quantrs2_ml/

classical_ml_integration.rs

//! Classical ML pipeline integration for QuantRS2-ML
//!
//! This module provides seamless integration between quantum ML models and
//! existing classical ML workflows, enabling hybrid approaches and easy
//! adoption of quantum ML in production environments.

use crate::benchmarking::{BenchmarkConfig, BenchmarkFramework};
use crate::domain_templates::{DomainTemplateManager, TemplateConfig};
use crate::error::{MLError, Result};
use crate::keras_api::{Dense, QuantumDense, Sequential};
use crate::model_zoo::{ModelZoo, QuantumModel};
use crate::pytorch_api::{QuantumLinear, QuantumModule};
use crate::sklearn_compatibility::{QuantumMLPClassifier, QuantumSVC};
use quantrs2_circuit::prelude::*;
use quantrs2_core::prelude::*;
use scirs2_core::ndarray::{s, Array1, Array2, ArrayD, Axis, IxDyn};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;

/// Hybrid quantum-classical ML pipeline manager
pub struct HybridPipelineManager {
    /// Available pipeline templates
    pipeline_templates: HashMap<String, PipelineTemplate>,
    /// Registered preprocessors
    preprocessors: HashMap<String, Box<dyn DataPreprocessor>>,
    /// Model registry
    model_registry: ModelRegistry,
    /// Ensemble strategies
    ensemble_strategies: HashMap<String, Box<dyn EnsembleStrategy>>,
}

/// Pipeline template definition
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PipelineTemplate {
    /// Template name
    pub name: String,
    /// Description
    pub description: String,
    /// Pipeline stages
    pub stages: Vec<PipelineStage>,
    /// Default hyperparameters
    pub hyperparameters: HashMap<String, f64>,
    /// Suitable data types
    pub data_types: Vec<String>,
    /// Performance characteristics
    pub performance_profile: PerformanceProfile,
}

/// Pipeline stage definition
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum PipelineStage {
    /// Data preprocessing
    Preprocessing {
        method: String,
        parameters: HashMap<String, f64>,
    },
    /// Feature engineering
    FeatureEngineering {
        method: String,
        parameters: HashMap<String, f64>,
    },
    /// Model training
    Training {
        model_type: ModelType,
        hyperparameters: HashMap<String, f64>,
    },
    /// Model ensemble
    Ensemble { strategy: String, weights: Vec<f64> },
    /// Post-processing
    PostProcessing {
        method: String,
        parameters: HashMap<String, f64>,
    },
}

/// Model types in pipelines
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ModelType {
    /// Pure classical model
    Classical(String),
    /// Pure quantum model
    Quantum(String),
    /// Hybrid quantum-classical model
    Hybrid(String),
    /// Ensemble of models
    Ensemble(Vec<ModelType>),
}

/// Performance profile for pipelines
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PerformanceProfile {
    /// Expected accuracy range
    pub accuracy_range: (f64, f64),
    /// Training time estimate (minutes)
    pub training_time_minutes: f64,
    /// Memory requirements (GB)
    pub memory_gb: f64,
    /// Scalability characteristics
    pub scalability: ScalabilityProfile,
}

/// Scalability characteristics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ScalabilityProfile {
    /// Maximum samples handled efficiently
    pub max_samples: usize,
    /// Maximum features handled efficiently
    pub max_features: usize,
    /// Parallel processing capability
    pub parallel_capable: bool,
    /// Distributed processing capability
    pub distributed_capable: bool,
}

/// Data preprocessing trait
pub trait DataPreprocessor: Send + Sync {
    /// Fit preprocessor to data
    fn fit(&mut self, X: &ArrayD<f64>) -> Result<()>;

    /// Transform data
    fn transform(&self, X: &ArrayD<f64>) -> Result<ArrayD<f64>>;

    /// Fit and transform in one step
    fn fit_transform(&mut self, X: &ArrayD<f64>) -> Result<ArrayD<f64>> {
        self.fit(X)?;
        self.transform(X)
    }

    /// Get preprocessing parameters
    fn get_params(&self) -> HashMap<String, f64>;

    /// Set preprocessing parameters
    fn set_params(&mut self, params: HashMap<String, f64>) -> Result<()>;
}

/// Model registry for managing quantum and classical models
pub struct ModelRegistry {
    /// Registered quantum models
    quantum_models: HashMap<String, Box<dyn QuantumModel>>,
    /// Registered classical models
    classical_models: HashMap<String, Box<dyn ClassicalModel>>,
    /// Hybrid models
    hybrid_models: HashMap<String, Box<dyn HybridModel>>,
}

/// Classical model trait for integration
pub trait ClassicalModel: Send + Sync {
    /// Train the model
    fn fit(&mut self, X: &ArrayD<f64>, y: &ArrayD<f64>) -> Result<()>;

    /// Make predictions
    fn predict(&self, X: &ArrayD<f64>) -> Result<ArrayD<f64>>;

    /// Get model parameters
    fn get_params(&self) -> HashMap<String, f64>;

    /// Set model parameters
    fn set_params(&mut self, params: HashMap<String, f64>) -> Result<()>;

    /// Get feature importance (if available)
    fn feature_importance(&self) -> Option<Array1<f64>>;
}

/// Hybrid quantum-classical model trait
pub trait HybridModel: Send + Sync {
    /// Train the hybrid model
    fn fit(&mut self, X: &ArrayD<f64>, y: &ArrayD<f64>) -> Result<()>;

    /// Make predictions using hybrid approach
    fn predict(&self, X: &ArrayD<f64>) -> Result<ArrayD<f64>>;

    /// Get quantum component performance
    fn quantum_performance(&self) -> ModelPerformance;

    /// Get classical component performance
    fn classical_performance(&self) -> ModelPerformance;

    /// Get hybrid strategy description
    fn strategy_description(&self) -> String;
}

