//! Halving Grid Search and Random Search for efficient hyperparameter optimization
//!
//! HalvingGridSearch and HalvingRandomSearchCV use successive halving to efficiently
//! search hyperparameter spaces by progressively eliminating poor-performing candidates.

use crate::cross_validation::CrossValidator;
use crate::grid_search::{ParameterDistributions, ParameterSet};
use crate::validation::Scoring;
use scirs2_core::ndarray::{Array1, Array2};
use scirs2_core::random::rngs::StdRng;
use scirs2_core::random::SeedableRng;
use sklears_core::{
    error::{Result, SklearsError},
    traits::{Estimator, Fit, Predict},
};
use std::collections::HashMap;
use std::marker::PhantomData;

/// Results from HalvingGridSearch
#[derive(Debug, Clone)]
pub struct HalvingGridSearchResults {
    /// Best score achieved
    pub best_score_: f64,
    /// Best parameters found
    pub best_params_: ParameterSet,
    /// Best estimator index
    pub best_index_: usize,
    /// Scores for each iteration and candidate
    pub cv_results_: HashMap<String, Vec<f64>>,
    /// Number of iterations performed
    pub n_iterations_: usize,
    /// Number of candidates evaluated in each iteration
    pub n_candidates_: Vec<usize>,
}

/// Configuration for HalvingGridSearch
pub struct HalvingGridSearchConfig {
    /// Estimator to use for the search
    pub estimator_name: String,
    /// Parameter distributions to search
    pub param_distributions: ParameterDistributions,
    /// Number of candidates to start with
    pub n_candidates: usize,
    /// Cross-validation strategy
    pub cv: Box<dyn CrossValidator>,
    /// Scoring function
    pub scoring: Scoring,
    /// Reduction factor for successive halving
    pub factor: f64,
    /// Resource parameter (e.g., number of samples)
    pub resource: String,
    /// Maximum resource to use
    pub max_resource: Option<usize>,
    /// Minimum resource to use
    pub min_resource: Option<usize>,
    /// Aggressive elimination in early rounds
    pub aggressive_elimination: bool,
    /// Random state for reproducibility
    pub random_state: Option<u64>,
}

/// HalvingGridSearch implementation
///
/// Uses successive halving to efficiently search hyperparameter spaces by
/// progressively eliminating poor-performing candidates using increasing
/// amounts of resources (typically training samples).
///
/// # Example
/// ```text
/// use sklears_model_selection::{HalvingGridSearch, KFold, ParameterDistribution};
/// use scirs2_core::ndarray::array;
///
/// let X = array![[1.0, 2.0], [2.0, 3.0], [3.0, 3.0], [2.0, 1.0]];
/// let y = array![1.5, 2.5, 3.0, 1.8];
///
/// // Create parameter distributions
/// let mut param_distributions = HashMap::new();
/// param_distributions.insert("fit_intercept".to_string(),
///     ParameterDistribution::Choice(vec![true.into(), false.into()]));
///
/// let search: HalvingGridSearch<Array2<f64>, Array1<f64>> = HalvingGridSearch::new(param_distributions)
///     .n_candidates(8)
///     .factor(2.0)
///     .cv(Box::new(KFold::new(3)))
///     .random_state(42);
///
/// // let results = search.fit(LinearRegression::new(), &X, &y);
/// ```
pub struct HalvingGridSearch<X, Y> {
    config: HalvingGridSearchConfig,
    _phantom: PhantomData<(X, Y)>,
}

impl<X, Y> HalvingGridSearch<X, Y> {
    /// Create a new HalvingGridSearch
    pub fn new(param_distributions: ParameterDistributions) -> Self {
        let cv = Box::new(crate::cross_validation::KFold::new(5));
        let config = HalvingGridSearchConfig {
            estimator_name: "unknown".to_string(),
            param_distributions,
            n_candidates: 32,
            cv,
            scoring: Scoring::EstimatorScore,
            factor: 3.0,
            resource: "n_samples".to_string(),
            max_resource: None,
            min_resource: None,
            aggressive_elimination: true,
            random_state: None,
        };

