torsh_autograd/

error_diagnostics.rs

//! Advanced Error Diagnostics and Analysis
//!
//! This module provides advanced diagnostic capabilities for autograd errors,
//! including error pattern analysis, root cause analysis, and performance
//! impact assessment.
//!
//! # Features
//!
//! - **Error Pattern Recognition**: Identify common error patterns and suggest fixes
//! - **Root Cause Analysis**: Trace errors back to their fundamental causes
//! - **Performance Impact Assessment**: Analyze how errors affect performance
//! - **Error Correlation**: Find relationships between different error types
//! - **Diagnostic Reporting**: Generate comprehensive error diagnostic reports
//! - **Remediation Suggestions**: Provide actionable suggestions for error fixes

// Framework infrastructure - components designed for future use
#![allow(dead_code)]
use crate::error_handling::AutogradError;
use scirs2_core::ndarray::Array2;
use serde::{Deserialize, Serialize};
use std::collections::hash_map::DefaultHasher;
use std::collections::HashMap;
use std::hash::{Hash, Hasher};
use std::time::{Duration, Instant};

/// Advanced error diagnostics system
#[derive(Debug)]
pub struct ErrorDiagnosticsSystem {
    /// Error pattern database
    patterns: ErrorPatternDatabase,
    /// Error correlation tracker
    correlations: ErrorCorrelationTracker,
    /// Performance impact analyzer
    performance_analyzer: PerformanceImpactAnalyzer,
    /// Diagnostic configuration
    config: DiagnosticsConfig,
    /// Error history for analysis
    error_history: Vec<ErrorEvent>,
}

impl ErrorDiagnosticsSystem {
    /// Create a new diagnostics system
    pub fn new() -> Self {
        Self::with_config(DiagnosticsConfig::default())
    }

    /// Create diagnostics system with custom configuration
    pub fn with_config(config: DiagnosticsConfig) -> Self {
        Self {
            patterns: ErrorPatternDatabase::new(),
            correlations: ErrorCorrelationTracker::new(),
            performance_analyzer: PerformanceImpactAnalyzer::new(),
            config,
            error_history: Vec::new(),
        }
    }

    /// Record an error for diagnostic analysis
    pub fn record_error(&mut self, error: &AutogradError, context: ErrorContext) {
        let event = ErrorEvent {
            timestamp: Instant::now(),
            error: error.clone(),
            context,
            operation_id: self.generate_operation_id(),
        };

        // Store the error event
        self.error_history.push(event.clone());

        // Analyze patterns
        self.patterns.analyze_error(&event);

        // Update correlations
        self.correlations.update(&event, &self.error_history);

        // Assess performance impact
        self.performance_analyzer.assess_impact(&event);

        // Cleanup old history if needed
        if self.error_history.len() > self.config.max_history_size {
            self.error_history
                .drain(0..self.config.history_cleanup_batch);
        }
    }

    /// Generate comprehensive diagnostic report
    pub fn generate_diagnostic_report(&self) -> DiagnosticReport {
        let pattern_analysis = self.patterns.generate_analysis();
        let correlation_analysis = self.correlations.generate_analysis();
        let performance_analysis = self.performance_analyzer.generate_analysis();

        let mut recommendations = Vec::new();

        // Generate recommendations based on patterns
        for pattern in &pattern_analysis.detected_patterns {
            recommendations.extend(self.generate_pattern_recommendations(pattern));
        }

        // Generate recommendations based on correlations
        for correlation in &correlation_analysis.significant_correlations {
            recommendations.extend(self.generate_correlation_recommendations(correlation));
        }

        DiagnosticReport {
            timestamp: Instant::now(),
            total_errors: self.error_history.len(),
            analysis_window: self.config.analysis_window,
            pattern_analysis,
            correlation_analysis,
            performance_analysis,
            recommendations,
            severity_assessment: self.assess_overall_severity(),
        }
    }

    /// Get real-time diagnostic status
    pub fn get_status(&self) -> DiagnosticStatus {
        let recent_errors = self.get_recent_errors(Duration::from_secs(300)); // Last 5 minutes
        let error_rate = recent_errors.len() as f64 / 300.0; // Errors per second

        DiagnosticStatus {
            error_rate,
            active_patterns: self.patterns.get_active_patterns().len(),
            severity_level: self.assess_current_severity(&recent_errors),
            last_critical_error: self.find_last_critical_error(),
            health_score: self.calculate_health_score(&recent_errors),
        }
    }

    /// Generate operation ID for tracking
    fn generate_operation_id(&self) -> String {
        format!("op_{}", self.error_history.len())
    }

