egutierrez
63a9cb5273
Datascience: aggregate_by_group, deduplicate_entities/relations, detect_drift, diff_entities/relations, extract_entities/relations_llm, hotness_score, melt, merge_graphs, pivot, build_entity/relation_schema_prompt. Finance: avellaneda_stoikov_quotes, generate_gbm_prices, generate_taker_order, hawkes_intensity + módulo finance.py. Cybersecurity: envelope_encrypt/decrypt + módulo cybersecurity.py. Pipelines: extraction_pipeline, monte_carlo_market, run_market_sim. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
87 lines
2.5 KiB
Python
87 lines
2.5 KiB
Python
"""detect_drift — detecta drift estadistico por z-score comparando metricas contra historial."""
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import math
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def detect_drift(
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history: list[dict],
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current: dict,
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fields: list[str],
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threshold: float = 2.0,
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) -> list[dict]:
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"""Detecta drift estadistico comparando metricas actuales contra el historial.
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Usa z-score: si |z| > threshold, el campo ha drifteado. Pensado para
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comparar metrics de executions sucesivas en operations.db.
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Args:
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history: Lista de dicts con metricas historicas. Cada dict puede
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contener cualquier combinacion de los campos indicados.
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current: Dict con las metricas de la ejecucion actual.
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fields: Lista de campos numericos a analizar.
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threshold: Umbral de z-score para considerar drift. Default 2.0.
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Returns:
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Lista de dicts con: field, current, mean, std, z_score, drifted.
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Si el historial tiene 0 o 1 punto, z_score=0.0 y drifted=False
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porque no hay suficiente informacion estadistica.
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"""
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results = []
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for field in fields:
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values = [
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float(h[field])
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for h in history
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if field in h and h[field] is not None
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]
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current_val = float(current.get(field, 0))
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if len(values) == 0:
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results.append({
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"field": field,
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"current": current_val,
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"mean": 0.0,
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"std": 0.0,
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"z_score": 0.0,
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"drifted": False,
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})
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continue
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n = len(values)
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mean = sum(values) / n
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if n < 2:
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# Un solo punto: no hay std, no podemos calcular z-score
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results.append({
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"field": field,
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"current": current_val,
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"mean": mean,
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"std": 0.0,
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"z_score": 0.0,
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"drifted": False,
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})
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continue
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variance = sum((v - mean) ** 2 for v in values) / n
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std = math.sqrt(variance)
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if std == 0.0:
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# Todos los valores identicos: z_score indeterminado, no drift
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z_score = 0.0
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drifted = False
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else:
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z_score = (current_val - mean) / std
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drifted = abs(z_score) > threshold
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results.append({
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"field": field,
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"current": current_val,
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"mean": mean,
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"std": std,
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"z_score": z_score,
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"drifted": drifted,
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})
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return results
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