scirs2-stats 0.3.3

//! Advanced advanced Error Enhancement System
//!
//! This module provides next-generation error handling with intelligent diagnostics,
//! context-aware recovery suggestions, and performance-optimized error reporting
//! for production-grade statistical computing.

use crate::error::StatsError;
use crate::error_standardization::PerformanceImpact;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::time::{Duration, Instant};

/// advanced Enhanced Error Context with Intelligent Diagnostics
#[derive(Debug, Clone)]
pub struct AdvancedErrorContext {
    /// Core error information
    pub error: StatsError,
    /// Context where the error occurred
    pub operation_context: OperationContext,
    /// Intelligent diagnostics
    pub diagnostics: IntelligentDiagnostics,
    /// Recovery strategies
    pub recovery_strategies: Vec<RecoveryStrategy>,
    /// Performance impact assessment
    pub performance_assessment: PerformanceAssessment,
    /// User experience recommendations
    pub ux_recommendations: UXRecommendations,
}

/// Comprehensive operation context
#[derive(Debug, Clone)]
pub struct OperationContext {
    /// Function name where error occurred
    pub function_name: String,
    /// Module path
    pub module_path: String,
    /// Input data characteristics
    pub data_characteristics: DataCharacteristics,
    /// Execution environment
    pub execution_environment: ExecutionEnvironment,
    /// Timestamp of error
    pub timestamp: Instant,
    /// Stack trace information (if available)
    pub stack_trace: Option<String>,
}

/// Intelligent error diagnostics
#[derive(Debug, Clone)]
pub struct IntelligentDiagnostics {
    /// Root cause analysis
    pub root_cause: RootCause,
    /// Probable causes ranked by likelihood
    pub probable_causes: Vec<(String, f64)>, // (cause, probability)
    /// Related errors that often occur together
    pub related_errors: Vec<String>,
    /// Data quality assessment
    pub data_quality: DataQuality,
    /// Computational complexity analysis
    pub complexity_analysis: ComplexityAnalysis,
}

/// Root cause classification
#[derive(Debug, Clone)]
pub enum RootCause {
    /// Input data issues
    DataIssue(DataIssueType),
    /// Algorithmic limitations
    AlgorithmicLimitation(AlgorithmIssue),
    /// Numerical instability
    NumericalInstability(NumericalIssue),
    /// Resource constraints
    ResourceConstraint(ResourceIssue),
    /// Configuration problems
    ConfigurationIssue(ConfigIssue),
    /// Unknown or complex interaction
    Unknown,
}

/// Types of data issues
#[derive(Debug, Clone)]
pub enum DataIssueType {
    InsufficientData,
    CorruptedData,
    OutOfRange,
    MissingValues,
    DimensionalityMismatch,
    NumericalPrecision,
}

/// Algorithmic issues
#[derive(Debug, Clone)]
pub enum AlgorithmIssue {
    ConvergenceFailure,
    NonApplicableDomain,
    ScalabilityLimit,
    PreconditionViolation,
}

/// Numerical computation issues
#[derive(Debug, Clone)]
pub enum NumericalIssue {
    Overflow,
    Underflow,
    LossOfPrecision,
    IllConditioned,
    RoundoffError,
}

/// Resource constraint issues
#[derive(Debug, Clone)]
pub enum ResourceIssue {
    InsufficientMemory,
    ComputationalTimeout,
    ThreadLimitExceeded,
    DiskSpaceExhausted,
}

/// Configuration issues
#[derive(Debug, Clone)]
pub enum ConfigIssue {
    InvalidParameter,
    ConflictingSettings,
    MissingDependency,
    VersionMismatch,
}

/// Data characteristics analysis
#[derive(Debug, Clone)]
pub struct DataCharacteristics {
    /// Size information
    pub size_info: SizeInfo,
    /// Data type information
    pub type_info: TypeInfo,
    /// Distribution characteristics
    pub distribution_info: DistributionInfo,
    /// Quality metrics
    pub quality_metrics: QualityMetrics,
}

/// Size information
#[derive(Debug, Clone)]
pub struct SizeInfo {
    pub dimensions: Vec<usize>,
    pub total_elements: usize,
    pub memory_footprint: usize, // in bytes
    pub sparsity: Option<f64>,   // if applicable
}