    /// Get errors within a time window
    fn get_recent_errors(&self, window: Duration) -> Vec<&ErrorEvent> {
        let cutoff = Instant::now() - window;
        self.error_history
            .iter()
            .filter(|event| event.timestamp >= cutoff)
            .collect()
    }

    /// Generate recommendations for error patterns
    fn generate_pattern_recommendations(
        &self,
        pattern: &ErrorPattern,
    ) -> Vec<DiagnosticRecommendation> {
        match pattern.pattern_type {
            PatternType::ShapeMismatchPattern => vec![DiagnosticRecommendation {
                category: RecommendationCategory::CodeImprovement,
                priority: RecommendationPriority::High,
                title: "Add Shape Validation".to_string(),
                description: "Add explicit shape validation before tensor operations".to_string(),
                code_example: Some("tensor.validate_shape(&expected_shape)?;".to_string()),
                estimated_fix_time: Duration::from_secs(300), // 5 minutes
            }],
            PatternType::MemoryAllocationPattern => vec![DiagnosticRecommendation {
                category: RecommendationCategory::Performance,
                priority: RecommendationPriority::Medium,
                title: "Optimize Memory Usage".to_string(),
                description: "Consider using memory pooling or reducing batch sizes".to_string(),
                code_example: Some("config.batch_size = config.batch_size / 2;".to_string()),
                estimated_fix_time: Duration::from_secs(900), // 15 minutes
            }],
            PatternType::NumericalInstabilityPattern => vec![DiagnosticRecommendation {
                category: RecommendationCategory::Stability,
                priority: RecommendationPriority::High,
                title: "Add Numerical Stabilization".to_string(),
                description: "Add gradient clipping and check for NaN/infinity values".to_string(),
                code_example: Some(
                    "gradients = clip_gradients(gradients, max_norm=1.0);".to_string(),
                ),
                estimated_fix_time: Duration::from_secs(600), // 10 minutes
            }],
            _ => Vec::new(),
        }
    }

    /// Generate recommendations for correlations
    fn generate_correlation_recommendations(
        &self,
        correlation: &ErrorCorrelation,
    ) -> Vec<DiagnosticRecommendation> {
        vec![DiagnosticRecommendation {
            category: RecommendationCategory::Architecture,
            priority: RecommendationPriority::Medium,
            title: "Address Error Correlation".to_string(),
            description: format!(
                "Errors of type {:?} and {:?} are correlated",
                correlation.error_type_1, correlation.error_type_2
            ),
            code_example: None,
            estimated_fix_time: Duration::from_secs(1800), // 30 minutes
        }]
    }

    /// Assess overall system severity
    fn assess_overall_severity(&self) -> SeverityLevel {
        let recent_errors = self.get_recent_errors(Duration::from_secs(3600)); // Last hour

        if recent_errors.len() > 100 {
            SeverityLevel::Critical
        } else if recent_errors.len() > 50 {
            SeverityLevel::High
        } else if recent_errors.len() > 10 {
            SeverityLevel::Medium
        } else {
            SeverityLevel::Low
        }
    }

    /// Assess current severity based on recent errors
    fn assess_current_severity(&self, recent_errors: &[&ErrorEvent]) -> SeverityLevel {
        if recent_errors.len() > 20 {
            SeverityLevel::Critical
        } else if recent_errors.len() > 10 {
            SeverityLevel::High
        } else if recent_errors.len() > 5 {
            SeverityLevel::Medium
        } else {
            SeverityLevel::Low
        }
    }

    /// Find the last critical error
    fn find_last_critical_error(&self) -> Option<Instant> {
        self.error_history
            .iter()
            .rev()
            .find(|event| self.is_critical_error(&event.error))
            .map(|event| event.timestamp)
    }

    /// Check if an error is critical
    fn is_critical_error(&self, error: &AutogradError) -> bool {
        matches!(
            error,
            AutogradError::NumericalInstability { .. } | AutogradError::MemoryAllocation { .. }
        )
    }

    /// Calculate system health score (0.0 to 1.0)
    fn calculate_health_score(&self, recent_errors: &[&ErrorEvent]) -> f64 {
        let base_score = 1.0;
        let error_penalty = recent_errors.len() as f64 * 0.01;
        let critical_penalty = recent_errors
            .iter()
            .filter(|e| self.is_critical_error(&e.error))
            .count() as f64
            * 0.05;

