sklears_dummy/validation/

mod.rs

//! Validation utilities for dummy estimators
//!
//! This module provides comprehensive validation and comparison tools for dummy estimators,
//! including cross-validation, bootstrap validation, and statistical analysis.

pub mod bootstrap_validation;
pub mod cross_validation;
pub mod data_splitting;
pub mod statistical_analysis;
pub mod strategy_comparison;
pub mod tests;
pub mod validation_core;
pub mod validation_metrics;
pub mod validation_utils;

// Re-export commonly used functions and types
pub use bootstrap_validation::*;
pub use cross_validation::*;
pub use data_splitting::*;
pub use statistical_analysis::*;
pub use strategy_comparison::*;
pub use validation_core::*;
pub use validation_metrics::*;
pub use validation_utils::*;

// Common types used across validation modules
use crate::dummy_classifier::Strategy as ClassifierStrategy;
use crate::dummy_regressor::Strategy as RegressorStrategy;
use scirs2_core::ndarray::{Array1, Array2};

/// Results of dummy strategy validation
#[derive(Debug, Clone)]
pub struct DummyValidationResult {
    /// strategy
    pub strategy: String,
    /// cv_scores
    pub cv_scores: Vec<f64>,
    /// mean_score
    pub mean_score: f64,
    /// std_score
    pub std_score: f64,
    /// confidence_interval
    pub confidence_interval: (f64, f64),
}

impl DummyValidationResult {
    /// Create a new DummyValidationResult
    pub fn new(mean_score: f64, std_score: f64, cv_scores: Vec<f64>, strategy: String) -> Self {
        // Calculate 95% confidence interval: mean ± 1.96 * (std / sqrt(n))
        let n = cv_scores.len() as f64;
        let margin = 1.96 * (std_score / n.sqrt());
        let confidence_interval = (mean_score - margin, mean_score + margin);

        Self {
            strategy,
            cv_scores,
            mean_score,
            std_score,
            confidence_interval,
        }
    }
}

/// Statistical validation results
#[derive(Debug, Clone)]
pub struct StatisticalValidationResult {
    /// test_statistic
    pub test_statistic: f64,
    /// p_value
    pub p_value: f64,
    /// critical_value
    pub critical_value: f64,
    /// is_significant
    pub is_significant: bool,
}

/// Strategy ranking information
#[derive(Debug, Clone)]
pub struct StrategyRanking {
    /// strategy
    pub strategy: String,
    /// rank
    pub rank: usize,
    /// score
    pub score: f64,
    /// tier
    pub tier: String,
}

/// Strategy recommendation
#[derive(Debug, Clone)]
pub struct StrategyRecommendation {
    /// recommended_strategy
    pub recommended_strategy: String,
    /// confidence
    pub confidence: f64,
    /// reasoning
    pub reasoning: String,
    /// alternatives
    pub alternatives: Vec<String>,
}

/// Dataset characteristics for analysis
#[derive(Debug, Clone)]
pub struct DatasetCharacteristics {
    /// n_samples
    pub n_samples: usize,
    /// n_features
    pub n_features: usize,
    /// target_type
    pub target_type: String,
    /// class_balance
    pub class_balance: Option<f64>,
    /// missing_values
    pub missing_values: f64,
}

/// Class distribution information
#[derive(Debug, Clone)]
pub struct ClassDistribution {
    /// classes
    pub classes: Vec<i32>,
    /// counts
    pub counts: Vec<usize>,
    /// proportions
    pub proportions: Vec<f64>,
}

/// Target distribution information
#[derive(Debug, Clone)]
pub struct TargetDistribution {
    /// mean
    pub mean: f64,
    /// std
    pub std: f64,
    /// min
    pub min: f64,
    /// max
    pub max: f64,
    /// percentiles
    pub percentiles: Vec<f64>,
}

