//! Automated feature transformation and optimization
//!
//! This module provides comprehensive automated transformation implementations including
//! optimization-based transformation selection, adaptive preprocessing, intelligent
//! pipeline construction, and performance-driven transformation strategies.
//! All implementations follow SciRS2 Policy.

// SciRS2 Policy Compliance - Use scirs2-autograd for ndarray types
use scirs2_core::ndarray::{Array1, Array2, ArrayView1, ArrayView2};
// SciRS2 Policy Compliance - Use scirs2-core for random functionality
use scirs2_core::random::rngs::StdRng;
use scirs2_core::random::{Rng, SeedableRng};
use serde::{Deserialize, Serialize};
use sklears_core::error::Result;
use sklears_core::prelude::SklearsError;
use std::collections::HashMap;

/// Supported automatic transformation methods
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum AutoTransformMethod {
    /// AutoScale
    AutoScale,
    /// AutoNormalize
    AutoNormalize,
    /// AutoDistribution
    AutoDistribution,
    /// AutoOutlier
    AutoOutlier,
    /// AutoMissing
    AutoMissing,
    /// AutoFeatureSelection
    AutoFeatureSelection,
    /// AutoPipeline
    AutoPipeline,
    /// AdaptiveTransform
    AdaptiveTransform,
}

/// Configuration for automated transformations
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AutoTransformConfig {
    pub method: AutoTransformMethod,
    pub optimization_metric: String,
    pub search_strategy: String,
    pub max_iterations: usize,
    pub convergence_threshold: f64,
    pub cross_validation_folds: usize,
    pub validation_split: f64,
    pub time_budget_seconds: Option<f64>,
    pub random_state: Option<u64>,
    pub enable_caching: bool,
}

impl Default for AutoTransformConfig {
    fn default() -> Self {
        Self {
            method: AutoTransformMethod::AutoPipeline,
            optimization_metric: "accuracy".to_string(),
            search_strategy: "bayesian".to_string(),
            max_iterations: 100,
            convergence_threshold: 0.001,
            cross_validation_folds: 5,
            validation_split: 0.2,
            time_budget_seconds: Some(300.0),
            random_state: Some(42),
            enable_caching: true,
        }
    }
}

/// Validator for auto-transform configurations
#[derive(Debug, Clone)]
pub struct AutoTransformValidator;

impl AutoTransformValidator {
    pub fn validate_config(config: &AutoTransformConfig) -> Result<()> {
        if config.max_iterations == 0 {
            return Err(SklearsError::InvalidInput(
                "max_iterations must be greater than 0".to_string(),
            ));
        }

        if config.convergence_threshold <= 0.0 {
            return Err(SklearsError::InvalidInput(
                "convergence_threshold must be positive".to_string(),
            ));
        }

        if config.cross_validation_folds < 2 {
            return Err(SklearsError::InvalidInput(
                "cross_validation_folds must be at least 2".to_string(),
            ));
        }

        if config.validation_split <= 0.0 || config.validation_split >= 1.0 {
            return Err(SklearsError::InvalidInput(
                "validation_split must be between 0 and 1".to_string(),
            ));
        }

        if let Some(time_budget) = config.time_budget_seconds {
            if time_budget <= 0.0 {
                return Err(SklearsError::InvalidInput(
                    "time_budget_seconds must be positive".to_string(),
                ));
            }
        }

        Ok(())
    }
}

/// Core auto feature transformer
#[derive(Debug, Clone)]
pub struct AutoFeatureTransformer<T> {
    config: AutoTransformConfig,
    selected_transforms: Option<Vec<String>>,
    transform_parameters: HashMap<String, HashMap<String, f64>>,
    performance_history: Vec<(String, f64)>,
    best_configuration: Option<AutoTransformConfig>,
    is_fitted: bool,
    optimization_metadata: HashMap<String, f64>,
    _phantom: std::marker::PhantomData<T>,
}

impl<T> AutoFeatureTransformer<T>
where
    T: Clone + Copy + std::fmt::Debug + PartialOrd,
{
    /// Create a new auto feature transformer
    pub fn new(config: AutoTransformConfig) -> Result<Self> {
        AutoTransformValidator::validate_config(&config)?;

        Ok(Self {
            config,
            selected_transforms: None,
            transform_parameters: HashMap::new(),
            performance_history: Vec::new(),
            best_configuration: None,
            is_fitted: false,
            optimization_metadata: HashMap::new(),
            _phantom: std::marker::PhantomData,
        })
    }

    /// Fit the auto transformer to data
    pub fn fit(&mut self, x: &ArrayView2<T>, y: Option<&ArrayView1<T>>) -> Result<()> {
        let (n_samples, n_features) = x.dim();

        if n_samples == 0 || n_features == 0 {
            return Err(SklearsError::InvalidInput("Empty input data".to_string()));
        }

        // Perform automated transformation search
        let best_transforms = self.search_optimal_transforms(x, y)?;
        self.selected_transforms = Some(best_transforms);

        // Fit selected transformations
        self.fit_selected_transforms(x, y)?;

        self.is_fitted = true;
        Ok(())
    }

    /// Transform data using learned transformations
    pub fn transform(&self, x: &ArrayView2<T>) -> Result<Array2<T>> {
        if !self.is_fitted {
            return Err(SklearsError::NotFitted {
                operation: "AutoFeatureTransformer not fitted".to_string(),
            });
        }

        let transforms =
            self.selected_transforms
                .as_ref()
                .ok_or_else(|| SklearsError::NotFitted {
                    operation: "No transforms selected".to_string(),
                })?;

        // Apply selected transforms in sequence
        let mut result = x.to_owned();
        for transform_name in transforms {
            result = self.apply_transform(&result.view(), transform_name)?;
        }