/// Type information
#[derive(Debug, Clone)]
pub struct TypeInfo {
    pub primary_type: String,
    pub precision: String, // f32, f64, etc.
    pub signed: bool,
    pub complex: bool,
}

/// Distribution characteristics
#[derive(Debug, Clone)]
pub struct DistributionInfo {
    pub range: Option<(f64, f64)>,
    pub mean: Option<f64>,
    pub variance: Option<f64>,
    pub skewness: Option<f64>,
    pub kurtosis: Option<f64>,
    pub outlier_percentage: Option<f64>,
}

/// Quality metrics
#[derive(Debug, Clone)]
pub struct QualityMetrics {
    pub completeness: f64,      // percentage of non-missing values
    pub consistency: f64,       // consistency score
    pub accuracy_estimate: f64, // estimated accuracy
    pub noise_level: f64,       // estimated noise level
}

/// Execution environment information
#[derive(Debug, Clone)]
pub struct ExecutionEnvironment {
    pub cpu_info: CpuInfo,
    pub memory_info: MemoryInfo,
    pub optimization_level: OptimizationLevel,
    pub threading_info: ThreadingInfo,
    pub feature_flags: HashMap<String, bool>,
}

/// CPU information
#[derive(Debug, Clone)]
pub struct CpuInfo {
    pub architecture: String,
    pub core_count: usize,
    pub simd_support: Vec<String>,
    pub cachesizes: Vec<usize>,
}

/// Memory information
#[derive(Debug, Clone)]
pub struct MemoryInfo {
    pub total_memory: usize,
    pub available_memory: usize,
    pub memory_pressure: f64, // 0.0 to 1.0
}

/// Optimization level
#[derive(Debug, Clone)]
pub enum OptimizationLevel {
    Debug,
    Release,
    Optimized,
    AdvancedOptimized,
}

/// Threading information
#[derive(Debug, Clone)]
pub struct ThreadingInfo {
    pub thread_count: usize,
    pub thread_affinity: Option<Vec<usize>>,
    pub numa_topology: Option<String>,
}

/// Data quality assessment
#[derive(Debug, Clone)]
pub struct DataQuality {
    pub overall_score: f64, // 0.0 to 1.0
    pub issues: Vec<DataQualityIssue>,
    pub recommendations: Vec<String>,
}

/// Data quality issues
#[derive(Debug, Clone)]
pub struct DataQualityIssue {
    pub issue_type: String,
    pub severity: QualitySeverity,
    pub affected_percentage: f64,
    pub description: String,
    pub remedy: String,
}

/// Quality issue severity
#[derive(Debug, Clone)]
pub enum QualitySeverity {
    Low,
    Medium,
    High,
    Critical,
}

/// Computational complexity analysis
#[derive(Debug, Clone)]
pub struct ComplexityAnalysis {
    pub time_complexity: String,
    pub space_complexity: String,
    pub scalability_assessment: ScalabilityAssessment,
    pub bottleneck_analysis: Vec<BottleneckInfo>,
}

/// Scalability assessment
#[derive(Debug, Clone)]
pub struct ScalabilityAssessment {
    pub current_performance: PerformanceMetrics,
    pub predicted_performance: Vec<(usize, PerformanceMetrics)>, // (size, metrics)
    pub scaling_factor: f64,
    pub recommended_maxsize: Option<usize>,
}

/// Performance metrics
#[derive(Debug, Clone)]
pub struct PerformanceMetrics {
    pub execution_time: Duration,
    pub memory_usage: usize,
    pub cache_efficiency: f64,
    pub cpu_utilization: f64,
}

/// Bottleneck information
#[derive(Debug, Clone)]
pub struct BottleneckInfo {
    pub component: String,
    pub impact_factor: f64,          // multiplier for performance impact
    pub optimization_potential: f64, // 0.0 to 1.0
    pub recommended_action: String,
}

/// Enhanced recovery strategy
#[derive(Debug, Clone)]
pub struct RecoveryStrategy {
    /// Strategy name
    pub name: String,
    /// Detailed description
    pub description: String,
    /// Implementation steps
    pub implementation_steps: Vec<ImplementationStep>,
    /// Expected success probability
    pub success_probability: f64,
    /// Performance impact
    pub performance_impact: PerformanceImpact,
    /// Risk assessment
    pub risk_assessment: RiskAssessment,
    /// Code example (if applicable)
    pub code_example: Option<String>,
}

