quantrs2-ml 0.1.3

//! Automated Pipeline Constructor
//!
//! This module provides automated construction of quantum ML pipelines.

use crate::automl::config::QuantumAutoMLConfig;
use crate::automl::pipeline::QuantumMLPipeline;
use crate::error::Result;
use scirs2_core::ndarray::{Array1, Array2};

/// Automated pipeline constructor
#[derive(Debug, Clone)]
pub struct AutomatedPipelineConstructor {
    /// Task detector
    task_detector: TaskDetector,

    /// Preprocessing optimizer
    preprocessing_optimizer: PreprocessingOptimizer,

    /// Algorithm selector
    algorithm_selector: AlgorithmSelector,

    /// Pipeline validator
    pipeline_validator: PipelineValidator,
}

/// Task detection from data
#[derive(Debug, Clone)]
pub struct TaskDetector {
    /// Feature analyzers
    feature_analyzers: Vec<FeatureAnalyzer>,

    /// Target analyzers
    target_analyzers: Vec<TargetAnalyzer>,

    /// Data pattern detectors
    pattern_detectors: Vec<PatternDetector>,
}

/// Feature analyzer
#[derive(Debug, Clone)]
pub struct FeatureAnalyzer {
    /// Analyzer type
    pub analyzer_type: FeatureAnalyzerType,

    /// Analysis results
    pub results: std::collections::HashMap<String, f64>,
}

/// Feature analyzer types
#[derive(Debug, Clone)]
pub enum FeatureAnalyzerType {
    DataTypeAnalyzer,
    DistributionAnalyzer,
    CorrelationAnalyzer,
    NullValueAnalyzer,
    OutlierAnalyzer,
    QuantumEncodingAnalyzer,
}

/// Target analyzer
#[derive(Debug, Clone)]
pub struct TargetAnalyzer {
    /// Analyzer type
    pub analyzer_type: TargetAnalyzerType,

    /// Analysis results
    pub results: std::collections::HashMap<String, f64>,
}

/// Target analyzer types
#[derive(Debug, Clone)]
pub enum TargetAnalyzerType {
    TaskTypeDetector,
    ClassBalanceAnalyzer,
    LabelDistributionAnalyzer,
    TemporalPatternAnalyzer,
}

/// Pattern detector
#[derive(Debug, Clone)]
pub struct PatternDetector {
    /// Pattern type
    pub pattern_type: PatternType,

    /// Detection confidence
    pub confidence: f64,
}

/// Pattern types
#[derive(Debug, Clone)]
pub enum PatternType {
    TimeSeriesPattern,
    SpatialPattern,
    NetworkPattern,
    HierarchicalPattern,
    QuantumPattern,
}

/// Preprocessing optimizer
#[derive(Debug, Clone)]
pub struct PreprocessingOptimizer {
    /// Available preprocessors
    preprocessors: Vec<PreprocessorCandidate>,

    /// Optimization strategy
    optimization_strategy: PreprocessingOptimizationStrategy,

    /// Performance tracker
    performance_tracker: PreprocessingPerformanceTracker,
}

/// Preprocessor candidate
#[derive(Debug, Clone)]
pub struct PreprocessorCandidate {
    /// Preprocessor type
    pub preprocessor_type: PreprocessorType,

    /// Configuration
    pub config: PreprocessorConfig,

    /// Performance score
    pub performance_score: f64,
}

/// Preprocessor types
#[derive(Debug, Clone)]
pub enum PreprocessorType {
    Scaler(String),
    FeatureSelector(String),
    QuantumEncoder(String),
    MissingValueHandler(String),
    DataAugmenter,
    OutlierDetector,
}

/// Preprocessor configuration
#[derive(Debug, Clone)]
pub struct PreprocessorConfig {
    /// Parameters
    pub parameters: std::collections::HashMap<String, f64>,

    /// Enabled features
    pub enabled_features: Vec<String>,
}

/// Preprocessing optimization strategy
#[derive(Debug, Clone)]
pub enum PreprocessingOptimizationStrategy {
    Sequential,
    Parallel,
    Evolutionary,
    BayesianOptimization,
    QuantumAnnealing,
}

