scirs2_stats/
advanced_error_enhancements_v2.rs

1//! Advanced advanced Error Enhancement System
2//!
3//! This module provides next-generation error handling with intelligent diagnostics,
4//! context-aware recovery suggestions, and performance-optimized error reporting
5//! for production-grade statistical computing.
6
7use crate::error::StatsError;
8use crate::error_standardization::PerformanceImpact;
9use serde::{Deserialize, Serialize};
10use std::collections::HashMap;
11use std::time::{Duration, Instant};
12
13/// advanced Enhanced Error Context with Intelligent Diagnostics
14#[derive(Debug, Clone)]
15pub struct AdvancedErrorContext {
16    /// Core error information
17    pub error: StatsError,
18    /// Context where the error occurred
19    pub operation_context: OperationContext,
20    /// Intelligent diagnostics
21    pub diagnostics: IntelligentDiagnostics,
22    /// Recovery strategies
23    pub recovery_strategies: Vec<RecoveryStrategy>,
24    /// Performance impact assessment
25    pub performance_assessment: PerformanceAssessment,
26    /// User experience recommendations
27    pub ux_recommendations: UXRecommendations,
28}
29
30/// Comprehensive operation context
31#[derive(Debug, Clone)]
32pub struct OperationContext {
33    /// Function name where error occurred
34    pub function_name: String,
35    /// Module path
36    pub module_path: String,
37    /// Input data characteristics
38    pub data_characteristics: DataCharacteristics,
39    /// Execution environment
40    pub execution_environment: ExecutionEnvironment,
41    /// Timestamp of error
42    pub timestamp: Instant,
43    /// Stack trace information (if available)
44    pub stack_trace: Option<String>,
45}
46
47/// Intelligent error diagnostics
48#[derive(Debug, Clone)]
49pub struct IntelligentDiagnostics {
50    /// Root cause analysis
51    pub root_cause: RootCause,
52    /// Probable causes ranked by likelihood
53    pub probable_causes: Vec<(String, f64)>, // (cause, probability)
54    /// Related errors that often occur together
55    pub related_errors: Vec<String>,
56    /// Data quality assessment
57    pub data_quality: DataQuality,
58    /// Computational complexity analysis
59    pub complexity_analysis: ComplexityAnalysis,
60}
61
62/// Root cause classification
63#[derive(Debug, Clone)]
64pub enum RootCause {
65    /// Input data issues
66    DataIssue(DataIssueType),
67    /// Algorithmic limitations
68    AlgorithmicLimitation(AlgorithmIssue),
69    /// Numerical instability
70    NumericalInstability(NumericalIssue),
71    /// Resource constraints
72    ResourceConstraint(ResourceIssue),
73    /// Configuration problems
74    ConfigurationIssue(ConfigIssue),
75    /// Unknown or complex interaction
76    Unknown,
77}
78
79/// Types of data issues
80#[derive(Debug, Clone)]
81pub enum DataIssueType {
82    InsufficientData,
83    CorruptedData,
84    OutOfRange,
85    MissingValues,
86    DimensionalityMismatch,
87    NumericalPrecision,
88}
89
90/// Algorithmic issues
91#[derive(Debug, Clone)]
92pub enum AlgorithmIssue {
93    ConvergenceFailure,
94    NonApplicableDomain,
95    ScalabilityLimit,
96    PreconditionViolation,
97}
98
99/// Numerical computation issues
100#[derive(Debug, Clone)]
101pub enum NumericalIssue {
102    Overflow,
103    Underflow,
104    LossOfPrecision,
105    IllConditioned,
106    RoundoffError,
107}
108
109/// Resource constraint issues
110#[derive(Debug, Clone)]
111pub enum ResourceIssue {
112    InsufficientMemory,
113    ComputationalTimeout,
114    ThreadLimitExceeded,
115    DiskSpaceExhausted,
116}
117
118/// Configuration issues
119#[derive(Debug, Clone)]
120pub enum ConfigIssue {
121    InvalidParameter,
122    ConflictingSettings,
123    MissingDependency,
124    VersionMismatch,
125}
126
127/// Data characteristics analysis
128#[derive(Debug, Clone)]
129pub struct DataCharacteristics {
130    /// Size information
131    pub size_info: SizeInfo,
132    /// Data type information
133    pub type_info: TypeInfo,
134    /// Distribution characteristics
135    pub distribution_info: DistributionInfo,
136    /// Quality metrics
137    pub quality_metrics: QualityMetrics,
138}
139
140/// Size information
141#[derive(Debug, Clone)]
142pub struct SizeInfo {
143    pub dimensions: Vec<usize>,
144    pub total_elements: usize,
145    pub memory_footprint: usize, // in bytes
146    pub sparsity: Option<f64>,   // if applicable
147}
148
149/// Type information
150#[derive(Debug, Clone)]
151pub struct TypeInfo {
152    pub primary_type: String,
153    pub precision: String, // f32, f64, etc.