/// Implementation step
#[derive(Debug, Clone)]
pub struct ImplementationStep {
    pub step_number: usize,
    pub action: String,
    pub details: String,
    pub validation: String,
    pub fallback: Option<String>,
}

/// Risk assessment for recovery strategy
#[derive(Debug, Clone)]
pub struct RiskAssessment {
    pub overall_risk: RiskLevel,
    pub data_loss_risk: RiskLevel,
    pub performance_degradation_risk: RiskLevel,
    pub compatibility_risk: RiskLevel,
}

/// Risk levels
#[derive(Debug, Clone)]
pub enum RiskLevel {
    VeryLow,
    Low,
    Medium,
    High,
    VeryHigh,
}

/// Performance impact assessment
#[derive(Debug, Clone)]
pub struct PerformanceAssessment {
    /// Current operation performance
    pub baseline_performance: PerformanceMetrics,
    /// Performance with different recovery strategies
    pub strategy_performance: HashMap<String, PerformanceMetrics>,
    /// Optimization recommendations
    pub optimization_recommendations: Vec<OptimizationRecommendation>,
}

/// Optimization recommendation
#[derive(Debug, Clone)]
pub struct OptimizationRecommendation {
    pub recommendation: String,
    pub expected_improvement: f64, // percentage improvement
    pub implementation_effort: EffortLevel,
    pub compatibility_impact: CompatibilityImpact,
}

/// Implementation effort levels
#[derive(Debug, Clone)]
pub enum EffortLevel {
    Trivial,  // < 1 hour
    Low,      // 1-4 hours
    Medium,   // 1-2 days
    High,     // 3-7 days
    VeryHigh, // > 1 week
}

/// Compatibility impact
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum CompatibilityImpact {
    None,
    Minor,
    Moderate,
    Major,
    Breaking,
}

/// User experience recommendations
#[derive(Debug, Clone)]
pub struct UXRecommendations {
    /// Error message improvements
    pub message_improvements: Vec<String>,
    /// User workflow suggestions
    pub workflow_suggestions: Vec<String>,
    /// Documentation references
    pub documentation_refs: Vec<DocumentationRef>,
    /// Interactive assistance options
    pub interactive_options: Vec<InteractiveOption>,
}

/// Documentation reference
#[derive(Debug, Clone)]
pub struct DocumentationRef {
    pub title: String,
    pub url: String,
    pub relevance_score: f64,
    pub section: Option<String>,
}

/// Interactive assistance option
#[derive(Debug, Clone)]
pub struct InteractiveOption {
    pub option_type: InteractiveType,
    pub description: String,
    pub action: String,
}

/// Types of interactive assistance
#[derive(Debug, Clone)]
pub enum InteractiveType {
    AutoFix,
    GuidedTutorial,
    ParameterWizard,
    DataValidation,
    AlternativeMethod,
}

/// advanced Error Enhancement Engine
pub struct AdvancedErrorEngine {
    /// Configuration for error analysis
    config: ErrorEngineConfig,
    /// Error history for learning
    error_history: Vec<AdvancedErrorContext>,
    /// Performance metrics cache
    performance_cache: HashMap<String, PerformanceMetrics>,
    /// Recovery success statistics
    recovery_stats: HashMap<String, RecoveryStatistics>,
}

/// Configuration for error engine
#[derive(Debug, Clone)]
pub struct ErrorEngineConfig {
    /// Enable deep diagnostics
    pub enable_deep_diagnostics: bool,
    /// Enable performance profiling
    pub enable_performance_profiling: bool,
    /// Enable learning from errors
    pub enable_learning: bool,
    /// Maximum analysis time
    pub max_analysis_time: Duration,
    /// Cache size limit
    pub cachesize_limit: usize,
}

/// Recovery statistics
#[derive(Debug, Clone)]
pub struct RecoveryStatistics {
    pub attempts: usize,
    pub successes: usize,
    pub average_performance_impact: f64,
    pub user_satisfaction: f64,
}

impl AdvancedErrorEngine {
    /// Create new error enhancement engine
    pub fn new(config: ErrorEngineConfig) -> Self {
        Self {
            config,
            error_history: Vec::new(),
            performance_cache: HashMap::new(),
            recovery_stats: HashMap::new(),
        }
    }

    /// Enhance error with advanced analysis
    pub fn enhance_error(
        &mut self,
        error: StatsError,
        context: OperationContext,
    ) -> AdvancedErrorContext {
        let start_time = Instant::now();