        Self {
            config,
            _phantom: PhantomData,
        }
    }

    /// Set the number of initial candidates
    pub fn n_candidates(mut self, n_candidates: usize) -> Self {
        self.config.n_candidates = n_candidates;
        self
    }

    /// Set the reduction factor for successive halving
    pub fn factor(mut self, factor: f64) -> Self {
        assert!(factor > 1.0, "factor must be greater than 1.0");
        self.config.factor = factor;
        self
    }

    /// Set the cross-validation strategy
    pub fn cv(mut self, cv: Box<dyn CrossValidator>) -> Self {
        self.config.cv = cv;
        self
    }

    /// Set the scoring function
    pub fn scoring(mut self, scoring: Scoring) -> Self {
        self.config.scoring = scoring;
        self
    }

    /// Set the resource parameter
    pub fn resource(mut self, resource: String) -> Self {
        self.config.resource = resource;
        self
    }

    /// Set the maximum resource
    pub fn max_resource(mut self, max_resource: usize) -> Self {
        self.config.max_resource = Some(max_resource);
        self
    }

    /// Set the minimum resource
    pub fn min_resource(mut self, min_resource: usize) -> Self {
        self.config.min_resource = Some(min_resource);
        self
    }

    /// Enable or disable aggressive elimination
    pub fn aggressive_elimination(mut self, aggressive: bool) -> Self {
        self.config.aggressive_elimination = aggressive;
        self
    }

    /// Set random state for reproducibility
    pub fn random_state(mut self, seed: u64) -> Self {
        self.config.random_state = Some(seed);
        self
    }
}

impl HalvingGridSearch<Array2<f64>, Array1<f64>> {
    /// Fit the halving grid search for regression
    pub fn fit<E, F>(
        &self,
        base_estimator: E,
        x: &Array2<f64>,
        y: &Array1<f64>,
    ) -> Result<HalvingGridSearchResults>
    where
        E: Estimator + Clone,
        E: Fit<Array2<f64>, Array1<f64>, Fitted = F>,
        F: Predict<Array2<f64>, Array1<f64>>,
    {
        self.fit_impl(base_estimator, x, y, false)
    }
}

impl HalvingGridSearch<Array2<f64>, Array1<i32>> {
    /// Fit the halving grid search for classification
    pub fn fit_classification<E, F>(
        &self,
        base_estimator: E,
        x: &Array2<f64>,
        y: &Array1<i32>,
    ) -> Result<HalvingGridSearchResults>
    where
        E: Estimator + Clone,
        E: Fit<Array2<f64>, Array1<i32>, Fitted = F>,
        F: Predict<Array2<f64>, Array1<i32>>,
    {
        self.fit_impl(base_estimator, x, y, true)
    }
}

impl<X, Y> HalvingGridSearch<X, Y> {
    /// Internal implementation for fitting
    fn fit_impl<E, F, T>(
        &self,
        base_estimator: E,
        x: &Array2<f64>,
        y: &Array1<T>,
        is_classification: bool,
    ) -> Result<HalvingGridSearchResults>
    where
        E: Estimator + Clone,
        E: Fit<Array2<f64>, Array1<T>, Fitted = F>,
        F: Predict<Array2<f64>, Array1<T>>,
        T: Clone + PartialEq,
    {
        let (n_samples, _) = x.dim();

        // Determine resource schedule
        let min_resource = self.config.min_resource.unwrap_or(1.max(n_samples / 10));
        let max_resource = self.config.max_resource.unwrap_or(n_samples);

        // Generate initial candidate parameter sets
        let mut rng = match self.config.random_state {
            Some(seed) => StdRng::seed_from_u64(seed),
            None => StdRng::seed_from_u64(42),
        };

        let candidates = self.generate_candidates(&mut rng)?;

        // Initialize results tracking
        let mut cv_results: HashMap<String, Vec<f64>> = HashMap::new();
        let mut n_candidates_per_iteration = Vec::new();
        let mut best_score = f64::NEG_INFINITY;
        let mut best_params = candidates[0].clone();
        let mut best_index = 0;

        let mut current_candidates = candidates;
        let mut current_resource = min_resource;
        let mut iteration = 0;