/// Data type enumeration
#[derive(Debug, Clone)]
pub enum DataType {
    /// Classification
    Classification,
    /// Regression
    Regression,
    /// Multiclass
    Multiclass,
    /// Multilabel
    Multilabel,
}

/// Permutation test results
#[derive(Debug, Clone)]
pub struct PermutationTestResult {
    /// observed_score
    pub observed_score: f64,
    /// null_distribution
    pub null_distribution: Vec<f64>,
    /// p_value
    pub p_value: f64,
    /// significance_level
    pub significance_level: f64,
    /// is_significant
    pub is_significant: bool,
}

/// Validation summary
#[derive(Debug, Clone)]
pub struct ValidationSummary {
    /// best_strategy
    pub best_strategy: String,
    /// all_results
    pub all_results: Vec<DummyValidationResult>,
    /// statistical_significance
    pub statistical_significance: StatisticalValidationResult,
    /// recommendation
    pub recommendation: StrategyRecommendation,
}

// Placeholder implementations for missing functions
// These should be implemented properly in the future

/// Analyze classification dataset characteristics
pub fn analyze_classification_dataset(x: &Array2<f64>, y: &Array1<i32>) -> DatasetCharacteristics {
    use std::collections::HashMap;

    let n_samples = x.nrows();
    let n_features = x.ncols();

    // Calculate class distribution
    let mut class_counts: HashMap<i32, usize> = HashMap::new();
    for &class in y.iter() {
        *class_counts.entry(class).or_insert(0) += 1;
    }

    // Calculate class balance as the ratio of smallest to largest class
    // Perfect balance = 1.0, completely imbalanced = 0.0
    let class_balance = if class_counts.len() > 1 {
        let counts: Vec<usize> = class_counts.values().copied().collect();
        let min_count = *counts.iter().min().unwrap() as f64;
        let max_count = *counts.iter().max().unwrap() as f64;
        Some(min_count / max_count)
    } else {
        None // Single class
    };

    /// DatasetCharacteristics
    DatasetCharacteristics {
        n_samples,
        n_features,
        target_type: "classification".to_string(),
        class_balance,
        missing_values: 0.0,
    }
}

/// Analyze regression dataset characteristics
pub fn analyze_regression_dataset(_x: &Array2<f64>, _y: &Array1<f64>) -> DatasetCharacteristics {
    // Placeholder implementation
    /// DatasetCharacteristics
    DatasetCharacteristics {
        n_samples: 100,
        n_features: 5,
        target_type: "regression".to_string(),
        class_balance: None,
        missing_values: 0.0,
    }
}

/// Get adaptive classification strategy
pub fn get_adaptive_classification_strategy(
    characteristics: &DatasetCharacteristics,
) -> ClassifierStrategy {
    // Use class balance to determine strategy
    if let Some(class_balance) = characteristics.class_balance {
        // If classes are well balanced (balance ratio > 0.7), use Stratified
        // This preserves the distribution while providing variety
        if class_balance > 0.7 {
            ClassifierStrategy::Stratified
        } else {
            // For imbalanced data, MostFrequent is often more appropriate
            ClassifierStrategy::MostFrequent
        }
    } else {
        // Fallback to MostFrequent if no balance information
        ClassifierStrategy::MostFrequent
    }
}

/// Get adaptive regression strategy
pub fn get_adaptive_regression_strategy(
    _characteristics: &DatasetCharacteristics,
) -> RegressorStrategy {
    // Placeholder implementation
    RegressorStrategy::Mean
}

/// Cross-validate dummy estimator
pub fn cross_validate_dummy(
    _estimator: &str,
    _x: &Array2<f64>,
    _y: &Array1<f64>,
    _cv: usize,
) -> DummyValidationResult {
    // Placeholder implementation
    /// DummyValidationResult
    DummyValidationResult {
        strategy: "placeholder".to_string(),
        cv_scores: vec![0.5, 0.6, 0.7],
        mean_score: 0.6,
        std_score: 0.1,
        confidence_interval: (0.5, 0.7),
    }
}