        // Perform intelligent diagnostics
        let diagnostics = self.analyze_error(&error, &context);

        // Generate recovery strategies
        let recovery_strategies = self.generate_recovery_strategies(&error, &diagnostics);

        // Assess performance impact
        let performance_assessment = self.assess_performance_impact(&error, &context);

        // Generate UX recommendations
        let ux_recommendations = self.generate_ux_recommendations(&error, &diagnostics);

        let enhanced_context = AdvancedErrorContext {
            error,
            operation_context: context,
            diagnostics,
            recovery_strategies,
            performance_assessment,
            ux_recommendations,
        };

        // Store for learning if enabled
        if self.config.enable_learning {
            self.error_history.push(enhanced_context.clone());

            // Limit history size
            if self.error_history.len() > self.config.cachesize_limit {
                self.error_history.remove(0);
            }
        }

        enhanced_context
    }

    /// Analyze error using intelligent diagnostics
    fn analyze_error(
        &self,
        error: &StatsError,
        context: &OperationContext,
    ) -> IntelligentDiagnostics {
        // Determine root cause
        let root_cause = self.determine_root_cause(error, context);

        // Calculate probable causes
        let probable_causes = self.calculate_probable_causes(error, context);

        // Find related errors
        let related_errors = self.find_related_errors(error);

        // Assess data quality
        let data_quality = self.assessdata_quality(&context.data_characteristics);

        // Analyze computational complexity
        let complexity_analysis = self.analyze_complexity(context);

        IntelligentDiagnostics {
            root_cause,
            probable_causes,
            related_errors,
            data_quality,
            complexity_analysis,
        }
    }

    /// Determine root cause of error
    fn determine_root_cause(&self, error: &StatsError, context: &OperationContext) -> RootCause {
        match error {
            StatsError::InvalidArgument(msg) if msg.contains("empty") => {
                RootCause::DataIssue(DataIssueType::InsufficientData)
            }
            StatsError::InvalidArgument(msg) if msg.contains("NaN") => {
                RootCause::DataIssue(DataIssueType::MissingValues)
            }
            StatsError::DimensionMismatch(_) => {
                RootCause::DataIssue(DataIssueType::DimensionalityMismatch)
            }
            StatsError::ComputationError(msg) if msg.contains("singular") => {
                RootCause::NumericalInstability(NumericalIssue::IllConditioned)
            }
            StatsError::ConvergenceError(_) => {
                RootCause::AlgorithmicLimitation(AlgorithmIssue::ConvergenceFailure)
            }
            StatsError::DomainError(_) => {
                RootCause::AlgorithmicLimitation(AlgorithmIssue::NonApplicableDomain)
            }
            _ => RootCause::Unknown,
        }
    }

    /// Calculate probable causes with probabilities
    fn calculate_probable_causes(
        &self,
        error: &StatsError,
        context: &OperationContext,
    ) -> Vec<(String, f64)> {
        let mut causes = Vec::new();

        // Base analysis on error type and context
        match error {
            StatsError::InvalidArgument(msg) if msg.contains("empty") => {
                causes.push(("Insufficient input data".to_string(), 0.9));
                causes.push((
                    "Data preprocessing removed all valid points".to_string(),
                    0.7,
                ));
                causes.push(("Incorrect data loading".to_string(), 0.5));
            }
            StatsError::ComputationError(_) => {
                causes.push(("Numerical instability".to_string(), 0.8));
                causes.push(("Matrix conditioning issues".to_string(), 0.6));
                causes.push(("Overflow/underflow in computation".to_string(), 0.4));
            }
            _ => {
                causes.push(("Input validation failure".to_string(), 0.6));
                causes.push(("Unexpected data characteristics".to_string(), 0.4));
            }
        }

        // Adjust probabilities based on data characteristics
        if context.data_characteristics.size_info.total_elements == 0 {
            if let Some(pos) = causes
                .iter()
                .position(|(cause_)| cause.contains("Insufficient"))
            {
                causes[pos].1 = 0.95;
            }
        }

        causes.sort_by(|a, b| b.1.partial_cmp(&a.1).expect("Operation failed"));
        causes
    }