154    pub signed: bool,
155    pub complex: bool,
156}
157
158/// Distribution characteristics
159#[derive(Debug, Clone)]
160pub struct DistributionInfo {
161    pub range: Option<(f64, f64)>,
162    pub mean: Option<f64>,
163    pub variance: Option<f64>,
164    pub skewness: Option<f64>,
165    pub kurtosis: Option<f64>,
166    pub outlier_percentage: Option<f64>,
167}
168
169/// Quality metrics
170#[derive(Debug, Clone)]
171pub struct QualityMetrics {
172    pub completeness: f64,      // percentage of non-missing values
173    pub consistency: f64,       // consistency score
174    pub accuracy_estimate: f64, // estimated accuracy
175    pub noise_level: f64,       // estimated noise level
176}
177
178/// Execution environment information
179#[derive(Debug, Clone)]
180pub struct ExecutionEnvironment {
181    pub cpu_info: CpuInfo,
182    pub memory_info: MemoryInfo,
183    pub optimization_level: OptimizationLevel,
184    pub threading_info: ThreadingInfo,
185    pub feature_flags: HashMap<String, bool>,
186}
187
188/// CPU information
189#[derive(Debug, Clone)]
190pub struct CpuInfo {
191    pub architecture: String,
192    pub core_count: usize,
193    pub simd_support: Vec<String>,
194    pub cachesizes: Vec<usize>,
195}
196
197/// Memory information
198#[derive(Debug, Clone)]
199pub struct MemoryInfo {
200    pub total_memory: usize,
201    pub available_memory: usize,
202    pub memory_pressure: f64, // 0.0 to 1.0
203}
204
205/// Optimization level
206#[derive(Debug, Clone)]
207pub enum OptimizationLevel {
208    Debug,
209    Release,
210    Optimized,
211    AdvancedOptimized,
212}
213
214/// Threading information
215#[derive(Debug, Clone)]
216pub struct ThreadingInfo {
217    pub thread_count: usize,
218    pub thread_affinity: Option<Vec<usize>>,
219    pub numa_topology: Option<String>,
220}
221
222/// Data quality assessment
223#[derive(Debug, Clone)]
224pub struct DataQuality {
225    pub overall_score: f64, // 0.0 to 1.0
226    pub issues: Vec<DataQualityIssue>,
227    pub recommendations: Vec<String>,
228}
229
230/// Data quality issues
231#[derive(Debug, Clone)]
232pub struct DataQualityIssue {
233    pub issue_type: String,
234    pub severity: QualitySeverity,
235    pub affected_percentage: f64,
236    pub description: String,
237    pub remedy: String,
238}
239
240/// Quality issue severity
241#[derive(Debug, Clone)]
242pub enum QualitySeverity {
243    Low,
244    Medium,
245    High,
246    Critical,
247}
248
249/// Computational complexity analysis
250#[derive(Debug, Clone)]
251pub struct ComplexityAnalysis {
252    pub time_complexity: String,
253    pub space_complexity: String,
254    pub scalability_assessment: ScalabilityAssessment,
255    pub bottleneck_analysis: Vec<BottleneckInfo>,
256}
257
258/// Scalability assessment
259#[derive(Debug, Clone)]
260pub struct ScalabilityAssessment {
261    pub current_performance: PerformanceMetrics,
262    pub predicted_performance: Vec<(usize, PerformanceMetrics)>, // (size, metrics)
263    pub scaling_factor: f64,
264    pub recommended_maxsize: Option<usize>,
265}
266
267/// Performance metrics
268#[derive(Debug, Clone)]
269pub struct PerformanceMetrics {
270    pub execution_time: Duration,
271    pub memory_usage: usize,
272    pub cache_efficiency: f64,
273    pub cpu_utilization: f64,
274}
275
276/// Bottleneck information
277#[derive(Debug, Clone)]
278pub struct BottleneckInfo {
279    pub component: String,
280    pub impact_factor: f64,          // multiplier for performance impact
281    pub optimization_potential: f64, // 0.0 to 1.0
282    pub recommended_action: String,
283}
284
285/// Enhanced recovery strategy
286#[derive(Debug, Clone)]
287pub struct RecoveryStrategy {
288    /// Strategy name
289    pub name: String,
290    /// Detailed description
291    pub description: String,
292    /// Implementation steps
293    pub implementation_steps: Vec<ImplementationStep>,
294    /// Expected success probability
295    pub success_probability: f64,
296    /// Performance impact
297    pub performance_impact: PerformanceImpact,
298    /// Risk assessment
299    pub risk_assessment: RiskAssessment,
300    /// Code example (if applicable)
301    pub code_example: Option<String>,
302}
303
304/// Implementation step
305#[derive(Debug, Clone)]
306pub struct ImplementationStep {
307    pub step_number: usize,
308    pub action: String,
309    pub details: String,
310    pub validation: String,
311    pub fallback: Option<String>,
312}
313
314/// Risk assessment for recovery strategy
315#[derive(Debug, Clone)]
316pub struct RiskAssessment {
317    pub overall_risk: RiskLevel,
318    pub data_loss_risk: RiskLevel,
319    pub performance_degradation_risk: RiskLevel,
320    pub compatibility_risk: RiskLevel,
321}
322
323/// Risk levels
324#[derive(Debug, Clone)]
325pub enum RiskLevel {
326    VeryLow,
327    Low,
328    Medium,
329    High,
330    VeryHigh,
331}
332
333/// Performance impact assessment
334#[derive(Debug, Clone)]
335pub struct PerformanceAssessment {
336    /// Current operation performance
337    pub baseline_performance: PerformanceMetrics,
338    /// Performance with different recovery strategies
339    pub strategy_performance: HashMap<String, PerformanceMetrics>,
340    /// Optimization recommendations
341    pub optimization_recommendations: Vec<OptimizationRecommendation>,
342}
343
