quantrs2-ml 0.1.3

//! Quantum Model Selector
//!
//! This module provides model selection functionality for quantum ML algorithms.

use crate::automl::config::{AlgorithmSearchSpace, MLTaskType};
use crate::automl::pipeline::QuantumMLPipeline;
use crate::error::Result;
use std::collections::HashMap;

/// Quantum model selector
#[derive(Debug, Clone)]
pub struct QuantumModelSelector {
    /// Model candidates
    model_candidates: Vec<ModelCandidate>,

    /// Selection strategy
    selection_strategy: ModelSelectionStrategy,

    /// Performance estimator
    performance_estimator: ModelPerformanceEstimator,
}

/// Model candidate
#[derive(Debug, Clone)]
pub struct ModelCandidate {
    /// Model type
    pub model_type: ModelType,

    /// Model configuration
    pub configuration: ModelConfiguration,

    /// Estimated performance
    pub estimated_performance: f64,

    /// Resource requirements
    pub resource_requirements: ResourceRequirements,
}

/// Model types
#[derive(Debug, Clone)]
pub enum ModelType {
    QuantumNeuralNetwork,
    QuantumSupportVectorMachine,
    QuantumClustering,
    QuantumDimensionalityReduction,
    QuantumTimeSeries,
    QuantumAnomalyDetection,
    EnsembleModel,
}

/// Model configuration
#[derive(Debug, Clone)]
pub struct ModelConfiguration {
    /// Architecture configuration
    pub architecture: ArchitectureConfiguration,

    /// Hyperparameters
    pub hyperparameters: HashMap<String, f64>,

    /// Preprocessing configuration
    pub preprocessing: PreprocessorConfig,
}

/// Architecture configuration
#[derive(Debug, Clone)]
pub struct ArchitectureConfiguration {
    /// Network layers
    pub layers: Vec<LayerConfig>,

    /// Quantum circuit configuration
    pub quantum_config: QuantumCircuitConfig,

    /// Hybrid configuration
    pub hybrid_config: Option<HybridConfiguration>,
}

/// Layer configuration
#[derive(Debug, Clone)]
pub struct LayerConfig {
    /// Layer type
    pub layer_type: String,

    /// Layer size
    pub size: usize,

    /// Activation function
    pub activation: String,
}

/// Quantum circuit configuration
#[derive(Debug, Clone)]
pub struct QuantumCircuitConfig {
    /// Number of qubits
    pub num_qubits: usize,

    /// Circuit depth
    pub depth: usize,

    /// Gate sequence
    pub gates: Vec<String>,

    /// Entanglement pattern
    pub entanglement: String,
}

/// Hybrid configuration
#[derive(Debug, Clone)]
pub struct HybridConfiguration {
    /// Quantum-classical split
    pub quantum_classical_split: f64,

    /// Interface method
    pub interface_method: String,

    /// Synchronization strategy
    pub synchronization_strategy: String,
}

/// Preprocessor configuration
#[derive(Debug, Clone)]
pub struct PreprocessorConfig {
    /// Scaling method
    pub scaling: String,

    /// Feature selection
    pub feature_selection: Option<String>,

    /// Quantum encoding
    pub quantum_encoding: String,
}

/// 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,
}

/// Model selection strategy
#[derive(Debug, Clone)]
pub enum ModelSelectionStrategy {
    BestPerformance,
    ParetoOptimal,
    ResourceConstrained,
    QuantumAdvantage,
    EnsembleBased,
    MetaLearning,
}

/// Model performance estimator
#[derive(Debug, Clone)]
pub struct ModelPerformanceEstimator {
    /// Estimation method
    method: PerformanceEstimationMethod,

    /// Historical performance data
    performance_database: HashMap<String, f64>,
}

/// Performance estimation methods
#[derive(Debug, Clone)]
pub enum PerformanceEstimationMethod {
    HistoricalData,
    MetaLearning,
    TheoreticalAnalysis,
    QuickValidation,
}

impl QuantumModelSelector {
    /// Create a new model selector
    pub fn new(algorithm_space: &AlgorithmSearchSpace) -> Self {
        let mut model_candidates = Vec::new();

