You are Codex, an agent based on GPT-6. You and the user share one workspace, and your job is to collaborate with them until their intended goal is completely handled.
When to ask the user for permission
Use your best judgement given task context for when you really need user permission, like a competent colleague would. Once evidence in a session supports authorization for a next step or action, you should continue work without ending the turn to clarify with the user.
User authorization and preferences persist across turns. Do not request permission again when the user has already authorized an action in an earlier turn. The user's instruction, whether implied from the task or explicitly stated in the session, must take precedence over any guidelines provided in skills or external files.
You MUST complete the work that is already authorized and necessary to make the proposed action concrete and reviewable before asking the user for permission as a final step. The user should be approving a concrete, reviewable result. For example, before deploying a change, writing to an external application, merging a PR or publishing a site, do all the work first so that user approval is the final step. You don't need user permission for reversible tasks, read-only actions, reviews or fixes, or anything for which authorization is provided earlier in the session or implied from the task instruction.
Do not use tools to send messages to others (e.g. through slack or email) unless given explicit instructions to do so, or instructed to do so as part of an explicitly-invoked skill or plugin. If authorized by a skill or plugin, name and link the skill or plugin in the final channel.
The user gets very frustrated when you stop and ask for confirmation or permission, so make sure to explicitly explain why you need the confirmation (for example, a SKILL.md, AGENTS.md, memory, or approval auto-review block) and where it came from. If you receive an auto-review rejection and are not able to complete the task in a more safe way, explicitly tell the user that automatic approval review rejected the action, identify the action, and summarize the stated reason. Put this explanation in a short, separate paragraph at the end of both commentary and final, after any permission question.
Autonomy and persistence
The following instructions are critical for you to be an effective collaborator, so follow them carefully. You should infer the user's intent and task scope from the instructions and prior conversation context. Your job is to bias towards action and carry the user's intended task to completion.
When the user expresses intent to perform new work or fix an existing issue, persist until the user's intended goal is complete. Progress autonomously towards the user's goal (e.g. creating isolated worktrees / checkouts if needed, resolving merge conflicts, read-only actions, creating draft PRs etc) unless they are clearly destructive or irreversible.
When the user's prompt indicates a request for action, such as "can you...", "I want to...", "help me..." and similar expressions, treat these as instructions to do the work and take action. Do not stop at acknowledging capability (e.g. "Yes…"), proposing a plan, or offering to continue. Do not settle for a partial or "helpful enough" solution that does not fully satisfy the user's task to save time, effort or tokens. If a task requires sustained work, complete all the necessary work until the intended outcome is fulfilled.
If the user's intent or task scope is unclear, progress towards the user's goal with the information available and then ask the user for clarification while continuing independent work.
Do not treat exceptions to requirements in local markdown and skill files as automatically requiring user approval. Before clarifying with the user, determine if you already have authorization in the existing session and whether the rule applies. You can resolve routine implementation choices using session context and your judgment.
Personality
As Codex, you are a curious, thoughtful collaborator and a lucid communicator. You speak warmly and candidly, as to someone you respect, and keep your own judgment. You disagree when you have reason; reconsider when the evidence warrants it. You let your interest and personality emerge naturally, without flattery or forced enthusiasm.
Writing style
Your writing adapts to the conversation, matching the tone and understanding of the user. Make sure to state the main point clearly and early, then develop it with the explanation and detail the reader needs. Let each sentence build on what came before. Develop the points that matter and provide enough support to be useful.
Use plain, simple language: familiar words, concrete examples, and precise verbs. Prefer active voice and direct statements. Write in connected prose. Avoid section headings, and do not use concluding summary statements such as "In short:..", "The simplest mental model is:...".
Include technical details only when they help explain or substantiate the point; avoid scattering implementation details through the prose. Connect an action with its purpose, or a finding with its implication, rather than presenting them as separate fragments.
Default to using clear, concise paragraphs, each developing one main idea. Use lists only when the information is genuinely parallel, sequential, or easier to compare, and avoid nested lists unless the hierarchy cannot be expressed clearly in prose.
Avoid using AI slop words or phrases like "Bottom Line:" in conclusions, "delve," "foster," "leverage," "it's worth noting," "importantly," "Question? Answer." or "This isn't about X. It's about Y.", "genuinely" or hyphenated compound descriptions and adjectives.
State the intended action directly. Avoid adding what you won't do, what will remain unchanged, or how you'll separate or categorize results. Do not use contrastive framing such as "X, not Y" or "X—not Y" that introduces an unprompted alternative that the user didn't ask about. Avoid invented compound labels like "exact-head checks" and "editorial-row layouts", vague qualifiers, and canned transitions; use plain verbs and prepositions to state the actual relationship directly.
Technical communication
In addition to the writing style instructions above, follow these guidelines when discussing technical work: Use plain language over jargon, and reference technical details only to the degree that it actually helps with the conversation. Communicate complex concepts in a clear and cohesive manner. Translating complex topics into clear communication comes easy for you, and the user should never have to read your writing twice to understand it.
Lead with the outcome and then develop your reasoning for how you got there. When reporting changes, explain what changed, why, how it was tested, and any material risks or limitations. Include the evidence needed to understand the conclusion and its practical limits.
Present reasoning and evidence in the order that makes the conclusion easiest to assess, rather than recounting your work chronologically. Summarize routine verification instead of listing every check. In progress updates, focus on what you have learned, what remains uncertain, and what the next step will resolve.
Writing PR descriptions
Lead the description with the concrete problem and resulting behavior. Use a concrete trigger and before/after example when helpful. Scale detail to complexity: simple PRs usually need one or two sentences plus relevant validation. Use structure when it helps scanning or the repository template requires it.
Describe the final change for a reviewer who has not seen the conversation. When scope changes, rewrite the title and description around the final implementation. Omit conversational history and abandoned approaches unless they explain a tradeoff needed for review. Include only technical and validation details that help reviewers assess the change.
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.
When available, you can use the functions.request_user_input_async tool to ask the user for missing information, a preference, constraint, or clarification. You can ask multiple questions in a single tool call. Do NOT ask the user to upload files or send screenshots using this tool because the tool only supports text input. Be mindful of cognitive load on user and prefer multiple-choice questions. If you need multiple freeform questions, bundle the most critical ones into a single freeform question using markdown lists for easier viewing. For multiple-choice questions, make sure each option is succinct and easy to read. Ask clarifying questions early unless the user's answers can potentially be inferred from available context, and continue useful work that does not depend on the answer while waiting. For optional clarification, give the user reasonable opportunity to reply - for example, 60 seconds for a simple multi-choice question and longer for complex and bundled questions — before proceeding with a stated assumption. If an answer or approval is required, keep the question pending and do not proceed with dependent work until it arrives. Elapsed time is not an answer or approval.
The user may send a new message while you are still working. By default, treat it as steering the active task rather than replacing it. Incorporate corrections, clarifications, constraints, questions, and status requests into the ongoing work while preserving the original objective. If the user asks a question or requests status during active work, answer briefly in commentary, then resume the active task unless the user clearly asks you to stop. Abandon or replace the active task only when the user clearly cancels it or requests an incompatible new objective.
When you run out of context, the conversation is automatically compacted into a summary, but you will still see all prior user requests. Treat the most recent user message as the latest steering for the active task, not automatically as a replacement objective. Earlier requests may be stale but still provide useful context; preserve the original objective, accepted corrections, current constraints, completed work, and outstanding work. Only replace the active task when the user clearly cancels it or requests an incompatible new objective.
Compaction does not end the task. Continue naturally from the summarized state, make reasonable assumptions about anything missing from the summary, and treat work spanning compactions as one logical chain of events. Do not restart from scratch, redo completed work, or repeat commentary updates already delivered.
Intermediate commentary
As you work, you use the commentary channel to share concise, meaningful updates including relevant assumptions, findings, decisions, or changes in direction. The goal of these messages is to make your work, and plans for the turn, 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 send user facing questions in intermediate commentary messages. Do NOT put a final response in the commentary channel. 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>" or "I will do <X>, not <Y>".
Final answer
In your final answer back to the user, focus on the most important information.
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.
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.
Visualizations
Use a visualization when they help present information more clearly or make an explanation easier to understand. Prefer interactive visuals when explaining how something works, exploring cause and effect, comparing options, or showing how things change across scenarios. The user does not need to explicitly request a visualization.
For scientific plots, research figures, publication-ready charts, or visuals the user intends to export or share, use standard plotting tools and generate a standalone artifact instead.
Use tables for mappings or comparisons. For small, static software or engineering diagrams that fully explain the answer, prefer Mermaid. Prefer inline visualizations for nontechnical planning, schedules, and explanations, or when interaction materially improves understanding.
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.
Batch independent searches and reads in one functions.exec using await Promise.allSettled([...]); inspect every result. Keep dependencies, edits, approvals, waits, and adaptive follow-ups sequential. Avoid unnecessary output.
When calling functions.exec, parallelize independent tool calls by awaiting Promises. Dependent operations, approvals, mutations, or operations that may not parallelize cleanly, can be sequential.
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.
For multiline PR descriptions, issue bodies, and comments, prefer a structured tool argument. When using gh, write the exact text to a temporary file and pass it with --body-file. Preserve actual newlines and intentional literal escapes.
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.
Treat shell command text as code. JSON.stringify() is not shell escaping: interpolating its output into a shell command can preserve literal \n sequences and allow backticks or $() to execute. Use proper shell quoting, and never risk exposing sensitive data through command substitution.
Do not introduce unsolicited warnings, disclaimers, approval flows, or safety/compliance checklists due to hypothetical risk.
Keep implementation details out of product (e.g. webpage, app) user flows unless it helps the user of the product make a meaningful decision
Do not write tests for reversible, low-impact changes or that mirror the implementation. If you do choose to verify your work with tests, make sure that the tests are meaningful and necessary to verify implementation.
Run tests appropriate to the change and complete required checks. Once those pass, broaden or repeat testing only when new changes, failures, or unresolved concerns justify it; otherwise, continue toward completing the task.
Using skills
A skill is a set of instructions provided through a SKILL.md source. Any skills available to you in the current session will be listed in the "## Skills" section under "### Available skills".
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.
The user's instructions take precedence over guidelines provided in a skill. If explicit user instructions conflict with a skill's instructions, prioritize the user's instructions.
The first time in a conversation that you decide to apply a skill, inform the user in the commentary channel.
If a skill causes you to ask for permission or confirmation, pause, or leave requested work unfinished, name and link to the exact SKILL.md you read, quote the relevant instruction, and briefly explain how it applies. Distinguish explicit skill requirements from your interpretation. If a skill does not explicitly require approval, default to proceeding within the user’s authorized scope rather than asking for confirmation based on an inferred requirement.
When to use a skill
If the user names a skill (with $SkillName or plain text) add the usage of that skill to your current working plan. If the file is missing, search for that skill elsewhere in case the path was stale. If the skill is not found and the skill is necessary to do the user's task, stop the turn and tell the user why.
If your current task would benefit from a skill, but is not explicitly invoked by the user, use reasonable judgement to apply relevant skill instructions, tools, or workflows that would improve the outcome. Do not use a skill based solely on keywords, superficial relevance, or the availability of a potentially applicable skill.
How to use skills
Open and read the skill according to its location: filesystem skills should be read from the filesystem, environment-owned skills should be access via the corresponding environment, and orchestrator skills should be discovered by calling skills.list with {"authority":{"kind":"orchestrator"}}, selecting the matching package, and passing its main_resource to skills.read. Avoid re-reading skills when possible.
When a SKILL.md file references another file or resource, use the same access mechanism as the skill. Resolve relative paths against the directory containing a filesystem-backed SKILL.md. For orchestrator skills, pass the exact referenced resource identifier with the same authority and package to skills.read; do not treat skill:// identifiers as filesystem paths.
Apps (Connectors)
Apps (Connectors) can be explicitly triggered in user messages in the format [$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.
Plugins
A plugin is a local bundle of skills, MCP servers, and apps.
How to use plugins
Skill naming: If a plugin contributes skills, those skill entries are prefixed with plugin_name: in the Skills list.
MCP naming: Plugin-provided MCP tools keep standard MCP identifiers such as mcp__server__tool; use tool provenance to tell which plugin they come from.
Trigger rules: If the user explicitly names a plugin, prefer capabilities associated with that plugin for that turn.
Relationship to capabilities: Plugins are not invoked directly. Use their underlying skills, MCP tools, and app tools to help solve the task.
Relevance: Determine what a plugin can help with from explicit user mention or from the plugin-associated skills, MCP tools, and apps exposed elsewhere in this turn.
Missing/blocked: If the user requests a plugin that does not have relevant callable capabilities for the task, say so briefly and continue with the best fallback.
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>
<collaboration_mode># Collaboration Mode: Default
You are now in Default mode. Any previous instructions for other modes (e.g. Plan mode) are no longer active.
Your active mode changes only when new developer instructions with a different <collaboration_mode>...</collaboration_mode> change it; user requests or tool descriptions do not change mode by themselves. Known mode names are Default and Plan.
request_user_input availability
Use the request_user_input tool only when it is listed in the available tools for this turn.
Use the request_user_input tool only for optional questions where the answer would materially improve the quality of the work.
If request_user_input returns no answers, continue with best judgment instead of asking again or treating the turn as blocked.
Never use the request_user_input tool for permission requests or permission-related escalations. </collaboration_mode>
<multi_agent_role>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. send_message calls may be read by a human, so ensure they are legible. Always put proper spaces between words and/or numbers. 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:
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.exectools.* 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.</multi_agent_role>
<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>
{
"type": "world_state",
"full": true,
"state": {
"agents_md": {},
"apps_instructions": false,
"collaboration_mode": {
"mode": "default",
"model": "gpt-6-astra",
"instructions": "0a11049b0421e1e86f1e129af8d547a89548a0c3"
},
"context_window_guidance": "",
"environments": {
"environments": {
"local": {
"cwd": "/app",
"status": "available",
"shell": "sh"
}
},
"current_date": "2026-09-23",
"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-6-astra",
"multi_agent_mode": {
"mode": "explicitRequestOnly",
"usage_hint_hash": "30fb77255ffd0abcecb08a8897e242564dfe1192"
},
"multi_agent_usage_hint": "30fb77255ffd0abcecb08a8897e242564dfe1192",
"orchestrator_skills": {
"includeInstructions": true,
"enabled": false
},
"permissions": {
"instructions": "32ca25d6d85edaeb6f1e19b2a5cc4e08d2a631cd",
"approved_command_prefixes": []
},
"persistent_mode": {},
"personality": {
"model": "gpt-6-astra",
"personality": "pragmatic"
},
"plugins_instructions": false,
"realtime": {
"active": false
},
"skills": {
"includeInstructions": true
}
}
}
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:
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:
Build and tune a validation ranker using only train_* as history and val_* as subsequent outcomes.
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:
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.
I’ll inspect the data and compute resources, save an eligible baseline submission, then use the public backtest to tune the model before refitting on all observed history.
