//! Fairness-aware tree construction algorithms
//!
//! This module provides algorithms and utilities for constructing decision trees
//! that minimize bias and ensure fairness across protected attributes.

use scirs2_core::ndarray::{Array1, Array2, ArrayView1, Axis};
use sklears_core::error::{Result, SklearsError};
use std::collections::HashMap;

/// Protected attribute information
#[derive(Debug, Clone)]
pub struct ProtectedAttribute {
    /// Name of the protected attribute
    pub name: String,
    /// Column index in the dataset
    pub column_index: usize,
    /// Unique values for categorical attributes
    pub values: Vec<String>,
    /// Whether this is a binary attribute
    pub is_binary: bool,
    /// Privileged group (for binary attributes)
    pub privileged_group: Option<String>,
}

impl ProtectedAttribute {
    /// Create a new protected attribute
    pub fn new(
        name: String,
        column_index: usize,
        values: Vec<String>,
        privileged_group: Option<String>,
    ) -> Self {
        let is_binary = values.len() == 2;
        Self {
            name,
            column_index,
            values,
            is_binary,
            privileged_group,
        }
    }

    /// Create a binary protected attribute
    pub fn binary(
        name: String,
        column_index: usize,
        privileged_group: String,
        unprivileged_group: String,
    ) -> Self {
        Self {
            name,
            column_index,
            values: vec![privileged_group.clone(), unprivileged_group],
            is_binary: true,
            privileged_group: Some(privileged_group),
        }
    }

    /// Check if a value belongs to the privileged group
    pub fn is_privileged(&self, value: &str) -> bool {
        if let Some(ref priv_group) = self.privileged_group {
            value == priv_group
        } else {
            false
        }
    }
}

/// Fairness metrics for evaluating bias
#[derive(Debug, Clone)]
pub struct FairnessMetrics {
    /// Demographic parity (statistical parity)
    pub demographic_parity: f64,
    /// Equalized odds
    pub equalized_odds: f64,
    /// Equal opportunity
    pub equal_opportunity: f64,
    /// Disparate impact ratio
    pub disparate_impact: f64,
    /// Calibration difference
    pub calibration: f64,
    /// Individual fairness measure
    pub individual_fairness: f64,
}

impl FairnessMetrics {
    /// Calculate fairness metrics for predictions
    pub fn calculate(
        y_true: &Array1<i32>,
        y_pred: &Array1<i32>,
        y_prob: Option<&Array2<f64>>,
        protected_attr: &Array1<i32>,
        privileged_group: i32,
    ) -> Result<Self> {
        let demographic_parity =
            Self::calculate_demographic_parity(y_pred, protected_attr, privileged_group)?;
        let equalized_odds =
            Self::calculate_equalized_odds(y_true, y_pred, protected_attr, privileged_group)?;
        let equal_opportunity =
            Self::calculate_equal_opportunity(y_true, y_pred, protected_attr, privileged_group)?;
        let disparate_impact =
            Self::calculate_disparate_impact(y_pred, protected_attr, privileged_group)?;

        let calibration = if let Some(probs) = y_prob {
            Self::calculate_calibration(y_true, probs, protected_attr, privileged_group)?
        } else {
            0.0
        };

        let individual_fairness = Self::calculate_individual_fairness(y_pred, protected_attr)?;

        Ok(Self {
            demographic_parity,
            equalized_odds,
            equal_opportunity,
            disparate_impact,
            calibration,
            individual_fairness,
        })
    }

    /// Calculate demographic parity (difference in positive prediction rates)
    fn calculate_demographic_parity(
        y_pred: &Array1<i32>,
        protected_attr: &Array1<i32>,
        privileged_group: i32,
    ) -> Result<f64> {
        let privileged_mask = protected_attr.mapv(|x| x == privileged_group);
        let unprivileged_mask = protected_attr.mapv(|x| x != privileged_group);

        let privileged_positive_rate = Self::positive_rate(y_pred, &privileged_mask);
        let unprivileged_positive_rate = Self::positive_rate(y_pred, &unprivileged_mask);