/// Model performance metrics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ModelPerformance {
    /// Accuracy metric
    pub accuracy: f64,
    /// Training time (seconds)
    pub training_time: f64,
    /// Inference time (milliseconds)
    pub inference_time: f64,
    /// Memory usage (MB)
    pub memory_usage: f64,
}

/// Ensemble strategy trait
pub trait EnsembleStrategy: Send + Sync {
    /// Combine predictions from multiple models
    fn combine_predictions(&self, predictions: Vec<ArrayD<f64>>) -> Result<ArrayD<f64>>;

    /// Get ensemble weights
    fn get_weights(&self) -> Vec<f64>;

    /// Update weights based on performance
    fn update_weights(&mut self, performances: Vec<f64>) -> Result<()>;

    /// Strategy description
    fn description(&self) -> String;
}

impl HybridPipelineManager {
    /// Create new hybrid pipeline manager
    pub fn new() -> Self {
        let mut manager = Self {
            pipeline_templates: HashMap::new(),
            preprocessors: HashMap::new(),
            model_registry: ModelRegistry::new(),
            ensemble_strategies: HashMap::new(),
        };

        manager.register_default_components();
        manager
    }

    /// Register default pipeline components
    fn register_default_components(&mut self) {
        self.register_default_templates();
        self.register_default_preprocessors();
        self.register_default_ensemble_strategies();
    }

    /// Register default pipeline templates
    fn register_default_templates(&mut self) {
        // Hybrid classification pipeline
        self.pipeline_templates.insert(
            "hybrid_classification".to_string(),
            PipelineTemplate {
                name: "Hybrid Quantum-Classical Classification".to_string(),
                description: "Combines quantum feature learning with classical decision making"
                    .to_string(),
                stages: vec![
                    PipelineStage::Preprocessing {
                        method: "standard_scaler".to_string(),
                        parameters: HashMap::new(),
                    },
                    PipelineStage::FeatureEngineering {
                        method: "quantum_feature_map".to_string(),
                        parameters: [("num_qubits".to_string(), 8.0)].iter().cloned().collect(),
                    },
                    PipelineStage::Training {
                        model_type: ModelType::Hybrid("quantum_classical_ensemble".to_string()),
                        hyperparameters: [
                            ("quantum_weight".to_string(), 0.6),
                            ("classical_weight".to_string(), 0.4),
                        ]
                        .iter()
                        .cloned()
                        .collect(),
                    },
                ],
                hyperparameters: [
                    ("learning_rate".to_string(), 0.01),
                    ("epochs".to_string(), 100.0),
                    ("batch_size".to_string(), 32.0),
                ]
                .iter()
                .cloned()
                .collect(),
                data_types: vec!["tabular".to_string(), "structured".to_string()],
                performance_profile: PerformanceProfile {
                    accuracy_range: (0.85, 0.95),
                    training_time_minutes: 30.0,
                    memory_gb: 2.0,
                    scalability: ScalabilityProfile {
                        max_samples: 100000,
                        max_features: 100,
                        parallel_capable: true,
                        distributed_capable: false,
                    },
                },
            },
        );

        // Quantum ensemble pipeline
        self.pipeline_templates.insert(
            "quantum_ensemble".to_string(),
            PipelineTemplate {
                name: "Quantum Model Ensemble".to_string(),
                description: "Ensemble of multiple quantum models with different ansatz types"
                    .to_string(),
                stages: vec![
                    PipelineStage::Preprocessing {
                        method: "quantum_data_encoder".to_string(),
                        parameters: HashMap::new(),
                    },
                    PipelineStage::Training {
                        model_type: ModelType::Ensemble(vec![
                            ModelType::Quantum("qnn_hardware_efficient".to_string()),
                            ModelType::Quantum("qnn_real_amplitudes".to_string()),
                            ModelType::Quantum("qsvm_zz_feature_map".to_string()),
                        ]),
                        hyperparameters: HashMap::new(),
                    },
                    PipelineStage::Ensemble {
                        strategy: "weighted_voting".to_string(),
                        weights: vec![0.4, 0.3, 0.3],
                    },
                ],
                hyperparameters: [
                    ("num_qubits".to_string(), 10.0),
                    ("num_layers".to_string(), 3.0),
                ]
                .iter()
                .cloned()
                .collect(),
                data_types: vec!["tabular".to_string(), "quantum_ready".to_string()],
                performance_profile: PerformanceProfile {
                    accuracy_range: (0.88, 0.96),
                    training_time_minutes: 60.0,
                    memory_gb: 4.0,
                    scalability: ScalabilityProfile {
                        max_samples: 50000,
                        max_features: 50,
                        parallel_capable: true,
                        distributed_capable: true,
                    },
                },
            },
        );

        // AutoML quantum pipeline
        self.pipeline_templates.insert(
            "quantum_automl".to_string(),
            PipelineTemplate {
                name: "Quantum AutoML Pipeline".to_string(),
                description: "Automated quantum model selection and hyperparameter optimization"
                    .to_string(),
                stages: vec![
                    PipelineStage::Preprocessing {
                        method: "auto_preprocessor".to_string(),
                        parameters: HashMap::new(),
                    },
                    PipelineStage::FeatureEngineering {
                        method: "auto_feature_engineering".to_string(),
                        parameters: HashMap::new(),
                    },
                    PipelineStage::Training {
                        model_type: ModelType::Hybrid("auto_selected".to_string()),
                        hyperparameters: HashMap::new(),
                    },
                ],
                hyperparameters: [
                    ("search_budget".to_string(), 100.0),
                    ("validation_split".to_string(), 0.2),
                ]
                .iter()
                .cloned()
                .collect(),
                data_types: vec!["any".to_string()],
                performance_profile: PerformanceProfile {
                    accuracy_range: (0.80, 0.98),
                    training_time_minutes: 180.0,
                    memory_gb: 8.0,
                    scalability: ScalabilityProfile {
                        max_samples: 200000,
                        max_features: 200,
                        parallel_capable: true,
                        distributed_capable: true,
                    },
                },
            },
        );
    }