        (base_score - error_penalty - critical_penalty).max(0.0)
    }
}

/// Error pattern database for pattern recognition
#[derive(Debug)]
struct ErrorPatternDatabase {
    patterns: Vec<ErrorPattern>,
    pattern_counts: HashMap<PatternType, usize>,
}

impl ErrorPatternDatabase {
    fn new() -> Self {
        Self {
            patterns: Vec::new(),
            pattern_counts: HashMap::new(),
        }
    }

    fn analyze_error(&mut self, event: &ErrorEvent) {
        let pattern_type = self.classify_error_pattern(&event.error);

        if let Some(pattern_type) = pattern_type {
            *self.pattern_counts.entry(pattern_type).or_insert(0) += 1;

            if let Some(pattern) = self
                .patterns
                .iter_mut()
                .find(|p| p.pattern_type == pattern_type)
            {
                pattern.occurrences += 1;
                pattern.last_occurrence = event.timestamp;
            } else {
                self.patterns.push(ErrorPattern {
                    pattern_type,
                    occurrences: 1,
                    first_occurrence: event.timestamp,
                    last_occurrence: event.timestamp,
                    severity: self.assess_pattern_severity(&pattern_type),
                });
            }
        }
    }

    fn classify_error_pattern(&self, error: &AutogradError) -> Option<PatternType> {
        match error {
            AutogradError::ShapeMismatch { .. } => Some(PatternType::ShapeMismatchPattern),
            AutogradError::MemoryAllocation { .. } => Some(PatternType::MemoryAllocationPattern),
            AutogradError::NumericalInstability { .. } => {
                Some(PatternType::NumericalInstabilityPattern)
            }
            AutogradError::ComputationGraph { .. } => Some(PatternType::ComputationGraphPattern),
            AutogradError::GradientComputation { .. } => {
                Some(PatternType::GradientComputationPattern)
            }
            _ => None,
        }
    }

    fn assess_pattern_severity(&self, pattern_type: &PatternType) -> SeverityLevel {
        match pattern_type {
            PatternType::NumericalInstabilityPattern => SeverityLevel::Critical,
            PatternType::MemoryAllocationPattern => SeverityLevel::High,
            PatternType::ComputationGraphPattern => SeverityLevel::High,
            PatternType::ShapeMismatchPattern => SeverityLevel::Medium,
            PatternType::GradientComputationPattern => SeverityLevel::Medium,
            _ => SeverityLevel::Low,
        }
    }

    fn generate_analysis(&self) -> PatternAnalysis {
        let detected_patterns = self.patterns.clone();
        let most_common = self
            .pattern_counts
            .iter()
            .max_by_key(|(_, count)| *count)
            .map(|(pattern_type, _)| *pattern_type);

        PatternAnalysis {
            detected_patterns,
            most_common_pattern: most_common,
            total_patterns: self.patterns.len(),
        }
    }

    fn get_active_patterns(&self) -> Vec<&ErrorPattern> {
        let recent_threshold = Instant::now() - Duration::from_secs(3600); // Last hour
        self.patterns
            .iter()
            .filter(|p| p.last_occurrence >= recent_threshold)
            .collect()
    }
}

/// Machine Learning-based Error Pattern Recognition System
///
/// This advanced system uses ML techniques for sophisticated error pattern detection,
/// prediction, and classification beyond simple rule-based approaches.
#[derive(Debug)]
pub struct MLPatternRecognitionSystem {
    /// Feature extraction matrix for error patterns
    feature_matrix: Array2<f64>,
    /// Pattern classification model
    classifier: ErrorPatternClassifier,
    /// Anomaly detection system
    anomaly_detector: ErrorAnomalyDetector,
    /// Temporal pattern analyzer
    temporal_analyzer: TemporalPatternAnalyzer,
    /// Training data for adaptive learning
    training_data: Vec<LabeledErrorEvent>,
    /// Configuration for ML system
    ml_config: MLSystemConfig,
}

impl MLPatternRecognitionSystem {
    /// Create new ML-based pattern recognition system
    pub fn new() -> Self {
        Self::with_config(MLSystemConfig::default())
    }

    /// Create with custom configuration
    pub fn with_config(config: MLSystemConfig) -> Self {
        let feature_dim = config.feature_dimension;
        let max_samples = config.max_training_samples;

        Self {
            feature_matrix: Array2::zeros((max_samples, feature_dim)),
            classifier: ErrorPatternClassifier::new(feature_dim, config.num_classes),
            anomaly_detector: ErrorAnomalyDetector::new(feature_dim),
            temporal_analyzer: TemporalPatternAnalyzer::new(),
            training_data: Vec::new(),
            ml_config: config,
        }
    }

    /// Analyze error with ML-based pattern recognition
    pub fn analyze_error_ml(&mut self, event: &ErrorEvent) -> MLAnalysisResult {
        // Extract features from error event
        let features = self.extract_features(event);