        Ok((privileged_positive_rate - unprivileged_positive_rate).abs())
    }

    /// Calculate equalized odds (difference in TPR and FPR)
    fn calculate_equalized_odds(
        y_true: &Array1<i32>,
        y_pred: &Array1<i32>,
        protected_attr: &Array1<i32>,
        privileged_group: i32,
    ) -> Result<f64> {
        let privileged_mask = protected_attr.mapv(|x| x == privileged_group);
        let unprivileged_mask = protected_attr.mapv(|x| x != privileged_group);

        let (priv_tpr, priv_fpr) = Self::calculate_tpr_fpr(y_true, y_pred, &privileged_mask);
        let (unpriv_tpr, unpriv_fpr) = Self::calculate_tpr_fpr(y_true, y_pred, &unprivileged_mask);

        let tpr_diff = (priv_tpr - unpriv_tpr).abs();
        let fpr_diff = (priv_fpr - unpriv_fpr).abs();

        Ok((tpr_diff + fpr_diff) / 2.0)
    }

    /// Calculate equal opportunity (difference in TPR only)
    fn calculate_equal_opportunity(
        y_true: &Array1<i32>,
        y_pred: &Array1<i32>,
        protected_attr: &Array1<i32>,
        privileged_group: i32,
    ) -> Result<f64> {
        let privileged_mask = protected_attr.mapv(|x| x == privileged_group);
        let unprivileged_mask = protected_attr.mapv(|x| x != privileged_group);

        let (priv_tpr, _) = Self::calculate_tpr_fpr(y_true, y_pred, &privileged_mask);
        let (unpriv_tpr, _) = Self::calculate_tpr_fpr(y_true, y_pred, &unprivileged_mask);

        Ok((priv_tpr - unpriv_tpr).abs())
    }

    /// Calculate disparate impact ratio
    fn calculate_disparate_impact(
        y_pred: &Array1<i32>,
        protected_attr: &Array1<i32>,
        privileged_group: i32,
    ) -> Result<f64> {
        let privileged_mask = protected_attr.mapv(|x| x == privileged_group);
        let unprivileged_mask = protected_attr.mapv(|x| x != privileged_group);

        let privileged_positive_rate = Self::positive_rate(y_pred, &privileged_mask);
        let unprivileged_positive_rate = Self::positive_rate(y_pred, &unprivileged_mask);

        if privileged_positive_rate > 0.0 {
            Ok(unprivileged_positive_rate / privileged_positive_rate)
        } else {
            Ok(1.0)
        }
    }

    /// Calculate calibration difference
    fn calculate_calibration(
        y_true: &Array1<i32>,
        y_prob: &Array2<f64>,
        protected_attr: &Array1<i32>,
        privileged_group: i32,
    ) -> Result<f64> {
        let privileged_mask = protected_attr.mapv(|x| x == privileged_group);
        let unprivileged_mask = protected_attr.mapv(|x| x != privileged_group);

        let priv_calibration = Self::calculate_group_calibration(y_true, y_prob, &privileged_mask);
        let unpriv_calibration =
            Self::calculate_group_calibration(y_true, y_prob, &unprivileged_mask);

        Ok((priv_calibration - unpriv_calibration).abs())
    }

    /// Calculate individual fairness (consistency across similar individuals)
    fn calculate_individual_fairness(
        y_pred: &Array1<i32>,
        protected_attr: &Array1<i32>,
    ) -> Result<f64> {
        // Simplified individual fairness: variance in predictions within groups
        let privileged_mask = protected_attr.mapv(|x| x == 1); // Assume binary

        let priv_predictions: Vec<f64> = y_pred
            .iter()
            .zip(privileged_mask.iter())
            .filter(|(_, &is_priv)| is_priv)
            .map(|(&pred, _)| pred as f64)
            .collect();

        let unpriv_predictions: Vec<f64> = y_pred
            .iter()
            .zip(privileged_mask.iter())
            .filter(|(_, &is_priv)| !is_priv)
            .map(|(&pred, _)| pred as f64)
            .collect();

        let priv_var = Self::calculate_variance(&priv_predictions);
        let unpriv_var = Self::calculate_variance(&unpriv_predictions);