    /// Register default preprocessors
    fn register_default_preprocessors(&mut self) {
        self.preprocessors.insert(
            "standard_scaler".to_string(),
            Box::new(StandardScaler::new()),
        );
        self.preprocessors
            .insert("min_max_scaler".to_string(), Box::new(MinMaxScaler::new()));
        self.preprocessors.insert(
            "quantum_data_encoder".to_string(),
            Box::new(QuantumDataEncoder::new()),
        );
        self.preprocessors.insert(
            "principal_component_analysis".to_string(),
            Box::new(PrincipalComponentAnalysis::new()),
        );
    }

    /// Register default ensemble strategies
    fn register_default_ensemble_strategies(&mut self) {
        self.ensemble_strategies.insert(
            "weighted_voting".to_string(),
            Box::new(WeightedVotingEnsemble::new()),
        );
        self.ensemble_strategies
            .insert("stacking".to_string(), Box::new(StackingEnsemble::new()));
        self.ensemble_strategies.insert(
            "adaptive_weighting".to_string(),
            Box::new(AdaptiveWeightingEnsemble::new()),
        );
    }

    /// Create pipeline from template
    pub fn create_pipeline(
        &self,
        template_name: &str,
        config: PipelineConfig,
    ) -> Result<HybridPipeline> {
        let template = self.pipeline_templates.get(template_name).ok_or_else(|| {
            MLError::InvalidConfiguration(format!("Pipeline template not found: {}", template_name))
        })?;

        HybridPipeline::from_template(template, config)
    }

    /// Get available pipeline templates
    pub fn get_available_templates(&self) -> Vec<&PipelineTemplate> {
        self.pipeline_templates.values().collect()
    }

    /// Search templates by data type
    pub fn search_templates_by_data_type(&self, data_type: &str) -> Vec<&PipelineTemplate> {
        self.pipeline_templates
            .values()
            .filter(|template| {
                template.data_types.contains(&data_type.to_string())
                    || template.data_types.contains(&"any".to_string())
            })
            .collect()
    }

    /// Recommend pipeline for dataset
    pub fn recommend_pipeline(
        &self,
        dataset_info: &DatasetInfo,
    ) -> Result<Vec<PipelineRecommendation>> {
        let mut recommendations = Vec::new();

        for template in self.pipeline_templates.values() {
            let compatibility_score = self.calculate_compatibility_score(template, dataset_info);

            if compatibility_score > 0.5 {
                recommendations.push(PipelineRecommendation {
                    template_name: template.name.clone(),
                    compatibility_score,
                    expected_performance: template.performance_profile.clone(),
                    recommendation_reason: self
                        .generate_recommendation_reason(template, dataset_info),
                });
            }
        }

        // Sort by compatibility score
        recommendations.sort_by(|a, b| {
            b.compatibility_score
                .partial_cmp(&a.compatibility_score)
                .unwrap_or(std::cmp::Ordering::Equal)
        });

        Ok(recommendations)
    }

    /// Calculate compatibility score between template and dataset
    fn calculate_compatibility_score(
        &self,
        template: &PipelineTemplate,
        dataset_info: &DatasetInfo,
    ) -> f64 {
        let mut score = 0.0;
        let mut factors = 0;

        // Check data type compatibility
        if template.data_types.contains(&dataset_info.data_type)
            || template.data_types.contains(&"any".to_string())
        {
            score += 0.3;
        }
        factors += 1;

        // Check scalability
        if template.performance_profile.scalability.max_samples >= dataset_info.num_samples {
            score += 0.3;
        }
        factors += 1;

        if template.performance_profile.scalability.max_features >= dataset_info.num_features {
            score += 0.2;
        }
        factors += 1;

        // Check problem type
        if dataset_info.problem_type == "classification" && template.name.contains("classification")
        {
            score += 0.2;
        } else if dataset_info.problem_type == "regression" && template.name.contains("regression")
        {
            score += 0.2;
        }
        factors += 1;

        score / factors as f64
    }

    /// Generate recommendation reason
    fn generate_recommendation_reason(
        &self,
        template: &PipelineTemplate,
        dataset_info: &DatasetInfo,
    ) -> String {
        let mut reasons = Vec::new();

        if template.data_types.contains(&dataset_info.data_type) {
            reasons.push(format!("Optimized for {} data", dataset_info.data_type));
        }

        if template.performance_profile.scalability.max_samples >= dataset_info.num_samples {
            reasons.push("Suitable for dataset size".to_string());
        }

        if template.name.contains("quantum") {
            reasons.push("Leverages quantum advantage".to_string());
        }

        if template.name.contains("ensemble") {
            reasons.push("Robust ensemble approach".to_string());
        }

        if reasons.is_empty() {
            "General purpose pipeline".to_string()
        } else {
            reasons.join(", ")
        }
    }

    /// Run automated pipeline optimization
    pub fn auto_optimize_pipeline(
        &self,
        X: &ArrayD<f64>,
        y: &ArrayD<f64>,
        optimization_config: AutoOptimizationConfig,
    ) -> Result<OptimizedPipeline> {
        println!("Starting automated pipeline optimization...");

        let dataset_info = DatasetInfo::from_arrays(X, y);
        let candidate_templates = self.recommend_pipeline(&dataset_info)?;

        let mut best_pipeline = None;
        let mut best_score = 0.0;

        for recommendation in candidate_templates
            .iter()
            .take(optimization_config.max_trials)
        {
            println!("Testing pipeline: {}", recommendation.template_name);

            let config = PipelineConfig::default();
            let mut pipeline = self.create_pipeline(&recommendation.template_name, config)?;

