rustorch 0.6.29

//! Comprehensive Data Validation & Quality Assurance Tests
//! データ検証・品質保証包括的テスト

use std::time::Duration;

// Basic validation framework tests
#[cfg(test)]
mod basic_validation_tests {
    use super::*;
    use std::collections::HashMap;

    /// Test basic data quality assessment concept
    #[test]
    fn test_basic_quality_assessment() {
        // Simulate quality dimensions and scores
        let mut dimensions = HashMap::new();
        dimensions.insert("completeness", 0.95);
        dimensions.insert("accuracy", 0.88);
        dimensions.insert("consistency", 0.92);

        let overall_score = dimensions.values().sum::<f64>() / dimensions.len() as f64;

        assert!(overall_score > 0.8);
        assert!(overall_score < 1.0);

        println!("Quality assessment: overall score = {:.3}", overall_score);
    }

    /// Test anomaly detection concept
    #[test]
    fn test_basic_anomaly_detection() {
        let data = [1.0, 2.0, 3.0, 100.0, 4.0, 5.0]; // Contains outlier

        // Z-Score anomaly detection
        let mean = data.iter().sum::<f64>() / data.len() as f64;
        let variance = data.iter().map(|x| (x - mean).powi(2)).sum::<f64>() / data.len() as f64;
        let std_dev = variance.sqrt();

        let mut anomalies = Vec::new();
        for (i, &value) in data.iter().enumerate() {
            let z_score = (value - mean).abs() / std_dev;
            if z_score > 2.0 {
                // 2-sigma threshold
                anomalies.push((i, value, z_score));
            }
        }

        assert!(!anomalies.is_empty());
        assert_eq!(anomalies[0].1, 100.0); // Should detect the outlier

        println!(
            "Anomalies detected: {} (outlier at index {})",
            anomalies.len(),
            anomalies[0].0
        );
    }

    /// Test consistency checking concept
    #[test]
    fn test_basic_consistency_checking() {
        // Test shape consistency
        let valid_shapes = vec![vec![3, 3], vec![10, 5], vec![1, 100]];

        let invalid_shapes = vec![
            vec![],     // Empty shape
            vec![0, 5], // Zero dimension
        ];

        // Valid shapes should pass
        for shape in valid_shapes {
            let is_consistent = !shape.is_empty() && shape.iter().all(|&dim| dim > 0);
            assert!(is_consistent, "Shape {:?} should be consistent", shape);
        }

        // Invalid shapes should fail
        for shape in invalid_shapes {
            let is_consistent = !shape.is_empty() && shape.iter().all(|&dim| dim > 0);
            assert!(!is_consistent, "Shape {:?} should be inconsistent", shape);
        }

        println!("Consistency checking: passed all shape validation tests");
    }

    /// Test real-time validation concept
    #[test]
    fn test_realtime_validation_concept() {
        let mut validation_buffer = Vec::new();
        let buffer_max_size = 5;

        // Simulate streaming validation results
        for i in 1..=10 {
            let validation_result = ValidationResult {
                quality_score: 0.8 + (i as f64 * 0.02),
                is_valid: true,
            };

            // Add to buffer
            validation_buffer.push(validation_result);

            // Maintain buffer size
            if validation_buffer.len() > buffer_max_size {
                validation_buffer.remove(0);
            }
        }

        assert_eq!(validation_buffer.len(), buffer_max_size);
        assert!(validation_buffer.iter().all(|r| r.is_valid));

        let avg_quality = validation_buffer
            .iter()
            .map(|r| r.quality_score)
            .sum::<f64>()
            / validation_buffer.len() as f64;

        assert!(avg_quality > 0.9);

        println!(
            "Real-time validation: buffer size = {}, avg quality = {:.3}",
            validation_buffer.len(),
            avg_quality
        );
    }