        Ok((priv_var - unpriv_var).abs())
    }

    /// Helper: Calculate positive prediction rate
    fn positive_rate(y_pred: &Array1<i32>, mask: &Array1<bool>) -> f64 {
        let total: i32 = mask.iter().map(|&x| if x { 1 } else { 0 }).sum();
        if total == 0 {
            return 0.0;
        }

        let positive: i32 = y_pred
            .iter()
            .zip(mask.iter())
            .map(|(&pred, &include)| if include && pred > 0 { 1 } else { 0 })
            .sum();

        positive as f64 / total as f64
    }

    /// Helper: Calculate TPR and FPR for a group
    fn calculate_tpr_fpr(
        y_true: &Array1<i32>,
        y_pred: &Array1<i32>,
        mask: &Array1<bool>,
    ) -> (f64, f64) {
        let mut tp = 0;
        let mut fp = 0;
        let mut tn = 0;
        let mut fn_count = 0;

        for ((&true_label, &pred_label), &include) in
            y_true.iter().zip(y_pred.iter()).zip(mask.iter())
        {
            if include {
                match (true_label > 0, pred_label > 0) {
                    (true, true) => tp += 1,
                    (false, true) => fp += 1,
                    (false, false) => tn += 1,
                    (true, false) => fn_count += 1,
                }
            }
        }

        let tpr = if tp + fn_count > 0 {
            tp as f64 / (tp + fn_count) as f64
        } else {
            0.0
        };

        let fpr = if fp + tn > 0 {
            fp as f64 / (fp + tn) as f64
        } else {
            0.0
        };

        (tpr, fpr)
    }

    /// Helper: Calculate calibration for a group
    fn calculate_group_calibration(
        y_true: &Array1<i32>,
        y_prob: &Array2<f64>,
        mask: &Array1<bool>,
    ) -> f64 {
        // Simplified calibration: mean absolute difference between predicted and actual rates
        let mut calibration_error = 0.0;
        let mut count = 0;

        for ((&true_label, prob_row), &include) in y_true
            .iter()
            .zip(y_prob.axis_iter(Axis(0)))
            .zip(mask.iter())
        {
            if include {
                let predicted_prob = prob_row[1]; // Assume binary classification
                let actual = if true_label > 0 { 1.0 } else { 0.0 };
                calibration_error += (predicted_prob - actual).abs();
                count += 1;
            }
        }

        if count > 0 {
            calibration_error / count as f64
        } else {
            0.0
        }
    }

    /// Helper: Calculate variance
    fn calculate_variance(values: &[f64]) -> f64 {
        if values.is_empty() {
            return 0.0;
        }

        let mean = values.iter().sum::<f64>() / values.len() as f64;
        let variance =
            values.iter().map(|&x| (x - mean).powi(2)).sum::<f64>() / values.len() as f64;

        variance
    }
}

/// Fairness constraints for tree construction
#[derive(Debug, Clone)]
pub struct FairnessConstraints {
    /// Maximum allowed demographic parity violation
    pub max_demographic_parity: f64,
    /// Maximum allowed equalized odds violation
    pub max_equalized_odds: f64,
    /// Minimum required disparate impact ratio
    pub min_disparate_impact: f64,
    /// Protected attributes to consider
    pub protected_attributes: Vec<ProtectedAttribute>,
    /// Fairness regularization weight
    pub fairness_weight: f64,
    /// Enable pre-processing fairness
    pub enable_preprocessing: bool,
    /// Enable in-processing fairness
    pub enable_inprocessing: bool,
    /// Enable post-processing fairness
    pub enable_postprocessing: bool,
}

impl Default for FairnessConstraints {
    fn default() -> Self {
        Self {
            max_demographic_parity: 0.1,
            max_equalized_odds: 0.1,
            min_disparate_impact: 0.8,
            protected_attributes: Vec::new(),
            fairness_weight: 0.1,
            enable_preprocessing: true,
            enable_inprocessing: true,
            enable_postprocessing: false,
        }
    }
}