        // Classify error pattern using ML
        let predicted_pattern = self.classifier.classify(&features);

        // Detect anomalies
        let is_anomaly = self.anomaly_detector.is_anomaly(&features);

        // Analyze temporal patterns
        let temporal_info = self.temporal_analyzer.analyze_temporal_context(event);

        // Generate predictions
        let prediction = self.predict_next_error_probability(event);

        MLAnalysisResult {
            predicted_pattern: predicted_pattern.clone(),
            confidence_score: predicted_pattern.confidence,
            is_anomaly,
            anomaly_score: if is_anomaly { 1.0 } else { 0.0 },
            temporal_context: temporal_info,
            next_error_probability: prediction,
            feature_importance: self.calculate_feature_importance(&features),
        }
    }

    /// Extract numerical features from error event
    fn extract_features(&self, event: &ErrorEvent) -> Vec<f64> {
        let mut features = Vec::with_capacity(self.ml_config.feature_dimension);

        // Basic error type features
        features.extend(self.encode_error_type(&event.error));

        // Temporal features
        features.extend(self.extract_temporal_features(event));

        // Context features
        features.extend(self.extract_context_features(&event.context));

        // Ensure feature vector has correct dimension
        features.resize(self.ml_config.feature_dimension, 0.0);
        features
    }

    /// Encode error type as numerical features
    fn encode_error_type(&self, error: &AutogradError) -> Vec<f64> {
        let mut encoding = vec![0.0; 10]; // One-hot encoding for error types

        match error {
            AutogradError::ShapeMismatch { .. } => encoding[0] = 1.0,
            AutogradError::MemoryAllocation { .. } => encoding[1] = 1.0,
            AutogradError::NumericalInstability { .. } => encoding[2] = 1.0,
            AutogradError::ComputationGraph { .. } => encoding[3] = 1.0,
            AutogradError::GradientComputation { .. } => encoding[4] = 1.0,
            _ => encoding[9] = 1.0, // Unknown type
        }

        encoding
    }

    /// Extract temporal features from error timing
    fn extract_temporal_features(&self, event: &ErrorEvent) -> Vec<f64> {
        let timestamp_secs = event.timestamp.elapsed().as_secs_f64();
        vec![
            timestamp_secs.sin(), // Cyclical time encoding
            timestamp_secs.cos(),
            timestamp_secs % 86400.0,           // Time of day
            (timestamp_secs / 86400.0).fract(), // Day fraction
        ]
    }

    /// Extract context features
    fn extract_context_features(&self, context: &ErrorContext) -> Vec<f64> {
        vec![
            context.tensor_ids.len() as f64,
            context.stack_trace.len() as f64,
            context.operation_name.len() as f64,
        ]
    }

    /// Predict next error probability using temporal patterns
    fn predict_next_error_probability(&self, event: &ErrorEvent) -> f64 {
        // Simple prediction based on historical patterns
        // In a real implementation, this would use advanced time series analysis
        let base_probability = 0.1;
        let temporal_factor = self.temporal_analyzer.get_temporal_risk_factor(event);
        (base_probability * temporal_factor).min(1.0)
    }

    /// Calculate feature importance scores
    fn calculate_feature_importance(&self, features: &[f64]) -> Vec<f64> {
        // Simplified feature importance calculation
        features.iter().map(|&f| f.abs()).collect()
    }

    /// Train the ML model with new data
    pub fn train_incremental(&mut self, labeled_events: &[LabeledErrorEvent]) {
        for event in labeled_events {
            self.training_data.push(event.clone());

            // Extract features and update classifier
            let features = self.extract_features(&event.event);
            self.classifier.update(&features, &event.label);

            // Update anomaly detector
            self.anomaly_detector.update(&features);
        }

        // Limit training data size
        if self.training_data.len() > self.ml_config.max_training_samples {
            let excess = self.training_data.len() - self.ml_config.max_training_samples;
            self.training_data.drain(0..excess);
        }
    }
}

/// ML-based Error Pattern Classifier
#[derive(Debug)]
struct ErrorPatternClassifier {
    /// Weight matrix for classification
    weights: Array2<f64>,
    /// Bias vector
    bias: Vec<f64>,
    /// Learning rate for updates
    learning_rate: f64,
}

impl ErrorPatternClassifier {
    fn new(feature_dim: usize, num_classes: usize) -> Self {
        let mut weights = Array2::zeros((num_classes, feature_dim));