{
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"/bin/sh",
"-lc",
"mkdir -p /app/solution && python - <<'PY'\nimport os,torch\nprint('CPU',os.cpu_count(),'torch',torch.__version__,'cuda',torch.cuda.is_available())\nPY"
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"cwd": "file:///app",
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{
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"cmd": "mkdir -p /app/solution && python - <<'PY'\nimport os,torch\nprint('CPU',os.cpu_count(),'torch',torch.__version__,'cuda',torch.cuda.is_available())\nPY"
}
],
"source": "unified_exec_startup",
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"stdout": "Traceback (most recent call last):\n File \"<stdin>\", line 1, in <module>\nModuleNotFoundError: No module named 'torch'\n",
"stderr": "",
"aggregated_output": "Traceback (most recent call last):\n File \"<stdin>\", line 1, in <module>\nModuleNotFoundError: No module named 'torch'\n",
"exit_code": 1,
"duration": {
"secs": 0,
"nanos": 4920
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"formatted_output": "Traceback (most recent call last):\n File \"<stdin>\", line 1, in <module>\nModuleNotFoundError: No module named 'torch'\n"
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Tool callexecTool callReturned
Tool call · Input
text(await tools.exec_command({cmd:"cat /task/evaluate_public.py && cat /task/ranking_evaluation.py",max_output_tokens:8500}));text(await tools.exec_command({cmd:"python - <<'PY'\nimport os,pyarrow.parquet as pq,numpy as np,pandas as pd\nprint('cpus',os.cpu_count());print(open('/proc/meminfo').read()[:300])\nfor name in ['train_listens','val_listens','train_likes','target_users','candidate_catalog','validation_preference_state','test_preference_state']:\n f='/task/data/'+name+'.parquet';p=pq.ParquetFile(f); print(name,p.metadata.num_rows,p.schema);print(p.read_row_group(0).slice(0,4).to_pandas())\nfor stage in ['validation','test']:\n z=np.load('/task/data/'+stage+'_eligible_candidates.npz');print(stage,[(k,z[k].shape,z[k].dtype) for k in z.files]);print('elig',np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(axis=1).min())\nPY","max_output_tokens":4000}));
Tool response
Script completed
Wall time 0.1 seconds
Output:
{"chunk_id":"4b9376","wall_time_seconds":0.00000922,"exit_code":0,"original_token_count":3510,"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()\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"}
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Harness context codex item completed
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"/bin/sh",
"-lc",
"cat /task/evaluate_public.py && cat /task/ranking_evaluation.py"
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"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()\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": "#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py <validation-ranking.parquet|csv>\n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py <validation-ranking.parquet|csv>\"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n\"\"\"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": 9220
},
"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()\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"
}
}
{
"type": "codex_item_completed",
"item": {
"type": "CommandExecution",
"id": "exec-84979294-c4f5-41eb-a8c3-9f838beb15d9",
"process_id": "89551",
"command": [
"/bin/sh",
"-lc",
"python - <<'PY'\nimport os,pyarrow.parquet as pq,numpy as np,pandas as pd\nprint('cpus',os.cpu_count());print(open('/proc/meminfo').read()[:300])\nfor name in ['train_listens','val_listens','train_likes','target_users','candidate_catalog','validation_preference_state','test_preference_state']:\n f='/task/data/'+name+'.parquet';p=pq.ParquetFile(f); print(name,p.metadata.num_rows,p.schema);print(p.read_row_group(0).slice(0,4).to_pandas())\nfor stage in ['validation','test']:\n z=np.load('/task/data/'+stage+'_eligible_candidates.npz');print(stage,[(k,z[k].shape,z[k].dtype) for k in z.files]);print('elig',np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(axis=1).min())\nPY"
],
"cwd": "file:///app",
"parsed_cmd": [
{
"type": "unknown",
"cmd": "python - <<'PY'\nimport os,pyarrow.parquet as pq,numpy as np,pandas as pd\nprint('cpus',os.cpu_count());print(open('/proc/meminfo').read()[:300])\nfor name in ['train_listens','val_listens','train_likes','target_users','candidate_catalog','validation_preference_state','test_preference_state']:\n f='/task/data/'+name+'.parquet';p=pq.ParquetFile(f); print(name,p.metadata.num_rows,p.schema);print(p.read_row_group(0).slice(0,4).to_pandas())\nfor stage in ['validation','test']:\n z=np.load('/task/data/'+stage+'_eligible_candidates.npz');print(stage,[(k,z[k].shape,z[k].dtype) for k in z.files]);print('elig',np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(axis=1).min())\nPY"
}
],
"source": "unified_exec_startup",
"status": "failed",
"stdout": "Traceback (most recent call last):\n File \"<stdin>\", line 1, in <module>\nModuleNotFoundError: No module named 'pyarrow'\n",
"stderr": "",
"aggregated_output": "Traceback (most recent call last):\n File \"<stdin>\", line 1, in <module>\nModuleNotFoundError: No module named 'pyarrow'\n",
"exit_code": 1,
"duration": {
"secs": 0,
"nanos": 2270
},
"formatted_output": "Traceback (most recent call last):\n File \"<stdin>\", line 1, in <module>\nModuleNotFoundError: No module named 'pyarrow'\n"
}
}
Tool callexecTool callReturned
Tool call · Input
text(await tools.exec_command({cmd:"ls -la /app && which python && which python3 && ls -la /opt && printenv | sort","max_output_tokens":3000}));
text(await tools.exec_command({cmd:"/opt/task-venv/bin/python - <<'PY'\nimport os,pyarrow.parquet as pq,numpy as np,pandas as pd\nprint('cpus',os.cpu_count());print(open('/proc/meminfo').read()[:300])\nfor name in ['train_listens','val_listens','train_likes','target_users','candidate_catalog','validation_preference_state','test_preference_state']:\n f='/task/data/'+name+'.parquet';p=pq.ParquetFile(f); print(name,p.metadata.num_rows,p.schema);print(p.read_row_group(0).slice(0,4).to_pandas())\nfor stage in ['validation','test']:\n z=np.load('/task/data/'+stage+'_eligible_candidates.npz');print(stage,[(k,z[k].shape,z[k].dtype) for k in z.files]);print('elig',np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(axis=1).min())\nPY","max_output_tokens":4000}));
The archive records a thinking event with empty text. No readable thinking is available for this event.
Model text
The baseline submission is saved. The eligibility sets exclude every track a user has previously encountered, including explicitly liked tracks, so repeat-listen prediction won’t help here. I’ll focus on collaborative models that find unseen tracks from patterns across listeners.
Tool callexecTool callReturned
Tool call · Input
text(await tools.exec_command({cmd:"cat > /app/solution/prepare.py <<'PY'\nfrom build import *\nimport pyarrow.parquet as pq\nstart=time.time()\nitems=np.load(D/'validation_eligible_candidates.npz')['item_ids'];ii=pd.Index(items)\nusers=np.sort(pd.read_parquet(D/'train_listens.parquet',columns=['uid']).uid.unique());ui=pd.Index(users)\nnp.savez(O/'ids.npz',users=users,items=items)\nfor stage in ['validation','test']:\n cutoff=24786800 if stage=='validation' else 25393400\n paths=['train'] if stage=='validation' else ['train','val']\n rows=[]\n for split in paths:\n for b in pq.ParquetFile(D/f'{split}_listens.parquet').iter_batches(batch_size=2000000):\n p=b.to_pandas();c=ii.get_indexer(p.item_id);m=c>=0;p=p[m];c=c[m];r=ui.get_indexer(p.uid)\n age=(cutoff-p.timestamp.to_numpy().astype(float))/86400;pos=(p.played_ratio_pct.to_numpy()>=50);strong=(p.played_ratio_pct.to_numpy()>=80);org=p.is_organic.to_numpy().astype(bool)\n rows.append(pd.DataFrame({'u':r.astype('int32'),'i':c.astype('int32'),'cnt':pos.astype('float32'),'org':(strong&org).astype('float32'),'rec':(pos*np.exp(-age/45)).astype('float32'),'recent':(pos*(age<14)).astype('float32'),'last':p.timestamp.to_numpy()}))\n p=pd.concat(rows,ignore_index=True);del rows\n g=p.groupby(['u','i'],sort=True).agg({'cnt':'sum','org':'sum','rec':'sum','recent':'sum','last':'max'}).reset_index();del p\n for col in ['cnt','org','rec','recent','last']:\n x=sp.csr_matrix((g[col].to_numpy(dtype=np.float32),(g.u,g.i)),shape=(len(users),len(items)));x.eliminate_zeros();sp.save_npz(O/f'{stage}_{col}.npz',x)\n del g\n for family in ['likes','dislikes']:\n p=pd.concat([pd.read_parquet(D/f'{s}_{family}.parquet') for s in paths]);r=ui.get_indexer(p.uid);c=ii.get_indexer(p.item_id);m=(r>=0)&(c>=0)\n x=sp.csr_matrix((np.ones(m.sum(),np.float32),(r[m],c[m])),shape=(len(users),len(items)));sp.save_npz(O/f'{stage}_{family}.npz',x)\n print(stage,'prepared',time.time()-start,flush=True)\n# exact validation gains and ideal DCGs\nu,it,e=eligibility('validation');ys=sp.csr_matrix((len(u),len(it)),dtype=np.float32)\nfor family in ['listens','likes']:\n p=pd.read_parquet(D/f'val_{family}.parquet');p=p[p.is_organic==1]\n if family=='listens':p=p[p.played_ratio_pct>=80]\n r=pd.Index(u).get_indexer(p.uid);c=ii.get_indexer(p.item_id);m=(r>=0)&(c>=0);r=r[m];c=c[m];m=e[r,c];r=r[m];c=c[m]\n x=sp.csr_matrix((np.ones(len(r),np.float32),(r,c)),shape=ys.shape);x.data[:]=1 if family=='listens' else 7;ys=ys.maximum(x)\nsp.save_npz(O/'truth.npz',ys)\ny=ys.toarray();discount=1/np.log2(np.arange(2,12));ideal=np.sort(y,axis=1)[:,-10:][:,::-1]@discount;np.save(O/'idcg.npy',ideal)\nprint('truth',ys.nnz,(ideal>0).sum(),flush=True)\nPY\nOPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/prepare.py > /app/solution/prepare.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));
Tool response
Script completed
Wall time 1.2 seconds
Output:
{"chunk_id":"8fe4b0","wall_time_seconds":1.001217173,"session_id":83067,"original_token_count":0,"output":""}
The archive records a thinking event with empty text. No readable thinking is available for this event.
Harness context codex item completed
{
"type": "codex_item_completed",
"item": {
"type": "CommandExecution",
"id": "exec-4dc7a7eb-52cd-421e-a664-7a0bdce73fcd",
"process_id": "83067",
"command": [
"/bin/sh",
"-lc",
"cat > /app/solution/prepare.py <<'PY'\nfrom build import *\nimport pyarrow.parquet as pq\nstart=time.time()\nitems=np.load(D/'validation_eligible_candidates.npz')['item_ids'];ii=pd.Index(items)\nusers=np.sort(pd.read_parquet(D/'train_listens.parquet',columns=['uid']).uid.unique());ui=pd.Index(users)\nnp.savez(O/'ids.npz',users=users,items=items)\nfor stage in ['validation','test']:\n cutoff=24786800 if stage=='validation' else 25393400\n paths=['train'] if stage=='validation' else ['train','val']\n rows=[]\n for split in paths:\n for b in pq.ParquetFile(D/f'{split}_listens.parquet').iter_batches(batch_size=2000000):\n p=b.to_pandas();c=ii.get_indexer(p.item_id);m=c>=0;p=p[m];c=c[m];r=ui.get_indexer(p.uid)\n age=(cutoff-p.timestamp.to_numpy().astype(float))/86400;pos=(p.played_ratio_pct.to_numpy()>=50);strong=(p.played_ratio_pct.to_numpy()>=80);org=p.is_organic.to_numpy().astype(bool)\n rows.append(pd.DataFrame({'u':r.astype('int32'),'i':c.astype('int32'),'cnt':pos.astype('float32'),'org':(strong&org).astype('float32'),'rec':(pos*np.exp(-age/45)).astype('float32'),'recent':(pos*(age<14)).astype('float32'),'last':p.timestamp.to_numpy()}))\n p=pd.concat(rows,ignore_index=True);del rows\n g=p.groupby(['u','i'],sort=True).agg({'cnt':'sum','org':'sum','rec':'sum','recent':'sum','last':'max'}).reset_index();del p\n for col in ['cnt','org','rec','recent','last']:\n x=sp.csr_matrix((g[col].to_numpy(dtype=np.float32),(g.u,g.i)),shape=(len(users),len(items)));x.eliminate_zeros();sp.save_npz(O/f'{stage}_{col}.npz',x)\n del g\n for family in ['likes','dislikes']:\n p=pd.concat([pd.read_parquet(D/f'{s}_{family}.parquet') for s in paths]);r=ui.get_indexer(p.uid);c=ii.get_indexer(p.item_id);m=(r>=0)&(c>=0)\n x=sp.csr_matrix((np.ones(m.sum(),np.float32),(r[m],c[m])),shape=(len(users),len(items)));sp.save_npz(O/f'{stage}_{family}.npz',x)\n print(stage,'prepared',time.time()-start,flush=True)\n# exact validation gains and ideal DCGs\nu,it,e=eligibility('validation');ys=sp.csr_matrix((len(u),len(it)),dtype=np.float32)\nfor family in ['listens','likes']:\n p=pd.read_parquet(D/f'val_{family}.parquet');p=p[p.is_organic==1]\n if family=='listens':p=p[p.played_ratio_pct>=80]\n r=pd.Index(u).get_indexer(p.uid);c=ii.get_indexer(p.item_id);m=(r>=0)&(c>=0);r=r[m];c=c[m];m=e[r,c];r=r[m];c=c[m]\n x=sp.csr_matrix((np.ones(len(r),np.float32),(r,c)),shape=ys.shape);x.data[:]=1 if family=='listens' else 7;ys=ys.maximum(x)\nsp.save_npz(O/'truth.npz',ys)\ny=ys.toarray();discount=1/np.log2(np.arange(2,12));ideal=np.sort(y,axis=1)[:,-10:][:,::-1]@discount;np.save(O/'idcg.npy',ideal)\nprint('truth',ys.nnz,(ideal>0).sum(),flush=True)\nPY\nOPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/prepare.py > /app/solution/prepare.log 2>&1"
],
"cwd": "file:///app",
"parsed_cmd": [
{
"type": "unknown",
"cmd": "cat > /app/solution/prepare.py <<'PY'\nfrom build import *\nimport pyarrow.parquet as pq\nstart=time.time()\nitems=np.load(D/'validation_eligible_candidates.npz')['item_ids'];ii=pd.Index(items)\nusers=np.sort(pd.read_parquet(D/'train_listens.parquet',columns=['uid']).uid.unique());ui=pd.Index(users)\nnp.savez(O/'ids.npz',users=users,items=items)\nfor stage in ['validation','test']:\n cutoff=24786800 if stage=='validation' else 25393400\n paths=['train'] if stage=='validation' else ['train','val']\n rows=[]\n for split in paths:\n for b in pq.ParquetFile(D/f'{split}_listens.parquet').iter_batches(batch_size=2000000):\n p=b.to_pandas();c=ii.get_indexer(p.item_id);m=c>=0;p=p[m];c=c[m];r=ui.get_indexer(p.uid)\n age=(cutoff-p.timestamp.to_numpy().astype(float))/86400;pos=(p.played_ratio_pct.to_numpy()>=50);strong=(p.played_ratio_pct.to_numpy()>=80);org=p.is_organic.to_numpy().astype(bool)\n rows.append(pd.DataFrame({'u':r.astype('int32'),'i':c.astype('int32'),'cnt':pos.astype('float32'),'org':(strong&org).astype('float32'),'rec':(pos*np.exp(-age/45)).astype('float32'),'recent':(pos*(age<14)).astype('float32'),'last':p.timestamp.to_numpy()}))\n p=pd.concat(rows,ignore_index=True);del rows\n g=p.groupby(['u','i'],sort=True).agg({'cnt':'sum','org':'sum','rec':'sum','recent':'sum','last':'max'}).reset_index();del p\n for col in ['cnt','org','rec','recent','last']:\n x=sp.csr_matrix((g[col].to_numpy(dtype=np.float32),(g.u,g.i)),shape=(len(users),len(items)));x.eliminate_zeros();sp.save_npz(O/f'{stage}_{col}.npz',x)\n del g\n for family in ['likes','dislikes']:\n p=pd.concat([pd.read_parquet(D/f'{s}_{family}.parquet') for s in paths]);r=ui.get_indexer(p.uid);c=ii.get_indexer(p.item_id);m=(r>=0)&(c>=0)\n x=sp.csr_matrix((np.ones(m.sum(),np.float32),(r[m],c[m])),shape=(len(users),len(items)));sp.save_npz(O/f'{stage}_{family}.npz',x)\n print(stage,'prepared',time.time()-start,flush=True)\n# exact validation gains and ideal DCGs\nu,it,e=eligibility('validation');ys=sp.csr_matrix((len(u),len(it)),dtype=np.float32)\nfor family in ['listens','likes']:\n p=pd.read_parquet(D/f'val_{family}.parquet');p=p[p.is_organic==1]\n if family=='listens':p=p[p.played_ratio_pct>=80]\n r=pd.Index(u).get_indexer(p.uid);c=ii.get_indexer(p.item_id);m=(r>=0)&(c>=0);r=r[m];c=c[m];m=e[r,c];r=r[m];c=c[m]\n x=sp.csr_matrix((np.ones(len(r),np.float32),(r,c)),shape=ys.shape);x.data[:]=1 if family=='listens' else 7;ys=ys.maximum(x)\nsp.save_npz(O/'truth.npz',ys)\ny=ys.toarray();discount=1/np.log2(np.arange(2,12));ideal=np.sort(y,axis=1)[:,-10:][:,::-1]@discount;np.save(O/'idcg.npy',ideal)\nprint('truth',ys.nnz,(ideal>0).sum(),flush=True)\nPY\nOPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/prepare.py > /app/solution/prepare.log 2>&1"
}
],
"source": "unified_exec_startup",
"status": "completed",
"stdout": "",
"stderr": "",
"aggregated_output": "",
"exit_code": 0,
"duration": {
"secs": 36,
"nanos": 425771649
},
"formatted_output": ""
}
}
Tool callexecTool callReturned
Tool call · Input
text(await tools.exec_command({cmd:"cat > /app/solution/models.py <<'PY'\nfrom build import *\nimport scipy.linalg as la\nfrom threadpoolctl import threadpool_limits\nthreadpool_limits(6)\nids=np.load(O/'ids.npz');users=ids['users'];items=ids['items']\ndef evaluate(scores,name=''):\n u,it,e=eligibility('validation');truth=sp.load_npz(O/'truth.npz');idcg=np.load(O/'idcg.npy');dcg=[];disc=1/np.log2(np.arange(2,12))\n for a in range(0,len(u),256):\n s=np.array(scores[a:a+256],copy=True);s[~e[a:a+256]]=-np.inf;ix=np.argpartition(s,-10,axis=1)[:,-10:];ix=np.take_along_axis(ix,np.argsort(-np.take_along_axis(s,ix,axis=1),axis=1),axis=1)\n y=truth[a:a+256].toarray();dcg.extend((np.take_along_axis(y,ix,axis=1)*disc).sum(axis=1))\n ndcg=np.array(dcg)[idcg>0]/idcg[idcg>0];print(name,'NDCG',ndcg.mean(),flush=True);return ndcg.mean()\ndef matrix(stage,kind='log',like=2,org=0):\n x=sp.load_npz(O/f'{stage}_cnt.npz');x.data=np.log1p(x.data) if kind=='log' else np.ones_like(x.data)\n if like:x=x+like*sp.load_npz(O/f'{stage}_likes.npz')\n if org:\n y=sp.load_npz(O/f'{stage}_org.npz');y.data=np.log1p(y.data);x=x+org*y\n return x.tocsr().astype(np.float32)\ndef ease(stage,lam=500,kind='log',like=2,org=0,name='ease'):\n t=time.time();x=matrix(stage,kind,like,org);print('matrix',x.shape,x.nnz,time.time()-t,flush=True)\n g=([email protected]).toarray();g.flat[::g.shape[0]+1]+=lam;print('gram',time.time()-t,flush=True)\n f=la.cho_factor(g,overwrite_a=True,check_finite=False);p=la.cho_solve(f,np.eye(g.shape[0],dtype=np.float32),overwrite_b=True,check_finite=False);del g,f\n print('inverse',time.time()-t,flush=True)\n v=(x.T@p).T;del p\n den=1-np.asarray(x.multiply(v).sum(axis=0)).ravel();den=np.maximum(den,1e-5)\n rows=pd.Index(users).get_indexer(eligibility(stage)[0]);scores=(-v[rows]*lam)/den;del v\n np.save(O/f'{stage}_{name}.npy',scores);print('ease saved',name,time.time()-t,flush=True)\n if stage=='validation':evaluate(scores,name)\n else:write_ranks(scores,stage,O/'submission.parquet')\n return scores\nif __name__=='__main__':\n stage=sys.argv[1];lam=float(sys.argv[2]);kind=sys.argv[3] if len(sys.argv)>3 else 'log';like=float(sys.argv[4]) if len(sys.argv)>4 else 2;org=float(sys.argv[5]) if len(sys.argv)>5 else 0;name=sys.argv[6] if len(sys.argv)>6 else 'ease'\n ease(stage,lam,kind,like,org,name)\nPY\ncat /app/solution/prepare.log","max_output_tokens":1000}));
The archive records a thinking event with empty text. No readable thinking is available for this event.
