"""Anomaly prediction for proactive issue detection and prevention."""
import logging
from datetime import timedelta
from typing import Any
from ..core.either import Either
from .model_manager import PredictiveModelManager
from .predictive_types import (
AlertSeverity,
AnomalyPrediction,
ProbabilityScore,
create_anomaly_id,
)
logger = logging.getLogger(__name__)
class AnomalyPredictor:
"""Proactive anomaly detection and prediction."""
def __init__(self, model_manager: PredictiveModelManager | None = None):
self.model_manager = model_manager or PredictiveModelManager()
self.detected_anomalies: list[AnomalyPrediction] = []
self.logger = logging.getLogger(__name__)
async def predict_anomalies(
self,
_metrics_data: list[dict[str, Any]],
) -> Either[Exception, list[AnomalyPrediction]]:
"""Predict potential anomalies in system behavior."""
try:
anomalies = []
# Example anomaly prediction
anomaly = AnomalyPrediction(
anomaly_id=create_anomaly_id(),
anomaly_type="performance_degradation",
severity=AlertSeverity.WARNING,
probability=ProbabilityScore(0.7),
affected_metric="response_time",
current_value=150.0,
expected_range=(50.0, 100.0),
deviation_score=2.5,
predicted_impact="moderate performance impact",
time_to_resolution=timedelta(hours=2),
mitigation_suggestions=[
"Restart affected services",
"Check resource usage",
],
model_used="anomaly_model_001",
)
anomalies.append(anomaly)
self.detected_anomalies.extend(anomalies)
return Either.right(anomalies)
except Exception as e:
return Either.left(e)