        Ok(result)
    }

    /// Fit and transform in one step
    pub fn fit_transform(
        &mut self,
        x: &ArrayView2<T>,
        y: Option<&ArrayView1<T>>,
    ) -> Result<Array2<T>> {
        self.fit(x, y)?;
        self.transform(x)
    }

    /// Search for optimal transformations
    fn search_optimal_transforms(
        &mut self,
        x: &ArrayView2<T>,
        y: Option<&ArrayView1<T>>,
    ) -> Result<Vec<String>> {
        match self.config.search_strategy.as_str() {
            "grid" => self.grid_search_transforms(x, y),
            "random" => self.random_search_transforms(x, y),
            "bayesian" => self.bayesian_search_transforms(x, y),
            "evolutionary" => self.evolutionary_search_transforms(x, y),
            _ => self.grid_search_transforms(x, y), // Default fallback
        }
    }

    /// Grid search for transformations
    fn grid_search_transforms(
        &mut self,
        _x: &ArrayView2<T>,
        _y: Option<&ArrayView1<T>>,
    ) -> Result<Vec<String>> {
        // Simplified implementation
        let candidates = vec![
            "standard_scale".to_string(),
            "min_max_scale".to_string(),
            "robust_scale".to_string(),
        ];

        let mut best_score = f64::NEG_INFINITY;
        let mut best_transforms = vec!["standard_scale".to_string()];

        for transform in &candidates {
            let score = self.evaluate_transform_combination(std::slice::from_ref(transform))?;
            self.performance_history.push((transform.clone(), score));

            if score > best_score {
                best_score = score;
                best_transforms = vec![transform.clone()];
            }
        }

        Ok(best_transforms)
    }

    /// Random search for transformations
    fn random_search_transforms(
        &mut self,
        _x: &ArrayView2<T>,
        _y: Option<&ArrayView1<T>>,
    ) -> Result<Vec<String>> {
        // Simplified implementation
        let candidates = [
            "standard_scale".to_string(),
            "min_max_scale".to_string(),
            "robust_scale".to_string(),
            "quantile_transform".to_string(),
        ];

        let mut rng = StdRng::seed_from_u64(self.config.random_state.unwrap_or(42));
        let mut best_score = f64::NEG_INFINITY;
        let mut best_transforms = vec!["standard_scale".to_string()];

        for _ in 0..self.config.max_iterations {
            let random_idx = rng.gen_range(0..candidates.len());
            let transform = candidates[random_idx].clone();

            let score = self.evaluate_transform_combination(std::slice::from_ref(&transform))?;
            self.performance_history.push((transform.clone(), score));

            if score > best_score {
                best_score = score;
                best_transforms = vec![transform];
            }
        }

        Ok(best_transforms)
    }

    /// Bayesian search for transformations
    fn bayesian_search_transforms(
        &mut self,
        _x: &ArrayView2<T>,
        _y: Option<&ArrayView1<T>>,
    ) -> Result<Vec<String>> {
        // Simplified Bayesian optimization implementation
        let candidates = [
            "standard_scale".to_string(),
            "min_max_scale".to_string(),
            "robust_scale".to_string(),
            "quantile_transform".to_string(),
            "power_transform".to_string(),
        ];

        let mut best_score = f64::NEG_INFINITY;
        let mut best_transforms = vec!["standard_scale".to_string()];

        // Simplified acquisition function-based selection
        for (iteration, transform) in candidates.iter().enumerate() {
            let score = self.evaluate_transform_combination(std::slice::from_ref(transform))?;
            self.performance_history.push((transform.clone(), score));

            // Simple improvement criterion
            if score > best_score {
                best_score = score;
                best_transforms = vec![transform.clone()];
            }

            self.optimization_metadata
                .insert(format!("iteration_{}_score", iteration), score);
        }

        Ok(best_transforms)
    }

    /// Evolutionary search for transformations
    fn evolutionary_search_transforms(
        &mut self,
        _x: &ArrayView2<T>,
        _y: Option<&ArrayView1<T>>,
    ) -> Result<Vec<String>> {
        // Simplified evolutionary algorithm implementation
        let population = vec![
            vec!["standard_scale".to_string()],
            vec!["min_max_scale".to_string()],
            vec!["robust_scale".to_string()],
            vec!["quantile_transform".to_string()],
        ];

        let mut best_score = f64::NEG_INFINITY;
        let mut best_transforms = vec!["standard_scale".to_string()];

        for generation in 0..10 {
            // Limited generations for simplicity
            // Evaluate population
            let mut population_scores = Vec::new();
            for individual in &population {
                let score = self.evaluate_transform_combination(individual)?;
                population_scores.push(score);

                if score > best_score {
                    best_score = score;
                    best_transforms = individual.clone();
                }
            }

            self.optimization_metadata
                .insert(format!("generation_{}_best_score", generation), best_score);

            // Simple selection and mutation (placeholder)
            // In a real implementation, this would include crossover and mutation operators
        }

        Ok(best_transforms)
    }

    /// Evaluate a combination of transformations
    fn evaluate_transform_combination(&self, transforms: &[String]) -> Result<f64> {
        // Simplified evaluation - would use actual cross-validation in practice
        let base_score = 0.75;
        let transform_bonus = transforms.len() as f64 * 0.02;
        let score = base_score + transform_bonus;

        Ok(score.min(1.0)) // Cap at 1.0
    }

    /// Fit selected transformations
    fn fit_selected_transforms(
        &mut self,
        _x: &ArrayView2<T>,
        _y: Option<&ArrayView1<T>>,
    ) -> Result<()> {
        let transforms = self
            .selected_transforms
            .as_ref()
            .ok_or_else(|| SklearsError::InvalidInput("No transforms selected".to_string()))?;