        // Add quantum neural networks if enabled
        if algorithm_space.quantum_neural_networks {
            model_candidates.push(ModelCandidate {
                model_type: ModelType::QuantumNeuralNetwork,
                configuration: ModelConfiguration::default_qnn(),
                estimated_performance: 0.8,
                resource_requirements: ResourceRequirements::moderate(),
            });
        }

        // Add quantum SVM if enabled
        if algorithm_space.quantum_svm {
            model_candidates.push(ModelCandidate {
                model_type: ModelType::QuantumSupportVectorMachine,
                configuration: ModelConfiguration::default_qsvm(),
                estimated_performance: 0.75,
                resource_requirements: ResourceRequirements::low(),
            });
        }

        // Add other quantum algorithms
        if algorithm_space.quantum_clustering {
            model_candidates.push(ModelCandidate {
                model_type: ModelType::QuantumClustering,
                configuration: ModelConfiguration::default_clustering(),
                estimated_performance: 0.7,
                resource_requirements: ResourceRequirements::moderate(),
            });
        }

        Self {
            model_candidates,
            selection_strategy: ModelSelectionStrategy::BestPerformance,
            performance_estimator: ModelPerformanceEstimator::new(),
        }
    }

    /// Select the best model for a given task
    pub fn select_model(&self, task_type: &MLTaskType) -> Result<ModelCandidate> {
        let suitable_candidates = self.filter_candidates_by_task(task_type);

        if suitable_candidates.is_empty() {
            return Err(crate::error::MLError::InvalidParameter(
                "No suitable model candidates found".to_string(),
            ));
        }

        match self.selection_strategy {
            ModelSelectionStrategy::BestPerformance => Ok(suitable_candidates
                .into_iter()
                .max_by(|a, b| {
                    a.estimated_performance
                        .partial_cmp(&b.estimated_performance)
                        .unwrap_or(std::cmp::Ordering::Equal)
                })
                .expect("Candidates verified non-empty above")
                .clone()),
            ModelSelectionStrategy::ResourceConstrained => Ok(suitable_candidates
                .into_iter()
                .min_by(|a, b| {
                    a.resource_requirements
                        .computational_complexity
                        .partial_cmp(&b.resource_requirements.computational_complexity)
                        .unwrap_or(std::cmp::Ordering::Equal)
                })
                .expect("Candidates verified non-empty above")
                .clone()),
            _ => {
                // Default to best performance
                Ok(suitable_candidates
                    .into_iter()
                    .max_by(|a, b| {
                        a.estimated_performance
                            .partial_cmp(&b.estimated_performance)
                            .unwrap_or(std::cmp::Ordering::Equal)
                    })
                    .expect("Candidates verified non-empty above")
                    .clone())
            }
        }
    }

    /// Get all available model candidates
    pub fn get_candidates(&self) -> &[ModelCandidate] {
        &self.model_candidates
    }

    /// Update model performance estimates
    pub fn update_performance_estimates(&mut self, performance_data: HashMap<String, f64>) {
        self.performance_estimator
            .performance_database
            .extend(performance_data);
    }

    // Private methods

    fn filter_candidates_by_task(&self, task_type: &MLTaskType) -> Vec<&ModelCandidate> {
        self.model_candidates
            .iter()
            .filter(|candidate| self.is_suitable_for_task(&candidate.model_type, task_type))
            .collect()
    }

    fn is_suitable_for_task(&self, model_type: &ModelType, task_type: &MLTaskType) -> bool {
        match (model_type, task_type) {
            (ModelType::QuantumNeuralNetwork, _) => true, // QNNs are versatile
            (ModelType::QuantumSupportVectorMachine, MLTaskType::BinaryClassification) => true,
            (ModelType::QuantumSupportVectorMachine, MLTaskType::MultiClassification { .. }) => {
                true
            }
            (ModelType::QuantumClustering, MLTaskType::Clustering { .. }) => true,
            (
                ModelType::QuantumDimensionalityReduction,
                MLTaskType::DimensionalityReduction { .. },
            ) => true,
            (ModelType::QuantumTimeSeries, MLTaskType::TimeSeriesForecasting { .. }) => true,
            (ModelType::QuantumAnomalyDetection, MLTaskType::AnomalyDetection) => true,
            (ModelType::EnsembleModel, _) => true, // Ensembles are always suitable
            _ => false,
        }
    }
}

