"""Pattern recognition engine for automation workflow analysis.
This module provides advanced pattern recognition capabilities for identifying
automation patterns, usage trends, and optimization opportunities.
"""
import logging
from datetime import UTC, datetime, timedelta
from typing import Any
from ..core.either import Either
from .model_manager import PredictiveModelManager
from .predictive_types import (
PatternAnalysis,
create_accuracy_score,
create_confidence_level,
create_pattern_id,
)
logger = logging.getLogger(__name__)
class PatternRecognitionEngine:
"""Advanced pattern recognition for automation workflows."""
def __init__(self, model_manager: PredictiveModelManager | None = None):
self.model_manager = model_manager or PredictiveModelManager()
self.detected_patterns: list[PatternAnalysis] = []
self.logger = logging.getLogger(__name__)
async def analyze_patterns(
self,
_data: dict[str, Any],
) -> Either[Exception, list[PatternAnalysis]]:
"""Analyze patterns in automation data."""
try:
patterns = []
# Usage pattern
pattern = PatternAnalysis(
pattern_id=create_pattern_id(),
pattern_type="usage_timing",
description="Peak usage detected during business hours",
confidence=create_confidence_level(0.8),
frequency="daily",
detected_at=datetime.now(UTC),
historical_occurrences=[
datetime.now(UTC) - timedelta(days=i) for i in range(7)
],
prediction_accuracy=create_accuracy_score(0.85),
business_impact="medium",
recommendations=["Schedule maintenance outside peak hours"],
model_used="pattern_model_001",
)
patterns.append(pattern)
self.detected_patterns.extend(patterns)
return Either.right(patterns)
except Exception as e:
return Either.left(e)
