//! Fluent API for Decomposition Pipelines
//!
//! This module provides a builder-pattern fluent API for composing complex
//! decomposition workflows with preprocessing, decomposition, and post-processing
//! steps in a clean, readable manner.
//!
//! Features:
//! - Fluent builder pattern for pipeline construction
//! - Configuration presets for common use cases
//! - Method chaining for intuitive API
//! - Serializable decomposition models
//! - Type-safe pipeline composition

use crate::modular_framework::{
    AlgorithmCapabilities, DecompositionAlgorithm, DecompositionComponents, DecompositionParams,
    ParamValue, PostprocessingStep, PreprocessingStep,
};
use crate::s;
use scirs2_core::ndarray::{Array1, Array2};
use serde::{Deserialize, Serialize};
use sklears_core::{
    error::{Result, SklearsError},
    types::Float,
};
use std::any::Any;

/// Fluent API builder for decomposition pipelines
pub struct DecompositionPipelineBuilder {
    preprocessing_steps: Vec<Box<dyn PreprocessingStep>>,
    algorithm: Option<Box<dyn DecompositionAlgorithm>>,
    postprocessing_steps: Vec<Box<dyn PostprocessingStep>>,
    params: DecompositionParams,
    config: PipelineConfig,
}

impl DecompositionPipelineBuilder {
    /// Create a new decomposition pipeline builder
    pub fn new() -> Self {
        Self {
            preprocessing_steps: Vec::new(),
            algorithm: None,
            postprocessing_steps: Vec::new(),
            params: DecompositionParams::default(),
            config: PipelineConfig::default(),
        }
    }

    /// Create builder from preset configuration
    pub fn from_preset(preset: DecompositionPreset) -> Self {
        let builder = Self::new();

        match preset {
            DecompositionPreset::StandardPCA { n_components } => builder
                .with_standardization()
                .with_pca(n_components)
                .with_variance_threshold(0.01),
            DecompositionPreset::RobustPCA { n_components } => builder
                .with_robust_scaling()
                .with_robust_pca(n_components)
                .with_outlier_removal(),
            DecompositionPreset::SparsePCA {
                n_components,
                alpha,
            } => builder
                .with_standardization()
                .with_sparse_pca(n_components, alpha)
                .with_sparsity_constraint(),
            DecompositionPreset::IncrementalPCA {
                n_components,
                batch_size,
            } => builder
                .with_batch_normalization(batch_size)
                .with_incremental_pca(n_components, batch_size),
            DecompositionPreset::KernelPCA {
                n_components,
                kernel,
            } => builder
                .with_standardization()
                .with_kernel_pca(n_components, kernel),
            DecompositionPreset::FastICA { n_components } => builder
                .with_centering()
                .with_whitening()
                .with_fast_ica(n_components),
            DecompositionPreset::NMF {
                n_components,
                init_method,
            } => builder
                .with_non_negative_scaling()
                .with_nmf(n_components, init_method),
            DecompositionPreset::FactorAnalysis { n_components } => builder
                .with_standardization()
                .with_factor_analysis(n_components)
                .with_rotation("varimax"),
            DecompositionPreset::TruncatedSVD { n_components } => {
                builder.with_truncated_svd(n_components)
            }
            DecompositionPreset::TimeSeriesSSA {
                window_length,
                n_components,
            } => builder
                .with_time_series_preprocessing(window_length)
                .with_ssa(window_length, n_components),
        }
    }

    /// Add preprocessing step: standardization
    pub fn with_standardization(mut self) -> Self {
        self.preprocessing_steps
            .push(Box::new(StandardizationStep::new()));
        self
    }

    /// Add preprocessing step: robust scaling
    pub fn with_robust_scaling(mut self) -> Self {
        self.preprocessing_steps
            .push(Box::new(RobustScalingStep::new()));
        self
    }

    /// Add preprocessing step: centering
    pub fn with_centering(mut self) -> Self {
        self.preprocessing_steps
            .push(Box::new(CenteringStep::new()));
        self
    }

    /// Add preprocessing step: whitening
    pub fn with_whitening(mut self) -> Self {
        self.preprocessing_steps
            .push(Box::new(WhiteningStep::new()));
        self
    }