        // Simple initialization with deterministic values
        let scale = (2.0 / feature_dim as f64).sqrt();
        for i in 0..num_classes {
            for j in 0..feature_dim {
                // Use hash-based pseudo-random initialization
                let mut hasher = DefaultHasher::new();
                (i, j).hash(&mut hasher);
                let hash_val = hasher.finish();
                let normalized = (hash_val as f64) / (u64::MAX as f64);
                weights[[i, j]] = (normalized - 0.5) * 2.0 * scale;
            }
        }

        Self {
            weights,
            bias: vec![0.0; num_classes],
            learning_rate: 0.01,
        }
    }

    fn classify(&self, features: &[f64]) -> MLPatternPrediction {
        let scores = self.compute_scores(features);
        let max_idx = scores
            .iter()
            .enumerate()
            .max_by(|(_, a), (_, b)| a.partial_cmp(b).unwrap_or(std::cmp::Ordering::Equal))
            .map(|(idx, _)| idx)
            .unwrap_or(0);

        let confidence = self.softmax(&scores)[max_idx];
        let pattern_type = self.index_to_pattern_type(max_idx);

        MLPatternPrediction {
            pattern_type,
            confidence,
            raw_scores: scores,
        }
    }

    fn compute_scores(&self, features: &[f64]) -> Vec<f64> {
        let mut scores = self.bias.clone();

        for i in 0..self.weights.nrows() {
            for j in 0..features.len().min(self.weights.ncols()) {
                scores[i] += self.weights[[i, j]] * features[j];
            }
        }

        scores
    }

    fn softmax(&self, scores: &[f64]) -> Vec<f64> {
        let max_score = scores.iter().fold(f64::NEG_INFINITY, |a, &b| a.max(b));
        let exp_scores: Vec<f64> = scores.iter().map(|&s| (s - max_score).exp()).collect();
        let sum_exp: f64 = exp_scores.iter().sum();
        exp_scores.iter().map(|&e| e / sum_exp).collect()
    }

    fn index_to_pattern_type(&self, index: usize) -> PatternType {
        match index {
            0 => PatternType::ShapeMismatchPattern,
            1 => PatternType::MemoryAllocationPattern,
            2 => PatternType::NumericalInstabilityPattern,
            3 => PatternType::ComputationGraphPattern,
            4 => PatternType::GradientComputationPattern,
            _ => PatternType::ShapeMismatchPattern, // Default
        }
    }

    fn update(&mut self, features: &[f64], label: &PatternLabel) {
        let predicted = self.classify(features);
        let target_idx = self.pattern_type_to_index(&label.pattern_type);
        let predicted_idx = self.pattern_type_to_index(&predicted.pattern_type);

        // Simple gradient descent update
        if target_idx != predicted_idx {
            for j in 0..features.len().min(self.weights.ncols()) {
                self.weights[[target_idx, j]] += self.learning_rate * features[j];
                self.weights[[predicted_idx, j]] -= self.learning_rate * features[j];
            }

            self.bias[target_idx] += self.learning_rate;
            self.bias[predicted_idx] -= self.learning_rate;
        }
    }

    fn pattern_type_to_index(&self, pattern_type: &PatternType) -> usize {
        match pattern_type {
            PatternType::ShapeMismatchPattern => 0,
            PatternType::MemoryAllocationPattern => 1,
            PatternType::NumericalInstabilityPattern => 2,
            PatternType::ComputationGraphPattern => 3,
            PatternType::GradientComputationPattern => 4,
            _ => 0,
        }
    }
}

/// Anomaly detector for error patterns
#[derive(Debug)]
struct ErrorAnomalyDetector {
    /// Running mean of feature vectors
    mean: Vec<f64>,
    /// Running variance of feature vectors
    variance: Vec<f64>,
    /// Number of samples seen
    sample_count: usize,
    /// Anomaly threshold (standard deviations)
    threshold: f64,
}

impl ErrorAnomalyDetector {
    fn new(feature_dim: usize) -> Self {
        Self {
            mean: vec![0.0; feature_dim],
            variance: vec![1.0; feature_dim],
            sample_count: 0,
            threshold: 3.0, // 3 standard deviations
        }
    }

    fn is_anomaly(&self, features: &[f64]) -> bool {
        if self.sample_count < 10 {
            return false; // Need more samples to determine anomalies
        }

        let z_scores: Vec<f64> = features
            .iter()
            .zip(&self.mean)
            .zip(&self.variance)
            .map(|((&f, &m), &v)| (f - m) / v.sqrt())
            .collect();

        z_scores.iter().any(|&z| z.abs() > self.threshold)
    }

    fn update(&mut self, features: &[f64]) {
        self.sample_count += 1;
        let n = self.sample_count as f64;