/// Fair splitting criterion that considers both accuracy and fairness
#[derive(Debug, Clone)]
pub struct FairSplittingCriterion {
    /// Standard splitting criterion (gini, entropy, etc.)
    pub base_criterion: SplitCriterion,
    /// Fairness constraints
    pub fairness_constraints: FairnessConstraints,
    /// Weight for fairness vs accuracy trade-off
    pub fairness_weight: f64,
}

#[derive(Debug, Clone)]
pub enum SplitCriterion {
    Gini,
    Entropy,
    MSE,
}

impl FairSplittingCriterion {
    /// Create a new fair splitting criterion
    pub fn new(base_criterion: SplitCriterion, fairness_constraints: FairnessConstraints) -> Self {
        let fairness_weight = fairness_constraints.fairness_weight;
        Self {
            base_criterion,
            fairness_constraints,
            fairness_weight,
        }
    }

    /// Evaluate a split considering both accuracy and fairness
    pub fn evaluate_split(
        &self,
        left_y: &Array1<i32>,
        right_y: &Array1<i32>,
        left_protected: &Array1<i32>,
        right_protected: &Array1<i32>,
        protected_attr: &ProtectedAttribute,
    ) -> Result<f64> {
        // Calculate standard impurity
        let left_impurity = self.calculate_impurity(left_y)?;
        let right_impurity = self.calculate_impurity(right_y)?;

        let n_left = left_y.len() as f64;
        let n_right = right_y.len() as f64;
        let n_total = n_left + n_right;

        let weighted_impurity =
            (n_left / n_total) * left_impurity + (n_right / n_total) * right_impurity;

        // Calculate fairness penalty
        let fairness_penalty = self.calculate_fairness_penalty(
            left_y,
            right_y,
            left_protected,
            right_protected,
            protected_attr,
        )?;

        // Combine accuracy and fairness
        let score = weighted_impurity + self.fairness_weight * fairness_penalty;

        Ok(score)
    }

    /// Calculate impurity based on the base criterion
    fn calculate_impurity(&self, y: &Array1<i32>) -> Result<f64> {
        if y.is_empty() {
            return Ok(0.0);
        }

        match &self.base_criterion {
            SplitCriterion::Gini => Ok(self.gini_impurity(y)),
            SplitCriterion::Entropy => Ok(self.entropy_impurity(y)),
            SplitCriterion::MSE => Ok(self.mse_impurity(y)),
        }
    }

    /// Calculate Gini impurity
    fn gini_impurity(&self, y: &Array1<i32>) -> f64 {
        let mut class_counts = HashMap::new();
        for &label in y.iter() {
            *class_counts.entry(label).or_insert(0) += 1;
        }

        let n = y.len() as f64;
        let mut gini = 1.0;

        for &count in class_counts.values() {
            let p = count as f64 / n;
            gini -= p * p;
        }

        gini
    }

    /// Calculate entropy impurity
    fn entropy_impurity(&self, y: &Array1<i32>) -> f64 {
        let mut class_counts = HashMap::new();
        for &label in y.iter() {
            *class_counts.entry(label).or_insert(0) += 1;
        }

        let n = y.len() as f64;
        let mut entropy = 0.0;

        for &count in class_counts.values() {
            if count > 0 {
                let p = count as f64 / n;
                entropy -= p * p.log2();
            }
        }

        entropy
    }

    /// Calculate MSE impurity (for regression)
    fn mse_impurity(&self, y: &Array1<i32>) -> f64 {
        if y.is_empty() {
            return 0.0;
        }

