Delete predictive_models.py

predictive_models.py

@@ -1,261 +0,0 @@
-import numpy as np
-import pandas as pd
-from typing import Dict, List, Optional, Tuple
-from datetime import datetime, timedelta
-import json
-import warnings
-from dataclasses import dataclass
-
-@dataclass
-class ForecastResult:
-    metric: str
-    predicted_value: float
-    confidence: float
-    trend: str  # "increasing", "decreasing", "stable"
-    time_to_threshold: Optional[timedelta] = None
-    risk_level: str = "low"  # low, medium, high, critical
-
-class SimplePredictiveEngine:
-    """
-    Lightweight forecasting engine optimized for Hugging Face Spaces
-    Uses statistical methods instead of heavy ML models
-    """
-    
-    def __init__(self, history_window: int = 50):
-        self.history_window = history_window
-        self.service_history: Dict[str, List] = {}
-        self.prediction_cache: Dict[str, ForecastResult] = {}
-        
-    def add_telemetry(self, service: str, event_data: Dict):
-        """Add telemetry data to service history"""
-        if service not in self.service_history:
-            self.service_history[service] = []
-        
-        # Store key metrics with timestamp
-        telemetry_point = {
-            'timestamp': datetime.now(),
-            'latency': event_data.get('latency_p99', 0),
-            'error_rate': event_data.get('error_rate', 0),
-            'throughput': event_data.get('throughput', 0),
-            'cpu_util': event_data.get('cpu_util'),
-            'memory_util': event_data.get('memory_util')
-        }
-        
-        self.service_history[service].append(telemetry_point)
-        
-        # Keep only recent history
-        if len(self.service_history[service]) > self.history_window:
-            self.service_history[service].pop(0)
-    
-    def forecast_service_health(self, service: str, lookahead_minutes: int = 15) -> List[ForecastResult]:
-        """Forecast service health metrics"""
-        if service not in self.service_history or len(self.service_history[service]) < 10:
-            return []
-        
-        history = self.service_history[service]
-        forecasts = []
-        
-        # Forecast latency
-        latency_forecast = self._forecast_latency(history, lookahead_minutes)
-        if latency_forecast:
-            forecasts.append(latency_forecast)
-        
-        # Forecast error rate
-        error_forecast = self._forecast_error_rate(history, lookahead_minutes)
-        if error_forecast:
-            forecasts.append(error_forecast)
-        
-        # Forecast resource utilization
-        resource_forecasts = self._forecast_resources(history, lookahead_minutes)
-        forecasts.extend(resource_forecasts)
-        
-        # Cache results
-        for forecast in forecasts:
-            cache_key = f"{service}_{forecast.metric}"
-            self.prediction_cache[cache_key] = forecast
-        
-        return forecasts
-    
-    def _forecast_latency(self, history: List, lookahead_minutes: int) -> Optional[ForecastResult]:
-        """Forecast latency using linear regression and trend analysis"""
-        try:
-            latencies = [point['latency'] for point in history[-20:]]  # Last 20 points
-            
-            if len(latencies) < 5:
-                return None
-            
-            # Simple linear trend
-            x = np.arange(len(latencies))
-            slope, intercept = np.polyfit(x, latencies, 1)
-            
-            # Predict next value
-            next_x = len(latencies)
-            predicted_latency = slope * next_x + intercept
-            
-            # Calculate confidence based on data quality
-            residuals = latencies - (slope * x + intercept)
-            confidence = max(0, 1 - (np.std(residuals) / max(1, np.mean(latencies))))
-            
-            # Determine trend
-            if slope > 5:  # Increasing by more than 5ms per interval
-                trend = "increasing"
-                risk = "high" if predicted_latency > 300 else "medium"
-            elif slope < -2:  # Decreasing
-                trend = "decreasing" 
-                risk = "low"
-            else:
-                trend = "stable"
-                risk = "low"
-            
-            # Calculate time to reach critical threshold (500ms)
-            time_to_critical = None
-            if slope > 0 and predicted_latency < 500:
-                time_to_critical = timedelta(
-                    minutes=lookahead_minutes * (500 - predicted_latency) / (predicted_latency - latencies[-1])
-                )
-            
-            return ForecastResult(
-                metric="latency",
-                predicted_value=predicted_latency,
-                confidence=confidence,
-                trend=trend,
-                time_to_threshold=time_to_critical,
-                risk_level=risk
-            )
-            
-        except Exception as e:
-            print(f"Latency forecast error: {e}")
-            return None
-    
-    def _forecast_error_rate(self, history: List, lookahead_minutes: int) -> Optional[ForecastResult]:
-        """Forecast error rate using exponential smoothing"""
-        try:
-            error_rates = [point['error_rate'] for point in history[-15:]]
-            
-            if len(error_rates) < 5:
-                return None
-            
-            # Exponential smoothing
-            alpha = 0.3  # Smoothing factor
-            forecast = error_rates[0]
-            for rate in error_rates[1:]:
-                forecast = alpha * rate + (1 - alpha) * forecast
-            
-            predicted_rate = forecast
-            
-            # Trend analysis
-            recent_trend = np.mean(error_rates[-3:]) - np.mean(error_rates[-6:-3])
-            
-            if recent_trend > 0.02:  # Increasing trend
-                trend = "increasing"
-                risk = "high" if predicted_rate > 0.1 else "medium"
-            elif recent_trend < -0.01:  # Decreasing
-                trend = "decreasing"
-                risk = "low"
-            else:
-                trend = "stable"
-                risk = "low"
-            
-            # Confidence based on volatility
-            confidence = max(0, 1 - (np.std(error_rates) / max(0.01, np.mean(error_rates))))
-            
-            return ForecastResult(
-                metric="error_rate",
-                predicted_value=predicted_rate,
-                confidence=confidence,
-                trend=trend,
-                risk_level=risk
-            )
-            
-        except Exception as e:
-            print(f"Error rate forecast error: {e}")
-            return None
-    
-    def _forecast_resources(self, history: List, lookahead_minutes: int) -> List[ForecastResult]:
-        """Forecast CPU and memory utilization"""
-        forecasts = []
-        
-        # CPU forecast
-        cpu_values = [point['cpu_util'] for point in history if point.get('cpu_util') is not None]
-        if len(cpu_values) >= 5:
-            try:
-                predicted_cpu = np.mean(cpu_values[-5:])  # Simple moving average
-                trend = "increasing" if cpu_values[-1] > np.mean(cpu_values[-10:-5]) else "stable"
-                
-                risk = "low"
-                if predicted_cpu > 0.8:
-                    risk = "critical" if predicted_cpu > 0.9 else "high"
-                elif predicted_cpu > 0.7:
-                    risk = "medium"
-                
-                forecasts.append(ForecastResult(
-                    metric="cpu_util",
-                    predicted_value=predicted_cpu,
-                    confidence=0.7,  # Moderate confidence for resources
-                    trend=trend,
-                    risk_level=risk
-                ))
-            except Exception as e:
-                print(f"CPU forecast error: {e}")
-        
-        # Memory forecast (similar approach)
-        memory_values = [point['memory_util'] for point in history if point.get('memory_util') is not None]
-        if len(memory_values) >= 5:
-            try:
-                predicted_memory = np.mean(memory_values[-5:])
-                trend = "increasing" if memory_values[-1] > np.mean(memory_values[-10:-5]) else "stable"
-                
-                risk = "low"
-                if predicted_memory > 0.8:
-                    risk = "critical" if predicted_memory > 0.9 else "high"
-                elif predicted_memory > 0.7:
-                    risk = "medium"
-                
-                forecasts.append(ForecastResult(
-                    metric="memory_util",
-                    predicted_value=predicted_memory,
-                    confidence=0.7,
-                    trend=trend,
-                    risk_level=risk
-                ))
-            except Exception as e:
-                print(f"Memory forecast error: {e}")
-        
-        return forecasts
-    
-    def get_predictive_insights(self, service: str) -> Dict[str, any]:
-        """Generate actionable insights from forecasts"""
-        forecasts = self.forecast_service_health(service)
-        
-        critical_risks = [f for f in forecasts if f.risk_level in ["high", "critical"]]
-        warnings = []
-        recommendations = []
-        
-        for forecast in critical_risks:
-            if forecast.metric == "latency" and forecast.risk_level in ["high", "critical"]:
-                warnings.append(f"📈 Latency expected to reach {forecast.predicted_value:.0f}ms")
-                if forecast.time_to_threshold:
-                    minutes = int(forecast.time_to_threshold.total_seconds() / 60)
-                    recommendations.append(f"⏰ Critical latency (~500ms) in ~{minutes} minutes")
-                recommendations.append("🔧 Consider scaling or optimizing dependencies")
-            
-            elif forecast.metric == "error_rate" and forecast.risk_level in ["high", "critical"]:
-                warnings.append(f"🚨 Errors expected to reach {forecast.predicted_value*100:.1f}%")
-                recommendations.append("🐛 Investigate recent deployments or dependency issues")
-            
-            elif forecast.metric == "cpu_util" and forecast.risk_level in ["high", "critical"]:
-                warnings.append(f"🔥 CPU expected at {forecast.predicted_value*100:.1f}%")
-                recommendations.append("⚡ Consider scaling compute resources")
-            
-            elif forecast.metric == "memory_util" and forecast.risk_level in ["high", "critical"]:
-                warnings.append(f"💾 Memory expected at {forecast.predicted_value*100:.1f}%")
-                recommendations.append("🧹 Check for memory leaks or optimize usage")
-        
-        return {
-            'service': service,
-            'forecasts': [f.__dict__ for f in forecasts],
-            'warnings': warnings[:3],  # Top 3 warnings
-            'recommendations': list(dict.fromkeys(recommendations))[:3],  # Unique top 3
-            'critical_risk_count': len(critical_risks),
-            'forecast_timestamp': datetime.now().isoformat()
-        }