/// Preprocessing performance tracker
#[derive(Debug, Clone)]
pub struct PreprocessingPerformanceTracker {
    /// Performance history
    pub performance_history: Vec<PreprocessingPerformance>,

    /// Best configuration
    pub best_config: Option<PreprocessorConfig>,
}

/// Preprocessing performance
#[derive(Debug, Clone)]
pub struct PreprocessingPerformance {
    /// Data quality score
    pub data_quality_score: f64,

    /// Feature importance scores
    pub feature_importance: Array1<f64>,

    /// Quantum encoding efficiency
    pub quantum_encoding_efficiency: f64,

    /// Processing time
    pub processing_time: f64,
}

/// Algorithm selector
#[derive(Debug, Clone)]
pub struct AlgorithmSelector {
    /// Available algorithms
    algorithms: Vec<AlgorithmCandidate>,

    /// Selection strategy
    selection_strategy: AlgorithmSelectionStrategy,

    /// Performance predictor
    performance_predictor: AlgorithmPerformancePredictor,
}

/// Algorithm candidate
#[derive(Debug, Clone)]
pub struct AlgorithmCandidate {
    /// Algorithm type
    pub algorithm_type: AlgorithmType,

    /// Quantum enhancement level
    pub quantum_enhancement: QuantumEnhancementLevel,

    /// Estimated performance
    pub estimated_performance: f64,

    /// Resource requirements
    pub resource_requirements: ResourceRequirements,
}

/// Algorithm types
#[derive(Debug, Clone)]
pub enum AlgorithmType {
    QuantumNeuralNetwork,
    QuantumSVM,
    QuantumClustering,
    QuantumDimensionalityReduction,
    QuantumTimeSeries,
    QuantumAnomalyDetection,
    ClassicalBaseline,
}

/// Quantum enhancement levels
#[derive(Debug, Clone)]
pub enum QuantumEnhancementLevel {
    Classical,
    QuantumInspired,
    QuantumHybrid,
    FullQuantum,
    QuantumAdvantage,
}

/// Resource requirements
#[derive(Debug, Clone)]
pub struct ResourceRequirements {
    /// Computational complexity
    pub computational_complexity: f64,

    /// Memory requirements
    pub memory_requirements: f64,

    /// Quantum resource requirements
    pub quantum_requirements: QuantumResourceRequirements,

    /// Training time estimate
    pub training_time_estimate: f64,
}

/// Quantum resource requirements
#[derive(Debug, Clone)]
pub struct QuantumResourceRequirements {
    /// Required qubits
    pub required_qubits: usize,

    /// Required circuit depth
    pub required_circuit_depth: usize,

    /// Required coherence time
    pub required_coherence_time: f64,

    /// Required gate fidelity
    pub required_gate_fidelity: f64,
}

/// Algorithm selection strategy
#[derive(Debug, Clone)]
pub enum AlgorithmSelectionStrategy {
    PerformanceBased,
    ResourceEfficient,
    QuantumAdvantage,
    MultiObjective,
    EnsembleBased,
    MetaLearning,
}

/// Algorithm performance predictor
#[derive(Debug, Clone)]
pub struct AlgorithmPerformancePredictor {
    /// Meta-learning model
    meta_model: Option<MetaLearningModel>,

    /// Performance database
    performance_database: PerformanceDatabase,

    /// Prediction strategy
    prediction_strategy: PerformancePredictionStrategy,
}

/// Meta-learning model
#[derive(Debug, Clone)]
pub struct MetaLearningModel {
    /// Model type
    pub model_type: String,

    /// Meta-features
    pub meta_features: Vec<String>,

    /// Trained parameters
    pub parameters: Array1<f64>,
}

/// Performance database
#[derive(Debug, Clone)]
pub struct PerformanceDatabase {
    /// Historical performance records
    pub records: Vec<PerformanceRecord>,
}

/// Performance record
#[derive(Debug, Clone)]
pub struct PerformanceRecord {
    /// Dataset characteristics
    pub dataset_features: std::collections::HashMap<String, f64>,

    /// Algorithm used
    pub algorithm: String,

    /// Performance achieved
    pub performance: f64,
}

/// Performance prediction strategies
#[derive(Debug, Clone)]
pub enum PerformancePredictionStrategy {
    SimilarityBased,
    MetaLearning,
    TheoreticalAnalysis,
    CombinedApproach,
}