/// Comprehensive validation for classifier
pub fn comprehensive_validation_classifier(
    _classifier: &str,
    _x: &Array2<f64>,
    _y: &Array1<i32>,
) -> ValidationSummary {
    // Placeholder implementation
    /// ValidationSummary
    ValidationSummary {
        best_strategy: "most_frequent".to_string(),
        all_results: vec![],
        statistical_significance: StatisticalValidationResult {
            test_statistic: 1.5,
            p_value: 0.05,
            critical_value: 1.96,
            is_significant: false,
        },
        recommendation: StrategyRecommendation {
            recommended_strategy: "most_frequent".to_string(),
            confidence: 0.8,
            reasoning: "Balanced dataset".to_string(),
            alternatives: vec!["stratified".to_string()],
        },
    }
}

/// Get best strategy
pub fn get_best_strategy(_results: &[DummyValidationResult]) -> String {
    "best_strategy".to_string()
}

/// Get ranking summary
pub fn get_ranking_summary(_results: &[DummyValidationResult]) -> Vec<StrategyRanking> {
    vec![]
}

/// Get strategies in tier
pub fn get_strategies_in_tier(_results: &[DummyValidationResult], _tier: &str) -> Vec<String> {
    vec![]
}

/// Permutation test for classifier
pub fn permutation_test_classifier(
    _classifier: &str,
    _x: &Array2<f64>,
    _y: &Array1<i32>,
) -> PermutationTestResult {
    // Placeholder implementation
    /// PermutationTestResult
    PermutationTestResult {
        observed_score: 0.8,
        null_distribution: vec![0.5; 100],
        p_value: 0.01,
        significance_level: 0.05,
        is_significant: true,
    }
}

/// Permutation test vs random classifier
pub fn permutation_test_vs_random_classifier(
    _classifier: &str,
    _x: &Array2<f64>,
    _y: &Array1<i32>,
) -> PermutationTestResult {
    // Placeholder implementation
    /// PermutationTestResult
    PermutationTestResult {
        observed_score: 0.8,
        null_distribution: vec![0.5; 100],
        p_value: 0.01,
        significance_level: 0.05,
        is_significant: true,
    }
}

/// Rank dummy strategies for classifier
pub fn rank_dummy_strategies_classifier(
    _strategies: &[ClassifierStrategy],
    _x: &Array2<f64>,
    _y: &Array1<i32>,
) -> Vec<StrategyRanking> {
    vec![]
}

/// Rank dummy strategies for regressor
pub fn rank_dummy_strategies_regressor(
    _strategies: &[RegressorStrategy],
    _x: &Array2<f64>,
    _y: &Array1<f64>,
) -> Vec<StrategyRanking> {
    vec![]
}

/// Recommend classification strategy
pub fn recommend_classification_strategy(
    _x: &Array2<f64>,
    _y: &Array1<i32>,
) -> StrategyRecommendation {
    /// StrategyRecommendation
    StrategyRecommendation {
        recommended_strategy: "most_frequent".to_string(),
        confidence: 0.8,
        reasoning: "Default recommendation".to_string(),
        alternatives: vec!["stratified".to_string()],
    }
}

/// Recommend regression strategy
pub fn recommend_regression_strategy(_x: &Array2<f64>, _y: &Array1<f64>) -> StrategyRecommendation {
    /// StrategyRecommendation
    StrategyRecommendation {
        recommended_strategy: "mean".to_string(),
        confidence: 0.8,
        reasoning: "Default recommendation".to_string(),
        alternatives: vec!["median".to_string()],
    }
}

/// Validate reproducibility
pub fn validate_reproducibility(
    _estimator: &str,
    _x: &Array2<f64>,
    _y: &Array1<f64>,
    _random_state: u64,
) -> bool {
    true // Placeholder - always return true
}