    /// Add preprocessing step: non-negative scaling
    pub fn with_non_negative_scaling(mut self) -> Self {
        self.preprocessing_steps
            .push(Box::new(NonNegativeScalingStep::new()));
        self
    }

    /// Add preprocessing step: batch normalization
    pub fn with_batch_normalization(mut self, batch_size: usize) -> Self {
        self.preprocessing_steps
            .push(Box::new(BatchNormalizationStep::new(batch_size)));
        self
    }

    /// Add preprocessing step: time series preprocessing
    pub fn with_time_series_preprocessing(mut self, window_length: usize) -> Self {
        self.preprocessing_steps
            .push(Box::new(TimeSeriesPreprocessingStep::new(window_length)));
        self
    }

    /// Set decomposition algorithm: PCA
    pub fn with_pca(mut self, n_components: usize) -> Self {
        self.params.n_components = Some(n_components);
        self.algorithm = Some(Box::new(PCAAlgorithm::new(n_components)));
        self
    }

    /// Set decomposition algorithm: Robust PCA
    pub fn with_robust_pca(mut self, n_components: usize) -> Self {
        self.params.n_components = Some(n_components);
        self.algorithm = Some(Box::new(RobustPCAAlgorithm::new(n_components)));
        self
    }

    /// Set decomposition algorithm: Sparse PCA
    pub fn with_sparse_pca(mut self, n_components: usize, alpha: Float) -> Self {
        self.params.n_components = Some(n_components);
        self.params
            .algorithm_specific
            .insert("alpha".to_string(), ParamValue::Float(alpha));
        self.algorithm = Some(Box::new(SparsePCAAlgorithm::new(n_components, alpha)));
        self
    }

    /// Set decomposition algorithm: Incremental PCA
    pub fn with_incremental_pca(mut self, n_components: usize, batch_size: usize) -> Self {
        self.params.n_components = Some(n_components);
        self.params.algorithm_specific.insert(
            "batch_size".to_string(),
            ParamValue::Integer(batch_size as i64),
        );
        self.algorithm = Some(Box::new(IncrementalPCAAlgorithm::new(
            n_components,
            batch_size,
        )));
        self
    }

    /// Set decomposition algorithm: Kernel PCA
    pub fn with_kernel_pca(mut self, n_components: usize, kernel: String) -> Self {
        self.params.n_components = Some(n_components);
        self.params
            .algorithm_specific
            .insert("kernel".to_string(), ParamValue::String(kernel.clone()));
        self.algorithm = Some(Box::new(KernelPCAAlgorithm::new(n_components, kernel)));
        self
    }

    /// Set decomposition algorithm: FastICA
    pub fn with_fast_ica(mut self, n_components: usize) -> Self {
        self.params.n_components = Some(n_components);
        self.algorithm = Some(Box::new(FastICAAlgorithm::new(n_components)));
        self
    }

    /// Set decomposition algorithm: NMF
    pub fn with_nmf(mut self, n_components: usize, init_method: String) -> Self {
        self.params.n_components = Some(n_components);
        self.params.algorithm_specific.insert(
            "init_method".to_string(),
            ParamValue::String(init_method.clone()),
        );
        self.algorithm = Some(Box::new(NMFAlgorithm::new(n_components, init_method)));
        self
    }

    /// Set decomposition algorithm: Factor Analysis
    pub fn with_factor_analysis(mut self, n_components: usize) -> Self {
        self.params.n_components = Some(n_components);
        self.algorithm = Some(Box::new(FactorAnalysisAlgorithm::new(n_components)));
        self
    }

    /// Set decomposition algorithm: Truncated SVD
    pub fn with_truncated_svd(mut self, n_components: usize) -> Self {
        self.params.n_components = Some(n_components);
        self.algorithm = Some(Box::new(TruncatedSVDAlgorithm::new(n_components)));
        self
    }

    /// Set decomposition algorithm: SSA (Singular Spectrum Analysis)
    pub fn with_ssa(mut self, window_length: usize, n_components: usize) -> Self {
        self.params.n_components = Some(n_components);
        self.params.algorithm_specific.insert(
            "window_length".to_string(),
            ParamValue::Integer(window_length as i64),
        );
        self.algorithm = Some(Box::new(SSAAlgorithm::new(window_length, n_components)));
        self
    }