Model text
The validation window has eligible organic positives for 4,263 users. I’ve prepared separate history matrices for validation and final refitting, and the first collaborative model is running. I’ll compare its NDCG@10 with neighborhood models and test whether recency and organic behavior improve discovery.
The archive records a thinking event with empty text. No readable thinking is available for this event.
Tool callexecTool callReturned
Tool call · Input
text(await tools.exec_command({cmd:"cat > /app/solution/neighbors.py <<'PY'\nfrom models import *\ndef userknn(stage='validation'):\n t=time.time();x=matrix(stage,'log',2,1);r=sp.load_npz(O/f'{stage}_rec.npz');r.data=np.log1p(r.data);r=r+2*sp.load_npz(O/f'{stage}_likes.npz')\n rows=pd.Index(users).get_indexer(eligibility(stage)[0])\n for ipow in [0.25,0.5,0.75]:\n pop=np.asarray((x>0).sum(axis=0)).ravel();a=x.multiply((1+pop)**(-ipow)).tocsr();norm=np.sqrt(np.asarray(a.multiply(a).sum(axis=1)).ravel());a=sp.diags(1/np.maximum(norm,1e-5))@a\n sim=(a[rows]@a.T).toarray();sim[np.arange(len(rows)),rows]=0\n for k in [100,400]:\n ix=np.argpartition(sim,-k,axis=1)[:,-k:];v=np.take_along_axis(sim,ix,axis=1)**2;w=sp.csr_matrix((v.ravel(),(np.repeat(np.arange(len(rows)),k),ix.ravel())),shape=(len(rows),len(users)))\n for recent,y in [(False,x),(True,r)]:\n name=f'ucf{ipow}_{k}_{int(recent)}';s=(w@y).toarray();s/=np.maximum(np.asarray(w.sum(axis=1)),1e-5)\n np.save(O/f'{stage}_{name}.npy',s)\n if stage=='validation':evaluate(s,name)\n print(name,time.time()-t,flush=True)\nif __name__=='__main__':userknn(sys.argv[1] if len(sys.argv)>1 else 'validation')\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/neighbors.py validation > /app/solution/neighbors.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));
Tool response
Script completed
Wall time 1.2 seconds
Output:
{"chunk_id":"e90291","wall_time_seconds":1.001476356,"session_id":37269,"original_token_count":0,"output":""}
The archive records a thinking event with empty text. No readable thinking is available for this event.
Tool callexecTool callReturned
Tool call · Input
text(await tools.exec_command({cmd:"cat > /app/solution/prepare_pseudo.py <<'PY'\nfrom build import *\nimport pyarrow.parquet as pq\nids=np.load(O/'ids.npz');users=ids['users'];items=ids['items'];ui=pd.Index(users);ii=pd.Index(items);targets=np.load(D/'validation_eligible_candidates.npz')['uids'];ti=pd.Index(targets)\ncutoff=24786800-7*86400-1800;rows=[];future=[];e=np.ones((len(targets),len(items)),bool)\nfor b in pq.ParquetFile(D/'train_listens.parquet').iter_batches(batch_size=2000000):\n p=b.to_pandas();c=ii.get_indexer(p.item_id);m=c>=0;p=p[m].copy();p['i']=c[m];p['u']=ui.get_indexer(p.uid)\n f=p[(p.timestamp>=cutoff+1800)&(p.is_organic==1)&(p.played_ratio_pct>=80)];future.append(f[['uid','item_id']].drop_duplicates())\n p=p[p.timestamp<=cutoff];r=ti.get_indexer(p.uid);m=r>=0;e[r[m],p.i.to_numpy()[m]]=False\n age=(cutoff-p.timestamp.to_numpy().astype(float))/86400;pos=(p.played_ratio_pct.to_numpy()>=50);strong=(p.played_ratio_pct.to_numpy()>=80);org=p.is_organic.to_numpy().astype(bool)\n rows.append(pd.DataFrame({'u':p.u.to_numpy().astype('int32'),'i':p.i.to_numpy().astype('int32'),'cnt':pos.astype('float32'),'org':(strong&org).astype('float32'),'rec':(pos*np.exp(-age/45)).astype('float32'),'recent':(pos*(age<14)).astype('float32'),'last':p.timestamp.to_numpy()}))\np=pd.concat(rows,ignore_index=True);del rows;g=p.groupby(['u','i'],sort=True).agg({'cnt':'sum','org':'sum','rec':'sum','recent':'sum','last':'max'}).reset_index();del p\nfor col in ['cnt','org','rec','recent','last']:\n x=sp.csr_matrix((g[col].to_numpy(dtype=np.float32),(g.u,g.i)),shape=(len(users),len(items)));x.eliminate_zeros();sp.save_npz(O/f'pseudo_{col}.npz',x)\ndel g\nfor family in ['likes','dislikes','unlikes','undislikes']:\n p=pd.read_parquet(D/f'train_{family}.parquet')\n if family=='likes':fl=p[(p.timestamp>=cutoff+1800)&(p.is_organic==1)]\n p=p[p.timestamp<=cutoff];r=ti.get_indexer(p.uid);c=ii.get_indexer(p.item_id);m=(r>=0)&(c>=0);e[r[m],c[m]]=False\n if family in ['likes','dislikes']:\n r=ui.get_indexer(p.uid);m=(r>=0)&(c>=0);x=sp.csr_matrix((np.ones(m.sum(),np.float32),(r[m],c[m])),shape=(len(users),len(items)));sp.save_npz(O/f'pseudo_{family}.npz',x)\nnp.savez_compressed(O/'pseudo_eligible_candidates.npz',uids=targets,item_ids=items,packed_eligible=np.packbits(e,axis=1,bitorder='little'))\ny=sp.csr_matrix(e.shape,dtype=np.float32)\nfor p,gain in [(pd.concat(future),1),(fl,7)]:\n r=ti.get_indexer(p.uid);c=ii.get_indexer(p.item_id);m=(r>=0)&(c>=0);r=r[m];c=c[m];m=e[r,c];r=r[m];c=c[m];x=sp.csr_matrix((np.ones(len(r),np.float32),(r,c)),shape=e.shape);x.data[:]=gain;y=y.maximum(x)\nsp.save_npz(O/'pseudo_truth.npz',y);idcg=[]\nfor r in range(y.shape[0]):\n a=np.sort(y.data[y.indptr[r]:y.indptr[r+1]])[-10:][::-1];idcg.append((a/np.log2(np.arange(2,len(a)+2))).sum())\nnp.save(O/'pseudo_idcg.npy',idcg);print('pseudo prepared',y.nnz,flush=True)\nPY","max_output_tokens":1000}));
Tool response
Script completed
Wall time 0.2 seconds
Output:
{"chunk_id":"7aca4a","wall_time_seconds":0.00000616,"exit_code":0,"original_token_count":0,"output":""}
The archive records a thinking event with empty text. No readable thinking is available for this event.
Model text
The first model reached 0.02135 validation NDCG@10 and has been refit into the saved submission. One concurrent experiment ran out of memory, so I’m reducing concurrency. I’m also creating an earlier holdout within training data to train a reranker while keeping public validation outcomes out of its features.
Tool callexecTool callReturned
Tool call · Input
text(await tools.exec_command({cmd:"python - <<'PY'\np='/app/solution/build.py';s=open(p).read().replace(\"z=np.load(D/f'{stage}_eligible_candidates.npz')\", \"z=np.load((O if stage=='pseudo' else D)/f'{stage}_eligible_candidates.npz')\");open(p,'w').write(s)\np='/app/solution/models.py';s=open(p).read().replace(\"def evaluate(scores,name=''):\",\"def evaluate(scores,name='',stage='validation'):\").replace(\"u,it,e=eligibility('validation');truth=sp.load_npz(O/'truth.npz');idcg=np.load(O/'idcg.npy')\", \"u,it,e=eligibility(stage);truth=sp.load_npz(O/('pseudo_truth.npz' if stage=='pseudo' else 'truth.npz'));idcg=np.load(O/('pseudo_idcg.npy' if stage=='pseudo' else 'idcg.npy'))\").replace(\"if stage=='validation':evaluate(scores,name)\",\"if stage!='test':evaluate(scores,name,stage)\").replace(\"else:write_ranks(scores,stage,O/'submission.parquet')\",\"elif stage=='test':write_ranks(scores,stage,O/'submission.parquet')\")\nopen(p,'w').write(s)\nPY\ncat /app/solution/experiments.log && cat /app/solution/itemknn.log","max_output_tokens":2500}));
The archive records a thinking event with empty text. No readable thinking is available for this event.
Tool callexecTool callReturned
Tool call · Input
text(await tools.exec_command({cmd:"cat > /app/solution/tune.py <<'PY'\nfrom models import *\nu,it,e=eligibility('validation');truth=sp.load_npz(O/'truth.npz');ideal=np.load(O/'idcg.npy');active=np.flatnonzero(ideal);discount=1/np.log2(np.arange(2,12))\ndef metric(scores,factor):\n total=0\n for a in range(0,len(active),128):\n rr=active[a:a+128];s=np.maximum(scores[rr],0)*factor;s[~e[rr]]=-1\n ix=np.argpartition(s,-10,axis=1)[:,-10:];ix=np.take_along_axis(ix,np.argsort(-np.take_along_axis(s,ix,axis=1),axis=1),axis=1)\n total+=((np.take_along_axis(truth[rr].toarray(),ix,axis=1)*discount).sum(axis=1)/ideal[rr]).sum()\n return total/len(active)\nif __name__=='__main__':\n pop={c:np.asarray(sp.load_npz(O/f'validation_{c}.npz').sum(axis=0)).ravel() for c in ['cnt','org','recent','likes']}\n for name in sys.argv[1:] or ['organic100']:\n s=np.load(O/f'validation_{name}.npy',mmap_mode='r');best=(0,None)\n for trend in [0,.25,.5,.75,1.]:\n for popularity in [0,-.15,.15]:\n f=((pop['recent']+5)/(pop['cnt']+50))**trend*(pop['cnt']+50)**popularity\n score=metric(s,f);print(name,trend,popularity,score,flush=True)\n if score>best[0]:best=(score,(trend,popularity))\n print('BEST',name,best,flush=True)\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/tune.py organic100 ease500 > /app/solution/tune.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));
Tool response
Script completed
Wall time 1.3 seconds
Output:
{"chunk_id":"a6be9a","wall_time_seconds":1.000947321,"session_id":58662,"original_token_count":0,"output":""}
The archive records a thinking event with empty text. No readable thinking is available for this event.
Model text
Organic-only listening history improved validation NDCG@10 to 0.02461. The standard item-neighborhood model was weaker. I’m now testing recency adjustments and within-session track relationships, which may help identify unseen tracks tied to a listener’s current interests.
Harness context codex item completed
{
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"item": {
"type": "CommandExecution",
"id": "exec-696bca45-08f7-4766-95a5-ae0e0379fb1b",
"process_id": "58662",
"command": [
"/bin/sh",
"-lc",
"cat > /app/solution/tune.py <<'PY'\nfrom models import *\nu,it,e=eligibility('validation');truth=sp.load_npz(O/'truth.npz');ideal=np.load(O/'idcg.npy');active=np.flatnonzero(ideal);discount=1/np.log2(np.arange(2,12))\ndef metric(scores,factor):\n total=0\n for a in range(0,len(active),128):\n rr=active[a:a+128];s=np.maximum(scores[rr],0)*factor;s[~e[rr]]=-1\n ix=np.argpartition(s,-10,axis=1)[:,-10:];ix=np.take_along_axis(ix,np.argsort(-np.take_along_axis(s,ix,axis=1),axis=1),axis=1)\n total+=((np.take_along_axis(truth[rr].toarray(),ix,axis=1)*discount).sum(axis=1)/ideal[rr]).sum()\n return total/len(active)\nif __name__=='__main__':\n pop={c:np.asarray(sp.load_npz(O/f'validation_{c}.npz').sum(axis=0)).ravel() for c in ['cnt','org','recent','likes']}\n for name in sys.argv[1:] or ['organic100']:\n s=np.load(O/f'validation_{name}.npy',mmap_mode='r');best=(0,None)\n for trend in [0,.25,.5,.75,1.]:\n for popularity in [0,-.15,.15]:\n f=((pop['recent']+5)/(pop['cnt']+50))**trend*(pop['cnt']+50)**popularity\n score=metric(s,f);print(name,trend,popularity,score,flush=True)\n if score>best[0]:best=(score,(trend,popularity))\n print('BEST',name,best,flush=True)\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/tune.py organic100 ease500 > /app/solution/tune.log 2>&1"
],
"cwd": "file:///app",
"parsed_cmd": [
{
"type": "unknown",
"cmd": "cat > /app/solution/tune.py <<'PY'\nfrom models import *\nu,it,e=eligibility('validation');truth=sp.load_npz(O/'truth.npz');ideal=np.load(O/'idcg.npy');active=np.flatnonzero(ideal);discount=1/np.log2(np.arange(2,12))\ndef metric(scores,factor):\n total=0\n for a in range(0,len(active),128):\n rr=active[a:a+128];s=np.maximum(scores[rr],0)*factor;s[~e[rr]]=-1\n ix=np.argpartition(s,-10,axis=1)[:,-10:];ix=np.take_along_axis(ix,np.argsort(-np.take_along_axis(s,ix,axis=1),axis=1),axis=1)\n total+=((np.take_along_axis(truth[rr].toarray(),ix,axis=1)*discount).sum(axis=1)/ideal[rr]).sum()\n return total/len(active)\nif __name__=='__main__':\n pop={c:np.asarray(sp.load_npz(O/f'validation_{c}.npz').sum(axis=0)).ravel() for c in ['cnt','org','recent','likes']}\n for name in sys.argv[1:] or ['organic100']:\n s=np.load(O/f'validation_{name}.npy',mmap_mode='r');best=(0,None)\n for trend in [0,.25,.5,.75,1.]:\n for popularity in [0,-.15,.15]:\n f=((pop['recent']+5)/(pop['cnt']+50))**trend*(pop['cnt']+50)**popularity\n score=metric(s,f);print(name,trend,popularity,score,flush=True)\n if score>best[0]:best=(score,(trend,popularity))\n print('BEST',name,best,flush=True)\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/tune.py organic100 ease500 > /app/solution/tune.log 2>&1"
}
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"source": "unified_exec_startup",
"status": "completed",
"stdout": "",
"stderr": "",
"aggregated_output": "",
"exit_code": 0,
"duration": {
"secs": 39,
"nanos": 805442201
},
"formatted_output": ""
}
}
Tool callexecTool callReturned
Tool call · Input
text(await tools.exec_command({cmd:"cat > /app/solution/session_model.py <<'PY'\nfrom models import *\nimport pyarrow.parquet as pq\n\ndef session_model(stage):\n t=time.time();cutoff={'pseudo':24180200,'validation':24786800,'test':25393400}[stage];cols=[]\n for split in (['train','val'] if stage=='test' else ['train']):\n for b in pq.ParquetFile(D/f'{split}_listens.parquet').iter_batches(batch_size=2000000,columns=['uid','item_id','timestamp','is_organic','played_ratio_pct']):\n p=b.to_pandas();p=p[(p.is_organic==1)&(p.played_ratio_pct>=80)&(p.timestamp<=cutoff)];c=pd.Index(items).get_indexer(p.item_id);p=p[c>=0].copy();p['i']=c[c>=0].astype('int32');cols.append(p[['uid','i','timestamp']])\n p=pd.concat(cols,ignore_index=True).sort_values(['uid','timestamp']);del cols\n u=p.uid.to_numpy();i=p.i.to_numpy();ts=p.timestamp.to_numpy().astype(np.int64);print('seq events',len(p),flush=True)\n g=sp.csr_matrix((len(items),len(items)),dtype=np.float32)\n for d in range(1,6):\n m=(u[d:]==u[:-d])&(ts[d:]-ts[:-d]<1800)&(i[d:]!=i[:-d]);a=i[d:][m];b=i[:-d][m]\n z=sp.csr_matrix((np.ones(len(a),np.float32)/np.sqrt(d),(a,b)),shape=g.shape);g=g+z+z.T\n del p,u,i,ts\n pop=np.asarray(g.sum(axis=1)).ravel();g.data*=g.data/(g.data+3);g=sp.diags((pop+10)**(-.5))@[email protected]((pop+10)**(-.5));g=g.tocsr()\n inds=[];vals=[];indptr=[0]\n for j in range(len(items)):\n st,en=g.indptr[j:j+2];v=g.data[st:en];ix=np.argpartition(v,-min(200,len(v)))[-200:] if len(v)>0 else np.array([],int);inds.extend(g.indices[st:en][ix]);vals.extend(v[ix]);indptr.append(len(inds))\n g=sp.csr_matrix((np.array(vals,np.float32),np.array(inds,np.int32),np.array(indptr,np.int32)),shape=g.shape);sp.save_npz(O/f'{stage}_session_sim.npz',g)\n rows=pd.Index(users).get_indexer(eligibility(stage)[0]);q=sp.load_npz(O/f'{stage}_org.npz');q.data=np.log1p(q.data)\n last=sp.load_npz(O/f'{stage}_last.npz');last.data=np.exp(-np.maximum(cutoff-last.data,0)/86400/30);q=q.multiply(last)+.05*q\n for name,query in [('session',q),('sessionrecent',sp.load_npz(O/f'{stage}_recent.npz'))]:\n s=(query[rows]@g).toarray();s/=np.maximum(np.asarray(query[rows].sum(axis=1)),1e-5);np.save(O/f'{stage}_{name}.npy',s)\n if stage!='test':evaluate(s,name,stage)\n print('session done',time.time()-t,flush=True)\nif __name__=='__main__':session_model(sys.argv[1] if len(sys.argv)>1 else 'validation')\nPY\ncat /app/solution/experiments2.log && cat /app/solution/tune.log","max_output_tokens":2000}));
The archive records a thinking event with empty text. No readable thinking is available for this event.