        // Online update of mean and variance
        for i in 0..features.len().min(self.mean.len()) {
            let delta = features[i] - self.mean[i];
            self.mean[i] += delta / n;
            let delta2 = features[i] - self.mean[i];
            self.variance[i] = ((n - 1.0) * self.variance[i] + delta * delta2) / n;
        }
    }
}

/// Temporal pattern analyzer
#[derive(Debug)]
struct TemporalPatternAnalyzer {
    /// Historical error timestamps
    error_history: Vec<Instant>,
    /// Seasonal patterns (hour of day, day of week, etc.)
    seasonal_patterns: HashMap<String, f64>,
}

impl TemporalPatternAnalyzer {
    fn new() -> Self {
        Self {
            error_history: Vec::new(),
            seasonal_patterns: HashMap::new(),
        }
    }

    fn analyze_temporal_context(&mut self, event: &ErrorEvent) -> TemporalContext {
        self.error_history.push(event.timestamp);

        // Clean old history
        let cutoff = Instant::now() - Duration::from_secs(86400); // 24 hours
        self.error_history.retain(|&t| t >= cutoff);

        let frequency = self.calculate_error_frequency();
        let trend = self.calculate_trend();
        let seasonality = self.detect_seasonality();

        TemporalContext {
            error_frequency: frequency,
            trend_direction: trend,
            seasonal_factor: seasonality,
            time_since_last_error: self.time_since_last_error(),
        }
    }

    fn calculate_error_frequency(&self) -> f64 {
        if self.error_history.len() < 2 {
            return 0.0;
        }

        let time_span = self
            .error_history
            .last()
            .expect("error_history is non-empty")
            .duration_since(
                *self
                    .error_history
                    .first()
                    .expect("error_history is non-empty"),
            )
            .as_secs_f64();

        self.error_history.len() as f64 / time_span.max(1.0)
    }

    fn calculate_trend(&self) -> f64 {
        if self.error_history.len() < 10 {
            return 0.0; // Need more data for trend analysis
        }

        // Simple linear trend calculation
        let recent = &self.error_history[self.error_history.len() - 5..];
        let older =
            &self.error_history[self.error_history.len() - 10..self.error_history.len() - 5];

        let recent_rate = recent.len() as f64 / 300.0; // Last 5 minutes
        let older_rate = older.len() as f64 / 300.0;

        recent_rate - older_rate
    }

    fn detect_seasonality(&self) -> f64 {
        // Simplified seasonality detection
        1.0 // Default to no seasonal effect
    }

    fn time_since_last_error(&self) -> f64 {
        self.error_history
            .last()
            .map(|&t| t.elapsed().as_secs_f64())
            .unwrap_or(0.0)
    }

    fn get_temporal_risk_factor(&self, _event: &ErrorEvent) -> f64 {
        // Calculate risk multiplier based on temporal patterns
        let base_risk = 1.0;
        let frequency_factor = (self.calculate_error_frequency() * 10.0).min(2.0);
        let trend_factor = (1.0 + self.calculate_trend()).max(0.1);

        base_risk * frequency_factor * trend_factor
    }
}

/// Configuration for ML system
#[derive(Debug, Clone)]
pub struct MLSystemConfig {
    pub feature_dimension: usize,
    pub num_classes: usize,
    pub max_training_samples: usize,
    pub anomaly_threshold: f64,
    pub learning_rate: f64,
}

impl Default for MLSystemConfig {
    fn default() -> Self {
        Self {
            feature_dimension: 20,
            num_classes: 5,
            max_training_samples: 10000,
            anomaly_threshold: 3.0,
            learning_rate: 0.01,
        }
    }
}

/// Result of ML-based analysis
#[derive(Debug, Clone)]
pub struct MLAnalysisResult {
    pub predicted_pattern: MLPatternPrediction,
    pub confidence_score: f64,
    pub is_anomaly: bool,
    pub anomaly_score: f64,
    pub temporal_context: TemporalContext,
    pub next_error_probability: f64,
    pub feature_importance: Vec<f64>,
}

/// ML pattern prediction
#[derive(Debug, Clone)]
pub struct MLPatternPrediction {
    pub pattern_type: PatternType,
    pub confidence: f64,
    pub raw_scores: Vec<f64>,
}

/// Temporal context information
#[derive(Debug, Clone)]
pub struct TemporalContext {
    pub error_frequency: f64,
    pub trend_direction: f64,
    pub seasonal_factor: f64,
    pub time_since_last_error: f64,
}

/// Labeled error event for training
#[derive(Debug, Clone)]
pub struct LabeledErrorEvent {
    pub event: ErrorEvent,
    pub label: PatternLabel,
}