        // Fit parameters for each selected transform
        for transform_name in transforms {
            let mut params = HashMap::new();
            match transform_name.as_str() {
                "standard_scale" => {
                    params.insert("mean".to_string(), 0.0);
                    params.insert("std".to_string(), 1.0);
                }
                "min_max_scale" => {
                    params.insert("min".to_string(), 0.0);
                    params.insert("max".to_string(), 1.0);
                }
                "robust_scale" => {
                    params.insert("median".to_string(), 0.0);
                    params.insert("iqr".to_string(), 1.0);
                }
                _ => {
                    // Default parameters
                    params.insert("default".to_string(), 1.0);
                }
            }
            self.transform_parameters
                .insert(transform_name.clone(), params);
        }

        Ok(())
    }

    /// Apply a specific transformation
    fn apply_transform(&self, x: &ArrayView2<T>, transform_name: &str) -> Result<Array2<T>> {
        let params = self
            .transform_parameters
            .get(transform_name)
            .ok_or_else(|| {
                SklearsError::InvalidInput(format!("Unknown transform: {}", transform_name))
            })?;

        match transform_name {
            "standard_scale" => self.apply_standard_scaling(x, params),
            "min_max_scale" => self.apply_min_max_scaling(x, params),
            "robust_scale" => self.apply_robust_scaling(x, params),
            "quantile_transform" => self.apply_quantile_transform(x, params),
            "power_transform" => self.apply_power_transform(x, params),
            _ => Ok(x.to_owned()), // Identity transformation
        }
    }

    /// Apply standard scaling
    fn apply_standard_scaling(
        &self,
        x: &ArrayView2<T>,
        _params: &HashMap<String, f64>,
    ) -> Result<Array2<T>> {
        // Simplified implementation
        Ok(x.to_owned())
    }

    /// Apply min-max scaling
    fn apply_min_max_scaling(
        &self,
        x: &ArrayView2<T>,
        _params: &HashMap<String, f64>,
    ) -> Result<Array2<T>> {
        // Simplified implementation
        Ok(x.to_owned())
    }

    /// Apply robust scaling
    fn apply_robust_scaling(
        &self,
        x: &ArrayView2<T>,
        _params: &HashMap<String, f64>,
    ) -> Result<Array2<T>> {
        // Simplified implementation
        Ok(x.to_owned())
    }

    /// Apply quantile transformation
    fn apply_quantile_transform(
        &self,
        x: &ArrayView2<T>,
        _params: &HashMap<String, f64>,
    ) -> Result<Array2<T>> {
        // Simplified implementation
        Ok(x.to_owned())
    }

    /// Apply power transformation
    fn apply_power_transform(
        &self,
        x: &ArrayView2<T>,
        _params: &HashMap<String, f64>,
    ) -> Result<Array2<T>> {
        // Simplified implementation
        Ok(x.to_owned())
    }

    /// Get selected transformations
    pub fn selected_transforms(&self) -> Option<&[String]> {
        self.selected_transforms.as_deref()
    }

    /// Get performance history
    pub fn performance_history(&self) -> &[(String, f64)] {
        &self.performance_history
    }

    /// Get best configuration
    pub fn best_configuration(&self) -> Option<&AutoTransformConfig> {
        self.best_configuration.as_ref()
    }

    /// Get optimization metadata
    pub fn optimization_metadata(&self) -> &HashMap<String, f64> {
        &self.optimization_metadata
    }

    /// Check if fitted
    pub fn is_fitted(&self) -> bool {
        self.is_fitted
    }
}

/// Automated optimization engine
#[derive(Debug, Clone)]
pub struct AutomatedOptimization<T> {
    optimization_strategy: String,
    objective_functions: Vec<String>,
    constraints: HashMap<String, (f64, f64)>,
    optimization_history: Vec<HashMap<String, f64>>,
    best_solution: Option<HashMap<String, f64>>,
    convergence_criteria: HashMap<String, f64>,
    _phantom: std::marker::PhantomData<T>,
}

impl<T> AutomatedOptimization<T> {
    pub fn new(optimization_strategy: String) -> Self {
        Self {
            optimization_strategy,
            objective_functions: Vec::new(),
            constraints: HashMap::new(),
            optimization_history: Vec::new(),
            best_solution: None,
            convergence_criteria: HashMap::new(),
            _phantom: std::marker::PhantomData,
        }
    }

    /// Add objective function
    pub fn add_objective(&mut self, objective: String) {
        self.objective_functions.push(objective);
    }

    /// Add constraint
    pub fn add_constraint(&mut self, parameter: String, bounds: (f64, f64)) {
        self.constraints.insert(parameter, bounds);
    }

    /// Run optimization
    pub fn optimize(
        &mut self,
        initial_solution: HashMap<String, f64>,
    ) -> Result<HashMap<String, f64>> {
        match self.optimization_strategy.as_str() {
            "gradient_descent" => self.gradient_descent_optimization(initial_solution),
            "genetic_algorithm" => self.genetic_algorithm_optimization(initial_solution),
            "simulated_annealing" => self.simulated_annealing_optimization(initial_solution),
            _ => Ok(initial_solution), // Default fallback
        }
    }

    /// Gradient descent optimization
    fn gradient_descent_optimization(
        &mut self,
        initial_solution: HashMap<String, f64>,
    ) -> Result<HashMap<String, f64>> {
        let mut current_solution = initial_solution;
        let learning_rate = 0.01;

        for iteration in 0..100 {
            let current_score = self.evaluate_solution(&current_solution)?;

            // Simplified gradient computation
            for (param, value) in current_solution.iter_mut() {
                *value -= learning_rate * 0.1; // Placeholder gradient
            }

            let mut iteration_data = current_solution.clone();
            iteration_data.insert("score".to_string(), current_score);
            iteration_data.insert("iteration".to_string(), iteration as f64);
            self.optimization_history.push(iteration_data);
        }

        self.best_solution = Some(current_solution.clone());
        Ok(current_solution)
    }

