Struct ModelEvaluator

pub struct ModelEvaluator<F: Float + Debug + 'static + Sum + Clone + Copy + FromPrimitive> { /* private fields */ }

Enhanced model evaluator

Implementations

impl<F: Float + Debug + 'static + Sum + Clone + Copy + FromPrimitive> ModelEvaluator<F>

pub fn new() -> Self

Create a new model evaluator

pub fn add_metric(&mut self, metric: EvaluationMetric)

Add evaluation metric

pub fn set_cross_validation(&mut self, strategy: CrossValidationStrategy)

Set cross-validation strategy

pub fn enable_bootstrap(&mut self, n_samples: usize)

Enable bootstrap confidence intervals

pub fn set_significance_level(&mut self, level: f64)

Set significance level for statistical tests

pub fn evaluate( &mut self, y_true: &ArrayD<F>, y_pred: &ArrayD<F>, model_name: Option<String>, ) -> Result<EvaluationResults<F>>

Evaluate model predictions

Examples found in repository

examples/neural_advanced_features.rs (lines 131-135)

fn demonstrate_enhanced_evaluation() -> Result<()> {
    println!("📊 Enhanced Model Evaluation Demonstration");
    println!("=========================================\n");

    // Create comprehensive evaluation pipeline
    let mut evaluator = EvaluationBuilder::<f64>::new()
        .with_classification_metrics()
        .with_regression_metrics()
        .with_cross_validation(CrossValidationStrategy::KFold {
            k: 5,
            shuffle: true,
        })
        .with_bootstrap(1000)
        .build();

    // Generate sample predictions and ground truth
    let y_true_class =
        Array::from_vec(vec![0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0]).into_dyn();
    let y_pred_class =
        Array::from_vec(vec![0.1, 0.9, 0.2, 0.8, 0.7, 0.3, 0.9, 0.1, 0.8, 0.2]).into_dyn();

    let y_true_reg =
        Array::from_vec(vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0]).into_dyn();
    let y_pred_reg =
        Array::from_vec(vec![1.1, 1.9, 3.2, 3.8, 5.1, 5.9, 7.1, 7.9, 9.2, 9.8]).into_dyn();

    println!("1. Evaluating classification model...");
    let class_results = evaluator.evaluate(
        &y_true_class,
        &y_pred_class,
        Some("classifier_model".to_string()),
    )?;
    println!(
        "   Classification metrics computed: {}",
        class_results.scores.len()
    );

    for (metric, score) in &class_results.scores {
        println!("   {}: {:.4}", metric, score.value);
    }

    println!("\n2. Evaluating regression model...");
    let reg_results = evaluator.evaluate(
        &y_true_reg,
        &y_pred_reg,
        Some("regression_model".to_string()),
    )?;
    println!(
        "   Regression metrics computed: {}",
        reg_results.scores.len()
    );

    for (metric, score) in &reg_results.scores {
        println!("   {}: {:.4}", metric, score.value);
    }

    // Generate comprehensive report
    println!("\n3. Comprehensive Evaluation Report:");
    println!("{}", evaluator.generate_report(&class_results));

    // Compare models
    println!("4. Statistical Model Comparison:");
    let comparison = evaluator.compare_models("classifier_model", "regression_model")?;
    if let Some(t_test) = &comparison.t_test {
        println!(
            "   T-test: t={:.3}, p={:.3}, significant={}",
            t_test.t_statistic, t_test.p_value, t_test.significant
        );
    }

    println!("✅ Enhanced evaluation demonstration completed!\n");
    Ok(())
}

pub fn compare_models( &mut self, model1_name: &str, model2_name: &str, ) -> Result<StatisticalTestResults<F>>

Compare two models using statistical tests

Examples found in repository

examples/neural_advanced_features.rs (line 166)

fn demonstrate_enhanced_evaluation() -> Result<()> {
    println!("📊 Enhanced Model Evaluation Demonstration");
    println!("=========================================\n");

    // Create comprehensive evaluation pipeline
    let mut evaluator = EvaluationBuilder::<f64>::new()
        .with_classification_metrics()
        .with_regression_metrics()
        .with_cross_validation(CrossValidationStrategy::KFold {
            k: 5,
            shuffle: true,
        })
        .with_bootstrap(1000)
        .build();