    /// Find related errors that often occur together
    fn find_related_errors(&self, &StatsError) -> Vec<String> {
        // In a real implementation, this would use machine learning
        // to find patterns in _error co-occurrence
        vec![
            "DimensionMismatch often precedes this _error".to_string(),
            "Consider checking for NaN values".to_string(),
            "Verify input data preprocessing".to_string(),
        ]
    }

    /// Assess data quality
    fn assessdata_quality(&self, characteristics: &DataCharacteristics) -> DataQuality {
        let mut overall_score = 1.0;
        let mut issues = Vec::new();

        // Check completeness
        if characteristics.quality_metrics.completeness < 0.95 {
            overall_score *= 0.9;
            issues.push(DataQualityIssue {
                issue_type: "Missing Data".to_string(),
                severity: if characteristics.quality_metrics.completeness < 0.8 {
                    QualitySeverity::High
                } else {
                    QualitySeverity::Medium
                },
                affected_percentage: (1.0 - characteristics.quality_metrics.completeness) * 100.0,
                description: "Dataset contains missing values".to_string(),
                remedy: "Consider imputation or removal of incomplete records".to_string(),
            });
        }

        // Check for outliers
        if let Some(outlier_pct) = characteristics.distribution_info.outlier_percentage {
            if outlier_pct > 5.0 {
                overall_score *= 0.95;
                issues.push(DataQualityIssue {
                    issue_type: "Outliers".to_string(),
                    severity: if outlier_pct > 15.0 {
                        QualitySeverity::High
                    } else {
                        QualitySeverity::Medium
                    },
                    affected_percentage: outlier_pct,
                    description: format!("Dataset contains {}% outliers", outlier_pct),
                    remedy: "Consider outlier detection and treatment".to_string(),
                });
            }
        }

        DataQuality {
            overall_score,
            issues,
            recommendations: vec![
                "Perform exploratory data analysis".to_string(),
                "Validate data preprocessing pipeline".to_string(),
                "Consider robust statistical methods".to_string(),
            ],
        }
    }

    /// Analyze computational complexity
    fn analyze_complexity(&self, context: &OperationContext) -> ComplexityAnalysis {
        let size = context.data_characteristics.size_info.total_elements;

        // Estimate complexity based on operation and data size
        let (time_complexity, space_complexity) = match context.function_name.as_str() {
            name if name.contains("sort") => ("O(n log n)".to_string(), "O(n)".to_string()),
            name if name.contains("corr") => ("O(n)".to_string(), "O(1)".to_string()),
            name if name.contains("matrix") => ("O(n³)".to_string(), "O(n²)".to_string(), _ => ("O(n)".to_string(), "O(1)".to_string()),
        };

        ComplexityAnalysis {
            time_complexity,
            space_complexity,
            scalability_assessment: ScalabilityAssessment {
                current_performance: PerformanceMetrics {
                    execution_time: Duration::from_micros(size as u64),
                    memory_usage: size * 8, // Assume 8 bytes per element
                    cache_efficiency: 0.8,
                    cpu_utilization: 0.6,
                },
                predicted_performance: vec![(
                    size * 10,
                    PerformanceMetrics {
                        execution_time: Duration::from_micros(size as u64 * 10),
                        memory_usage: size * 80,
                        cache_efficiency: 0.7,
                        cpu_utilization: 0.8,
                    },
                )],
                scaling_factor: 1.0,
                recommended_maxsize: Some(1_000_000),
            },
            bottleneck_analysis: vec![BottleneckInfo {
                component: "Memory allocation".to_string(),
                impact_factor: 1.5,
                optimization_potential: 0.3,
                recommended_action: "Use memory pools or streaming algorithms".to_string(),
            }],
        }
    }

    /// Generate recovery strategies
    fn generate_recovery_strategies(
        &self,
        error: &StatsError,
        diagnostics: &IntelligentDiagnostics,
    ) -> Vec<RecoveryStrategy> {
        let mut strategies = Vec::new();

