-4,1e-3];vals={a:[] for a in alphas}\nfor st in range(0,len(qrows),128):\n qr=qrows[st:st+128];S=U[qr]@V;S-=S.mean(1)[:,None];sd=S.std(1);sd[sd==0]=1;S/=sd[:,None]\n for a in alphas:\n  Z=gb[None,:]+a*S;Z[~bits[qr]]=-np.inf;top=np.argpartition(Z,-10,axis=1)[:,-10:]\n  for j,row in enumerate(top):row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[a].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\nfor a in alphas:print(a,np.mean(vals[a]))\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport sys\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();nitem=len(items);target=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64);ur={int(x):i for i,x in enumerate(target)};ic={int(x):i for i,x in enumerate(items)}\nps=pd.read_parquet(P+'/validation_preference_state.parquet');ps=ps[ps.state=='liked'];rr=np.array([ur.get(int(x),-1) for x in ps.uid]);cc=np.array([ic.get(int(x),-1) for x in ps.item_id]);ok=(rr>=0)&(cc>=0);X=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(rr[ok],cc[ok])),shape=(len(target),nitem));X.data[:]=1\nE=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qrows=np.array([ur[u] for u in gold]);quid=[int(target[r]) for r in qrows]\nwith np.load(P+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=nitem,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(nitem,np.float32);z[o]=1-np.arange(nitem)/nitem;return z\ngb=rs(F[0,2])+2*rs(L[1,1]);sv=TruncatedSVD(n_components=32,n_iter=7,random_state=42);U=sv.fit_transform(X);V=sv.components_\nalphas=[0,1e-7,3e-7,1e-6,3e-6,1e-5,3e-5,1e-4,3e-4,1e-3];vals={a:[] for a in alphas}\nfor st in range(0,len(qrows),128):\n qr=qrows[st:st+128];S=U[qr]@V;S-=S.mean(1)[:,None];sd=S.std(1);sd[sd==0]=1;S/=sd[:,None]\n for a in alphas:\n  Z=gb[None,:]+a*S;Z[~bits[qr]]=-np.inf;top=np.argpartition(Z,-10,axis=1)[:,-10:]\n  for j,row in enumerate(top):row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[a].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\nfor a in alphas:print(a,np.mean(vals[a]))\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "0 0.03495591411760116\n1e-07 0.03495608415986733\n3e-07 0.03495608415986733\n1e-06 0.03495608415986733\n3e-06 0.03495114874763764\n1e-05 0.03526063346151137\n3e-05 0.03542977359260937\n0.0001 0.035877024136109305\n0.0003 0.03568162253059627\n0.001 0.033818241776565706\n",
    "stderr": "",
    "aggregated_output": "0 0.03495591411760116\n1e-07 0.03495608415986733\n3e-07 0.03495608415986733\n1e-06 0.03495608415986733\n3e-06 0.03495114874763764\n1e-05 0.03526063346151137\n3e-05 0.03542977359260937\n0.0001 0.035877024136109305\n0.0003 0.03568162253059627\n0.001 0.033818241776565706\n",
    "exit_code": 0,
    "duration": {
      "secs": 10,
      "nanos": 762249920
    },
    "formatted_output": "0 0.03495591411760116\n1e-07 0.03495608415986733\n3e-07 0.03495608415986733\n1e-06 0.03495608415986733\n3e-06 0.03495114874763764\n1e-05 0.03526063346151137\n3e-05 0.03542977359260937\n0.0001 0.035877024136109305\n0.0003 0.03568162253059627\n0.001 0.033818241776565706\n"
  }
}
45

Agent message

+14:42
Thinking unavailable No text recorded

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

46

exec command

+14:44
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd\nx=pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint(x[['positive_listens','organic_positive_listens']].sum())\nprint(x[['positive_listens','organic_positive_listens']].quantile([.5,.9,.99,1]))\nPY",workdir:"/app",yield_time_ms:10000,max_output_tokens:5000});
text(r.output);

Tool response

Script completed
Wall time 0.8 seconds
Output:

positive_listens            20056329.0
organic_positive_listens     9417793.0
dtype: float64
      positive_listens  organic_positive_listens
0.50            354.00                    162.00
0.90           1367.00                    640.00
0.99           5052.16                   2548.08
1.00          26615.00                  20620.00
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "response_id": "resp_0fdbf3e20a3ba82b016ab5c989d9ac87d29b0baf9dc3b315f2",
  "usage": {
    "input_tokens": 46810,
    "cached_input_tokens": 45568,
    "cache_write_input_tokens": 0,
    "output_tokens": 700,
    "reasoning_output_tokens": 574,
    "total_tokens": 47510
  },
  "turn_token_usage": {
    "input_tokens": 803156,
    "cached_input_tokens": 762752,
    "cache_write_input_tokens": 0,
    "output_tokens": 19920,
    "reasoning_output_tokens": 9554,
    "total_tokens": 823076
  },
  "thread_token_usage": {
    "input_tokens": 803156,
    "cached_input_tokens": 762752,
    "cache_write_input_tokens": 0,
    "output_tokens": 19920,
    "reasoning_output_tokens": 9554,
    "total_tokens": 823076
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-4c38087a-e9e4-4569-96a8-4edb742e53f6",
    "process_id": "61431",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd\nx=pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint(x[['positive_listens','organic_positive_listens']].sum())\nprint(x[['positive_listens','organic_positive_listens']].quantile([.5,.9,.99,1]))\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd\nx=pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint(x[['positive_listens','organic_positive_listens']].sum())\nprint(x[['positive_listens','organic_positive_listens']].quantile([.5,.9,.99,1]))\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "positive_listens            20056329.0\norganic_positive_listens     9417793.0\ndtype: float64\n      positive_listens  organic_positive_listens\n0.50            354.00                    162.00\n0.90           1367.00                    640.00\n0.99           5052.16                   2548.08\n1.00          26615.00                  20620.00\n",
    "stderr": "",
    "aggregated_output": "positive_listens            20056329.0\norganic_positive_listens     9417793.0\ndtype: float64\n      positive_listens  organic_positive_listens\n0.50            354.00                    162.00\n0.90           1367.00                    640.00\n0.99           5052.16                   2548.08\n1.00          26615.00                  20620.00\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 557417630
    },
    "formatted_output": "positive_listens            20056329.0\norganic_positive_listens     9417793.0\ndtype: float64\n      positive_listens  organic_positive_listens\n0.50            354.00                    162.00\n0.90           1367.00                    640.00\n0.99           5052.16                   2548.08\n1.00          26615.00                  20620.00\n"
  }
}
47

exec command

+14:59
Thinking unavailable No text recorded

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

Model text

Active-like SVD provides a real, if modest, lift: the best blended validation score is now about 0.03588. I’m expanding the representation to the full positive-listen graph; it contains roughly 20 million candidate-track events and should capture taste structure that sparse explicit likes miss.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python -u - <<'PY'\nimport pyarrow.parquet as pq, pandas as pd, numpy as np\nfrom scipy import sparse\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');ids=cat.item_id.to_numpy(np.int64);n=len(ids);sid=np.sort(ids);s2c=np.argsort(ids)\nrows=[];cols=[];orgs=[]\npf=pq.ParquetFile(P+'/train_listens.parquet')\nfor bi,b in enumerate(pf.iter_batches(batch_size=2_000_000,columns=['uid','item_id','is_organic','played_ratio_pct'])):\n a=b.to_pydict();ratio=np.asarray(a['played_ratio_pct'],np.uint16);m=ratio>=50;it=np.asarray(a['item_id'],np.int64)[m];p=np.searchsorted(sid,it);ok=(p<n)&(sid[np.minimum(p,n-1)]==it);rows.append((np.asarray(a['uid'],np.int32)[m][ok]//100-1).astype(np.int32));cols.append(s2c[p[ok]].astype(np.int32));orgs.append(np.asarray(a['is_organic'],np.uint8)[m][ok]);print('batch',bi,'kept',sum(map(len,rows)),flush=True)\nr=np.concatenate(rows);c=np.concatenate(cols);o=np.concatenate(orgs);print('coo',len(r),flush=True);del rows,cols,orgs\nX=sparse.coo_matrix((np.ones(len(r),np.float32),(r,c)),shape=(10000,n)).tocsr();X.sum_duplicates();print('X',X.nnz,flush=True)\nsel=o==1;XO=sparse.coo_matrix((np.ones(sel.sum(),np.float32),(r[sel],c[sel])),shape=(10000,n)).tocsr();XO.sum_duplicates();print('XO',XO.nnz,flush=True)\nsparse.save_npz('/app/solution/listen_counts_train.npz',X,compressed=True);sparse.save_npz('/app/solution/organic_counts_train.npz',XO,compressed=True)\nPY",workdir:"/app",yield_time_ms:30000,max_output_tokens:12000});
if(r.session_id){text(`SESSION_ID=${r.session_id}`);text(r.output);}else{text(r.output);}

Tool response

Script completed
Wall time 30.2 seconds
Output:

SESSION_ID=16905
batch 0 kept 908896
batch 1 kept 1785737
batch 2 kept 2709445
batch 3 kept 3652603
batch 4 kept 4527184
batch 5 kept 5470718
batch 6 kept 6418602
batch 7 kept 7394955
batch 8 kept 8248997
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "response_id": "resp_0fdbf3e20a3ba82b016ab5c99b6e2487d2bfb6b0c2b4ed02e1",
  "usage": {
    "input_tokens": 47632,
    "cached_input_tokens": 46592,
    "cache_write_input_tokens": 0,
    "output_tokens": 1116,
    "reasoning_output_tokens": 516,
    "total_tokens": 48748
  },
  "turn_token_usage": {
    "input_tokens": 850788,
    "cached_input_tokens": 809344,
    "cache_write_input_tokens": 0,
    "output_tokens": 21036,
    "reasoning_output_tokens": 10070,
    "total_tokens": 871824
  },
  "thread_token_usage": {
    "input_tokens": 850788,
    "cached_input_tokens": 809344,
    "cache_write_input_tokens": 0,
    "output_tokens": 21036,
    "reasoning_output_tokens": 10070,
    "total_tokens": 871824
  }
}
48

exec command

+15:39
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:16905,chars:"",yield_time_ms:30000,max_output_tokens:12000});
text(r.output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

batch 9 kept 9196517
batch 10 kept 10155336
batch 11 kept 11064652
batch 12 kept 11970245
batch 13 kept 12909457
batch 14 kept 13800518
batch 15 kept 14730189
batch 16 kept 15667516
batch 17 kept 16586586
batch 18 kept 17502704
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "response_id": "resp_0fdbf3e20a3ba82b016ab5c9cf0d9c87d2974cef2c3ace05da",
  "usage": {
    "input_tokens": 48856,
    "cached_input_tokens": 47488,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
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  },
  "turn_token_usage": {
    "input_tokens": 899644,
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    "cache_write_input_tokens": 0,
    "output_tokens": 21089,
    "reasoning_output_tokens": 10076,
    "total_tokens": 920733
  },
  "thread_token_usage": {
    "input_tokens": 899644,
    "cached_input_tokens": 856832,
    "cache_write_input_tokens": 0,
    "output_tokens": 21089,
    "reasoning_output_tokens": 10076,
    "total_tokens": 920733
  }
}
49

exec command

+16:11
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:16905,chars:"",yield_time_ms:30000,max_output_tokens:12000});
text(r.output);

Tool response

Script completed
Wall time 9.8 seconds
Output:

batch 19 kept 18436772
batch 20 kept 19367547
batch 21 kept 20056329
coo 20056329
X 5564926
XO 2060124
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "response_id": "resp_0fdbf3e20a3ba82b016ab5c9ef3cb887d2b3306d02a01cb0d7",
  "usage": {
    "input_tokens": 49022,
    "cached_input_tokens": 48640,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
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  },
  "turn_token_usage": {
    "input_tokens": 948666,
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  "thread_token_usage": {
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    "cache_write_input_tokens": 0,
    "output_tokens": 21142,
    "reasoning_output_tokens": 10082,
    "total_tokens": 969808
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-9f76517f-7459-4d8b-ae90-a020514e9813",
    "process_id": "16905",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python -u - <<'PY'\nimport pyarrow.parquet as pq, pandas as pd, numpy as np\nfrom scipy import sparse\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');ids=cat.item_id.to_numpy(np.int64);n=len(ids);sid=np.sort(ids);s2c=np.argsort(ids)\nrows=[];cols=[];orgs=[]\npf=pq.ParquetFile(P+'/train_listens.parquet')\nfor bi,b in enumerate(pf.iter_batches(batch_size=2_000_000,columns=['uid','item_id','is_organic','played_ratio_pct'])):\n a=b.to_pydict();ratio=np.asarray(a['played_ratio_pct'],np.uint16);m=ratio>=50;it=np.asarray(a['item_id'],np.int64)[m];p=np.searchsorted(sid,it);ok=(p<n)&(sid[np.minimum(p,n-1)]==it);rows.append((np.asarray(a['uid'],np.int32)[m][ok]//100-1).astype(np.int32));cols.append(s2c[p[ok]].astype(np.int32));orgs.append(np.asarray(a['is_organic'],np.uint8)[m][ok]);print('batch',bi,'kept',sum(map(len,rows)),flush=True)\nr=np.concatenate(rows);c=np.concatenate(cols);o=np.concatenate(orgs);print('coo',len(r),flush=True);del rows,cols,orgs\nX=sparse.coo_matrix((np.ones(len(r),np.float32),(r,c)),shape=(10000,n)).tocsr();X.sum_duplicates();print('X',X.nnz,flush=True)\nsel=o==1;XO=sparse.coo_matrix((np.ones(sel.sum(),np.float32),(r[sel],c[sel])),shape=(10000,n)).tocsr();XO.sum_duplicates();print('XO',XO.nnz,flush=True)\nsparse.save_npz('/app/solution/listen_counts_train.npz',X,compressed=True);sparse.save_npz('/app/solution/organic_counts_train.npz',XO,compressed=True)\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport pyarrow.parquet as pq, pandas as pd, numpy as np\nfrom scipy import sparse\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');ids=cat.item_id.to_numpy(np.int64);n=len(ids);sid=np.sort(ids);s2c=np.argsort(ids)\nrows=[];cols=[];orgs=[]\npf=pq.ParquetFile(P+'/train_listens.parquet')\nfor bi,b in enumerate(pf.iter_batches(batch_size=2_000_000,columns=['uid','item_id','is_organic','played_ratio_pct'])):\n a=b.to_pydict();ratio=np.asarray(a['played_ratio_pct'],np.uint16);m=ratio>=50;it=np.asarray(a['item_id'],np.int64)[m];p=np.searchsorted(sid,it);ok=(p<n)&(sid[np.minimum(p,n-1)]==it);rows.append((np.asarray(a['uid'],np.int32)[m][ok]//100-1).astype(np.int32));cols.append(s2c[p[ok]].astype(np.int32));orgs.append(np.asarray(a['is_organic'],np.uint8)[m][ok]);print('batch',bi,'kept',sum(map(len,rows)),flush=True)\nr=np.concatenate(rows);c=np.concatenate(cols);o=np.concatenate(orgs);print('coo',len(r),flush=True);del rows,cols,orgs\nX=sparse.coo_matrix((np.ones(len(r),np.float32),(r,c)),shape=(10000,n)).tocsr();X.sum_duplicates();print('X',X.nnz,flush=True)\nsel=o==1;XO=sparse.coo_matrix((np.ones(sel.sum(),np.float32),(r[sel],c[sel])),shape=(10000,n)).tocsr();XO.sum_duplicates();print('XO',XO.nnz,flush=True)\nsparse.save_npz('/app/solution/listen_counts_train.npz',X,compressed=True);sparse.save_npz('/app/solution/organic_counts_train.npz',XO,compressed=True)\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "batch 0 kept 908896\nbatch 1 kept 1785737\nbatch 2 kept 2709445\nbatch 3 kept 3652603\nbatch 4 kept 4527184\nbatch 5 kept 5470718\nbatch 6 kept 6418602\nbatch 7 kept 7394955\nbatch 8 kept 8248997\nbatch 9 kept 9196517\nbatch 10 kept 10155336\nbatch 11 kept 11064652\nbatch 12 kept 11970245\nbatch 13 kept 12909457\nbatch 14 kept 13800518\nbatch 15 kept 14730189\nbatch 16 kept 15667516\nbatch 17 kept 16586586\nbatch 18 kept 17502704\nbatch 19 kept 18436772\nbatch 20 kept 19367547\nbatch 21 kept 20056329\ncoo 20056329\nX 5564926\nXO 2060124\n",
    "stderr": "",
    "aggregated_output": "batch 0 kept 908896\nbatch 1 kept 1785737\nbatch 2 kept 2709445\nbatch 3 kept 3652603\nbatch 4 kept 4527184\nbatch 5 kept 5470718\nbatch 6 kept 6418602\nbatch 7 kept 7394955\nbatch 8 kept 8248997\nbatch 9 kept 9196517\nbatch 10 kept 10155336\nbatch 11 kept 11064652\nbatch 12 kept 11970245\nbatch 13 kept 12909457\nbatch 14 kept 13800518\nbatch 15 kept 14730189\nbatch 16 kept 15667516\nbatch 17 kept 16586586\nbatch 18 kept 17502704\nbatch 19 kept 18436772\nbatch 20 kept 19367547\nbatch 21 kept 20056329\ncoo 20056329\nX 5564926\nXO 2060124\n",
    "exit_code": 0,
    "duration": {
      "secs": 74,
      "nanos": 217487608
    },
    "formatted_output": "batch 0 kept 908896\nbatch 1 kept 1785737\nbatch 2 kept 2709445\nbatch 3 kept 3652603\nbatch 4 kept 4527184\nbatch 5 kept 5470718\nbatch 6 kept 6418602\nbatch 7 kept 7394955\nbatch 8 kept 8248997\nbatch 9 kept 9196517\nbatch 10 kept 10155336\nbatch 11 kept 11064652\nbatch 12 kept 11970245\nbatch 13 kept 12909457\nbatch 14 kept 13800518\nbatch 15 kept 14730189\nbatch 16 kept 15667516\nbatch 17 kept 16586586\nbatch 18 kept 17502704\nbatch 19 kept 18436772\nbatch 20 kept 19367547\nbatch 21 kept 20056329\ncoo 20056329\nX 5564926\nXO 2060124\n"
  }
}
50

exec command

+16:35
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python -u - <<'PY'\nimport sys,time\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();nitem=len(items);target=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64);qmap={int(x):i for i,x in enumerate(target)}\nX=sparse.load_npz('/app/solution/listen_counts_train.npz').astype(np.float32);X.data[:]=1\nE=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qidx=np.array([qmap[u] for u in gold]); xrows=(target[qidx]//100-1).astype(int); quid=[int(target[r]) for r in qidx]\nwith np.load(P+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=nitem,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(nitem,np.float32);z[o]=1-np.arange(nitem)/nitem;return z\ngb=rs(F[0,2])+2*rs(L[1,1])\nprint('fit',X.shape,X.nnz,flush=True);sv=TruncatedSVD(n_components=96,n_iter=5,random_state=42);U=sv.fit_transform(X);V=sv.components_;print('variance',sv.explained_variance_ratio_.sum(),flush=True)\nalphas=[0,3e-6,1e-5,3e-5,1e-4,2e-4,3e-4,5e-4,8e-4,1e-3,2e-3];\nfor nc in [16,32,64,96]:\n vals={a:[] for a in alphas}\n for st in range(0,len(xrows),128):\n  xr=xrows[st:st+128]; br=qidx[st:st+128];S=U[xr,:nc]@V[:nc];S-=S.mean(1)[:,None];sd=S.std(1);sd[sd==0]=1;S/=sd[:,None]\n  for a in alphas:\n   Z=gb[None,:]+a*S;Z[~bits[br]]=-np.inf;top=np.argpartition(Z,-10,axis=1)[:,-10:]\n   for j,row in enumerate(top):row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[a].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\n for a in alphas:print('RES',nc,a,np.mean(vals[a]),flush=True)\nnp.savez_compressed('/app/solution/svd_binary_train.npz',U=U.astype(np.float32),V=V.astype(np.float32))\nPY",workdir:"/app",yield_time_ms:30000,max_output_tokens:20000});
if(r.session_id){text(`SESSION_ID=${r.session_id}`);text(r.output);}else{text(r.output);}

Tool response

Script completed
Wall time 30.2 seconds
Output:

SESSION_ID=44227
fit (10000, 30000) 5564926
variance 0.32360658
RES 16 0 0.03495591411760116
RES 16 3e-06 0.034973235658446705
RES 16 1e-05 0.03500126758146793
RES 16 3e-05 0.035207605075085836
RES 16 0.0001 0.035310353716967675
RES 16 0.0002 0.03561747562209513
RES 16 0.0003 0.035237045829663804
RES 16 0.0005 0.03435119179985207
RES 16 0.0008 0.033242982136058606
RES 16 0.001 0.03215413969124974
RES 16 0.002 0.028746468668758678
RES 32 0 0.03495591411760116
RES 32 3e-06 0.03496830024621702
RES 32 1e-05 0.03505211299686844
RES 32 3e-05 0.03535116761579805
RES 32 0.0001 0.03565403310497617
RES 32 0.0002 0.03584872212789105
RES 32 0.0003 0.036103044199023375
RES 32 0.0005 0.035921291768678996
RES 32 0.0008 0.03538058357845926
RES 32 0.001 0.03506861058046967
RES 32 0.002 0.03263534564478926
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "response_id": "resp_0fdbf3e20a3ba82b016ab5c9fd3b4487d2b83203a5d4542150",
  "usage": {
    "input_tokens": 49144,
    "cached_input_tokens": 48896,
    "cache_write_input_tokens": 0,
    "output_tokens": 1270,
    "reasoning_output_tokens": 455,
    "total_tokens": 50414
  },
  "turn_token_usage": {
    "input_tokens": 997810,
    "cached_input_tokens": 954368,
    "cache_write_input_tokens": 0,
    "output_tokens": 22412,
    "reasoning_output_tokens": 10537,
    "total_tokens": 1020222
  },
  "thread_token_usage": {
    "input_tokens": 997810,
    "cached_input_tokens": 954368,
    "cache_write_input_tokens": 0,
    "output_tokens": 22412,
    "reasoning_output_tokens": 10537,
    "total_tokens": 1020222
  }
}
51

exec command

+17:20
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:44227,chars:"",yield_time_ms:30000,max_output_tokens:20000});
text(r.output);

Tool response

Script completed
Wall time 6.5 seconds
Output:

RES 64 0 0.03495591411760116
RES 64 3e-06 0.034993551524214905
RES 64 1e-05 0.035022907026576885
RES 64 3e-05 0.0353965064416447
RES 64 0.0001 0.035983215487622976
RES 64 0.0002 0.03647827593270166
RES 64 0.0003 0.03654102379252188
RES 64 0.0005 0.036780080343714106
RES 64 0.0008 0.036294447809657245
RES 64 0.001 0.036200086618208074
RES 64 0.002 0.03471478678621616
RES 96 0 0.03495591411760116
RES 96 3e-06 0.034993551524214905
RES 96 1e-05 0.035042989079468234
RES 96 3e-05 0.035286688873380365
RES 96 0.0001 0.03613227395223899
RES 96 0.0002 0.036574598354717935
RES 96 0.0003 0.036778200151850265
RES 96 0.0005 0.03675568153586478
RES 96 0.0008 0.036988795291506504
RES 96 0.001 0.03631790863192337
RES 96 0.002 0.03579480515436143
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "response_id": "resp_0fdbf3e20a3ba82b016ab5ca33d56487d2bc6f687e46e3d20f",
  "usage": {
    "input_tokens": 50850,
    "cached_input_tokens": 49024,
    "cache_write_input_tokens": 0,
    "output_tokens": 61,
    "reasoning_output_tokens": 14,
    "total_tokens": 50911
  },
  "turn_token_usage": {
    "input_tokens": 1048660,
    "cached_input_tokens": 1003392,
    "cache_write_input_tokens": 0,
    "output_tokens": 22473,
    "reasoning_output_tokens": 10551,
    "total_tokens": 1071133
  },
  "thread_token_usage": {
    "input_tokens": 1048660,
    "cached_input_tokens": 1003392,
    "cache_write_input_tokens": 0,
    "output_tokens": 22473,
    "reasoning_output_tokens": 10551,
    "total_tokens": 1071133
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-477075bb-3c25-49aa-8254-f084ff4749f3",
    "process_id": "44227",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python -u - <<'PY'\nimport sys,time\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();nitem=len(items);target=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64);qmap={int(x):i for i,x in enumerate(target)}\nX=sparse.load_npz('/app/solution/listen_counts_train.npz').astype(np.float32);X.data[:]=1\nE=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qidx=np.array([qmap[u] for u in gold]); xrows=(target[qidx]//100-1).astype(int); quid=[int(target[r]) for r in qidx]\nwith np.load(P+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=nitem,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(nitem,np.float32);z[o]=1-np.arange(nitem)/nitem;return z\ngb=rs(F[0,2])+2*rs(L[1,1])\nprint('fit',X.shape,X.nnz,flush=True);sv=TruncatedSVD(n_components=96,n_iter=5,random_state=42);U=sv.fit_transform(X);V=sv.components_;print('variance',sv.explained_variance_ratio_.sum(),flush=True)\nalphas=[0,3e-6,1e-5,3e-5,1e-4,2e-4,3e-4,5e-4,8e-4,1e-3,2e-3];\nfor nc in [16,32,64,96]:\n vals={a:[] for a in alphas}\n for st in range(0,len(xrows),128):\n  xr=xrows[st:st+128]; br=qidx[st:st+128];S=U[xr,:nc]@V[:nc];S-=S.mean(1)[:,None];sd=S.std(1);sd[sd==0]=1;S/=sd[:,None]\n  for a in alphas:\n   Z=gb[None,:]+a*S;Z[~bits[br]]=-np.inf;top=np.argpartition(Z,-10,axis=1)[:,-10:]\n   for j,row in enumerate(top):row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[a].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\n for a in alphas:print('RES',nc,a,np.mean(vals[a]),flush=True)\nnp.savez_compressed('/app/solution/svd_binary_train.npz',U=U.astype(np.float32),V=V.astype(np.float32))\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport sys,time\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();nitem=len(items);target=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64);qmap={int(x):i for i,x in enumerate(target)}\nX=sparse.load_npz('/app/solution/listen_counts_train.npz').astype(np.float32);X.data[:]=1\nE=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qidx=np.array([qmap[u] for u in gold]); xrows=(target[qidx]//100-1).astype(int); quid=[int(target[r]) for r in qidx]\nwith np.load(P+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=nitem,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(nitem,np.float32);z[o]=1-np.arange(nitem)/nitem;return z\ngb=rs(F[0,2])+2*rs(L[1,1])\nprint('fit',X.shape,X.nnz,flush=True);sv=TruncatedSVD(n_components=96,n_iter=5,random_state=42);U=sv.fit_transform(X);V=sv.components_;print('variance',sv.explained_variance_ratio_.sum(),flush=True)\nalphas=[0,3e-6,1e-5,3e-5,1e-4,2e-4,3e-4,5e-4,8e-4,1e-3,2e-3];\nfor nc in [16,32,64,96]:\n vals={a:[] for a in alphas}\n for st in range(0,len(xrows),128):\n  xr=xrows[st:st+128]; br=qidx[st:st+128];S=U[xr,:nc]@V[:nc];S-=S.mean(1)[:,None];sd=S.std(1);sd[sd==0]=1;S/=sd[:,None]\n  for a in alphas:\n   Z=gb[None,:]+a*S;Z[~bits[br]]=-np.inf;top=np.argpartition(Z,-10,axis=1)[:,-10:]\n   for j,row in enumerate(top):row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[a].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\n for a in alphas:print('RES',nc,a,np.mean(vals[a]),flush=True)\nnp.savez_compressed('/app/solution/svd_binary_train.npz',U=U.astype(np.float32),V=V.astype(np.float32))\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "fit (10000, 30000) 5564926\nvariance 0.32360658\nRES 16 0 0.03495591411760116\nRES 16 3e-06 0.034973235658446705\nRES 16 1e-05 0.03500126758146793\nRES 16 3e-05 0.035207605075085836\nRES 16 0.0001 0.035310353716967675\nRES 16 0.0002 0.03561747562209513\nRES 16 0.0003 0.035237045829663804\nRES 16 0.0005 0.03435119179985207\nRES 16 0.0008 0.033242982136058606\nRES 16 0.001 0.03215413969124974\nRES 16 0.002 0.028746468668758678\nRES 32 0 0.03495591411760116\nRES 32 3e-06 0.03496830024621702\nRES 32 1e-05 0.03505211299686844\nRES 32 3e-05 0.03535116761579805\nRES 32 0.0001 0.03565403310497617\nRES 32 0.0002 0.03584872212789105\nRES 32 0.0003 0.036103044199023375\nRES 32 0.0005 0.035921291768678996\nRES 32 0.0008 0.03538058357845926\nRES 32 0.001 0.03506861058046967\nRES 32 0.002 0.03263534564478926\nRES 64 0 0.03495591411760116\nRES 64 3e-06 0.034993551524214905\nRES 64 1e-05 0.035022907026576885\nRES 64 3e-05 0.0353965064416447\nRES 64 0.0001 0.035983215487622976\nRES 64 0.0002 0.03647827593270166\nRES 64 0.0003 0.03654102379252188\nRES 64 0.0005 0.036780080343714106\nRES 64 0.0008 0.036294447809657245\nRES 64 0.001 0.036200086618208074\nRES 64 0.002 0.03471478678621616\nRES 96 0 0.03495591411760116\nRES 96 3e-06 0.034993551524214905\nRES 96 1e-05 0.035042989079468234\nRES 96 3e-05 0.035286688873380365\nRES 96 0.0001 0.03613227395223899\nRES 96 0.0002 0.036574598354717935\nRES 96 0.0003 0.036778200151850265\nRES 96 0.0005 0.03675568153586478\nRES 96 0.0008 0.036988795291506504\nRES 96 0.001 0.03631790863192337\nRES 96 0.002 0.03579480515436143\n",
    "stderr": "",
    "aggregated_output": "fit (10000, 30000) 5564926\nvariance 0.32360658\nRES 16 0 0.03495591411760116\nRES 16 3e-06 0.034973235658446705\nRES 16 1e-05 0.03500126758146793\nRES 16 3e-05 0.035207605075085836\nRES 16 0.0001 0.035310353716967675\nRES 16 0.0002 0.03561747562209513\nRES 16 0.0003 0.035237045829663804\nRES 16 0.0005 0.03435119179985207\nRES 16 0.0008 0.033242982136058606\nRES 16 0.001 0.03215413969124974\nRES 16 0.002 0.028746468668758678\nRES 32 0 0.03495591411760116\nRES 32 3e-06 0.03496830024621702\nRES 32 1e-05 0.03505211299686844\nRES 32 3e-05 0.03535116761579805\nRES 32 0.0001 0.03565403310497617\nRES 32 0.0002 0.03584872212789105\nRES 32 0.0003 0.036103044199023375\nRES 32 0.0005 0.035921291768678996\nRES 32 0.0008 0.03538058357845926\nRES 32 0.001 0.03506861058046967\nRES 32 0.002 0.03263534564478926\nRES 64 0 0.03495591411760116\nRES 64 3e-06 0.034993551524214905\nRES 64 1e-05 0.035022907026576885\nRES 64 3e-05 0.0353965064416447\nRES 64 0.0001 0.035983215487622976\nRES 64 0.0002 0.03647827593270166\nRES 64 0.0003 0.03654102379252188\nRES 64 0.0005 0.036780080343714106\nRES 64 0.0008 0.036294447809657245\nRES 64 0.001 0.036200086618208074\nRES 64 0.002 0.03471478678621616\nRES 96 0 0.03495591411760116\nRES 96 3e-06 0.034993551524214905\nRES 96 1e-05 0.035042989079468234\nRES 96 3e-05 0.035286688873380365\nRES 96 0.0001 0.03613227395223899\nRES 96 0.0002 0.036574598354717935\nRES 96 0.0003 0.036778200151850265\nRES 96 0.0005 0.03675568153586478\nRES 96 0.0008 0.036988795291506504\nRES 96 0.001 0.03631790863192337\nRES 96 0.002 0.03579480515436143\n",
    "exit_code": 0,
    "duration": {
      "secs": 39,
      "nanos": 116710117
    },
    "formatted_output": "fit (10000, 30000) 5564926\nvariance 0.32360658\nRES 16 0 0.03495591411760116\nRES 16 3e-06 0.034973235658446705\nRES 16 1e-05 0.03500126758146793\nRES 16 3e-05 0.035207605075085836\nRES 16 0.0001 0.035310353716967675\nRES 16 0.0002 0.03561747562209513\nRES 16 0.0003 0.035237045829663804\nRES 16 0.0005 0.03435119179985207\nRES 16 0.0008 0.033242982136058606\nRES 16 0.001 0.03215413969124974\nRES 16 0.002 0.028746468668758678\nRES 32 0 0.03495591411760116\nRES 32 3e-06 0.03496830024621702\nRES 32 1e-05 0.03505211299686844\nRES 32 3e-05 0.03535116761579805\nRES 32 0.0001 0.03565403310497617\nRES 32 0.0002 0.03584872212789105\nRES 32 0.0003 0.036103044199023375\nRES 32 0.0005 0.035921291768678996\nRES 32 0.0008 0.03538058357845926\nRES 32 0.001 0.03506861058046967\nRES 32 0.002 0.03263534564478926\nRES 64 0 0.03495591411760116\nRES 64 3e-06 0.034993551524214905\nRES 64 1e-05 0.035022907026576885\nRES 64 3e-05 0.0353965064416447\nRES 64 0.0001 0.035983215487622976\nRES 64 0.0002 0.03647827593270166\nRES 64 0.0003 0.03654102379252188\nRES 64 0.0005 0.036780080343714106\nRES 64 0.0008 0.036294447809657245\nRES 64 0.001 0.036200086618208074\nRES 64 0.002 0.03471478678621616\nRES 96 0 0.03495591411760116\nRES 96 3e-06 0.034993551524214905\nRES 96 1e-05 0.035042989079468234\nRES 96 3e-05 0.035286688873380365\nRES 96 0.0001 0.03613227395223899\nRES 96 0.0002 0.036574598354717935\nRES 96 0.0003 0.036778200151850265\nRES 96 0.0005 0.03675568153586478\nRES 96 0.0008 0.036988795291506504\nRES 96 0.001 0.03631790863192337\nRES 96 0.002 0.03579480515436143\n"
  }
}
52

exec command

+17:30
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python -u - <<'PY'\nimport sys\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');it=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();n=len(it);E=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(pd.read_parquet(P+'/target_users.parquet').uid.astype(int)));g=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x,tie=pr):o=np.lexsort((tie,-x));z=np.empty(n);z[o]=1-np.arange(n)/n;return z\ndef ev(s):\n o=np.lexsort((pr,-s));v=[]\n for u,gg in gold.items():\n  a=[];r=E.uid_row[u]\n  for j in o[:100]:\n   if (int(E.packed[r,j//8])>>(j%8))&1:a.append(int(it[j]))\n   if len(a)==10:break\n  v.append(ndcg_at_k(a,gg,10))\n return np.mean(v)\nr7=rs(F[0,2]);\nfeatures=[('likeorg3',rs(L[1,1])),('likeorg7',rs(L[1,2])),('likeall3',rs(L[0,1])),('likeblend',rs(L[1,1])+0.3*rs(L[1,2])),('likeorg3_rawtie',rs(L[1,1],-F[0,2]))]\nfor name,r in features:\n for a in [.5,.75,1,1.25,1.5,1.75,2,2.25,2.5,3,4,5,7,10]: print(name,a,ev(r7+a*r))\nPY",workdir:"/app",yield_time_ms:30000,max_output_tokens:12000});
if(r.session_id){text(`SESSION_ID=${r.session_id}`);text(r.output);}else{text(r.output);}

Tool response

Script completed
Wall time 10.2 seconds
Output:

likeorg3 0.5 0.033963631976805006
likeorg3 0.75 0.03413670092434041
likeorg3 1 0.03478906582342955
likeorg3 1.25 0.03481941028493119
likeorg3 1.5 0.0348618509450571
likeorg3 1.75 0.034886024028319376
likeorg3 2 0.034957401307281685
likeorg3 2.25 0.03453638825389476
likeorg3 2.5 0.0342649138091844
likeorg3 3 0.03410615910380185
likeorg3 4 0.03386415382699404
likeorg3 5 0.03361298271630871
likeorg3 7 0.033363475304940986
likeorg3 10 0.03312374318430003
likeorg7 0.5 0.03300521469452717
likeorg7 0.75 0.032714753661767
likeorg7 1 0.03215135411560338
likeorg7 1.25 0.031975020627536085
likeorg7 1.5 0.03187190903460598
likeorg7 1.75 0.03166283041459479
likeorg7 2 0.031683216162269004
likeorg7 2.25 0.03175334192483856
likeorg7 2.5 0.031735352730559725
likeorg7 3 0.03165587557469718
likeorg7 4 0.031219386489485065
likeorg7 5 0.03118128422998665
likeorg7 7 0.030461659029017908
likeorg7 10 0.030506893990931268
likeall3 0.5 0.034027207369405336
likeall3 0.75 0.03421020290779107
likeall3 1 0.03447505310436017
likeall3 1.25 0.03448857389772924
likeall3 1.5 0.034469006991614586
likeall3 1.75 0.03413722504781785
likeall3 2 0.03418546580534684
likeall3 2.25 0.03414946999074894
likeall3 2.5 0.034291482503797974
likeall3 3 0.03391757674421942
likeall3 4 0.03311989908179825
likeall3 5 0.03288138166394025
likeall3 7 0.03294791771345674
likeall3 10 0.03226151795615059
likeblend 0.5 0.03409316057929037
likeblend 0.75 0.03426779707219418
likeblend 1 0.03460844146056106
likeblend 1.25 0.0346986227865933
likeblend 1.5 0.03433258726225442
likeblend 1.75 0.034233449048533565
likeblend 2 0.0342574963923634
likeblend 2.25 0.03421577784838721
likeblend 2.5 0.03384182230443158
likeblend 3 0.0334504252092217
likeblend 4 0.033291197097675446
likeblend 5 0.033092381999362604
likeblend 7 0.03257359081248193
likeblend 10 0.0325665198876556
likeorg3_rawtie 0.5 0.033880949387223004
likeorg3_rawtie 0.75 0.03385470160526581
likeorg3_rawtie 1 0.03409585099446252
likeorg3_rawtie 1.25 0.03430167766570564
likeorg3_rawtie 1.5 0.034557518727019704
likeorg3_rawtie 1.75 0.0341957909376445
likeorg3_rawtie 2 0.03410001970826477
likeorg3_rawtie 2.25 0.03415115970020574
likeorg3_rawtie 2.5 0.0341368847678014
likeorg3_rawtie 3 0.034302700665116176
likeorg3_rawtie 4 0.03401202335041622
likeorg3_rawtie 5 0.03369932438727578
likeorg3_rawtie 7 0.033007819899746244
likeorg3_rawtie 10 0.032861566939448
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "response_id": "resp_0fdbf3e20a3ba82b016ab5ca3cc13087d2b7c39a21e67de4ac",
  "usage": {
    "input_tokens": 51320,
    "cached_input_tokens": 50688,
    "cache_write_input_tokens": 0,
    "output_tokens": 575,
    "reasoning_output_tokens": 46,
    "total_tokens": 51895
  },
  "turn_token_usage": {
    "input_tokens": 1099980,
    "cached_input_tokens": 1054080,
    "cache_write_input_tokens": 0,
    "output_tokens": 23048,
    "reasoning_output_tokens": 10597,
    "total_tokens": 1123028
  },
  "thread_token_usage": {
    "input_tokens": 1099980,
    "cached_input_tokens": 1054080,
    "cache_write_input_tokens": 0,
    "output_tokens": 23048,
    "reasoning_output_tokens": 10597,
    "total_tokens": 1123028
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-f3dd3635-31e4-4bd5-aa95-c066fb14b9ed",
    "process_id": "16197",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python -u - <<'PY'\nimport sys\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');it=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();n=len(it);E=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(pd.read_parquet(P+'/target_users.parquet').uid.astype(int)));g=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x,tie=pr):o=np.lexsort((tie,-x));z=np.empty(n);z[o]=1-np.arange(n)/n;return z\ndef ev(s):\n o=np.lexsort((pr,-s));v=[]\n for u,gg in gold.items():\n  a=[];r=E.uid_row[u]\n  for j in o[:100]:\n   if (int(E.packed[r,j//8])>>(j%8))&1:a.append(int(it[j]))\n   if len(a)==10:break\n  v.append(ndcg_at_k(a,gg,10))\n return np.mean(v)\nr7=rs(F[0,2]);\nfeatures=[('likeorg3',rs(L[1,1])),('likeorg7',rs(L[1,2])),('likeall3',rs(L[0,1])),('likeblend',rs(L[1,1])+0.3*rs(L[1,2])),('likeorg3_rawtie',rs(L[1,1],-F[0,2]))]\nfor name,r in features:\n for a in [.5,.75,1,1.25,1.5,1.75,2,2.25,2.5,3,4,5,7,10]: print(name,a,ev(r7+a*r))\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport sys\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');it=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();n=len(it);E=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(pd.read_parquet(P+'/target_users.parquet').uid.astype(int)));g=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x,tie=pr):o=np.lexsort((tie,-x));z=np.empty(n);z[o]=1-np.arange(n)/n;return z\ndef ev(s):\n o=np.lexsort((pr,-s));v=[]\n for u,gg in gold.items():\n  a=[];r=E.uid_row[u]\n  for j in o[:100]:\n   if (int(E.packed[r,j//8])>>(j%8))&1:a.append(int(it[j]))\n   if len(a)==10:break\n  v.append(ndcg_at_k(a,gg,10))\n return np.mean(v)\nr7=rs(F[0,2]);\nfeatures=[('likeorg3',rs(L[1,1])),('likeorg7',rs(L[1,2])),('likeall3',rs(L[0,1])),('likeblend',rs(L[1,1])+0.3*rs(L[1,2])),('likeorg3_rawtie',rs(L[1,1],-F[0,2]))]\nfor name,r in features:\n for a in [.5,.75,1,1.25,1.5,1.75,2,2.25,2.5,3,4,5,7,10]: print(name,a,ev(r7+a*r))\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "likeorg3 0.5 0.033963631976805006\nlikeorg3 0.75 0.03413670092434041\nlikeorg3 1 0.03478906582342955\nlikeorg3 1.25 0.03481941028493119\nlikeorg3 1.5 0.0348618509450571\nlikeorg3 1.75 0.034886024028319376\nlikeorg3 2 0.034957401307281685\nlikeorg3 2.25 0.03453638825389476\nlikeorg3 2.5 0.0342649138091844\nlikeorg3 3 0.03410615910380185\nlikeorg3 4 0.03386415382699404\nlikeorg3 5 0.03361298271630871\nlikeorg3 7 0.033363475304940986\nlikeorg3 10 0.03312374318430003\nlikeorg7 0.5 0.03300521469452717\nlikeorg7 0.75 0.032714753661767\nlikeorg7 1 0.03215135411560338\nlikeorg7 1.25 0.031975020627536085\nlikeorg7 1.5 0.03187190903460598\nlikeorg7 1.75 0.03166283041459479\nlikeorg7 2 0.031683216162269004\nlikeorg7 2.25 0.03175334192483856\nlikeorg7 2.5 0.031735352730559725\nlikeorg7 3 0.03165587557469718\nlikeorg7 4 0.031219386489485065\nlikeorg7 5 0.03118128422998665\nlikeorg7 7 0.030461659029017908\nlikeorg7 10 0.030506893990931268\nlikeall3 0.5 0.034027207369405336\nlikeall3 0.75 0.03421020290779107\nlikeall3 1 0.03447505310436017\nlikeall3 1.25 0.03448857389772924\nlikeall3 1.5 0.034469006991614586\nlikeall3 1.75 0.03413722504781785\nlikeall3 2 0.03418546580534684\nlikeall3 2.25 0.03414946999074894\nlikeall3 2.5 0.034291482503797974\nlikeall3 3 0.03391757674421942\nlikeall3 4 0.03311989908179825\nlikeall3 5 0.03288138166394025\nlikeall3 7 0.03294791771345674\nlikeall3 10 0.03226151795615059\nlikeblend 0.5 0.03409316057929037\nlikeblend 0.75 0.03426779707219418\nlikeblend 1 0.03460844146056106\nlikeblend 1.25 0.0346986227865933\nlikeblend 1.5 0.03433258726225442\nlikeblend 1.75 0.034233449048533565\nlikeblend 2 0.0342574963923634\nlikeblend 2.25 0.03421577784838721\nlikeblend 2.5 0.03384182230443158\nlikeblend 3 0.0334504252092217\nlikeblend 4 0.033291197097675446\nlikeblend 5 0.033092381999362604\nlikeblend 7 0.03257359081248193\nlikeblend 10 0.0325665198876556\nlikeorg3_rawtie 0.5 0.033880949387223004\nlikeorg3_rawtie 0.75 0.03385470160526581\nlikeorg3_rawtie 1 0.03409585099446252\nlikeorg3_rawtie 1.25 0.03430167766570564\nlikeorg3_rawtie 1.5 0.034557518727019704\nlikeorg3_rawtie 1.75 0.0341957909376445\nlikeorg3_rawtie 2 0.03410001970826477\nlikeorg3_rawtie 2.25 0.03415115970020574\nlikeorg3_rawtie 2.5 0.0341368847678014\nlikeorg3_rawtie 3 0.034302700665116176\nlikeorg3_rawtie 4 0.03401202335041622\nlikeorg3_rawtie 5 0.03369932438727578\nlikeorg3_rawtie 7 0.033007819899746244\nlikeorg3_rawtie 10 0.032861566939448\n",
    "stderr": "",
    "aggregated_output": "likeorg3 0.5 0.033963631976805006\nlikeorg3 0.75 0.03413670092434041\nlikeorg3 1 0.03478906582342955\nlikeorg3 1.25 0.03481941028493119\nlikeorg3 1.5 0.0348618509450571\nlikeorg3 1.75 0.034886024028319376\nlikeorg3 2 0.034957401307281685\nlikeorg3 2.25 0.03453638825389476\nlikeorg3 2.5 0.0342649138091844\nlikeorg3 3 0.03410615910380185\nlikeorg3 4 0.03386415382699404\nlikeorg3 5 0.03361298271630871\nlikeorg3 7 0.033363475304940986\nlikeorg3 10 0.03312374318430003\nlikeorg7 0.5 0.03300521469452717\nlikeorg7 0.75 0.032714753661767\nlikeorg7 1 0.03215135411560338\nlikeorg7 1.25 0.031975020627536085\nlikeorg7 1.5 0.03187190903460598\nlikeorg7 1.75 0.03166283041459479\nlikeorg7 2 0.031683216162269004\nlikeorg7 2.25 0.03175334192483856\nlikeorg7 2.5 0.031735352730559725\nlikeorg7 3 0.03165587557469718\nlikeorg7 4 0.031219386489485065\nlikeorg7 5 0.03118128422998665\nlikeorg7 7 0.030461659029017908\nlikeorg7 10 0.030506893990931268\nlikeall3 0.5 0.034027207369405336\nlikeall3 0.75 0.03421020290779107\nlikeall3 1 0.03447505310436017\nlikeall3 1.25 0.03448857389772924\nlikeall3 1.5 0.034469006991614586\nlikeall3 1.75 0.03413722504781785\nlikeall3 2 0.03418546580534684\nlikeall3 2.25 0.03414946999074894\nlikeall3 2.5 0.034291482503797974\nlikeall3 3 0.03391757674421942\nlikeall3 4 0.03311989908179825\nlikeall3 5 0.03288138166394025\nlikeall3 7 0.03294791771345674\nlikeall3 10 0.03226151795615059\nlikeblend 0.5 0.03409316057929037\nlikeblend 0.75 0.03426779707219418\nlikeblend 1 0.03460844146056106\nlikeblend 1.25 0.0346986227865933\nlikeblend 1.5 0.03433258726225442\nlikeblend 1.75 0.034233449048533565\nlikeblend 2 0.0342574963923634\nlikeblend 2.25 0.03421577784838721\nlikeblend 2.5 0.03384182230443158\nlikeblend 3 0.0334504252092217\nlikeblend 4 0.033291197097675446\nlikeblend 5 0.033092381999362604\nlikeblend 7 0.03257359081248193\nlikeblend 10 0.0325665198876556\nlikeorg3_rawtie 0.5 0.033880949387223004\nlikeorg3_rawtie 0.75 0.03385470160526581\nlikeorg3_rawtie 1 0.03409585099446252\nlikeorg3_rawtie 1.25 0.03430167766570564\nlikeorg3_rawtie 1.5 0.034557518727019704\nlikeorg3_rawtie 1.75 0.0341957909376445\nlikeorg3_rawtie 2 0.03410001970826477\nlikeorg3_rawtie 2.25 0.03415115970020574\nlikeorg3_rawtie 2.5 0.0341368847678014\nlikeorg3_rawtie 3 0.034302700665116176\nlikeorg3_rawtie 4 0.03401202335041622\nlikeorg3_rawtie 5 0.03369932438727578\nlikeorg3_rawtie 7 0.033007819899746244\nlikeorg3_rawtie 10 0.032861566939448\n",
    "exit_code": 0,
    "duration": {
      "secs": 9,
      "nanos": 990549708
    },
    "formatted_output": "likeorg3 0.5 0.033963631976805006\nlikeorg3 0.75 0.03413670092434041\nlikeorg3 1 0.03478906582342955\nlikeorg3 1.25 0.03481941028493119\nlikeorg3 1.5 0.0348618509450571\nlikeorg3 1.75 0.034886024028319376\nlikeorg3 2 0.034957401307281685\nlikeorg3 2.25 0.03453638825389476\nlikeorg3 2.5 0.0342649138091844\nlikeorg3 3 0.03410615910380185\nlikeorg3 4 0.03386415382699404\nlikeorg3 5 0.03361298271630871\nlikeorg3 7 0.033363475304940986\nlikeorg3 10 0.03312374318430003\nlikeorg7 0.5 0.03300521469452717\nlikeorg7 0.75 0.032714753661767\nlikeorg7 1 0.03215135411560338\nlikeorg7 1.25 0.031975020627536085\nlikeorg7 1.5 0.03187190903460598\nlikeorg7 1.75 0.03166283041459479\nlikeorg7 2 0.031683216162269004\nlikeorg7 2.25 0.03175334192483856\nlikeorg7 2.5 0.031735352730559725\nlikeorg7 3 0.03165587557469718\nlikeorg7 4 0.031219386489485065\nlikeorg7 5 0.03118128422998665\nlikeorg7 7 0.030461659029017908\nlikeorg7 10 0.030506893990931268\nlikeall3 0.5 0.034027207369405336\nlikeall3 0.75 0.03421020290779107\nlikeall3 1 0.03447505310436017\nlikeall3 1.25 0.03448857389772924\nlikeall3 1.5 0.034469006991614586\nlikeall3 1.75 0.03413722504781785\nlikeall3 2 0.03418546580534684\nlikeall3 2.25 0.03414946999074894\nlikeall3 2.5 0.034291482503797974\nlikeall3 3 0.03391757674421942\nlikeall3 4 0.03311989908179825\nlikeall3 5 0.03288138166394025\nlikeall3 7 0.03294791771345674\nlikeall3 10 0.03226151795615059\nlikeblend 0.5 0.03409316057929037\nlikeblend 0.75 0.03426779707219418\nlikeblend 1 0.03460844146056106\nlikeblend 1.25 0.0346986227865933\nlikeblend 1.5 0.03433258726225442\nlikeblend 1.75 0.034233449048533565\nlikeblend 2 0.0342574963923634\nlikeblend 2.25 0.03421577784838721\nlikeblend 2.5 0.03384182230443158\nlikeblend 3 0.0334504252092217\nlikeblend 4 0.033291197097675446\nlikeblend 5 0.033092381999362604\nlikeblend 7 0.03257359081248193\nlikeblend 10 0.0325665198876556\nlikeorg3_rawtie 0.5 0.033880949387223004\nlikeorg3_rawtie 0.75 0.03385470160526581\nlikeorg3_rawtie 1 0.03409585099446252\nlikeorg3_rawtie 1.25 0.03430167766570564\nlikeorg3_rawtie 1.5 0.034557518727019704\nlikeorg3_rawtie 1.75 0.0341957909376445\nlikeorg3_rawtie 2 0.03410001970826477\nlikeorg3_rawtie 2.25 0.03415115970020574\nlikeorg3_rawtie 2.5 0.0341368847678014\nlikeorg3_rawtie 3 0.034302700665116176\nlikeorg3_rawtie 4 0.03401202335041622\nlikeorg3_rawtie 5 0.03369932438727578\nlikeorg3_rawtie 7 0.033007819899746244\nlikeorg3_rawtie 10 0.032861566939448\n"
  }
}
53

exec command

+17:54
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 full listen graph improves the blended score again, to roughly 0.03699. Its best behavior uses a broad 96-dimensional taste representation with a deliberately small reranking weight, so the strong release-trend prior remains dominant. I’m checking count weighting and an ensemble of implicit and explicit taste before locking the final refit.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python -u - <<'PY'\nimport sys\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();nitem=len(items);target=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64);qm={int(x):i for i,x in enumerate(target)}\nX=sparse.load_npz('/app/solution/listen_counts_train.npz').astype(np.float32);X.data=np.log1p(X.data)\nE=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qidx=np.array([qm[u] for u in gold]);xrows=(target[qidx]//100-1).astype(int);quid=[int(target[r]) for r in qidx]\nwith np.load(P+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=nitem,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(nitem,np.float32);z[o]=1-np.arange(nitem)/nitem;return z\ngb=rs(F[0,2])+2*rs(L[1,1]);print('fit log',flush=True);sv=TruncatedSVD(n_components=96,n_iter=5,random_state=42);U=sv.fit_transform(X);V=sv.components_;print('var',sv.explained_variance_ratio_.sum(),flush=True)\nalphas=[0,3e-5,1e-4,2e-4,3e-4,5e-4,8e-4,1e-3,1.5e-3,2e-3];\nfor nc in [32,64,96]:\n vals={a:[] for a in alphas}\n for st in range(0,len(xrows),128):\n  xr=xrows[st:st+128];br=qidx[st:st+128];S=U[xr,:nc]@V[:nc];S-=S.mean(1)[:,None];sd=S.std(1);sd[sd==0]=1;S/=sd[:,None]\n  for a in alphas:\n   Z=gb[None,:]+a*S;Z[~bits[br]]=-np.inf;top=np.argpartition(Z,-10,axis=1)[:,-10:]\n   for j,row in enumerate(top):row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[a].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\n for a in alphas:print('RES',nc,a,np.mean(vals[a]),flush=True)\nnp.savez_compressed('/app/solution/svd_log_train.npz',U=U.astype(np.float32),V=V.astype(np.float32))\nPY",workdir:"/app",yield_time_ms:30000,max_output_tokens:18000});
if(r.session_id){text(`SESSION_ID=${r.session_id}`);text(r.output);}else{text(r.output);}