344/// Optimization recommendation
345#[derive(Debug, Clone)]
346pub struct OptimizationRecommendation {
347    pub recommendation: String,
348    pub expected_improvement: f64, // percentage improvement
349    pub implementation_effort: EffortLevel,
350    pub compatibility_impact: CompatibilityImpact,
351}
352
353/// Implementation effort levels
354#[derive(Debug, Clone)]
355pub enum EffortLevel {
356    Trivial,  // < 1 hour
357    Low,      // 1-4 hours
358    Medium,   // 1-2 days
359    High,     // 3-7 days
360    VeryHigh, // > 1 week
361}
362
363/// Compatibility impact
364#[derive(Debug, Clone, Serialize, Deserialize)]
365pub enum CompatibilityImpact {
366    None,
367    Minor,
368    Moderate,
369    Major,
370    Breaking,
371}
372
373/// User experience recommendations
374#[derive(Debug, Clone)]
375pub struct UXRecommendations {
376    /// Error message improvements
377    pub message_improvements: Vec<String>,
378    /// User workflow suggestions
379    pub workflow_suggestions: Vec<String>,
380    /// Documentation references
381    pub documentation_refs: Vec<DocumentationRef>,
382    /// Interactive assistance options
383    pub interactive_options: Vec<InteractiveOption>,
384}
385
386/// Documentation reference
387#[derive(Debug, Clone)]
388pub struct DocumentationRef {
389    pub title: String,
390    pub url: String,
391    pub relevance_score: f64,
392    pub section: Option<String>,
393}
394
395/// Interactive assistance option
396#[derive(Debug, Clone)]
397pub struct InteractiveOption {
398    pub option_type: InteractiveType,
399    pub description: String,
400    pub action: String,
401}
402
403/// Types of interactive assistance
404#[derive(Debug, Clone)]
405pub enum InteractiveType {
406    AutoFix,
407    GuidedTutorial,
408    ParameterWizard,
409    DataValidation,
410    AlternativeMethod,
411}
412
413/// advanced Error Enhancement Engine
414pub struct AdvancedErrorEngine {
415    /// Configuration for error analysis
416    config: ErrorEngineConfig,
417    /// Error history for learning
418    error_history: Vec<AdvancedErrorContext>,
419    /// Performance metrics cache
420    performance_cache: HashMap<String, PerformanceMetrics>,
421    /// Recovery success statistics
422    recovery_stats: HashMap<String, RecoveryStatistics>,
423}
424
425/// Configuration for error engine
426#[derive(Debug, Clone)]
427pub struct ErrorEngineConfig {
428    /// Enable deep diagnostics
429    pub enable_deep_diagnostics: bool,
430    /// Enable performance profiling
431    pub enable_performance_profiling: bool,
432    /// Enable learning from errors
433    pub enable_learning: bool,
434    /// Maximum analysis time
435    pub max_analysis_time: Duration,
436    /// Cache size limit
437    pub cachesize_limit: usize,
438}
439
440/// Recovery statistics
441#[derive(Debug, Clone)]
442pub struct RecoveryStatistics {
443    pub attempts: usize,
444    pub successes: usize,
445    pub average_performance_impact: f64,
446    pub user_satisfaction: f64,
447}
448
449impl AdvancedErrorEngine {
450    /// Create new error enhancement engine
451    pub fn new(config: ErrorEngineConfig) -> Self {
452        Self {
453            config,
454            error_history: Vec::new(),
455            performance_cache: HashMap::new(),
456            recovery_stats: HashMap::new(),
457        }
458    }
459
460    /// Enhance error with advanced analysis
461    pub fn enhance_error(
462        &mut self,
463        error: StatsError,
464        context: OperationContext,
465    ) -> AdvancedErrorContext {
466        let start_time = Instant::now();
467
468        // Perform intelligent diagnostics
469        let diagnostics = self.analyze_error(&error, &context);
470
471        // Generate recovery strategies
472        let recovery_strategies = self.generate_recovery_strategies(&error, &diagnostics);
473
474        // Assess performance impact
475        let performance_assessment = self.assess_performance_impact(&error, &context);
476
477        // Generate UX recommendations
478        let ux_recommendations = self.generate_ux_recommendations(&error, &diagnostics);
479
480        let enhanced_context = AdvancedErrorContext {
481            error,
482            operation_context: context,
483            diagnostics,
484            recovery_strategies,
485            performance_assessment,
486            ux_recommendations,
487        };
488
489        // Store for learning if enabled
490        if self.config.enable_learning {
491            self.error_history.push(enhanced_context.clone());
492
493            // Limit history size
494            if self.error_history.len() > self.config.cachesize_limit {
495                self.error_history.remove(0);
496            }
497        }
498
499        enhanced_context
500    }
501
502    /// Analyze error using intelligent diagnostics
503    fn analyze_error(
504        &self,
505        error: &StatsError,
506        context: &OperationContext,
507    ) -> IntelligentDiagnostics {
508        // Determine root cause
509        let root_cause = self.determine_root_cause(error, context);
510
511        // Calculate probable causes
512        let probable_causes = self.calculate_probable_causes(error, context);
513
514        // Find related errors
515        let related_errors = self.find_related_errors(error);
516