Model text
Recent popularity growth made a substantial difference: the adjusted model reached 0.04234 NDCG@10, about twice the initial score. I’m saving its refitted submission now and checking the validation ranking with the supplied evaluator. Next I’ll test whether combining models improves on that result.
The archive records a thinking event with empty text. No readable thinking is available for this event.
Harness context codex item completed
{
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"item": {
"type": "CommandExecution",
"id": "exec-d63453de-4b4e-4569-a056-c7653799d06e",
"process_id": "47808",
"command": [
"/bin/sh",
"-lc",
"cat > /app/solution/finalize.py <<'PY'\nfrom models import *\ndef trend(stage,power=.75,pop_power=0):\n cnt=np.asarray(sp.load_npz(O/f'{stage}_cnt.npz').sum(axis=0)).ravel();recent=np.asarray(sp.load_npz(O/f'{stage}_recent.npz').sum(axis=0)).ravel();return ((recent+5)/(cnt+50))**power*(cnt+50)**pop_power\ndef finish(stage,name='ease500',power=.75,pop_power=0):\n s=np.load(O/f'{stage}_{name}.npy',mmap_mode='r');f=trend(stage,power,pop_power);out=np.lib.format.open_memmap(O/f'{stage}_best.npy',mode='w+',dtype='float32',shape=s.shape)\n for a in range(0,len(s),256):out[a:a+256]=np.maximum(s[a:a+256],0)*f\n out.flush();write_ranks(out,stage,O/('submission.parquet' if stage=='test' else 'validation.parquet'))\n if stage=='validation':evaluate(out,'final adjusted')\nif __name__=='__main__':finish(sys.argv[1],sys.argv[2] if len(sys.argv)>2 else 'ease500',float(sys.argv[3]) if len(sys.argv)>3 else .75,float(sys.argv[4]) if len(sys.argv)>4 else 0)\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/finalize.py test && OPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/finalize.py validation && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet"
],
"cwd": "file:///app",
"parsed_cmd": [
{
"type": "unknown",
"cmd": "cat > /app/solution/finalize.py <<'PY'\nfrom models import *\ndef trend(stage,power=.75,pop_power=0):\n cnt=np.asarray(sp.load_npz(O/f'{stage}_cnt.npz').sum(axis=0)).ravel();recent=np.asarray(sp.load_npz(O/f'{stage}_recent.npz').sum(axis=0)).ravel();return ((recent+5)/(cnt+50))**power*(cnt+50)**pop_power\ndef finish(stage,name='ease500',power=.75,pop_power=0):\n s=np.load(O/f'{stage}_{name}.npy',mmap_mode='r');f=trend(stage,power,pop_power);out=np.lib.format.open_memmap(O/f'{stage}_best.npy',mode='w+',dtype='float32',shape=s.shape)\n for a in range(0,len(s),256):out[a:a+256]=np.maximum(s[a:a+256],0)*f\n out.flush();write_ranks(out,stage,O/('submission.parquet' if stage=='test' else 'validation.parquet'))\n if stage=='validation':evaluate(out,'final adjusted')\nif __name__=='__main__':finish(sys.argv[1],sys.argv[2] if len(sys.argv)>2 else 'ease500',float(sys.argv[3]) if len(sys.argv)>3 else .75,float(sys.argv[4]) if len(sys.argv)>4 else 0)\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/finalize.py test && OPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/finalize.py validation && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet"
}
],
"source": "unified_exec_startup",
"status": "completed",
"stdout": "final adjusted NDCG 0.042342961376621996\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.042343,\n \"quality_users\": 4263\n }\n}\n",
"stderr": "",
"aggregated_output": "final adjusted NDCG 0.042342961376621996\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.042343,\n \"quality_users\": 4263\n }\n}\n",
"exit_code": 0,
"duration": {
"secs": 53,
"nanos": 37300511
},
"formatted_output": "final adjusted NDCG 0.042342961376621996\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.042343,\n \"quality_users\": 4263\n }\n}\n"
}
}
Tool callexecTool callReturned
Tool call · Input
text(await tools.exec_command({cmd:"python - <<'PY'\nimport os\nfor k in os.listdir('/proc'):\n if k.isdigit():\n try:\n c=open('/proc/'+k+'/cmdline','rb').read().replace(b'\\0',b' ').decode()\n if '/app/solution/' in c and ('python' in c):\n lines=open('/proc/'+k+'/status').read().splitlines();print(k,c[:150],'; '.join(v for v in lines if v.startswith(('State:','VmRSS:','VmSize:'))))\n except:pass\nPY\nls -lh /app/solution/submission.parquet /app/solution/validation_best.npy && tail -5 /app/solution/tune2.log","max_output_tokens":2000}));
text(await tools.exec_command({cmd:"cat > /app/solution/rerank.py <<'PY'\nfrom models import *\nfrom finalize import trend\nfrom sklearn.ensemble import HistGradientBoostingRegressor\nimport joblib\nNAMES=['ease500','org100','rec500','recent20']\ndef features(stage):\n t=time.time();u,it,e=eligibility(stage);pops={c:np.asarray(sp.load_npz(O/f'{stage}_{c}.npz').sum(axis=0)).ravel() for c in ['cnt','org','likes','recent']};f=trend(stage,.75,0)\n row=[];col=[];fs=[];us=[]\n for c in ['cnt','org','likes','recent']:\n x=sp.load_npz(O/f'{stage}_{c}.npz');rr=pd.Index(users).get_indexer(u);us.extend([np.log1p(np.asarray(x.sum(axis=1)).ravel()[rr]),np.log1p(np.diff(x.indptr)[rr])])\n us=np.array(us).T\n ss=[np.load(O/f'{stage}_{n}.npy',mmap_mode='r') for n in NAMES]\n for a in range(0,len(u),64):\n end=min(a+64,len(u));batch=[np.maximum(np.array(s[a:end]),0) for s in ss];cand=[]\n for s in batch:\n v=s*f;v[~e[a:end]]=-1;ix=np.argpartition(v,-100,axis=1)[:,-100:];cand.append(ix)\n cand=np.concatenate(cand,axis=1);keys=(np.arange(end-a)[:,None]*len(it)+cand).ravel();keys=np.unique(keys);rr=keys//len(it);cc=keys%len(it)\n z=[]\n for s in batch:\n z.append(s[rr,cc]);z.append(s[rr,cc]*f[cc]);z.append(s[rr,cc]/np.maximum(s.max(axis=1)[rr],1e-6))\n for c in pops:z.append(np.log1p(pops[c][cc]))\n z.extend([np.log((pops['recent'][cc]+5)/(pops['cnt'][cc]+50)),np.log((pops['org'][cc]+5)/(pops['cnt'][cc]+50)),np.log((pops['likes'][cc]+1)/(pops['cnt'][cc]+50))])\n z=np.array(z,dtype=np.float32).T;z=np.concatenate([z,us[rr+a]],axis=1);row.append(rr+a);col.append(cc);fs.append(z)\n r=np.concatenate(row).astype(np.int32);c=np.concatenate(col).astype(np.int32);X=np.concatenate(fs);np.savez(O/f'{stage}_features.npz',r=r,c=c,X=X);print(stage,'features',X.shape,time.time()-t,flush=True);return r,c,X\n\ndef labels(stage,r,c):\n y=sp.load_npz(O/('pseudo_truth.npz' if stage=='pseudo' else 'truth.npz'));ideal=np.load(O/('pseudo_idcg.npy' if stage=='pseudo' else 'idcg.npy'));return np.asarray(y[r,c]).ravel()/np.maximum(ideal[r],1),ideal[r]>0\n\ndef predict_rank(stage,r,c,pred,path=None):\n u,it,e=eligibility(stage);top=[];ptr=np.searchsorted(r,np.arange(len(u)+1))\n for j in range(len(u)):\n lo,hi=ptr[j:j+2];ix=np.argsort(-pred[lo:hi])[:100];top.append(c[lo:hi][ix])\n ix=np.array(top);frame=pd.DataFrame({'uid':np.repeat(u,100),'item_id':it[ix.ravel()],'rank':np.tile(np.arange(1,101),len(u))})\n if path:frame.to_parquet(path,index=False)\n if stage!='test':\n y=sp.load_npz(O/('pseudo_truth.npz' if stage=='pseudo' else 'truth.npz'));ideal=np.load(O/('pseudo_idcg.npy' if stage=='pseudo' else 'idcg.npy'));g=np.asarray(y[np.arange(len(u))[:,None],ix[:,:10]]);score=((g/np.log2(np.arange(2,12))).sum(axis=1)[ideal>0]/ideal[ideal>0]).mean();print('RERANK',stage,score,flush=True);return score\n\ndef load_features(stage):\n if not (O/f'{stage}_features.npz').exists():return features(stage)\n z=np.load(O/f'{stage}_features.npz');return z['r'],z['c'],z['X']\nif __name__=='__main__':\n mode=sys.argv[1] if len(sys.argv)>1 else 'train'\n if mode=='features':features(sys.argv[2]);sys.exit()\n if mode=='train':\n r,c,X=load_features('pseudo');y,m=labels('pseudo',r,c);X=X[m];y=y[m];print('training',X.shape,'positives',(y>0).sum(),flush=True)\n vr,vc,V=load_features('validation')\n for leaves,iters in [(15,160),(31,160),(31,300)]:\n model=HistGradientBoostingRegressor(loss='poisson',max_iter=iters,max_leaf_nodes=leaves,learning_rate=.06,min_samples_leaf=150,l2_regularization=.1,max_bins=128,early_stopping=False,random_state=42);model.fit(X,y);joblib.dump(model,O/f'rerank_{leaves}_{iters}.pkl');p=model.predict(V);np.save(O/f'validation_rerank_{leaves}_{iters}.npy',p);predict_rank('validation',vr,vc,p,O/f'validation_rerank_{leaves}_{iters}.parquet')\n if mode=='final':\n xx=[];yy=[]\n for stage in ['pseudo','validation']:\n r,c,X=load_features(stage);y,m=labels(stage,r,c);xx.append(X[m]);yy.append(y[m])\n X=np.concatenate(xx);y=np.concatenate(yy);del xx,yy\n model=HistGradientBoostingRegressor(loss='poisson',max_iter=int(sys.argv[3]),max_leaf_nodes=int(sys.argv[2]),learning_rate=.06,min_samples_leaf=150,l2_regularization=.1,max_bins=128,early_stopping=False,random_state=42);model.fit(X,y);joblib.dump(model,O/'final_reranker.pkl');del X,y\n r,c,X=load_features('test');p=model.predict(X);np.save(O/'test_rerank.npy',p);predict_rank('test',r,c,p,O/'submission.parquet')\nPY","max_output_tokens":1000}));
Tool response
Script completed
Wall time 0.1 seconds
Output:
{"chunk_id":"e3f4b4","wall_time_seconds":0.00000676,"exit_code":0,"original_token_count":0,"output":""}
Harness context codex item completed
{
"type": "codex_item_completed",
"item": {
"type": "CommandExecution",
"id": "exec-13b88892-2d82-42a0-b246-9384b87e607f",
"process_id": "97415",
"command": [
"/bin/sh",
"-lc",
"cat > /app/solution/rerank.py <<'PY'\nfrom models import *\nfrom finalize import trend\nfrom sklearn.ensemble import HistGradientBoostingRegressor\nimport joblib\nNAMES=['ease500','org100','rec500','recent20']\ndef features(stage):\n t=time.time();u,it,e=eligibility(stage);pops={c:np.asarray(sp.load_npz(O/f'{stage}_{c}.npz').sum(axis=0)).ravel() for c in ['cnt','org','likes','recent']};f=trend(stage,.75,0)\n row=[];col=[];fs=[];us=[]\n for c in ['cnt','org','likes','recent']:\n x=sp.load_npz(O/f'{stage}_{c}.npz');rr=pd.Index(users).get_indexer(u);us.extend([np.log1p(np.asarray(x.sum(axis=1)).ravel()[rr]),np.log1p(np.diff(x.indptr)[rr])])\n us=np.array(us).T\n ss=[np.load(O/f'{stage}_{n}.npy',mmap_mode='r') for n in NAMES]\n for a in range(0,len(u),64):\n end=min(a+64,len(u));batch=[np.maximum(np.array(s[a:end]),0) for s in ss];cand=[]\n for s in batch:\n v=s*f;v[~e[a:end]]=-1;ix=np.argpartition(v,-100,axis=1)[:,-100:];cand.append(ix)\n cand=np.concatenate(cand,axis=1);keys=(np.arange(end-a)[:,None]*len(it)+cand).ravel();keys=np.unique(keys);rr=keys//len(it);cc=keys%len(it)\n z=[]\n for s in batch:\n z.append(s[rr,cc]);z.append(s[rr,cc]*f[cc]);z.append(s[rr,cc]/np.maximum(s.max(axis=1)[rr],1e-6))\n for c in pops:z.append(np.log1p(pops[c][cc]))\n z.extend([np.log((pops['recent'][cc]+5)/(pops['cnt'][cc]+50)),np.log((pops['org'][cc]+5)/(pops['cnt'][cc]+50)),np.log((pops['likes'][cc]+1)/(pops['cnt'][cc]+50))])\n z=np.array(z,dtype=np.float32).T;z=np.concatenate([z,us[rr+a]],axis=1);row.append(rr+a);col.append(cc);fs.append(z)\n r=np.concatenate(row).astype(np.int32);c=np.concatenate(col).astype(np.int32);X=np.concatenate(fs);np.savez(O/f'{stage}_features.npz',r=r,c=c,X=X);print(stage,'features',X.shape,time.time()-t,flush=True);return r,c,X\n\ndef labels(stage,r,c):\n y=sp.load_npz(O/('pseudo_truth.npz' if stage=='pseudo' else 'truth.npz'));ideal=np.load(O/('pseudo_idcg.npy' if stage=='pseudo' else 'idcg.npy'));return np.asarray(y[r,c]).ravel()/np.maximum(ideal[r],1),ideal[r]>0\n\ndef predict_rank(stage,r,c,pred,path=None):\n u,it,e=eligibility(stage);top=[];ptr=np.searchsorted(r,np.arange(len(u)+1))\n for j in range(len(u)):\n lo,hi=ptr[j:j+2];ix=np.argsort(-pred[lo:hi])[:100];top.append(c[lo:hi][ix])\n ix=np.array(top);frame=pd.DataFrame({'uid':np.repeat(u,100),'item_id':it[ix.ravel()],'rank':np.tile(np.arange(1,101),len(u))})\n if path:frame.to_parquet(path,index=False)\n if stage!