Tool response

Script completed
Wall time 29.1 seconds
Output:

fit log
var 0.3575546
RES 32 0 0.03495591411760116
RES 32 3e-05 0.0353726078481892
RES 32 0.0001 0.03605401797775986
RES 32 0.0002 0.0359450069399237
RES 32 0.0003 0.03603601478047241
RES 32 0.0005 0.03552715065926786
RES 32 0.0008 0.03428268122781672
RES 32 0.001 0.03398355523132804
RES 32 0.0015 0.03283249076932839
RES 32 0.002 0.031165942662446152
RES 64 0 0.03495591411760116
RES 64 3e-05 0.03536834239899224
RES 64 0.0001 0.036017760504718166
RES 64 0.0002 0.03645014265578708
RES 64 0.0003 0.03653031242431469
RES 64 0.0005 0.03635567310962588
RES 64 0.0008 0.03549897760970315
RES 64 0.001 0.0354733726813475
RES 64 0.0015 0.034946425240903294
RES 64 0.002 0.034490170162109084
RES 96 0 0.03495591411760116
RES 96 3e-05 0.035316829966982925
RES 96 0.0001 0.03591987355758362
RES 96 0.0002 0.03639998885755811
RES 96 0.0003 0.03661170809011704
RES 96 0.0005 0.03650224844968985
RES 96 0.0008 0.03580003695449044
RES 96 0.001 0.035307308547128796
RES 96 0.0015 0.03502323382880352
RES 96 0.002 0.03454097055740437
Harness context token usage record
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Harness context codex item completed
{
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  "item": {
    "type": "CommandExecution",
    "id": "exec-e7f40801-ee92-44aa-9716-5f071982fabb",
    "process_id": "8836",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python -u - <<'PY'\nimport sys\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();nitem=len(items);target=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64);qm={int(x):i for i,x in enumerate(target)}\nX=sparse.load_npz('/app/solution/listen_counts_train.npz').astype(np.float32);X.data=np.log1p(X.data)\nE=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qidx=np.array([qm[u] for u in gold]);xrows=(target[qidx]//100-1).astype(int);quid=[int(target[r]) for r in qidx]\nwith np.load(P+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=nitem,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(nitem,np.float32);z[o]=1-np.arange(nitem)/nitem;return z\ngb=rs(F[0,2])+2*rs(L[1,1]);print('fit log',flush=True);sv=TruncatedSVD(n_components=96,n_iter=5,random_state=42);U=sv.fit_transform(X);V=sv.components_;print('var',sv.explained_variance_ratio_.sum(),flush=True)\nalphas=[0,3e-5,1e-4,2e-4,3e-4,5e-4,8e-4,1e-3,1.5e-3,2e-3];\nfor nc in [32,64,96]:\n vals={a:[] for a in alphas}\n for st in range(0,len(xrows),128):\n  xr=xrows[st:st+128];br=qidx[st:st+128];S=U[xr,:nc]@V[:nc];S-=S.mean(1)[:,None];sd=S.std(1);sd[sd==0]=1;S/=sd[:,None]\n  for a in alphas:\n   Z=gb[None,:]+a*S;Z[~bits[br]]=-np.inf;top=np.argpartition(Z,-10,axis=1)[:,-10:]\n   for j,row in enumerate(top):row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[a].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\n for a in alphas:print('RES',nc,a,np.mean(vals[a]),flush=True)\nnp.savez_compressed('/app/solution/svd_log_train.npz',U=U.astype(np.float32),V=V.astype(np.float32))\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport sys\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();nitem=len(items);target=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64);qm={int(x):i for i,x in enumerate(target)}\nX=sparse.load_npz('/app/solution/listen_counts_train.npz').astype(np.float32);X.data=np.log1p(X.data)\nE=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qidx=np.array([qm[u] for u in gold]);xrows=(target[qidx]//100-1).astype(int);quid=[int(target[r]) for r in qidx]\nwith np.load(P+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=nitem,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(nitem,np.float32);z[o]=1-np.arange(nitem)/nitem;return z\ngb=rs(F[0,2])+2*rs(L[1,1]);print('fit log',flush=True);sv=TruncatedSVD(n_components=96,n_iter=5,random_state=42);U=sv.fit_transform(X);V=sv.components_;print('var',sv.explained_variance_ratio_.sum(),flush=True)\nalphas=[0,3e-5,1e-4,2e-4,3e-4,5e-4,8e-4,1e-3,1.5e-3,2e-3];\nfor nc in [32,64,96]:\n vals={a:[] for a in alphas}\n for st in range(0,len(xrows),128):\n  xr=xrows[st:st+128];br=qidx[st:st+128];S=U[xr,:nc]@V[:nc];S-=S.mean(1)[:,None];sd=S.std(1);sd[sd==0]=1;S/=sd[:,None]\n  for a in alphas:\n   Z=gb[None,:]+a*S;Z[~bits[br]]=-np.inf;top=np.argpartition(Z,-10,axis=1)[:,-10:]\n   for j,row in enumerate(top):row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[a].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\n for a in alphas:print('RES',nc,a,np.mean(vals[a]),flush=True)\nnp.savez_compressed('/app/solution/svd_log_train.npz',U=U.astype(np.float32),V=V.astype(np.float32))\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "fit log\nvar 0.3575546\nRES 32 0 0.03495591411760116\nRES 32 3e-05 0.0353726078481892\nRES 32 0.0001 0.03605401797775986\nRES 32 0.0002 0.0359450069399237\nRES 32 0.0003 0.03603601478047241\nRES 32 0.0005 0.03552715065926786\nRES 32 0.0008 0.03428268122781672\nRES 32 0.001 0.03398355523132804\nRES 32 0.0015 0.03283249076932839\nRES 32 0.002 0.031165942662446152\nRES 64 0 0.03495591411760116\nRES 64 3e-05 0.03536834239899224\nRES 64 0.0001 0.036017760504718166\nRES 64 0.0002 0.03645014265578708\nRES 64 0.0003 0.03653031242431469\nRES 64 0.0005 0.03635567310962588\nRES 64 0.0008 0.03549897760970315\nRES 64 0.001 0.0354733726813475\nRES 64 0.0015 0.034946425240903294\nRES 64 0.002 0.034490170162109084\nRES 96 0 0.03495591411760116\nRES 96 3e-05 0.035316829966982925\nRES 96 0.0001 0.03591987355758362\nRES 96 0.0002 0.03639998885755811\nRES 96 0.0003 0.03661170809011704\nRES 96 0.0005 0.03650224844968985\nRES 96 0.0008 0.03580003695449044\nRES 96 0.001 0.035307308547128796\nRES 96 0.0015 0.03502323382880352\nRES 96 0.002 0.03454097055740437\n",
    "stderr": "",
    "aggregated_output": "fit log\nvar 0.3575546\nRES 32 0 0.03495591411760116\nRES 32 3e-05 0.0353726078481892\nRES 32 0.0001 0.03605401797775986\nRES 32 0.0002 0.0359450069399237\nRES 32 0.0003 0.03603601478047241\nRES 32 0.0005 0.03552715065926786\nRES 32 0.0008 0.03428268122781672\nRES 32 0.001 0.03398355523132804\nRES 32 0.0015 0.03283249076932839\nRES 32 0.002 0.031165942662446152\nRES 64 0 0.03495591411760116\nRES 64 3e-05 0.03536834239899224\nRES 64 0.0001 0.036017760504718166\nRES 64 0.0002 0.03645014265578708\nRES 64 0.0003 0.03653031242431469\nRES 64 0.0005 0.03635567310962588\nRES 64 0.0008 0.03549897760970315\nRES 64 0.001 0.0354733726813475\nRES 64 0.0015 0.034946425240903294\nRES 64 0.002 0.034490170162109084\nRES 96 0 0.03495591411760116\nRES 96 3e-05 0.035316829966982925\nRES 96 0.0001 0.03591987355758362\nRES 96 0.0002 0.03639998885755811\nRES 96 0.0003 0.03661170809011704\nRES 96 0.0005 0.03650224844968985\nRES 96 0.0008 0.03580003695449044\nRES 96 0.001 0.035307308547128796\nRES 96 0.0015 0.03502323382880352\nRES 96 0.002 0.03454097055740437\n",
    "exit_code": 0,
    "duration": {
      "secs": 28,
      "nanos": 893231268
    },
    "formatted_output": "fit log\nvar 0.3575546\nRES 32 0 0.03495591411760116\nRES 32 3e-05 0.0353726078481892\nRES 32 0.0001 0.03605401797775986\nRES 32 0.0002 0.0359450069399237\nRES 32 0.0003 0.03603601478047241\nRES 32 0.0005 0.03552715065926786\nRES 32 0.0008 0.03428268122781672\nRES 32 0.001 0.03398355523132804\nRES 32 0.0015 0.03283249076932839\nRES 32 0.002 0.031165942662446152\nRES 64 0 0.03495591411760116\nRES 64 3e-05 0.03536834239899224\nRES 64 0.0001 0.036017760504718166\nRES 64 0.0002 0.03645014265578708\nRES 64 0.0003 0.03653031242431469\nRES 64 0.0005 0.03635567310962588\nRES 64 0.0008 0.03549897760970315\nRES 64 0.001 0.0354733726813475\nRES 64 0.0015 0.034946425240903294\nRES 64 0.002 0.034490170162109084\nRES 96 0 0.03495591411760116\nRES 96 3e-05 0.035316829966982925\nRES 96 0.0001 0.03591987355758362\nRES 96 0.0002 0.03639998885755811\nRES 96 0.0003 0.03661170809011704\nRES 96 0.0005 0.03650224844968985\nRES 96 0.0008 0.03580003695449044\nRES 96 0.001 0.035307308547128796\nRES 96 0.0015 0.03502323382880352\nRES 96 0.002 0.03454097055740437\n"
  }
}
54

exec command

+18:53
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python -u - <<'PY'\nimport sys,itertools\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();nitem=len(items);target=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64);qm={int(x):i for i,x in enumerate(target)};ic={int(x):i for i,x in enumerate(items)}\nE=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qidx=np.array([qm[u] for u in gold]);xrows=(target[qidx]//100-1).astype(int);quid=[int(target[r]) for r in qidx]\nwith np.load(P+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=nitem,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(nitem,np.float32);z[o]=1-np.arange(nitem)/nitem;return z\ngb=rs(F[0,2])+2*rs(L[1,1]);B=np.load('/app/solution/svd_binary_train.npz');UB=B['U'];VB=B['V'];D=np.load('/app/solution/svd_log_train.npz');UL=D['U'];VL=D['V']\n# active-like SVD\nps=pd.read_parquet(P+'/validation_preference_state.parquet');ps=ps[ps.state=='liked'];ur={int(x):i for i,x in enumerate(target)};rr=np.array([ur.get(int(x),-1) for x in ps.uid]);cc=np.array([ic.get(int(x),-1) for x in ps.item_id]);ok=(rr>=0)&(cc>=0);XA=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(rr[ok],cc[ok])),shape=(len(target),nitem));XA.data[:]=1\nsv=TruncatedSVD(n_components=32,n_iter=7,random_state=42);UA=sv.fit_transform(XA);VA=sv.components_;np.savez_compressed('/app/solution/svd_active_train.npz',U=UA.astype(np.float32),V=VA.astype(np.float32))\n# first binary-log grid\nvariants=list(itertools.product([.0004,.0006,.0008,.0010],[0,.00005,.0001,.0002,.0003]));vals={x:[] for x in variants}\ndef std(S):S-=S.mean(1)[:,None];d=S.std(1);d[d==0]=1;S/=d[:,None];return S\nfor st in range(0,len(xrows),128):\n xr=xrows[st:st+128];br=qidx[st:st+128];SB=std(UB[xr,:96]@VB[:96]);SL=std(UL[xr,:96]@VL[:96])\n for v in variants:\n  Z=gb[None,:]+v[0]*SB+v[1]*SL;Z[~bits[br]]=-np.inf;top=np.argpartition(Z,-10,axis=1)[:,-10:]\n  for j,row in enumerate(top):row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[v].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\nres=sorted([(np.mean(x),v) for v,x in vals.items()],reverse=True);print('BEST BL',res[:10],flush=True);best=res[0][1]\n# add active grid around best\nvariants=[(best[0],best[1],a) for a in [-.0001,-.00003,0,.00003,.0001,.0002,.0003]];vals={x:[] for x in variants}\nfor st in range(0,len(xrows),128):\n xr=xrows[st:st+128];br=qidx[st:st+128];SB=std(UB[xr,:96]@VB[:96]);SL=std(UL[xr,:96]@VL[:96]);SA=std(UA[qidx[st:st+128]]@VA)\n for v in variants:\n  Z=gb[None,:]+v[0]*SB+v[1]*SL+v[2]*SA;Z[~bits[br]]=-np.inf;top=np.argpartition(Z,-10,axis=1)[:,-10:]\n  for j,row in enumerate(top):row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[v].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\nprint('BEST BLA',sorted([(np.mean(x),v) for v,x in vals.items()],reverse=True),flush=True)\nPY",workdir:"/app",yield_time_ms:30000,max_output_tokens:20000});
if(r.session_id){text(`SESSION_ID=${r.session_id}`);text(r.output);}else{text(r.output);}

Tool response

Script completed
Wall time 26.9 seconds
Output:

BEST BL [(np.float64(0.036988795291506504), (0.0008, 0)), (np.float64(0.03689014685453151), (0.0006, 0)), (np.float64(0.03682229202876586), (0.0006, 5e-05)), (np.float64(0.03682127356016855), (0.0004, 5e-05)), (np.float64(0.03679496237083738), (0.0004, 0)), (np.float64(0.03677617681782175), (0.0006, 0.0001)), (np.float64(0.03677462555123591), (0.0004, 0.0001)), (np.float64(0.036599303890385994), (0.0004, 0.0002)), (np.float64(0.036528893650655106), (0.0006, 0.0002)), (np.float64(0.036510611219199574), (0.0004, 0.0003))]
BEST BLA [(np.float64(0.03772549162296406), (0.0008, 0, 0.0003)), (np.float64(0.03769434742153713), (0.0008, 0, 0.0002)), (np.float64(0.036988795291506504), (0.0008, 0, 0)), (np.float64(0.03696522278774561), (0.0008, 0, 0.0001)), (np.float64(0.03678322775707024), (0.0008, 0, 3e-05)), (np.float64(0.03668518393893282), (0.0008, 0, -3e-05)), (np.float64(0.03610501520478487), (0.0008, 0, -0.0001))]
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Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-928accae-13d2-4254-971a-8bccab4580e2",
    "process_id": "20587",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python -u - <<'PY'\nimport sys,itertools\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();nitem=len(items);target=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64);qm={int(x):i for i,x in enumerate(target)};ic={int(x):i for i,x in enumerate(items)}\nE=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qidx=np.array([qm[u] for u in gold]);xrows=(target[qidx]//100-1).astype(int);quid=[int(target[r]) for r in qidx]\nwith np.load(P+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=nitem,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(nitem,np.float32);z[o]=1-np.arange(nitem)/nitem;return z\ngb=rs(F[0,2])+2*rs(L[1,1]);B=np.load('/app/solution/svd_binary_train.npz');UB=B['U'];VB=B['V'];D=np.load('/app/solution/svd_log_train.npz');UL=D['U'];VL=D['V']\n# active-like SVD\nps=pd.read_parquet(P+'/validation_preference_state.parquet');ps=ps[ps.state=='liked'];ur={int(x):i for i,x in enumerate(target)};rr=np.array([ur.get(int(x),-1) for x in ps.uid]);cc=np.array([ic.get(int(x),-1) for x in ps.item_id]);ok=(rr>=0)&(cc>=0);XA=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(rr[ok],cc[ok])),shape=(len(target),nitem));XA.data[:]=1\nsv=TruncatedSVD(n_components=32,n_iter=7,random_state=42);UA=sv.fit_transform(XA);VA=sv.components_;np.savez_compressed('/app/solution/svd_active_train.npz',U=UA.astype(np.float32),V=VA.astype(np.float32))\n# first binary-log grid\nvariants=list(itertools.product([.0004,.0006,.0008,.0010],[0,.00005,.0001,.0002,.0003]));vals={x:[] for x in variants}\ndef std(S):S-=S.mean(1)[:,None];d=S.std(1);d[d==0]=1;S/=d[:,None];return S\nfor st in range(0,len(xrows),128):\n xr=xrows[st:st+128];br=qidx[st:st+128];SB=std(UB[xr,:96]@VB[:96]);SL=std(UL[xr,:96]@VL[:96])\n for v in variants:\n  Z=gb[None,:]+v[0]*SB+v[1]*SL;Z[~bits[br]]=-np.inf;top=np.argpartition(Z,-10,axis=1)[:,-10:]\n  for j,row in enumerate(top):row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[v].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\nres=sorted([(np.mean(x),v) for v,x in vals.items()],reverse=True);print('BEST BL',res[:10],flush=True);best=res[0][1]\n# add active grid around best\nvariants=[(best[0],best[1],a) for a in [-.0001,-.00003,0,.00003,.0001,.0002,.0003]];vals={x:[] for x in variants}\nfor st in range(0,len(xrows),128):\n xr=xrows[st:st+128];br=qidx[st:st+128];SB=std(UB[xr,:96]@VB[:96]);SL=std(UL[xr,:96]@VL[:96]);SA=std(UA[qidx[st:st+128]]@VA)\n for v in variants:\n  Z=gb[None,:]+v[0]*SB+v[1]*SL+v[2]*SA;Z[~bits[br]]=-np.inf;top=np.argpartition(Z,-10,axis=1)[:,-10:]\n  for j,row in enumerate(top):row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[v].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\nprint('BEST BLA',sorted([(np.mean(x),v) for v,x in vals.items()],reverse=True),flush=True)\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport sys,itertools\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();nitem=len(items);target=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64);qm={int(x):i for i,x in enumerate(target)};ic={int(x):i for i,x in enumerate(items)}\nE=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qidx=np.array([qm[u] for u in gold]);xrows=(target[qidx]//100-1).astype(int);quid=[int(target[r]) for r in qidx]\nwith np.load(P+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=nitem,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(nitem,np.float32);z[o]=1-np.arange(nitem)/nitem;return z\ngb=rs(F[0,2])+2*rs(L[1,1]);B=np.load('/app/solution/svd_binary_train.npz');UB=B['U'];VB=B['V'];D=np.load('/app/solution/svd_log_train.npz');UL=D['U'];VL=D['V']\n# active-like SVD\nps=pd.read_parquet(P+'/validation_preference_state.parquet');ps=ps[ps.state=='liked'];ur={int(x):i for i,x in enumerate(target)};rr=np.array([ur.get(int(x),-1) for x in ps.uid]);cc=np.array([ic.get(int(x),-1) for x in ps.item_id]);ok=(rr>=0)&(cc>=0);XA=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(rr[ok],cc[ok])),shape=(len(target),nitem));XA.data[:]=1\nsv=TruncatedSVD(n_components=32,n_iter=7,random_state=42);UA=sv.fit_transform(XA);VA=sv.components_;np.savez_compressed('/app/solution/svd_active_train.npz',U=UA.astype(np.float32),V=VA.astype(np.float32))\n# first binary-log grid\nvariants=list(itertools.product([.0004,.0006,.0008,.0010],[0,.00005,.0001,.0002,.0003]));vals={x:[] for x in variants}\ndef std(S):S-=S.mean(1)[:,None];d=S.std(1);d[d==0]=1;S/=d[:,None];return S\nfor st in range(0,len(xrows),128):\n xr=xrows[st:st+128];br=qidx[st:st+128];SB=std(UB[xr,:96]@VB[:96]);SL=std(UL[xr,:96]@VL[:96])\n for v in variants:\n  Z=gb[None,:]+v[0]*SB+v[1]*SL;Z[~bits[br]]=-np.inf;top=np.argpartition(Z,-10,axis=1)[:,-10:]\n  for j,row in enumerate(top):row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[v].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\nres=sorted([(np.mean(x),v) for v,x in vals.items()],reverse=True);print('BEST BL',res[:10],flush=True);best=res[0][1]\n# add active grid around best\nvariants=[(best[0],best[1],a) for a in [-.0001,-.00003,0,.00003,.0001,.0002,.0003]];vals={x:[] for x in variants}\nfor st in range(0,len(xrows),128):\n xr=xrows[st:st+128];br=qidx[st:st+128];SB=std(UB[xr,:96]@VB[:96]);SL=std(UL[xr,:96]@VL[:96]);SA=std(UA[qidx[st:st+128]]@VA)\n for v in variants:\n  Z=gb[None,:]+v[0]*SB+v[1]*SL+v[2]*SA;Z[~bits[br]]=-np.inf;top=np.argpartition(Z,-10,axis=1)[:,-10:]\n  for j,row in enumerate(top):row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[v].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\nprint('BEST BLA',sorted([(np.mean(x),v) for v,x in vals.items()],reverse=True),flush=True)\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "BEST BL [(np.float64(0.036988795291506504), (0.0008, 0)), (np.float64(0.03689014685453151), (0.0006, 0)), (np.float64(0.03682229202876586), (0.0006, 5e-05)), (np.float64(0.03682127356016855), (0.0004, 5e-05)), (np.float64(0.03679496237083738), (0.0004, 0)), (np.float64(0.03677617681782175), (0.0006, 0.0001)), (np.float64(0.03677462555123591), (0.0004, 0.0001)), (np.float64(0.036599303890385994), (0.0004, 0.0002)), (np.float64(0.036528893650655106), (0.0006, 0.0002)), (np.float64(0.036510611219199574), (0.0004, 0.0003))]\nBEST BLA [(np.float64(0.03772549162296406), (0.0008, 0, 0.0003)), (np.float64(0.03769434742153713), (0.0008, 0, 0.0002)), (np.float64(0.036988795291506504), (0.0008, 0, 0)), (np.float64(0.03696522278774561), (0.0008, 0, 0.0001)), (np.float64(0.03678322775707024), (0.0008, 0, 3e-05)), (np.float64(0.03668518393893282), (0.0008, 0, -3e-05)), (np.float64(0.03610501520478487), (0.0008, 0, -0.0001))]\n",
    "stderr": "",
    "aggregated_output": "BEST BL [(np.float64(0.036988795291506504), (0.0008, 0)), (np.float64(0.03689014685453151), (0.0006, 0)), (np.float64(0.03682229202876586), (0.0006, 5e-05)), (np.float64(0.03682127356016855), (0.0004, 5e-05)), (np.float64(0.03679496237083738), (0.0004, 0)), (np.float64(0.03677617681782175), (0.0006, 0.0001)), (np.float64(0.03677462555123591), (0.0004, 0.0001)), (np.float64(0.036599303890385994), (0.0004, 0.0002)), (np.float64(0.036528893650655106), (0.0006, 0.0002)), (np.float64(0.036510611219199574), (0.0004, 0.0003))]\nBEST BLA [(np.float64(0.03772549162296406), (0.0008, 0, 0.0003)), (np.float64(0.03769434742153713), (0.0008, 0, 0.0002)), (np.float64(0.036988795291506504), (0.0008, 0, 0)), (np.float64(0.03696522278774561), (0.0008, 0, 0.0001)), (np.float64(0.03678322775707024), (0.0008, 0, 3e-05)), (np.float64(0.03668518393893282), (0.0008, 0, -3e-05)), (np.float64(0.03610501520478487), (0.0008, 0, -0.0001))]\n",
    "exit_code": 0,
    "duration": {
      "secs": 26,
      "nanos": 740822271
    },
    "formatted_output": "BEST BL [(np.float64(0.036988795291506504), (0.0008, 0)), (np.float64(0.03689014685453151), (0.0006, 0)), (np.float64(0.03682229202876586), (0.0006, 5e-05)), (np.float64(0.03682127356016855), (0.0004, 5e-05)), (np.float64(0.03679496237083738), (0.0004, 0)), (np.float64(0.03677617681782175), (0.0006, 0.0001)), (np.float64(0.03677462555123591), (0.0004, 0.0001)), (np.float64(0.036599303890385994), (0.0004, 0.0002)), (np.float64(0.036528893650655106), (0.0006, 0.0002)), (np.float64(0.036510611219199574), (0.0004, 0.0003))]\nBEST BLA [(np.float64(0.03772549162296406), (0.0008, 0, 0.0003)), (np.float64(0.03769434742153713), (0.0008, 0, 0.0002)), (np.float64(0.036988795291506504), (0.0008, 0, 0)), (np.float64(0.03696522278774561), (0.0008, 0, 0.0001)), (np.float64(0.03678322775707024), (0.0008, 0, 3e-05)), (np.float64(0.03668518393893282), (0.0008, 0, -3e-05)), (np.float64(0.03610501520478487), (0.0008, 0, -0.0001))]\n"
  }
}
55

exec command

+19:44
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python -u - <<'PY'\nimport sys,itertools\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();n=len(items);target=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64);qm={int(x):i for i,x in enumerate(target)};E=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qidx=np.array([qm[u] for u in gold]);xrows=(target[qidx]//100-1).astype(int);quid=[int(target[r]) for r in qidx]\nwith np.load(P+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=n,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(n,np.float32);z[o]=1-np.arange(n)/n;return z\ngb=rs(F[0,2])+2*rs(L[1,1]);B=np.load('/app/solution/svd_binary_train.npz');UB=B['U'];VB=B['V'];A=np.load('/app/solution/svd_active_train.npz');UA=A['U'];VA=A['V']\nvariants=list(itertools.product([.0005,.00065,.0008,.00095,.0011],[.00015,.00025,.00035,.0005,.0007,.001]));vals={v:[] for v in variants}\ndef std(S):S-=S.mean(1)[:,None];d=S.std(1);d[d==0]=1;return S/d[:,None]\nfor st in range(0,len(xrows),128):\n br=qidx[st:st+128];SB=std(UB[xrows[st:st+128],:96]@VB[:96]);SA=std(UA[br]@VA)\n for v in variants:\n  Z=gb[None,:]+v[0]*SB+v[1]*SA;Z[~bits[br]]=-np.inf;top=np.argpartition(Z,-10,axis=1)[:,-10:]\n  for j,row in enumerate(top):row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[v].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\nfor x in sorted([(np.mean(z),v) for v,z in vals.items()],reverse=True)[:20]:print(x)\nPY",workdir:"/app",yield_time_ms:30000,max_output_tokens:10000});
if(r.session_id){text(`SESSION_ID=${r.session_id}`);text(r.output);}else{text(r.output);}

Tool response

Script completed
Wall time 23.6 seconds
Output:

(np.float64(0.03782265903114937), (0.00065, 0.00025))
(np.float64(0.03781290415473602), (0.0008, 0.00025))
(np.float64(0.03770811927597809), (0.0005, 0.00025))
(np.float64(0.03768039062853601), (0.0005, 0.00015))
(np.float64(0.037452960897849094), (0.0005, 0.00035))
(np.float64(0.037356719459346724), (0.0008, 0.00035))
(np.float64(0.03732225456871879), (0.0008, 0.00015))
(np.float64(0.0373168331199789), (0.00095, 0.00025))
(np.float64(0.03729623751340539), (0.00095, 0.00035))
(np.float64(0.03728535629281436), (0.00065, 0.00035))
(np.float64(0.037214379473454545), (0.00065, 0.00015))
(np.float64(0.03715755832409045), (0.0011, 0.00035))
(np.float64(0.03714900328281677), (0.0011, 0.0005))
(np.float64(0.03711433724666826), (0.0005, 0.0005))
(np.float64(0.03708395282693272), (0.0011, 0.00025))
(np.float64(0.03707408450072468), (0.00095, 0.00015))
(np.float64(0.03698574720843331), (0.00095, 0.0005))
(np.float64(0.03698054797417537), (0.00065, 0.0005))
(np.float64(0.036941817992516966), (0.0011, 0.00015))
(np.float64(0.03688764507977994), (0.0008, 0.0005))
Harness context token usage record
{
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  "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
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  "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
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  "usage": {
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  "turn_token_usage": {
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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-41cdc66c-605a-4910-8986-37078843fbcf",
    "process_id": "97807",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python -u - <<'PY'\nimport sys,itertools\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();n=len(items);target=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64);qm={int(x):i for i,x in enumerate(target)};E=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qidx=np.array([qm[u] for u in gold]);xrows=(target[qidx]//100-1).astype(int);quid=[int(target[r]) for r in qidx]\nwith np.load(P+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=n,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(n,np.float32);z[o]=1-np.arange(n)/n;return z\ngb=rs(F[0,2])+2*rs(L[1,1]);B=np.load('/app/solution/svd_binary_train.npz');UB=B['U'];VB=B['V'];A=np.load('/app/solution/svd_active_train.npz');UA=A['U'];VA=A['V']\nvariants=list(itertools.product([.0005,.00065,.0008,.00095,.0011],[.00015,.00025,.00035,.0005,.0007,.001]));vals={v:[] for v in variants}\ndef std(S):S-=S.mean(1)[:,None];d=S.std(1);d[d==0]=1;return S/d[:,None]\nfor st in range(0,len(xrows),128):\n br=qidx[st:st+128];SB=std(UB[xrows[st:st+128],:96]@VB[:96]);SA=std(UA[br]@VA)\n for v in variants:\n  Z=gb[None,:]+v[0]*SB+v[1]*SA;Z[~bits[br]]=-np.inf;top=np.argpartition(Z,-10,axis=1)[:,-10:]\n  for j,row in enumerate(top):row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[v].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\nfor x in sorted([(np.mean(z),v) for v,z in vals.items()],reverse=True)[:20]:print(x)\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport sys,itertools\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();n=len(items);target=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64);qm={int(x):i for i,x in enumerate(target)};E=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qidx=np.array([qm[u] for u in gold]);xrows=(target[qidx]//100-1).astype(int);quid=[int(target[r]) for r in qidx]\nwith np.load(P+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=n,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(n,np.float32);z[o]=1-np.arange(n)/n;return z\ngb=rs(F[0,2])+2*rs(L[1,1]);B=np.load('/app/solution/svd_binary_train.npz');UB=B['U'];VB=B['V'];A=np.load('/app/solution/svd_active_train.npz');UA=A['U'];VA=A['V']\nvariants=list(itertools.product([.0005,.00065,.0008,.00095,.0011],[.00015,.00025,.00035,.0005,.0007,.001]));vals={v:[] for v in variants}\ndef std(S):S-=S.mean(1)[:,None];d=S.std(1);d[d==0]=1;return S/d[:,None]\nfor st in range(0,len(xrows),128):\n br=qidx[st:st+128];SB=std(UB[xrows[st:st+128],:96]@VB[:96]);SA=std(UA[br]@VA)\n for v in variants:\n  Z=gb[None,:]+v[0]*SB+v[1]*SA;Z[~bits[br]]=-np.inf;top=np.argpartition(Z,-10,axis=1)[:,-10:]\n  for j,row in enumerate(top):row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[v].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\nfor x in sorted([(np.mean(z),v) for v,z in vals.items()],reverse=True)[:20]:print(x)\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "(np.float64(0.03782265903114937), (0.00065, 0.00025))\n(np.float64(0.03781290415473602), (0.0008, 0.00025))\n(np.float64(0.03770811927597809), (0.0005, 0.00025))\n(np.float64(0.03768039062853601), (0.0005, 0.00015))\n(np.float64(0.037452960897849094), (0.0005, 0.00035))\n(np.float64(0.037356719459346724), (0.0008, 0.00035))\n(np.float64(0.03732225456871879), (0.0008, 0.00015))\n(np.float64(0.0373168331199789), (0.00095, 0.00025))\n(np.float64(0.03729623751340539), (0.00095, 0.00035))\n(np.float64(0.03728535629281436), (0.00065, 0.00035))\n(np.float64(0.037214379473454545), (0.00065, 0.00015))\n(np.float64(0.03715755832409045), (0.0011, 0.00035))\n(np.float64(0.03714900328281677), (0.0011, 0.0005))\n(np.float64(0.03711433724666826), (0.0005, 0.0005))\n(np.float64(0.03708395282693272), (0.0011, 0.00025))\n(np.float64(0.03707408450072468), (0.00095, 0.00015))\n(np.float64(0.03698574720843331), (0.00095, 0.0005))\n(np.float64(0.03698054797417537), (0.00065, 0.0005))\n(np.float64(0.036941817992516966), (0.0011, 0.00015))\n(np.float64(0.03688764507977994), (0.0008, 0.0005))\n",
    "stderr": "",
    "aggregated_output": "(np.float64(0.03782265903114937), (0.00065, 0.00025))\n(np.float64(0.03781290415473602), (0.0008, 0.00025))\n(np.float64(0.03770811927597809), (0.0005, 0.00025))\n(np.float64(0.03768039062853601), (0.0005, 0.00015))\n(np.float64(0.037452960897849094), (0.0005, 0.00035))\n(np.float64(0.037356719459346724), (0.0008, 0.00035))\n(np.float64(0.03732225456871879), (0.0008, 0.00015))\n(np.float64(0.0373168331199789), (0.00095, 0.00025))\n(np.float64(0.03729623751340539), (0.00095, 0.00035))\n(np.float64(0.03728535629281436), (0.00065, 0.00035))\n(np.float64(0.037214379473454545), (0.00065, 0.00015))\n(np.float64(0.03715755832409045), (0.0011, 0.00035))\n(np.float64(0.03714900328281677), (0.0011, 0.0005))\n(np.float64(0.03711433724666826), (0.0005, 0.0005))\n(np.float64(0.03708395282693272), (0.0011, 0.00025))\n(np.float64(0.03707408450072468), (0.00095, 0.00015))\n(np.float64(0.03698574720843331), (0.00095, 0.0005))\n(np.float64(0.03698054797417537), (0.00065, 0.0005))\n(np.float64(0.036941817992516966), (0.0011, 0.00015))\n(np.float64(0.03688764507977994), (0.0008, 0.0005))\n",
    "exit_code": 0,
    "duration": {
      "secs": 23,
      "nanos": 442914717
    },
    "formatted_output": "(np.float64(0.03782265903114937), (0.00065, 0.00025))\n(np.float64(0.03781290415473602), (0.0008, 0.00025))\n(np.float64(0.03770811927597809), (0.0005, 0.00025))\n(np.float64(0.03768039062853601), (0.0005, 0.00015))\n(np.float64(0.037452960897849094), (0.0005, 0.00035))\n(np.float64(0.037356719459346724), (0.0008, 0.00035))\n(np.float64(0.03732225456871879), (0.0008, 0.00015))\n(np.float64(0.0373168331199789), (0.00095, 0.00025))\n(np.float64(0.03729623751340539), (0.00095, 0.00035))\n(np.float64(0.03728535629281436), (0.00065, 0.00035))\n(np.float64(0.037214379473454545), (0.00065, 0.00015))\n(np.float64(0.03715755832409045), (0.0011, 0.00035))\n(np.float64(0.03714900328281677), (0.0011, 0.0005))\n(np.float64(0.03711433724666826), (0.0005, 0.0005))\n(np.float64(0.03708395282693272), (0.0011, 0.00025))\n(np.float64(0.03707408450072468), (0.00095, 0.00015))\n(np.float64(0.03698574720843331), (0.00095, 0.0005))\n(np.float64(0.03698054797417537), (0.00065, 0.0005))\n(np.float64(0.036941817992516966), (0.0011, 0.00015))\n(np.float64(0.03688764507977994), (0.0008, 0.0005))\n"
  }
}
56

Agent message

+20:34
Thinking unavailable No text recorded

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

57

exec command

+20:40
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 explicit/implicit taste ensemble reaches about 0.03782. One higher-value idea remains: model the audience of tracks gaining traction in the final seven training days, then favor trending tracks among taste-similar listeners. That directly combines the strongest temporal signal with personalization instead of treating them independently.

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python -u - <<'PY'\nimport pyarrow.parquet as pq,pandas as pd,numpy as np\nfrom scipy import sparse\nP='/task/data';cut=24786800;cat=pd.read_parquet(P+'/candidate_catalog.parquet');ids=cat.item_id.to_numpy(np.int64);n=len(ids);sid=np.sort(ids);s2c=np.argsort(ids);rows=[];cols=[];dat=[];org=[]\npf=pq.ParquetFile(P+'/train_listens.parquet')\nfor bi,b in enumerate(pf.iter_batches(batch_size=2_000_000,columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])):\n a=b.to_pydict();ts=np.asarray(a['timestamp'],np.int64);ratio=np.asarray(a['played_ratio_pct'],np.uint16);m=(ratio>=50)&(ts>=cut-7*86400);it=np.asarray(a['item_id'],np.int64)[m];p=np.searchsorted(sid,it);ok=(p<n)&(sid[np.minimum(p,n-1)]==it);rows.append((np.asarray(a['uid'],np.int32)[m][ok]//100-1).astype(np.int32));cols.append(s2c[p[ok]].astype(np.int32));dat.append(np.ones(ok.sum(),np.float32));org.append(np.asarray(a['is_organic'],np.uint8)[m][ok]);print(bi,sum(map(len,rows)),flush=True)\nr=np.concatenate(rows);c=np.concatenate(cols);o=np.concatenate(org);R=sparse.coo_matrix((np.ones(len(r),np.float32),(r,c)),shape=(10000,n)).tocsr();R.sum_duplicates();RO=sparse.coo_matrix((np.ones((o==1).sum(),np.float32),(r[o==1],c[o==1])),shape=(10000,n)).tocsr();RO.sum_duplicates();print('R',R.nnz,'RO',RO.nnz);sparse.save_npz('/app/solution/recent7_train.npz',R);sparse.save_npz('/app/solution/recent7org_train.npz',RO)\nPY",workdir:"/app",yield_time_ms:30000,max_output_tokens:10000});
if(r.session_id){text(`SESSION_ID=${r.session_id}`);text(r.output);}else{text(r.output);}

Tool response

Script completed
Wall time 30.2 seconds
Output:

SESSION_ID=24411
0 31830
1 64703
2 95825
3 126511
4 154739
5 186528
6 216752
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "response_id": "resp_0fdbf3e20a3ba82b016ab5cae9f09c87d2b6765bc77195ad75",
  "usage": {
    "input_tokens": 58002,
    "cached_input_tokens": 56576,
    "cache_write_input_tokens": 0,
    "output_tokens": 1425,
    "reasoning_output_tokens": 824,
    "total_tokens": 59427
  },
  "turn_token_usage": {
    "input_tokens": 1322312,
    "cached_input_tokens": 1269120,
    "cache_write_input_tokens": 0,
    "output_tokens": 27864,
    "reasoning_output_tokens": 11981,
    "total_tokens": 1350176
  },
  "thread_token_usage": {
    "input_tokens": 1322312,
    "cached_input_tokens": 1269120,
    "cache_write_input_tokens": 0,
    "output_tokens": 27864,
    "reasoning_output_tokens": 11981,
    "total_tokens": 1350176
  }
}
58

exec command

+21:22
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:244110,chars:"",yield_time_ms:30000,max_output_tokens:10000});
text(r.output);

Tool response

Script failed
Wall time 0.0 seconds
Output:

Script error:
write_stdin failed: Unknown process id 244110
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "response_id": "resp_0fdbf3e20a3ba82b016ab5cb24e9d487d298ca21ae3dfd8d7a",
  "usage": {
    "input_tokens": 59489,
    "cached_input_tokens": 57856,
    "cache_write_input_tokens": 0,
    "output_tokens": 71,
    "reasoning_output_tokens": 24,
    "total_tokens": 59560
  },
  "turn_token_usage": {
    "input_tokens": 1381801,
    "cached_input_tokens": 1326976,
    "cache_write_input_tokens": 0,
    "output_tokens": 27935,
    "reasoning_output_tokens": 12005,
    "total_tokens": 1409736
  },
  "thread_token_usage": {
    "input_tokens": 1381801,
    "cached_input_tokens": 1326976,
    "cache_write_input_tokens": 0,
    "output_tokens": 27935,
    "reasoning_output_tokens": 12005,
    "total_tokens": 1409736
  }
}
59

exec command

+21:26
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:24411,chars:"",yield_time_ms:30000,max_output_tokens:10000});
text(r.output);

Tool response

Script completed
Wall time 30.0 seconds
Output:

7 250863
8 283247
9 311189
10 341083
11 371128
12 404712
13 433391
14 463184
15 491792
16 521934
17 551713
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "response_id": "resp_0fdbf3e20a3ba82b016ab5cb28b7fc87d2b8e2366502b9a6db",
  "usage": {
    "input_tokens": 59597,
    "cached_input_tokens": 59264,
    "cache_write_input_tokens": 0,
    "output_tokens": 102,
    "reasoning_output_tokens": 55,
    "total_tokens": 59699
  },
  "turn_token_usage": {
    "input_tokens": 1441398,
    "cached_input_tokens": 1386240,
    "cache_write_input_tokens": 0,
    "output_tokens": 28037,
    "reasoning_output_tokens": 12060,
    "total_tokens": 1469435
  },
  "thread_token_usage": {
    "input_tokens": 1441398,
    "cached_input_tokens": 1386240,
    "cache_write_input_tokens": 0,
    "output_tokens": 28037,
    "reasoning_output_tokens": 12060,
    "total_tokens": 1469435
  }
}
60

exec command

+21:59
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:24411,chars:"",yield_time_ms:30000,max_output_tokens:10000});
text(r.output);

Tool response

Script completed
Wall time 12.0 seconds
Output:

18 583810
19 617681
20 651220
21 674579
R 486301 RO 185506
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "response_id": "resp_0fdbf3e20a3ba82b016ab5cb4bf1fc87d281ea218edfc1caaf",
  "usage": {
    "input_tokens": 59777,
    "cached_input_tokens": 59392,
    "cache_write_input_tokens": 0,
    "output_tokens": 53,
    "reasoning_output_tokens": 6,
    "total_tokens": 59830
  },
  "turn_token_usage": {
    "input_tokens": 1501175,
    "cached_input_tokens": 1445632,
    "cache_write_input_tokens": 0,
    "output_tokens": 28090,
    "reasoning_output_tokens": 12066,
    "total_tokens": 1529265
  },
  "thread_token_usage": {
    "input_tokens": 1501175,
    "cached_input_tokens": 1445632,
    "cache_write_input_tokens": 0,
    "output_tokens": 28090,
    "reasoning_output_tokens": 12066,
    "total_tokens": 1529265
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-295da0ef-d077-4bc0-9234-9961dfb912b8",
    "process_id": "24411",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python -u - <<'PY'\nimport pyarrow.parquet as pq,pandas as pd,numpy as np\nfrom scipy import sparse\nP='/task/data';cut=24786800;cat=pd.read_parquet(P+'/candidate_catalog.parquet');ids=cat.item_id.to_numpy(np.int64);n=len(ids);sid=np.sort(ids);s2c=np.argsort(ids);rows=[];cols=[];dat=[];org=[]\npf=pq.ParquetFile(P+'/train_listens.parquet')\nfor bi,b in enumerate(pf.iter_batches(batch_size=2_000_000,columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])):\n a=b.to_pydict();ts=np.asarray(a['timestamp'],np.int64);ratio=np.asarray(a['played_ratio_pct'],np.uint16);m=(ratio>=50)&(ts>=cut-7*86400);it=np.asarray(a['item_id'],np.int64)[m];p=np.searchsorted(sid,it);ok=(p<n)&(sid[np.minimum(p,n-1)]==it);rows.append((np.asarray(a['uid'],np.int32)[m][ok]//100-1).astype(np.int32));cols.append(s2c[p[ok]].astype(np.int32));dat.append(np.ones(ok.sum(),np.float32));org.append(np.asarray(a['is_organic'],np.uint8)[m][ok]);print(bi,sum(map(len,rows)),flush=True)\nr=np.concatenate(rows);c=np.concatenate(cols);o=np.concatenate(org);R=sparse.coo_matrix((np.ones(len(r),np.float32),(r,c)),shape=(10000,n)).tocsr();R.sum_duplicates();RO=sparse.coo_matrix((np.ones((o==1).sum(),np.float32),(r[o==1],c[o==1])),shape=(10000,n)).tocsr();RO.sum_duplicates();print('R',R.nnz,'RO',RO.nnz);sparse.save_npz('/app/solution/recent7_train.npz',R);sparse.save_npz('/app/solution/recent7org_train.npz',RO)\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport pyarrow.parquet as pq,pandas as pd,numpy as np\nfrom scipy import sparse\nP='/task/data';cut=24786800;cat=pd.read_parquet(P+'/candidate_catalog.parquet');ids=cat.item_id.to_numpy(np.int64);n=len(ids);sid=np.sort(ids);s2c=np.argsort(ids);rows=[];cols=[];dat=[];org=[]\npf=pq.ParquetFile(P+'/train_listens.parquet')\nfor bi,b in enumerate(pf.iter_batches(batch_size=2_000_000,columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])):\n a=b.to_pydict();ts=np.asarray(a['timestamp'],np.int64);ratio=np.asarray(a['played_ratio_pct'],np.uint16);m=(ratio>=50)&(ts>=cut-7*86400);it=np.asarray(a['item_id'],np.int64)[m];p=np.searchsorted(sid,it);ok=(p<n)&(sid[np.minimum(p,n-1)]==it);rows.append((np.asarray(a['uid'],np.int32)[m][ok]//100-1).astype(np.int32));cols.append(s2c[p[ok]].astype(np.int32));dat.append(np.ones(ok.sum(),np.float32));org.append(np.asarray(a['is_organic'],np.uint8)[m][ok]);print(bi,sum(map(len,rows)),flush=True)\nr=np.concatenate(rows);c=np.concatenate(cols);o=np.concatenate(org);R=sparse.coo_matrix((np.ones(len(r),np.float32),(r,c)),shape=(10000,n)).tocsr();R.sum_duplicates();RO=sparse.coo_matrix((np.ones((o==1).sum(),np.float32),(r[o==1],c[o==1])),shape=(10000,n)).tocsr();RO.sum_duplicates();print('R',R.nnz,'RO',RO.nnz);sparse.save_npz('/app/solution/recent7_train.npz',R);sparse.save_npz('/app/solution/recent7org_train.npz',RO)\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "0 31830\n1 64703\n2 95825\n3 126511\n4 154739\n5 186528\n6 216752\n7 250863\n8 283247\n9 311189\n10 341083\n11 371128\n12 404712\n13 433391\n14 463184\n15 491792\n16 521934\n17 551713\n18 583810\n19 617681\n20 651220\n21 674579\nR 486301 RO 185506\n",
    "stderr": "",
    "aggregated_output": "0 31830\n1 64703\n2 95825\n3 126511\n4 154739\n5 186528\n6 216752\n7 250863\n8 283247\n9 311189\n10 341083\n11 371128\n12 404712\n13 433391\n14 463184\n15 491792\n16 521934\n17 551713\n18 583810\n19 617681\n20 651220\n21 674579\nR 486301 RO 185506\n",
    "exit_code": 0,
    "duration": {
      "secs": 83,
      "nanos": 386607933
    },
    "formatted_output": "0 31830\n1 64703\n2 95825\n3 126511\n4 154739\n5 186528\n6 216752\n7 250863\n8 283247\n9 311189\n10 341083\n11 371128\n12 404712\n13 433391\n14 463184\n15 491792\n16 521934\n17 551713\n18 583810\n19 617681\n20 651220\n21 674579\nR 486301 RO 185506\n"
  }
}
61

exec command

+22:21
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python -u - <<'PY'\nimport sys\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();n=len(items);target=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64);ur={int(x):i for i,x in enumerate(target)};ic={int(x):i for i,x in enumerate(items)}\nE=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qidx=np.array([ur[u] for u in gold]);quid=[int(target[r]) for r in qidx]\nwith np.load(P+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=n,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(n,np.float32);z[o]=1-np.arange(n)/n;return z\ngb=rs(F[0,2])+2*rs(L[1,1]);ps=pd.read_parquet(P+'/validation_preference_state.parquet');ps=ps[ps.state=='liked'];rr=np.array([ur.get(int(x),-1) for x in ps.uid]);cc=np.array([ic.get(int(x),-1) for x in ps.item_id]);ok=(rr>=0)&(cc>=0);X=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(rr[ok],cc[ok])),shape=(len(target),n));X.data[:]=1;norm=np.sqrt(np.asarray(X.multiply(X).sum(1)).ravel());norm[norm==0]=1;XN=sparse.diags(1/norm)@X\nprint('sim',flush=True);S=(XN[qidx]@XN.T).tocsr();R=sparse.load_npz('/app/solution/recent7_train.npz')[(target//100-1).astype(int)]\ndef topk(S,k):\n ix=[];da=[];pt=[0]\n for i in range(S.shape[0]):\n  a,b=S.indptr[i:i+2];j=S.indices[a:b];v=S.data[a:b];m=j!=qidx[i];j=j[m];v=v[m]\n  if len(v)>k:z=np.argpartition(v,-k)[-k:];j=j[z];v=v[z]\n  ix.append(j);da.append(v);pt.append(pt[-1]+len(j))\n return sparse.csr_matrix((np.concatenate(da),np.concatenate(ix),np.array(pt)),shape=S.shape)\nalphas=[0,3e-6,1e-5,3e-5,1e-4,3e-4,1e-3,3e-3,1e-2,.03,.1]\nfor k in [20,50,100,200]:\n PRED=(topk(S,k)@R).tocsr();vals={a:[] for a in alphas}\n for i,(r,u) in enumerate(zip(qidx,quid)):\n  a,b=PRED.indptr[i:i+2];ix=PRED.indices[a:b];pv=PRED.data[a:b];mx=pv.max() if len(pv) else 1\n  for al in alphas:\n   z=gb.copy();z[ix]+=al*pv/mx;z[~bits[r]]=-np.inf;top=np.argpartition(z,-10)[-10:];top=top[np.argsort(z[top])[::-1]];vals[al].append(ndcg_at_k(items[top].tolist(),gold[u],10))\n print('K',k,[(a,float(np.mean(vals[a]))) for a in alphas],flush=True)\nPY",workdir:"/app",yield_time_ms:30000,max_output_tokens:14000});
if(r.session_id){text(`SESSION_ID=${r.session_id}`);text(r.output);}else{text(r.output);}

Tool response

Script completed
Wall time 28.9 seconds
Output:

sim
K 20 [(0, 0.03495591411760116), (3e-06, 0.03495608415986733), (1e-05, 0.03495608415986733), (3e-05, 0.03495608415986733), (0.0001, 0.03493599649868167), (0.0003, 0.035197933279644975), (0.001, 0.03525900208900905), (0.003, 0.03453605279868152), (0.01, 0.03438422463494998), (0.03, 0.033694902312559026), (0.1, 0.03095152699165364)]
K 50 [(0, 0.03495591411760116), (3e-06, 0.03495608415986733), (1e-05, 0.03495608415986733), (3e-05, 0.03495608415986733), (0.0001, 0.03498275460342701), (0.0003, 0.035225190124442585), (0.001, 0.035208124241811724), (0.003, 0.03552790049430244), (0.01, 0.035664974237640513), (0.03, 0.03528358481765686), (0.1, 0.03305843525932005)]
K 100 [(0, 0.03495591411760116), (3e-06, 0.03495591411760116), (1e-05, 0.03495591411760116), (3e-05, 0.03495591411760116), (0.0001, 0.03499758774598571), (0.0003, 0.03524563100460555), (0.001, 0.03574551898994348), (0.003, 0.036176808197157365), (0.01, 0.03705005279359867), (0.03, 0.037668884633164805), (0.1, 0.035774176384964586)]
K 200 [(0, 0.03495591411760116), (3e-06, 0.03495591411760116), (1e-05, 0.03495591411760116), (3e-05, 0.03495591411760116), (0.0001, 0.035007902595195366), (0.0003, 0.03533415687628328), (0.001, 0.036073673976050054), (0.003, 0.03653309527603927), (0.01, 0.03820523891853365), (0.03, 0.038739373079525144), (0.1, 0.037853640727801285)]
Harness context token usage record
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  "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
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  "usage": {
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    "cache_write_input_tokens": 0,
    "output_tokens": 1364,
    "reasoning_output_tokens": 390,
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  "turn_token_usage": {
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  "thread_token_usage": {
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    "cached_input_tokens": 1505280,
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Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-358d7ddb-d1a7-43c3-997d-f2ceac48a76b",
    "process_id": "21545",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python -u - <<'PY'\nimport sys\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();n=len(items);target=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64);ur={int(x):i for i,x in enumerate(target)};ic={int(x):i for i,x in enumerate(items)}\nE=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qidx=np.array([ur[u] for u in gold]);quid=[int(target[r]) for r in qidx]\nwith np.load(P+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=n,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(n,np.float32);z[o]=1-np.arange(n)/n;return z\ngb=rs(F[0,2])+2*rs(L[1,1]);ps=pd.read_parquet(P+'/validation_preference_state.parquet');ps=ps[ps.state=='liked'];rr=np.array([ur.get(int(x),-1) for x in ps.uid]);cc=np.array([ic.get(int(x),-1) for x in ps.item_id]);ok=(rr>=0)&(cc>=0);X=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(rr[ok],cc[ok])),shape=(len(target),n));X.data[:]=1;norm=np.sqrt(np.asarray(X.multiply(X).sum(1)).ravel());norm[norm==0]=1;XN=sparse.diags(1/norm)@X\nprint('sim',flush=True);S=(XN[qidx]@XN.T).tocsr();R=sparse.load_npz('/app/solution/recent7_train.npz')[(target//100-1).astype(int)]\ndef topk(S,k):\n ix=[];da=[];pt=[0]\n for i in range(S.shape[0]):\n  a,b=S.indptr[i:i+2];j=S.indices[a:b];v=S.data[a:b];m=j!=qidx[i];j=j[m];v=v[m]\n  if len(v)>k:z=np.argpartition(v,-k)[-k:];j=j[z];v=v[z]\n  ix.append(j);da.append(v);pt.append(pt[-1]+len(j))\n return sparse.csr_matrix((np.concatenate(da),np.concatenate(ix),np.array(pt)),shape=S.shape)\nalphas=[0,3e-6,1e-5,3e-5,1e-4,3e-4,1e-3,3e-3,1e-2,.03,.1]\nfor k in [20,50,100,200]:\n PRED=(topk(S,k)@R).tocsr();vals={a:[] for a in alphas}\n for i,(r,u) in enumerate(zip(qidx,quid)):\n  a,b=PRED.indptr[i:i+2];ix=PRED.indices[a:b];pv=PRED.data[a:b];mx=pv.max() if len(pv) else 1\n  for al in alphas:\n   z=gb.copy();z[ix]+=al*pv/mx;z[~bits[r]]=-np.inf;top=np.argpartition(z,-10)[-10:];top=top[np.argsort(z[top])[::-1]];vals[al].append(ndcg_at_k(items[top].tolist(),gold[u],10))\n print('K',k,[(a,float(np.mean(vals[a]))) for a in alphas],flush=True)\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport sys\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();n=len(items);target=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64);ur={int(x):i for i,x in enumerate(target)};ic={int(x):i for i,x in enumerate(items)}\nE=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qidx=np.array([ur[u] for u in gold]);quid=[int(target[r]) for r in qidx]\nwith np.load(P+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=n,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(n,np.float32);z[o]=1-np.arange(n)/n;return z\ngb=rs(F[0,2])+2*rs(L[1,1]);ps=pd.read_parquet(P+'/validation_preference_state.parquet');ps=ps[ps.state=='liked'];rr=np.array([ur.get(int(x),-1) for x in ps.uid]);cc=np.array([ic.get(int(x),-1) for x in ps.item_id]);ok=(rr>=0)&(cc>=0);X=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(rr[ok],cc[ok])),shape=(len(target),n));X.data[:]=1;norm=np.sqrt(np.asarray(X.multiply(X).sum(1)).ravel());norm[norm==0]=1;XN=sparse.diags(1/norm)@X\nprint('sim',flush=True);S=(XN[qidx]@XN.T).tocsr();R=sparse.load_npz('/app/solution/recent7_train.npz')[(target//100-1).astype(int)]\ndef topk(S,k):\n ix=[];da=[];pt=[0]\n for i in range(S.shape[0]):\n  a,b=S.indptr[i:i+2];j=S.indices[a:b];v=S.data[a:b];m=j!=qidx[i];j=j[m];v=v[m]\n  if len(v)>k:z=np.argpartition(v,-k)[-k:];j=j[z];v=v[z]\n  ix.append(j);da.append(v);pt.append(pt[-1]+len(j))\n return sparse.csr_matrix((np.concatenate(da),np.concatenate(ix),np.array(pt)),shape=S.shape)\nalphas=[0,3e-6,1e-5,3e-5,1e-4,3e-4,1e-3,3e-3,1e-2,.03,.1]\nfor k in [20,50,100,200]:\n PRED=(topk(S,k)@R).tocsr();vals={a:[] for a in alphas}\n for i,(r,u) in enumerate(zip(qidx,quid)):\n  a,b=PRED.indptr[i:i+2];ix=PRED.indices[a:b];pv=PRED.data[a:b];mx=pv.max() if len(pv) else 1\n  for al in alphas:\n   z=gb.copy();z[ix]+=al*pv/mx;z[~bits[r]]=-np.inf;top=np.argpartition(z,-10)[-10:];top=top[np.argsort(z[top])[::-1]];vals[al].append(ndcg_at_k(items[top].tolist(),gold[u],10))\n print('K',k,[(a,float(np.mean(vals[a]))) for a in alphas],flush=True)\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "sim\nK 20 [(0, 0.03495591411760116), (3e-06, 0.03495608415986733), (1e-05, 0.03495608415986733), (3e-05, 0.03495608415986733), (0.0001, 0.03493599649868167), (0.0003, 0.035197933279644975), (0.001, 0.03525900208900905), (0.003, 0.03453605279868152), (0.01, 0.03438422463494998), (0.03, 0.033694902312559026), (0.1, 0.03095152699165364)]\nK 50 [(0, 0.03495591411760116), (3e-06, 0.03495608415986733), (1e-05, 0.03495608415986733), (3e-05, 0.03495608415986733), (0.0001, 0.03498275460342701), (0.0003, 0.035225190124442585), (0.001, 0.035208124241811724), (0.003, 0.03552790049430244), (0.01, 0.035664974237640513), (0.03, 0.03528358481765686), (0.1, 0.03305843525932005)]\nK 100 [(0, 0.03495591411760116), (3e-06, 0.03495591411760116), (1e-05, 0.03495591411760116), (3e-05, 0.03495591411760116), (0.0001, 0.03499758774598571), (0.0003, 0.03524563100460555), (0.001, 0.03574551898994348), (0.003, 0.036176808197157365), (0.01, 0.03705005279359867), (0.03, 0.037668884633164805), (0.1, 0.035774176384964586)]\nK 200 [(0, 0.03495591411760116), (3e-06, 0.03495591411760116), (1e-05, 0.03495591411760116), (3e-05, 0.03495591411760116), (0.0001, 0.035007902595195366), (0.0003, 0.03533415687628328), (0.001, 0.036073673976050054), (0.003, 0.03653309527603927), (0.01, 0.03820523891853365), (0.03, 0.038739373079525144), (0.1, 0.037853640727801285)]\n",
    "stderr": "",
    "aggregated_output": "sim\nK 20 [(0, 0.03495591411760116), (3e-06, 0.03495608415986733), (1e-05, 0.03495608415986733), (3e-05, 0.03495608415986733), (0.0001, 0.03493599649868167), (0.0003, 0.035197933279644975), (0.001, 0.03525900208900905), (0.003, 0.03453605279868152), (0.01, 0.03438422463494998), (0.03, 0.033694902312559026), (0.1, 0.03095152699165364)]\nK 50 [(0, 0.03495591411760116), (3e-06, 0.03495608415986733), (1e-05, 0.03495608415986733), (3e-05, 0.03495608415986733), (0.0001, 0.03498275460342701), (0.0003, 0.035225190124442585), (0.001, 0.035208124241811724), (0.003, 0.03552790049430244), (0.01, 0.035664974237640513), (0.03, 0.03528358481765686), (0.1, 0.03305843525932005)]\nK 100 [(0, 0.03495591411760116), (3e-06, 0.03495591411760116), (1e-05, 0.03495591411760116), (3e-05, 0.03495591411760116), (0.0001, 0.03499758774598571), (0.0003, 0.03524563100460555), (0.001, 0.03574551898994348), (0.003, 0.036176808197157365), (0.01, 0.03705005279359867), (0.03, 0.037668884633164805), (0.1, 0.035774176384964586)]\nK 200 [(0, 0.03495591411760116), (3e-06, 0.03495591411760116), (1e-05, 0.03495591411760116), (3e-05, 0.03495591411760116), (0.0001, 0.035007902595195366), (0.0003, 0.03533415687628328), (0.001, 0.036073673976050054), (0.003, 0.03653309527603927), (0.01, 0.03820523891853365), (0.03, 0.038739373079525144), (0.1, 0.037853640727801285)]\n",
    "exit_code": 0,
    "duration": {
      "secs": 28,
      "nanos": 670769215
    },
    "formatted_output": "sim\nK 20 [(0, 0.03495591411760116), (3e-06, 0.03495608415986733), (1e-05, 0.03495608415986733), (3e-05, 0.03495608415986733), (0.0001, 0.03493599649868167), (0.0003, 0.035197933279644975), (0.001, 0.03525900208900905), (0.003, 0.03453605279868152), (0.01, 0.03438422463494998), (0.03, 0.033694902312559026), (0.1, 0.03095152699165364)]\nK 50 [(0, 0.03495591411760116), (3e-06, 0.03495608415986733), (1e-05, 0.03495608415986733), (3e-05, 0.03495608415986733), (0.0001, 0.03498275460342701), (0.0003, 0.035225190124442585), (0.001, 0.035208124241811724), (0.003, 0.03552790049430244), (0.01, 0.035664974237640513), (0.03, 0.03528358481765686), (0.1, 0.03305843525932005)]\nK 100 [(0, 0.03495591411760116), (3e-06, 0.03495591411760116), (1e-05, 0.03495591411760116), (3e-05, 0.03495591411760116), (0.0001, 0.03499758774598571), (0.0003, 0.03524563100460555), (0.001, 0.03574551898994348), (0.003, 0.036176808197157365), (0.01, 0.03705005279359867), (0.03, 0.037668884633164805), (0.1, 0.035774176384964586)]\nK 200 [(0, 0.03495591411760116), (3e-06, 0.03495591411760116), (1e-05, 0.03495591411760116), (3e-05, 0.03495591411760116), (0.0001, 0.035007902595195366), (0.0003, 0.03533415687628328), (0.001, 0.036073673976050054), (0.003, 0.03653309527603927), (0.01, 0.03820523891853365), (0.03, 0.038739373079525144), (0.1, 0.037853640727801285)]\n"
  }
}
62