    // Generate sample predictions and ground truth
    let y_true_class =
        Array::from_vec(vec![0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0]).into_dyn();
    let y_pred_class =
        Array::from_vec(vec![0.1, 0.9, 0.2, 0.8, 0.7, 0.3, 0.9, 0.1, 0.8, 0.2]).into_dyn();

    let y_true_reg =
        Array::from_vec(vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0]).into_dyn();
    let y_pred_reg =
        Array::from_vec(vec![1.1, 1.9, 3.2, 3.8, 5.1, 5.9, 7.1, 7.9, 9.2, 9.8]).into_dyn();

    println!("1. Evaluating classification model...");
    let class_results = evaluator.evaluate(
        &y_true_class,
        &y_pred_class,
        Some("classifier_model".to_string()),
    )?;
    println!(
        "   Classification metrics computed: {}",
        class_results.scores.len()
    );

    for (metric, score) in &class_results.scores {
        println!("   {}: {:.4}", metric, score.value);
    }

    println!("\n2. Evaluating regression model...");
    let reg_results = evaluator.evaluate(
        &y_true_reg,
        &y_pred_reg,
        Some("regression_model".to_string()),
    )?;
    println!(
        "   Regression metrics computed: {}",
        reg_results.scores.len()
    );

    for (metric, score) in &reg_results.scores {
        println!("   {}: {:.4}", metric, score.value);
    }

    // Generate comprehensive report
    println!("\n3. Comprehensive Evaluation Report:");
    println!("{}", evaluator.generate_report(&class_results));

    // Compare models
    println!("4. Statistical Model Comparison:");
    let comparison = evaluator.compare_models("classifier_model", "regression_model")?;
    if let Some(t_test) = &comparison.t_test {
        println!(
            "   T-test: t={:.3}, p={:.3}, significant={}",
            t_test.t_statistic, t_test.p_value, t_test.significant
        );
    }

    println!("✅ Enhanced evaluation demonstration completed!\n");
    Ok(())
}

pub fn generate_report(&self, results: &EvaluationResults<F>) -> String

Generate comprehensive evaluation report

Examples found in repository

examples/neural_advanced_features.rs (line 162)

fn demonstrate_enhanced_evaluation() -> Result<()> {
    println!("📊 Enhanced Model Evaluation Demonstration");
    println!("=========================================\n");

    // Create comprehensive evaluation pipeline
    let mut evaluator = EvaluationBuilder::<f64>::new()
        .with_classification_metrics()
        .with_regression_metrics()
        .with_cross_validation(CrossValidationStrategy::KFold {
            k: 5,
            shuffle: true,
        })
        .with_bootstrap(1000)
        .build();

    // Generate sample predictions and ground truth
    let y_true_class =
        Array::from_vec(vec![0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0]).into_dyn();
    let y_pred_class =
        Array::from_vec(vec![0.1, 0.9, 0.2, 0.8, 0.7, 0.3, 0.9, 0.1, 0.8, 0.2]).into_dyn();

    let y_true_reg =
        Array::from_vec(vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0]).into_dyn();
    let y_pred_reg =
        Array::from_vec(vec![1.1, 1.9, 3.2, 3.8, 5.1, 5.9, 7.1, 7.9, 9.2, 9.8]).into_dyn();

    println!("1. Evaluating classification model...");
    let class_results = evaluator.evaluate(
        &y_true_class,
        &y_pred_class,
        Some("classifier_model".to_string()),
    )?;
    println!(
        "   Classification metrics computed: {}",
        class_results.scores.len()
    );

    for (metric, score) in &class_results.scores {
        println!("   {}: {:.4}", metric, score.value);
    }

    println!("\n2. Evaluating regression model...");
    let reg_results = evaluator.evaluate(
        &y_true_reg,
        &y_pred_reg,
        Some("regression_model".to_string()),
    )?;
    println!(
        "   Regression metrics computed: {}",
        reg_results.scores.len()
    );

    for (metric, score) in &reg_results.scores {
        println!("   {}: {:.4}", metric, score.value);
    }

    // Generate comprehensive report
    println!("\n3. Comprehensive Evaluation Report:");
    println!("{}", evaluator.generate_report(&class_results));

    // Compare models
    println!("4. Statistical Model Comparison:");
    let comparison = evaluator.compare_models("classifier_model", "regression_model")?;
    if let Some(t_test) = &comparison.t_test {
        println!(
            "   T-test: t={:.3}, p={:.3}, significant={}",
            t_test.t_statistic, t_test.p_value, t_test.significant
        );
    }

    println!("✅ Enhanced evaluation demonstration completed!\n");
    Ok(())
}

pub fn get_cached_results( &self, model_name: &str, ) -> Option<&EvaluationResults<F>>

Get cached evaluation results

pub fn clear_cache(&mut self)

Clear results cache

Trait Implementations

impl<F: Float + Debug + 'static + Sum + Clone + Copy + FromPrimitive> Default for ModelEvaluator<F>

fn default() -> Self

Returns the “default value” for a type.