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- .claude/CLAUDE.md - .claude/commands/subagentes.md - .claude/rules/INDEX.md - .mcp.json - bash/functions/cybersecurity/analyze_dns.md - bash/functions/cybersecurity/audit_http_headers.md - bash/functions/cybersecurity/audit_ssh_config.md - bash/functions/cybersecurity/check_firewall.md - bash/functions/cybersecurity/detect_suspicious_users.md - bash/functions/cybersecurity/encrypt_file.md - ... Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
67 lines
2.8 KiB
Markdown
67 lines
2.8 KiB
Markdown
---
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name: detect_drift
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kind: function
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lang: py
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domain: datascience
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version: "1.0.0"
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purity: pure
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signature: "def detect_drift(history: list[dict], current: dict, fields: list[str], threshold: float = 2.0) -> list[dict]"
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description: "Detecta drift estadistico comparando metricas de la ejecucion actual contra el historial usando z-score. Si |z| > threshold, el campo ha drifteado. Util para monitorizar executions en operations.db."
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tags: [drift, statistics, z-score, monitoring, executions, operations, datascience, pendiente-usar]
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uses_functions: []
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uses_types: []
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returns: []
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returns_optional: false
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error_type: ""
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imports: [math]
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params:
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- name: history
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desc: "lista de dicts con metricas historicas (ej: [{'records_out': 100, 'duration_ms': 500}, ...])"
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- name: current
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desc: "dict con metricas actuales a comparar (ej: {'records_out': 50, 'duration_ms': 2000})"
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- name: fields
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desc: "lista de nombres de campos a monitorizar en current vs history (ej: ['records_out', 'duration_ms'])"
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- name: threshold
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desc: "umbral de z-score para declarar drift (tipico: 2.0-3.0). Mayor = menos sensible."
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output: "lista de dicts con {field, current, mean, std, z_score, drifted} para cada campo monitoreado"
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tested: true
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tests:
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- "campo con drift claro (z > threshold)"
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- "campo estable (z < threshold)"
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- "historial con un solo punto → std=0, no puede calcular → drifted=False con nota"
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- "historial vacio → todos drifted=False"
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- "threshold custom"
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test_file_path: "python/functions/datascience/detect_drift_test.py"
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file_path: "python/functions/datascience/detect_drift.py"
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---
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## Ejemplo
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```python
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history = [
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{"records_out": 100, "duration_ms": 500},
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{"records_out": 105, "duration_ms": 480},
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{"records_out": 98, "duration_ms": 510},
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]
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current = {"records_out": 50, "duration_ms": 2000}
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results = detect_drift(history, current, ["records_out", "duration_ms"])
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# [
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# {"field": "records_out", "current": 50, "mean": 101.0, "std": 3.6, "z_score": -14.2, "drifted": True},
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# {"field": "duration_ms", "current": 2000, "mean": 496.7, "std": 15.3, "z_score": 98.3, "drifted": True},
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# ]
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```
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## Notas
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Funcion pura. Solo stdlib (`math`).
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El z-score usa desviacion estandar poblacional (dividir por N, no N-1) para ser consistente con historial de cualquier tamanio.
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Casos especiales:
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- **Historial vacio**: z_score=0.0, drifted=False para todos los campos.
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- **Un solo punto en historial**: std=0.0, z_score=0.0, drifted=False. No hay suficiente historia para calcular variabilidad.
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- **Std=0 con N>=2**: todos los valores historicos identicos. z_score=0.0, drifted=False (cualquier desviacion seria tecnicamente infinita, pero se asume que el sistema es muy estable).
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Pensado para el paso ANALIZAR del bucle reactivo: comparar `metrics` de la ejecucion actual con executions historicas de `operations.db`.
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