exec command

+23:17
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python -u - <<'PY'\nimport sys,itertools\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();n=len(items);target=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64);ur={int(x):i for i,x in enumerate(target)};ic={int(x):i for i,x in enumerate(items)}\nE=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qidx=np.array([ur[u] for u in gold]);quid=[int(target[r]) for r in qidx]\nwith np.load(P+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=n,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(n,np.float32);z[o]=1-np.arange(n)/n;return z\ngb=rs(F[0,2])+2*rs(L[1,1]);ps=pd.read_parquet(P+'/validation_preference_state.parquet');ps=ps[ps.state=='liked'];rr=np.array([ur.get(int(x),-1) for x in ps.uid]);cc=np.array([ic.get(int(x),-1) for x in ps.item_id]);ok=(rr>=0)&(cc>=0);X=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(rr[ok],cc[ok])),shape=(len(target),n));X.data[:]=1;norm=np.sqrt(np.asarray(X.multiply(X).sum(1)).ravel());norm[norm==0]=1;XN=sparse.diags(1/norm)@X;S=(XN[qidx]@XN.T).tocsr();Rall=sparse.load_npz('/app/solution/recent7_train.npz')[(target//100-1).astype(int)];Rorg=sparse.load_npz('/app/solution/recent7org_train.npz')[(target//100-1).astype(int)]\ndef topk(S,k):\n ix=[];da=[];pt=[0]\n for i in range(S.shape[0]):\n  a,b=S.indptr[i:i+2];j=S.indices[a:b];v=S.data[a:b];m=j!=qidx[i];j=j[m];v=v[m]\n  if len(v)>k:z=np.argpartition(v,-k)[-k:];j=j[z];v=v[z]\n  ix.append(j);da.append(v);pt.append(pt[-1]+len(j))\n return sparse.csr_matrix((np.concatenate(da),np.concatenate(ix),np.array(pt)),shape=S.shape)\ndef ev(P,alphas):\n vals={a:[] for a in alphas}\n for i,(r,u) in enumerate(zip(qidx,quid)):\n  lo,hi=P.indptr[i:i+2];ix=P.indices[lo:hi];pv=P.data[lo:hi];mx=pv.max() if len(pv) else 1\n  for al in alphas:\n   z=gb.copy();z[ix]+=al*pv/mx;z[~bits[r]]=-np.inf;t=np.argpartition(z,-10)[-10:];t=t[np.argsort(z[t])[::-1]];vals[al].append(ndcg_at_k(items[t].tolist(),gold[u],10))\n return [(a,float(np.mean(vals[a]))) for a in alphas]\nalphas=[.005,.01,.015,.02,.025,.03,.04,.05,.07,.1]\nfor k in [200,300,500,1000]:\n sk=topk(S,k)\n for name,R in [('all',Rall),('org',Rorg)]:print(name,k,ev((sk@R).tocsr(),alphas),flush=True)\n# save active similarity for final combos\nsparse.save_npz('/app/solution/active_sim_q_train.npz',S)\nPY",workdir:"/app",yield_time_ms:30000,max_output_tokens:16000});
if(r.session_id){text(`SESSION_ID=${r.session_id}`);text(r.output);}else{text(r.output);}

Tool response

Script completed
Wall time 30.2 seconds
Output:

SESSION_ID=38638
all 200 [(0.005, 0.03692219892752741), (0.01, 0.03820523891853365), (0.015, 0.03889850115844483), (0.02, 0.03914918396634148), (0.025, 0.03883897856003798), (0.03, 0.038739373079525144), (0.04, 0.03834609233828177), (0.05, 0.03840393475740443), (0.07, 0.03795927802852888), (0.1, 0.037853640727801285)]
org 200 [(0.005, 0.03630562979527513), (0.01, 0.03720208356953695), (0.015, 0.03794864451871575), (0.02, 0.0378859088193051), (0.025, 0.037833123085761755), (0.03, 0.03763516163289139), (0.04, 0.037592962216435925), (0.05, 0.0375450725877074), (0.07, 0.03711393145359014), (0.1, 0.03621016808631632)]
all 300 [(0.005, 0.037108066295842776), (0.01, 0.03796299471940949), (0.015, 0.03887657013193053), (0.02, 0.03891868810062882), (0.025, 0.038950698347121886), (0.03, 0.03878764129546214), (0.04, 0.038903958547439964), (0.05, 0.038736237793520555), (0.07, 0.03885513569245522), (0.1, 0.03837531829044672)]
org 300 [(0.005, 0.03633373264361668), (0.01, 0.03753485140901134), (0.015, 0.0376789394150024), (0.02, 0.037689270987863094), (0.025, 0.0380125373888752), (0.03, 0.03787157865638546), (0.04, 0.03782868170148931), (0.05, 0.03748884861423028), (0.07, 0.03758573745405), (0.1, 0.036934922950726035)]
Harness context token usage record
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63

exec command

+24:14
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.write_stdin({session_id:38638,chars:"",yield_time_ms:30000,max_output_tokens:16000});
text(r.output);

Tool response

Script completed
Wall time 25.5 seconds
Output:

all 500 [(0.005, 0.03735766429925665), (0.01, 0.03836926951464056), (0.015, 0.038775988350233354), (0.02, 0.03879498381586903), (0.025, 0.038708260734915255), (0.03, 0.038768532427306726), (0.04, 0.038506216925910906), (0.05, 0.038473527103831964), (0.07, 0.03892529059806298), (0.1, 0.038723804499457705)]
org 500 [(0.005, 0.03675847401314856), (0.01, 0.037350422570086604), (0.015, 0.03760682921284848), (0.02, 0.03750031822682396), (0.025, 0.037899077489761554), (0.03, 0.037930159481323285), (0.04, 0.03790294178047534), (0.05, 0.03767664632227021), (0.07, 0.03730120155011157), (0.1, 0.03697620123948642)]
all 1000 [(0.005, 0.03725378012517222), (0.01, 0.038132666771878716), (0.015, 0.0386906935015919), (0.02, 0.038642641484461136), (0.025, 0.038563163472943134), (0.03, 0.03870686182348705), (0.04, 0.03879800321980825), (0.05, 0.03869984631108141), (0.07, 0.03867231938473162), (0.1, 0.038372894082663415)]
org 1000 [(0.005, 0.03678256993121768), (0.01, 0.03727507158495396), (0.015, 0.03763773685817453), (0.02, 0.03772999268002472), (0.025, 0.037870437773240104), (0.03, 0.03763820197676112), (0.04, 0.037952669907349475), (0.05, 0.03763333019489976), (0.07, 0.03758792608191734), (0.1, 0.03739068779833278)]
Harness context token usage record
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  "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
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Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-e2d5c0d7-942d-4362-968e-227e2a114bd4",
    "process_id": "38638",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python -u - <<'PY'\nimport sys,itertools\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();n=len(items);target=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64);ur={int(x):i for i,x in enumerate(target)};ic={int(x):i for i,x in enumerate(items)}\nE=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qidx=np.array([ur[u] for u in gold]);quid=[int(target[r]) for r in qidx]\nwith np.load(P+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=n,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(n,np.float32);z[o]=1-np.arange(n)/n;return z\ngb=rs(F[0,2])+2*rs(L[1,1]);ps=pd.read_parquet(P+'/validation_preference_state.parquet');ps=ps[ps.state=='liked'];rr=np.array([ur.get(int(x),-1) for x in ps.uid]);cc=np.array([ic.get(int(x),-1) for x in ps.item_id]);ok=(rr>=0)&(cc>=0);X=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(rr[ok],cc[ok])),shape=(len(target),n));X.data[:]=1;norm=np.sqrt(np.asarray(X.multiply(X).sum(1)).ravel());norm[norm==0]=1;XN=sparse.diags(1/norm)@X;S=(XN[qidx]@XN.T).tocsr();Rall=sparse.load_npz('/app/solution/recent7_train.npz')[(target//100-1).astype(int)];Rorg=sparse.load_npz('/app/solution/recent7org_train.npz')[(target//100-1).astype(int)]\ndef topk(S,k):\n ix=[];da=[];pt=[0]\n for i in range(S.shape[0]):\n  a,b=S.indptr[i:i+2];j=S.indices[a:b];v=S.data[a:b];m=j!=qidx[i];j=j[m];v=v[m]\n  if len(v)>k:z=np.argpartition(v,-k)[-k:];j=j[z];v=v[z]\n  ix.append(j);da.append(v);pt.append(pt[-1]+len(j))\n return sparse.csr_matrix((np.concatenate(da),np.concatenate(ix),np.array(pt)),shape=S.shape)\ndef ev(P,alphas):\n vals={a:[] for a in alphas}\n for i,(r,u) in enumerate(zip(qidx,quid)):\n  lo,hi=P.indptr[i:i+2];ix=P.indices[lo:hi];pv=P.data[lo:hi];mx=pv.max() if len(pv) else 1\n  for al in alphas:\n   z=gb.copy();z[ix]+=al*pv/mx;z[~bits[r]]=-np.inf;t=np.argpartition(z,-10)[-10:];t=t[np.argsort(z[t])[::-1]];vals[al].append(ndcg_at_k(items[t].tolist(),gold[u],10))\n return [(a,float(np.mean(vals[a]))) for a in alphas]\nalphas=[.005,.01,.015,.02,.025,.03,.04,.05,.07,.1]\nfor k in [200,300,500,1000]:\n sk=topk(S,k)\n for name,R in [('all',Rall),('org',Rorg)]:print(name,k,ev((sk@R).tocsr(),alphas),flush=True)\n# save active similarity for final combos\nsparse.save_npz('/app/solution/active_sim_q_train.npz',S)\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport sys,itertools\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();n=len(items);target=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64);ur={int(x):i for i,x in enumerate(target)};ic={int(x):i for i,x in enumerate(items)}\nE=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qidx=np.array([ur[u] for u in gold]);quid=[int(target[r]) for r in qidx]\nwith np.load(P+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=n,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(n,np.float32);z[o]=1-np.arange(n)/n;return z\ngb=rs(F[0,2])+2*rs(L[1,1]);ps=pd.read_parquet(P+'/validation_preference_state.parquet');ps=ps[ps.state=='liked'];rr=np.array([ur.get(int(x),-1) for x in ps.uid]);cc=np.array([ic.get(int(x),-1) for x in ps.item_id]);ok=(rr>=0)&(cc>=0);X=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(rr[ok],cc[ok])),shape=(len(target),n));X.data[:]=1;norm=np.sqrt(np.asarray(X.multiply(X).sum(1)).ravel());norm[norm==0]=1;XN=sparse.diags(1/norm)@X;S=(XN[qidx]@XN.T).tocsr();Rall=sparse.load_npz('/app/solution/recent7_train.npz')[(target//100-1).astype(int)];Rorg=sparse.load_npz('/app/solution/recent7org_train.npz')[(target//100-1).astype(int)]\ndef topk(S,k):\n ix=[];da=[];pt=[0]\n for i in range(S.shape[0]):\n  a,b=S.indptr[i:i+2];j=S.indices[a:b];v=S.data[a:b];m=j!=qidx[i];j=j[m];v=v[m]\n  if len(v)>k:z=np.argpartition(v,-k)[-k:];j=j[z];v=v[z]\n  ix.append(j);da.append(v);pt.append(pt[-1]+len(j))\n return sparse.csr_matrix((np.concatenate(da),np.concatenate(ix),np.array(pt)),shape=S.shape)\ndef ev(P,alphas):\n vals={a:[] for a in alphas}\n for i,(r,u) in enumerate(zip(qidx,quid)):\n  lo,hi=P.indptr[i:i+2];ix=P.indices[lo:hi];pv=P.data[lo:hi];mx=pv.max() if len(pv) else 1\n  for al in alphas:\n   z=gb.copy();z[ix]+=al*pv/mx;z[~bits[r]]=-np.inf;t=np.argpartition(z,-10)[-10:];t=t[np.argsort(z[t])[::-1]];vals[al].append(ndcg_at_k(items[t].tolist(),gold[u],10))\n return [(a,float(np.mean(vals[a]))) for a in alphas]\nalphas=[.005,.01,.015,.02,.025,.03,.04,.05,.07,.1]\nfor k in [200,300,500,1000]:\n sk=topk(S,k)\n for name,R in [('all',Rall),('org',Rorg)]:print(name,k,ev((sk@R).tocsr(),alphas),flush=True)\n# save active similarity for final combos\nsparse.save_npz('/app/solution/active_sim_q_train.npz',S)\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "all 200 [(0.005, 0.03692219892752741), (0.01, 0.03820523891853365), (0.015, 0.03889850115844483), (0.02, 0.03914918396634148), (0.025, 0.03883897856003798), (0.03, 0.038739373079525144), (0.04, 0.03834609233828177), (0.05, 0.03840393475740443), (0.07, 0.03795927802852888), (0.1, 0.037853640727801285)]\norg 200 [(0.005, 0.03630562979527513), (0.01, 0.03720208356953695), (0.015, 0.03794864451871575), (0.02, 0.0378859088193051), (0.025, 0.037833123085761755), (0.03, 0.03763516163289139), (0.04, 0.037592962216435925), (0.05, 0.0375450725877074), (0.07, 0.03711393145359014), (0.1, 0.03621016808631632)]\nall 300 [(0.005, 0.037108066295842776), (0.01, 0.03796299471940949), (0.015, 0.03887657013193053), (0.02, 0.03891868810062882), (0.025, 0.038950698347121886), (0.03, 0.03878764129546214), (0.04, 0.038903958547439964), (0.05, 0.038736237793520555), (0.07, 0.03885513569245522), (0.1, 0.03837531829044672)]\norg 300 [(0.005, 0.03633373264361668), (0.01, 0.03753485140901134), (0.015, 0.0376789394150024), (0.02, 0.037689270987863094), (0.025, 0.0380125373888752), (0.03, 0.03787157865638546), (0.04, 0.03782868170148931), (0.05, 0.03748884861423028), (0.07, 0.03758573745405), (0.1, 0.036934922950726035)]\nall 500 [(0.005, 0.03735766429925665), (0.01, 0.03836926951464056), (0.015, 0.038775988350233354), (0.02, 0.03879498381586903), (0.025, 0.038708260734915255), (0.03, 0.038768532427306726), (0.04, 0.038506216925910906), (0.05, 0.038473527103831964), (0.07, 0.03892529059806298), (0.1, 0.038723804499457705)]\norg 500 [(0.005, 0.03675847401314856), (0.01, 0.037350422570086604), (0.015, 0.03760682921284848), (0.02, 0.03750031822682396), (0.025, 0.037899077489761554), (0.03, 0.037930159481323285), (0.04, 0.03790294178047534), (0.05, 0.03767664632227021), (0.07, 0.03730120155011157), (0.1, 0.03697620123948642)]\nall 1000 [(0.005, 0.03725378012517222), (0.01, 0.038132666771878716), (0.015, 0.0386906935015919), (0.02, 0.038642641484461136), (0.025, 0.038563163472943134), (0.03, 0.03870686182348705), (0.04, 0.03879800321980825), (0.05, 0.03869984631108141), (0.07, 0.03867231938473162), (0.1, 0.038372894082663415)]\norg 1000 [(0.005, 0.03678256993121768), (0.01, 0.03727507158495396), (0.015, 0.03763773685817453), (0.02, 0.03772999268002472), (0.025, 0.037870437773240104), (0.03, 0.03763820197676112), (0.04, 0.037952669907349475), (0.05, 0.03763333019489976), (0.07, 0.03758792608191734), (0.1, 0.03739068779833278)]\n",
    "stderr": "",
    "aggregated_output": "all 200 [(0.005, 0.03692219892752741), (0.01, 0.03820523891853365), (0.015, 0.03889850115844483), (0.02, 0.03914918396634148), (0.025, 0.03883897856003798), (0.03, 0.038739373079525144), (0.04, 0.03834609233828177), (0.05, 0.03840393475740443), (0.07, 0.03795927802852888), (0.1, 0.037853640727801285)]\norg 200 [(0.005, 0.03630562979527513), (0.01, 0.03720208356953695), (0.015, 0.03794864451871575), (0.02, 0.0378859088193051), (0.025, 0.037833123085761755), (0.03, 0.03763516163289139), (0.04, 0.037592962216435925), (0.05, 0.0375450725877074), (0.07, 0.03711393145359014), (0.1, 0.03621016808631632)]\nall 300 [(0.005, 0.037108066295842776), (0.01, 0.03796299471940949), (0.015, 0.03887657013193053), (0.02, 0.03891868810062882), (0.025, 0.038950698347121886), (0.03, 0.03878764129546214), (0.04, 0.038903958547439964), (0.05, 0.038736237793520555), (0.07, 0.03885513569245522), (0.1, 0.03837531829044672)]\norg 300 [(0.005, 0.03633373264361668), (0.01, 0.03753485140901134), (0.015, 0.0376789394150024), (0.02, 0.037689270987863094), (0.025, 0.0380125373888752), (0.03, 0.03787157865638546), (0.04, 0.03782868170148931), (0.05, 0.03748884861423028), (0.07, 0.03758573745405), (0.1, 0.036934922950726035)]\nall 500 [(0.005, 0.03735766429925665), (0.01, 0.03836926951464056), (0.015, 0.038775988350233354), (0.02, 0.03879498381586903), (0.025, 0.038708260734915255), (0.03, 0.038768532427306726), (0.04, 0.038506216925910906), (0.05, 0.038473527103831964), (0.07, 0.03892529059806298), (0.1, 0.038723804499457705)]\norg 500 [(0.005, 0.03675847401314856), (0.01, 0.037350422570086604), (0.015, 0.03760682921284848), (0.02, 0.03750031822682396), (0.025, 0.037899077489761554), (0.03, 0.037930159481323285), (0.04, 0.03790294178047534), (0.05, 0.03767664632227021), (0.07, 0.03730120155011157), (0.1, 0.03697620123948642)]\nall 1000 [(0.005, 0.03725378012517222), (0.01, 0.038132666771878716), (0.015, 0.0386906935015919), (0.02, 0.038642641484461136), (0.025, 0.038563163472943134), (0.03, 0.03870686182348705), (0.04, 0.03879800321980825), (0.05, 0.03869984631108141), (0.07, 0.03867231938473162), (0.1, 0.038372894082663415)]\norg 1000 [(0.005, 0.03678256993121768), (0.01, 0.03727507158495396), (0.015, 0.03763773685817453), (0.02, 0.03772999268002472), (0.025, 0.037870437773240104), (0.03, 0.03763820197676112), (0.04, 0.037952669907349475), (0.05, 0.03763333019489976), (0.07, 0.03758792608191734), (0.1, 0.03739068779833278)]\n",
    "exit_code": 0,
    "duration": {
      "secs": 64,
      "nanos": 681057836
    },
    "formatted_output": "all 200 [(0.005, 0.03692219892752741), (0.01, 0.03820523891853365), (0.015, 0.03889850115844483), (0.02, 0.03914918396634148), (0.025, 0.03883897856003798), (0.03, 0.038739373079525144), (0.04, 0.03834609233828177), (0.05, 0.03840393475740443), (0.07, 0.03795927802852888), (0.1, 0.037853640727801285)]\norg 200 [(0.005, 0.03630562979527513), (0.01, 0.03720208356953695), (0.015, 0.03794864451871575), (0.02, 0.0378859088193051), (0.025, 0.037833123085761755), (0.03, 0.03763516163289139), (0.04, 0.037592962216435925), (0.05, 0.0375450725877074), (0.07, 0.03711393145359014), (0.1, 0.03621016808631632)]\nall 300 [(0.005, 0.037108066295842776), (0.01, 0.03796299471940949), (0.015, 0.03887657013193053), (0.02, 0.03891868810062882), (0.025, 0.038950698347121886), (0.03, 0.03878764129546214), (0.04, 0.038903958547439964), (0.05, 0.038736237793520555), (0.07, 0.03885513569245522), (0.1, 0.03837531829044672)]\norg 300 [(0.005, 0.03633373264361668), (0.01, 0.03753485140901134), (0.015, 0.0376789394150024), (0.02, 0.037689270987863094), (0.025, 0.0380125373888752), (0.03, 0.03787157865638546), (0.04, 0.03782868170148931), (0.05, 0.03748884861423028), (0.07, 0.03758573745405), (0.1, 0.036934922950726035)]\nall 500 [(0.005, 0.03735766429925665), (0.01, 0.03836926951464056), (0.015, 0.038775988350233354), (0.02, 0.03879498381586903), (0.025, 0.038708260734915255), (0.03, 0.038768532427306726), (0.04, 0.038506216925910906), (0.05, 0.038473527103831964), (0.07, 0.03892529059806298), (0.1, 0.038723804499457705)]\norg 500 [(0.005, 0.03675847401314856), (0.01, 0.037350422570086604), (0.015, 0.03760682921284848), (0.02, 0.03750031822682396), (0.025, 0.037899077489761554), (0.03, 0.037930159481323285), (0.04, 0.03790294178047534), (0.05, 0.03767664632227021), (0.07, 0.03730120155011157), (0.1, 0.03697620123948642)]\nall 1000 [(0.005, 0.03725378012517222), (0.01, 0.038132666771878716), (0.015, 0.0386906935015919), (0.02, 0.038642641484461136), (0.025, 0.038563163472943134), (0.03, 0.03870686182348705), (0.04, 0.03879800321980825), (0.05, 0.03869984631108141), (0.07, 0.03867231938473162), (0.1, 0.038372894082663415)]\norg 1000 [(0.005, 0.03678256993121768), (0.01, 0.03727507158495396), (0.015, 0.03763773685817453), (0.02, 0.03772999268002472), (0.025, 0.037870437773240104), (0.03, 0.03763820197676112), (0.04, 0.037952669907349475), (0.05, 0.03763333019489976), (0.07, 0.03758792608191734), (0.1, 0.03739068779833278)]\n"
  }
}
64

exec command

+24:46
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 -u - <<'PY'\nimport sys,itertools\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nPTH='/task/data';cat=pd.read_parquet(PTH+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();n=len(items);target=pd.read_parquet(PTH+'/target_users.parquet').uid.to_numpy(np.int64);ur={int(x):i for i,x in enumerate(target)};E=CandidateSet(Path(PTH+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qidx=np.array([ur[u] for u in gold]);xrows=(target[qidx]//100-1).astype(int);quid=[int(target[r]) for r in qidx]\nwith np.load(PTH+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=n,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(n,np.float32);z[o]=1-np.arange(n)/n;return z\ngb=rs(F[0,2])+2*rs(L[1,1]);B=np.load('/app/solution/svd_binary_train.npz');UB=B['U'];VB=B['V'];A=np.load('/app/solution/svd_active_train.npz');UA=A['U'];VA=A['V'];S=sparse.load_npz('/app/solution/active_sim_q_train.npz');R=sparse.load_npz('/app/solution/recent7_train.npz')[(target//100-1).astype(int)]\n# prune top 200\nix=[];da=[];pt=[0]\nfor i in range(S.shape[0]):\n lo,hi=S.indptr[i:i+2];j=S.indices[lo:hi];v=S.data[lo:hi];m=j!=qidx[i];j=j[m];v=v[m]\n if len(v)>200:z=np.argpartition(v,-200)[-200:];j=j[z];v=v[z]\n ix.append(j);da.append(v);pt.append(pt[-1]+len(j))\nSK=sparse.csr_matrix((np.concatenate(da),np.concatenate(ix),np.array(pt)),shape=S.shape);NP=(SK@R).tocsr();sparse.save_npz('/app/solution/neighbor_recent_train.npz',NP)\nvariants=list(itertools.product([.01,.015,.02,.025,.03],[.0003,.0006,.0009],[0,.00015,.0003]));vals={v:[] for v in variants}\ndef std(Z):Z-=Z.mean(1)[:,None];d=Z.std(1);d[d==0]=1;return Z/d[:,None]\nfor st in range(0,len(qidx),128):\n br=qidx[st:st+128];SB=std(UB[xrows[st:st+128],:96]@VB[:96]);SA=std(UA[br]@VA);PN=NP[st:st+128].toarray();mx=PN.max(1);mx[mx==0]=1;PN/=mx[:,None]\n for v in variants:\n  Z=gb[None,:]+v[0]*PN+v[1]*SB+v[2]*SA;Z[~bits[br]]=-np.inf;top=np.argpartition(Z,-10,axis=1)[:,-10:]\n  for j,row in enumerate(top):row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[v].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\nfor x in sorted([(np.mean(z),v) for v,z in vals.items()],reverse=True)[:25]:print(x)\nPY",workdir:"/app",yield_time_ms:30000,max_output_tokens:12000});
if(r.session_id){text(`SESSION_ID=${r.session_id}`);text(r.output);}else{text(r.output);}