    /// Test quality reporting concept
    #[test]
    fn test_quality_reporting_concept() {
        let quality_data = QualityReportData {
            total_validations: 1000,
            successful_validations: 950,
            average_quality_score: 0.89,
            issues_by_category: {
                let mut categories = HashMap::new();
                categories.insert("missing_data".to_string(), 25);
                categories.insert("range_violation".to_string(), 15);
                categories.insert("format_error".to_string(), 10);
                categories
            },
            performance_metrics: PerformanceMetrics {
                avg_validation_time_ms: 2.5,
                throughput_per_sec: 400.0,
                resource_efficiency: 0.85,
            },
        };

        // Generate basic report
        let report = generate_quality_report(&quality_data);

        assert!(report.contains("Success Rate"));
        assert!(report.contains("89.0%")); // Average quality score
        assert!(report.contains("95.0%")); // Success rate

        println!("Quality Report:\n{}", report);
    }

    /// Test framework integration concept
    #[test]
    fn test_framework_integration() {
        let mut framework_stats = FrameworkStats {
            validations_performed: 0,
            quality_scores: Vec::new(),
            anomalies_detected: 0,
            consistency_violations: 0,
            processing_times: Vec::new(),
        };

        // Simulate framework operations
        for _ in 0..100 {
            // Simulate validation
            framework_stats.validations_performed += 1;
            framework_stats
                .quality_scores
                .push(0.85 + rand::random::<f64>() * 0.1);
            framework_stats
                .processing_times
                .push(Duration::from_millis(1 + (rand::random::<u64>() % 5)));

            // Occasionally detect anomalies/violations
            if rand::random::<f64>() < 0.05 {
                framework_stats.anomalies_detected += 1;
            }
            if rand::random::<f64>() < 0.03 {
                framework_stats.consistency_violations += 1;
            }
        }

        // Verify framework performance
        assert_eq!(framework_stats.validations_performed, 100);
        assert!(framework_stats.quality_scores.len() == 100);

        let avg_quality = framework_stats.quality_scores.iter().sum::<f64>() / 100.0;
        let avg_processing_time = framework_stats
            .processing_times
            .iter()
            .map(|d| d.as_millis() as f64)
            .sum::<f64>()
            / 100.0;

        assert!(avg_quality > 0.8);
        assert!(avg_processing_time < 10.0);

        println!("Framework Integration Test:");
        println!("  Validations: {}", framework_stats.validations_performed);
        println!("  Avg Quality: {:.3}", avg_quality);
        println!("  Avg Time: {:.1}ms", avg_processing_time);
        println!("  Anomalies: {}", framework_stats.anomalies_detected);
        println!("  Violations: {}", framework_stats.consistency_violations);
    }

    // Helper structures and functions for testing

    #[derive(Debug, Clone)]
    struct ValidationResult {
        quality_score: f64,
        is_valid: bool,
    }

    #[derive(Debug)]
    struct QualityReportData {
        total_validations: usize,
        successful_validations: usize,
        average_quality_score: f64,
        issues_by_category: HashMap<String, usize>,
        performance_metrics: PerformanceMetrics,
    }

    #[derive(Debug)]
    struct PerformanceMetrics {
        avg_validation_time_ms: f64,
        throughput_per_sec: f64,
        resource_efficiency: f64,
    }

    #[derive(Debug)]
    struct FrameworkStats {
        validations_performed: usize,
        quality_scores: Vec<f64>,
        anomalies_detected: usize,
        consistency_violations: usize,
        processing_times: Vec<Duration>,
    }

    fn generate_quality_report(data: &QualityReportData) -> String {
        let success_rate =
            (data.successful_validations as f64 / data.total_validations as f64) * 100.0;

        format!(
            "📊 Data Quality Report\n\
             ====================\n\
             Total Validations: {}\n\
             Success Rate: {:.1}%\n\
             Average Quality Score: {:.1}%\n\
             \n\
             Issues by Category:\n\
             {}\n\
             \n\
             Performance Metrics:\n\
             - Avg Validation Time: {:.1}ms\n\
             - Throughput: {:.0}/sec\n\
             - Resource Efficiency: {:.1}%",
            data.total_validations,
            success_rate,
            data.average_quality_score * 100.0,
            data.issues_by_category
                .iter()
                .map(|(category, count)| format!("- {}: {}", category, count))
                .collect::<Vec<_>>()
                .join("\n"),
            data.performance_metrics.avg_validation_time_ms,
            data.performance_metrics.throughput_per_sec,
            data.performance_metrics.resource_efficiency * 100.0
        )
    }
}