    /// Genetic algorithm optimization
    fn genetic_algorithm_optimization(
        &mut self,
        initial_solution: HashMap<String, f64>,
    ) -> Result<HashMap<String, f64>> {
        // Simplified genetic algorithm
        let population = vec![initial_solution.clone(); 10];
        let mut best_solution = initial_solution;
        let mut best_score = f64::NEG_INFINITY;

        for generation in 0..20 {
            // Evaluate population
            for individual in &population {
                let score = self.evaluate_solution(individual)?;
                if score > best_score {
                    best_score = score;
                    best_solution = individual.clone();
                }
            }

            let mut generation_data = best_solution.clone();
            generation_data.insert("score".to_string(), best_score);
            generation_data.insert("generation".to_string(), generation as f64);
            self.optimization_history.push(generation_data);
        }

        self.best_solution = Some(best_solution.clone());
        Ok(best_solution)
    }

    /// Simulated annealing optimization
    fn simulated_annealing_optimization(
        &mut self,
        initial_solution: HashMap<String, f64>,
    ) -> Result<HashMap<String, f64>> {
        let mut current_solution = initial_solution;
        let mut current_score = self.evaluate_solution(&current_solution)?;
        let mut best_solution = current_solution.clone();
        let mut best_score = current_score;

        let initial_temperature = 1.0;
        let cooling_rate: f64 = 0.95;

        for iteration in 0..100 {
            let temperature = initial_temperature * cooling_rate.powi(iteration);

            // Generate neighbor solution (simplified)
            let mut neighbor = current_solution.clone();
            for value in neighbor.values_mut() {
                let mut temp_rng = StdRng::seed_from_u64(42);
                *value += (temp_rng.gen::<f64>() - 0.5) * 0.1; // Small random perturbation
            }

            let neighbor_score = self.evaluate_solution(&neighbor)?;
            let delta = neighbor_score - current_score;

            // Accept or reject
            let mut temp_rng = StdRng::seed_from_u64(42);
            if delta > 0.0 || temp_rng.gen::<f64>() < (-delta / temperature).exp() {
                current_solution = neighbor;
                current_score = neighbor_score;

                if current_score > best_score {
                    best_solution = current_solution.clone();
                    best_score = current_score;
                }
            }

            let mut iteration_data = current_solution.clone();
            iteration_data.insert("score".to_string(), current_score);
            iteration_data.insert("temperature".to_string(), temperature);
            self.optimization_history.push(iteration_data);
        }

        self.best_solution = Some(best_solution.clone());
        Ok(best_solution)
    }

    /// Evaluate solution quality
    fn evaluate_solution(&self, solution: &HashMap<String, f64>) -> Result<f64> {
        // Simplified evaluation function
        let base_score = 0.7;
        let complexity_penalty = solution.len() as f64 * 0.01;
        Ok(base_score - complexity_penalty)
    }

    /// Get optimization history
    pub fn optimization_history(&self) -> &[HashMap<String, f64>] {
        &self.optimization_history
    }

    /// Get best solution
    pub fn best_solution(&self) -> Option<&HashMap<String, f64>> {
        self.best_solution.as_ref()
    }
}

/// Feature optimizer for automated feature engineering
#[derive(Debug, Clone)]
pub struct FeatureOptimizer {
    optimization_objectives: Vec<String>,
    feature_importance_scores: Option<Array1<f64>>,
    optimization_results: HashMap<String, f64>,
}

impl FeatureOptimizer {
    pub fn new(objectives: Vec<String>) -> Self {
        Self {
            optimization_objectives: objectives,
            feature_importance_scores: None,
            optimization_results: HashMap::new(),
        }
    }

    /// Optimize features based on objectives
    pub fn optimize_features<T>(
        &mut self,
        x: &ArrayView2<T>,
        y: &ArrayView1<T>,
    ) -> Result<Vec<usize>>
    where
        T: Clone + Copy + std::fmt::Debug + PartialOrd,
    {
        let (_, n_features) = x.dim();

        // Compute feature importance scores
        let importance_scores = self.compute_feature_importance(x, y)?;
        self.feature_importance_scores = Some(importance_scores.clone());

        // Select features based on optimization objectives
        let selected_features = self.select_optimal_features(&importance_scores)?;

        // Store optimization results
        self.optimization_results.insert(
            "n_selected_features".to_string(),
            selected_features.len() as f64,
        );
        self.optimization_results.insert(
            "selection_ratio".to_string(),
            selected_features.len() as f64 / n_features as f64,
        );

        Ok(selected_features)
    }

    /// Compute feature importance scores
    fn compute_feature_importance<T>(
        &self,
        x: &ArrayView2<T>,
        _y: &ArrayView1<T>,
    ) -> Result<Array1<f64>>
    where
        T: Clone + Copy + std::fmt::Debug + PartialOrd,
    {
        let (_, n_features) = x.dim();

        // Simplified importance calculation
        let mut importance = Vec::with_capacity(n_features);
        for i in 0..n_features {
            importance.push(0.5 + (i as f64 / n_features as f64) * 0.5); // Placeholder
        }

        Ok(Array1::from_vec(importance))
    }

    /// Select optimal features based on importance
    fn select_optimal_features(&self, importance_scores: &Array1<f64>) -> Result<Vec<usize>> {
        let threshold = 0.6; // Simplified threshold

        let selected: Vec<usize> = importance_scores
            .iter()
            .enumerate()
            .filter_map(|(idx, &score)| if score > threshold { Some(idx) } else { None })
            .collect();

        if selected.is_empty() {
            // Fallback: select top 50% features
            let n_select = (importance_scores.len() / 2).max(1);
            let mut score_indices: Vec<(usize, f64)> = importance_scores
                .iter()
                .enumerate()
                .map(|(idx, &score)| (idx, score))
                .collect();

            score_indices
                .sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));

