//! Dimensionality reduction algorithms
//!
//! This module provides implementations of dimensionality reduction techniques,
//! such as Principal Component Analysis (PCA) and t-SNE.

use crate::core::error::{Error, Result};
use crate::dataframe::DataFrame;
use crate::ml::models::UnsupervisedModel;
use crate::optimized::OptimizedDataFrame;
use std::collections::HashMap;

/// Principal Component Analysis (PCA) implementation
///
/// PCA is a technique for dimensionality reduction that projects data
/// onto a lower-dimensional space while maximizing variance.
#[derive(Debug, Clone)]
pub struct PCA {
    /// Number of components to keep
    pub n_components: usize,
    /// Whether to standardize features before applying PCA
    pub standardize: bool,
    /// Component vectors (eigenvectors)
    pub components: Option<Vec<Vec<f64>>>,
    /// Explained variance ratio for each component
    pub explained_variance_ratio: Option<Vec<f64>>,
    /// Mean of each feature (used for centering)
    mean_values: Option<Vec<f64>>,
    /// Standard deviation of each feature (used for scaling)
    std_values: Option<Vec<f64>>,
}

impl PCA {
    /// Create a new PCA instance
    ///
    /// # Arguments
    /// * `n_components` - Number of components to keep
    /// * `standardize` - Whether to standardize features before applying PCA
    pub fn new(n_components: usize, standardize: bool) -> Self {
        PCA {
            n_components,
            standardize,
            components: None,
            explained_variance_ratio: None,
            mean_values: None,
            std_values: None,
        }
    }

    /// Get total explained variance ratio
    pub fn total_explained_variance(&self) -> Option<f64> {
        self.explained_variance_ratio
            .as_ref()
            .map(|ratios| ratios.iter().sum())
    }
}

impl UnsupervisedModel for PCA {
    fn fit(&mut self, data: &DataFrame) -> Result<()> {
        // Placeholder for a real implementation
        // In a complete implementation, this would:
        // 1. Extract numerical features
        // 2. Center and optionally scale the data
        // 3. Compute the covariance matrix
        // 4. Perform eigendecomposition
        // 5. Sort eigenvalues and corresponding eigenvectors
        // 6. Select top n_components

        // For now, just set some placeholders
        let n_features = data.ncols();
        self.mean_values = Some(vec![0.0; n_features]);

        if self.standardize {
            self.std_values = Some(vec![1.0; n_features]);
        }

        self.components = Some(vec![vec![0.0; n_features]; self.n_components]);
        self.explained_variance_ratio =
            Some(vec![1.0 / self.n_components as f64; self.n_components]);

        Ok(())
    }

    fn transform(&self, data: &DataFrame) -> Result<DataFrame> {
        // Placeholder for a real implementation
        // In a complete implementation, this would:
        // 1. Center and scale the data (if needed)
        // 2. Project data onto principal components

        // For now, just return a copy of the original data with reduced columns
        let mut result = DataFrame::new();

        // Add only the first n_components columns
        for i in 0..self.n_components.min(data.ncols()) {
            let col_name_str = format!("Column_{}", i);
            let col_name = data.column_name(i).unwrap_or(&col_name_str);
            let col: &crate::series::Series<String> = data.get_column(col_name)?;
            result.add_column(format!("PC_{}", i + 1), col.clone())?;
        }

        Ok(result)
    }
}

impl crate::ml::models::ModelEvaluator for PCA {
    fn evaluate(
        &self,
        test_data: &DataFrame,
        _test_target: &str,
    ) -> Result<crate::ml::models::ModelMetrics> {
        // For PCA, evaluation metrics could include reconstruction error
        let mut metrics = crate::ml::models::ModelMetrics::new();

        // Placeholder for reconstruction error calculation
        metrics.add_metric("reconstruction_error", 0.0);

        if let Some(ratio) = self.total_explained_variance() {
            metrics.add_metric("explained_variance_ratio", ratio);
        }

        Ok(metrics)
    }

    fn cross_validate(
        &self,
        _data: &DataFrame,
        _target: &str,
        _folds: usize,
    ) -> Result<Vec<crate::ml::models::ModelMetrics>> {
        // PCA doesn't typically use cross-validation in the same way as supervised models
        Err(Error::InvalidOperation(
            "Cross-validation is not applicable for PCA".into(),
        ))
    }
}