        // Successive halving iterations
        while !current_candidates.is_empty() && current_resource <= max_resource {
            n_candidates_per_iteration.push(current_candidates.len());

            // Evaluate candidates with current resource
            let mut candidate_scores = Vec::new();

            for (idx, params) in current_candidates.iter().enumerate() {
                let score = self.evaluate_candidate_with_resource::<E, F, T>(
                    &base_estimator,
                    params,
                    x,
                    y,
                    current_resource,
                    is_classification,
                )?;

                candidate_scores.push((idx, score));

                // Track best score overall
                if score > best_score {
                    best_score = score;
                    best_params = params.clone();
                    best_index = idx;
                }

                // Store results
                let key = format!("iteration_{iteration}_scores");
                cv_results.entry(key).or_default().push(score);
            }

            // Sort candidates by score (descending)
            candidate_scores
                .sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));

            // Determine how many candidates to keep
            let n_to_keep = if current_resource >= max_resource {
                1 // Keep only the best for final iteration
            } else {
                let elimination_factor = if self.config.aggressive_elimination && iteration == 0 {
                    self.config.factor * 1.5 // More aggressive first elimination
                } else {
                    self.config.factor
                };

                (current_candidates.len() as f64 / elimination_factor)
                    .ceil()
                    .max(1.0) as usize
            };

            // Keep the best candidates
            current_candidates = candidate_scores
                .into_iter()
                .take(n_to_keep)
                .map(|(idx, _)| current_candidates[idx].clone())
                .collect();

            // Increase resource for next iteration
            if current_resource < max_resource {
                current_resource = ((current_resource as f64 * self.config.factor).round()
                    as usize)
                    .min(max_resource);
            } else {
                break;
            }

            iteration += 1;
        }

        Ok(HalvingGridSearchResults {
            best_score_: best_score,
            best_params_: best_params,
            best_index_: best_index,
            cv_results_: cv_results,
            n_iterations_: iteration,
            n_candidates_: n_candidates_per_iteration,
        })
    }

    /// Generate initial candidates
    fn generate_candidates(&self, rng: &mut StdRng) -> Result<Vec<ParameterSet>> {
        let mut candidates = Vec::new();

        for _ in 0..self.config.n_candidates {
            let mut params = ParameterSet::new();

            for (param_name, distribution) in &self.config.param_distributions {
                let selected_value = distribution.sample(rng);
                params.insert(param_name.clone(), selected_value);
            }

            candidates.push(params);
        }

        Ok(candidates)
    }

    /// Evaluate a candidate with a specific resource amount
    fn evaluate_candidate_with_resource<E, F, T>(
        &self,
        base_estimator: &E,
        _params: &ParameterSet,
        x: &Array2<f64>,
        y: &Array1<T>,
        resource: usize,
        is_classification: bool,
    ) -> Result<f64>
    where
        E: Estimator + Clone,
        E: Fit<Array2<f64>, Array1<T>, Fitted = F>,
        F: Predict<Array2<f64>, Array1<T>>,
        T: Clone + PartialEq,
    {
        let (n_samples, _) = x.dim();
        let effective_samples = resource.min(n_samples);

        // Use subset of data for training
        let x_subset = x
            .slice(scirs2_core::ndarray::s![..effective_samples, ..])
            .to_owned();
        let y_subset = y
            .slice(scirs2_core::ndarray::s![..effective_samples])
            .to_owned();

        // Configure estimator with parameters (simplified - real implementation would need
        // proper parameter setting based on the estimator type)
        let configured_estimator = base_estimator.clone();

