Struct AdvancedDatasetAnalyzer 

pub struct AdvancedDatasetAnalyzer { /* private fields */ }

Advanced dataset analyzer with configurable options

Implementations

impl AdvancedDatasetAnalyzer

pub fn new() -> Self

Create a new analyzer with default settings

Examples found in repository

examples/advanced_showcase.rs (line 87)

fn demonstrate_advanced_analytics(dataset: &Dataset) -> Result<(), Box<dyn std::error::Error>> {
    println!("\n🧠 Advanced Analytics Demonstration");
    println!("==========================================");

    // Quick quality assessment
    println!("📈 Running quick quality assessment...");
    let quick_quality = quick_quality_assessment(dataset)?;
    println!("   Quality Score: {quick_quality:.3}");

    // Comprehensive advanced-analysis
    println!("🔬 Running comprehensive advanced-analysis...");
    let start_time = Instant::now();

    let analyzer = AdvancedDatasetAnalyzer::new()
        .with_gpu(true)
        .with_advanced_precision(true)
        .with_significance_threshold(0.01);

    let metrics = analyzer.analyze_dataset_quality(dataset)?;
    let analysis_time = start_time.elapsed();

    println!("   Analysis completed in: {analysis_time:?}");
    println!("   Complexity Score: {:.3}", metrics.complexity_score);
    println!("   Entropy: {:.3}", metrics.entropy);
    println!("   Outlier Score: {:.3}", metrics.outlier_score);
    println!("   ML Quality Score: {:.3}", metrics.ml_quality_score);

    // Display normality assessment
    println!("   Normality Assessment:");
    println!(
        "     Overall Normality: {:.3}",
        metrics.normality_assessment.overall_normality
    );
    println!(
        "     Shapiro-Wilk (avg): {:.3}",
        metrics.normality_assessment.shapiro_wilk_scores.mean()
    );

    // Display correlation insights
    println!("   Correlation Insights:");
    println!(
        "     Feature Importance (top 3): {:?}",
        metrics
            .correlation_insights
            .feature_importance
            .iter()
            .take(3)
            .map(|&x| format!("{x:.3}"))
            .collect::<Vec<_>>()
    );

    Ok(())
}

pub fn with_gpu(self, enabled: bool) -> Self

Enable GPU acceleration

Examples found in repository

examples/advanced_showcase.rs (line 88)

fn demonstrate_advanced_analytics(dataset: &Dataset) -> Result<(), Box<dyn std::error::Error>> {
    println!("\n🧠 Advanced Analytics Demonstration");
    println!("==========================================");

    // Quick quality assessment
    println!("📈 Running quick quality assessment...");
    let quick_quality = quick_quality_assessment(dataset)?;
    println!("   Quality Score: {quick_quality:.3}");

    // Comprehensive advanced-analysis
    println!("🔬 Running comprehensive advanced-analysis...");
    let start_time = Instant::now();

    let analyzer = AdvancedDatasetAnalyzer::new()
        .with_gpu(true)
        .with_advanced_precision(true)
        .with_significance_threshold(0.01);

    let metrics = analyzer.analyze_dataset_quality(dataset)?;
    let analysis_time = start_time.elapsed();

    println!("   Analysis completed in: {analysis_time:?}");
    println!("   Complexity Score: {:.3}", metrics.complexity_score);
    println!("   Entropy: {:.3}", metrics.entropy);
    println!("   Outlier Score: {:.3}", metrics.outlier_score);
    println!("   ML Quality Score: {:.3}", metrics.ml_quality_score);

    // Display normality assessment
    println!("   Normality Assessment:");
    println!(
        "     Overall Normality: {:.3}",
        metrics.normality_assessment.overall_normality
    );
    println!(
        "     Shapiro-Wilk (avg): {:.3}",
        metrics.normality_assessment.shapiro_wilk_scores.mean()
    );

    // Display correlation insights
    println!("   Correlation Insights:");
    println!(
        "     Feature Importance (top 3): {:?}",
        metrics
            .correlation_insights
            .feature_importance
            .iter()
            .take(3)
            .map(|&x| format!("{x:.3}"))
            .collect::<Vec<_>>()
    );

    Ok(())
}

pub fn with_advanced_precision(self, enabled: bool) -> Self

Enable advanced precision calculations

Examples found in repository

examples/advanced_showcase.rs (line 89)

fn demonstrate_advanced_analytics(dataset: &Dataset) -> Result<(), Box<dyn std::error::Error>> {
    println!("\n🧠 Advanced Analytics Demonstration");
    println!("==========================================");

    // Quick quality assessment
    println!("📈 Running quick quality assessment...");
    let quick_quality = quick_quality_assessment(dataset)?;
    println!("   Quality Score: {quick_quality:.3}");

    // Comprehensive advanced-analysis
    println!("🔬 Running comprehensive advanced-analysis...");
    let start_time = Instant::now();

    let analyzer = AdvancedDatasetAnalyzer::new()
        .with_gpu(true)
        .with_advanced_precision(true)
        .with_significance_threshold(0.01);

    let metrics = analyzer.analyze_dataset_quality(dataset)?;
    let analysis_time = start_time.elapsed();

    println!("   Analysis completed in: {analysis_time:?}");
    println!("   Complexity Score: {:.3}", metrics.complexity_score);
    println!("   Entropy: {:.3}", metrics.entropy);
    println!("   Outlier Score: {:.3}", metrics.outlier_score);
    println!("   ML Quality Score: {:.3}", metrics.ml_quality_score);