        match &diagnostics.root_cause {
            RootCause::DataIssue(DataIssueType::InsufficientData) => {
                strategies.push(RecoveryStrategy {
                    name: "Data Augmentation".to_string(),
                    description: "Increase dataset size through augmentation techniques"
                        .to_string(),
                    implementation_steps: vec![
                        ImplementationStep {
                            step_number: 1,
                            action: "Collect additional data".to_string(),
                            details: "Gather more samples from the same population".to_string(),
                            validation: "Verify new data follows same distribution".to_string(),
                            fallback: Some("Use synthetic data generation".to_string()),
                        },
                        ImplementationStep {
                            step_number: 2,
                            action: "Validate data quality".to_string(),
                            details: "Ensure augmented data maintains statistical properties"
                                .to_string(),
                            validation: "Run statistical tests for consistency".to_string(),
                            fallback: None,
                        },
                    ],
                    success_probability: 0.8,
                    performance_impact: PerformanceImpact::Moderate,
                    risk_assessment: RiskAssessment {
                        overall_risk: RiskLevel::Low,
                        data_loss_risk: RiskLevel::VeryLow,
                        performance_degradation_risk: RiskLevel::Medium,
                        compatibility_risk: RiskLevel::VeryLow,
                    },
                    code_example: Some(
                        r#"
// Example: Bootstrap resampling for data augmentation
use scirs2_core::ndarray::Array1;
use scirs2_core::random::{rng, seq::SliceRandom};

#[allow(dead_code)]
fn bootstrap_augment(data: &Array1<f64>, targetsize: usize) -> Array1<f64> {
    let mut rng = scirs2_core::random::thread_rng();
    let mut augmented = Vec::with_capacity(targetsize);
    
    for _ in 0..targetsize {
        let sample = data.as_slice().expect("Operation failed").choose(&mut rng).expect("Operation failed");
        augmented.push(*sample);
    }
    
    Array1::from(augmented)
}
"#
                        .to_string(),
                    ),
                });
            }
            RootCause::NumericalInstability(_) => {
                strategies.push(RecoveryStrategy {
                    name: "Regularization".to_string(),
                    description: "Add regularization to improve numerical stability".to_string(),
                    implementation_steps: vec![ImplementationStep {
                        step_number: 1,
                        action: "Add ridge regularization".to_string(),
                        details: "Include L2 penalty term in computation".to_string(),
                        validation: "Check condition number improvement".to_string(),
                        fallback: Some("Use higher precision arithmetic".to_string()),
                    }],
                    success_probability: 0.9,
                    performance_impact: PerformanceImpact::Minimal,
                    risk_assessment: RiskAssessment {
                        overall_risk: RiskLevel::VeryLow,
                        data_loss_risk: RiskLevel::VeryLow,
                        performance_degradation_risk: RiskLevel::Low,
                        compatibility_risk: RiskLevel::VeryLow,
                    },
                    code_example: Some(
                        r#"
// Example: Ridge regularization for matrix operations
#[allow(dead_code)]
fn add_ridge_regularization(matrix: &mut Array2<f64>, lambda: f64) {
    for i in 0.._matrix.nrows().min(_matrix.ncols()) {
        matrix[[i, i]] += lambda;
    }
}
"#
                        .to_string(),
                    ),
                });
            }
            _ => {
                strategies.push(RecoveryStrategy {
                    name: "Robust Alternative".to_string(),
                    description: "Use robust statistical methods less sensitive to outliers"
                        .to_string(),
                    implementation_steps: vec![ImplementationStep {
                        step_number: 1,
                        action: "Switch to robust estimator".to_string(),
                        details: "Use median-based or M-estimators".to_string(),
                        validation: "Compare results with original method".to_string(),
                        fallback: None,
                    }],
                    success_probability: 0.7,
                    performance_impact: PerformanceImpact::Moderate,
                    risk_assessment: RiskAssessment {
                        overall_risk: RiskLevel::Low,
                        data_loss_risk: RiskLevel::VeryLow,
                        performance_degradation_risk: RiskLevel::Medium,
                        compatibility_risk: RiskLevel::Low,
                    },
                    code_example: None,
                });
            }
        }

        strategies
    }

    /// Assess performance impact
    fn assess_performance_impact(
        &self, &StatsError,
        context: &OperationContext,
    ) -> PerformanceAssessment {
        let baseline = PerformanceMetrics {
            execution_time: Duration::from_millis(100),
            memory_usage: context.data_characteristics.size_info.memory_footprint,
            cache_efficiency: 0.8,
            cpu_utilization: 0.6,
        };

        let mut strategy_performance = HashMap::new();
        strategy_performance.insert(
            "Regularization".to_string(),
            PerformanceMetrics {
                execution_time: Duration::from_millis(105),
                memory_usage: baseline.memory_usage,
                cache_efficiency: 0.8,
                cpu_utilization: 0.6,
            },
        );