        let mean = y.iter().map(|&x| x as f64).sum::<f64>() / y.len() as f64;
        let mse = y.iter().map(|&x| (x as f64 - mean).powi(2)).sum::<f64>() / y.len() as f64;

        mse
    }

    /// Calculate fairness penalty for a split
    fn calculate_fairness_penalty(
        &self,
        left_y: &Array1<i32>,
        right_y: &Array1<i32>,
        left_protected: &Array1<i32>,
        right_protected: &Array1<i32>,
        protected_attr: &ProtectedAttribute,
    ) -> Result<f64> {
        let privileged_value = if let Some(ref priv_group) = protected_attr.privileged_group {
            // Convert string to int (simplified)
            if priv_group == "1" || priv_group.to_lowercase() == "true" {
                1
            } else {
                0
            }
        } else {
            1 // Default privileged group
        };

        // Calculate demographic parity violation for left split
        let left_dp = if !left_y.is_empty() {
            FairnessMetrics::calculate_demographic_parity(left_y, left_protected, privileged_value)
                .unwrap_or(0.0)
        } else {
            0.0
        };

        // Calculate demographic parity violation for right split
        let right_dp = if !right_y.is_empty() {
            FairnessMetrics::calculate_demographic_parity(
                right_y,
                right_protected,
                privileged_value,
            )
            .unwrap_or(0.0)
        } else {
            0.0
        };

        // Calculate weighted fairness penalty
        let n_left = left_y.len() as f64;
        let n_right = right_y.len() as f64;
        let n_total = n_left + n_right;

        if n_total > 0.0 {
            let weighted_penalty = (n_left / n_total) * left_dp + (n_right / n_total) * right_dp;
            Ok(weighted_penalty)
        } else {
            Ok(0.0)
        }
    }
}

/// Fair decision tree builder
pub struct FairDecisionTreeBuilder {
    /// Maximum tree depth
    pub max_depth: Option<usize>,
    /// Minimum samples per split
    pub min_samples_split: usize,
    /// Minimum samples per leaf
    pub min_samples_leaf: usize,
    /// Fair splitting criterion
    pub criterion: FairSplittingCriterion,
    /// Random seed
    pub random_state: Option<u64>,
}

impl FairDecisionTreeBuilder {
    /// Create a new fair decision tree builder
    pub fn new(criterion: FairSplittingCriterion) -> Self {
        Self {
            max_depth: None,
            min_samples_split: 2,
            min_samples_leaf: 1,
            criterion,
            random_state: None,
        }
    }

    /// Build a fair decision tree
    pub fn fit(
        &self,
        x: &Array2<f64>,
        y: &Array1<i32>,
        protected_attrs: &Array2<i32>,
    ) -> Result<FairDecisionTree> {
        let n_samples = x.nrows();
        let sample_indices: Vec<usize> = (0..n_samples).collect();

        let root = self.build_tree_recursive(x, y, protected_attrs, &sample_indices, 0)?;

        Ok(FairDecisionTree {
            root,
            feature_importances: self.calculate_feature_importances(x, y)?,
            fairness_metrics: None,
            protected_attributes: self
                .criterion
                .fairness_constraints
                .protected_attributes
                .clone(),
        })
    }

    /// Recursively build the tree
    fn build_tree_recursive(
        &self,
        x: &Array2<f64>,
        y: &Array1<i32>,
        protected_attrs: &Array2<i32>,
        sample_indices: &[usize],
        depth: usize,
    ) -> Result<FairTreeNode> {
        let n_samples = sample_indices.len();

        // Check stopping criteria
        if self.should_stop(n_samples, depth, y, sample_indices) {
            return self.create_leaf_node(y, sample_indices);
        }

        // Find best fair split
        let best_split = self.find_best_fair_split(x, y, protected_attrs, sample_indices)?;

        if let Some((feature_idx, threshold, left_indices, right_indices)) = best_split {
            // Recursively build left and right subtrees
            let left_child = Box::new(self.build_tree_recursive(
                x,
                y,
                protected_attrs,
                &left_indices,
                depth + 1,
            )?);

            let right_child = Box::new(self.build_tree_recursive(
                x,
                y,
                protected_attrs,
                &right_indices,
                depth + 1,
            )?);