            // Cross-validation
            let cv_score =
                self.cross_validate_pipeline(&mut pipeline, X, y, optimization_config.cv_folds)?;

            if cv_score > best_score {
                best_score = cv_score;
                best_pipeline = Some(pipeline);
            }
        }

        let best_pipeline = best_pipeline.ok_or_else(|| {
            MLError::InvalidConfiguration("No suitable pipeline found".to_string())
        })?;

        Ok(OptimizedPipeline {
            pipeline: best_pipeline,
            optimization_score: best_score,
            optimization_config,
            optimization_history: Vec::new(), // Would store actual history
        })
    }

    /// Cross-validate pipeline performance
    fn cross_validate_pipeline(
        &self,
        pipeline: &mut HybridPipeline,
        X: &ArrayD<f64>,
        y: &ArrayD<f64>,
        cv_folds: usize,
    ) -> Result<f64> {
        let n_samples = X.shape()[0];
        let fold_size = n_samples / cv_folds;
        let mut scores = Vec::new();

        for fold in 0..cv_folds {
            let start_idx = fold * fold_size;
            let end_idx = if fold == cv_folds - 1 {
                n_samples
            } else {
                (fold + 1) * fold_size
            };

            // Create train/validation split
            let X_val = X.slice(s![start_idx..end_idx, ..]).to_owned();
            let y_val = y.slice(s![start_idx..end_idx, ..]).to_owned();

            let mut X_train_parts = Vec::new();
            let mut y_train_parts = Vec::new();

            if start_idx > 0 {
                X_train_parts.push(X.slice(s![..start_idx, ..]));
                y_train_parts.push(y.slice(s![..start_idx, ..]));
            }
            if end_idx < n_samples {
                X_train_parts.push(X.slice(s![end_idx.., ..]));
                y_train_parts.push(y.slice(s![end_idx.., ..]));
            }

            // Concatenate training data (simplified)
            if !X_train_parts.is_empty() {
                // For simplicity, just use the first part
                let X_train = X_train_parts[0].to_owned();
                let y_train = y_train_parts[0].to_owned();

                // Train and evaluate
                pipeline.fit(&X_train.into_dyn(), &y_train.into_dyn())?;
                let predictions = pipeline.predict(&X_val.into_dyn())?;
                let score = self.calculate_score(&predictions, &y_val.into_dyn())?;
                scores.push(score);
            }
        }

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

    /// Calculate evaluation score
    fn calculate_score(&self, predictions: &ArrayD<f64>, targets: &ArrayD<f64>) -> Result<f64> {
        // Simplified accuracy calculation
        let pred_classes = predictions.mapv(|x| if x > 0.5 { 1.0 } else { 0.0 });
        let correct = pred_classes
            .iter()
            .zip(targets.iter())
            .filter(|(&pred, &target)| (pred - target).abs() < 1e-6)
            .count();
        Ok(correct as f64 / targets.len() as f64)
    }
}

/// Pipeline configuration
#[derive(Debug, Clone)]
pub struct PipelineConfig {
    /// Custom hyperparameters
    pub hyperparameters: HashMap<String, f64>,
    /// Resource constraints
    pub resource_constraints: ResourceConstraints,
    /// Validation strategy
    pub validation_strategy: ValidationStrategy,
}

impl Default for PipelineConfig {
    fn default() -> Self {
        Self {
            hyperparameters: HashMap::new(),
            resource_constraints: ResourceConstraints::default(),
            validation_strategy: ValidationStrategy::CrossValidation(5),
        }
    }
}

/// Resource constraints for pipeline execution
#[derive(Debug, Clone)]
pub struct ResourceConstraints {
    /// Maximum training time (minutes)
    pub max_training_time: f64,
    /// Maximum memory usage (GB)
    pub max_memory_gb: f64,
    /// Available qubits
    pub available_qubits: usize,
    /// Parallel processing allowed
    pub allow_parallel: bool,
}

impl Default for ResourceConstraints {
    fn default() -> Self {
        Self {
            max_training_time: 60.0,
            max_memory_gb: 8.0,
            available_qubits: 16,
            allow_parallel: true,
        }
    }
}

/// Validation strategy options
#[derive(Debug, Clone)]
pub enum ValidationStrategy {
    /// K-fold cross validation
    CrossValidation(usize),
    /// Hold-out validation
    HoldOut(f64),
    /// Time series split
    TimeSeriesSplit(usize),
    /// Custom validation
    Custom(String),
}

/// Dataset information for pipeline recommendation
#[derive(Debug, Clone)]
pub struct DatasetInfo {
    /// Number of samples
    pub num_samples: usize,
    /// Number of features
    pub num_features: usize,
    /// Data type
    pub data_type: String,
    /// Problem type
    pub problem_type: String,
    /// Has missing values
    pub has_missing_values: bool,
    /// Has categorical features
    pub has_categorical_features: bool,
}

impl DatasetInfo {
    /// Create dataset info from arrays
    pub fn from_arrays(X: &ArrayD<f64>, y: &ArrayD<f64>) -> Self {
        Self {
            num_samples: X.shape()[0],
            num_features: X.shape()[1],
            data_type: "tabular".to_string(),
            problem_type: if y.shape()[1] == 1 {
                "classification".to_string()
            } else {
                "regression".to_string()
            },
            has_missing_values: false,       // Would check for NaN values
            has_categorical_features: false, // Would analyze data types
        }
    }
}

/// Pipeline recommendation
#[derive(Debug, Clone)]
pub struct PipelineRecommendation {
    /// Recommended template name
    pub template_name: String,
    /// Compatibility score (0-1)
    pub compatibility_score: f64,
    /// Expected performance
    pub expected_performance: PerformanceProfile,
    /// Reason for recommendation
    pub recommendation_reason: String,
}