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

    /// Test advanced quality metrics calculation
    #[test]
    fn test_advanced_quality_metrics() {
        let quality_scores = vec![0.95, 0.87, 0.92, 0.89, 0.94, 0.86, 0.91];

        // Calculate advanced statistics
        let mean = quality_scores.iter().sum::<f64>() / quality_scores.len() as f64;
        let variance = quality_scores
            .iter()
            .map(|x| (x - mean).powi(2))
            .sum::<f64>()
            / quality_scores.len() as f64;
        let std_dev = variance.sqrt();

        // Calculate percentiles
        let mut sorted_scores = quality_scores.clone();
        sorted_scores.sort_by(|a, b| a.partial_cmp(b).unwrap());
        let median = sorted_scores[sorted_scores.len() / 2];
        let p95 = sorted_scores[(sorted_scores.len() as f64 * 0.95) as usize];

        assert!(mean > 0.8);
        assert!(std_dev < 0.1); // Should have low variance for good quality
        assert!(median > 0.85);
        assert!(p95 > 0.9);

        println!("Advanced Quality Metrics:");
        println!("  Mean: {:.3}", mean);
        println!("  Std Dev: {:.3}", std_dev);
        println!("  Median: {:.3}", median);
        println!("  95th Percentile: {:.3}", p95);
    }

    /// Test trend analysis
    #[test]
    fn test_trend_analysis() {
        // Generate trending quality data
        let mut quality_history = Vec::new();
        for i in 1..=20 {
            let base_quality = 0.8;
            let trend = i as f64 * 0.005; // Slight upward trend
            let noise = (rand::random::<f64>() - 0.5) * 0.05;
            quality_history.push(base_quality + trend + noise);
        }

        // Simple linear regression for trend detection
        let n = quality_history.len() as f64;
        let sum_x: f64 = (1..=20).map(|i| i as f64).sum();
        let sum_y: f64 = quality_history.iter().sum();
        let sum_xy: f64 = quality_history
            .iter()
            .enumerate()
            .map(|(i, &y)| (i + 1) as f64 * y)
            .sum();
        let sum_x2: f64 = (1..=20).map(|i| (i as f64).powi(2)).sum();

        let slope = (n * sum_xy - sum_x * sum_y) / (n * sum_x2 - sum_x * sum_x);
        let trend_direction = if slope > 0.001 {
            "Improving"
        } else if slope < -0.001 {
            "Declining"
        } else {
            "Stable"
        };

        println!("Trend Analysis:");
        println!("  Slope: {:.6}", slope);
        println!("  Direction: {}", trend_direction);
        println!("  Data points: {}", quality_history.len());

        // Should detect upward trend
        assert!(slope > 0.0);
    }

    /// Test performance validation
    #[test]
    fn test_performance_validation() {
        let mut processing_times = Vec::new();
        let performance_budget_ms = 5.0;

        // Simulate validation processing times
        for _ in 0..1000 {
            let processing_time = 1.0 + rand::random::<f64>() * 3.0; // 1-4ms range
            processing_times.push(processing_time);
        }

        let avg_time = processing_times.iter().sum::<f64>() / processing_times.len() as f64;
        let max_time = processing_times.iter().fold(0.0f64, |a, &b| a.max(b));
        let budget_violations = processing_times
            .iter()
            .filter(|&&time| time > performance_budget_ms)
            .count();

        let violation_rate = budget_violations as f64 / processing_times.len() as f64;

        assert!(avg_time < performance_budget_ms);
        assert!(violation_rate < 0.05); // Less than 5% violations

        println!("Performance Validation:");
        println!("  Average Time: {:.2}ms", avg_time);
        println!("  Max Time: {:.2}ms", max_time);
        println!("  Budget: {:.1}ms", performance_budget_ms);
        println!("  Violation Rate: {:.1}%", violation_rate * 100.0);
    }