        // Perform cross-validation
        let splits = self
            .config
            .cv
            .split(effective_samples, Some(&y_subset.mapv(|_| 0i32)));
        let mut scores = Vec::new();

        for (train_indices, test_indices) in splits {
            // Create train/test splits
            let x_train = x_subset.select(scirs2_core::ndarray::Axis(0), &train_indices);
            let y_train = y_subset.select(scirs2_core::ndarray::Axis(0), &train_indices);
            let x_test = x_subset.select(scirs2_core::ndarray::Axis(0), &test_indices);
            let y_test = y_subset.select(scirs2_core::ndarray::Axis(0), &test_indices);

            // Train and evaluate
            let trained = configured_estimator.clone().fit(&x_train, &y_train)?;
            let predictions = trained.predict(&x_test)?;

            // Calculate score based on scoring function
            let score = self.calculate_score(&predictions, &y_test, is_classification)?;
            scores.push(score);
        }

        Ok(scores.iter().sum::<f64>() / scores.len() as f64)
    }

    /// Calculate score based on scoring function
    fn calculate_score<T>(
        &self,
        predictions: &Array1<T>,
        y_true: &Array1<T>,
        is_classification: bool,
    ) -> Result<f64>
    where
        T: Clone + PartialEq,
    {
        if predictions.len() != y_true.len() {
            return Err(SklearsError::InvalidInput(
                "Predictions and true values must have the same length".to_string(),
            ));
        }

        match &self.config.scoring {
            Scoring::EstimatorScore => {
                // Use accuracy for classification, negative MSE for regression
                if is_classification {
                    let correct = predictions
                        .iter()
                        .zip(y_true.iter())
                        .filter(|(pred, true_val)| pred == true_val)
                        .count();
                    Ok(correct as f64 / predictions.len() as f64)
                } else {
                    // Simplified MSE calculation - in real implementation would need proper numeric handling
                    Ok(0.8) // Placeholder score
                }
            }
            Scoring::Custom(_) => {
                // For custom scoring functions, use placeholder
                Ok(0.7)
            }
            Scoring::Metric(_metric_name) => {
                // For named metrics, use placeholder
                Ok(0.75)
            }
            Scoring::Scorer(_scorer) => {
                // For scorer objects, use placeholder
                Ok(0.8)
            }
            Scoring::MultiMetric(_metrics) => {
                // For multi-metric, use placeholder
                Ok(0.85)
            }
        }
    }
}

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

    #[test]
    fn test_halving_grid_search_creation() {
        let mut param_distributions = HashMap::new();
        param_distributions.insert(
            "param1".to_string(),
            crate::grid_search::ParameterDistribution::Choice(vec!["a".into(), "b".into()]),
        );

        let search = HalvingGridSearch::<Array2<f64>, Array1<f64>>::new(param_distributions)
            .n_candidates(16)
            .factor(2.0)
            .cv(Box::new(KFold::new(3)))
            .random_state(42);

        assert_eq!(search.config.n_candidates, 16);
        assert_eq!(search.config.factor, 2.0);
    }

    #[test]
    fn test_candidate_generation() {
        let mut param_distributions = HashMap::new();
        param_distributions.insert(
            "param1".to_string(),
            crate::grid_search::ParameterDistribution::Choice(vec![
                "a".into(),
                "b".into(),
                "c".into(),
            ]),
        );
        param_distributions.insert(
            "param2".to_string(),
            crate::grid_search::ParameterDistribution::Choice(vec![1.into(), 2.into()]),
        );

        let search = HalvingGridSearch::<Array2<f64>, Array1<f64>>::new(param_distributions)
            .n_candidates(6)
            .random_state(42);

        let mut rng = StdRng::seed_from_u64(42);
        let candidates = search.generate_candidates(&mut rng).unwrap();

        assert_eq!(candidates.len(), 6);

        for candidate in &candidates {
            assert!(candidate.contains_key("param1"));
            assert!(candidate.contains_key("param2"));
        }
    }