='test':\n y=sp.load_npz(O/('pseudo_truth.npz' if stage=='pseudo' else 'truth.npz'));ideal=np.load(O/('pseudo_idcg.npy' if stage=='pseudo' else 'idcg.npy'));g=np.asarray(y[np.arange(len(u))[:,None],ix[:,:10]]);score=((g/np.log2(np.arange(2,12))).sum(axis=1)[ideal>0]/ideal[ideal>0]).mean();print('RERANK',stage,score,flush=True);return score\n\ndef load_features(stage):\n if not (O/f'{stage}_features.npz').exists():return features(stage)\n z=np.load(O/f'{stage}_features.npz');return z['r'],z['c'],z['X']\nif __name__=='__main__':\n mode=sys.argv[1] if len(sys.argv)>1 else 'train'\n if mode=='features':features(sys.argv[2]);sys.exit()\n if mode=='train':\n r,c,X=load_features('pseudo');y,m=labels('pseudo',r,c);X=X[m];y=y[m];print('training',X.shape,'positives',(y>0).sum(),flush=True)\n vr,vc,V=load_features('validation')\n for leaves,iters in [(15,160),(31,160),(31,300)]:\n model=HistGradientBoostingRegressor(loss='poisson',max_iter=iters,max_leaf_nodes=leaves,learning_rate=.06,min_samples_leaf=150,l2_regularization=.1,max_bins=128,early_stopping=False,random_state=42);model.fit(X,y);joblib.dump(model,O/f'rerank_{leaves}_{iters}.pkl');p=model.predict(V);np.save(O/f'validation_rerank_{leaves}_{iters}.npy',p);predict_rank('validation',vr,vc,p,O/f'validation_rerank_{leaves}_{iters}.parquet')\n if mode=='final':\n xx=[];yy=[]\n for stage in ['pseudo','validation']:\n r,c,X=load_features(stage);y,m=labels(stage,r,c);xx.append(X[m]);yy.append(y[m])\n X=np.concatenate(xx);y=np.concatenate(yy);del xx,yy\n model=HistGradientBoostingRegressor(loss='poisson',max_iter=int(sys.argv[3]),max_leaf_nodes=int(sys.argv[2]),learning_rate=.06,min_samples_leaf=150,l2_regularization=.1,max_bins=128,early_stopping=False,random_state=42);model.fit(X,y);joblib.dump(model,O/'final_reranker.pkl');del X,y\n r,c,X=load_features('test');p=model.predict(X);np.save(O/'test_rerank.npy',p);predict_rank('test',r,c,p,O/'submission.parquet')\nPY"
],
"cwd": "file:///app",
"parsed_cmd": [
{
"type": "unknown",
"cmd": "cat > /app/solution/rerank.py <<'PY'\nfrom models import *\nfrom finalize import trend\nfrom sklearn.ensemble import HistGradientBoostingRegressor\nimport joblib\nNAMES=['ease500','org100','rec500','recent20']\ndef features(stage):\n t=time.time();u,it,e=eligibility(stage);pops={c:np.asarray(sp.load_npz(O/f'{stage}_{c}.npz').sum(axis=0)).ravel() for c in ['cnt','org','likes','recent']};f=trend(stage,.75,0)\n row=[];col=[];fs=[];us=[]\n for c in ['cnt','org','likes','recent']:\n x=sp.load_npz(O/f'{stage}_{c}.npz');rr=pd.Index(users).get_indexer(u);us.extend([np.log1p(np.asarray(x.sum(axis=1)).ravel()[rr]),np.log1p(np.diff(x.indptr)[rr])])\n us=np.array(us).T\n ss=[np.load(O/f'{stage}_{n}.npy',mmap_mode='r') for n in NAMES]\n for a in range(0,len(u),64):\n end=min(a+64,len(u));batch=[np.maximum(np.array(s[a:end]),0) for s in ss];cand=[]\n for s in batch:\n v=s*f;v[~e[a:end]]=-1;ix=np.argpartition(v,-100,axis=1)[:,-100:];cand.append(ix)\n cand=np.concatenate(cand,axis=1);keys=(np.arange(end-a)[:,None]*len(it)+cand).ravel();keys=np.unique(keys);rr=keys//len(it);cc=keys%len(it)\n z=[]\n for s in batch:\n z.append(s[rr,cc]);z.append(s[rr,cc]*f[cc]);z.append(s[rr,cc]/np.maximum(s.max(axis=1)[rr],1e-6))\n for c in pops:z.append(np.log1p(pops[c][cc]))\n z.extend([np.log((pops['recent'][cc]+5)/(pops['cnt'][cc]+50)),np.log((pops['org'][cc]+5)/(pops['cnt'][cc]+50)),np.log((pops['likes'][cc]+1)/(pops['cnt'][cc]+50))])\n z=np.array(z,dtype=np.float32).T;z=np.concatenate([z,us[rr+a]],axis=1);row.append(rr+a);col.append(cc);fs.append(z)\n r=np.concatenate(row).astype(np.int32);c=np.concatenate(col).astype(np.int32);X=np.concatenate(fs);np.savez(O/f'{stage}_features.npz',r=r,c=c,X=X);print(stage,'features',X.shape,time.time()-t,flush=True);return r,c,X\n\ndef labels(stage,r,c):\n y=sp.load_npz(O/('pseudo_truth.npz' if stage=='pseudo' else 'truth.npz'));ideal=np.load(O/('pseudo_idcg.npy' if stage=='pseudo' else 'idcg.npy'));return np.asarray(y[r,c]).ravel()/np.maximum(ideal[r],1),ideal[r]>0\n\ndef predict_rank(stage,r,c,pred,path=None):\n u,it,e=eligibility(stage);top=[];ptr=np.searchsorted(r,np.arange(len(u)+1))\n for j in range(len(u)):\n lo,hi=ptr[j:j+2];ix=np.argsort(-pred[lo:hi])[:100];top.append(c[lo:hi][ix])\n ix=np.array(top);frame=pd.DataFrame({'uid':np.repeat(u,100),'item_id':it[ix.ravel()],'rank':np.tile(np.arange(1,101),len(u))})\n if path:frame.to_parquet(path,index=False)\n if stage!='test':\n y=sp.load_npz(O/('pseudo_truth.npz' if stage=='pseudo' else 'truth.npz'));ideal=np.load(O/('pseudo_idcg.npy' if stage=='pseudo' else 'idcg.npy'));g=np.asarray(y[np.arange(len(u))[:,None],ix[:,:10]]);score=((g/np.log2(np.arange(2,12))).sum(axis=1)[ideal>0]/ideal[ideal>0]).mean();print('RERANK',stage,score,flush=True);return score\n\ndef load_features(stage):\n if not (O/f'{stage}_features.npz').exists():return features(stage)\n z=np.load(O/f'{stage}_features.npz');return z['r'],z['c'],z['X']\nif __name__=='__main__':\n mode=sys.argv[1] if len(sys.argv)>1 else 'train'\n if mode=='features':features(sys.argv[2]);sys.exit()\n if mode=='train':\n r,c,X=load_features('pseudo');y,m=labels('pseudo',r,c);X=X[m];y=y[m];print('training',X.shape,'positives',(y>0).sum(),flush=True)\n vr,vc,V=load_features('validation')\n for leaves,iters in [(15,160),(31,160),(31,300)]:\n model=HistGradientBoostingRegressor(loss='poisson',max_iter=iters,max_leaf_nodes=leaves,learning_rate=.06,min_samples_leaf=150,l2_regularization=.1,max_bins=128,early_stopping=False,random_state=42);model.fit(X,y);joblib.dump(model,O/f'rerank_{leaves}_{iters}.pkl');p=model.predict(V);np.save(O/f'validation_rerank_{leaves}_{iters}.npy',p);predict_rank('validation',vr,vc,p,O/f'validation_rerank_{leaves}_{iters}.parquet')\n if mode=='final':\n xx=[];yy=[]\n for stage in ['pseudo','validation']:\n r,c,X=load_features(stage);y,m=labels(stage,r,c);xx.append(X[m]);yy.append(y[m])\n X=np.concatenate(xx);y=np.concatenate(yy);del xx,yy\n model=HistGradientBoostingRegressor(loss='poisson',max_iter=int(sys.argv[3]),max_leaf_nodes=int(sys.argv[2]),learning_rate=.06,min_samples_leaf=150,l2_regularization=.1,max_bins=128,early_stopping=False,random_state=42);model.fit(X,y);joblib.dump(model,O/'final_reranker.pkl');del X,y\n r,c,X=load_features('test');p=model.predict(X);np.save(O/'test_rerank.npy',p);predict_rank('test',r,c,p,O/'submission.parquet')\nPY"
}
],
"source": "unified_exec_startup",
"status": "completed",
"stdout": "",
"stderr": "",
"aggregated_output": "",
"exit_code": 0,
"duration": {
"secs": 0,
"nanos": 6760
},
"formatted_output": ""
}
}
The archive records a thinking event with empty text. No readable thinking is available for this event.
Tool callexecTool callReturned
Tool call · Input
text(await tools.exec_command({cmd:"python - <<'PY'\np='/app/solution/models.py';s=open(p).read().replace(\"elif stage=='test':write_ranks(scores,stage,O/'submission.parquet')\", \"# Test ranking is written only by the selected finalizer.\");open(p,'w').write(s)\nPY\ncat > /app/solution/test_models.py <<'PY'\nfrom models import *\nfrom finalize import finish\nfor lam,kind,like,org,name in [(500,'rec',2,0,'rec500'),(100,'org',0,0,'org100'),(20,'recent',1,0,'recent20')]:\n ease('test',lam,kind,like,org,name)\n if name=='rec500':finish('test','rec500',.5,-.15)\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/rerank.py features validation > /app/solution/val_features.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));
Tool response
Script completed
Wall time 1.2 seconds
Output:
{"chunk_id":"bacce8","wall_time_seconds":1.001136588,"session_id":13500,"original_token_count":0,"output":""}
The archive records a thinking event with empty text. No readable thinking is available for this event.
Model text
The supplied evaluator confirmed 0.042343. A model using recency-weighted histories has since reached 0.04421 with a popularity adjustment. The session model also shows promise as an additional signal; I’ll include it in the reranker trained on the earlier holdout and evaluate that on the public window.
The archive records a thinking event with empty text. No readable thinking is available for this event.
Tool callexecTool callReturned
Tool call · Input
text(await tools.exec_command({cmd:"python - <<'PY'\nimport os\nfor k in os.listdir('/proc'):\n if k.isdigit():\n try:\n c=open('/proc/'+k+'/cmdline','rb').read().replace(b'\\0',b' ').decode()\n if c.startswith('/opt/task-venv/bin/python'):\n lines=open('/proc/'+k+'/status').read().splitlines();print(k,c,'; '.join(v for v in lines if v.startswith(('State:','VmRSS:'))))\n except:pass\nPY\ncat /app/solution/val_features.log && cat /app/solution/rerank.log","max_output_tokens":1000}));text(await tools.clock__curr_time({}));
Script completed
Wall time 0.2 seconds
Output:
{"chunk_id":"70954e","wall_time_seconds":0.00000943,"exit_code":0,"original_token_count":815,"output":"pseudo features (2570474, 30) 48.36058521270752\ntraining (1259702, 30) positives 8008\nTraceback (most recent call last):\n File \"/app/solution/rerank.py\", line 51, in <module>\n model=HistGradientBoostingRegressor(loss='poisson',max_iter=iters,max_leaf_nodes=leaves,learning_rate=.06,min_samples_leaf=150,l2_regularization=.1,max_bins=128,early_stopping=False,random_state=42);model.fit(X,y);joblib.dump(model,O/f'rerank_{leaves}_{iters}.pkl');p=model.predict(V);np.save(O/f'validation_rerank_{leaves}_{iters}.npy',p);predict_rank('validation',vr,vc,p,O/f'validation_rerank_{leaves}_{iters}.parquet')\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/rerank.py\", line 39, in predict_rank\n y=sp.load_npz(O/('pseudo_truth.npz' if stage=='pseudo' else 'truth.npz'));ideal=np.load(O/('pseudo_idcg.npy' if stage=='pseudo' else 'idcg.npy'));g=np.asarray(y[np.arange(len(u))[:,None],ix[:,:10]]);score=((g/np.log2(np.arange(2,12))).sum(axis=1)[ideal>0]/ideal[ideal>0]).mean();print('RERANK',stage,score,flush=True);return score\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/_core/_methods.py\", line 52, in _sum\n return umr_sum(a, axis, dtype, out, keepdims, initial, where)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nnumpy.exceptions.AxisError: axis 1 is out of bounds for array of dimension 1\nTraceback (most recent call last):\n File \"/app/solution/blend.py\", line 8, in <module>\n p=sum(w*s for w,s in zip(weights,ss));score=predict_rank('validation',r,c,p);print(weights,score,flush=True)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/rerank.py\", line 39, in predict_rank\n y=sp.load_npz(O/('pseudo_truth.npz' if stage=='pseudo' else 'truth.npz'));ideal=np.load(O/('pseudo_idcg.npy' if stage=='pseudo' else 'idcg.npy'));g=np.asarray(y[np.arange(len(u))[:,None],ix[:,:10]]);score=((g/np.log2(np.arange(2,12))).sum(axis=1)[ideal>0]/ideal[ideal>0]).mean();print('RERANK',stage,score,flush=True);return score\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/_core/_methods.py\", line 52, in _sum\n return umr_sum(a, axis, dtype, out, keepdims, initial, where)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nnumpy.exceptions.AxisError: axis 1 is out of bounds for array of dimension 1\nseq events 8990594\nsession done 27.22570300102234\n"}
{"current_time":"2026-09-23 05:34:24 UTC"}
The archive records a thinking event with empty text. No readable thinking is available for this event.
Tool callexecTool callReturned
Tool call · Input
text(await tools.exec_command({cmd:"OPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/rerank.py features test > /app/solution/test_features.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));
Tool response
Script completed
Wall time 1.2 seconds
Output:
{"chunk_id":"ef01bf","wall_time_seconds":1.001101086,"session_id":12697,"original_token_count":0,"output":""}
The archive records a thinking event with empty text. No readable thinking is available for this event.