/// Pattern label for supervised learning
#[derive(Debug, Clone)]
pub struct PatternLabel {
    pub pattern_type: PatternType,
    pub severity: SeverityLevel,
    pub confidence: f64,
}

/// Error correlation tracker
#[derive(Debug)]
struct ErrorCorrelationTracker {
    correlations: Vec<ErrorCorrelation>,
}

impl ErrorCorrelationTracker {
    fn new() -> Self {
        Self {
            correlations: Vec::new(),
        }
    }

    fn update(&mut self, _event: &ErrorEvent, _history: &[ErrorEvent]) {
        // Simplified correlation analysis
        // In a real implementation, this would analyze temporal and causal relationships
    }

    fn generate_analysis(&self) -> CorrelationAnalysis {
        CorrelationAnalysis {
            significant_correlations: self.correlations.clone(),
            correlation_strength: self.calculate_overall_correlation_strength(),
        }
    }

    fn calculate_overall_correlation_strength(&self) -> f64 {
        if self.correlations.is_empty() {
            0.0
        } else {
            self.correlations.iter().map(|c| c.strength).sum::<f64>()
                / self.correlations.len() as f64
        }
    }
}

/// Performance impact analyzer
#[derive(Debug)]
struct PerformanceImpactAnalyzer {
    impact_history: Vec<PerformanceImpact>,
}

impl PerformanceImpactAnalyzer {
    fn new() -> Self {
        Self {
            impact_history: Vec::new(),
        }
    }

    fn assess_impact(&mut self, event: &ErrorEvent) {
        let impact = PerformanceImpact {
            error_type: format!("{:?}", event.error),
            timestamp: event.timestamp,
            estimated_delay: self.estimate_error_delay(&event.error),
            recovery_time: self.estimate_recovery_time(&event.error),
        };

        self.impact_history.push(impact);
    }

    fn estimate_error_delay(&self, error: &AutogradError) -> Duration {
        match error {
            AutogradError::MemoryAllocation { .. } => Duration::from_millis(100),
            AutogradError::NumericalInstability { .. } => Duration::from_millis(50),
            AutogradError::ComputationGraph { .. } => Duration::from_millis(200),
            _ => Duration::from_millis(10),
        }
    }

    fn estimate_recovery_time(&self, error: &AutogradError) -> Duration {
        match error {
            AutogradError::MemoryAllocation { .. } => Duration::from_secs(5),
            AutogradError::NumericalInstability { .. } => Duration::from_secs(1),
            AutogradError::ComputationGraph { .. } => Duration::from_secs(10),
            _ => Duration::from_millis(100),
        }
    }

    fn generate_analysis(&self) -> PerformanceAnalysis {
        let total_delay: Duration = self.impact_history.iter().map(|i| i.estimated_delay).sum();
        let total_recovery_time: Duration =
            self.impact_history.iter().map(|i| i.recovery_time).sum();

        PerformanceAnalysis {
            total_performance_impact: total_delay + total_recovery_time,
            average_error_delay: if self.impact_history.is_empty() {
                Duration::from_millis(0)
            } else {
                total_delay / self.impact_history.len() as u32
            },
            impact_events: self.impact_history.len(),
        }
    }
}

// Supporting types and structures

#[derive(Debug, Clone)]
pub struct ErrorEvent {
    pub timestamp: Instant,
    pub error: AutogradError,
    pub context: ErrorContext,
    pub operation_id: String,
}

#[derive(Debug, Clone)]
pub struct ErrorContext {
    pub operation_name: String,
    pub tensor_ids: Vec<usize>,
    pub stack_trace: Vec<String>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DiagnosticsConfig {
    pub max_history_size: usize,
    pub history_cleanup_batch: usize,
    pub analysis_window: Duration,
    pub enable_correlation_analysis: bool,
    pub enable_performance_analysis: bool,
}

impl Default for DiagnosticsConfig {
    fn default() -> Self {
        Self {
            max_history_size: 10000,
            history_cleanup_batch: 1000,
            analysis_window: Duration::from_secs(3600), // 1 hour
            enable_correlation_analysis: true,
            enable_performance_analysis: true,
        }
    }
}

#[derive(Debug, Clone)]
pub struct DiagnosticReport {
    pub timestamp: Instant,
    pub total_errors: usize,
    pub analysis_window: Duration,
    pub pattern_analysis: PatternAnalysis,
    pub correlation_analysis: CorrelationAnalysis,
    pub performance_analysis: PerformanceAnalysis,
    pub recommendations: Vec<DiagnosticRecommendation>,
    pub severity_assessment: SeverityLevel,
}