517        // Assess data quality
518        let data_quality = self.assessdata_quality(&context.data_characteristics);
519
520        // Analyze computational complexity
521        let complexity_analysis = self.analyze_complexity(context);
522
523        IntelligentDiagnostics {
524            root_cause,
525            probable_causes,
526            related_errors,
527            data_quality,
528            complexity_analysis,
529        }
530    }
531
532    /// Determine root cause of error
533    fn determine_root_cause(&self, error: &StatsError, context: &OperationContext) -> RootCause {
534        match error {
535            StatsError::InvalidArgument(msg) if msg.contains("empty") => {
536                RootCause::DataIssue(DataIssueType::InsufficientData)
537            }
538            StatsError::InvalidArgument(msg) if msg.contains("NaN") => {
539                RootCause::DataIssue(DataIssueType::MissingValues)
540            }
541            StatsError::DimensionMismatch(_) => {
542                RootCause::DataIssue(DataIssueType::DimensionalityMismatch)
543            }
544            StatsError::ComputationError(msg) if msg.contains("singular") => {
545                RootCause::NumericalInstability(NumericalIssue::IllConditioned)
546            }
547            StatsError::ConvergenceError(_) => {
548                RootCause::AlgorithmicLimitation(AlgorithmIssue::ConvergenceFailure)
549            }
550            StatsError::DomainError(_) => {
551                RootCause::AlgorithmicLimitation(AlgorithmIssue::NonApplicableDomain)
552            }
553            _ => RootCause::Unknown,
554        }
555    }
556
557    /// Calculate probable causes with probabilities
558    fn calculate_probable_causes(
559        &self,
560        error: &StatsError,
561        context: &OperationContext,
562    ) -> Vec<(String, f64)> {
563        let mut causes = Vec::new();
564
565        // Base analysis on error type and context
566        match error {
567            StatsError::InvalidArgument(msg) if msg.contains("empty") => {
568                causes.push(("Insufficient input data".to_string(), 0.9));
569                causes.push((
570                    "Data preprocessing removed all valid points".to_string(),
571                    0.7,
572                ));
573                causes.push(("Incorrect data loading".to_string(), 0.5));
574            }
575            StatsError::ComputationError(_) => {
576                causes.push(("Numerical instability".to_string(), 0.8));
577                causes.push(("Matrix conditioning issues".to_string(), 0.6));
578                causes.push(("Overflow/underflow in computation".to_string(), 0.4));
579            }
580            _ => {
581                causes.push(("Input validation failure".to_string(), 0.6));
582                causes.push(("Unexpected data characteristics".to_string(), 0.4));
583            }
584        }
585
586        // Adjust probabilities based on data characteristics
587        if context.data_characteristics.size_info.total_elements == 0 {
588            if let Some(pos) = causes
589                .iter()
590                .position(|(cause_, _)| cause_.contains("Insufficient"))
591            {
592                causes[pos].1 = 0.95;
593            }
594        }
595
596        causes.sort_by(|a, b| b.1.partial_cmp(&a.1).expect("Operation failed"));
597        causes
598    }
599
600    /// Find related errors that often occur together
601    fn find_related_errors(&self, _error: &StatsError) -> Vec<String> {
602        // In a real implementation, this would use machine learning
603        // to find patterns in _error co-occurrence
604        vec![
605            "DimensionMismatch often precedes this _error".to_string(),
606            "Consider checking for NaN values".to_string(),
607            "Verify input data preprocessing".to_string(),
608        ]
609    }
610
611    /// Assess data quality
612    fn assessdata_quality(&self, characteristics: &DataCharacteristics) -> DataQuality {
613        let mut overall_score = 1.0;
614        let mut issues = Vec::new();
615
616        // Check completeness
617        if characteristics.quality_metrics.completeness < 0.95 {
618            overall_score *= 0.9;
619            issues.push(DataQualityIssue {
620                issue_type: "Missing Data".to_string(),
621                severity: if characteristics.quality_metrics.completeness < 0.8 {
622                    QualitySeverity::High
623                } else {
624                    QualitySeverity::Medium
625                },
626                affected_percentage: (1.0 - characteristics.quality_metrics.completeness) * 100.0,
627                description: "Dataset contains missing values".to_string(),
628                remedy: "Consider imputation or removal of incomplete records".to_string(),
629            });
630        }
631
632        // Check for outliers
633        if let Some(outlier_pct) = characteristics.distribution_info.outlier_percentage {
634            if outlier_pct > 5.0 {
635                overall_score *= 0.95;
636                issues.push(DataQualityIssue {
637                    issue_type: "Outliers".to_string(),
638                    severity: if outlier_pct > 15.0 {
639                        QualitySeverity::High
640                    } else {