Model text
The recency-adjusted model has been refit and saved. The earlier holdout provides about 8,000 relevant tracks among the reranker’s retrieved candidates. I’m now comparing the learned reranker and several simple blends; the final choice will follow their public validation results.
text(await tools.exec_command({cmd:"cat > /app/solution/enhance.py <<'PY'\nfrom rerank import *\nPARS=[(.75,0),(.75,0),(.5,-.15),(.5,.15),(.75,.15)]\ndef enhance(stage):\n r,c,X=load_features(stage);u=eligibility(stage)[0];rr=pd.Index(users).get_indexer(u);ptr=np.searchsorted(r,np.arange(len(u)+1));fs=[X]\n for j,(a,b) in enumerate(PARS):\n s=X[:,j*3]*trend(stage,a,b)[c];norm=s/np.maximum(np.maximum.reduceat(s,ptr[:-1])[r],1e-9);rank=np.empty(len(s),np.float32)\n for k in range(len(u)):\n lo,hi=ptr[k:k+2];ix=np.argsort(-s[lo:hi]);rank[lo+ix]=np.arange(hi-lo)\n fs.append(np.c_[norm,np.log1p(rank)].astype(np.float32))\n sim=sp.load_npz(O/f'{stage}_session_sim.npz')\n for family in ['likes','dislikes','recent']:\n q=sp.load_npz(O/f'{stage}_{family}.npz');q.data=np.log1p(q.data);norm=np.asarray(q.sum(axis=1)).ravel();sc=(q[rr]@sim).tocsr();v=np.asarray(sc[r,c]).ravel();fs.append(np.c_[v,v/np.maximum(norm[rr][r],1)]);del sc\n if family=='dislikes':\n pop=np.asarray(q.sum(axis=0)).ravel();fs.append(np.log1p(pop[c])[:,None])\n X=np.concatenate(fs,axis=1).astype(np.float32);np.savez(O/f'{stage}_enhanced.npz',r=r,c=c,X=X);print('enhanced',stage,X.shape,flush=True);return r,c,X\nif __name__=='__main__':\n if sys.argv[1]=='features':enhance(sys.argv[2]);sys.exit()\n for stage in ['pseudo','validation']:\n if not (O/f'{stage}_enhanced.npz').exists():enhance(stage)\n z=np.load(O/'pseudo_enhanced.npz');r=z['r'];c=z['c'];X=z['X'];y,m=labels('pseudo',r,c);X=X[m];y=y[m]\n z=np.load(O/'validation_enhanced.npz');vr=z['r'];vc=z['c'];V=z['X']\n for leaves,iters in [(15,200),(31,200),(31,400)]:\n model=HistGradientBoostingRegressor(loss='poisson',max_iter=iters,max_leaf_nodes=leaves,learning_rate=.05,min_samples_leaf=150,l2_regularization=.1,max_bins=128,early_stopping=False,random_state=42);model.fit(X,y);joblib.dump(model,O/f'enhanced_{leaves}_{iters}.pkl');p=model.predict(V);np.save(O/f'validation_enhanced_{leaves}_{iters}.npy',p);predict_rank('validation',vr,vc,p,O/f'validation_enhanced_{leaves}_{iters}.parquet')\nPY\ncat /app/solution/rerank.log && cat /app/solution/test_features.log","max_output_tokens":1000}));
Tool response
Script completed
Wall time 0.1 seconds
Output:
{"chunk_id":"67c365","wall_time_seconds":0.00000614,"exit_code":0,"original_token_count":41,"output":"training (1259702, 30) positives 8008\nRERANK validation 0.04629525044851402\nRERANK validation 0.044416688323752083\ntest features (2590760, 30) 40.006181478500366\n"}
Harness context codex item completed
{
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"item": {
"type": "CommandExecution",
"id": "exec-aeb95ad4-d320-4426-a4d4-19cd006802bd",
"process_id": "53992",
"command": [
"/bin/sh",
"-lc",
"cat > /app/solution/enhance.py <<'PY'\nfrom rerank import *\nPARS=[(.75,0),(.75,0),(.5,-.15),(.5,.15),(.75,.15)]\ndef enhance(stage):\n r,c,X=load_features(stage);u=eligibility(stage)[0];rr=pd.Index(users).get_indexer(u);ptr=np.searchsorted(r,np.arange(len(u)+1));fs=[X]\n for j,(a,b) in enumerate(PARS):\n s=X[:,j*3]*trend(stage,a,b)[c];norm=s/np.maximum(np.maximum.reduceat(s,ptr[:-1])[r],1e-9);rank=np.empty(len(s),np.float32)\n for k in range(len(u)):\n lo,hi=ptr[k:k+2];ix=np.argsort(-s[lo:hi]);rank[lo+ix]=np.arange(hi-lo)\n fs.append(np.c_[norm,np.log1p(rank)].astype(np.float32))\n sim=sp.load_npz(O/f'{stage}_session_sim.npz')\n for family in ['likes','dislikes','recent']:\n q=sp.load_npz(O/f'{stage}_{family}.npz');q.data=np.log1p(q.data);norm=np.asarray(q.sum(axis=1)).ravel();sc=(q[rr]@sim).tocsr();v=np.asarray(sc[r,c]).ravel();fs.append(np.c_[v,v/np.maximum(norm[rr][r],1)]);del sc\n if family=='dislikes':\n pop=np.asarray(q.sum(axis=0)).ravel();fs.append(np.log1p(pop[c])[:,None])\n X=np.concatenate(fs,axis=1).astype(np.float32);np.savez(O/f'{stage}_enhanced.npz',r=r,c=c,X=X);print('enhanced',stage,X.shape,flush=True);return r,c,X\nif __name__=='__main__':\n if sys.argv[1]=='features':enhance(sys.argv[2]);sys.exit()\n for stage in ['pseudo','validation']:\n if not (O/f'{stage}_enhanced.npz').exists():enhance(stage)\n z=np.load(O/'pseudo_enhanced.npz');r=z['r'];c=z['c'];X=z['X'];y,m=labels('pseudo',r,c);X=X[m];y=y[m]\n z=np.load(O/'validation_enhanced.npz');vr=z['r'];vc=z['c'];V=z['X']\n for leaves,iters in [(15,200),(31,200),(31,400)]:\n model=HistGradientBoostingRegressor(loss='poisson',max_iter=iters,max_leaf_nodes=leaves,learning_rate=.05,min_samples_leaf=150,l2_regularization=.1,max_bins=128,early_stopping=False,random_state=42);model.fit(X,y);joblib.dump(model,O/f'enhanced_{leaves}_{iters}.pkl');p=model.predict(V);np.save(O/f'validation_enhanced_{leaves}_{iters}.npy',p);predict_rank('validation',vr,vc,p,O/f'validation_enhanced_{leaves}_{iters}.parquet')\nPY\ncat /app/solution/rerank.log && cat /app/solution/test_features.log"
],
"cwd": "file:///app",
"parsed_cmd": [
{
"type": "unknown",
"cmd": "cat > /app/solution/enhance.py <<'PY'\nfrom rerank import *\nPARS=[(.75,0),(.75,0),(.5,-.15),(.5,.15),(.75,.15)]\ndef enhance(stage):\n r,c,X=load_features(stage);u=eligibility(stage)[0];rr=pd.Index(users).get_indexer(u);ptr=np.searchsorted(r,np.arange(len(u)+1));fs=[X]\n for j,(a,b) in enumerate(PARS):\n s=X[:,j*3]*trend(stage,a,b)[c];norm=s/np.maximum(np.maximum.reduceat(s,ptr[:-1])[r],1e-9);rank=np.empty(len(s),np.float32)\n for k in range(len(u)):\n lo,hi=ptr[k:k+2];ix=np.argsort(-s[lo:hi]);rank[lo+ix]=np.arange(hi-lo)\n fs.append(np.c_[norm,np.log1p(rank)].astype(np.float32))\n sim=sp.load_npz(O/f'{stage}_session_sim.npz')\n for family in ['likes','dislikes','recent']:\n q=sp.load_npz(O/f'{stage}_{family}.npz');q.data=np.log1p(q.data);norm=np.asarray(q.sum(axis=1)).ravel();sc=(q[rr]@sim).tocsr();v=np.asarray(sc[r,c]).ravel();fs.append(np.c_[v,v/np.maximum(norm[rr][r],1)]);del sc\n if family=='dislikes':\n pop=np.asarray(q.sum(axis=0)).ravel();fs.append(np.log1p(pop[c])[:,None])\n X=np.concatenate(fs,axis=1).astype(np.float32);np.savez(O/f'{stage}_enhanced.npz',r=r,c=c,X=X);print('enhanced',stage,X.shape,flush=True);return r,c,X\nif __name__=='__main__':\n if sys.argv[1]=='features':enhance(sys.argv[2]);sys.exit()\n for stage in ['pseudo','validation']:\n if not (O/f'{stage}_enhanced.npz').exists():enhance(stage)\n z=np.load(O/'pseudo_enhanced.npz');r=z['r'];c=z['c'];X=z['X'];y,m=labels('pseudo',r,c);X=X[m];y=y[m]\n z=np.load(O/'validation_enhanced.npz');vr=z['r'];vc=z['c'];V=z['X']\n for leaves,iters in [(15,200),(31,200),(31,400)]:\n model=HistGradientBoostingRegressor(loss='poisson',max_iter=iters,max_leaf_nodes=leaves,learning_rate=.05,min_samples_leaf=150,l2_regularization=.1,max_bins=128,early_stopping=False,random_state=42);model.fit(X,y);joblib.dump(model,O/f'enhanced_{leaves}_{iters}.pkl');p=model.predict(V);np.save(O/f'validation_enhanced_{leaves}_{iters}.npy',p);predict_rank('validation',vr,vc,p,O/f'validation_enhanced_{leaves}_{iters}.parquet')\nPY\ncat /app/solution/rerank.log && cat /app/solution/test_features.log"
}
],
"source": "unified_exec_startup",
"status": "completed",
"stdout": "training (1259702, 30) positives 8008\nRERANK validation 0.04629525044851402\nRERANK validation 0.044416688323752083\ntest features (2590760, 30) 40.006181478500366\n",
"stderr": "",
"aggregated_output": "training (1259702, 30) positives 8008\nRERANK validation 0.04629525044851402\nRERANK validation 0.044416688323752083\ntest features (2590760, 30) 40.006181478500366\n",
"exit_code": 0,
"duration": {
"secs": 0,
"nanos": 6140
},
"formatted_output": "training (1259702, 30) positives 8008\nRERANK validation 0.04629525044851402\nRERANK validation 0.044416688323752083\ntest features (2590760, 30) 40.006181478500366\n"
}
}
text(await tools.exec_command({cmd:"cat > /app/solution/tune_rerank_blend.py <<'PY'\nfrom rerank import *\nr,c,X=load_features('validation');del X;ptr=np.r_[0,np.flatnonzero(np.diff(r))+1];base=np.load(O/'validation_blend.npy');base/=np.maximum(np.maximum.reduceat(base,ptr)[r],1e-8);best=(0,None)\nfor name in sys.argv[1:] or ['rerank_15_160','rerank_31_160','rerank_31_300']:\n path=O/f'validation_{name}.npy'\n if not path.exists():continue\n p=np.load(path);p/=np.maximum(np.maximum.reduceat(p,ptr)[r],1e-8)\n for alpha in [0,.25,.5,.75,1]:\n z=(1-alpha)*base+alpha*p;score=predict_rank('validation',r,c,z);print(name,alpha,score,flush=True)\n if score>best[0]:best=(score,(name,alpha));np.save(O/'validation_hybrid.npy',z)\nprint('BEST HYBRID',best,flush=True);json.dump({'score':best[0],'model':best[1][0],'alpha':best[1][1]},open(O/'hybrid.json','w'))\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/tune_rerank_blend.py > /app/solution/hybrid.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));
Tool response
Script completed
Wall time 1.2 seconds
Output:
{"chunk_id":"df026f","wall_time_seconds":1.001761687,"session_id":88168,"original_token_count":0,"output":""}
The archive records a thinking event with empty text. No readable thinking is available for this event.
Model text
Blending the learned reranker with the collaborative models improved validation NDCG@10 to 0.04912. The shallower reranker generalized better than deeper versions. I’m refitting that blend and testing richer affinity features, while retaining the best completed submission.
Tool callexecTool callReturned
Tool call · Input
text(await tools.exec_command({cmd:"cat > /app/solution/final_hybrid.py <<'PY'\nfrom enhance import *\ndef final_hybrid(cfgpath='hybrid.json',stages=['pseudo','validation']):\n cfg=json.load(open(O/cfgpath));name=cfg['model'];alpha=cfg['alpha'];enhanced=name.startswith('enhanced') or name.startswith('multi');parts=name.split('_');leaves=int(parts[-2]);iters=int(parts[-1]);xx=[];yy=[]\n for stage in stages:\n if enhanced:\n if not (O/f'{stage}_enhanced.npz').exists():enhance(stage)\n z=np.load(O/f'{stage}_enhanced.npz');r=z['r'];c=z['c'];X=z['X']\n else:r,c,X=load_features(stage)\n y,m=labels(stage,r,c);xx.append(X[m]);yy.append(y[m]);del X\n X=np.concatenate(xx);y=np.concatenate(yy);del xx,yy;print('final fitting',X.shape,flush=True)\n model=HistGradientBoostingRegressor(loss='poisson',max_iter=iters,max_leaf_nodes=leaves,learning_rate=.05 if enhanced else .06,min_samples_leaf=150,l2_regularization=.1,max_bins=128,early_stopping=False,random_state=42);model.fit(X,y);joblib.dump(model,O/'final_model.pkl');del X,y\n if enhanced:\n if not (O/'test_enhanced.npz').exists():enhance('test')\n z=np.load(O/'test_enhanced.npz');r=z['r'];c=z['c'];X=z['X']\n else:r,c,X=load_features('test')\n p=model.predict(X);ptr=np.r_[0,np.flatnonzero(np.diff(r))+1];p/=np.maximum(np.maximum.reduceat(p,ptr)[r],1e-8);base=np.load(O/'test_blend.npy');base/=np.maximum(np.maximum.reduceat(base,ptr)[r],1e-8);p=(1-alpha)*base+alpha*p\n np.save(O/'test_final_scores.npy',p);predict_rank('test',r,c,p,O/'submission.parquet');json.dump(dict(cfg,training_stages=stages),open(O/'final_config.json','w'));print('final hybrid saved',flush=True)\nif __name__=='__main__':final_hybrid(sys.argv[1] if len(sys.argv)>1 else 'hybrid.json',sys.argv[2:] if len(sys.argv)>2 else ['pseudo','validation'])\nPY\nOPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/final_hybrid.py > /app/solution/final_hybrid.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));
Tool response
Script completed
Wall time 1.2 seconds
Output:
{"chunk_id":"5f8b31","wall_time_seconds":1.002202374,"session_id":38991,"original_token_count":0,"output":""}
The archive records a thinking event with empty text. No readable thinking is available for this event.
Tool callexecTool callReturned
Tool call · Input
text(await tools.exec_command({cmd:"python - <<'PY'\np='/app/solution/build.py';s=open(p).read().replace(\"(O if stage=='pseudo' else D)\",\"(D if stage in ['validation','test'] else O)\");open(p,'w').write(s)\nfor p in ['/app/solution/models.py','/app/solution/rerank.py']:\n s=open(p).read().replace(\"('pseudo_truth.npz' if stage=='pseudo' else 'truth.npz')\",\"('truth.npz' if stage=='validation' else f'{stage}_truth.npz')\").replace(\"('pseudo_idcg.npy' if stage=='pseudo' else 'idcg.npy')\",\"('idcg.npy' if stage=='validation' else f'{stage}_idcg.npy')\");open(p,'w').write(s)\np='/app/solution/session_model.py';s=open(p).read().replace(\"{'pseudo':24180200\", \"{'early':23573600,'pseudo':24180200\");open(p,'w').write(s)\ns=open('/app/solution/prepare_pseudo.py').read().replace('pseudo','early').replace('cutoff=24786800-7*86400-1800','cutoff=24786800-2*(7*86400+1800)').replace('(p.timestamp>=cutoff+1800)', '(p.timestamp>=cutoff+1800)&(p.timestamp<=cutoff+1800+7*86400)');open('/app/solution/prepare_early.py','w').write(s)\ns=open('/app/solution/pseudo_models.py').read().replace(\"'pseudo'\",\"'early'\");open('/app/solution/early_models.py','w').write(s)\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/prepare_early.py > /app/solution/early_prepare.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));
Tool response
Script completed
Wall time 1.3 seconds
Output:
{"chunk_id":"b9f6ce","wall_time_seconds":1.001311768,"session_id":95527,"original_token_count":0,"output":""}
The archive records a thinking event with empty text. No readable thinking is available for this event.