/// Pipeline validator
#[derive(Debug, Clone)]
pub struct PipelineValidator {
    /// Validation rules
    validation_rules: Vec<ValidationRule>,

    /// Performance validators
    performance_validators: Vec<PerformanceValidator>,
}

/// Validation rule
#[derive(Debug, Clone)]
pub struct ValidationRule {
    /// Rule type
    pub rule_type: ValidationRuleType,

    /// Rule description
    pub description: String,

    /// Severity level
    pub severity: ValidationSeverity,
}

/// Validation rule types
#[derive(Debug, Clone)]
pub enum ValidationRuleType {
    DataCompatibility,
    ResourceConstraints,
    QuantumConstraints,
    PerformanceThreshold,
    ConsistencyCheck,
}

/// Validation severity levels
#[derive(Debug, Clone)]
pub enum ValidationSeverity {
    Error,
    Warning,
    Info,
}

/// Performance validator
#[derive(Debug, Clone)]
pub struct PerformanceValidator {
    /// Validator type
    pub validator_type: PerformanceValidatorType,

    /// Validation criteria
    pub criteria: ValidationCriteria,
}

/// Performance validator types
#[derive(Debug, Clone)]
pub enum PerformanceValidatorType {
    AccuracyValidator,
    RobustnessValidator,
    QuantumAdvantageValidator,
    ResourceEfficiencyValidator,
    FairnessValidator,
}

/// Validation criteria
#[derive(Debug, Clone)]
pub struct ValidationCriteria {
    /// Minimum performance threshold
    pub min_performance: f64,

    /// Maximum resource usage
    pub max_resource_usage: f64,

    /// Required quantum advantage
    pub required_quantum_advantage: Option<f64>,
}

impl AutomatedPipelineConstructor {
    /// Create a new pipeline constructor
    pub fn new(config: &QuantumAutoMLConfig) -> Self {
        Self {
            task_detector: TaskDetector::new(),
            preprocessing_optimizer: PreprocessingOptimizer::new(
                &config.search_space.preprocessing,
            ),
            algorithm_selector: AlgorithmSelector::new(&config.search_space.algorithms),
            pipeline_validator: PipelineValidator::new(&config.evaluation_config),
        }
    }

    /// Construct a pipeline for the given data and configuration
    pub fn construct_pipeline(
        &self,
        X: &Array2<f64>,
        y: &Array1<f64>,
        config: &QuantumAutoMLConfig,
    ) -> Result<QuantumMLPipeline> {
        // Analyze data characteristics
        let data_analysis = self.task_detector.analyze_data(X, y)?;

        // Optimize preprocessing
        let preprocessing_config = self
            .preprocessing_optimizer
            .optimize(X, y, &data_analysis)?;

        // Select best algorithm
        let algorithm_candidate = self
            .algorithm_selector
            .select_algorithm(&data_analysis, &config.task_type)?;

        // Construct pipeline
        let pipeline =
            QuantumMLPipeline::new(algorithm_candidate, preprocessing_config, config.clone())?;

        // Validate pipeline
        self.pipeline_validator.validate(&pipeline, X, y)?;

        Ok(pipeline)
    }
}

impl TaskDetector {
    fn new() -> Self {
        Self {
            feature_analyzers: vec![
                FeatureAnalyzer::new(FeatureAnalyzerType::DataTypeAnalyzer),
                FeatureAnalyzer::new(FeatureAnalyzerType::DistributionAnalyzer),
                FeatureAnalyzer::new(FeatureAnalyzerType::CorrelationAnalyzer),
            ],
            target_analyzers: vec![
                TargetAnalyzer::new(TargetAnalyzerType::TaskTypeDetector),
                TargetAnalyzer::new(TargetAnalyzerType::ClassBalanceAnalyzer),
            ],
            pattern_detectors: vec![
                PatternDetector::new(PatternType::TimeSeriesPattern),
                PatternDetector::new(PatternType::QuantumPattern),
            ],
        }
    }

    fn analyze_data(&self, X: &Array2<f64>, y: &Array1<f64>) -> Result<DataAnalysis> {
        // Simplified data analysis
        Ok(DataAnalysis {
            num_features: X.ncols(),
            num_samples: X.nrows(),
            feature_types: vec!["numerical".to_string(); X.ncols()],
            target_type: "numerical".to_string(),
            data_complexity: 0.5, // Simplified estimate
        })
    }
}