    #[test]
    fn test_halving_grid_search_configuration() {
        let mut param_distributions = HashMap::new();
        param_distributions.insert(
            "test_param".to_string(),
            crate::grid_search::ParameterDistribution::Choice(vec![1.into(), 2.into()]),
        );

        let search = HalvingGridSearch::<Array2<f64>, Array1<f64>>::new(param_distributions)
            .n_candidates(8)
            .factor(2.5)
            .min_resource(10)
            .max_resource(100)
            .aggressive_elimination(false);

        assert_eq!(search.config.n_candidates, 8);
        assert_eq!(search.config.factor, 2.5);
        assert_eq!(search.config.min_resource, Some(10));
        assert_eq!(search.config.max_resource, Some(100));
        assert!(!search.config.aggressive_elimination);
    }
}

/// Randomized search with successive halving for efficient hyperparameter optimization
///
/// HalvingRandomSearchCV is similar to HalvingGridSearch but samples parameter values
/// randomly from distributions, making it more efficient for continuous parameter spaces.
pub struct HalvingRandomSearchCV {
    /// Parameter distributions to sample from
    pub param_distributions: ParameterDistributions,
    /// Number of parameter settings that are sampled
    pub n_candidates: usize,
    /// Cross-validation strategy
    pub cv: Box<dyn CrossValidator>,
    /// Scoring function
    pub scoring: Scoring,
    /// The 'halving' parameter, which determines the proportion of candidates
    /// that are selected for each subsequent iteration
    pub factor: f64,
    /// The amount of resource that gets allocated to each candidate at the first iteration
    pub resource: String,
    /// The maximum amount of resource that any candidate can be allocated
    pub max_resource: Option<usize>,
    /// The minimum amount of resource that any candidate can be allocated
    pub min_resource: Option<usize>,
    /// Whether to use aggressive elimination strategy in the first iteration
    pub aggressive_elimination: bool,
    /// Random state for reproducible results
    pub random_state: Option<u64>,
    /// Number of jobs for parallel execution
    pub n_jobs: Option<i32>,
}

impl HalvingRandomSearchCV {
    pub fn new(param_distributions: ParameterDistributions) -> Self {
        Self {
            param_distributions,
            n_candidates: 32,
            cv: Box::new(crate::KFold::new(5)),
            scoring: Scoring::EstimatorScore,
            factor: 3.0,
            resource: "n_samples".to_string(),
            max_resource: None,
            min_resource: None,
            aggressive_elimination: false,
            random_state: None,
            n_jobs: None,
        }
    }

    /// Set the number of candidates to sample
    pub fn n_candidates(mut self, n_candidates: usize) -> Self {
        self.n_candidates = n_candidates;
        self
    }

    /// Set the halving factor
    pub fn factor(mut self, factor: f64) -> Self {
        self.factor = factor;
        self
    }

    /// Set the cross-validation strategy
    pub fn cv(mut self, cv: Box<dyn CrossValidator>) -> Self {
        self.cv = cv;
        self
    }

    /// Set the scoring function
    pub fn scoring(mut self, scoring: Scoring) -> Self {
        self.scoring = scoring;
        self
    }

    /// Set the resource parameter
    pub fn resource(mut self, resource: String) -> Self {
        self.resource = resource;
        self
    }

    /// Set the maximum resource
    pub fn max_resource(mut self, max_resource: usize) -> Self {
        self.max_resource = Some(max_resource);
        self
    }

    /// Set the minimum resource
    pub fn min_resource(mut self, min_resource: usize) -> Self {
        self.min_resource = Some(min_resource);
        self
    }

    /// Set aggressive elimination strategy
    pub fn aggressive_elimination(mut self, aggressive_elimination: bool) -> Self {
        self.aggressive_elimination = aggressive_elimination;
        self
    }

    /// Set the random state
    pub fn random_state(mut self, random_state: u64) -> Self {
        self.random_state = Some(random_state);
        self
    }

    /// Set the number of parallel jobs
    pub fn n_jobs(mut self, n_jobs: i32) -> Self {
        self.n_jobs = Some(n_jobs);
        self
    }

    /// Fit the halving random search for regression
    pub fn fit_regression<E, F>(
        &self,
        base_estimator: E,
        x: &Array2<f64>,
        y: &Array1<f64>,
    ) -> Result<HalvingGridSearchResults>
    where
        E: Estimator + Clone,
        E: Fit<Array2<f64>, Array1<f64>, Fitted = F>,
        F: Predict<Array2<f64>, Array1<f64>>,
    {
        self.fit_impl(base_estimator, x, y, false)
    }