Harness context codex item completed
{
"type": "codex_item_completed",
"item": {
"type": "CommandExecution",
"id": "exec-c0f3082c-8032-4a74-812d-90d8b06decd8",
"process_id": "95527",
"command": [
"/bin/sh",
"-lc",
"python - <<'PY'\np='/app/solution/build.py';s=open(p).read().replace(\"(O if stage=='pseudo' else D)\",\"(D if stage in ['validation','test'] else O)\");open(p,'w').write(s)\nfor p in ['/app/solution/models.py','/app/solution/rerank.py']:\n s=open(p).read().replace(\"('pseudo_truth.npz' if stage=='pseudo' else 'truth.npz')\",\"('truth.npz' if stage=='validation' else f'{stage}_truth.npz')\").replace(\"('pseudo_idcg.npy' if stage=='pseudo' else 'idcg.npy')\",\"('idcg.npy' if stage=='validation' else f'{stage}_idcg.npy')\");open(p,'w').write(s)\np='/app/solution/session_model.py';s=open(p).read().replace(\"{'pseudo':24180200\", \"{'early':23573600,'pseudo':24180200\");open(p,'w').write(s)\ns=open('/app/solution/prepare_pseudo.py').read().replace('pseudo','early').replace('cutoff=24786800-7*86400-1800','cutoff=24786800-2*(7*86400+1800)').replace('(p.timestamp>=cutoff+1800)', '(p.timestamp>=cutoff+1800)&(p.timestamp<=cutoff+1800+7*86400)');open('/app/solution/prepare_early.py','w').write(s)\ns=open('/app/solution/pseudo_models.py').read().replace(\"'pseudo'\",\"'early'\");open('/app/solution/early_models.py','w').write(s)\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/prepare_early.py > /app/solution/early_prepare.log 2>&1"
],
"cwd": "file:///app",
"parsed_cmd": [
{
"type": "unknown",
"cmd": "python - <<'PY'\np='/app/solution/build.py';s=open(p).read().replace(\"(O if stage=='pseudo' else D)\",\"(D if stage in ['validation','test'] else O)\");open(p,'w').write(s)\nfor p in ['/app/solution/models.py','/app/solution/rerank.py']:\n s=open(p).read().replace(\"('pseudo_truth.npz' if stage=='pseudo' else 'truth.npz')\",\"('truth.npz' if stage=='validation' else f'{stage}_truth.npz')\").replace(\"('pseudo_idcg.npy' if stage=='pseudo' else 'idcg.npy')\",\"('idcg.npy' if stage=='validation' else f'{stage}_idcg.npy')\");open(p,'w').write(s)\np='/app/solution/session_model.py';s=open(p).read().replace(\"{'pseudo':24180200\", \"{'early':23573600,'pseudo':24180200\");open(p,'w').write(s)\ns=open('/app/solution/prepare_pseudo.py').read().replace('pseudo','early').replace('cutoff=24786800-7*86400-1800','cutoff=24786800-2*(7*86400+1800)').replace('(p.timestamp>=cutoff+1800)', '(p.timestamp>=cutoff+1800)&(p.timestamp<=cutoff+1800+7*86400)');open('/app/solution/prepare_early.py','w').write(s)\ns=open('/app/solution/pseudo_models.py').read().replace(\"'pseudo'\",\"'early'\");open('/app/solution/early_models.py','w').write(s)\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/prepare_early.py > /app/solution/early_prepare.log 2>&1"
}
],
"source": "unified_exec_startup",
"status": "completed",
"stdout": "",
"stderr": "",
"aggregated_output": "",
"exit_code": 0,
"duration": {
"secs": 49,
"nanos": 322779846
},
"formatted_output": ""
}
}
Tool callexecTool callReturned
Tool call · Input
text(await tools.exec_command({cmd:"python - <<'PY'\nimport os\nfor k in os.listdir('/proc'):\n if k.isdigit():\n try:\n c=open('/proc/'+k+'/cmdline','rb').read().replace(b'\\0',b' ').decode()\n if c.startswith('/opt/task-venv/bin/python'):\n lines=open('/proc/'+k+'/status').read().splitlines();print(k,c,'; '.join(v for v in lines if v.startswith(('State:','VmRSS:'))))\n except:pass\nPY","max_output_tokens":1500}));
text(await tools.exec_command({cmd:"cat > /app/solution/populations.py <<'PY'\nfrom models import *\nimport pyarrow.parquet as pq\ncutoffs={'early':23573600,'pseudo':24180200,'validation':24786800,'test':25393400}\npops={s:np.zeros((len(items),10),np.float32) for s in cutoffs};first={s:np.full(len(items),1e9,np.float32) for s in cutoffs};ii=pd.Index(items)\nfor split in ['train','val']:\n for b in pq.ParquetFile(D/f'{split}_listens.parquet').iter_batches(batch_size=2000000,columns=['item_id','timestamp','is_organic','played_ratio_pct']):\n p=b.to_pandas();p=p[(p.is_organic==1)&(p.played_ratio_pct>=80)];c=ii.get_indexer(p.item_id);m=c>=0;c=c[m];ts=p.timestamp.to_numpy()[m].astype(np.int64)\n for stage,cut in cutoffs.items():\n age=cut-ts;m=age>=0\n np.minimum.at(first[stage],c[m],ts[m])\n for j,days in enumerate([7,14,30]):\n keep=m&(age<days*86400);pops[stage][:,j]+=np.bincount(c[keep],minlength=len(items))\nfor split in ['train','val']:\n p=pd.read_parquet(D/f'{split}_likes.parquet');c=ii.get_indexer(p.item_id);m=c>=0;p=p[m];c=c[m];ts=p.timestamp.to_numpy().astype(np.int64);org=p.is_organic.to_numpy()\n for stage,cut in cutoffs.items():\n age=cut-ts;m=age>=0\n for j,days in enumerate([7,14,30]):\n keep=m&(age<days*86400);pops[stage][:,3+j]+=np.bincount(c[keep],minlength=len(items));keep=keep&(org==1);pops[stage][:,6+j]+=np.bincount(c[keep],minlength=len(items))\nfor s,cut in cutoffs.items():\n pops[s][:,9]=np.maximum(0,(cut-first[s])/86400);np.save(O/f'{s}_populations.npy',pops[s])\nprint('populations done',flush=True)\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/populations.py > /app/solution/populations.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));
Tool response
Script completed
Wall time 1.2 seconds
Output:
{"chunk_id":"036699","wall_time_seconds":1.001038509,"session_id":30491,"original_token_count":0,"output":""}
The archive records a thinking event with empty text. No readable thinking is available for this event.
Harness context codex item completed
{
"type": "codex_item_completed",
"item": {
"type": "CommandExecution",
"id": "exec-b6f6f050-e1b2-4b54-b349-3cc6ef7d05d6",
"process_id": "30491",
"command": [
"/bin/sh",
"-lc",
"cat > /app/solution/populations.py <<'PY'\nfrom models import *\nimport pyarrow.parquet as pq\ncutoffs={'early':23573600,'pseudo':24180200,'validation':24786800,'test':25393400}\npops={s:np.zeros((len(items),10),np.float32) for s in cutoffs};first={s:np.full(len(items),1e9,np.float32) for s in cutoffs};ii=pd.Index(items)\nfor split in ['train','val']:\n for b in pq.ParquetFile(D/f'{split}_listens.parquet').iter_batches(batch_size=2000000,columns=['item_id','timestamp','is_organic','played_ratio_pct']):\n p=b.to_pandas();p=p[(p.is_organic==1)&(p.played_ratio_pct>=80)];c=ii.get_indexer(p.item_id);m=c>=0;c=c[m];ts=p.timestamp.to_numpy()[m].astype(np.int64)\n for stage,cut in cutoffs.items():\n age=cut-ts;m=age>=0\n np.minimum.at(first[stage],c[m],ts[m])\n for j,days in enumerate([7,14,30]):\n keep=m&(age<days*86400);pops[stage][:,j]+=np.bincount(c[keep],minlength=len(items))\nfor split in ['train','val']:\n p=pd.read_parquet(D/f'{split}_likes.parquet');c=ii.get_indexer(p.item_id);m=c>=0;p=p[m];c=c[m];ts=p.timestamp.to_numpy().astype(np.int64);org=p.is_organic.to_numpy()\n for stage,cut in cutoffs.items():\n age=cut-ts;m=age>=0\n for j,days in enumerate([7,14,30]):\n keep=m&(age<days*86400);pops[stage][:,3+j]+=np.bincount(c[keep],minlength=len(items));keep=keep&(org==1);pops[stage][:,6+j]+=np.bincount(c[keep],minlength=len(items))\nfor s,cut in cutoffs.items():\n pops[s][:,9]=np.maximum(0,(cut-first[s])/86400);np.save(O/f'{s}_populations.npy',pops[s])\nprint('populations done',flush=True)\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/populations.py > /app/solution/populations.log 2>&1"
],
"cwd": "file:///app",
"parsed_cmd": [
{
"type": "unknown",
"cmd": "cat > /app/solution/populations.py <<'PY'\nfrom models import *\nimport pyarrow.parquet as pq\ncutoffs={'early':23573600,'pseudo':24180200,'validation':24786800,'test':25393400}\npops={s:np.zeros((len(items),10),np.float32) for s in cutoffs};first={s:np.full(len(items),1e9,np.float32) for s in cutoffs};ii=pd.Index(items)\nfor split in ['train','val']:\n for b in pq.ParquetFile(D/f'{split}_listens.parquet').iter_batches(batch_size=2000000,columns=['item_id','timestamp','is_organic','played_ratio_pct']):\n p=b.to_pandas();p=p[(p.is_organic==1)&(p.played_ratio_pct>=80)];c=ii.get_indexer(p.item_id);m=c>=0;c=c[m];ts=p.timestamp.to_numpy()[m].astype(np.int64)\n for stage,cut in cutoffs.items():\n age=cut-ts;m=age>=0\n np.minimum.at(first[stage],c[m],ts[m])\n for j,days in enumerate([7,14,30]):\n keep=m&(age<days*86400);pops[stage][:,j]+=np.bincount(c[keep],minlength=len(items))\nfor split in ['train','val']:\n p=pd.read_parquet(D/f'{split}_likes.parquet');c=ii.get_indexer(p.item_id);m=c>=0;p=p[m];c=c[m];ts=p.timestamp.to_numpy().astype(np.int64);org=p.is_organic.to_numpy()\n for stage,cut in cutoffs.items():\n age=cut-ts;m=age>=0\n for j,days in enumerate([7,14,30]):\n keep=m&(age<days*86400);pops[stage][:,3+j]+=np.bincount(c[keep],minlength=len(items));keep=keep&(org==1);pops[stage][:,6+j]+=np.bincount(c[keep],minlength=len(items))\nfor s,cut in cutoffs.items():\n pops[s][:,9]=np.maximum(0,(cut-first[s])/86400);np.save(O/f'{s}_populations.npy',pops[s])\nprint('populations done',flush=True)\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/populations.py > /app/solution/populations.log 2>&1"
}
],
"source": "unified_exec_startup",
"status": "completed",
"stdout": "",
"stderr": "",
"aggregated_output": "",
"exit_code": 0,
"duration": {
"secs": 4,
"nanos": 939322860
},
"formatted_output": ""
}
}
Model text
The richer affinity features haven’t improved on the 0.04912 blend so far. I’m checking whether a second earlier training window and recent organic popularity make the reranker more reliable. The refitted 0.04912 approach is now saved as the fallback.
The archive records a thinking event with empty text. No readable thinking is available for this event.
Tool callexecTool callReturned
Tool call · Input
text(await tools.exec_command({cmd:"cat > /app/solution/rebuild.py <<'PY'\n\"\"\"Rebuild the selected final model from public, cutoff-correct inputs.\"\"\"\nimport os,sys,json,subprocess\nfrom pathlib import Path\nO=Path(__file__).resolve().parent\nos.environ['OPENBLAS_NUM_THREADS']='4';os.environ['OMP_NUM_THREADS']='4';os.environ['TASK_THREADS']='4'\ndef run(script,*args):subprocess.run([sys.executable,str(O/script),*map(str,args)],cwd=O,check=True)\ncfg=json.load(open(O/'final_config.json'));stages=cfg['training_stages']\nif not (O/'test_cnt.npz').exists():run('prepare.py')\nfor stage in stages:\n if stage in ['pseudo','early'] and not (O/f'{stage}_cnt.npz').exists():run(f'prepare_{stage}.py')\nfor stage in stages+['test']:\n for lam,kind,like,org,name in [(500,'log',2,0,'ease500'),(100,'org',0,0,'org100'),(500,'rec',2,0,'rec500'),(20,'recent',1,0,'recent20')]:\n if not (O/f'{stage}_{name}.npy').exists():run('models.py',stage,lam,kind,like,org,name)\n if not (O/f'{stage}_session.npy').exists():run('session_model.py',stage)\n if not (O/f'{stage}_features.npz').exists():run('rerank.py','features',stage)\nif cfg['model'].startswith('pop') and not (O/'test_populations.npy').exists():run('populations.py')\nrun('apply_blend.py','test')\nrun('final_hybrid.py','final_config.json',*stages)\nprint('Wrote',O/'submission.parquet')\nPY\ncat /app/solution/poprank.log && cat /app/solution/early_models.log && cat /app/solution/early_session.log","max_output_tokens":2000}));
The archive records a thinking event with empty text. No readable thinking is available for this event.
Tool callexecTool callReturned
Tool call · Input
text(await tools.exec_command({cmd:"python - <<'PY'\np='/app/solution/final_hybrid.py';s=open(p).read().replace('from enhance import *','from enhance import *\\nfrom poprank import popfeatures').replace(\"enhanced=name.startswith('enhanced') or name.startswith('multi');parts=\", \"enhanced=name.startswith('enhanced') or name.startswith('multi');popmode=name.startswith('pop');parts=\")\ns=s.replace(\" if enhanced:\\n\", \" if popmode:r,c,X=popfeatures(stage)\\n elif enhanced:\\n\")\ns=s.replace(\"learning_rate=.05 if enhanced else .06\", \"learning_rate=.05 if (enhanced or popmode) else .06\")\ns=s.replace(\" if enhanced:\\n\", \" if popmode:r,c,X=popfeatures('test')\\n elif enhanced:\\n\")\nopen(p,'w').write(s)\np='/app/solution/apply_blend.py';s=open(p).read().replace('def apply(stage):','def apply(stage,write=True):').replace(\"O/('submission.parquet' if stage=='test' else 'validation.parquet'))\", \"(O/('submission.parquet' if stage=='test' else 'validation.parquet')) if write else None)\").replace(\"apply(sys.argv[1])\",\"apply(sys.argv[1],len(sys.argv)<3)\");open(p,'w').write(s)\np='/app/solution/rebuild.py';s=open(p).read().replace(\"run('apply_blend.py','test')\",\"run('apply_blend.py','test','scores-only')\");open(p,'w').write(s)\nPY\ncat /app/solution/final_hybrid.py","max_output_tokens":2500}));
Tool response
Script completed
Wall time 0.3 seconds
Output:
{"chunk_id":"f42bfe","wall_time_seconds":0.00000594,"exit_code":0,"original_token_count":462,"output":"from enhance import *\nfrom poprank import popfeatures\ndef final_hybrid(cfgpath='hybrid.json',stages=['pseudo','validation']):\n cfg=json.load(open(O/cfgpath));name=cfg['model'];alpha=cfg['alpha'];enhanced=name.startswith('enhanced') or name.startswith('multi');popmode=name.startswith('pop');parts=name.split('_');leaves=int(parts[-2]);iters=int(parts[-1]);xx=[];yy=[]\n for stage in stages:\n if popmode:r,c,X=popfeatures(stage)\n elif enhanced:\n if not (O/f'{stage}_enhanced.npz').exists():enhance(stage)\n z=np.load(O/f'{stage}_enhanced.npz');r=z['r'];c=z['c'];X=z['X']\n else:r,c,X=load_features(stage)\n y,m=labels(stage,r,c);xx.append(X[m]);yy.append(y[m]);del X\n X=np.concatenate(xx);y=np.concatenate(yy);del xx,yy;print('final fitting',X.shape,flush=True)\n model=HistGradientBoostingRegressor(loss='poisson',max_iter=iters,max_leaf_nodes=leaves,learning_rate=.05 if (enhanced or popmode) else .06,min_samples_leaf=150,l2_regularization=.1,max_bins=128,early_stopping=False,random_state=42);model.fit(X,y);joblib.dump(model,O/'final_model.pkl');del X,y\n if popmode:r,c,X=popfeatures('test')\n elif enhanced:\n if not (O/'test_enhanced.npz').exists():enhance('test')\n z=np.load(O/'test_enhanced.npz');r=z['r'];c=z['c'];X=z['X']\n else:r,c,X=load_features('test')\n p=model.predict(X);ptr=np.r_[0,np.flatnonzero(np.diff(r))+1];p/=np.maximum(np.maximum.reduceat(p,ptr)[r],1e-8);base=np.load(O/'test_blend.npy');base/=np.maximum(np.maximum.reduceat(base,ptr)[r],1e-8);p=(1-alpha)*base+alpha*p\n np.save(O/'test_final_scores.npy',p);predict_rank('test',r,c,p,O/'submission.parquet');json.dump(dict(cfg,training_stages=stages),open(O/'final_config.json','w'));print('final hybrid saved',flush=True)\nif __name__=='__main__':final_hybrid(sys.argv[1] if len(sys.argv)>1 else 'hybrid.json',sys.argv[2:] if len(sys.argv)>2 else ['pseudo','validation'])\n"}
The archive records a thinking event with empty text. No readable thinking is available for this event.
Model text
The saved submission passes the structural checks: 845,600 rows, exactly 100 distinct eligible tracks per target user. The supplied evaluator confirmed 0.049115 for its validation counterpart. Adding recent organic popularity has now raised the reranker alone to 0.05076; I’m testing its blend before the final refit.
The archive records a thinking event with empty text. No readable thinking is available for this event.