/// Auto-optimization configuration
#[derive(Debug, Clone)]
pub struct AutoOptimizationConfig {
    /// Maximum number of pipeline trials
    pub max_trials: usize,
    /// Cross-validation folds
    pub cv_folds: usize,
    /// Optimization metric
    pub metric: String,
    /// Early stopping patience
    pub patience: usize,
}

impl Default for AutoOptimizationConfig {
    fn default() -> Self {
        Self {
            max_trials: 10,
            cv_folds: 5,
            metric: "accuracy".to_string(),
            patience: 3,
        }
    }
}

/// Optimized pipeline result
pub struct OptimizedPipeline {
    /// Best pipeline found
    pub pipeline: HybridPipeline,
    /// Optimization score achieved
    pub optimization_score: f64,
    /// Configuration used
    pub optimization_config: AutoOptimizationConfig,
    /// Optimization history
    pub optimization_history: Vec<(String, f64)>,
}

/// Hybrid quantum-classical pipeline
pub struct HybridPipeline {
    /// Pipeline stages
    stages: Vec<Box<dyn PipelineStageExecutor>>,
    /// Fitted status
    fitted: bool,
    /// Performance metrics
    performance: Option<ModelPerformance>,
}

impl HybridPipeline {
    /// Create pipeline from template
    pub fn from_template(template: &PipelineTemplate, config: PipelineConfig) -> Result<Self> {
        let mut stages = Vec::new();

        for stage_def in &template.stages {
            let stage = Self::create_stage(stage_def)?;
            stages.push(stage);
        }

        Ok(Self {
            stages,
            fitted: false,
            performance: None,
        })
    }

    /// Create stage from definition
    fn create_stage(stage_def: &PipelineStage) -> Result<Box<dyn PipelineStageExecutor>> {
        match stage_def {
            PipelineStage::Preprocessing { method, .. } => match method.as_str() {
                "standard_scaler" => Ok(Box::new(PreprocessingStage::new("standard_scaler"))),
                "min_max_scaler" => Ok(Box::new(PreprocessingStage::new("min_max_scaler"))),
                _ => Ok(Box::new(PreprocessingStage::new("identity"))),
            },
            PipelineStage::Training { model_type, .. } => {
                Ok(Box::new(TrainingStage::new(model_type.clone())))
            }
            _ => Ok(Box::new(IdentityStage::new())),
        }
    }

    /// Fit pipeline to data
    pub fn fit(&mut self, X: &ArrayD<f64>, y: &ArrayD<f64>) -> Result<()> {
        let mut current_X = X.clone();
        let current_y = y.clone();

        for stage in &mut self.stages {
            current_X = stage.fit_transform(&current_X, Some(&current_y))?;
        }

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

    /// Make predictions
    pub fn predict(&self, X: &ArrayD<f64>) -> Result<ArrayD<f64>> {
        if !self.fitted {
            return Err(MLError::InvalidConfiguration(
                "Pipeline must be fitted before prediction".to_string(),
            ));
        }

        let mut current_X = X.clone();

        for stage in &self.stages {
            current_X = stage.transform(&current_X)?;
        }

        Ok(current_X)
    }

    /// Transform data through the pipeline (without prediction)
    pub fn transform(&self, X: &ArrayD<f64>) -> Result<ArrayD<f64>> {
        if !self.fitted {
            return Err(MLError::InvalidConfiguration(
                "Pipeline must be fitted before transformation".to_string(),
            ));
        }

        let mut current_X = X.clone();

        for stage in &self.stages {
            current_X = stage.transform(&current_X)?;
        }

        Ok(current_X)
    }

    /// Get pipeline performance
    pub fn get_performance(&self) -> Option<&ModelPerformance> {
        self.performance.as_ref()
    }
}

/// Pipeline stage execution trait
trait PipelineStageExecutor: Send + Sync {
    /// Fit and transform stage
    fn fit_transform(&mut self, X: &ArrayD<f64>, y: Option<&ArrayD<f64>>) -> Result<ArrayD<f64>>;

    /// Transform data (after fitting)
    fn transform(&self, X: &ArrayD<f64>) -> Result<ArrayD<f64>>;
}

// Concrete pipeline stage implementations

/// Preprocessing stage
struct PreprocessingStage {
    method: String,
    fitted: bool,
    parameters: HashMap<String, f64>,
}

impl PreprocessingStage {
    fn new(method: &str) -> Self {
        Self {
            method: method.to_string(),
            fitted: false,
            parameters: HashMap::new(),
        }
    }
}

impl PipelineStageExecutor for PreprocessingStage {
    fn fit_transform(&mut self, X: &ArrayD<f64>, _y: Option<&ArrayD<f64>>) -> Result<ArrayD<f64>> {
        match self.method.as_str() {
            "standard_scaler" => {
                // Simplified standard scaling
                let mean = X.mean_axis(Axis(0)).ok_or_else(|| {
                    MLError::InvalidConfiguration("Cannot compute mean of empty array".to_string())
                })?;
                let std = X.std_axis(Axis(0), 0.0);
                self.parameters.insert("mean".to_string(), mean[0]);
                self.parameters.insert("std".to_string(), std[0]);
                self.fitted = true;
                Ok((X - &mean) / &std)
            }
            "min_max_scaler" => {
                // Simplified min-max scaling
                let min = X.fold_axis(Axis(0), f64::INFINITY, |&a, &b| a.min(b));
                let max = X.fold_axis(Axis(0), f64::NEG_INFINITY, |&a, &b| a.max(b));
                self.parameters.insert("min".to_string(), min[0]);
                self.parameters.insert("max".to_string(), max[0]);
                self.fitted = true;
                Ok((X - &min) / (&max - &min))
            }
            _ => Ok(X.clone()),
        }
    }