    /// Add post-processing step: variance threshold
    pub fn with_variance_threshold(mut self, threshold: Float) -> Self {
        self.postprocessing_steps
            .push(Box::new(VarianceThresholdStep::new(threshold)));
        self
    }

    /// Add post-processing step: outlier removal
    pub fn with_outlier_removal(mut self) -> Self {
        self.postprocessing_steps
            .push(Box::new(OutlierRemovalStep::new()));
        self
    }

    /// Add post-processing step: sparsity constraint
    pub fn with_sparsity_constraint(mut self) -> Self {
        self.postprocessing_steps
            .push(Box::new(SparsityConstraintStep::new()));
        self
    }

    /// Add post-processing step: rotation (e.g., varimax)
    pub fn with_rotation(mut self, method: &str) -> Self {
        self.postprocessing_steps
            .push(Box::new(RotationStep::new(method.to_string())));
        self
    }

    /// Set number of components
    pub fn n_components(mut self, n: usize) -> Self {
        self.params.n_components = Some(n);
        self
    }

    /// Set tolerance
    pub fn tolerance(mut self, tol: Float) -> Self {
        self.params.tolerance = Some(tol);
        self
    }

    /// Set maximum iterations
    pub fn max_iterations(mut self, max_iter: usize) -> Self {
        self.params.max_iterations = Some(max_iter);
        self
    }

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

    /// Enable verbose output
    pub fn verbose(mut self, verbose: bool) -> Self {
        self.config.verbose = verbose;
        self
    }

    /// Enable automatic parameter tuning
    pub fn auto_tune(mut self, enabled: bool) -> Self {
        self.config.auto_tune = enabled;
        self
    }

    /// Set validation split ratio
    pub fn validation_split(mut self, ratio: Float) -> Self {
        self.config.validation_split = Some(ratio);
        self
    }

    /// Build the decomposition pipeline
    pub fn build(self) -> Result<DecompositionPipeline> {
        if self.algorithm.is_none() {
            return Err(SklearsError::InvalidInput(
                "No decomposition algorithm specified".to_string(),
            ));
        }

        Ok(DecompositionPipeline {
            preprocessing_steps: self.preprocessing_steps,
            algorithm: self.algorithm.unwrap(),
            postprocessing_steps: self.postprocessing_steps,
            params: self.params,
            config: self.config,
            fitted: false,
        })
    }
}

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

/// Configuration presets for common decomposition use cases
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum DecompositionPreset {
    /// Standard PCA with centering and scaling
    StandardPCA { n_components: usize },

    /// Robust PCA resistant to outliers
    RobustPCA { n_components: usize },

    /// Sparse PCA for interpretable components
    SparsePCA { n_components: usize, alpha: Float },

    /// Incremental PCA for large datasets
    IncrementalPCA {
        n_components: usize,
        batch_size: usize,
    },

    /// Kernel PCA for non-linear dimensionality reduction
    KernelPCA { n_components: usize, kernel: String },

    /// FastICA for independent component analysis
    FastICA { n_components: usize },

    /// NMF for non-negative matrix factorization
    NMF {
        n_components: usize,
        init_method: String,
    },

    /// Factor Analysis with rotation
    FactorAnalysis { n_components: usize },

    /// Truncated SVD for sparse matrices
    TruncatedSVD { n_components: usize },

    /// Singular Spectrum Analysis for time series
    TimeSeriesSSA {
        window_length: usize,
        n_components: usize,
    },
}

/// Pipeline configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PipelineConfig {
    pub verbose: bool,
    pub auto_tune: bool,
    pub validation_split: Option<Float>,
    pub early_stopping: bool,
    pub save_intermediate: bool,
}

impl Default for PipelineConfig {
    fn default() -> Self {
        Self {
            verbose: false,
            auto_tune: false,
            validation_split: None,
            early_stopping: false,
            save_intermediate: false,
        }
    }
}

/// Decomposition pipeline that chains preprocessing, decomposition, and post-processing
pub struct DecompositionPipeline {
    preprocessing_steps: Vec<Box<dyn PreprocessingStep>>,
    algorithm: Box<dyn DecompositionAlgorithm>,
    postprocessing_steps: Vec<Box<dyn PostprocessingStep>>,
    params: DecompositionParams,
    config: PipelineConfig,
    fitted: bool,
}

impl DecompositionPipeline {
    /// Fit the pipeline to data
    pub fn fit(&mut self, data: &Array2<Float>) -> Result<&mut Self> {
        if self.config.verbose {
            println!("Starting decomposition pipeline...");
        }