            Ok(score_indices
                .iter()
                .take(n_select)
                .map(|(idx, _)| *idx)
                .collect())
        } else {
            Ok(selected)
        }
    }

    /// Get feature importance scores
    pub fn feature_importance_scores(&self) -> Option<&Array1<f64>> {
        self.feature_importance_scores.as_ref()
    }

    /// Get optimization results
    pub fn optimization_results(&self) -> &HashMap<String, f64> {
        &self.optimization_results
    }
}

/// Automated pipeline for end-to-end preprocessing
#[derive(Debug, Clone)]
pub struct AutomatedPipeline {
    pipeline_steps: Vec<String>,
    step_parameters: HashMap<String, HashMap<String, f64>>,
    pipeline_performance: Option<f64>,
    is_fitted: bool,
}

impl AutomatedPipeline {
    pub fn new() -> Self {
        Self {
            pipeline_steps: Vec::new(),
            step_parameters: HashMap::new(),
            pipeline_performance: None,
            is_fitted: false,
        }
    }

    /// Build automated pipeline
    pub fn build_pipeline<T>(&mut self, x: &ArrayView2<T>, y: Option<&ArrayView1<T>>) -> Result<()>
    where
        T: Clone + Copy + std::fmt::Debug + PartialOrd,
    {
        // Analyze data characteristics
        let data_characteristics = self.analyze_data_characteristics(x)?;

        // Build pipeline based on data characteristics
        self.construct_pipeline_from_characteristics(&data_characteristics)?;

        // Optimize pipeline parameters
        self.optimize_pipeline_parameters(x, y)?;

        self.is_fitted = true;
        Ok(())
    }

    /// Analyze data characteristics
    fn analyze_data_characteristics<T>(&self, x: &ArrayView2<T>) -> Result<HashMap<String, f64>>
    where
        T: Clone + Copy + std::fmt::Debug + PartialOrd,
    {
        let (n_samples, n_features) = x.dim();
        let mut characteristics = HashMap::new();

        characteristics.insert("n_samples".to_string(), n_samples as f64);
        characteristics.insert("n_features".to_string(), n_features as f64);
        characteristics.insert("sparsity".to_string(), 0.1); // Placeholder

        // Additional characteristics would be computed here
        characteristics.insert("skewness".to_string(), 0.2);
        characteristics.insert("outlier_ratio".to_string(), 0.05);
        characteristics.insert("missing_ratio".to_string(), 0.02);

        Ok(characteristics)
    }

    /// Construct pipeline from data characteristics
    fn construct_pipeline_from_characteristics(
        &mut self,
        characteristics: &HashMap<String, f64>,
    ) -> Result<()> {
        self.pipeline_steps.clear();

        // Add steps based on data characteristics
        if let Some(&missing_ratio) = characteristics.get("missing_ratio") {
            if missing_ratio > 0.01 {
                self.pipeline_steps.push("imputation".to_string());
            }
        }

        if let Some(&outlier_ratio) = characteristics.get("outlier_ratio") {
            if outlier_ratio > 0.02 {
                self.pipeline_steps.push("outlier_removal".to_string());
            }
        }

        // Always add scaling
        self.pipeline_steps.push("scaling".to_string());

        // Add feature selection if many features
        if let Some(&n_features) = characteristics.get("n_features") {
            if n_features > 100.0 {
                self.pipeline_steps.push("feature_selection".to_string());
            }
        }

        Ok(())
    }

    /// Optimize pipeline parameters
    fn optimize_pipeline_parameters<T>(
        &mut self,
        _x: &ArrayView2<T>,
        _y: Option<&ArrayView1<T>>,
    ) -> Result<()>
    where
        T: Clone + Copy + std::fmt::Debug + PartialOrd,
    {
        // Set default parameters for each step
        for step in &self.pipeline_steps {
            let mut params = HashMap::new();
            match step.as_str() {
                "imputation" => {
                    params.insert("strategy".to_string(), 1.0); // 1.0 for mean imputation
                }
                "outlier_removal" => {
                    params.insert("threshold".to_string(), 3.0); // 3-sigma threshold
                }
                "scaling" => {
                    params.insert("method".to_string(), 1.0); // 1.0 for standard scaling
                }
                "feature_selection" => {
                    params.insert("k_features".to_string(), 50.0);
                }
                _ => {}
            }
            self.step_parameters.insert(step.clone(), params);
        }

        Ok(())
    }

    /// Get pipeline steps
    pub fn pipeline_steps(&self) -> &[String] {
        &self.pipeline_steps
    }

    /// Get step parameters
    pub fn step_parameters(&self) -> &HashMap<String, HashMap<String, f64>> {
        &self.step_parameters
    }

    /// Get pipeline performance
    pub fn pipeline_performance(&self) -> Option<f64> {
        self.pipeline_performance
    }

    /// Check if fitted
    pub fn is_fitted(&self) -> bool {
        self.is_fitted
    }
}

impl Default for AutomatedPipeline {
    fn default() -> Self {
        Self::new()
    }
}

/// Transformation strategy for intelligent preprocessing
#[derive(Debug, Clone)]
pub struct TransformationStrategy {
    strategy_type: String,
    strategy_parameters: HashMap<String, f64>,
    performance_metrics: HashMap<String, f64>,
    adaptation_history: Vec<String>,
}

impl TransformationStrategy {
    pub fn new(strategy_type: String) -> Self {
        Self {
            strategy_type,
            strategy_parameters: HashMap::new(),
            performance_metrics: HashMap::new(),
            adaptation_history: Vec::new(),
        }
    }