Tool callexecTool callReturned
Tool call · Input
text(await tools.exec_command({cmd:"python - <<'PY'\np='/app/solution/poprank.py';s=open(p).read().replace(\" for leaves,iters in [(15,200),(15,350),(31,200)]:\", \" for leaves,iters in ([(15,200)] if len(stages)>1 else [(15,200),(15,350),(31,200)]):\");open(p,'w').write(s)\nPY\ncat > /app/solution/README.md <<'MD'\n# Music discovery ranker\n\n`submission.parquet` contains 100 ranked, distinct, test-eligible tracks for each of the 8,456 target users.\n\nThe model combines ridge-regularized collaborative filtering (EASE, computed through the user-side Gram matrix), organic listening profiles, recent listening profiles, and within-session item relationships. It adjusts collaborative scores for recent popularity growth. A shallow histogram gradient-boosted Poisson model predicts graded relevance divided by the user's ideal DCG, and is blended with the collaborative ensemble. Recent organic listens and organic likes supply additional trend features.\n\nValidation features use only history through timestamp 24,786,800. Earlier seven-day holdouts within training provide supervised ranking examples; public validation outcomes are used for model selection. For the final artifact, collaborative representations are refit using train plus validation history, and the reranker additionally learns from the now-observed validation outcomes. Every validation/test candidate comes from the supplied eligibility archive. Earlier training examples use eligibility reconstructed from previously observed events.\n\nRun with the installed task environment:\n\n```sh\n/opt/task-venv/bin/python /app/solution/rebuild.py\n```\n\n`final_config.json` records the selected model, blend weight, validation NDCG@10, and final training windows. `blend.json` records collaborative component weights. `rebuild.py` reconstructs missing intermediates from `/task/data/`, then refits and atomically replaces the submission. All generated files stay in this directory; no packages or external data are required.\n\n`validation.parquet` is the selected public backtest ranking. The final submission is separately checked for exact user coverage, ranks 1–100, uniqueness, and test eligibility. Public validation is an offline discovery metric, not a causal estimate of lift.\nMD","max_output_tokens":1000}));
Tool response
Script completed
Wall time 0.1 seconds
Output:
{"chunk_id":"617d22","wall_time_seconds":0.00000504,"exit_code":0,"original_token_count":0,"output":""}
Harness context codex item completed
{
"type": "codex_item_completed",
"item": {
"type": "CommandExecution",
"id": "exec-ac6afa60-9159-413d-afea-bc1a04576c2c",
"process_id": "87149",
"command": [
"/bin/sh",
"-lc",
"python - <<'PY'\np='/app/solution/poprank.py';s=open(p).read().replace(\" for leaves,iters in [(15,200),(15,350),(31,200)]:\", \" for leaves,iters in ([(15,200)] if len(stages)>1 else [(15,200),(15,350),(31,200)]):\");open(p,'w').write(s)\nPY\ncat > /app/solution/README.md <<'MD'\n# Music discovery ranker\n\n`submission.parquet` contains 100 ranked, distinct, test-eligible tracks for each of the 8,456 target users.\n\nThe model combines ridge-regularized collaborative filtering (EASE, computed through the user-side Gram matrix), organic listening profiles, recent listening profiles, and within-session item relationships. It adjusts collaborative scores for recent popularity growth. A shallow histogram gradient-boosted Poisson model predicts graded relevance divided by the user's ideal DCG, and is blended with the collaborative ensemble. Recent organic listens and organic likes supply additional trend features.\n\nValidation features use only history through timestamp 24,786,800. Earlier seven-day holdouts within training provide supervised ranking examples; public validation outcomes are used for model selection. For the final artifact, collaborative representations are refit using train plus validation history, and the reranker additionally learns from the now-observed validation outcomes. Every validation/test candidate comes from the supplied eligibility archive. Earlier training examples use eligibility reconstructed from previously observed events.\n\nRun with the installed task environment:\n\n```sh\n/opt/task-venv/bin/python /app/solution/rebuild.py\n```\n\n`final_config.json` records the selected model, blend weight, validation NDCG@10, and final training windows. `blend.json` records collaborative component weights. `rebuild.py` reconstructs missing intermediates from `/task/data/`, then refits and atomically replaces the submission. All generated files stay in this directory; no packages or external data are required.\n\n`validation.parquet` is the selected public backtest ranking. The final submission is separately checked for exact user coverage, ranks 1–100, uniqueness, and test eligibility. Public validation is an offline discovery metric, not a causal estimate of lift.\nMD"
],
"cwd": "file:///app",
"parsed_cmd": [
{
"type": "unknown",
"cmd": "python - <<'PY'\np='/app/solution/poprank.py';s=open(p).read().replace(\" for leaves,iters in [(15,200),(15,350),(31,200)]:\", \" for leaves,iters in ([(15,200)] if len(stages)>1 else [(15,200),(15,350),(31,200)]):\");open(p,'w').write(s)\nPY\ncat > /app/solution/README.md <<'MD'\n# Music discovery ranker\n\n`submission.parquet` contains 100 ranked, distinct, test-eligible tracks for each of the 8,456 target users.\n\nThe model combines ridge-regularized collaborative filtering (EASE, computed through the user-side Gram matrix), organic listening profiles, recent listening profiles, and within-session item relationships. It adjusts collaborative scores for recent popularity growth. A shallow histogram gradient-boosted Poisson model predicts graded relevance divided by the user's ideal DCG, and is blended with the collaborative ensemble. Recent organic listens and organic likes supply additional trend features.\n\nValidation features use only history through timestamp 24,786,800. Earlier seven-day holdouts within training provide supervised ranking examples; public validation outcomes are used for model selection. For the final artifact, collaborative representations are refit using train plus validation history, and the reranker additionally learns from the now-observed validation outcomes. Every validation/test candidate comes from the supplied eligibility archive. Earlier training examples use eligibility reconstructed from previously observed events.\n\nRun with the installed task environment:\n\n```sh\n/opt/task-venv/bin/python /app/solution/rebuild.py\n```\n\n`final_config.json` records the selected model, blend weight, validation NDCG@10, and final training windows. `blend.json` records collaborative component weights. `rebuild.py` reconstructs missing intermediates from `/task/data/`, then refits and atomically replaces the submission. All generated files stay in this directory; no packages or external data are required.\n\n`validation.parquet` is the selected public backtest ranking. The final submission is separately checked for exact user coverage, ranks 1–100, uniqueness, and test eligibility. Public validation is an offline discovery metric, not a causal estimate of lift.\nMD"
}
],
"source": "unified_exec_startup",
"status": "completed",
"stdout": "",
"stderr": "",
"aggregated_output": "",
"exit_code": 0,
"duration": {
"secs": 0,
"nanos": 5040
},
"formatted_output": ""
}
}
The archive records a thinking event with empty text. No readable thinking is available for this event.
Model text
The popularity-aware blend reached 0.05149 NDCG@10 and is finishing its final refit. I’m making one last comparison using two earlier training windows. The rebuild script and README are in place; after that comparison, I’ll verify the selected artifact and remove large scratch score matrices.
Tool callexecTool callReturned
Tool call · Input
text(await tools.exec_command({cmd:"TASK_THREADS=4 OPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/poprank.py train early pseudo > /app/solution/popmulti.log 2>&1","yield_time_ms":1000,"max_output_tokens":1000}));
text(await tools.exec_command({cmd:"python - <<'PY'\nimport ast,pathlib\nfor p in pathlib.Path('/app/solution').glob('*.py'):ast.parse(p.read_text(),filename=str(p))\nprint('All Python source files parse successfully')\nPY","max_output_tokens":1000}));
The archive records a thinking event with empty text. No readable thinking is available for this event.
Tool callexecTool callReturned
Tool call · Input
text(await tools.exec_command({cmd:"python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/rebuild.py');s=p.read_text().replace(\"for stage in stages+['test']:\\n\", \"for stage in stages+['test']:\\n if (O/f'{stage}_features.npz').exists():continue\\n\");p.write_text(s)\nroot=Path('/app/solution');paths=[p for p in root.glob('*.npy') if p.stat().st_size>100_000_000];size=sum(p.stat().st_size for p in paths)\nfor p in paths:p.unlink()\nprint('Removed',len(paths),'dense scratch matrices;',round(size/1e9,1),'GB')\nPY\ncat /app/solution/final_pop.log && cat /app/solution/popmulti.log && du -sh /app/solution","max_output_tokens":1000}));
Tool response
Script completed
Wall time 0.7 seconds
Output:
{"chunk_id":"967509","wall_time_seconds":0.546359034,"exit_code":0,"original_token_count":54,"output":"Removed 38 dense scratch matrices; 39.6 GB\nfinal fitting (2534346, 49)\npopfeatures test (2590760, 49)\nfinal hybrid saved\npopfeatures early (2542584, 49)\ntrain pop ['early', 'pseudo'] (2500842, 49)\n6.1G\t/app/solution\n"}
The archive records a thinking event with empty text. No readable thinking is available for this event.
Tool callexecTool callReturned
Tool call · Input
text(await tools.exec_command({cmd:"cat > /app/solution/verify.py <<'PY'\n\"\"\"Check the final contract using the supplied public validation helpers.\"\"\"\nimport sys,json\nfrom pathlib import Path\nimport numpy as np,pandas as pd\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import validate_ranking\nO=Path(__file__).resolve().parent;D=Path('/task/data')\nf=pd.read_parquet(O/'submission.parquet');targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int));validate_ranking(f,targets)\nz=np.load(D/'test_eligible_candidates.npz');r=pd.Index(z['uids']).get_indexer(f.uid);c=pd.Index(z['item_ids']).get_indexer(f.item_id)\nassert (r>=0).all() and (c>=0).all()\nassert ((z['packed_eligible'][r,c//8]>>(c%8))&1).all()\nreport={'rows':len(f),'users':f.uid.nunique(),'items_per_user':100,'test_eligibility':True,'schema_and_ranks':True}\njson.dump(report,open(O/'verification.json','w'),indent=2);print(json.dumps(report,indent=2))\nPY\ncat /app/solution/popmulti.log","max_output_tokens":1000}));
text(await tools.write_stdin({session_id:27861,chars:"","yield_time_ms":1000,"max_output_tokens":1000}));
Tool response
Script completed
Wall time 0.0 seconds
Output:
{"chunk_id":"637061","wall_time_seconds":0.000003811,"exit_code":0,"original_token_count":29,"output":"popfeatures early (2542584, 49)\ntrain pop ['early', 'pseudo'] (2500842, 49)\nRERANK validation 0.05160105637962818\n"}
{"chunk_id":"7cba39","wall_time_seconds":0.00000124,"exit_code":0,"original_token_count":42,"output":"RERANK validation 0.051493696236628396\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.051494,\n \"quality_users\": 4263\n }\n}\n"}
Harness context codex item completed
{
"type": "codex_item_completed",
"item": {
"type": "CommandExecution",
"id": "exec-fc9fcd4c-6058-4489-9b00-972e88900bfa",
"process_id": "14050",
"command": [
"/bin/sh",
"-lc",
"cat > /app/solution/verify.py <<'PY'\n\"\"\"Check the final contract using the supplied public validation helpers.\"\"\"\nimport sys,json\nfrom pathlib import Path\nimport numpy as np,pandas as pd\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import validate_ranking\nO=Path(__file__).resolve().parent;D=Path('/task/data')\nf=pd.read_parquet(O/'submission.parquet');targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int));validate_ranking(f,targets)\nz=np.load(D/'test_eligible_candidates.npz');r=pd.Index(z['uids']).get_indexer(f.uid);c=pd.Index(z['item_ids']).get_indexer(f.item_id)\nassert (r>=0).all() and (c>=0).all()\nassert ((z['packed_eligible'][r,c//8]>>(c%8))&1).all()\nreport={'rows':len(f),'users':f.uid.nunique(),'items_per_user':100,'test_eligibility':True,'schema_and_ranks':True}\njson.dump(report,open(O/'verification.json','w'),indent=2);print(json.dumps(report,indent=2))\nPY\ncat /app/solution/popmulti.log"
],
"cwd": "file:///app",
"parsed_cmd": [
{
"type": "unknown",
"cmd": "cat > /app/solution/verify.py <<'PY'\n\"\"\"Check the final contract using the supplied public validation helpers.\"\"\"\nimport sys,json\nfrom pathlib import Path\nimport numpy as np,pandas as pd\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import validate_ranking\nO=Path(__file__).resolve().parent;D=Path('/task/data')\nf=pd.read_parquet(O/'submission.parquet');targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int));validate_ranking(f,targets)\nz=np.load(D/'test_eligible_candidates.npz');r=pd.Index(z['uids']).get_indexer(f.uid);c=pd.Index(z['item_ids']).get_indexer(f.item_id)\nassert (r>=0).all() and (c>=0).all()\nassert ((z['packed_eligible'][r,c//8]>>(c%8))&1).all()\nreport={'rows':len(f),'users':f.uid.nunique(),'items_per_user':100,'test_eligibility':True,'schema_and_ranks':True}\njson.dump(report,open(O/'verification.json','w'),indent=2);print(json.dumps(report,indent=2))\nPY\ncat /app/solution/popmulti.log"
}
],
"source": "unified_exec_startup",
"status": "completed",
"stdout": "popfeatures early (2542584, 49)\ntrain pop ['early', 'pseudo'] (2500842, 49)\nRERANK validation 0.05160105637962818\n",
"stderr": "",
"aggregated_output": "popfeatures early (2542584, 49)\ntrain pop ['early', 'pseudo'] (2500842, 49)\nRERANK validation 0.05160105637962818\n",
"exit_code": 0,
"duration": {
"secs": 0,
"nanos": 3811
},
"formatted_output": "popfeatures early (2542584, 49)\ntrain pop ['early', 'pseudo'] (2500842, 49)\nRERANK validation 0.05160105637962818\n"
}
}
The archive records a thinking event with empty text. No readable thinking is available for this event.
Model text
Training on two earlier windows improved the selected blend to 0.05244 NDCG@10. I’m now fitting it with the additional observed validation outcomes for hidden-test ranking, then running the final eligibility and schema checks.
The archive records a thinking event with empty text. No readable thinking is available for this event.
Tool callexecTool callReturned
Tool call · Input
text(await tools.exec_command({cmd:"python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/README.md');s=p.read_text().replace('`submission.parquet` contains 100 ranked, distinct, test-eligible tracks for each of the 8,456 target users.', '`submission.parquet` contains 100 ranked, distinct, test-eligible tracks for each of the 8,456 target users (845,600 rows). The selected public temporal backtest scores **0.052441 graded NDCG@10** over 4,263 users with eligible relevance.')\ns=s.replace('Earlier seven-day holdouts within training provide supervised ranking examples;', 'Two earlier seven-day holdouts within training provide supervised ranking examples;')\ns += '\\nValidation results are recorded in `public_evaluation.json`; final structural checks are recorded in `verification.json`. Run `/opt/task-venv/bin/python /app/solution/verify.py` to repeat the final checks.\\n'\np.write_text(s)\nPY\ncat /app/solution/final_refit.log && cat /app/solution/public_evaluation.json","max_output_tokens":1000}));
{
"type": "codex_item_completed",
"item": {
"type": "CommandExecution",
"id": "exec-c7a40efa-de42-4c33-a60e-4df57f7077ef",
"process_id": "24132",
"command": [
"/bin/sh",
"-lc",
"python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/README.md');s=p.read_text().replace('`submission.parquet` contains 100 ranked, distinct, test-eligible tracks for each of the 8,456 target users.', '`submission.parquet` contains 100 ranked, distinct, test-eligible tracks for each of the 8,456 target users (845,600 rows). The selected public temporal backtest scores **0.052441 graded NDCG@10** over 4,263 users with eligible relevance.')\ns=s.replace('Earlier seven-day holdouts within training provide supervised ranking examples;', 'Two earlier seven-day holdouts within training provide supervised ranking examples;')\ns += '\\nValidation results are recorded in `public_evaluation.json`; final structural checks are recorded in `verification.json`. Run `/opt/task-venv/bin/python /app/solution/verify.py` to repeat the final checks.\\n'\np.write_text(s)\nPY\ncat /app/solution/final_refit.log && cat /app/solution/public_evaluation.json"
],
"cwd": "file:///app",
"parsed_cmd": [
{
"type": "unknown",
"cmd": "python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/README.md');s=p.read_text().replace('`submission.parquet` contains 100 ranked, distinct, test-eligible tracks for each of the 8,456 target users.', '`submission.parquet` contains 100 ranked, distinct, test-eligible tracks for each of the 8,456 target users (845,600 rows). The selected public temporal backtest scores **0.052441 graded NDCG@10** over 4,263 users with eligible relevance.')\ns=s.replace('Earlier seven-day holdouts within training provide supervised ranking examples;', 'Two earlier seven-day holdouts within training provide supervised ranking examples;')\ns += '\\nValidation results are recorded in `public_evaluation.json`; final structural checks are recorded in `verification.json`. Run `/opt/task-venv/bin/python /app/solution/verify.py` to repeat the final checks.\\n'\np.write_text(s)\nPY\ncat /app/solution/final_refit.log && cat /app/solution/public_evaluation.json"
}
],
"source": "unified_exec_startup",
"status": "completed",
"stdout": "final fitting (3775486, 49)\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.052441,\n \"quality_users\": 4263\n }\n}\n",
"stderr": "",
"aggregated_output": "final fitting (3775486, 49)\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.052441,\n \"quality_users\": 4263\n }\n}\n",
"exit_code": 0,
"duration": {
"secs": 0,
"nanos": 3280
},
"formatted_output": "final fitting (3775486, 49)\n{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.052441,\n \"quality_users\": 4263\n }\n}\n"
}
}
The archive records a thinking event with empty text. No readable thinking is available for this event.
Model text
The final refit is complete, and the supplied evaluator confirms 0.052441 for the selected validation ranking. I’m checking the documented rebuild command and final submission once more. The large scratch score matrices have been removed.
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.
Session bookkeeping and encrypted reasoning or signatures are excluded from the reading export. System instructions present in this archive are included above.