        // Apply preprocessing
        let mut preprocessed_data = data.clone();
        for (idx, step) in self.preprocessing_steps.iter_mut().enumerate() {
            if self.config.verbose {
                println!("  Applying preprocessing step {}: {}", idx + 1, step.name());
            }
            preprocessed_data = step.process(&preprocessed_data)?;
        }

        // Fit decomposition algorithm
        if self.config.verbose {
            println!("  Fitting algorithm: {}", self.algorithm.name());
        }
        self.algorithm.fit(&preprocessed_data, &self.params)?;

        self.fitted = true;

        if self.config.verbose {
            println!("Pipeline fitted successfully!");
        }

        Ok(self)
    }

    /// Transform data using the fitted pipeline
    pub fn transform(&self, data: &Array2<Float>) -> Result<Array2<Float>> {
        if !self.fitted {
            return Err(SklearsError::InvalidInput(
                "Pipeline must be fitted before transform".to_string(),
            ));
        }

        // Apply preprocessing
        let mut preprocessed_data = data.clone();
        for _step in &self.preprocessing_steps {
            // Note: This is a simplified implementation
            // In a full implementation, steps would need interior mutability
            // or we would clone the step for processing
            preprocessed_data = preprocessed_data.clone();
        }

        // Transform using algorithm
        let transformed_data = self.algorithm.transform(&preprocessed_data)?;

        Ok(transformed_data)
    }

    /// Fit and transform in one step
    pub fn fit_transform(&mut self, data: &Array2<Float>) -> Result<Array2<Float>> {
        self.fit(data)?;
        self.transform(data)
    }

    /// Inverse transform if supported
    pub fn inverse_transform(&self, data: &Array2<Float>) -> Result<Array2<Float>> {
        if !self.fitted {
            return Err(SklearsError::InvalidInput(
                "Pipeline must be fitted before inverse_transform".to_string(),
            ));
        }

        // Inverse transform using algorithm
        let mut result = self.algorithm.inverse_transform(data)?;

        // Apply inverse preprocessing in reverse order
        for step in self.preprocessing_steps.iter().rev() {
            result = step.inverse_process(&result)?;
        }

        Ok(result)
    }

    /// Get decomposition components
    pub fn get_components(&self) -> Result<DecompositionComponents> {
        if !self.fitted {
            return Err(SklearsError::InvalidInput(
                "Pipeline must be fitted before getting components".to_string(),
            ));
        }

        self.algorithm.get_components()
    }

    /// Get explained variance ratio if available
    pub fn explained_variance_ratio(&self) -> Result<Array1<Float>> {
        let components = self.get_components()?;
        components.explained_variance_ratio.ok_or_else(|| {
            SklearsError::InvalidInput("No explained variance available".to_string())
        })
    }

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

    /// Serialize pipeline to JSON
    pub fn to_json(&self) -> Result<String> {
        let serializable_pipeline = SerializablePipeline {
            algorithm_name: self.algorithm.name().to_string(),
            params: self.params.clone(),
            config: self.config.clone(),
        };

        // Simplified JSON serialization without serde_json dependency
        Ok(format!(
            "{{\"algorithm\":\"{}\"}}",
            serializable_pipeline.algorithm_name
        ))
    }

    /// Deserialize pipeline from JSON
    pub fn from_json(_json: &str) -> Result<Self> {
        // Implementation would reconstruct pipeline from JSON
        Err(SklearsError::InvalidInput(
            "Deserialization not yet implemented".to_string(),
        ))
    }
}

/// Serializable representation of pipeline
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SerializablePipeline {
    pub algorithm_name: String,
    pub params: DecompositionParams,
    pub config: PipelineConfig,
}

// Placeholder implementations for preprocessing steps
// These would be fully implemented in the actual codebase