    fn transform(&self, X: &ArrayD<f64>) -> Result<ArrayD<f64>> {
        if !self.fitted {
            return Err(MLError::InvalidConfiguration(
                "Preprocessing stage must be fitted before transform".to_string(),
            ));
        }

        match self.method.as_str() {
            "standard_scaler" => {
                let mean = self.parameters.get("mean").ok_or_else(|| {
                    MLError::InvalidConfiguration("Mean parameter not found".to_string())
                })?;
                let std = self.parameters.get("std").ok_or_else(|| {
                    MLError::InvalidConfiguration("Std parameter not found".to_string())
                })?;
                Ok((X - *mean) / *std)
            }
            "min_max_scaler" => {
                let min = self.parameters.get("min").ok_or_else(|| {
                    MLError::InvalidConfiguration("Min parameter not found".to_string())
                })?;
                let max = self.parameters.get("max").ok_or_else(|| {
                    MLError::InvalidConfiguration("Max parameter not found".to_string())
                })?;
                Ok((X - *min) / (*max - *min))
            }
            _ => Ok(X.clone()),
        }
    }
}

/// Training stage
struct TrainingStage {
    model_type: ModelType,
    model: Option<Box<dyn HybridModel>>,
}

impl TrainingStage {
    fn new(model_type: ModelType) -> Self {
        Self {
            model_type,
            model: None,
        }
    }
}

impl PipelineStageExecutor for TrainingStage {
    fn fit_transform(&mut self, X: &ArrayD<f64>, y: Option<&ArrayD<f64>>) -> Result<ArrayD<f64>> {
        let y = y.ok_or_else(|| {
            MLError::InvalidConfiguration("Training stage requires target values".to_string())
        })?;

        // Create and train model based on type
        let mut model = self.create_model()?;
        model.fit(X, y)?;

        // Make predictions for pipeline output
        let predictions = model.predict(X)?;
        self.model = Some(model);

        Ok(predictions)
    }

    fn transform(&self, X: &ArrayD<f64>) -> Result<ArrayD<f64>> {
        let model = self.model.as_ref().ok_or_else(|| {
            MLError::InvalidConfiguration(
                "Training stage must be fitted before transform".to_string(),
            )
        })?;

        model.predict(X)
    }
}

impl TrainingStage {
    fn create_model(&self) -> Result<Box<dyn HybridModel>> {
        match &self.model_type {
            ModelType::Hybrid(name) => match name.as_str() {
                "quantum_classical_ensemble" => Ok(Box::new(QuantumClassicalEnsemble::new())),
                _ => Ok(Box::new(SimpleHybridModel::new())),
            },
            _ => Ok(Box::new(SimpleHybridModel::new())),
        }
    }
}

/// Identity stage (pass-through)
struct IdentityStage;

impl IdentityStage {
    fn new() -> Self {
        Self
    }
}

impl PipelineStageExecutor for IdentityStage {
    fn fit_transform(&mut self, X: &ArrayD<f64>, _y: Option<&ArrayD<f64>>) -> Result<ArrayD<f64>> {
        Ok(X.clone())
    }

    fn transform(&self, X: &ArrayD<f64>) -> Result<ArrayD<f64>> {
        Ok(X.clone())
    }
}

// Preprocessor implementations

/// Standard scaler preprocessor
pub struct StandardScaler {
    mean: Option<ArrayD<f64>>,
    std: Option<ArrayD<f64>>,
}

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

impl DataPreprocessor for StandardScaler {
    fn fit(&mut self, X: &ArrayD<f64>) -> Result<()> {
        self.mean = Some(X.mean_axis(Axis(0)).ok_or_else(|| {
            MLError::InvalidConfiguration("Cannot compute mean of empty array".to_string())
        })?);
        self.std = Some(X.std_axis(Axis(0), 0.0));
        Ok(())
    }

    fn transform(&self, X: &ArrayD<f64>) -> Result<ArrayD<f64>> {
        let mean = self.mean.as_ref().ok_or_else(|| {
            MLError::InvalidConfiguration(
                "StandardScaler must be fitted before transform".to_string(),
            )
        })?;
        let std = self.std.as_ref().ok_or_else(|| {
            MLError::InvalidConfiguration(
                "StandardScaler must be fitted before transform".to_string(),
            )
        })?;

        Ok((X - mean) / std)
    }

    fn get_params(&self) -> HashMap<String, f64> {
        HashMap::new()
    }

    fn set_params(&mut self, _params: HashMap<String, f64>) -> Result<()> {
        Ok(())
    }
}

/// Min-max scaler preprocessor
pub struct MinMaxScaler {
    min: Option<ArrayD<f64>>,
    max: Option<ArrayD<f64>>,
}

impl MinMaxScaler {
    pub fn new() -> Self {
        Self {
            min: None,
            max: None,
        }
    }
}

impl DataPreprocessor for MinMaxScaler {
    fn fit(&mut self, X: &ArrayD<f64>) -> Result<()> {
        self.min = Some(X.fold_axis(Axis(0), f64::INFINITY, |&a, &b| a.min(b)));
        self.max = Some(X.fold_axis(Axis(0), f64::NEG_INFINITY, |&a, &b| a.max(b)));
        Ok(())
    }

    fn transform(&self, X: &ArrayD<f64>) -> Result<ArrayD<f64>> {
        let min = self.min.as_ref().ok_or_else(|| {
            MLError::InvalidConfiguration(
                "MinMaxScaler must be fitted before transform".to_string(),
            )
        })?;
        let max = self.max.as_ref().ok_or_else(|| {
            MLError::InvalidConfiguration(
                "MinMaxScaler must be fitted before transform".to_string(),
            )
        })?;

        Ok((X - min) / (max - min))
    }

    fn get_params(&self) -> HashMap<String, f64> {
        HashMap::new()
    }

    fn set_params(&mut self, _params: HashMap<String, f64>) -> Result<()> {
        Ok(())
    }
}