        PerformanceAssessment {
            baseline_performance: baseline,
            strategy_performance,
            optimization_recommendations: vec![OptimizationRecommendation {
                recommendation: "Use SIMD operations for large datasets".to_string(),
                expected_improvement: 25.0,
                implementation_effort: EffortLevel::Low,
                compatibility_impact: CompatibilityImpact::None,
            }],
        }
    }

    /// Generate UX recommendations
    fn generate_ux_recommendations(
        &self,
        error: &StatsError, diagnostics: &IntelligentDiagnostics,
    ) -> UXRecommendations {
        UXRecommendations {
            message_improvements: vec![
                "Add data size information to error message".to_string(),
                "Include suggested parameter ranges".to_string(),
                "Provide link to troubleshooting guide".to_string(),
            ],
            workflow_suggestions: vec![
                "Validate input data before processing".to_string(),
                "Use exploratory data analysis first".to_string(),
                "Consider preprocessing pipeline".to_string(),
            ],
            documentation_refs: vec![DocumentationRef {
                title: "Input Validation Best Practices".to_string(),
                url: "https://docs.scirs2.rs/validation".to_string(),
                relevance_score: 0.9,
                section: Some("Data Preprocessing".to_string()),
            }],
            interactive_options: vec![
                InteractiveOption {
                    option_type: InteractiveType::AutoFix,
                    description: "Automatically apply recommended fix".to_string(),
                    action: "apply_regularization".to_string(),
                },
                InteractiveOption {
                    option_type: InteractiveType::DataValidation,
                    description: "Run comprehensive data validation".to_string(),
                    action: "validatedata_quality".to_string(),
                },
            ],
        }
    }
}

impl Default for ErrorEngineConfig {
    fn default() -> Self {
        Self {
            enable_deep_diagnostics: true,
            enable_performance_profiling: true,
            enable_learning: true,
            max_analysis_time: Duration::from_millis(100),
            cachesize_limit: 1000,
        }
    }
}

/// Convenience function to create enhanced error context
#[allow(dead_code)]
pub fn create_enhanced_error_context(
    error: StatsError,
    function_name: &str,
    module_path: &str,
    datasize: usize,
) -> AdvancedErrorContext {
    let mut engine = AdvancedErrorEngine::new(ErrorEngineConfig::default());

    let context = OperationContext {
        function_name: function_name.to_string(),
        module_path: module_path.to_string(),
        data_characteristics: DataCharacteristics {
            size_info: SizeInfo {
                dimensions: vec![datasize],
                total_elements: datasize,
                memory_footprint: datasize * 8,
                sparsity: None,
            },
            type_info: TypeInfo {
                primary_type: "f64".to_string(),
                precision: "64-bit".to_string(),
                signed: true,
                complex: false,
            },
            distribution_info: DistributionInfo {
                range: None,
                mean: None,
                variance: None,
                skewness: None,
                kurtosis: None,
                outlier_percentage: None,
            },
            quality_metrics: QualityMetrics {
                completeness: 1.0,
                consistency: 1.0,
                accuracy_estimate: 0.95,
                noise_level: 0.05,
            },
        },
        execution_environment: ExecutionEnvironment {
            cpu_info: CpuInfo {
                architecture: "x86_64".to_string(),
                core_count: num_cpus::get(),
                simd_support: vec!["AVX2".to_string(), "SSE4.2".to_string()],
                cachesizes: vec![32_768, 262_144, 8_388_608], // L1, L2, L3
            },
            memory_info: MemoryInfo {
                total_memory: 16_000_000_000,    // 16GB
                available_memory: 8_000_000_000, // 8GB
                memory_pressure: 0.3,
            },
            optimization_level: OptimizationLevel::Release,
            threading_info: ThreadingInfo {
                thread_count: num_cpus::get(),
                thread_affinity: None,
                numa_topology: None,
            },
            feature_flags: HashMap::new(),
        },
        timestamp: Instant::now(),
        stack_trace: None,
    };

    engine.enhance_error(error, context)
}

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

    #[test]
    fn test_error_enhancement_creation() {
        let error = StatsError::invalid_argument("Test error");
        let enhanced = create_enhanced_error_context(error, "test_function", "test_module", 100);

        assert_eq!(enhanced.operation_context.function_name, "test_function");
        assert_eq!(enhanced.operation_context.module_path, "test_module");
        assert_eq!(
            enhanced
                .operation_context
                .data_characteristics
                .size_info
                .total_elements,
            100
        );
        assert!(!enhanced.recovery_strategies.is_empty());
    }