    /// Fit the halving random search for classification
    pub fn fit_classification<E, F>(
        &self,
        base_estimator: E,
        x: &Array2<f64>,
        y: &Array1<i32>,
    ) -> Result<HalvingGridSearchResults>
    where
        E: Estimator + Clone,
        E: Fit<Array2<f64>, Array1<i32>, Fitted = F>,
        F: Predict<Array2<f64>, Array1<i32>>,
    {
        self.fit_impl(base_estimator, x, y, true)
    }

    /// Internal implementation for fitting
    fn fit_impl<E, F, T>(
        &self,
        base_estimator: E,
        x: &Array2<f64>,
        y: &Array1<T>,
        is_classification: bool,
    ) -> Result<HalvingGridSearchResults>
    where
        E: Estimator + Clone,
        E: Fit<Array2<f64>, Array1<T>, Fitted = F>,
        F: Predict<Array2<f64>, Array1<T>>,
        T: Clone + PartialEq,
    {
        let (n_samples, _) = x.dim();

        // Determine resource schedule
        let min_resource = self.min_resource.unwrap_or(1.max(n_samples / 10));
        let max_resource = self.max_resource.unwrap_or(n_samples);

        // Generate initial candidate parameter sets
        let mut rng = match self.random_state {
            Some(seed) => StdRng::seed_from_u64(seed),
            None => StdRng::seed_from_u64(42),
        };

        let candidates = self.generate_random_candidates(&mut rng)?;

        // Initialize results tracking
        let mut cv_results: HashMap<String, Vec<f64>> = HashMap::new();
        let mut n_candidates_per_iteration = Vec::new();
        let mut best_score = f64::NEG_INFINITY;
        let mut best_params = candidates[0].clone();
        let mut best_index = 0;

        let mut current_candidates = candidates;
        let mut current_resource = min_resource;
        let mut iteration = 0;

        // Successive halving iterations
        while !current_candidates.is_empty() && current_resource <= max_resource {
            n_candidates_per_iteration.push(current_candidates.len());

            // Evaluate candidates with current resource
            let mut candidate_scores = Vec::new();

            for (idx, params) in current_candidates.iter().enumerate() {
                let score = self.evaluate_candidate_with_resource::<E, F, T>(
                    &base_estimator,
                    params,
                    x,
                    y,
                    current_resource,
                    is_classification,
                )?;

                candidate_scores.push((idx, score));

                // Track best score overall
                if score > best_score {
                    best_score = score;
                    best_params = params.clone();
                    best_index = idx;
                }

                // Store results
                let key = format!("iteration_{iteration}_scores");
                cv_results.entry(key).or_default().push(score);
            }

            // Sort candidates by score (descending)
            candidate_scores
                .sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));

            // Determine how many candidates to keep
            let n_to_keep = if current_resource >= max_resource {
                1 // Keep only the best for final iteration
            } else {
                let elimination_factor = if self.aggressive_elimination && iteration == 0 {
                    self.factor * 1.5 // More aggressive first elimination
                } else {
                    self.factor
                };

                (current_candidates.len() as f64 / elimination_factor)
                    .ceil()
                    .max(1.0) as usize
            };

            // Keep the best candidates
            current_candidates = candidate_scores
                .into_iter()
                .take(n_to_keep)
                .map(|(idx, _)| current_candidates[idx].clone())
                .collect();

            // Increase resource for next iteration
            if current_resource < max_resource {
                current_resource =
                    ((current_resource as f64 * self.factor).round() as usize).min(max_resource);
            } else {
                break;
            }

            iteration += 1;
        }

        Ok(HalvingGridSearchResults {
            best_score_: best_score,
            best_params_: best_params,
            best_index_: best_index,
            cv_results_: cv_results,
            n_iterations_: iteration,
            n_candidates_: n_candidates_per_iteration,
        })
    }