641                        QualitySeverity::Medium
642                    },
643                    affected_percentage: outlier_pct,
644                    description: format!("Dataset contains {}% outliers", outlier_pct),
645                    remedy: "Consider outlier detection and treatment".to_string(),
646                });
647            }
648        }
649
650        DataQuality {
651            overall_score,
652            issues,
653            recommendations: vec![
654                "Perform exploratory data analysis".to_string(),
655                "Validate data preprocessing pipeline".to_string(),
656                "Consider robust statistical methods".to_string(),
657            ],
658        }
659    }
660
661    /// Analyze computational complexity
662    fn analyze_complexity(&self, context: &OperationContext) -> ComplexityAnalysis {
663        let size = context.data_characteristics.size_info.total_elements;
664
665        // Estimate complexity based on operation and data size
666        let (time_complexity, space_complexity) = match context.function_name.as_str() {
667            name if name.contains("sort") => ("O(n log n)".to_string(), "O(n)".to_string()),
668            name if name.contains("corr") => ("O(n)".to_string(), "O(1)".to_string()),
669            name if name.contains("matrix") => ("O(n³)".to_string(), "O(n²)".to_string()),
670            _ => ("O(n)".to_string(), "O(1)".to_string()),
671        };
672
673        ComplexityAnalysis {
674            time_complexity,
675            space_complexity,
676            scalability_assessment: ScalabilityAssessment {
677                current_performance: PerformanceMetrics {
678                    execution_time: Duration::from_micros(size as u64),
679                    memory_usage: size * 8, // Assume 8 bytes per element
680                    cache_efficiency: 0.8,
681                    cpu_utilization: 0.6,
682                },
683                predicted_performance: vec![(
684                    size * 10,
685                    PerformanceMetrics {
686                        execution_time: Duration::from_micros(size as u64 * 10),
687                        memory_usage: size * 80,
688                        cache_efficiency: 0.7,
689                        cpu_utilization: 0.8,
690                    },
691                )],
692                scaling_factor: 1.0,
693                recommended_maxsize: Some(1_000_000),
694            },
695            bottleneck_analysis: vec![BottleneckInfo {
696                component: "Memory allocation".to_string(),
697                impact_factor: 1.5,
698                optimization_potential: 0.3,
699                recommended_action: "Use memory pools or streaming algorithms".to_string(),
700            }],
701        }
702    }
703
704    /// Generate recovery strategies
705    fn generate_recovery_strategies(
706        &self,
707        error: &StatsError,
708        diagnostics: &IntelligentDiagnostics,
709    ) -> Vec<RecoveryStrategy> {
710        let mut strategies = Vec::new();
711
712        match &diagnostics.root_cause {
713            RootCause::DataIssue(DataIssueType::InsufficientData) => {
714                strategies.push(RecoveryStrategy {
715                    name: "Data Augmentation".to_string(),
716                    description: "Increase dataset size through augmentation techniques"
717                        .to_string(),
718                    implementation_steps: vec![
719                        ImplementationStep {
720                            step_number: 1,
721                            action: "Collect additional data".to_string(),
722                            details: "Gather more samples from the same population".to_string(),
723                            validation: "Verify new data follows same distribution".to_string(),
724                            fallback: Some("Use synthetic data generation".to_string()),
725                        },
726                        ImplementationStep {
727                            step_number: 2,
728                            action: "Validate data quality".to_string(),
729                            details: "Ensure augmented data maintains statistical properties"
730                                .to_string(),
731                            validation: "Run statistical tests for consistency".to_string(),
732                            fallback: None,
733                        },
734                    ],
735                    success_probability: 0.8,
736                    performance_impact: PerformanceImpact::Moderate,
737                    risk_assessment: RiskAssessment {
738                        overall_risk: RiskLevel::Low,
739                        data_loss_risk: RiskLevel::VeryLow,
740                        performance_degradation_risk: RiskLevel::Medium,
741                        compatibility_risk: RiskLevel::VeryLow,
742                    },
743                    code_example: Some(
744                        r#"
745// Example: Bootstrap resampling for data augmentation
746use scirs2_core::ndarray::Array1;
747use scirs2_core::random::{rng, seq::SliceRandom};
748
749#[allow(dead_code)]
750fn bootstrap_augment(data: &Array1<f64>, targetsize: usize) -> Array1<f64> {
751    let mut rng = scirs2_core::random::thread_rng();
