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docs(simple_metrics): document min samples guard and adaptive state step (SIENTIAPDE-2191)

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PedroHMCosme
2026-10-07 16:14:43 -03:00
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README.md
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@@ -489,14 +489,16 @@ The **SimpleMetrics** workflow calculates regression metrics (RMSE, MSE, MAE, R
1. **Data Loading**: Loads prediction vs target data via a JOIN query 1. **Data Loading**: Loads prediction vs target data via a JOIN query
2. **Metrics Calculation**: Calculates configured regression metrics; alerts on `simple_metrics_thresholds` breaches (one alert per model, the message names the window) unless the window has fewer rows than `simple_metrics_min_samples` 2. **Metrics Calculation**: Calculates configured regression metrics; alerts on `simple_metrics_thresholds` breaches (one alert per model, the message names the window) unless the window has fewer rows than `simple_metrics_min_samples`
3. **Data Export**: Exports metrics to PostgreSQL 3. **Data Export**: Exports metrics to PostgreSQL
4. **Adaptive State** (only with thresholds): when the schedule's `pipelines` doc has `frequency_alert`, escalates `adaptive_state` on a breach and recovers after the window is renewed without one (`min(12, ceil(interval_minutes / frequency_alert))` runs; empty or under-sampled windows count as healthy). The orchestrator runs the schedule at `frequency_alert` while escalated
#### Architecture Diagram #### Architecture Diagram
```mermaid ```mermaid
flowchart LR flowchart LR
A[1. load_custom_query] --> B[2. calculate_simple_metrics] --> C[3. export_data_to_postgres] A[1. load_custom_query] --> B[2. calculate_simple_metrics] --> C[3. export_data_to_postgres] --> D[4. update_simple_metrics_adaptive_state]
A -.-> Database[(Database)] A -.-> Database[(Database)]
C -.-> PostgreSQL[(PostgreSQL)] C -.-> PostgreSQL[(PostgreSQL)]
D -.-> MongoDB[(MongoDB pipelines)]
``` ```
#### Input Parameters #### Input Parameters