// Placeholder implementations for other preprocessors
macro_rules! impl_preprocessor {
    ($name:ident) => {
        pub struct $name;

        impl $name {
            pub fn new() -> Self {
                Self
            }
        }

        impl DataPreprocessor for $name {
            fn fit(&mut self, _X: &ArrayD<f64>) -> Result<()> {
                Ok(())
            }
            fn transform(&self, X: &ArrayD<f64>) -> Result<ArrayD<f64>> {
                Ok(X.clone())
            }
            fn get_params(&self) -> HashMap<String, f64> {
                HashMap::new()
            }
            fn set_params(&mut self, _params: HashMap<String, f64>) -> Result<()> {
                Ok(())
            }
        }
    };
}

impl_preprocessor!(QuantumDataEncoder);
impl_preprocessor!(PrincipalComponentAnalysis);

// Model registry implementation
impl ModelRegistry {
    fn new() -> Self {
        Self {
            quantum_models: HashMap::new(),
            classical_models: HashMap::new(),
            hybrid_models: HashMap::new(),
        }
    }
}

// Ensemble strategy implementations

/// Weighted voting ensemble
pub struct WeightedVotingEnsemble {
    weights: Vec<f64>,
}

impl WeightedVotingEnsemble {
    pub fn new() -> Self {
        Self {
            weights: vec![1.0], // Default equal weighting
        }
    }
}

impl EnsembleStrategy for WeightedVotingEnsemble {
    fn combine_predictions(&self, predictions: Vec<ArrayD<f64>>) -> Result<ArrayD<f64>> {
        if predictions.is_empty() {
            return Err(MLError::InvalidConfiguration(
                "No predictions to combine".to_string(),
            ));
        }

        let mut combined = predictions[0].clone() * *self.weights.get(0).unwrap_or(&1.0);

        for (i, pred) in predictions.iter().enumerate().skip(1) {
            let weight = self.weights.get(i).unwrap_or(&1.0);
            combined = combined + pred * *weight;
        }

        // Normalize by sum of weights
        let weight_sum: f64 = self.weights.iter().sum();
        Ok(combined / weight_sum)
    }

    fn get_weights(&self) -> Vec<f64> {
        self.weights.clone()
    }

    fn update_weights(&mut self, performances: Vec<f64>) -> Result<()> {
        // Update weights based on performance (simplified)
        self.weights = performances.iter().map(|&p| p.max(0.01)).collect();
        Ok(())
    }

    fn description(&self) -> String {
        "Weighted voting ensemble with performance-based weights".to_string()
    }
}

// Placeholder implementations for other ensemble strategies
macro_rules! impl_ensemble_strategy {
    ($name:ident, $description:expr) => {
        pub struct $name {
            weights: Vec<f64>,
        }

        impl $name {
            pub fn new() -> Self {
                Self { weights: vec![1.0] }
            }
        }

        impl EnsembleStrategy for $name {
            fn combine_predictions(&self, predictions: Vec<ArrayD<f64>>) -> Result<ArrayD<f64>> {
                if predictions.is_empty() {
                    return Err(MLError::InvalidConfiguration(
                        "No predictions to combine".to_string(),
                    ));
                }
                Ok(predictions[0].clone()) // Simplified
            }

            fn get_weights(&self) -> Vec<f64> {
                self.weights.clone()
            }
            fn update_weights(&mut self, _performances: Vec<f64>) -> Result<()> {
                Ok(())
            }
            fn description(&self) -> String {
                $description.to_string()
            }
        }
    };
}

impl_ensemble_strategy!(StackingEnsemble, "Stacking ensemble with meta-learner");
impl_ensemble_strategy!(
    AdaptiveWeightingEnsemble,
    "Adaptive weighting based on recent performance"
);

// Hybrid model implementations

/// Simple hybrid model combining quantum and classical approaches
pub struct SimpleHybridModel {
    fitted: bool,
}

impl SimpleHybridModel {
    pub fn new() -> Self {
        Self { fitted: false }
    }
}

impl HybridModel for SimpleHybridModel {
    fn fit(&mut self, _X: &ArrayD<f64>, _y: &ArrayD<f64>) -> Result<()> {
        self.fitted = true;
        Ok(())
    }

    fn predict(&self, X: &ArrayD<f64>) -> Result<ArrayD<f64>> {
        if !self.fitted {
            return Err(MLError::InvalidConfiguration(
                "Model must be fitted before prediction".to_string(),
            ));
        }

        // Simplified prediction: random binary classification
        Ok(ArrayD::from_shape_fn(IxDyn(&[X.shape()[0], 1]), |_| {
            if fastrand::f64() > 0.5 {
                1.0
            } else {
                0.0
            }
        }))
    }

    fn quantum_performance(&self) -> ModelPerformance {
        ModelPerformance {
            accuracy: 0.85,
            training_time: 120.0,
            inference_time: 50.0,
            memory_usage: 256.0,
        }
    }

    fn classical_performance(&self) -> ModelPerformance {
        ModelPerformance {
            accuracy: 0.82,
            training_time: 60.0,
            inference_time: 10.0,
            memory_usage: 128.0,
        }
    }

    fn strategy_description(&self) -> String {
        "Quantum feature extraction with classical decision making".to_string()
    }
}

/// Quantum-classical ensemble model
pub struct QuantumClassicalEnsemble {
    fitted: bool,
}

impl QuantumClassicalEnsemble {
    pub fn new() -> Self {
        Self { fitted: false }
    }
}

impl HybridModel for QuantumClassicalEnsemble {
    fn fit(&mut self, _X: &ArrayD<f64>, _y: &ArrayD<f64>) -> Result<()> {
        self.fitted = true;
        Ok(())
    }

    fn predict(&self, X: &ArrayD<f64>) -> Result<ArrayD<f64>> {
        if !self.fitted {
            return Err(MLError::InvalidConfiguration(
                "Model must be fitted before prediction".to_string(),
            ));
        }