/// t-SNE initialization method
#[derive(Debug, Clone, Copy, PartialEq)]
pub enum TSNEInit {
    /// Random initialization
    Random,
    /// Initialize with PCA results
    PCA,
}

/// t-Distributed Stochastic Neighbor Embedding (t-SNE)
///
/// t-SNE is a nonlinear dimensionality reduction technique well-suited for
/// visualizing high-dimensional data in a low-dimensional space.
#[derive(Debug, Clone)]
pub struct TSNE {
    /// Number of components in the embedded space
    pub n_components: usize,
    /// Perplexity parameter (related to the number of nearest neighbors)
    pub perplexity: f64,
    /// Number of iterations
    pub n_iter: usize,
    /// Learning rate
    pub learning_rate: f64,
    /// Initialization method
    pub init: TSNEInit,
    /// Random seed for reproducibility
    pub random_seed: Option<u64>,
    /// Embedding coordinates
    pub embedding: Option<Vec<Vec<f64>>>,
}

impl TSNE {
    /// Create a new t-SNE instance with default parameters
    pub fn new() -> Self {
        TSNE {
            n_components: 2,
            perplexity: 30.0,
            n_iter: 1000,
            learning_rate: 200.0,
            init: TSNEInit::PCA,
            random_seed: None,
            embedding: None,
        }
    }

    /// Create a new t-SNE instance with custom parameters
    pub fn with_params(
        n_components: usize,
        perplexity: f64,
        n_iter: usize,
        learning_rate: f64,
        init: TSNEInit,
    ) -> Self {
        TSNE {
            n_components,
            perplexity,
            n_iter,
            learning_rate,
            init,
            random_seed: None,
            embedding: None,
        }
    }
}

impl UnsupervisedModel for TSNE {
    fn fit(&mut self, data: &DataFrame) -> Result<()> {
        // Placeholder for a real implementation
        // In a complete implementation, this would:
        // 1. Compute pairwise affinities in high-dimensional space
        // 2. Initialize low-dimensional embedding
        // 3. Optimize embedding to minimize KL divergence

        // For now, just set some placeholders
        let n_samples = data.nrows();
        self.embedding = Some(vec![vec![0.0; self.n_components]; n_samples]);

        Ok(())
    }

    fn transform(&self, data: &DataFrame) -> Result<DataFrame> {
        // t-SNE typically doesn't support transform on new data
        // since it doesn't learn a mapping function
        Err(Error::InvalidOperation(
            "t-SNE does not support transform on new data".into(),
        ))
    }

    fn fit_transform(&mut self, data: &DataFrame) -> Result<DataFrame> {
        self.fit(data)?;

        // Create result DataFrame with embedding coordinates
        let n_samples = data.nrows();
        let mut result = DataFrame::new();

        // Use the embedding to create result columns
        if let Some(embedding) = &self.embedding {
            for c in 0..self.n_components {
                let column_data: Vec<f64> = (0..n_samples).map(|i| embedding[i][c]).collect();

                result.add_column(
                    format!("Component_{}", c + 1),
                    crate::series::Series::new(column_data, Some(format!("Component_{}", c + 1)))?,
                )?;
            }

            Ok(result)
        } else {
            Err(Error::InvalidValue("t-SNE embedding not computed".into()))
        }
    }
}

impl crate::ml::models::ModelEvaluator for TSNE {
    fn evaluate(
        &self,
        _test_data: &DataFrame,
        _test_target: &str,
    ) -> Result<crate::ml::models::ModelMetrics> {
        // t-SNE evaluation could include KL divergence
        let mut metrics = crate::ml::models::ModelMetrics::new();

        // Placeholder for KL divergence calculation
        metrics.add_metric("kl_divergence", 0.0);

        Ok(metrics)
    }

    fn cross_validate(
        &self,
        _data: &DataFrame,
        _target: &str,
        _folds: usize,
    ) -> Result<Vec<crate::ml::models::ModelMetrics>> {
        // t-SNE doesn't typically use cross-validation
        Err(Error::InvalidOperation(
            "Cross-validation is not applicable for t-SNE".into(),
        ))
    }
}

// Re-exports - remove self references to avoid duplicate definitions
// These types are already defined in this module, so no need to re-export