#[derive(Clone)]
struct StandardizationStep {
    mean: Option<Array1<Float>>,
    std: Option<Array1<Float>>,
}

impl StandardizationStep {
    fn new() -> Self {
        Self {
            mean: None,
            std: None,
        }
    }
}

impl PreprocessingStep for StandardizationStep {
    fn name(&self) -> &str {
        "Standardization"
    }

    fn process(&mut self, data: &Array2<Float>) -> Result<Array2<Float>> {
        let mean = data.mean_axis(scirs2_core::ndarray::Axis(0)).unwrap();
        let std = data
            .mapv(|x| x.powi(2))
            .mean_axis(scirs2_core::ndarray::Axis(0))
            .unwrap()
            .mapv(|x| x.sqrt());

        self.mean = Some(mean.clone());
        self.std = Some(std.clone());

        let result = (data - &mean) / &std;
        Ok(result)
    }

    fn is_fitted(&self) -> bool {
        self.mean.is_some()
    }

    fn clone_step(&self) -> Box<dyn PreprocessingStep> {
        Box::new(self.clone())
    }
}

#[derive(Clone)]
struct RobustScalingStep;
impl RobustScalingStep {
    fn new() -> Self {
        Self
    }
}
impl PreprocessingStep for RobustScalingStep {
    fn name(&self) -> &str {
        "RobustScaling"
    }
    fn process(&mut self, data: &Array2<Float>) -> Result<Array2<Float>> {
        Ok(data.clone())
    }
    fn is_fitted(&self) -> bool {
        true
    }
    fn clone_step(&self) -> Box<dyn PreprocessingStep> {
        Box::new(self.clone())
    }
}

#[derive(Clone)]
struct CenteringStep {
    mean: Option<Array1<Float>>,
}
impl CenteringStep {
    fn new() -> Self {
        Self { mean: None }
    }
}
impl PreprocessingStep for CenteringStep {
    fn name(&self) -> &str {
        "Centering"
    }
    fn process(&mut self, data: &Array2<Float>) -> Result<Array2<Float>> {
        let mean = data.mean_axis(scirs2_core::ndarray::Axis(0)).unwrap();
        self.mean = Some(mean.clone());
        Ok(data - &mean)
    }
    fn is_fitted(&self) -> bool {
        self.mean.is_some()
    }
    fn clone_step(&self) -> Box<dyn PreprocessingStep> {
        Box::new(self.clone())
    }
}

#[derive(Clone)]
struct WhiteningStep;
impl WhiteningStep {
    fn new() -> Self {
        Self
    }
}
impl PreprocessingStep for WhiteningStep {
    fn name(&self) -> &str {
        "Whitening"
    }
    fn process(&mut self, data: &Array2<Float>) -> Result<Array2<Float>> {
        Ok(data.clone())
    }
    fn is_fitted(&self) -> bool {
        true
    }
    fn clone_step(&self) -> Box<dyn PreprocessingStep> {
        Box::new(self.clone())
    }
}

#[derive(Clone)]
struct NonNegativeScalingStep;
impl NonNegativeScalingStep {
    fn new() -> Self {
        Self
    }
}
impl PreprocessingStep for NonNegativeScalingStep {
    fn name(&self) -> &str {
        "NonNegativeScaling"
    }
    fn process(&mut self, data: &Array2<Float>) -> Result<Array2<Float>> {
        Ok(data.clone())
    }
    fn is_fitted(&self) -> bool {
        true
    }
    fn clone_step(&self) -> Box<dyn PreprocessingStep> {
        Box::new(self.clone())
    }
}

#[derive(Clone)]
struct BatchNormalizationStep {
    batch_size: usize,
}
impl BatchNormalizationStep {
    fn new(batch_size: usize) -> Self {
        Self { batch_size }
    }
}
impl PreprocessingStep for BatchNormalizationStep {
    fn name(&self) -> &str {
        "BatchNormalization"
    }
    fn process(&mut self, data: &Array2<Float>) -> Result<Array2<Float>> {
        Ok(data.clone())
    }
    fn is_fitted(&self) -> bool {
        true
    }
    fn clone_step(&self) -> Box<dyn PreprocessingStep> {
        Box::new(self.clone())
    }
}

#[derive(Clone)]
struct TimeSeriesPreprocessingStep {
    window_length: usize,
}
impl TimeSeriesPreprocessingStep {
    fn new(window_length: usize) -> Self {
        Self { window_length }
    }
}
impl PreprocessingStep for TimeSeriesPreprocessingStep {
    fn name(&self) -> &str {
        "TimeSeriesPreprocessing"
    }
    fn process(&mut self, data: &Array2<Float>) -> Result<Array2<Float>> {
        Ok(data.clone())
    }
    fn is_fitted(&self) -> bool {
        true
    }
    fn clone_step(&self) -> Box<dyn PreprocessingStep> {
        Box::new(self.clone())
    }
}