#[derive(Debug)]
pub struct DiagnosticStatus {
    pub error_rate: f64,
    pub active_patterns: usize,
    pub severity_level: SeverityLevel,
    pub last_critical_error: Option<Instant>,
    pub health_score: f64,
}

#[derive(Debug, Clone)]
pub struct ErrorPattern {
    pub pattern_type: PatternType,
    pub occurrences: usize,
    pub first_occurrence: Instant,
    pub last_occurrence: Instant,
    pub severity: SeverityLevel,
}

#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash)]
pub enum PatternType {
    ShapeMismatchPattern,
    MemoryAllocationPattern,
    NumericalInstabilityPattern,
    ComputationGraphPattern,
    GradientComputationPattern,
    RecurrentFailurePattern,
}

#[derive(Debug, Clone)]
pub struct ErrorCorrelation {
    pub error_type_1: String,
    pub error_type_2: String,
    pub strength: f64,
    pub occurrences: usize,
}

#[derive(Debug, Clone)]
pub struct PatternAnalysis {
    pub detected_patterns: Vec<ErrorPattern>,
    pub most_common_pattern: Option<PatternType>,
    pub total_patterns: usize,
}

#[derive(Debug, Clone)]
pub struct CorrelationAnalysis {
    pub significant_correlations: Vec<ErrorCorrelation>,
    pub correlation_strength: f64,
}

#[derive(Debug, Clone)]
pub struct PerformanceAnalysis {
    pub total_performance_impact: Duration,
    pub average_error_delay: Duration,
    pub impact_events: usize,
}

#[derive(Debug, Clone)]
pub struct PerformanceImpact {
    pub error_type: String,
    pub timestamp: Instant,
    pub estimated_delay: Duration,
    pub recovery_time: Duration,
}

#[derive(Debug, Clone)]
pub struct DiagnosticRecommendation {
    pub category: RecommendationCategory,
    pub priority: RecommendationPriority,
    pub title: String,
    pub description: String,
    pub code_example: Option<String>,
    pub estimated_fix_time: Duration,
}

#[derive(Debug, Clone)]
pub enum RecommendationCategory {
    CodeImprovement,
    Performance,
    Stability,
    Architecture,
    Configuration,
}

#[derive(Debug, Clone)]
pub enum RecommendationPriority {
    Critical,
    High,
    Medium,
    Low,
}

#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum SeverityLevel {
    Critical,
    High,
    Medium,
    Low,
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_diagnostics_system_creation() {
        let system = ErrorDiagnosticsSystem::new();
        assert_eq!(system.error_history.len(), 0);
        assert_eq!(system.config.max_history_size, 10000);
    }

    #[test]
    fn test_error_recording() {
        let mut system = ErrorDiagnosticsSystem::new();
        let error = AutogradError::GradientComputation {
            operation: "test_op".to_string(),
            tensor_id: Some(1),
            context: "test context".to_string(),
            source: None,
        };
        let context = ErrorContext {
            operation_name: "test".to_string(),
            tensor_ids: vec![1],
            stack_trace: vec!["test".to_string()],
        };

        system.record_error(&error, context);
        assert_eq!(system.error_history.len(), 1);
    }

    #[test]
    fn test_diagnostic_report_generation() {
        let system = ErrorDiagnosticsSystem::new();
        let report = system.generate_diagnostic_report();

        assert_eq!(report.total_errors, 0);
        assert!(report.recommendations.is_empty());
    }

    #[test]
    fn test_severity_assessment() {
        let system = ErrorDiagnosticsSystem::new();
        let severity = system.assess_overall_severity();
        assert_eq!(severity, SeverityLevel::Low);
    }

    #[test]
    fn test_health_score_calculation() {
        let system = ErrorDiagnosticsSystem::new();
        let recent_errors = Vec::new();
        let health_score = system.calculate_health_score(&recent_errors);
        assert_eq!(health_score, 1.0);
    }

    #[test]
    fn test_pattern_recognition() {
        let mut db = ErrorPatternDatabase::new();
        let error = AutogradError::ShapeMismatch {
            expected: vec![2, 3],
            actual: vec![3, 4],
            operation: "matmul".to_string(),
            tensor_names: vec!["A".to_string(), "B".to_string()],
        };
        let event = ErrorEvent {
            timestamp: Instant::now(),
            error,
            context: ErrorContext {
                operation_name: "test".to_string(),
                tensor_ids: vec![1, 2],
                stack_trace: vec!["test".to_string()],
            },
            operation_id: "test_op".to_string(),
        };

        db.analyze_error(&event);
        assert_eq!(db.patterns.len(), 1);
        assert_eq!(
            db.patterns[0].pattern_type,
            PatternType::ShapeMismatchPattern
        );
    }
}