            Ok(FairTreeNode {
                is_leaf: false,
                feature_idx: Some(feature_idx),
                threshold: Some(threshold),
                prediction: None,
                left_child: Some(left_child),
                right_child: Some(right_child),
                sample_count: n_samples,
                fairness_score: 0.0,
            })
        } else {
            // No valid split found, create leaf
            self.create_leaf_node(y, sample_indices)
        }
    }

    /// Check if we should stop splitting
    fn should_stop(
        &self,
        n_samples: usize,
        depth: usize,
        y: &Array1<i32>,
        sample_indices: &[usize],
    ) -> bool {
        // Check minimum samples
        if n_samples < self.min_samples_split {
            return true;
        }

        // Check maximum depth
        if let Some(max_depth) = self.max_depth {
            if depth >= max_depth {
                return true;
            }
        }

        // Check if all samples have the same label
        let first_label = y[sample_indices[0]];
        if sample_indices.iter().all(|&idx| y[idx] == first_label) {
            return true;
        }

        false
    }

    /// Create a leaf node
    fn create_leaf_node(&self, y: &Array1<i32>, sample_indices: &[usize]) -> Result<FairTreeNode> {
        // Calculate majority class
        let mut class_counts = HashMap::new();
        for &idx in sample_indices {
            *class_counts.entry(y[idx]).or_insert(0) += 1;
        }

        let prediction = class_counts
            .iter()
            .max_by_key(|(_, &count)| count)
            .map(|(&class, _)| class)
            .unwrap_or(0);

        Ok(FairTreeNode {
            is_leaf: true,
            feature_idx: None,
            threshold: None,
            prediction: Some(prediction),
            left_child: None,
            right_child: None,
            sample_count: sample_indices.len(),
            fairness_score: 0.0,
        })
    }

    /// Find the best fair split
    fn find_best_fair_split(
        &self,
        x: &Array2<f64>,
        y: &Array1<i32>,
        protected_attrs: &Array2<i32>,
        sample_indices: &[usize],
    ) -> Result<Option<(usize, f64, Vec<usize>, Vec<usize>)>> {
        let n_features = x.ncols();
        let mut best_score = f64::INFINITY;
        let mut best_split = None;

        for feature_idx in 0..n_features {
            // Get unique values for this feature
            let mut feature_values: Vec<f64> = sample_indices
                .iter()
                .map(|&idx| x[[idx, feature_idx]])
                .collect();
            feature_values.sort_by(|a, b| a.partial_cmp(b).unwrap());
            feature_values.dedup_by(|a, b| (*a - *b).abs() < 1e-10);

            // Try different thresholds
            for i in 0..feature_values.len() - 1 {
                let threshold = (feature_values[i] + feature_values[i + 1]) / 2.0;

                // Split samples
                let (left_indices, right_indices) =
                    self.split_samples(x, sample_indices, feature_idx, threshold);

                // Check minimum samples constraint
                if left_indices.len() < self.min_samples_leaf
                    || right_indices.len() < self.min_samples_leaf
                {
                    continue;
                }

                // Evaluate split fairness
                let score = self.evaluate_split_fairness(
                    y,
                    protected_attrs,
                    &left_indices,
                    &right_indices,
                )?;

                if score < best_score {
                    best_score = score;
                    best_split = Some((feature_idx, threshold, left_indices, right_indices));
                }
            }
        }

        Ok(best_split)
    }

    /// Split samples based on feature and threshold
    fn split_samples(
        &self,
        x: &Array2<f64>,
        sample_indices: &[usize],
        feature_idx: usize,
        threshold: f64,
    ) -> (Vec<usize>, Vec<usize>) {
        let mut left_indices = Vec::new();
        let mut right_indices = Vec::new();

        for &idx in sample_indices {
            if x[[idx, feature_idx]] <= threshold {
                left_indices.push(idx);
            } else {
                right_indices.push(idx);
            }
        }