    /// Test comprehensive framework validation
    #[test]
    fn test_comprehensive_framework_validation() {
        let start_time = std::time::Instant::now();

        // Simulate comprehensive validation framework
        let mut framework = ValidationFrameworkSimulator::new();

        // Run validation cycles
        for cycle in 1..=10 {
            let cycle_result = framework.run_validation_cycle(cycle);
            assert!(cycle_result.success);

            if cycle_result.quality_score < 0.8 {
                eprintln!(
                    "Quality alert in cycle {}: score = {:.3}",
                    cycle, cycle_result.quality_score
                );
            }
        }

        let framework_stats = framework.get_statistics();
        let total_time = start_time.elapsed();

        // Validate framework performance
        assert_eq!(framework_stats.cycles_completed, 10);
        assert!(framework_stats.average_quality > 0.8);
        assert!(framework_stats.anomalies_detected < framework_stats.total_validations / 10);
        assert!(total_time < Duration::from_secs(1)); // Should complete quickly

        println!("Comprehensive Framework Validation:");
        println!("  Cycles: {}", framework_stats.cycles_completed);
        println!("  Total Validations: {}", framework_stats.total_validations);
        println!("  Avg Quality: {:.3}", framework_stats.average_quality);
        println!("  Anomalies: {}", framework_stats.anomalies_detected);
        println!("  Total Time: {:.2}ms", total_time.as_secs_f64() * 1000.0);
    }

    // Helper simulator for comprehensive testing
    struct ValidationFrameworkSimulator {
        cycles_completed: usize,
        total_validations: usize,
        quality_scores: Vec<f64>,
        anomalies_detected: usize,
        consistency_violations: usize,
    }

    struct CycleResult {
        success: bool,
        quality_score: f64,
    }

    struct FrameworkStatistics {
        cycles_completed: usize,
        total_validations: usize,
        average_quality: f64,
        anomalies_detected: usize,
    }

    impl ValidationFrameworkSimulator {
        fn new() -> Self {
            Self {
                cycles_completed: 0,
                total_validations: 0,
                quality_scores: Vec::new(),
                anomalies_detected: 0,
                consistency_violations: 0,
            }
        }

        fn run_validation_cycle(&mut self, cycle: usize) -> CycleResult {
            let validations_in_cycle = 50 + (cycle * 10);
            let mut cycle_quality_scores = Vec::new();
            let mut _cycle_anomalies = 0;

            for _ in 0..validations_in_cycle {
                // Simulate validation with slight quality degradation over cycles
                let base_quality = 0.9 - (cycle as f64 * 0.01);
                let noise = (rand::random::<f64>() - 0.5) * 0.1;
                let quality_score = (base_quality + noise).clamp(0.5, 1.0);

                cycle_quality_scores.push(quality_score);
                self.quality_scores.push(quality_score);
                self.total_validations += 1;

                // Simulate occasional anomalies
                if rand::random::<f64>() < 0.02 {
                    _cycle_anomalies += 1;
                    self.anomalies_detected += 1;
                }

                // Simulate consistency violations
                if rand::random::<f64>() < 0.01 {
                    self.consistency_violations += 1;
                }
            }

            self.cycles_completed += 1;
            let cycle_avg_quality =
                cycle_quality_scores.iter().sum::<f64>() / cycle_quality_scores.len() as f64;

            CycleResult {
                success: cycle_avg_quality > 0.7,
                quality_score: cycle_avg_quality,
            }
        }

        fn get_statistics(&self) -> FrameworkStatistics {
            let average_quality = if self.quality_scores.is_empty() {
                0.0
            } else {
                self.quality_scores.iter().sum::<f64>() / self.quality_scores.len() as f64
            };

            FrameworkStatistics {
                cycles_completed: self.cycles_completed,
                total_validations: self.total_validations,
                average_quality,
                anomalies_detected: self.anomalies_detected,
            }
        }
    }
}