// Placeholder implementations for algorithms

struct PCAAlgorithm {
    n_components: usize,
    components: Option<Array2<Float>>,
}
impl PCAAlgorithm {
    fn new(n_components: usize) -> Self {
        Self {
            n_components,
            components: None,
        }
    }
}
impl DecompositionAlgorithm for PCAAlgorithm {
    fn name(&self) -> &str {
        "PCA"
    }
    fn description(&self) -> &str {
        "Principal Component Analysis"
    }
    fn capabilities(&self) -> AlgorithmCapabilities {
        AlgorithmCapabilities::default()
    }
    fn validate_params(&self, _params: &DecompositionParams) -> Result<()> {
        Ok(())
    }
    fn fit(&mut self, data: &Array2<Float>, _params: &DecompositionParams) -> Result<()> {
        self.components = Some(data.slice(s![.., ..self.n_components]).to_owned());
        Ok(())
    }
    fn transform(&self, data: &Array2<Float>) -> Result<Array2<Float>> {
        Ok(data.slice(s![.., ..self.n_components]).to_owned())
    }
    fn get_components(&self) -> Result<DecompositionComponents> {
        Ok(DecompositionComponents::default())
    }
    fn is_fitted(&self) -> bool {
        self.components.is_some()
    }
    fn clone_algorithm(&self) -> Box<dyn DecompositionAlgorithm> {
        Box::new(Self {
            n_components: self.n_components,
            components: self.components.clone(),
        })
    }
    fn as_any(&self) -> &dyn Any {
        self
    }
}

// Additional placeholder algorithm implementations...
macro_rules! impl_placeholder_algorithm {
    ($name:ident, $display_name:expr, $($field:ident: $field_type:ty),*) => {
        struct $name {
            n_components: usize,
            $(
                $field: $field_type,
            )*
            components: Option<Array2<Float>>,
        }
        impl $name {
            fn new(n_components: usize $(, $field: $field_type)*) -> Self {
                Self {
                    n_components,
                    $(
                        $field,
                    )*
                    components: None,
                }
            }
        }
        impl DecompositionAlgorithm for $name {
            fn name(&self) -> &str { $display_name }
            fn description(&self) -> &str { $display_name }
            fn capabilities(&self) -> AlgorithmCapabilities { AlgorithmCapabilities::default() }
            fn validate_params(&self, _params: &DecompositionParams) -> Result<()> { Ok(()) }
            fn fit(&mut self, data: &Array2<Float>, _params: &DecompositionParams) -> Result<()> {
                self.components = Some(data.slice(s![.., ..self.n_components]).to_owned());
                Ok(())
            }
            fn transform(&self, data: &Array2<Float>) -> Result<Array2<Float>> {
                Ok(data.slice(s![.., ..self.n_components]).to_owned())
            }
            fn get_components(&self) -> Result<DecompositionComponents> {
                Ok(DecompositionComponents::default())
            }
            fn is_fitted(&self) -> bool { self.components.is_some() }
            fn clone_algorithm(&self) -> Box<dyn DecompositionAlgorithm> {
                Box::new(Self {
                    n_components: self.n_components,
                    $(
                        $field: self.$field.clone(),
                    )*
                    components: self.components.clone(),
                })
            }
            fn as_any(&self) -> &dyn Any { self }
        }
    };
}

impl_placeholder_algorithm!(RobustPCAAlgorithm, "RobustPCA",);
impl_placeholder_algorithm!(SparsePCAAlgorithm, "SparsePCA", alpha: Float);
impl_placeholder_algorithm!(IncrementalPCAAlgorithm, "IncrementalPCA", batch_size: usize);
impl_placeholder_algorithm!(KernelPCAAlgorithm, "KernelPCA", kernel: String);
impl_placeholder_algorithm!(FastICAAlgorithm, "FastICA",);
impl_placeholder_algorithm!(NMFAlgorithm, "NMF", init_method: String);
impl_placeholder_algorithm!(FactorAnalysisAlgorithm, "FactorAnalysis",);
impl_placeholder_algorithm!(TruncatedSVDAlgorithm, "TruncatedSVD",);
impl_placeholder_algorithm!(SSAAlgorithm, "SSA", window_length: usize);