752    let mut augmented = Vec::with_capacity(targetsize);
753    
754    for _ in 0..targetsize {
755        let sample = data.as_slice().expect("Operation failed").choose(&mut rng).expect("Operation failed");
756        augmented.push(*sample);
757    }
758    
759    Array1::from(augmented)
760}
761"#
762                        .to_string(),
763                    ),
764                });
765            }
766            RootCause::NumericalInstability(_) => {
767                strategies.push(RecoveryStrategy {
768                    name: "Regularization".to_string(),
769                    description: "Add regularization to improve numerical stability".to_string(),
770                    implementation_steps: vec![ImplementationStep {
771                        step_number: 1,
772                        action: "Add ridge regularization".to_string(),
773                        details: "Include L2 penalty term in computation".to_string(),
774                        validation: "Check condition number improvement".to_string(),
775                        fallback: Some("Use higher precision arithmetic".to_string()),
776                    }],
777                    success_probability: 0.9,
778                    performance_impact: PerformanceImpact::Minimal,
779                    risk_assessment: RiskAssessment {
780                        overall_risk: RiskLevel::VeryLow,
781                        data_loss_risk: RiskLevel::VeryLow,
782                        performance_degradation_risk: RiskLevel::Low,
783                        compatibility_risk: RiskLevel::VeryLow,
784                    },
785                    code_example: Some(
786                        r#"
787// Example: Ridge regularization for matrix operations
788#[allow(dead_code)]
789fn add_ridge_regularization(matrix: &mut Array2<f64>, lambda: f64) {
790    for i in 0.._matrix.nrows().min(_matrix.ncols()) {
791        matrix[[i, i]] += lambda;
792    }
793}
794"#
795                        .to_string(),
796                    ),
797                });
798            }
799            _ => {
800                strategies.push(RecoveryStrategy {
801                    name: "Robust Alternative".to_string(),
802                    description: "Use robust statistical methods less sensitive to outliers"
803                        .to_string(),
804                    implementation_steps: vec![ImplementationStep {
805                        step_number: 1,
806                        action: "Switch to robust estimator".to_string(),
807                        details: "Use median-based or M-estimators".to_string(),
808                        validation: "Compare results with original method".to_string(),
809                        fallback: None,
810                    }],
811                    success_probability: 0.7,
812                    performance_impact: PerformanceImpact::Moderate,
813                    risk_assessment: RiskAssessment {
814                        overall_risk: RiskLevel::Low,
815                        data_loss_risk: RiskLevel::VeryLow,
816                        performance_degradation_risk: RiskLevel::Medium,
817                        compatibility_risk: RiskLevel::Low,
818                    },
819                    code_example: None,
820                });
821            }
822        }
823
824        strategies
825    }
826
827    /// Assess performance impact
828    fn assess_performance_impact(
829        &self,
830        _error: &StatsError,
831        context: &OperationContext,
832    ) -> PerformanceAssessment {
833        let baseline = PerformanceMetrics {
834            execution_time: Duration::from_millis(100),
835            memory_usage: context.data_characteristics.size_info.memory_footprint,
836            cache_efficiency: 0.8,
837            cpu_utilization: 0.6,
838        };
839
840        let mut strategy_performance = HashMap::new();
841        strategy_performance.insert(
842            "Regularization".to_string(),
843            PerformanceMetrics {
844                execution_time: Duration::from_millis(105),
845                memory_usage: baseline.memory_usage,
846                cache_efficiency: 0.8,
847                cpu_utilization: 0.6,
848            },
849        );
850
851        PerformanceAssessment {
852            baseline_performance: baseline,
853            strategy_performance,
854            optimization_recommendations: vec![OptimizationRecommendation {
855                recommendation: "Use SIMD operations for large datasets".to_string(),
856                expected_improvement: 25.0,
857                implementation_effort: EffortLevel::Low,
858                compatibility_impact: CompatibilityImpact::None,
859            }],
860        }
861    }
862
863    /// Generate UX recommendations
864    fn generate_ux_recommendations(
865        &self,
866        error: &StatsError,
867        diagnostics: &IntelligentDiagnostics,
868    ) -> UXRecommendations {
869        UXRecommendations {
870            message_improvements: vec![
871                "Add data size information to error message".to_string(),
872                "Include suggested parameter ranges".to_string(),
873                "Provide link to troubleshooting guide".to_string(),
874            ],
875            workflow_suggestions: vec![