        (left_indices, right_indices)
    }

    /// Evaluate split fairness
    fn evaluate_split_fairness(
        &self,
        y: &Array1<i32>,
        protected_attrs: &Array2<i32>,
        left_indices: &[usize],
        right_indices: &[usize],
    ) -> Result<f64> {
        // Extract labels for left and right splits
        let left_y: Array1<i32> = left_indices.iter().map(|&idx| y[idx]).collect();
        let right_y: Array1<i32> = right_indices.iter().map(|&idx| y[idx]).collect();

        // For simplicity, use first protected attribute
        if !self
            .criterion
            .fairness_constraints
            .protected_attributes
            .is_empty()
        {
            let protected_attr = &self.criterion.fairness_constraints.protected_attributes[0];
            let attr_col = protected_attr.column_index;

            let left_protected: Array1<i32> = left_indices
                .iter()
                .map(|&idx| protected_attrs[[idx, attr_col]])
                .collect();
            let right_protected: Array1<i32> = right_indices
                .iter()
                .map(|&idx| protected_attrs[[idx, attr_col]])
                .collect();

            self.criterion.evaluate_split(
                &left_y,
                &right_y,
                &left_protected,
                &right_protected,
                protected_attr,
            )
        } else {
            // No protected attributes, use standard impurity
            let left_impurity = self.criterion.calculate_impurity(&left_y)?;
            let right_impurity = self.criterion.calculate_impurity(&right_y)?;

            let n_left = left_y.len() as f64;
            let n_right = right_y.len() as f64;
            let n_total = n_left + n_right;

            Ok((n_left / n_total) * left_impurity + (n_right / n_total) * right_impurity)
        }
    }

    /// Calculate feature importances
    fn calculate_feature_importances(
        &self,
        x: &Array2<f64>,
        y: &Array1<i32>,
    ) -> Result<Array1<f64>> {
        let n_features = x.ncols();
        Ok(Array1::ones(n_features) / n_features as f64) // Placeholder
    }
}

/// Fair decision tree node
#[derive(Debug, Clone)]
pub struct FairTreeNode {
    /// Whether this is a leaf node
    pub is_leaf: bool,
    /// Feature index for splitting (None for leaf nodes)
    pub feature_idx: Option<usize>,
    /// Threshold for splitting (None for leaf nodes)
    pub threshold: Option<f64>,
    /// Prediction value (Some for leaf nodes)
    pub prediction: Option<i32>,
    /// Left child node
    pub left_child: Option<Box<FairTreeNode>>,
    /// Right child node
    pub right_child: Option<Box<FairTreeNode>>,
    /// Number of samples in this node
    pub sample_count: usize,
    /// Fairness score for this node
    pub fairness_score: f64,
}

/// Fair decision tree
#[derive(Debug)]
pub struct FairDecisionTree {
    /// Root node of the tree
    pub root: FairTreeNode,
    /// Feature importances
    pub feature_importances: Array1<f64>,
    /// Fairness metrics
    pub fairness_metrics: Option<FairnessMetrics>,
    /// Protected attributes used during training
    pub protected_attributes: Vec<ProtectedAttribute>,
}

impl FairDecisionTree {
    /// Predict labels for new samples
    pub fn predict(&self, x: &Array2<f64>) -> Result<Array1<i32>> {
        let n_samples = x.nrows();
        let mut predictions = Array1::zeros(n_samples);

        for i in 0..n_samples {
            let sample = x.row(i);
            predictions[i] = self.predict_single(&self.root, sample.view())?;
        }