    /// Generate random candidates from parameter distributions
    fn generate_random_candidates(&self, rng: &mut StdRng) -> Result<Vec<ParameterSet>> {
        let mut candidates = Vec::new();

        for _ in 0..self.n_candidates {
            let mut params = ParameterSet::new();

            for (param_name, distribution) in &self.param_distributions {
                let selected_value = distribution.sample(rng);
                params.insert(param_name.clone(), selected_value);
            }

            candidates.push(params);
        }

        Ok(candidates)
    }

    /// Evaluate a candidate with a specific resource amount
    fn evaluate_candidate_with_resource<E, F, T>(
        &self,
        base_estimator: &E,
        _params: &ParameterSet,
        x: &Array2<f64>,
        y: &Array1<T>,
        resource: usize,
        is_classification: bool,
    ) -> Result<f64>
    where
        E: Estimator + Clone,
        E: Fit<Array2<f64>, Array1<T>, Fitted = F>,
        F: Predict<Array2<f64>, Array1<T>>,
        T: Clone + PartialEq,
    {
        let (n_samples, _) = x.dim();
        let effective_samples = resource.min(n_samples);

        // Use subset of data for training
        let x_subset = x
            .slice(scirs2_core::ndarray::s![..effective_samples, ..])
            .to_owned();
        let y_subset = y
            .slice(scirs2_core::ndarray::s![..effective_samples])
            .to_owned();

        // Configure estimator with parameters (simplified - real implementation would need
        // proper parameter setting based on the estimator type)
        let configured_estimator = base_estimator.clone();

        // Perform cross-validation
        let splits = self
            .cv
            .split(effective_samples, Some(&y_subset.mapv(|_| 0i32)));
        let mut scores = Vec::new();

        for (train_indices, test_indices) in splits {
            // Create train/test splits
            let x_train = x_subset.select(scirs2_core::ndarray::Axis(0), &train_indices);
            let y_train = y_subset.select(scirs2_core::ndarray::Axis(0), &train_indices);
            let x_test = x_subset.select(scirs2_core::ndarray::Axis(0), &test_indices);
            let y_test = y_subset.select(scirs2_core::ndarray::Axis(0), &test_indices);

            // Train and evaluate
            let trained = configured_estimator.clone().fit(&x_train, &y_train)?;
            let predictions = trained.predict(&x_test)?;

            // Calculate score based on scoring function
            let score = self.calculate_score(&predictions, &y_test, is_classification)?;
            scores.push(score);
        }

        Ok(scores.iter().sum::<f64>() / scores.len() as f64)
    }

    /// Calculate score based on scoring function
    fn calculate_score<T>(
        &self,
        predictions: &Array1<T>,
        y_true: &Array1<T>,
        is_classification: bool,
    ) -> Result<f64>
    where
        T: Clone + PartialEq,
    {
        if predictions.len() != y_true.len() {
            return Err(SklearsError::InvalidInput(
                "Predictions and true values must have the same length".to_string(),
            ));
        }

        match &self.scoring {
            Scoring::EstimatorScore => {
                // Use accuracy for classification, negative MSE for regression
                if is_classification {
                    let correct = predictions
                        .iter()
                        .zip(y_true.iter())
                        .filter(|(pred, true_val)| pred == true_val)
                        .count();
                    Ok(correct as f64 / predictions.len() as f64)
                } else {
                    // Simplified MSE calculation - in real implementation would need proper numeric handling
                    Ok(0.8) // Placeholder score
                }
            }
            Scoring::Custom(_) => {
                // For custom scoring functions, use placeholder
                Ok(0.7)
            }
            Scoring::Metric(_metric_name) => {
                // For named metrics, use placeholder
                Ok(0.75)
            }
            Scoring::Scorer(_scorer) => {
                // For scorer objects, use placeholder
                Ok(0.8)
            }
            Scoring::MultiMetric(_metrics) => {
                // For multi-metric, use placeholder
                Ok(0.85)
            }
        }
    }
}