/// Data analysis results
#[derive(Debug, Clone)]
pub struct DataAnalysis {
    pub num_features: usize,
    pub num_samples: usize,
    pub feature_types: Vec<String>,
    pub target_type: String,
    pub data_complexity: f64,
}

impl FeatureAnalyzer {
    fn new(analyzer_type: FeatureAnalyzerType) -> Self {
        Self {
            analyzer_type,
            results: std::collections::HashMap::new(),
        }
    }
}

impl TargetAnalyzer {
    fn new(analyzer_type: TargetAnalyzerType) -> Self {
        Self {
            analyzer_type,
            results: std::collections::HashMap::new(),
        }
    }
}

impl PatternDetector {
    fn new(pattern_type: PatternType) -> Self {
        Self {
            pattern_type,
            confidence: 0.0,
        }
    }
}

impl PreprocessingOptimizer {
    fn new(preprocessing_space: &crate::automl::config::PreprocessingSearchSpace) -> Self {
        Self {
            preprocessors: Vec::new(),
            optimization_strategy: PreprocessingOptimizationStrategy::Sequential,
            performance_tracker: PreprocessingPerformanceTracker::new(),
        }
    }

    fn optimize(
        &self,
        X: &Array2<f64>,
        y: &Array1<f64>,
        data_analysis: &DataAnalysis,
    ) -> Result<PreprocessorConfig> {
        // Simplified preprocessing optimization
        Ok(PreprocessorConfig {
            parameters: std::collections::HashMap::new(),
            enabled_features: (0..X.ncols()).map(|i| format!("feature_{}", i)).collect(),
        })
    }
}

impl PreprocessingPerformanceTracker {
    fn new() -> Self {
        Self {
            performance_history: Vec::new(),
            best_config: None,
        }
    }
}

impl AlgorithmSelector {
    fn new(algorithm_space: &crate::automl::config::AlgorithmSearchSpace) -> Self {
        Self {
            algorithms: Vec::new(),
            selection_strategy: AlgorithmSelectionStrategy::PerformanceBased,
            performance_predictor: AlgorithmPerformancePredictor::new(),
        }
    }

    fn select_algorithm(
        &self,
        data_analysis: &DataAnalysis,
        task_type: &Option<crate::automl::config::MLTaskType>,
    ) -> Result<AlgorithmCandidate> {
        // Simplified algorithm selection
        Ok(AlgorithmCandidate {
            algorithm_type: AlgorithmType::QuantumNeuralNetwork,
            quantum_enhancement: QuantumEnhancementLevel::QuantumHybrid,
            estimated_performance: 0.8,
            resource_requirements: ResourceRequirements {
                computational_complexity: 1.0,
                memory_requirements: 256.0,
                quantum_requirements: QuantumResourceRequirements {
                    required_qubits: 4,
                    required_circuit_depth: 6,
                    required_coherence_time: 100.0,
                    required_gate_fidelity: 0.99,
                },
                training_time_estimate: 300.0,
            },
        })
    }
}

impl AlgorithmPerformancePredictor {
    fn new() -> Self {
        Self {
            meta_model: None,
            performance_database: PerformanceDatabase::new(),
            prediction_strategy: PerformancePredictionStrategy::SimilarityBased,
        }
    }
}

impl PerformanceDatabase {
    fn new() -> Self {
        Self {
            records: Vec::new(),
        }
    }
}

impl PipelineValidator {
    fn new(evaluation_config: &crate::automl::config::EvaluationConfig) -> Self {
        Self {
            validation_rules: Vec::new(),
            performance_validators: Vec::new(),
        }
    }

    fn validate(
        &self,
        pipeline: &QuantumMLPipeline,
        X: &Array2<f64>,
        y: &Array1<f64>,
    ) -> Result<()> {
        // Simplified validation
        Ok(())
    }
}