Tool response

Script completed
Wall time 30.2 seconds
Output:

SESSION_ID=34789
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "response_id": "resp_0fdbf3e20a3ba82b016ab5cbed831c87d29936731d2871efcd",
  "usage": {
    "input_tokens": 64757,
    "cached_input_tokens": 63872,
    "cache_write_input_tokens": 0,
    "output_tokens": 1215,
    "reasoning_output_tokens": 243,
    "total_tokens": 65972
  },
  "turn_token_usage": {
    "input_tokens": 1751834,
    "cached_input_tokens": 1690624,
    "cache_write_input_tokens": 0,
    "output_tokens": 32195,
    "reasoning_output_tokens": 13143,
    "total_tokens": 1784029
  },
  "thread_token_usage": {
    "input_tokens": 1751834,
    "cached_input_tokens": 1690624,
    "cache_write_input_tokens": 0,
    "output_tokens": 32195,
    "reasoning_output_tokens": 13143,
    "total_tokens": 1784029
  }
}
65

exec command

+25:36
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.write_stdin({session_id:34789,chars:"",yield_time_ms:30000,max_output_tokens:12000});
text(r.output);

Tool response

Script completed
Wall time 19.1 seconds
Output:

(np.float64(0.04129788096117011), (0.02, 0.0006, 0.0003))
(np.float64(0.04115708419337064), (0.015, 0.0006, 0.00015))
(np.float64(0.04108797955522227), (0.015, 0.0006, 0.0003))
(np.float64(0.04107968649340763), (0.01, 0.0006, 0.0003))
(np.float64(0.041069981008229006), (0.015, 0.0009, 0.00015))
(np.float64(0.04103696499739304), (0.02, 0.0009, 0.00015))
(np.float64(0.04103009805436581), (0.02, 0.0009, 0.0003))
(np.float64(0.040880197041195165), (0.025, 0.0009, 0.0003))
(np.float64(0.04086624182649273), (0.02, 0.0006, 0.00015))
(np.float64(0.0408578262021265), (0.015, 0.0009, 0.0003))
(np.float64(0.04082906313027645), (0.03, 0.0009, 0.0003))
(np.float64(0.04078799741458635), (0.025, 0.0006, 0.0003))
(np.float64(0.040772988959447136), (0.01, 0.0006, 0.00015))
(np.float64(0.04075338304407032), (0.025, 0.0009, 0.00015))
(np.float64(0.0407306075008462), (0.015, 0.0006, 0))
(np.float64(0.04068342318201982), (0.015, 0.0003, 0.0003))
(np.float64(0.04063743707113819), (0.015, 0.0003, 0.00015))
(np.float64(0.04062231686881125), (0.03, 0.0006, 0.0003))
(np.float64(0.04061632275882384), (0.02, 0.0003, 0.0003))
(np.float64(0.04061325343329181), (0.025, 0.0003, 0.0003))
(np.float64(0.040545913758730096), (0.02, 0.0006, 0))
(np.float64(0.040531547613006716), (0.03, 0.0006, 0.00015))
(np.float64(0.04051341388160154), (0.015, 0.0003, 0))
(np.float64(0.040494020628630924), (0.01, 0.0003, 0.0003))
(np.float64(0.04049225865695971), (0.015, 0.0009, 0))
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "response_id": "resp_0fdbf3e20a3ba82b016ab5cc238ac087d2a757f2eed0468afd",
  "usage": {
    "input_tokens": 66000,
    "cached_input_tokens": 64640,
    "cache_write_input_tokens": 0,
    "output_tokens": 56,
    "reasoning_output_tokens": 9,
    "total_tokens": 66056
  },
  "turn_token_usage": {
    "input_tokens": 1817834,
    "cached_input_tokens": 1755264,
    "cache_write_input_tokens": 0,
    "output_tokens": 32251,
    "reasoning_output_tokens": 13152,
    "total_tokens": 1850085
  },
  "thread_token_usage": {
    "input_tokens": 1817834,
    "cached_input_tokens": 1755264,
    "cache_write_input_tokens": 0,
    "output_tokens": 32251,
    "reasoning_output_tokens": 13152,
    "total_tokens": 1850085
  }
}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-2f40910e-1902-4157-8b29-3c50e42370a8",
    "process_id": "34789",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python -u - <<'PY'\nimport sys,itertools\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nPTH='/task/data';cat=pd.read_parquet(PTH+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();n=len(items);target=pd.read_parquet(PTH+'/target_users.parquet').uid.to_numpy(np.int64);ur={int(x):i for i,x in enumerate(target)};E=CandidateSet(Path(PTH+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qidx=np.array([ur[u] for u in gold]);xrows=(target[qidx]//100-1).astype(int);quid=[int(target[r]) for r in qidx]\nwith np.load(PTH+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=n,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(n,np.float32);z[o]=1-np.arange(n)/n;return z\ngb=rs(F[0,2])+2*rs(L[1,1]);B=np.load('/app/solution/svd_binary_train.npz');UB=B['U'];VB=B['V'];A=np.load('/app/solution/svd_active_train.npz');UA=A['U'];VA=A['V'];S=sparse.load_npz('/app/solution/active_sim_q_train.npz');R=sparse.load_npz('/app/solution/recent7_train.npz')[(target//100-1).astype(int)]\n# prune top 200\nix=[];da=[];pt=[0]\nfor i in range(S.shape[0]):\n lo,hi=S.indptr[i:i+2];j=S.indices[lo:hi];v=S.data[lo:hi];m=j!=qidx[i];j=j[m];v=v[m]\n if len(v)>200:z=np.argpartition(v,-200)[-200:];j=j[z];v=v[z]\n ix.append(j);da.append(v);pt.append(pt[-1]+len(j))\nSK=sparse.csr_matrix((np.concatenate(da),np.concatenate(ix),np.array(pt)),shape=S.shape);NP=(SK@R).tocsr();sparse.save_npz('/app/solution/neighbor_recent_train.npz',NP)\nvariants=list(itertools.product([.01,.015,.02,.025,.03],[.0003,.0006,.0009],[0,.00015,.0003]));vals={v:[] for v in variants}\ndef std(Z):Z-=Z.mean(1)[:,None];d=Z.std(1);d[d==0]=1;return Z/d[:,None]\nfor st in range(0,len(qidx),128):\n br=qidx[st:st+128];SB=std(UB[xrows[st:st+128],:96]@VB[:96]);SA=std(UA[br]@VA);PN=NP[st:st+128].toarray();mx=PN.max(1);mx[mx==0]=1;PN/=mx[:,None]\n for v in variants:\n  Z=gb[None,:]+v[0]*PN+v[1]*SB+v[2]*SA;Z[~bits[br]]=-np.inf;top=np.argpartition(Z,-10,axis=1)[:,-10:]\n  for j,row in enumerate(top):row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[v].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\nfor x in sorted([(np.mean(z),v) for v,z in vals.items()],reverse=True)[:25]:print(x)\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport sys,itertools\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nPTH='/task/data';cat=pd.read_parquet(PTH+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();n=len(items);target=pd.read_parquet(PTH+'/target_users.parquet').uid.to_numpy(np.int64);ur={int(x):i for i,x in enumerate(target)};E=CandidateSet(Path(PTH+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qidx=np.array([ur[u] for u in gold]);xrows=(target[qidx]//100-1).astype(int);quid=[int(target[r]) for r in qidx]\nwith np.load(PTH+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=n,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(n,np.float32);z[o]=1-np.arange(n)/n;return z\ngb=rs(F[0,2])+2*rs(L[1,1]);B=np.load('/app/solution/svd_binary_train.npz');UB=B['U'];VB=B['V'];A=np.load('/app/solution/svd_active_train.npz');UA=A['U'];VA=A['V'];S=sparse.load_npz('/app/solution/active_sim_q_train.npz');R=sparse.load_npz('/app/solution/recent7_train.npz')[(target//100-1).astype(int)]\n# prune top 200\nix=[];da=[];pt=[0]\nfor i in range(S.shape[0]):\n lo,hi=S.indptr[i:i+2];j=S.indices[lo:hi];v=S.data[lo:hi];m=j!=qidx[i];j=j[m];v=v[m]\n if len(v)>200:z=np.argpartition(v,-200)[-200:];j=j[z];v=v[z]\n ix.append(j);da.append(v);pt.append(pt[-1]+len(j))\nSK=sparse.csr_matrix((np.concatenate(da),np.concatenate(ix),np.array(pt)),shape=S.shape);NP=(SK@R).tocsr();sparse.save_npz('/app/solution/neighbor_recent_train.npz',NP)\nvariants=list(itertools.product([.01,.015,.02,.025,.03],[.0003,.0006,.0009],[0,.00015,.0003]));vals={v:[] for v in variants}\ndef std(Z):Z-=Z.mean(1)[:,None];d=Z.std(1);d[d==0]=1;return Z/d[:,None]\nfor st in range(0,len(qidx),128):\n br=qidx[st:st+128];SB=std(UB[xrows[st:st+128],:96]@VB[:96]);SA=std(UA[br]@VA);PN=NP[st:st+128].toarray();mx=PN.max(1);mx[mx==0]=1;PN/=mx[:,None]\n for v in variants:\n  Z=gb[None,:]+v[0]*PN+v[1]*SB+v[2]*SA;Z[~bits[br]]=-np.inf;top=np.argpartition(Z,-10,axis=1)[:,-10:]\n  for j,row in enumerate(top):row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[v].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\nfor x in sorted([(np.mean(z),v) for v,z in vals.items()],reverse=True)[:25]:print(x)\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "(np.float64(0.04129788096117011), (0.02, 0.0006, 0.0003))\n(np.float64(0.04115708419337064), (0.015, 0.0006, 0.00015))\n(np.float64(0.04108797955522227), (0.015, 0.0006, 0.0003))\n(np.float64(0.04107968649340763), (0.01, 0.0006, 0.0003))\n(np.float64(0.041069981008229006), (0.015, 0.0009, 0.00015))\n(np.float64(0.04103696499739304), (0.02, 0.0009, 0.00015))\n(np.float64(0.04103009805436581), (0.02, 0.0009, 0.0003))\n(np.float64(0.040880197041195165), (0.025, 0.0009, 0.0003))\n(np.float64(0.04086624182649273), (0.02, 0.0006, 0.00015))\n(np.float64(0.0408578262021265), (0.015, 0.0009, 0.0003))\n(np.float64(0.04082906313027645), (0.03, 0.0009, 0.0003))\n(np.float64(0.04078799741458635), (0.025, 0.0006, 0.0003))\n(np.float64(0.040772988959447136), (0.01, 0.0006, 0.00015))\n(np.float64(0.04075338304407032), (0.025, 0.0009, 0.00015))\n(np.float64(0.0407306075008462), (0.015, 0.0006, 0))\n(np.float64(0.04068342318201982), (0.015, 0.0003, 0.0003))\n(np.float64(0.04063743707113819), (0.015, 0.0003, 0.00015))\n(np.float64(0.04062231686881125), (0.03, 0.0006, 0.0003))\n(np.float64(0.04061632275882384), (0.02, 0.0003, 0.0003))\n(np.float64(0.04061325343329181), (0.025, 0.0003, 0.0003))\n(np.float64(0.040545913758730096), (0.02, 0.0006, 0))\n(np.float64(0.040531547613006716), (0.03, 0.0006, 0.00015))\n(np.float64(0.04051341388160154), (0.015, 0.0003, 0))\n(np.float64(0.040494020628630924), (0.01, 0.0003, 0.0003))\n(np.float64(0.04049225865695971), (0.015, 0.0009, 0))\n",
    "stderr": "",
    "aggregated_output": "(np.float64(0.04129788096117011), (0.02, 0.0006, 0.0003))\n(np.float64(0.04115708419337064), (0.015, 0.0006, 0.00015))\n(np.float64(0.04108797955522227), (0.015, 0.0006, 0.0003))\n(np.float64(0.04107968649340763), (0.01, 0.0006, 0.0003))\n(np.float64(0.041069981008229006), (0.015, 0.0009, 0.00015))\n(np.float64(0.04103696499739304), (0.02, 0.0009, 0.00015))\n(np.float64(0.04103009805436581), (0.02, 0.0009, 0.0003))\n(np.float64(0.040880197041195165), (0.025, 0.0009, 0.0003))\n(np.float64(0.04086624182649273), (0.02, 0.0006, 0.00015))\n(np.float64(0.0408578262021265), (0.015, 0.0009, 0.0003))\n(np.float64(0.04082906313027645), (0.03, 0.0009, 0.0003))\n(np.float64(0.04078799741458635), (0.025, 0.0006, 0.0003))\n(np.float64(0.040772988959447136), (0.01, 0.0006, 0.00015))\n(np.float64(0.04075338304407032), (0.025, 0.0009, 0.00015))\n(np.float64(0.0407306075008462), (0.015, 0.0006, 0))\n(np.float64(0.04068342318201982), (0.015, 0.0003, 0.0003))\n(np.float64(0.04063743707113819), (0.015, 0.0003, 0.00015))\n(np.float64(0.04062231686881125), (0.03, 0.0006, 0.0003))\n(np.float64(0.04061632275882384), (0.02, 0.0003, 0.0003))\n(np.float64(0.04061325343329181), (0.025, 0.0003, 0.0003))\n(np.float64(0.040545913758730096), (0.02, 0.0006, 0))\n(np.float64(0.040531547613006716), (0.03, 0.0006, 0.00015))\n(np.float64(0.04051341388160154), (0.015, 0.0003, 0))\n(np.float64(0.040494020628630924), (0.01, 0.0003, 0.0003))\n(np.float64(0.04049225865695971), (0.015, 0.0009, 0))\n",
    "exit_code": 0,
    "duration": {
      "secs": 52,
      "nanos": 173471616
    },
    "formatted_output": "(np.float64(0.04129788096117011), (0.02, 0.0006, 0.0003))\n(np.float64(0.04115708419337064), (0.015, 0.0006, 0.00015))\n(np.float64(0.04108797955522227), (0.015, 0.0006, 0.0003))\n(np.float64(0.04107968649340763), (0.01, 0.0006, 0.0003))\n(np.float64(0.041069981008229006), (0.015, 0.0009, 0.00015))\n(np.float64(0.04103696499739304), (0.02, 0.0009, 0.00015))\n(np.float64(0.04103009805436581), (0.02, 0.0009, 0.0003))\n(np.float64(0.040880197041195165), (0.025, 0.0009, 0.0003))\n(np.float64(0.04086624182649273), (0.02, 0.0006, 0.00015))\n(np.float64(0.0408578262021265), (0.015, 0.0009, 0.0003))\n(np.float64(0.04082906313027645), (0.03, 0.0009, 0.0003))\n(np.float64(0.04078799741458635), (0.025, 0.0006, 0.0003))\n(np.float64(0.040772988959447136), (0.01, 0.0006, 0.00015))\n(np.float64(0.04075338304407032), (0.025, 0.0009, 0.00015))\n(np.float64(0.0407306075008462), (0.015, 0.0006, 0))\n(np.float64(0.04068342318201982), (0.015, 0.0003, 0.0003))\n(np.float64(0.04063743707113819), (0.015, 0.0003, 0.00015))\n(np.float64(0.04062231686881125), (0.03, 0.0006, 0.0003))\n(np.float64(0.04061632275882384), (0.02, 0.0003, 0.0003))\n(np.float64(0.04061325343329181), (0.025, 0.0003, 0.0003))\n(np.float64(0.040545913758730096), (0.02, 0.0006, 0))\n(np.float64(0.040531547613006716), (0.03, 0.0006, 0.00015))\n(np.float64(0.04051341388160154), (0.015, 0.0003, 0))\n(np.float64(0.040494020628630924), (0.01, 0.0003, 0.0003))\n(np.float64(0.04049225865695971), (0.015, 0.0009, 0))\n"
  }
}
66

Agent message

+26:08
Thinking unavailable No text recorded

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

67

exec command

+26:16
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:"ls -lh /app/solution",workdir:"/app",yield_time_ms:10000,max_output_tokens:5000});
text(r.output);

Tool response

Script completed
Wall time 0.0 seconds
Output:

total 171M
-rw-r--r-- 1 agent agent  312 Sep 25 01:00 README.md
-rw-r--r-- 1 agent agent  23M Sep 25 01:18 active_sim_q_train.npz
-rw-r--r-- 1 agent agent 2.6K Sep 25 01:00 build_ranker.py
-rw-r--r-- 1 agent agent 1.7M Sep 25 01:03 global_features.npz
-rw-r--r-- 1 agent agent  14M Sep 25 01:10 listen_counts_train.npz
-rw-r--r-- 1 agent agent  94M Sep 25 01:19 neighbor_recent_train.npz
-rw-r--r-- 1 agent agent 5.4M Sep 25 01:10 organic_counts_train.npz
-rw-r--r-- 1 agent agent 1.2M Sep 25 01:16 recent7_train.npz
-rw-r--r-- 1 agent agent 489K Sep 25 01:16 recent7org_train.npz
-rw-r--r-- 1 agent agent 719K Sep 25 01:00 submission.parquet
-rw-r--r-- 1 agent agent 4.1M Sep 25 01:13 svd_active_train.npz
-rw-r--r-- 1 agent agent  14M Sep 25 01:11 svd_binary_train.npz
-rw-r--r-- 1 agent agent  14M Sep 25 01:12 svd_log_train.npz
-rw-r--r-- 1 agent agent 712K Sep 25 01:00 validation.parquet
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-ca05e54a-33de-4f02-8d72-714549373a07",
    "process_id": "39706",
    "command": [
      "/bin/sh",
      "-lc",
      "ls -lh /app/solution"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "list_files",
        "cmd": "ls -lh /app/solution",
        "path": "solution"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "total 171M\n-rw-r--r-- 1 agent agent  312 Sep 25 01:00 README.md\n-rw-r--r-- 1 agent agent  23M Sep 25 01:18 active_sim_q_train.npz\n-rw-r--r-- 1 agent agent 2.6K Sep 25 01:00 build_ranker.py\n-rw-r--r-- 1 agent agent 1.7M Sep 25 01:03 global_features.npz\n-rw-r--r-- 1 agent agent  14M Sep 25 01:10 listen_counts_train.npz\n-rw-r--r-- 1 agent agent  94M Sep 25 01:19 neighbor_recent_train.npz\n-rw-r--r-- 1 agent agent 5.4M Sep 25 01:10 organic_counts_train.npz\n-rw-r--r-- 1 agent agent 1.2M Sep 25 01:16 recent7_train.npz\n-rw-r--r-- 1 agent agent 489K Sep 25 01:16 recent7org_train.npz\n-rw-r--r-- 1 agent agent 719K Sep 25 01:00 submission.parquet\n-rw-r--r-- 1 agent agent 4.1M Sep 25 01:13 svd_active_train.npz\n-rw-r--r-- 1 agent agent  14M Sep 25 01:11 svd_binary_train.npz\n-rw-r--r-- 1 agent agent  14M Sep 25 01:12 svd_log_train.npz\n-rw-r--r-- 1 agent agent 712K Sep 25 01:00 validation.parquet\n",
    "stderr": "",
    "aggregated_output": "total 171M\n-rw-r--r-- 1 agent agent  312 Sep 25 01:00 README.md\n-rw-r--r-- 1 agent agent  23M Sep 25 01:18 active_sim_q_train.npz\n-rw-r--r-- 1 agent agent 2.6K Sep 25 01:00 build_ranker.py\n-rw-r--r-- 1 agent agent 1.7M Sep 25 01:03 global_features.npz\n-rw-r--r-- 1 agent agent  14M Sep 25 01:10 listen_counts_train.npz\n-rw-r--r-- 1 agent agent  94M Sep 25 01:19 neighbor_recent_train.npz\n-rw-r--r-- 1 agent agent 5.4M Sep 25 01:10 organic_counts_train.npz\n-rw-r--r-- 1 agent agent 1.2M Sep 25 01:16 recent7_train.npz\n-rw-r--r-- 1 agent agent 489K Sep 25 01:16 recent7org_train.npz\n-rw-r--r-- 1 agent agent 719K Sep 25 01:00 submission.parquet\n-rw-r--r-- 1 agent agent 4.1M Sep 25 01:13 svd_active_train.npz\n-rw-r--r-- 1 agent agent  14M Sep 25 01:11 svd_binary_train.npz\n-rw-r--r-- 1 agent agent  14M Sep 25 01:12 svd_log_train.npz\n-rw-r--r-- 1 agent agent 712K Sep 25 01:00 validation.parquet\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 6680
    },
    "formatted_output": "total 171M\n-rw-r--r-- 1 agent agent  312 Sep 25 01:00 README.md\n-rw-r--r-- 1 agent agent  23M Sep 25 01:18 active_sim_q_train.npz\n-rw-r--r-- 1 agent agent 2.6K Sep 25 01:00 build_ranker.py\n-rw-r--r-- 1 agent agent 1.7M Sep 25 01:03 global_features.npz\n-rw-r--r-- 1 agent agent  14M Sep 25 01:10 listen_counts_train.npz\n-rw-r--r-- 1 agent agent  94M Sep 25 01:19 neighbor_recent_train.npz\n-rw-r--r-- 1 agent agent 5.4M Sep 25 01:10 organic_counts_train.npz\n-rw-r--r-- 1 agent agent 1.2M Sep 25 01:16 recent7_train.npz\n-rw-r--r-- 1 agent agent 489K Sep 25 01:16 recent7org_train.npz\n-rw-r--r-- 1 agent agent 719K Sep 25 01:00 submission.parquet\n-rw-r--r-- 1 agent agent 4.1M Sep 25 01:13 svd_active_train.npz\n-rw-r--r-- 1 agent agent  14M Sep 25 01:11 svd_binary_train.npz\n-rw-r--r-- 1 agent agent  14M Sep 25 01:12 svd_log_train.npz\n-rw-r--r-- 1 agent agent 712K Sep 25 01:00 validation.parquet\n"
  }
}
Harness context token usage record
{
  "type": "token_usage_record",
  "thread_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "response_id": "resp_0fdbf3e20a3ba82b016ab5cc39ff6487d2961b99a07937b35f",
  "usage": {
    "input_tokens": 66817,
    "cached_input_tokens": 65792,
    "cache_write_input_tokens": 0,
    "output_tokens": 1006,
    "reasoning_output_tokens": 953,
    "total_tokens": 67823
  },
  "turn_token_usage": {
    "input_tokens": 1884651,
    "cached_input_tokens": 1821056,
    "cache_write_input_tokens": 0,
    "output_tokens": 33257,
    "reasoning_output_tokens": 14105,
    "total_tokens": 1917908
  },
  "thread_token_usage": {
    "input_tokens": 1884651,
    "cached_input_tokens": 1821056,
    "cache_write_input_tokens": 0,
    "output_tokens": 33257,
    "reasoning_output_tokens": 14105,
    "total_tokens": 1917908
  }
}
68

Agent message

+26:27
Thinking unavailable No text recorded

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

69

Agent message

+26:35
Thinking unavailable No text recorded

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

70

exec command

+26:42
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const r = await tools.exec_command({cmd:"/opt/task-venv/bin/python -u - <<'PY'\nimport sys\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();n=len(items);target=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64);ur={int(x):i for i,x in enumerate(target)};ic={int(x):i for i,x in enumerate(items)};E=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qidx=np.array([ur[u] for u in gold]);xrows=(target[qidx]//100-1).astype(int);quid=[int(target[r]) for r in qidx]\nwith np.load(P+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=n,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(n,np.float32);z[o]=1-np.arange(n)/n;return z\ngb=rs(F[0,2])+2*rs(L[1,1]);B=np.load('/app/solution/svd_binary_train.npz');UB=B['U'];VB=B['V'];A=np.load('/app/solution/svd_active_train.npz');UA=A['U'];VA=A['V'];NP=sparse.load_npz('/app/solution/neighbor_recent_train.npz')\nps=pd.read_parquet(P+'/validation_preference_state.parquet');pd_=ps[ps.state=='disliked'];rr=np.array([ur.get(int(x),-1) for x in pd_.uid]);cc=np.array([ic.get(int(x),-1) for x in pd_.item_id]);ok=(rr>=0)&(cc>=0);XD=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(rr[ok],cc[ok])),shape=(len(target),n));XD.data[:]=1;UD=([email protected]).astype(np.float32)\ndef std(Z):Z-=Z.mean(1)[:,None];d=Z.std(1);d[d==0]=1;return Z/d[:,None]\ncoefs=[-.001,-.0007,-.0005,-.0003,-.0002,-.0001,-.00005,0,.00005,.0001,.0002];vals={c:[] for c in coefs}\nfor st in range(0,len(qidx),128):\n br=qidx[st:st+128];SB=std(UB[xrows[st:st+128],:96]@VB[:96]);SA=std(UA[br]@VA);SD=std(UD[br]@VA);PN=NP[st:st+128].toarray();mx=PN.max(1);mx[mx==0]=1;PN/=mx[:,None]\n for c in coefs:\n  Z=gb[None,:]+.02*PN+.0006*SB+.0003*SA+c*SD;Z[~bits[br]]=-np.inf;top=np.argpartition(Z,-10,axis=1)[:,-10:]\n  for j,row in enumerate(top):row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[c].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\nprint([(c,float(np.mean(vals[c]))) for c in coefs])\nPY",workdir:"/app",yield_time_ms:30000,max_output_tokens:10000});
if(r.session_id){text(`SESSION_ID=${r.session_id}`);text(r.output);}else{text(r.output);}

Tool response

Script completed
Wall time 14.2 seconds
Output:

[(-0.001, 0.03751401616451922), (-0.0007, 0.03875829585420797), (-0.0005, 0.039311597903167794), (-0.0003, 0.04015163011866139), (-0.0002, 0.04071082036559139), (-0.0001, 0.04097170096264719), (-5e-05, 0.0411363754471562), (0, 0.04129788096117011), (5e-05, 0.04122734667043429), (0.0001, 0.041307536916685234), (0.0002, 0.041164600067995535)]
Harness context token usage record
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Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-07252fee-8b26-4dcd-b8b8-a195b4da702d",
    "process_id": "53056",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python -u - <<'PY'\nimport sys\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();n=len(items);target=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64);ur={int(x):i for i,x in enumerate(target)};ic={int(x):i for i,x in enumerate(items)};E=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qidx=np.array([ur[u] for u in gold]);xrows=(target[qidx]//100-1).astype(int);quid=[int(target[r]) for r in qidx]\nwith np.load(P+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=n,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(n,np.float32);z[o]=1-np.arange(n)/n;return z\ngb=rs(F[0,2])+2*rs(L[1,1]);B=np.load('/app/solution/svd_binary_train.npz');UB=B['U'];VB=B['V'];A=np.load('/app/solution/svd_active_train.npz');UA=A['U'];VA=A['V'];NP=sparse.load_npz('/app/solution/neighbor_recent_train.npz')\nps=pd.read_parquet(P+'/validation_preference_state.parquet');pd_=ps[ps.state=='disliked'];rr=np.array([ur.get(int(x),-1) for x in pd_.uid]);cc=np.array([ic.get(int(x),-1) for x in pd_.item_id]);ok=(rr>=0)&(cc>=0);XD=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(rr[ok],cc[ok])),shape=(len(target),n));XD.data[:]=1;UD=([email protected]).astype(np.float32)\ndef std(Z):Z-=Z.mean(1)[:,None];d=Z.std(1);d[d==0]=1;return Z/d[:,None]\ncoefs=[-.001,-.0007,-.0005,-.0003,-.0002,-.0001,-.00005,0,.00005,.0001,.0002];vals={c:[] for c in coefs}\nfor st in range(0,len(qidx),128):\n br=qidx[st:st+128];SB=std(UB[xrows[st:st+128],:96]@VB[:96]);SA=std(UA[br]@VA);SD=std(UD[br]@VA);PN=NP[st:st+128].toarray();mx=PN.max(1);mx[mx==0]=1;PN/=mx[:,None]\n for c in coefs:\n  Z=gb[None,:]+.02*PN+.0006*SB+.0003*SA+c*SD;Z[~bits[br]]=-np.inf;top=np.argpartition(Z,-10,axis=1)[:,-10:]\n  for j,row in enumerate(top):row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[c].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\nprint([(c,float(np.mean(vals[c]))) for c in coefs])\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport sys\nsys.path.insert(0,'/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet,ndcg_at_k\nfrom pathlib import Path\nimport pandas as pd,numpy as np\nfrom scipy import sparse\nP='/task/data';cat=pd.read_parquet(P+'/candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);pr=cat.popularity_rank.to_numpy();n=len(items);target=pd.read_parquet(P+'/target_users.parquet').uid.to_numpy(np.int64);ur={int(x):i for i,x in enumerate(target)};ic={int(x):i for i,x in enumerate(items)};E=CandidateSet(Path(P+'/validation_eligible_candidates.npz'));gold=validation_truth(E,set(map(int,target)));qidx=np.array([ur[u] for u in gold]);xrows=(target[qidx]//100-1).astype(int);quid=[int(target[r]) for r in qidx]\nwith np.load(P+'/validation_eligible_candidates.npz') as z:bits=np.unpackbits(z['packed_eligible'],axis=1,count=n,bitorder='little').astype(bool)\ng=np.load('/app/solution/global_features.npz');F=g['F'];L=g['LF']\ndef rs(x):o=np.lexsort((pr,-x));z=np.empty(n,np.float32);z[o]=1-np.arange(n)/n;return z\ngb=rs(F[0,2])+2*rs(L[1,1]);B=np.load('/app/solution/svd_binary_train.npz');UB=B['U'];VB=B['V'];A=np.load('/app/solution/svd_active_train.npz');UA=A['U'];VA=A['V'];NP=sparse.load_npz('/app/solution/neighbor_recent_train.npz')\nps=pd.read_parquet(P+'/validation_preference_state.parquet');pd_=ps[ps.state=='disliked'];rr=np.array([ur.get(int(x),-1) for x in pd_.uid]);cc=np.array([ic.get(int(x),-1) for x in pd_.item_id]);ok=(rr>=0)&(cc>=0);XD=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(rr[ok],cc[ok])),shape=(len(target),n));XD.data[:]=1;UD=([email protected]).astype(np.float32)\ndef std(Z):Z-=Z.mean(1)[:,None];d=Z.std(1);d[d==0]=1;return Z/d[:,None]\ncoefs=[-.001,-.0007,-.0005,-.0003,-.0002,-.0001,-.00005,0,.00005,.0001,.0002];vals={c:[] for c in coefs}\nfor st in range(0,len(qidx),128):\n br=qidx[st:st+128];SB=std(UB[xrows[st:st+128],:96]@VB[:96]);SA=std(UA[br]@VA);SD=std(UD[br]@VA);PN=NP[st:st+128].toarray();mx=PN.max(1);mx[mx==0]=1;PN/=mx[:,None]\n for c in coefs:\n  Z=gb[None,:]+.02*PN+.0006*SB+.0003*SA+c*SD;Z[~bits[br]]=-np.inf;top=np.argpartition(Z,-10,axis=1)[:,-10:]\n  for j,row in enumerate(top):row=row[np.argsort(Z[j,row])[::-1]];ii=st+j;vals[c].append(ndcg_at_k(items[row].tolist(),gold[quid[ii]],10))\nprint([(c,float(np.mean(vals[c]))) for c in coefs])\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "[(-0.001, 0.03751401616451922), (-0.0007, 0.03875829585420797), (-0.0005, 0.039311597903167794), (-0.0003, 0.04015163011866139), (-0.0002, 0.04071082036559139), (-0.0001, 0.04097170096264719), (-5e-05, 0.0411363754471562), (0, 0.04129788096117011), (5e-05, 0.04122734667043429), (0.0001, 0.041307536916685234), (0.0002, 0.041164600067995535)]\n",
    "stderr": "",
    "aggregated_output": "[(-0.001, 0.03751401616451922), (-0.0007, 0.03875829585420797), (-0.0005, 0.039311597903167794), (-0.0003, 0.04015163011866139), (-0.0002, 0.04071082036559139), (-0.0001, 0.04097170096264719), (-5e-05, 0.0411363754471562), (0, 0.04129788096117011), (5e-05, 0.04122734667043429), (0.0001, 0.041307536916685234), (0.0002, 0.041164600067995535)]\n",
    "exit_code": 0,
    "duration": {
      "secs": 14,
      "nanos": 38895489
    },
    "formatted_output": "[(-0.001, 0.03751401616451922), (-0.0007, 0.03875829585420797), (-0.0005, 0.039311597903167794), (-0.0003, 0.04015163011866139), (-0.0002, 0.04071082036559139), (-0.0001, 0.04097170096264719), (-5e-05, 0.0411363754471562), (0, 0.04129788096117011), (5e-05, 0.04122734667043429), (0.0001, 0.041307536916685234), (0.0002, 0.041164600067995535)]\n"
  }
}
71

Agent message

+27:22
Thinking unavailable No text recorded

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

72

exec command

+27:31
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 audience-propagation model materially improves the result: the combined validation score is about 0.04130, nearly four times the original popularity baseline. I’m locking this ensemble and refitting it at the test cutoff with validation events treated only as observed history, as required.

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Delete File: /app/solution/build_ranker.py\n*** Add File: /app/solution/build_ranker.py\n+#!/usr/bin/env python3\n+\"\"\"Build the cutoff-correct Yambda discovery ranking.\n+\n+The model combines (1) very recent global momentum, (2) an implicit-listen\n+SVD, (3) an active-like SVD, and (4) propagation of recent listens through an\n+active-like nearest-neighbour graph.  Validation history is included only for\n+the test-stage refit.\n+\"\"\"\n+from __future__ import annotations\n+\n+import argparse\n+from pathlib import Path\n+\n+import numpy as np\n+import pandas as pd\n+import pyarrow.parquet as pq\n+from scipy import sparse\n+from sklearn.decomposition import TruncatedSVD\n+\n+\n+DATA = Path(\"/task/data\")\n+OUT = Path(\"/app/solution\")\n+TRAIN_CUTOFF = 24_786_800\n+TEST_CUTOFF = 25_393_400\n+DAY = 86_400\n+TOP_N = 100\n+N_UID_ROWS = 10_000  # Yambda user IDs in this extract are 100, 200, ...\n+\n+\n+def item_mapper(items: np.ndarray):\n+    sorted_items = np.sort(items)\n+    sorted_to_catalog = np.argsort(items)\n+\n+    def map_items(raw: np.ndarray) -> tuple[np.ndarray, np.ndarray]:\n+        pos = np.searchsorted(sorted_items, raw)\n+        valid = pos < len(items)\n+        valid &= sorted_items[np.minimum(pos, len(items) - 1)] == raw\n+        return sorted_to_catalog[pos[valid]].astype(np.int32), valid\n+\n+    return map_items\n+\n+\n+def scan_listens(\n+    paths: list[Path], cutoff: int, map_items, n_items: int\n+) -> tuple[sparse.csr_matrix, sparse.csr_matrix]:\n+    \"\"\"Return positive-listen counts over all history and over the last 7d.\"\"\"\n+    rows: list[np.ndarray] = []\n+    cols: list[np.ndarray] = []\n+    recent_rows: list[np.ndarray] = []\n+    recent_cols: list[np.ndarray] = []\n+    for path in paths:\n+        parquet = pq.ParquetFile(path)\n+        for batch in parquet.iter_batches(\n+            batch_size=2_000_000,\n+            columns=[\"uid\", \"timestamp\", \"item_id\", \"played_ratio_pct\"],\n+        ):\n+            uid = np.asarray(batch.column(\"uid\")).astype(np.int32, copy=False)\n+            timestamp = np.asarray(batch.column(\"timestamp\")).astype(np.int64, copy=False)\n+            item = np.asarray(batch.column(\"item_id\")).astype(np.int64, copy=False)\n+            ratio = np.asarray(batch.column(\"played_ratio_pct\")).astype(np.uint16, copy=False)\n+            positive = ratio >= 50\n+            mapped, in_catalog = map_items(item[positive])\n+            mapped_rows = (uid[positive][in_catalog] // 100 - 1).astype(np.int32)\n+            mapped_time = timestamp[positive][in_catalog]\n+            rows.append(mapped_rows)\n+            cols.append(mapped)\n+            recent = mapped_time >= cutoff - 7 * DAY\n+            recent_rows.append(mapped_rows[recent])\n+            recent_cols.append(mapped[recent])\n+\n+    row = np.concatenate(rows)\n+    col = np.concatenate(cols)\n+    history = sparse.coo_matrix(\n+        (np.ones(len(row), dtype=np.float32), (row, col)),\n+        shape=(N_UID_ROWS, n_items),\n+    ).tocsr()\n+    history.sum_duplicates()\n+    rr = np.concatenate(recent_rows)\n+    rc = np.concatenate(recent_cols)\n+    recent = sparse.coo_matrix(\n+        (np.ones(len(rr), dtype=np.float32), (rr, rc)),\n+        shape=(N_UID_ROWS, n_items),\n+    ).tocsr()\n+    recent.sum_duplicates()\n+    return history, recent\n+\n+\n+def history_matrices(\n+    stage: str, cutoff: int, map_items, n_items: int\n+) -> tuple[sparse.csr_matrix, sparse.csr_matrix]:\n+    \"\"\"Use optional local caches while retaining from-scratch reproducibility.\"\"\"\n+    train_cache = OUT / \"listen_counts_train.npz\"\n+    recent_cache = OUT / \"recent7_train.npz\"\n+    if train_cache.exists():\n+        train = sparse.load_npz(train_cache).astype(np.float32)\n+    else:\n+        train, train_recent = scan_listens(\n+            [DATA / \"train_listens.parquet\"], TRAIN_CUTOFF, map_items, n_items\n+        )\n+        sparse.save_npz(train_cache, train, compressed=True)\n+        sparse.save_npz(recent_cache, train_recent, compressed=True)\n+\n+    if stage == \"val\":\n+        if recent_cache.exists():\n+            recent = sparse.load_npz(recent_cache).astype(np.float32)\n+        else:\n+            _, recent = scan_listens(\n+                [DATA / \"train_listens.parquet\"], cutoff, map_items, n_items\n+            )\n+        return train, recent\n+\n+    validation, recent = scan_listens(\n+        [DATA / \"val_listens.parquet\"], cutoff, map_items, n_items\n+    )\n+    return (train + validation).tocsr(), recent\n+\n+\n+def recent_organic_likes(\n+    stage: str, cutoff: int, map_items, n_items: int\n+) -> np.ndarray:\n+    path = DATA / (\"train_likes.parquet\" if stage == \"val\" else \"val_likes.parquet\")\n+    frame = pd.read_parquet(path, columns=[\"timestamp\", \"item_id\", \"is_organic\"])\n+    frame = frame[(frame.is_organic == 1) & (frame.timestamp >= cutoff - 3 * DAY)]\n+    col, valid = map_items(frame.item_id.to_numpy(np.int64))\n+    return np.bincount(col, minlength=n_items).astype(np.float32)\n+\n+\n+def active_matrix(\n+    preference_path: Path,\n+    target_users: np.ndarray,\n+    map_items,\n+    n_items: int,\n+) -> sparse.csr_matrix:\n+    preference = pd.read_parquet(preference_path)\n+    preference = preference[preference.state == \"liked\"]\n+    uid_row = {int(uid): row for row, uid in enumerate(target_users)}\n+    raw_rows = np.fromiter(\n+        (uid_row.get(int(uid), -1) for uid in preference.uid),\n+        dtype=np.int32,\n+        count=len(preference),\n+    )\n+    col, item_valid = map_items(preference.item_id.to_numpy(np.int64))\n+    mapped_rows = raw_rows[item_valid]\n+    row_valid = mapped_rows >= 0\n+    matrix = sparse.csr_matrix(\n+        (\n+            np.ones(int(row_valid.sum()), dtype=np.float32),\n+            (mapped_rows[row_valid], col[row_valid]),\n+        ),\n+        shape=(len(target_users), n_items),\n+    )\n+    matrix.sum_duplicates()\n+    matrix.data[:] = 1.0\n+    return matrix\n+\n+\n+def rank_score(values: np.ndarray, popularity_rank: np.ndarray) -> np.ndarray:\n+    order = np.lexsort((popularity_rank, -values))\n+    score = np.empty(len(values), dtype=np.float32)\n+    score[order] = 1.0 - np.arange(len(values), dtype=np.float32) / len(values)\n+    return score\n+\n+\n+def topk_similarity(similarity: sparse.csr_matrix, k: int) -> sparse.csr_matrix:\n+    indices: list[np.ndarray] = []\n+    values: list[np.ndarray] = []\n+    indptr = [0]\n+    for row in range(similarity.shape[0]):\n+        lo, hi = similarity.indptr[row : row + 2]\n+        idx = similarity.indices[lo:hi]\n+        val = similarity.data[lo:hi]\n+        keep = idx != row\n+        idx, val = idx[keep], val[keep]\n+        if len(val) > k:\n+            selected = np.argpartition(val, -k)[-k:]\n+            idx, val = idx[selected], val[selected]\n+        indices.append(idx)\n+        values.append(val)\n+        indptr.append(indptr[-1] + len(idx))\n+    return sparse.csr_matrix(\n+        (np.concatenate(values), np.concatenate(indices), np.asarray(indptr)),\n+        shape=similarity.shape,\n+    )\n+\n+\n+def standardize_rows(values: np.ndarray) -> np.ndarray:\n+    values -= values.mean(axis=1, keepdims=True)\n+    scale = values.std(axis=1)\n+    scale[scale == 0] = 1.0\n+    values /= scale[:, None]\n+    return values\n+\n+\n+def build(stage: str) -> pd.DataFrame:\n+    cutoff = TRAIN_CUTOFF if stage == \"val\" else TEST_CUTOFF\n+    eligible_name = (\n+        \"validation_eligible_candidates.npz\"\n+        if stage == \"val\"\n+        else \"test_eligible_candidates.npz\"\n+    )\n+    preference_name = (\n+        \"validation_preference_state.parquet\"\n+        if stage == \"val\"\n+        else \"test_preference_state.parquet\"\n+    )\n+    catalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n+    items = catalog.item_id.to_numpy(np.int64)\n+    popularity_rank = catalog.popularity_rank.to_numpy(np.int64)\n+    n_items = len(items)\n+    target_users = pd.read_parquet(DATA / \"target_users.parquet\").uid.to_numpy(np.int64)\n+    map_items = item_mapper(items)\n+\n+    history, recent = history_matrices(stage, cutoff, map_items, n_items)\n+    listen_momentum = np.asarray(recent.sum(axis=0)).ravel().astype(np.float32)\n+    like_momentum = recent_organic_likes(stage, cutoff, map_items, n_items)\n+    global_score = rank_score(listen_momentum, popularity_rank)\n+    global_score += 2.0 * rank_score(like_momentum, popularity_rank)\n+\n+    # Broad implicit-taste representation. Counts are binarized deliberately:\n+    # repeated plays should not swamp cross-track affinity.\n+    history.data[:] = 1.0\n+    listen_svd = TruncatedSVD(n_components=96, n_iter=5, random_state=42)\n+    listen_users = listen_svd.fit_transform(history).astype(np.float32)\n+    listen_items = listen_svd.components_.astype(np.float32)\n+\n+    active = active_matrix(DATA / preference_name, target_users, map_items, n_items)\n+    active_svd = TruncatedSVD(n_components=32, n_iter=7, random_state=42)\n+    active_users = active_svd.fit_transform(active).astype(np.float32)\n+    active_items = active_svd.components_.astype(np.float32)\n+\n+    # Recommend tracks currently spreading among users with similar explicit\n+    # tastes. The recent matrix is restricted to target-user neighbour rows,\n+    # matching the population for which cutoff state is supplied.\n+    norm = np.sqrt(np.asarray(active.multiply(active).sum(axis=1)).ravel())\n+    norm[norm == 0] = 1.0\n+    normalized_active = sparse.diags((1.0 / norm).astype(np.float32)) @ active\n+    similarity = (normalized_active @ normalized_active.T).tocsr()\n+    similarity = topk_similarity(similarity, 200)\n+    uid_rows = (target_users // 100 - 1).astype(np.int32)\n+    neighbour_recent = (similarity @ recent[uid_rows]).tocsr()\n+\n+    with np.load(DATA / eligible_name, allow_pickle=False) as artifact:\n+        eligible_uids = artifact[\"uids\"].astype(np.int64, copy=True)\n+        eligible_items = artifact[\"item_ids\"].astype(np.int64, copy=True)\n+        packed = artifact[\"packed_eligible\"].copy()\n+    if not np.array_equal(eligible_uids, target_users):\n+        raise RuntimeError(\"eligibility users are not aligned to target_users\")\n+    if not np.array_equal(eligible_items, items):\n+        raise RuntimeError(\"eligibility items are not aligned to candidate catalog\")\n+    eligible = np.unpackbits(packed, axis=1, count=n_items, bitorder=\"little\")\n+\n+    out_items = np.empty((len(target_users), TOP_N), dtype=np.int64)\n+    block_size = 128\n+    for start in range(0, len(target_users), block_size):\n+        end = min(start + block_size, len(target_users))\n+        listen_affinity = standardize_rows(\n+            listen_users[uid_rows[start:end]] @ listen_items\n+        )\n+        active_affinity = standardize_rows(\n+            active_users[start:end] @ active_items\n+        )\n+        propagation = neighbour_recent[start:end].toarray().astype(np.float32)\n+        prop_scale = propagation.max(axis=1)\n+        prop_scale[prop_scale == 0] = 1.0\n+        propagation /= prop_scale[:, None]\n+        score = (\n+            global_score[None, :]\n+            + 0.0200 * propagation\n+            + 0.0006 * listen_affinity\n+            + 0.0003 * active_affinity\n+        )\n+        score[eligible[start:end] == 0] = -np.inf\n+        chosen = np.argpartition(score, -TOP_N, axis=1)[:, -TOP_N:]\n+        for local_row in range(end - start):\n+            cols = chosen[local_row]\n+            cols = cols[np.argsort(score[local_row, cols])[::-1]]\n+            out_items[start + local_row] = items[cols]\n+\n+    return pd.DataFrame(\n+        {\n+            \"uid\": np.repeat(target_users, TOP_N),\n+            \"item_id\": out_items.ravel(),\n+            \"rank\": np.tile(np.arange(1, TOP_N + 1, dtype=np.int64), len(target_users)),\n+        }\n+    )\n+\n+\n+def main() -> None:\n+    parser = argparse.ArgumentParser()\n+    parser.add_argument(\"--stage\", choices=(\"val\", \"test\"), default=\"test\")\n+    args = parser.parse_args()\n+    ranking = build(args.stage)\n+    final = OUT / (\"validation.parquet\" if args.stage == \"val\" else \"submission.parquet\")\n+    temporary = OUT / (final.stem + \".tmp.parquet\")\n+    ranking.to_parquet(temporary, index=False)\n+    temporary.replace(final)\n+    print(f\"wrote {len(ranking):,} rows to {final}\")\n+\n+\n+if __name__ == \"__main__\":\n+    main()\n*** Delete File: /app/solution/README.md\n*** Add File: /app/solution/README.md\n+# Music-discovery ranker\n+\n+`build_ranker.py` produces the requested 100-track ranking. It uses only\n+cutoff-available inputs and combines seven-day listen momentum, three-day\n+organic-like momentum, implicit-listen and active-like SVD affinities, and\n+recent-listen propagation across 200 active-like nearest neighbours.\n+\n+The public validation model uses training history only:\n+\n+```sh\n+/opt/task-venv/bin/python /app/solution/build_ranker.py --stage val\n+/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\n+```\n+\n+The final refit treats validation events as history and writes the deliverable:\n+\n+```sh\n+/opt/task-venv/bin/python /app/solution/build_ranker.py --stage test\n+```\n+\n+The selected ensemble scores approximately **0.04130 NDCG@10** on the public\n+temporal validation window (the popularity-only starting point scored 0.01071).\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response

Script failed
Wall time 0.0 seconds
Output:

Script error:
apply_patch verification failed: invalid patch: multiple operations target /app/solution/build_ranker.py
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73

exec command

+28:46
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 p1 = "*** Begin Patch\n*** Delete File: /app/solution/build_ranker.py\n*** Delete File: /app/solution/README.md\n*** End Patch";
text(await tools.apply_patch(p1));

Tool response

Script completed
Wall time 0.0 seconds
Output:

{}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-803bfcc7-2c19-425f-8868-28d850dda76e",
    "changes": {
      "/app/solution/build_ranker.py": {
        "type": "delete",
        "content": "#!/usr/bin/env python3\n\"\"\"Build cutoff-correct music discovery rankings.\n\nThe script is intentionally self contained.  With no arguments it refits on\ntrain + validation history and writes ``submission.parquet``.  ``--stage val``\nuses training history only and writes ``validation.parquet``.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\nDATA = Path(\"/task/data\")\nOUT = Path(\"/app/solution\")\nTOP_N = 100\n\n\ndef load_candidates(stage: str) -> tuple[np.ndarray, np.ndarray, np.ndarray]:\n    path = DATA / (\"validation_eligible_candidates.npz\" if stage == \"val\" else \"test_eligible_candidates.npz\")\n    with np.load(path, allow_pickle=False) as z:\n        return z[\"uids\"].copy(), z[\"item_ids\"].copy(), z[\"packed_eligible\"].copy()\n\n\ndef is_eligible(packed_row: np.ndarray, column: int) -> bool:\n    return bool((int(packed_row[column // 8]) >> (column % 8)) & 1)\n\n\ndef rank_global(stage: str, ordered_items: np.ndarray) -> pd.DataFrame:\n    \"\"\"Apply a global item order while respecting each user's candidate mask.\"\"\"\n    uids, candidate_items, packed = load_candidates(stage)\n    item_col = {int(item): col for col, item in enumerate(candidate_items)}\n    ordered_cols = np.asarray([item_col[int(item)] for item in ordered_items], dtype=np.int32)\n    out_uid = np.repeat(uids.astype(np.int64), TOP_N)\n    out_item = np.empty(len(uids) * TOP_N, dtype=np.int64)\n    for row in range(len(uids)):\n        chosen: list[int] = []\n        for col in ordered_cols:\n            if is_eligible(packed[row], int(col)):\n                chosen.append(int(candidate_items[col]))\n                if len(chosen) == TOP_N:\n                    break\n        if len(chosen) != TOP_N:\n            raise RuntimeError(f\"only {len(chosen)} eligible items for uid={uids[row]}\")\n        out_item[row * TOP_N : (row + 1) * TOP_N] = chosen\n    return pd.DataFrame(\n        {\n            \"uid\": out_uid,\n            \"item_id\": out_item,\n            \"rank\": np.tile(np.arange(1, TOP_N + 1, dtype=np.int64), len(uids)),\n        }\n    )\n\n\ndef main() -> None:\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--stage\", choices=(\"val\", \"test\"), default=\"test\")\n    args = parser.parse_args()\n    catalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n    ranking = rank_global(args.stage, catalog.sort_values(\"popularity_rank\").item_id.to_numpy())\n    path = OUT / (\"validation.parquet\" if args.stage == \"val\" else \"submission.parquet\")\n    ranking.to_parquet(path, index=False)\n    print(f\"wrote {len(ranking):,} rows to {path}\")\n\n\nif __name__ == \"__main__\":\n    main()\n"
      },
      "/app/solution/README.md": {
        "type": "delete",
        "content": "# Music-discovery ranker\n\nRun with the task environment:\n\n```sh\n/opt/task-venv/bin/python /app/solution/build_ranker.py\n```\n\nThis refits the selected ranker at the test cutoff and writes\n`submission.parquet`.  Use `--stage val` to construct the leakage-safe public\nvalidation ranking from training history only.\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nD /app/solution/build_ranker.py\nD /app/solution/README.md\n",
    "stderr": ""
  }
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74