// Placeholder implementations for post-processing steps

#[derive(Clone)]
struct VarianceThresholdStep {
    threshold: Float,
}
impl VarianceThresholdStep {
    fn new(threshold: Float) -> Self {
        Self { threshold }
    }
}
impl PostprocessingStep for VarianceThresholdStep {
    fn name(&self) -> &str {
        "VarianceThreshold"
    }
    fn process(&self, components: DecompositionComponents) -> Result<DecompositionComponents> {
        Ok(components)
    }
    fn clone_step(&self) -> Box<dyn PostprocessingStep> {
        Box::new(self.clone())
    }
}

#[derive(Clone)]
struct OutlierRemovalStep;
impl OutlierRemovalStep {
    fn new() -> Self {
        Self
    }
}
impl PostprocessingStep for OutlierRemovalStep {
    fn name(&self) -> &str {
        "OutlierRemoval"
    }
    fn process(&self, components: DecompositionComponents) -> Result<DecompositionComponents> {
        Ok(components)
    }
    fn clone_step(&self) -> Box<dyn PostprocessingStep> {
        Box::new(self.clone())
    }
}

#[derive(Clone)]
struct SparsityConstraintStep;
impl SparsityConstraintStep {
    fn new() -> Self {
        Self
    }
}
impl PostprocessingStep for SparsityConstraintStep {
    fn name(&self) -> &str {
        "SparsityConstraint"
    }
    fn process(&self, components: DecompositionComponents) -> Result<DecompositionComponents> {
        Ok(components)
    }
    fn clone_step(&self) -> Box<dyn PostprocessingStep> {
        Box::new(self.clone())
    }
}

#[derive(Clone)]
struct RotationStep {
    method: String,
}
impl RotationStep {
    fn new(method: String) -> Self {
        Self { method }
    }
}
impl PostprocessingStep for RotationStep {
    fn name(&self) -> &str {
        "Rotation"
    }
    fn process(&self, components: DecompositionComponents) -> Result<DecompositionComponents> {
        Ok(components)
    }
    fn clone_step(&self) -> Box<dyn PostprocessingStep> {
        Box::new(self.clone())
    }
}

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

    #[test]
    fn test_pipeline_builder() {
        let builder = DecompositionPipelineBuilder::new()
            .with_standardization()
            .with_pca(3)
            .n_components(3)
            .tolerance(1e-6);

        assert!(builder.build().is_ok());
    }

    #[test]
    fn test_preset_standard_pca() {
        let builder = DecompositionPipelineBuilder::from_preset(DecompositionPreset::StandardPCA {
            n_components: 5,
        });

        let pipeline = builder.build();
        assert!(pipeline.is_ok());
    }

    #[test]
    fn test_pipeline_fit_transform() {
        use scirs2_core::random::thread_rng;
        let mut rng = thread_rng();

        let data = Array2::from_shape_fn((50, 10), |(i, j)| {
            (i + j) as Float + rng.gen_range(-0.1..0.1)
        });

        let mut pipeline = DecompositionPipelineBuilder::new()
            .with_standardization()
            .with_pca(3)
            .build()
            .unwrap();

        let result = pipeline.fit_transform(&data);
        assert!(result.is_ok());
    }

    #[test]
    fn test_fluent_api_chaining() {
        let pipeline = DecompositionPipelineBuilder::new()
            .with_standardization()
            .with_pca(5)
            .n_components(5)
            .tolerance(1e-6)
            .max_iterations(100)
            .random_seed(42)
            .verbose(false)
            .build();

        assert!(pipeline.is_ok());
    }

    #[test]
    fn test_serialization() {
        let mut pipeline = DecompositionPipelineBuilder::new()
            .with_pca(3)
            .build()
            .unwrap();

        use scirs2_core::random::thread_rng;
        let mut rng = thread_rng();
        let data = Array2::from_shape_fn((20, 5), |(i, j)| {
            (i + j) as Float + rng.gen_range(-0.1..0.1)
        });

        pipeline.fit(&data).unwrap();

        let json = pipeline.to_json();
        assert!(json.is_ok());
    }
}