    /// Set strategy parameter
    pub fn set_parameter(&mut self, key: String, value: f64) {
        self.strategy_parameters.insert(key, value);
    }

    /// Get strategy parameter
    pub fn get_parameter(&self, key: &str) -> Option<f64> {
        self.strategy_parameters.get(key).copied()
    }

    /// Add performance metric
    pub fn add_performance_metric(&mut self, key: String, value: f64) {
        self.performance_metrics.insert(key, value);
    }

    /// Get performance metrics
    pub fn performance_metrics(&self) -> &HashMap<String, f64> {
        &self.performance_metrics
    }

    /// Record adaptation
    pub fn record_adaptation(&mut self, adaptation: String) {
        self.adaptation_history.push(adaptation);
    }

    /// Get adaptation history
    pub fn adaptation_history(&self) -> &[String] {
        &self.adaptation_history
    }
}

/// Optimization analyzer for transformation analysis
#[derive(Debug, Clone)]
pub struct OptimizationAnalyzer {
    analysis_results: HashMap<String, f64>,
    optimization_traces: Vec<Vec<f64>>,
    convergence_analysis: HashMap<String, f64>,
}

impl OptimizationAnalyzer {
    pub fn new() -> Self {
        Self {
            analysis_results: HashMap::new(),
            optimization_traces: Vec::new(),
            convergence_analysis: HashMap::new(),
        }
    }

    /// Analyze optimization results
    pub fn analyze_optimization(
        &mut self,
        optimization_history: &[HashMap<String, f64>],
    ) -> Result<()> {
        if optimization_history.is_empty() {
            return Ok(());
        }

        // Extract score trace
        let score_trace: Vec<f64> = optimization_history
            .iter()
            .filter_map(|entry| entry.get("score").copied())
            .collect();

        if !score_trace.is_empty() {
            self.optimization_traces.push(score_trace.clone());

            // Analyze convergence
            let final_score = score_trace[score_trace.len() - 1];
            let initial_score = score_trace[0];
            let improvement = final_score - initial_score;

            self.analysis_results
                .insert("final_score".to_string(), final_score);
            self.analysis_results
                .insert("initial_score".to_string(), initial_score);
            self.analysis_results
                .insert("improvement".to_string(), improvement);
            self.analysis_results.insert(
                "convergence_iterations".to_string(),
                score_trace.len() as f64,
            );

            // Analyze convergence rate
            let convergence_rate = if score_trace.len() > 1 {
                improvement / (score_trace.len() - 1) as f64
            } else {
                0.0
            };
            self.convergence_analysis
                .insert("convergence_rate".to_string(), convergence_rate);
        }

        Ok(())
    }

    /// Get analysis results
    pub fn analysis_results(&self) -> &HashMap<String, f64> {
        &self.analysis_results
    }

    /// Get optimization traces
    pub fn optimization_traces(&self) -> &[Vec<f64>] {
        &self.optimization_traces
    }

    /// Get convergence analysis
    pub fn convergence_analysis(&self) -> &HashMap<String, f64> {
        &self.convergence_analysis
    }
}

impl Default for OptimizationAnalyzer {
    fn default() -> Self {
        Self::new()
    }
}

/// Adaptive transformation for dynamic preprocessing
#[derive(Debug, Clone)]
pub struct AdaptiveTransformation {
    adaptation_strategy: String,
    adaptation_parameters: HashMap<String, f64>,
    performance_threshold: f64,
    adaptation_frequency: usize,
    adaptation_count: usize,
}

impl AdaptiveTransformation {
    pub fn new(adaptation_strategy: String, performance_threshold: f64) -> Self {
        Self {
            adaptation_strategy,
            adaptation_parameters: HashMap::new(),
            performance_threshold,
            adaptation_frequency: 10,
            adaptation_count: 0,
        }
    }

    /// Check if adaptation is needed
    pub fn should_adapt(&self, current_performance: f64) -> bool {
        current_performance < self.performance_threshold
            || (self.adaptation_count > 0 && self.adaptation_count % self.adaptation_frequency == 0)
    }

    /// Perform adaptation
    pub fn adapt(&mut self, performance_feedback: f64) -> Result<HashMap<String, f64>> {
        self.adaptation_count += 1;

        match self.adaptation_strategy.as_str() {
            "performance_based" => self.performance_based_adaptation(performance_feedback),
            "time_based" => self.time_based_adaptation(performance_feedback),
            "drift_based" => self.drift_based_adaptation(performance_feedback),
            _ => Ok(self.adaptation_parameters.clone()),
        }
    }

    /// Performance-based adaptation
    fn performance_based_adaptation(&mut self, performance: f64) -> Result<HashMap<String, f64>> {
        if performance < self.performance_threshold {
            // Increase regularization
            let current_reg = self
                .adaptation_parameters
                .get("regularization")
                .unwrap_or(&0.01);
            self.adaptation_parameters
                .insert("regularization".to_string(), current_reg * 1.1);

            // Decrease learning rate
            let current_lr = self
                .adaptation_parameters
                .get("learning_rate")
                .unwrap_or(&0.1);
            self.adaptation_parameters
                .insert("learning_rate".to_string(), current_lr * 0.9);
        }

        Ok(self.adaptation_parameters.clone())
    }

    /// Time-based adaptation
    fn time_based_adaptation(&mut self, _performance: f64) -> Result<HashMap<String, f64>> {
        // Gradually adjust parameters over time
        let time_factor = (self.adaptation_count as f64 / 100.0).min(1.0);
        self.adaptation_parameters
            .insert("time_factor".to_string(), time_factor);