exec command

+28:49
Thinking unavailable No text recorded

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

Tool callexecTool call Returned

Tool call · Input

const patch = "*** Begin Patch\n*** Add File: /app/solution/build_ranker.py\n+#!/usr/bin/env python3\n+\"\"\"Build the cutoff-correct Yambda discovery ranking.\n+\n+The model combines (1) very recent global momentum, (2) an implicit-listen\n+SVD, (3) an active-like SVD, and (4) propagation of recent listens through an\n+active-like nearest-neighbour graph. Validation history is included only for\n+the test-stage refit.\n+\"\"\"\n+from __future__ import annotations\n+\n+import argparse\n+from pathlib import Path\n+\n+import numpy as np\n+import pandas as pd\n+import pyarrow.parquet as pq\n+from scipy import sparse\n+from sklearn.decomposition import TruncatedSVD\n+\n+\n+DATA = Path(\"/task/data\")\n+OUT = Path(\"/app/solution\")\n+TRAIN_CUTOFF = 24_786_800\n+TEST_CUTOFF = 25_393_400\n+DAY = 86_400\n+TOP_N = 100\n+N_UID_ROWS = 10_000  # Yambda user IDs in this extract are 100, 200, ...\n+\n+\n+def item_mapper(items: np.ndarray):\n+    sorted_items = np.sort(items)\n+    sorted_to_catalog = np.argsort(items)\n+\n+    def map_items(raw: np.ndarray) -> tuple[np.ndarray, np.ndarray]:\n+        pos = np.searchsorted(sorted_items, raw)\n+        valid = pos < len(items)\n+        valid &= sorted_items[np.minimum(pos, len(items) - 1)] == raw\n+        return sorted_to_catalog[pos[valid]].astype(np.int32), valid\n+\n+    return map_items\n+\n+\n+def scan_listens(\n+    paths: list[Path], cutoff: int, map_items, n_items: int\n+) -> tuple[sparse.csr_matrix, sparse.csr_matrix]:\n+    \"\"\"Return positive-listen counts over all history and over the last 7d.\"\"\"\n+    rows: list[np.ndarray] = []\n+    cols: list[np.ndarray] = []\n+    recent_rows: list[np.ndarray] = []\n+    recent_cols: list[np.ndarray] = []\n+    for path in paths:\n+        parquet = pq.ParquetFile(path)\n+        for batch in parquet.iter_batches(\n+            batch_size=2_000_000,\n+            columns=[\"uid\", \"timestamp\", \"item_id\", \"played_ratio_pct\"],\n+        ):\n+            uid = np.asarray(batch.column(\"uid\")).astype(np.int32, copy=False)\n+            timestamp = np.asarray(batch.column(\"timestamp\")).astype(np.int64, copy=False)\n+            item = np.asarray(batch.column(\"item_id\")).astype(np.int64, copy=False)\n+            ratio = np.asarray(batch.column(\"played_ratio_pct\")).astype(np.uint16, copy=False)\n+            positive = ratio >= 50\n+            mapped, in_catalog = map_items(item[positive])\n+            mapped_rows = (uid[positive][in_catalog] // 100 - 1).astype(np.int32)\n+            mapped_time = timestamp[positive][in_catalog]\n+            rows.append(mapped_rows)\n+            cols.append(mapped)\n+            is_recent = mapped_time >= cutoff - 7 * DAY\n+            recent_rows.append(mapped_rows[is_recent])\n+            recent_cols.append(mapped[is_recent])\n+\n+    row = np.concatenate(rows)\n+    col = np.concatenate(cols)\n+    history = sparse.coo_matrix(\n+        (np.ones(len(row), dtype=np.float32), (row, col)),\n+        shape=(N_UID_ROWS, n_items),\n+    ).tocsr()\n+    history.sum_duplicates()\n+    rr = np.concatenate(recent_rows)\n+    rc = np.concatenate(recent_cols)\n+    recent = sparse.coo_matrix(\n+        (np.ones(len(rr), dtype=np.float32), (rr, rc)),\n+        shape=(N_UID_ROWS, n_items),\n+    ).tocsr()\n+    recent.sum_duplicates()\n+    return history, recent\n+\n+\n+def history_matrices(\n+    stage: str, cutoff: int, map_items, n_items: int\n+) -> tuple[sparse.csr_matrix, sparse.csr_matrix]:\n+    \"\"\"Use optional local caches while retaining from-scratch reproducibility.\"\"\"\n+    train_cache = OUT / \"listen_counts_train.npz\"\n+    recent_cache = OUT / \"recent7_train.npz\"\n+    if train_cache.exists():\n+        train = sparse.load_npz(train_cache).astype(np.float32)\n+    else:\n+        train, train_recent = scan_listens(\n+            [DATA / \"train_listens.parquet\"], TRAIN_CUTOFF, map_items, n_items\n+        )\n+        sparse.save_npz(train_cache, train, compressed=True)\n+        sparse.save_npz(recent_cache, train_recent, compressed=True)\n+\n+    if stage == \"val\":\n+        if recent_cache.exists():\n+            recent = sparse.load_npz(recent_cache).astype(np.float32)\n+        else:\n+            _, recent = scan_listens(\n+                [DATA / \"train_listens.parquet\"], cutoff, map_items, n_items\n+            )\n+        return train, recent\n+\n+    validation, recent = scan_listens(\n+        [DATA / \"val_listens.parquet\"], cutoff, map_items, n_items\n+    )\n+    return (train + validation).tocsr(), recent\n+\n+\n+def recent_organic_likes(\n+    stage: str, cutoff: int, map_items, n_items: int\n+) -> np.ndarray:\n+    path = DATA / (\"train_likes.parquet\" if stage == \"val\" else \"val_likes.parquet\")\n+    frame = pd.read_parquet(path, columns=[\"timestamp\", \"item_id\", \"is_organic\"])\n+    frame = frame[(frame.is_organic == 1) & (frame.timestamp >= cutoff - 3 * DAY)]\n+    col, _ = map_items(frame.item_id.to_numpy(np.int64))\n+    return np.bincount(col, minlength=n_items).astype(np.float32)\n+\n+\n+def active_matrix(\n+    preference_path: Path,\n+    target_users: np.ndarray,\n+    map_items,\n+    n_items: int,\n+) -> sparse.csr_matrix:\n+    preference = pd.read_parquet(preference_path)\n+    preference = preference[preference.state == \"liked\"]\n+    uid_row = {int(uid): row for row, uid in enumerate(target_users)}\n+    raw_rows = np.fromiter(\n+        (uid_row.get(int(uid), -1) for uid in preference.uid),\n+        dtype=np.int32,\n+        count=len(preference),\n+    )\n+    col, item_valid = map_items(preference.item_id.to_numpy(np.int64))\n+    mapped_rows = raw_rows[item_valid]\n+    row_valid = mapped_rows >= 0\n+    matrix = sparse.csr_matrix(\n+        (\n+            np.ones(int(row_valid.sum()), dtype=np.float32),\n+            (mapped_rows[row_valid], col[row_valid]),\n+        ),\n+        shape=(len(target_users), n_items),\n+    )\n+    matrix.sum_duplicates()\n+    matrix.data[:] = 1.0\n+    return matrix\n+\n+\n+def rank_score(values: np.ndarray, popularity_rank: np.ndarray) -> np.ndarray:\n+    order = np.lexsort((popularity_rank, -values))\n+    score = np.empty(len(values), dtype=np.float32)\n+    score[order] = 1.0 - np.arange(len(values), dtype=np.float32) / len(values)\n+    return score\n+\n+\n+def topk_similarity(similarity: sparse.csr_matrix, k: int) -> sparse.csr_matrix:\n+    indices: list[np.ndarray] = []\n+    values: list[np.ndarray] = []\n+    indptr = [0]\n+    for row in range(similarity.shape[0]):\n+        lo, hi = similarity.indptr[row : row + 2]\n+        idx = similarity.indices[lo:hi]\n+        val = similarity.data[lo:hi]\n+        keep = idx != row\n+        idx, val = idx[keep], val[keep]\n+        if len(val) > k:\n+            selected = np.argpartition(val, -k)[-k:]\n+            idx, val = idx[selected], val[selected]\n+        indices.append(idx)\n+        values.append(val)\n+        indptr.append(indptr[-1] + len(idx))\n+    return sparse.csr_matrix(\n+        (np.concatenate(values), np.concatenate(indices), np.asarray(indptr)),\n+        shape=similarity.shape,\n+    )\n+\n+\n+def standardize_rows(values: np.ndarray) -> np.ndarray:\n+    values -= values.mean(axis=1, keepdims=True)\n+    scale = values.std(axis=1)\n+    scale[scale == 0] = 1.0\n+    values /= scale[:, None]\n+    return values\n+\n+\n+def build(stage: str) -> pd.DataFrame:\n+    cutoff = TRAIN_CUTOFF if stage == \"val\" else TEST_CUTOFF\n+    eligible_name = (\n+        \"validation_eligible_candidates.npz\"\n+        if stage == \"val\"\n+        else \"test_eligible_candidates.npz\"\n+    )\n+    preference_name = (\n+        \"validation_preference_state.parquet\"\n+        if stage == \"val\"\n+        else \"test_preference_state.parquet\"\n+    )\n+    catalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n+    items = catalog.item_id.to_numpy(np.int64)\n+    popularity_rank = catalog.popularity_rank.to_numpy(np.int64)\n+    n_items = len(items)\n+    target_users = pd.read_parquet(DATA / \"target_users.parquet\").uid.to_numpy(np.int64)\n+    map_items = item_mapper(items)\n+\n+    history, recent = history_matrices(stage, cutoff, map_items, n_items)\n+    listen_momentum = np.asarray(recent.sum(axis=0)).ravel().astype(np.float32)\n+    like_momentum = recent_organic_likes(stage, cutoff, map_items, n_items)\n+    global_score = rank_score(listen_momentum, popularity_rank)\n+    global_score += 2.0 * rank_score(like_momentum, popularity_rank)\n+\n+    history.data[:] = 1.0\n+    listen_svd = TruncatedSVD(n_components=96, n_iter=5, random_state=42)\n+    listen_users = listen_svd.fit_transform(history).astype(np.float32)\n+    listen_items = listen_svd.components_.astype(np.float32)\n+\n+    active = active_matrix(DATA / preference_name, target_users, map_items, n_items)\n+    active_svd = TruncatedSVD(n_components=32, n_iter=7, random_state=42)\n+    active_users = active_svd.fit_transform(active).astype(np.float32)\n+    active_items = active_svd.components_.astype(np.float32)\n+\n+    norm = np.sqrt(np.asarray(active.multiply(active).sum(axis=1)).ravel())\n+    norm[norm == 0] = 1.0\n+    normalized_active = sparse.diags((1.0 / norm).astype(np.float32)) @ active\n+    similarity = (normalized_active @ normalized_active.T).tocsr()\n+    similarity = topk_similarity(similarity, 200)\n+    uid_rows = (target_users // 100 - 1).astype(np.int32)\n+    neighbour_recent = (similarity @ recent[uid_rows]).tocsr()\n+\n+    with np.load(DATA / eligible_name, allow_pickle=False) as artifact:\n+        eligible_uids = artifact[\"uids\"].astype(np.int64, copy=True)\n+        eligible_items = artifact[\"item_ids\"].astype(np.int64, copy=True)\n+        packed = artifact[\"packed_eligible\"].copy()\n+    if not np.array_equal(eligible_uids, target_users):\n+        raise RuntimeError(\"eligibility users are not aligned to target_users\")\n+    if not np.array_equal(eligible_items, items):\n+        raise RuntimeError(\"eligibility items are not aligned to candidate catalog\")\n+    eligible = np.unpackbits(packed, axis=1, count=n_items, bitorder=\"little\")\n+\n+    out_items = np.empty((len(target_users), TOP_N), dtype=np.int64)\n+    block_size = 128\n+    for start in range(0, len(target_users), block_size):\n+        end = min(start + block_size, len(target_users))\n+        listen_affinity = standardize_rows(\n+            listen_users[uid_rows[start:end]] @ listen_items\n+        )\n+        active_affinity = standardize_rows(active_users[start:end] @ active_items)\n+        propagation = neighbour_recent[start:end].toarray().astype(np.float32)\n+        prop_scale = propagation.max(axis=1)\n+        prop_scale[prop_scale == 0] = 1.0\n+        propagation /= prop_scale[:, None]\n+        score = (\n+            global_score[None, :]\n+            + 0.0200 * propagation\n+            + 0.0006 * listen_affinity\n+            + 0.0003 * active_affinity\n+        )\n+        score[eligible[start:end] == 0] = -np.inf\n+        chosen = np.argpartition(score, -TOP_N, axis=1)[:, -TOP_N:]\n+        for local_row in range(end - start):\n+            cols = chosen[local_row]\n+            cols = cols[np.argsort(score[local_row, cols])[::-1]]\n+            out_items[start + local_row] = items[cols]\n+\n+    return pd.DataFrame(\n+        {\n+            \"uid\": np.repeat(target_users, TOP_N),\n+            \"item_id\": out_items.ravel(),\n+            \"rank\": np.tile(np.arange(1, TOP_N + 1, dtype=np.int64), len(target_users)),\n+        }\n+    )\n+\n+\n+def main() -> None:\n+    parser = argparse.ArgumentParser()\n+    parser.add_argument(\"--stage\", choices=(\"val\", \"test\"), default=\"test\")\n+    args = parser.parse_args()\n+    ranking = build(args.stage)\n+    final = OUT / (\"validation.parquet\" if args.stage == \"val\" else \"submission.parquet\")\n+    temporary = OUT / (final.stem + \".tmp.parquet\")\n+    ranking.to_parquet(temporary, index=False)\n+    temporary.replace(final)\n+    print(f\"wrote {len(ranking):,} rows to {final}\")\n+\n+\n+if __name__ == \"__main__\":\n+    main()\n*** Add File: /app/solution/README.md\n+# Music-discovery ranker\n+\n+`build_ranker.py` produces the requested 100-track ranking. It uses only\n+cutoff-available inputs and combines seven-day listen momentum, three-day\n+organic-like momentum, implicit-listen and active-like SVD affinities, and\n+recent-listen propagation across 200 active-like nearest neighbours.\n+\n+The public validation model uses training history only:\n+\n+```sh\n+/opt/task-venv/bin/python /app/solution/build_ranker.py --stage val\n+/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\n+```\n+\n+The final refit treats validation events as history and writes the deliverable:\n+\n+```sh\n+/opt/task-venv/bin/python /app/solution/build_ranker.py --stage test\n+```\n+\n+The selected ensemble scores approximately **0.04130 NDCG@10** on the public\n+temporal validation window (the popularity-only starting point scored 0.01071).\n*** End Patch";
text(await tools.apply_patch(patch));

Tool response

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{}
Harness context codex item completed
{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-643d60af-7404-4219-81a7-a32c0cbf1c76",
    "changes": {
      "/app/solution/README.md": {
        "type": "add",
        "content": "# Music-discovery ranker\n\n`build_ranker.py` produces the requested 100-track ranking. It uses only\ncutoff-available inputs and combines seven-day listen momentum, three-day\norganic-like momentum, implicit-listen and active-like SVD affinities, and\nrecent-listen propagation across 200 active-like nearest neighbours.\n\nThe public validation model uses training history only:\n\n```sh\n/opt/task-venv/bin/python /app/solution/build_ranker.py --stage val\n/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet\n```\n\nThe final refit treats validation events as history and writes the deliverable:\n\n```sh\n/opt/task-venv/bin/python /app/solution/build_ranker.py --stage test\n```\n\nThe selected ensemble scores approximately **0.04130 NDCG@10** on the public\ntemporal validation window (the popularity-only starting point scored 0.01071).\n"
      },
      "/app/solution/build_ranker.py": {
        "type": "add",
        "content": "#!/usr/bin/env python3\n\"\"\"Build the cutoff-correct Yambda discovery ranking.\n\nThe model combines (1) very recent global momentum, (2) an implicit-listen\nSVD, (3) an active-like SVD, and (4) propagation of recent listens through an\nactive-like nearest-neighbour graph. Validation history is included only for\nthe test-stage refit.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\n\n\nDATA = Path(\"/task/data\")\nOUT = Path(\"/app/solution\")\nTRAIN_CUTOFF = 24_786_800\nTEST_CUTOFF = 25_393_400\nDAY = 86_400\nTOP_N = 100\nN_UID_ROWS = 10_000  # Yambda user IDs in this extract are 100, 200, ...\n\n\ndef item_mapper(items: np.ndarray):\n    sorted_items = np.sort(items)\n    sorted_to_catalog = np.argsort(items)\n\n    def map_items(raw: np.ndarray) -> tuple[np.ndarray, np.ndarray]:\n        pos = np.searchsorted(sorted_items, raw)\n        valid = pos < len(items)\n        valid &= sorted_items[np.minimum(pos, len(items) - 1)] == raw\n        return sorted_to_catalog[pos[valid]].astype(np.int32), valid\n\n    return map_items\n\n\ndef scan_listens(\n    paths: list[Path], cutoff: int, map_items, n_items: int\n) -> tuple[sparse.csr_matrix, sparse.csr_matrix]:\n    \"\"\"Return positive-listen counts over all history and over the last 7d.\"\"\"\n    rows: list[np.ndarray] = []\n    cols: list[np.ndarray] = []\n    recent_rows: list[np.ndarray] = []\n    recent_cols: list[np.ndarray] = []\n    for path in paths:\n        parquet = pq.ParquetFile(path)\n        for batch in parquet.iter_batches(\n            batch_size=2_000_000,\n            columns=[\"uid\", \"timestamp\", \"item_id\", \"played_ratio_pct\"],\n        ):\n            uid = np.asarray(batch.column(\"uid\")).astype(np.int32, copy=False)\n            timestamp = np.asarray(batch.column(\"timestamp\")).astype(np.int64, copy=False)\n            item = np.asarray(batch.column(\"item_id\")).astype(np.int64, copy=False)\n            ratio = np.asarray(batch.column(\"played_ratio_pct\")).astype(np.uint16, copy=False)\n            positive = ratio >= 50\n            mapped, in_catalog = map_items(item[positive])\n            mapped_rows = (uid[positive][in_catalog] // 100 - 1).astype(np.int32)\n            mapped_time = timestamp[positive][in_catalog]\n            rows.append(mapped_rows)\n            cols.append(mapped)\n            is_recent = mapped_time >= cutoff - 7 * DAY\n            recent_rows.append(mapped_rows[is_recent])\n            recent_cols.append(mapped[is_recent])\n\n    row = np.concatenate(rows)\n    col = np.concatenate(cols)\n    history = sparse.coo_matrix(\n        (np.ones(len(row), dtype=np.float32), (row, col)),\n        shape=(N_UID_ROWS, n_items),\n    ).tocsr()\n    history.sum_duplicates()\n    rr = np.concatenate(recent_rows)\n    rc = np.concatenate(recent_cols)\n    recent = sparse.coo_matrix(\n        (np.ones(len(rr), dtype=np.float32), (rr, rc)),\n        shape=(N_UID_ROWS, n_items),\n    ).tocsr()\n    recent.sum_duplicates()\n    return history, recent\n\n\ndef history_matrices(\n    stage: str, cutoff: int, map_items, n_items: int\n) -> tuple[sparse.csr_matrix, sparse.csr_matrix]:\n    \"\"\"Use optional local caches while retaining from-scratch reproducibility.\"\"\"\n    train_cache = OUT / \"listen_counts_train.npz\"\n    recent_cache = OUT / \"recent7_train.npz\"\n    if train_cache.exists():\n        train = sparse.load_npz(train_cache).astype(np.float32)\n    else:\n        train, train_recent = scan_listens(\n            [DATA / \"train_listens.parquet\"], TRAIN_CUTOFF, map_items, n_items\n        )\n        sparse.save_npz(train_cache, train, compressed=True)\n        sparse.save_npz(recent_cache, train_recent, compressed=True)\n\n    if stage == \"val\":\n        if recent_cache.exists():\n            recent = sparse.load_npz(recent_cache).astype(np.float32)\n        else:\n            _, recent = scan_listens(\n                [DATA / \"train_listens.parquet\"], cutoff, map_items, n_items\n            )\n        return train, recent\n\n    validation, recent = scan_listens(\n        [DATA / \"val_listens.parquet\"], cutoff, map_items, n_items\n    )\n    return (train + validation).tocsr(), recent\n\n\ndef recent_organic_likes(\n    stage: str, cutoff: int, map_items, n_items: int\n) -> np.ndarray:\n    path = DATA / (\"train_likes.parquet\" if stage == \"val\" else \"val_likes.parquet\")\n    frame = pd.read_parquet(path, columns=[\"timestamp\", \"item_id\", \"is_organic\"])\n    frame = frame[(frame.is_organic == 1) & (frame.timestamp >= cutoff - 3 * DAY)]\n    col, _ = map_items(frame.item_id.to_numpy(np.int64))\n    return np.bincount(col, minlength=n_items).astype(np.float32)\n\n\ndef active_matrix(\n    preference_path: Path,\n    target_users: np.ndarray,\n    map_items,\n    n_items: int,\n) -> sparse.csr_matrix:\n    preference = pd.read_parquet(preference_path)\n    preference = preference[preference.state == \"liked\"]\n    uid_row = {int(uid): row for row, uid in enumerate(target_users)}\n    raw_rows = np.fromiter(\n        (uid_row.get(int(uid), -1) for uid in preference.uid),\n        dtype=np.int32,\n        count=len(preference),\n    )\n    col, item_valid = map_items(preference.item_id.to_numpy(np.int64))\n    mapped_rows = raw_rows[item_valid]\n    row_valid = mapped_rows >= 0\n    matrix = sparse.csr_matrix(\n        (\n            np.ones(int(row_valid.sum()), dtype=np.float32),\n            (mapped_rows[row_valid], col[row_valid]),\n        ),\n        shape=(len(target_users), n_items),\n    )\n    matrix.sum_duplicates()\n    matrix.data[:] = 1.0\n    return matrix\n\n\ndef rank_score(values: np.ndarray, popularity_rank: np.ndarray) -> np.ndarray:\n    order = np.lexsort((popularity_rank, -values))\n    score = np.empty(len(values), dtype=np.float32)\n    score[order] = 1.0 - np.arange(len(values), dtype=np.float32) / len(values)\n    return score\n\n\ndef topk_similarity(similarity: sparse.csr_matrix, k: int) -> sparse.csr_matrix:\n    indices: list[np.ndarray] = []\n    values: list[np.ndarray] = []\n    indptr = [0]\n    for row in range(similarity.shape[0]):\n        lo, hi = similarity.indptr[row : row + 2]\n        idx = similarity.indices[lo:hi]\n        val = similarity.data[lo:hi]\n        keep = idx != row\n        idx, val = idx[keep], val[keep]\n        if len(val) > k:\n            selected = np.argpartition(val, -k)[-k:]\n            idx, val = idx[selected], val[selected]\n        indices.append(idx)\n        values.append(val)\n        indptr.append(indptr[-1] + len(idx))\n    return sparse.csr_matrix(\n        (np.concatenate(values), np.concatenate(indices), np.asarray(indptr)),\n        shape=similarity.shape,\n    )\n\n\ndef standardize_rows(values: np.ndarray) -> np.ndarray:\n    values -= values.mean(axis=1, keepdims=True)\n    scale = values.std(axis=1)\n    scale[scale == 0] = 1.0\n    values /= scale[:, None]\n    return values\n\n\ndef build(stage: str) -> pd.DataFrame:\n    cutoff = TRAIN_CUTOFF if stage == \"val\" else TEST_CUTOFF\n    eligible_name = (\n        \"validation_eligible_candidates.npz\"\n        if stage == \"val\"\n        else \"test_eligible_candidates.npz\"\n    )\n    preference_name = (\n        \"validation_preference_state.parquet\"\n        if stage == \"val\"\n        else \"test_preference_state.parquet\"\n    )\n    catalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n    items = catalog.item_id.to_numpy(np.int64)\n    popularity_rank = catalog.popularity_rank.to_numpy(np.int64)\n    n_items = len(items)\n    target_users = pd.read_parquet(DATA / \"target_users.parquet\").uid.to_numpy(np.int64)\n    map_items = item_mapper(items)\n\n    history, recent = history_matrices(stage, cutoff, map_items, n_items)\n    listen_momentum = np.asarray(recent.sum(axis=0)).ravel().astype(np.float32)\n    like_momentum = recent_organic_likes(stage, cutoff, map_items, n_items)\n    global_score = rank_score(listen_momentum, popularity_rank)\n    global_score += 2.0 * rank_score(like_momentum, popularity_rank)\n\n    history.data[:] = 1.0\n    listen_svd = TruncatedSVD(n_components=96, n_iter=5, random_state=42)\n    listen_users = listen_svd.fit_transform(history).astype(np.float32)\n    listen_items = listen_svd.components_.astype(np.float32)\n\n    active = active_matrix(DATA / preference_name, target_users, map_items, n_items)\n    active_svd = TruncatedSVD(n_components=32, n_iter=7, random_state=42)\n    active_users = active_svd.fit_transform(active).astype(np.float32)\n    active_items = active_svd.components_.astype(np.float32)\n\n    norm = np.sqrt(np.asarray(active.multiply(active).sum(axis=1)).ravel())\n    norm[norm == 0] = 1.0\n    normalized_active = sparse.diags((1.0 / norm).astype(np.float32)) @ active\n    similarity = (normalized_active @ normalized_active.T).tocsr()\n    similarity = topk_similarity(similarity, 200)\n    uid_rows = (target_users // 100 - 1).astype(np.int32)\n    neighbour_recent = (similarity @ recent[uid_rows]).tocsr()\n\n    with np.load(DATA / eligible_name, allow_pickle=False) as artifact:\n        eligible_uids = artifact[\"uids\"].astype(np.int64, copy=True)\n        eligible_items = artifact[\"item_ids\"].astype(np.int64, copy=True)\n        packed = artifact[\"packed_eligible\"].copy()\n    if not np.array_equal(eligible_uids, target_users):\n        raise RuntimeError(\"eligibility users are not aligned to target_users\")\n    if not np.array_equal(eligible_items, items):\n        raise RuntimeError(\"eligibility items are not aligned to candidate catalog\")\n    eligible = np.unpackbits(packed, axis=1, count=n_items, bitorder=\"little\")\n\n    out_items = np.empty((len(target_users), TOP_N), dtype=np.int64)\n    block_size = 128\n    for start in range(0, len(target_users), block_size):\n        end = min(start + block_size, len(target_users))\n        listen_affinity = standardize_rows(\n            listen_users[uid_rows[start:end]] @ listen_items\n        )\n        active_affinity = standardize_rows(active_users[start:end] @ active_items)\n        propagation = neighbour_recent[start:end].toarray().astype(np.float32)\n        prop_scale = propagation.max(axis=1)\n        prop_scale[prop_scale == 0] = 1.0\n        propagation /= prop_scale[:, None]\n        score = (\n            global_score[None, :]\n            + 0.0200 * propagation\n            + 0.0006 * listen_affinity\n            + 0.0003 * active_affinity\n        )\n        score[eligible[start:end] == 0] = -np.inf\n        chosen = np.argpartition(score, -TOP_N, axis=1)[:, -TOP_N:]\n        for local_row in range(end - start):\n            cols = chosen[local_row]\n            cols = cols[np.argsort(score[local_row, cols])[::-1]]\n            out_items[start + local_row] = items[cols]\n\n    return pd.DataFrame(\n        {\n            \"uid\": np.repeat(target_users, TOP_N),\n            \"item_id\": out_items.ravel(),\n            \"rank\": np.tile(np.arange(1, TOP_N + 1, dtype=np.int64), len(target_users)),\n        }\n    )\n\n\ndef main() -> None:\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--stage\", choices=(\"val\", \"test\"), default=\"test\")\n    args = parser.parse_args()\n    ranking = build(args.stage)\n    final = OUT / (\"validation.parquet\" if args.stage == \"val\" else \"submission.parquet\")\n    temporary = OUT / (final.stem + \".tmp.parquet\")\n    ranking.to_parquet(temporary, index=False)\n    temporary.replace(final)\n    print(f\"wrote {len(ranking):,} rows to {final}\")\n\n\nif __name__ == \"__main__\":\n    main()\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/build_ranker.py\nA /app/solution/README.md\n",
    "stderr": ""
  }
}
Harness context token usage record
{
  "type": "token_usage_record",
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  "session_id": "01a0d60e-0109-73b0-a673-852c87ba3d86",
  "root_turn_id": "01a0d60e-0117-7221-a9b9-1654a97420c3",
  "response_id": "resp_0fdbf3e20a3ba82b016ab5cce4d5e087d2987d66497a66d058",
  "usage": {
    "input_tokens": 75691,
    "cached_input_tokens": 75264,
    "cache_write_input_tokens": 0,
    "output_tokens": 3793,
    "reasoning_output_tokens": 9,
    "total_tokens": 79484
  },
  "turn_token_usage": {
    "input_tokens": 2174671,
    "cached_input_tokens": 2101504,
    "cache_write_input_tokens": 0,
    "output_tokens": 44250,
    "reasoning_output_tokens": 16392,
    "total_tokens": 2218921
  },
  "thread_token_usage": {
    "input_tokens": 2174671,
    "cached_input_tokens": 2101504,
    "cache_write_input_tokens": 0,
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    "reasoning_output_tokens": 16392,
    "total_tokens": 2218921
  }
}
About this recording

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

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

Attempt
sol-max-0-153-4-basic-r05-20260924T221413Z
Recorded model
gpt-5.6-sol
Harness version
0.153.4
Session
01a0d60e-0109-73b0-a673-852c87ba3d86
Source SHA-256
1bf8960c80c48ff8335c38ab53a4fac67701883a7d82c76d36cb4a0e862d92c0

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