876                "Validate input data before processing".to_string(),
877                "Use exploratory data analysis first".to_string(),
878                "Consider preprocessing pipeline".to_string(),
879            ],
880            documentation_refs: vec![DocumentationRef {
881                title: "Input Validation Best Practices".to_string(),
882                url: "https://docs.scirs2.rs/validation".to_string(),
883                relevance_score: 0.9,
884                section: Some("Data Preprocessing".to_string()),
885            }],
886            interactive_options: vec![
887                InteractiveOption {
888                    option_type: InteractiveType::AutoFix,
889                    description: "Automatically apply recommended fix".to_string(),
890                    action: "apply_regularization".to_string(),
891                },
892                InteractiveOption {
893                    option_type: InteractiveType::DataValidation,
894                    description: "Run comprehensive data validation".to_string(),
895                    action: "validatedata_quality".to_string(),
896                },
897            ],
898        }
899    }
900}
901
902impl Default for ErrorEngineConfig {
903    fn default() -> Self {
904        Self {
905            enable_deep_diagnostics: true,
906            enable_performance_profiling: true,
907            enable_learning: true,
908            max_analysis_time: Duration::from_millis(100),
909            cachesize_limit: 1000,
910        }
911    }
912}
913
914/// Convenience function to create enhanced error context
915#[allow(dead_code)]
916pub fn create_enhanced_error_context(
917    error: StatsError,
918    function_name: &str,
919    module_path: &str,
920    datasize: usize,
921) -> AdvancedErrorContext {
922    let mut engine = AdvancedErrorEngine::new(ErrorEngineConfig::default());
923
924    let context = OperationContext {
925        function_name: function_name.to_string(),
926        module_path: module_path.to_string(),
927        data_characteristics: DataCharacteristics {
928            size_info: SizeInfo {
929                dimensions: vec![datasize],
930                total_elements: datasize,
931                memory_footprint: datasize * 8,
932                sparsity: None,
933            },
934            type_info: TypeInfo {
935                primary_type: "f64".to_string(),
936                precision: "64-bit".to_string(),
937                signed: true,
938                complex: false,
939            },
940            distribution_info: DistributionInfo {
941                range: None,
942                mean: None,
943                variance: None,
944                skewness: None,
945                kurtosis: None,
946                outlier_percentage: None,
947            },
948            quality_metrics: QualityMetrics {
949                completeness: 1.0,
950                consistency: 1.0,
951                accuracy_estimate: 0.95,
952                noise_level: 0.05,
953            },
954        },
955        execution_environment: ExecutionEnvironment {
956            cpu_info: CpuInfo {
957                architecture: "x86_64".to_string(),
958                core_count: num_cpus::get(),
959                simd_support: vec!["AVX2".to_string(), "SSE4.2".to_string()],
960                cachesizes: vec![32_768, 262_144, 8_388_608], // L1, L2, L3
961            },
962            memory_info: MemoryInfo {
963                total_memory: 16_000_000_000u64 as usize, // 16GB (saturates on 32-bit)
964                available_memory: 8_000_000_000u64 as usize, // 8GB (saturates on 32-bit)
965                memory_pressure: 0.3,
966            },
967            optimization_level: OptimizationLevel::Release,
968            threading_info: ThreadingInfo {
969                thread_count: num_cpus::get(),
970                thread_affinity: None,
971                numa_topology: None,
972            },
973            feature_flags: HashMap::new(),
974        },
975        timestamp: Instant::now(),
976        stack_trace: None,
977    };
978
979    engine.enhance_error(error, context)
980}
981
982#[cfg(test)]
983mod tests {
984    use super::*;
985
986    #[test]
987    fn test_error_enhancement_creation() {
988        let error = StatsError::invalid_argument("Test error");
989        let enhanced = create_enhanced_error_context(error, "test_function", "test_module", 100);
990
991        assert_eq!(enhanced.operation_context.function_name, "test_function");
992        assert_eq!(enhanced.operation_context.module_path, "test_module");
993        assert_eq!(
994            enhanced
995                .operation_context
996                .data_characteristics
997                .size_info
998                .total_elements,
999            100
1000        );
1001        assert!(!enhanced.recovery_strategies.is_empty());
1002    }
1003
1004    #[test]
1005    fn test_root_cause_analysis() {
1006        let engine = AdvancedErrorEngine::new(ErrorEngineConfig::default());
1007        let error = StatsError::invalid_argument("Array 'x' cannot be empty");
1008        let context = OperationContext {
1009            function_name: "mean".to_string(),
1010            module_path: "descriptive".to_string(),
1011            data_characteristics: DataCharacteristics {