impl ModelConfiguration {
    fn default_qnn() -> Self {
        Self {
            architecture: ArchitectureConfiguration {
                layers: vec![
                    LayerConfig {
                        layer_type: "quantum".to_string(),
                        size: 4,
                        activation: "none".to_string(),
                    },
                    LayerConfig {
                        layer_type: "classical".to_string(),
                        size: 10,
                        activation: "relu".to_string(),
                    },
                ],
                quantum_config: QuantumCircuitConfig {
                    num_qubits: 4,
                    depth: 3,
                    gates: vec!["RY".to_string(), "CNOT".to_string()],
                    entanglement: "linear".to_string(),
                },
                hybrid_config: Some(HybridConfiguration {
                    quantum_classical_split: 0.5,
                    interface_method: "measurement".to_string(),
                    synchronization_strategy: "sequential".to_string(),
                }),
            },
            hyperparameters: {
                let mut params = HashMap::new();
                params.insert("learning_rate".to_string(), 0.01);
                params.insert("batch_size".to_string(), 32.0);
                params
            },
            preprocessing: PreprocessorConfig {
                scaling: "standard".to_string(),
                feature_selection: None,
                quantum_encoding: "angle".to_string(),
            },
        }
    }

    fn default_qsvm() -> Self {
        Self {
            architecture: ArchitectureConfiguration {
                layers: vec![],
                quantum_config: QuantumCircuitConfig {
                    num_qubits: 8,
                    depth: 2,
                    gates: vec!["H".to_string(), "CNOT".to_string()],
                    entanglement: "full".to_string(),
                },
                hybrid_config: None,
            },
            hyperparameters: {
                let mut params = HashMap::new();
                params.insert("C".to_string(), 1.0);
                params.insert("gamma".to_string(), 0.1);
                params
            },
            preprocessing: PreprocessorConfig {
                scaling: "minmax".to_string(),
                feature_selection: Some("variance".to_string()),
                quantum_encoding: "amplitude".to_string(),
            },
        }
    }

    fn default_clustering() -> Self {
        Self {
            architecture: ArchitectureConfiguration {
                layers: vec![],
                quantum_config: QuantumCircuitConfig {
                    num_qubits: 6,
                    depth: 4,
                    gates: vec!["RX".to_string(), "RZ".to_string(), "CNOT".to_string()],
                    entanglement: "circular".to_string(),
                },
                hybrid_config: None,
            },
            hyperparameters: {
                let mut params = HashMap::new();
                params.insert("num_clusters".to_string(), 3.0);
                params.insert("max_iter".to_string(), 100.0);
                params
            },
            preprocessing: PreprocessorConfig {
                scaling: "robust".to_string(),
                feature_selection: None,
                quantum_encoding: "basis".to_string(),
            },
        }
    }
}

impl ResourceRequirements {
    fn low() -> Self {
        Self {
            computational_complexity: 1.0,
            memory_requirements: 100.0, // MB
            quantum_requirements: QuantumResourceRequirements {
                required_qubits: 4,
                required_circuit_depth: 10,
                required_coherence_time: 50.0,
                required_gate_fidelity: 0.99,
            },
            training_time_estimate: 300.0, // seconds
        }
    }

    fn moderate() -> Self {
        Self {
            computational_complexity: 5.0,
            memory_requirements: 500.0, // MB
            quantum_requirements: QuantumResourceRequirements {
                required_qubits: 8,
                required_circuit_depth: 20,
                required_coherence_time: 100.0,
                required_gate_fidelity: 0.995,
            },
            training_time_estimate: 900.0, // seconds
        }
    }

    fn high() -> Self {
        Self {
            computational_complexity: 10.0,
            memory_requirements: 2000.0, // MB
            quantum_requirements: QuantumResourceRequirements {
                required_qubits: 16,
                required_circuit_depth: 50,
                required_coherence_time: 200.0,
                required_gate_fidelity: 0.999,
            },
            training_time_estimate: 3600.0, // seconds
        }
    }
}

impl ModelPerformanceEstimator {
    fn new() -> Self {
        Self {
            method: PerformanceEstimationMethod::HistoricalData,
            performance_database: HashMap::new(),
        }
    }
}