    // Display normality assessment
    println!("   Normality Assessment:");
    println!(
        "     Overall Normality: {:.3}",
        metrics.normality_assessment.overall_normality
    );
    println!(
        "     Shapiro-Wilk (avg): {:.3}",
        metrics.normality_assessment.shapiro_wilk_scores.mean()
    );

    // Display correlation insights
    println!("   Correlation Insights:");
    println!(
        "     Feature Importance (top 3): {:?}",
        metrics
            .correlation_insights
            .feature_importance
            .iter()
            .take(3)
            .map(|&x| format!("{x:.3}"))
            .collect::<Vec<_>>()
    );

    Ok(())
}

pub fn with_significance_threshold(self, threshold: f64) -> Self

Set significance threshold for statistical tests

Examples found in repository

examples/advanced_showcase.rs (line 90)

fn demonstrate_advanced_analytics(dataset: &Dataset) -> Result<(), Box<dyn std::error::Error>> {
    println!("\n🧠 Advanced Analytics Demonstration");
    println!("==========================================");

    // Quick quality assessment
    println!("📈 Running quick quality assessment...");
    let quick_quality = quick_quality_assessment(dataset)?;
    println!("   Quality Score: {quick_quality:.3}");

    // Comprehensive advanced-analysis
    println!("🔬 Running comprehensive advanced-analysis...");
    let start_time = Instant::now();

    let analyzer = AdvancedDatasetAnalyzer::new()
        .with_gpu(true)
        .with_advanced_precision(true)
        .with_significance_threshold(0.01);

    let metrics = analyzer.analyze_dataset_quality(dataset)?;
    let analysis_time = start_time.elapsed();

    println!("   Analysis completed in: {analysis_time:?}");
    println!("   Complexity Score: {:.3}", metrics.complexity_score);
    println!("   Entropy: {:.3}", metrics.entropy);
    println!("   Outlier Score: {:.3}", metrics.outlier_score);
    println!("   ML Quality Score: {:.3}", metrics.ml_quality_score);

    // Display normality assessment
    println!("   Normality Assessment:");
    println!(
        "     Overall Normality: {:.3}",
        metrics.normality_assessment.overall_normality
    );
    println!(
        "     Shapiro-Wilk (avg): {:.3}",
        metrics.normality_assessment.shapiro_wilk_scores.mean()
    );

    // Display correlation insights
    println!("   Correlation Insights:");
    println!(
        "     Feature Importance (top 3): {:?}",
        metrics
            .correlation_insights
            .feature_importance
            .iter()
            .take(3)
            .map(|&x| format!("{x:.3}"))
            .collect::<Vec<_>>()
    );

    Ok(())
}

pub fn analyze_dataset_quality( &self, dataset: &Dataset, ) -> Result<AdvancedQualityMetrics, Box<dyn Error>>

Analyze dataset quality with advanced metrics

Examples found in repository

examples/advanced_showcase.rs (line 92)

fn demonstrate_advanced_analytics(dataset: &Dataset) -> Result<(), Box<dyn std::error::Error>> {
    println!("\n🧠 Advanced Analytics Demonstration");
    println!("==========================================");

    // Quick quality assessment
    println!("📈 Running quick quality assessment...");
    let quick_quality = quick_quality_assessment(dataset)?;
    println!("   Quality Score: {quick_quality:.3}");

    // Comprehensive advanced-analysis
    println!("🔬 Running comprehensive advanced-analysis...");
    let start_time = Instant::now();

    let analyzer = AdvancedDatasetAnalyzer::new()
        .with_gpu(true)
        .with_advanced_precision(true)
        .with_significance_threshold(0.01);

    let metrics = analyzer.analyze_dataset_quality(dataset)?;
    let analysis_time = start_time.elapsed();

    println!("   Analysis completed in: {analysis_time:?}");
    println!("   Complexity Score: {:.3}", metrics.complexity_score);
    println!("   Entropy: {:.3}", metrics.entropy);
    println!("   Outlier Score: {:.3}", metrics.outlier_score);
    println!("   ML Quality Score: {:.3}", metrics.ml_quality_score);

    // Display normality assessment
    println!("   Normality Assessment:");
    println!(
        "     Overall Normality: {:.3}",
        metrics.normality_assessment.overall_normality
    );
    println!(
        "     Shapiro-Wilk (avg): {:.3}",
        metrics.normality_assessment.shapiro_wilk_scores.mean()
    );

    // Display correlation insights
    println!("   Correlation Insights:");
    println!(
        "     Feature Importance (top 3): {:?}",
        metrics
            .correlation_insights
            .feature_importance
            .iter()
            .take(3)
            .map(|&x| format!("{x:.3}"))
            .collect::<Vec<_>>()
    );

    Ok(())
}

Trait Implementations

impl Clone for AdvancedDatasetAnalyzer

fn clone(&self) -> AdvancedDatasetAnalyzer

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for AdvancedDatasetAnalyzer

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for AdvancedDatasetAnalyzer

fn default() -> Self

Returns the “default value” for a type.