1012                size_info: SizeInfo {
1013                    dimensions: vec![0],
1014                    total_elements: 0,
1015                    memory_footprint: 0,
1016                    sparsity: None,
1017                },
1018                type_info: TypeInfo {
1019                    primary_type: "f64".to_string(),
1020                    precision: "64-bit".to_string(),
1021                    signed: true,
1022                    complex: false,
1023                },
1024                distribution_info: DistributionInfo {
1025                    range: None,
1026                    mean: None,
1027                    variance: None,
1028                    skewness: None,
1029                    kurtosis: None,
1030                    outlier_percentage: None,
1031                },
1032                quality_metrics: QualityMetrics {
1033                    completeness: 0.0,
1034                    consistency: 1.0,
1035                    accuracy_estimate: 0.0,
1036                    noise_level: 0.0,
1037                },
1038            },
1039            execution_environment: ExecutionEnvironment {
1040                cpu_info: CpuInfo {
1041                    architecture: "x86_64".to_string(),
1042                    core_count: 4,
1043                    simd_support: vec!["AVX2".to_string()],
1044                    cachesizes: vec![32768],
1045                },
1046                memory_info: MemoryInfo {
1047                    total_memory: 8000000000,
1048                    available_memory: 4000000000,
1049                    memory_pressure: 0.2,
1050                },
1051                optimization_level: OptimizationLevel::Release,
1052                threading_info: ThreadingInfo {
1053                    thread_count: 4,
1054                    thread_affinity: None,
1055                    numa_topology: None,
1056                },
1057                feature_flags: HashMap::new(),
1058            },
1059            timestamp: Instant::now(),
1060            stack_trace: None,
1061        };
1062
1063        let root_cause = engine.determine_root_cause(&error, &context);
1064
1065        match root_cause {
1066            RootCause::DataIssue(DataIssueType::InsufficientData) => {
1067                // This is the expected root cause
1068            }
1069            _ => panic!("Expected InsufficientData root cause"),
1070        }
1071    }
1072
1073    #[test]
1074    fn test_recovery_strategy_generation() {
1075        let mut engine = AdvancedErrorEngine::new(ErrorEngineConfig::default());
1076        let error = StatsError::invalid_argument("Array 'x' cannot be empty");
1077        let context = OperationContext {
1078            function_name: "mean".to_string(),
1079            module_path: "descriptive".to_string(),
1080            data_characteristics: DataCharacteristics {
1081                size_info: SizeInfo {
1082                    dimensions: vec![0],
1083                    total_elements: 0,
1084                    memory_footprint: 0,
1085                    sparsity: None,
1086                },
1087                type_info: TypeInfo {
1088                    primary_type: "f64".to_string(),
1089                    precision: "64-bit".to_string(),
1090                    signed: true,
1091                    complex: false,
1092                },
1093                distribution_info: DistributionInfo {
1094                    range: None,
1095                    mean: None,
1096                    variance: None,
1097                    skewness: None,
1098                    kurtosis: None,
1099                    outlier_percentage: None,
1100                },
1101                quality_metrics: QualityMetrics {
1102                    completeness: 0.0,
1103                    consistency: 1.0,
1104                    accuracy_estimate: 0.0,
1105                    noise_level: 0.0,
1106                },
1107            },
1108            execution_environment: ExecutionEnvironment {
1109                cpu_info: CpuInfo {
1110                    architecture: "x86_64".to_string(),
1111                    core_count: 4,
1112                    simd_support: vec!["AVX2".to_string()],
1113                    cachesizes: vec![32768],
1114                },
1115                memory_info: MemoryInfo {
1116                    total_memory: 8000000000,
1117                    available_memory: 4000000000,
1118                    memory_pressure: 0.2,
1119                },
1120                optimization_level: OptimizationLevel::Release,
1121                threading_info: ThreadingInfo {
1122                    thread_count: 4,
1123                    thread_affinity: None,
1124                    numa_topology: None,
1125                },
1126                feature_flags: HashMap::new(),
1127            },
1128            timestamp: Instant::now(),
1129            stack_trace: None,
1130        };
1131
1132        let enhanced = engine.enhance_error(error, context);
1133
1134        assert!(!enhanced.recovery_strategies.is_empty());
1135        assert!(enhanced
1136            .recovery_strategies
1137            .iter()
1138            .any(|s| s.name.contains("Data Augmentation")));
1139    }
1140}
scirs2_stats/advanced_error_enhancements_v2.rs

scirs2_stats/
advanced_error_enhancements_v2.rs