    #[test]
    fn test_root_cause_analysis() {
        let engine = AdvancedErrorEngine::new(ErrorEngineConfig::default());
        let error = StatsError::invalid_argument("Array 'x' cannot be empty");
        let context = OperationContext {
            function_name: "mean".to_string(),
            module_path: "descriptive".to_string(),
            data_characteristics: DataCharacteristics {
                size_info: SizeInfo {
                    dimensions: vec![0],
                    total_elements: 0,
                    memory_footprint: 0,
                    sparsity: None,
                },
                type_info: TypeInfo {
                    primary_type: "f64".to_string(),
                    precision: "64-bit".to_string(),
                    signed: true,
                    complex: false,
                },
                distribution_info: DistributionInfo {
                    range: None,
                    mean: None,
                    variance: None,
                    skewness: None,
                    kurtosis: None,
                    outlier_percentage: None,
                },
                quality_metrics: QualityMetrics {
                    completeness: 0.0,
                    consistency: 1.0,
                    accuracy_estimate: 0.0,
                    noise_level: 0.0,
                },
            },
            execution_environment: ExecutionEnvironment {
                cpu_info: CpuInfo {
                    architecture: "x86_64".to_string(),
                    core_count: 4,
                    simd_support: vec!["AVX2".to_string()],
                    cachesizes: vec![32768],
                },
                memory_info: MemoryInfo {
                    total_memory: 8000000000,
                    available_memory: 4000000000,
                    memory_pressure: 0.2,
                },
                optimization_level: OptimizationLevel::Release,
                threading_info: ThreadingInfo {
                    thread_count: 4,
                    thread_affinity: None,
                    numa_topology: None,
                },
                feature_flags: HashMap::new(),
            },
            timestamp: Instant::now(),
            stack_trace: None,
        };

        let root_cause = engine.determine_root_cause(&error, &context);

        match root_cause {
            RootCause::DataIssue(DataIssueType::InsufficientData) => {
                // This is the expected root cause
            }
            _ => panic!("Expected InsufficientData root cause"),
        }
    }

    #[test]
    fn test_recovery_strategy_generation() {
        let mut engine = AdvancedErrorEngine::new(ErrorEngineConfig::default());
        let error = StatsError::invalid_argument("Array 'x' cannot be empty");
        let context = OperationContext {
            function_name: "mean".to_string(),
            module_path: "descriptive".to_string(),
            data_characteristics: DataCharacteristics {
                size_info: SizeInfo {
                    dimensions: vec![0],
                    total_elements: 0,
                    memory_footprint: 0,
                    sparsity: None,
                },
                type_info: TypeInfo {
                    primary_type: "f64".to_string(),
                    precision: "64-bit".to_string(),
                    signed: true,
                    complex: false,
                },
                distribution_info: DistributionInfo {
                    range: None,
                    mean: None,
                    variance: None,
                    skewness: None,
                    kurtosis: None,
                    outlier_percentage: None,
                },
                quality_metrics: QualityMetrics {
                    completeness: 0.0,
                    consistency: 1.0,
                    accuracy_estimate: 0.0,
                    noise_level: 0.0,
                },
            },
            execution_environment: ExecutionEnvironment {
                cpu_info: CpuInfo {
                    architecture: "x86_64".to_string(),
                    core_count: 4,
                    simd_support: vec!["AVX2".to_string()],
                    cachesizes: vec![32768],
                },
                memory_info: MemoryInfo {
                    total_memory: 8000000000,
                    available_memory: 4000000000,
                    memory_pressure: 0.2,
                },
                optimization_level: OptimizationLevel::Release,
                threading_info: ThreadingInfo {
                    thread_count: 4,
                    thread_affinity: None,
                    numa_topology: None,
                },
                feature_flags: HashMap::new(),
            },
            timestamp: Instant::now(),
            stack_trace: None,
        };

        let enhanced = engine.enhance_error(error, context);

        assert!(!enhanced.recovery_strategies.is_empty());
        assert!(enhanced
            .recovery_strategies
            .iter()
            .any(|s| s.name.contains("Data Augmentation")));
    }
}