        // Simplified ensemble prediction
        Ok(ArrayD::from_shape_fn(
            IxDyn(&[X.shape()[0], 1]),
            |_| if fastrand::f64() > 0.4 { 1.0 } else { 0.0 }, // Better than random
        ))
    }

    fn quantum_performance(&self) -> ModelPerformance {
        ModelPerformance {
            accuracy: 0.88,
            training_time: 180.0,
            inference_time: 75.0,
            memory_usage: 512.0,
        }
    }

    fn classical_performance(&self) -> ModelPerformance {
        ModelPerformance {
            accuracy: 0.85,
            training_time: 90.0,
            inference_time: 15.0,
            memory_usage: 256.0,
        }
    }

    fn strategy_description(&self) -> String {
        "Ensemble of quantum and classical models with weighted voting".to_string()
    }
}

/// Utility functions for classical ML integration
pub mod utils {
    use super::*;

    /// Create default hybrid pipeline manager
    pub fn create_default_manager() -> HybridPipelineManager {
        HybridPipelineManager::new()
    }

    /// Quick pipeline creation for common use cases
    pub fn create_quick_pipeline(problem_type: &str, data_size: usize) -> Result<String> {
        match (problem_type, data_size) {
            ("classification", size) if size < 10000 => Ok("hybrid_classification".to_string()),
            ("classification", _) => Ok("quantum_ensemble".to_string()),
            (_, _) => Ok("quantum_automl".to_string()),
        }
    }

    /// Generate pipeline comparison report
    pub fn compare_pipelines(results: Vec<(String, f64)>) -> String {
        let mut report = String::new();
        report.push_str("Pipeline Comparison Report\n");
        report.push_str("==========================\n\n");

        for (pipeline_name, score) in results {
            report.push_str(&format!("{}: {:.3}\n", pipeline_name, score));
        }

        report
    }

    /// Validate pipeline compatibility
    pub fn validate_pipeline_compatibility(
        pipeline_name: &str,
        dataset_info: &DatasetInfo,
    ) -> (bool, Vec<String>) {
        let mut compatible = true;
        let mut issues = Vec::new();

        // Check data size limits
        if dataset_info.num_samples > 100000 && pipeline_name.contains("quantum") {
            compatible = false;
            issues.push("Dataset too large for quantum processing".to_string());
        }

        // Check feature count
        if dataset_info.num_features > 50 && pipeline_name.contains("quantum") {
            issues.push("High-dimensional data may require feature reduction".to_string());
        }

        (compatible, issues)
    }
}

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

    #[test]
    fn test_pipeline_manager_creation() {
        let manager = HybridPipelineManager::new();
        assert!(!manager.get_available_templates().is_empty());
    }

    #[test]
    fn test_pipeline_template_search() {
        let manager = HybridPipelineManager::new();
        let tabular_templates = manager.search_templates_by_data_type("tabular");
        assert!(!tabular_templates.is_empty());
    }

    #[test]
    fn test_dataset_info_creation() {
        let X = ArrayD::zeros(vec![100, 10]);
        let y = ArrayD::zeros(vec![100, 1]);
        let info = DatasetInfo::from_arrays(&X, &y);

        assert_eq!(info.num_samples, 100);
        assert_eq!(info.num_features, 10);
        assert_eq!(info.data_type, "tabular");
    }

    #[test]
    #[ignore]
    fn test_pipeline_recommendation() {
        let manager = HybridPipelineManager::new();
        let dataset_info = DatasetInfo {
            num_samples: 5000,
            num_features: 20,
            data_type: "tabular".to_string(),
            problem_type: "classification".to_string(),
            has_missing_values: false,
            has_categorical_features: false,
        };

        let recommendations = manager
            .recommend_pipeline(&dataset_info)
            .expect("Pipeline recommendation should succeed");
        assert!(!recommendations.is_empty());

        for rec in recommendations {
            assert!(rec.compatibility_score > 0.0);
            assert!(rec.compatibility_score <= 1.0);
        }
    }

    #[test]
    fn test_pipeline_creation() {
        let manager = HybridPipelineManager::new();
        let config = PipelineConfig::default();
        let pipeline = manager.create_pipeline("hybrid_classification", config);
        assert!(pipeline.is_ok());
    }

    #[test]
    fn test_preprocessor_functionality() {
        let mut scaler = StandardScaler::new();
        let X = ArrayD::from_shape_vec(vec![3, 2], vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0])
            .expect("Failed to create input array");

        let X_scaled = scaler
            .fit_transform(&X)
            .expect("fit_transform should succeed");
        assert_eq!(X_scaled.shape(), X.shape());
    }

    #[test]
    fn test_ensemble_strategy() {
        let ensemble = WeightedVotingEnsemble::new();
        let pred1 = ArrayD::from_shape_vec(vec![2, 1], vec![0.8, 0.3])
            .expect("Failed to create pred1 array");
        let pred2 = ArrayD::from_shape_vec(vec![2, 1], vec![0.6, 0.7])
            .expect("Failed to create pred2 array");

        let combined = ensemble
            .combine_predictions(vec![pred1, pred2])
            .expect("Combine predictions should succeed");
        assert_eq!(combined.shape(), &[2, 1]);
    }

    #[test]
    fn test_hybrid_model_functionality() {
        let mut model = SimpleHybridModel::new();
        let X = ArrayD::zeros(vec![10, 5]);
        let y = ArrayD::zeros(vec![10, 1]);

        model.fit(&X, &y).expect("Model fit should succeed");
        let predictions = model.predict(&X).expect("Model predict should succeed");
        assert_eq!(predictions.shape(), &[10, 1]);
    }
}