        Ok(predictions)
    }

    /// Predict a single sample
    fn predict_single(&self, node: &FairTreeNode, sample: ArrayView1<f64>) -> Result<i32> {
        if node.is_leaf {
            Ok(node.prediction.unwrap_or(0))
        } else {
            let feature_idx = node.feature_idx.unwrap();
            let threshold = node.threshold.unwrap();

            if sample[feature_idx] <= threshold {
                if let Some(ref left_child) = node.left_child {
                    self.predict_single(left_child, sample)
                } else {
                    Ok(0)
                }
            } else {
                if let Some(ref right_child) = node.right_child {
                    self.predict_single(right_child, sample)
                } else {
                    Ok(0)
                }
            }
        }
    }

    /// Evaluate fairness metrics on test data
    pub fn evaluate_fairness(
        &mut self,
        x: &Array2<f64>,
        y_true: &Array1<i32>,
        protected_attrs: &Array2<i32>,
    ) -> Result<&FairnessMetrics> {
        let y_pred = self.predict(x)?;

        // For simplicity, use first protected attribute
        if !self.protected_attributes.is_empty() {
            let protected_attr = &self.protected_attributes[0];
            let attr_col = protected_attr.column_index;
            let protected_values: Array1<i32> = (0..x.nrows())
                .map(|i| protected_attrs[[i, attr_col]])
                .collect();

            let privileged_value = if let Some(ref priv_group) = protected_attr.privileged_group {
                if priv_group == "1" || priv_group.to_lowercase() == "true" {
                    1
                } else {
                    0
                }
            } else {
                1
            };

            let metrics = FairnessMetrics::calculate(
                y_true,
                &y_pred,
                None,
                &protected_values,
                privileged_value,
            )?;

            self.fairness_metrics = Some(metrics);
            Ok(self.fairness_metrics.as_ref().unwrap())
        } else {
            Err(sklears_core::error::SklearsError::InvalidData {
                reason: "No protected attributes defined".to_string(),
            })
        }
    }

    /// Get feature importances
    pub fn feature_importances(&self) -> &Array1<f64> {
        &self.feature_importances
    }
}

#[allow(non_snake_case)]
#[cfg(test)]
mod tests {
    use super::*;
    use scirs2_core::ndarray::Array2;

    #[test]
    fn test_protected_attribute_creation() {
        let attr = ProtectedAttribute::binary(
            "gender".to_string(),
            0,
            "male".to_string(),
            "female".to_string(),
        );

        assert_eq!(attr.name, "gender");
        assert!(attr.is_binary);
        assert!(attr.is_privileged("male"));
        assert!(!attr.is_privileged("female"));
    }

    #[test]
    fn test_fairness_metrics_calculation() {
        let y_true = Array1::from(vec![1, 1, 0, 0, 1, 0]);
        let y_pred = Array1::from(vec![1, 0, 0, 1, 1, 0]);
        let protected = Array1::from(vec![1, 1, 0, 0, 1, 0]);

        let metrics = FairnessMetrics::calculate(&y_true, &y_pred, None, &protected, 1);

        assert!(metrics.is_ok());
        let metrics = metrics.unwrap();
        assert!(metrics.demographic_parity >= 0.0);
    }

    #[test]
    fn test_fair_splitting_criterion() {
        let constraints = FairnessConstraints::default();
        let criterion = FairSplittingCriterion::new(SplitCriterion::Gini, constraints);

        let left_y = Array1::from(vec![1, 1, 0]);
        let right_y = Array1::from(vec![0, 0, 1]);
        let left_protected = Array1::from(vec![1, 0, 1]);
        let right_protected = Array1::from(vec![0, 1, 0]);

        let attr =
            ProtectedAttribute::binary("test".to_string(), 0, "1".to_string(), "0".to_string());

        let score =
            criterion.evaluate_split(&left_y, &right_y, &left_protected, &right_protected, &attr);

        assert!(score.is_ok());
    }

    #[test]
    fn test_fair_decision_tree_creation() {
        let constraints = FairnessConstraints::default();
        let criterion = FairSplittingCriterion::new(SplitCriterion::Gini, constraints);

        let builder = FairDecisionTreeBuilder::new(criterion);
        assert_eq!(builder.min_samples_split, 2);
        assert_eq!(builder.min_samples_leaf, 1);
    }
}