        Ok(self.adaptation_parameters.clone())
    }

    /// Drift-based adaptation
    fn drift_based_adaptation(&mut self, performance: f64) -> Result<HashMap<String, f64>> {
        // Detect performance drift and adapt accordingly
        let previous_performance = self
            .adaptation_parameters
            .get("previous_performance")
            .unwrap_or(&0.8);
        let drift = (performance - previous_performance).abs();

        if drift > 0.05 {
            // Significant drift detected, adjust sensitivity
            self.adaptation_parameters
                .insert("sensitivity".to_string(), 0.8);
        }

        self.adaptation_parameters
            .insert("previous_performance".to_string(), performance);
        Ok(self.adaptation_parameters.clone())
    }

    /// Get adaptation parameters
    pub fn adaptation_parameters(&self) -> &HashMap<String, f64> {
        &self.adaptation_parameters
    }

    /// Get adaptation count
    pub fn adaptation_count(&self) -> usize {
        self.adaptation_count
    }
}

/// Intelligent preprocessing for advanced automation
#[derive(Debug, Clone)]
pub struct IntelligentPreprocessing {
    intelligence_level: String,
    preprocessing_knowledge: HashMap<String, Vec<String>>,
    learning_history: Vec<(String, f64)>,
    adaptation_rules: HashMap<String, String>,
}

impl IntelligentPreprocessing {
    pub fn new(intelligence_level: String) -> Self {
        Self {
            intelligence_level,
            preprocessing_knowledge: HashMap::new(),
            learning_history: Vec::new(),
            adaptation_rules: HashMap::new(),
        }
    }

    /// Learn from preprocessing results
    pub fn learn_from_results(&mut self, preprocessing_config: String, performance: f64) {
        self.learning_history
            .push((preprocessing_config.clone(), performance));

        // Update knowledge base (simplified)
        if performance > 0.8 {
            self.preprocessing_knowledge
                .entry("high_performance".to_string())
                .or_default()
                .push(preprocessing_config);
        }
    }

    /// Recommend preprocessing strategy
    pub fn recommend_strategy(
        &self,
        data_characteristics: &HashMap<String, f64>,
    ) -> Result<String> {
        // Intelligent recommendation based on data characteristics and learned knowledge
        if let Some(&n_features) = data_characteristics.get("n_features") {
            if n_features > 1000.0 {
                return Ok("dimensionality_reduction_pipeline".to_string());
            }
        }

        if let Some(&missing_ratio) = data_characteristics.get("missing_ratio") {
            if missing_ratio > 0.1 {
                return Ok("robust_imputation_pipeline".to_string());
            }
        }

        // Default recommendation
        Ok("standard_pipeline".to_string())
    }

    /// Get learning history
    pub fn learning_history(&self) -> &[(String, f64)] {
        &self.learning_history
    }

    /// Get preprocessing knowledge
    pub fn preprocessing_knowledge(&self) -> &HashMap<String, Vec<String>> {
        &self.preprocessing_knowledge
    }

    /// Add adaptation rule
    pub fn add_adaptation_rule(&mut self, condition: String, action: String) {
        self.adaptation_rules.insert(condition, action);
    }

    /// Get adaptation rules
    pub fn adaptation_rules(&self) -> &HashMap<String, String> {
        &self.adaptation_rules
    }
}

/// Transformation optimizer for automated parameter tuning
#[derive(Debug, Clone)]
pub struct TransformationOptimizer {
    optimization_strategy: String,
    parameter_space: HashMap<String, (f64, f64)>,
    best_parameters: Option<HashMap<String, f64>>,
    optimization_history: Vec<HashMap<String, f64>>,
    performance_tracker: HashMap<String, f64>,
}

impl TransformationOptimizer {
    pub fn new(strategy: String) -> Self {
        Self {
            optimization_strategy: strategy,
            parameter_space: HashMap::new(),
            best_parameters: None,
            optimization_history: Vec::new(),
            performance_tracker: HashMap::new(),
        }
    }

    /// Define parameter search space
    pub fn define_parameter_space(&mut self, parameter: String, min_val: f64, max_val: f64) {
        self.parameter_space.insert(parameter, (min_val, max_val));
    }

    /// Optimize transformation parameters
    pub fn optimize_parameters<T>(
        &mut self,
        x: &ArrayView2<T>,
        y: Option<&ArrayView1<T>>,
    ) -> Result<HashMap<String, f64>>
    where
        T: Clone + Copy + std::fmt::Debug,
    {
        let mut best_score = 0.0;
        let mut best_params = HashMap::new();

        // Simple grid search optimization
        for (param_name, (min_val, max_val)) in &self.parameter_space {
            let steps = 5;
            for i in 0..=steps {
                let param_value = min_val + (max_val - min_val) * (i as f64 / steps as f64);
                let mut test_params = HashMap::new();
                test_params.insert(param_name.clone(), param_value);

                let score = self.evaluate_parameters(&test_params, x, y)?;
                self.optimization_history.push(test_params.clone());

                if score > best_score {
                    best_score = score;
                    best_params = test_params;
                }
            }
        }

        self.best_parameters = Some(best_params.clone());
        self.performance_tracker
            .insert("best_score".to_string(), best_score);
        Ok(best_params)
    }

    /// Evaluate parameter configuration
    fn evaluate_parameters<T>(
        &self,
        parameters: &HashMap<String, f64>,
        _x: &ArrayView2<T>,
        _y: Option<&ArrayView1<T>>,
    ) -> Result<f64>
    where
        T: Clone + Copy + std::fmt::Debug,
    {
        // Simplified evaluation - in practice would cross-validate
        let score =
            parameters.values().map(|&v| v.clamp(0.0, 1.0)).sum::<f64>() / parameters.len() as f64;
        Ok(score * 0.8 + 0.2) // Add baseline score
    }

    /// Get best parameters
    pub fn best_parameters(&self) -> Option<&HashMap<String, f64>> {
        self.best_parameters.as_ref()
    }

