majiayu000 Learn Timeout
General-purpose timeout estimation that replaces fragmented Ridge/Logistic models with a unified GradientBoosting-based predictor.
Commands
./run.sh collect # Gather training data from all sources
./run.sh train # Train both models
./run.sh predict '{"task_type":"pdf_extraction","page_count":400}'
./run.sh observe --task-id X --actual-seconds Y
./run.sh status # Model health dashboard
./run.sh benchmark # Classifier-lab backbone comparison
Prediction Output
{
"estimated_seconds": 4200,
"confidence_interval": [2800, 6300],
"risk_probability": 0.35,
"risk_label": "medium",
"recommended_timeout_seconds": 6300,
"features_used": ["page_count", "table_pages", "domain"],
"model_version": "2026-02-13_v1",
"duration_model_available": true,
"risk_model_available": true
}
Task Types
| task_type | Key Features |
|---|---|
pdf_extraction |
page_count, tables, figures, file_size, domain |
llm_api_call |
prompt_tokens, model, provider, image_count |
subprocess |
command_type, input_size, complexity_hints |
remediation |
issue_count, issue_severity, skill_name |
Training Data Sources
- Corpus
profile.json+timings.jsonl(S00 features + actual durations) - Supervisor run logs (
extract_timeoutevents) - Aggregate reports (extraction timing events)
- Observation feedback loop (
data/observations.jsonl)