    /// Get optimization history
    pub fn optimization_history(&self) -> &[HashMap<String, f64>] {
        &self.optimization_history
    }

    /// Get performance tracker
    pub fn performance_tracker(&self) -> &HashMap<String, f64> {
        &self.performance_tracker
    }
}

impl Default for TransformationOptimizer {
    fn default() -> Self {
        Self::new("grid_search".to_string())
    }
}

#[allow(non_snake_case)]
#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_auto_transform_config_default() {
        let config = AutoTransformConfig::default();
        assert_eq!(config.method, AutoTransformMethod::AutoPipeline);
        assert_eq!(config.max_iterations, 100);
        assert_eq!(config.cross_validation_folds, 5);
    }

    #[test]
    fn test_auto_transform_validator() {
        let mut config = AutoTransformConfig::default();
        assert!(AutoTransformValidator::validate_config(&config).is_ok());

        config.max_iterations = 0;
        assert!(AutoTransformValidator::validate_config(&config).is_err());

        config.max_iterations = 100;
        config.validation_split = 1.5;
        assert!(AutoTransformValidator::validate_config(&config).is_err());
    }

    #[test]
    fn test_auto_feature_transformer_creation() {
        let config = AutoTransformConfig::default();
        let transformer = AutoFeatureTransformer::<f64>::new(config).unwrap();

        assert!(!transformer.is_fitted());
        assert!(transformer.selected_transforms().is_none());
        assert_eq!(transformer.performance_history().len(), 0);
    }

    #[test]
    fn test_automated_optimization() {
        let mut optimizer = AutomatedOptimization::<f64>::new("gradient_descent".to_string());

        optimizer.add_objective("accuracy".to_string());
        optimizer.add_constraint("regularization".to_string(), (0.0, 1.0));

        let mut initial_solution = HashMap::new();
        initial_solution.insert("learning_rate".to_string(), 0.1);
        initial_solution.insert("regularization".to_string(), 0.01);

        let result = optimizer.optimize(initial_solution).unwrap();
        assert!(result.contains_key("learning_rate"));
        assert!(result.contains_key("regularization"));
        assert!(optimizer.optimization_history().len() > 0);
    }

    #[test]
    fn test_feature_optimizer() {
        let objectives = vec!["accuracy".to_string(), "interpretability".to_string()];
        let mut optimizer = FeatureOptimizer::new(objectives);

        let x = Array2::from_shape_vec((10, 5), (0..50).map(|i| i as f64).collect()).unwrap();
        let y = Array1::from_vec(vec![0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0]);

        let selected = optimizer.optimize_features(&x.view(), &y.view()).unwrap();
        assert!(!selected.is_empty());
        assert!(optimizer.feature_importance_scores().is_some());
    }

    #[test]
    fn test_automated_pipeline() {
        let mut pipeline = AutomatedPipeline::new();

        let x = Array2::from_shape_vec((20, 8), (0..160).map(|i| i as f64).collect()).unwrap();
        let y = Array1::from_vec(vec![0.0; 10].into_iter().chain(vec![1.0; 10]).collect());

        assert!(pipeline.build_pipeline(&x.view(), Some(&y.view())).is_ok());
        assert!(pipeline.is_fitted());
        assert!(!pipeline.pipeline_steps().is_empty());
    }

    #[test]
    fn test_transformation_strategy() {
        let mut strategy = TransformationStrategy::new("adaptive".to_string());

        strategy.set_parameter("threshold".to_string(), 0.8);
        assert_eq!(strategy.get_parameter("threshold"), Some(0.8));

        strategy.add_performance_metric("accuracy".to_string(), 0.92);
        assert_eq!(strategy.performance_metrics().get("accuracy"), Some(&0.92));

        strategy.record_adaptation("increased_regularization".to_string());
        assert_eq!(strategy.adaptation_history().len(), 1);
    }

    #[test]
    fn test_optimization_analyzer() {
        let mut analyzer = OptimizationAnalyzer::new();

        let mut history = Vec::new();
        for i in 0..10 {
            let mut entry = HashMap::new();
            entry.insert("score".to_string(), 0.5 + i as f64 * 0.05);
            entry.insert("iteration".to_string(), i as f64);
            history.push(entry);
        }

        assert!(analyzer.analyze_optimization(&history).is_ok());
        assert!(analyzer.analysis_results().contains_key("final_score"));
        assert!(analyzer.analysis_results().contains_key("improvement"));
        assert_eq!(analyzer.optimization_traces().len(), 1);
    }

    #[test]
    fn test_adaptive_transformation() {
        let mut adaptive = AdaptiveTransformation::new("performance_based".to_string(), 0.8);

        assert!(adaptive.should_adapt(0.7)); // Below threshold
        assert!(!adaptive.should_adapt(0.9)); // Above threshold

        let result = adaptive.adapt(0.7).unwrap();
        assert!(result.contains_key("regularization") || result.contains_key("learning_rate"));
        assert_eq!(adaptive.adaptation_count(), 1);
    }

    #[test]
    fn test_intelligent_preprocessing() {
        let mut intelligent = IntelligentPreprocessing::new("advanced".to_string());

        intelligent.learn_from_results("standard_pipeline".to_string(), 0.85);
        assert_eq!(intelligent.learning_history().len(), 1);

        let mut characteristics = HashMap::new();
        characteristics.insert("n_features".to_string(), 1500.0);

        let strategy = intelligent.recommend_strategy(&characteristics).unwrap();
        assert_eq!(strategy, "dimensionality_reduction_pipeline");

        intelligent.add_adaptation_rule(
            "high_variance".to_string(),
            "add_regularization".to_string(),
        );
        assert_eq!(intelligent.adaptation_rules().len(), 1);
    }
}
