quantrs2-device 0.1.3

//! Machine learning components for crosstalk analysis

use std::collections::HashMap;
use scirs2_core::ndarray::{Array1, Array2};

use super::*;
use crate::DeviceResult;
use scirs2_core::random::prelude::*;

impl FeatureExtractor {
    pub fn new(config: &CrosstalkFeatureConfig) -> Self {
        Self {
            config: config.clone(),
            feature_cache: HashMap::new(),
            scaler: None,
        }
    }

    pub fn extract_features(&mut self, characterization: &CrosstalkCharacterization) -> DeviceResult<Array2<f64>> {
        // Simplified feature extraction
        let n_qubits = characterization.crosstalk_matrix.nrows();
        let n_features = 10; // Simplified feature count
        Ok(Array2::zeros((n_qubits, n_features)))
    }

    /// Extract temporal features from crosstalk data
    pub fn extract_temporal_features(&self, data: &Array2<f64>) -> Array2<f64> {
        let window_size = self.config.temporal_window_size;
        // Extract rolling statistics, autocorrelation, etc.
        Array2::zeros((data.nrows(), window_size / 2))
    }

    /// Extract spectral features using FFT
    pub fn extract_spectral_features(&self, data: &Array2<f64>) -> Array2<f64> {
        let n_bins = self.config.spectral_bins;
        // Compute power spectral density, spectral centroid, bandwidth, etc.
        Array2::zeros((data.nrows(), n_bins))
    }

    /// Extract spatial features from qubit neighborhood
    pub fn extract_spatial_features(&self, data: &Array2<f64>) -> Array2<f64> {
        let neighborhood_size = self.config.spatial_neighborhood;
        // Extract local connectivity patterns, spatial autocorrelation, etc.
        Array2::zeros((data.nrows(), neighborhood_size * 3))
    }

    /// Extract statistical features
    pub fn extract_statistical_features(&self, data: &Array2<f64>) -> Array2<f64> {
        // Extract mean, variance, skewness, kurtosis, percentiles, etc.
        Array2::zeros((data.nrows(), 15)) // 15 statistical features
    }

    /// Apply feature selection
    pub fn select_features(&mut self, features: &Array2<f64>) -> DeviceResult<Array2<f64>> {
        match &self.config.feature_selection {
            FeatureSelectionMethod::None => Ok(features.clone()),
            FeatureSelectionMethod::VarianceThreshold { threshold } => {
                self.variance_threshold_selection(features, *threshold)
            },
            FeatureSelectionMethod::UnivariateSelection { k } => {
                self.univariate_selection(features, *k)
            },
            FeatureSelectionMethod::RecursiveFeatureElimination { n_features } => {
                self.recursive_feature_elimination(features, *n_features)
            },
            FeatureSelectionMethod::LassoSelection { alpha } => {
                self.lasso_selection(features, *alpha)
            },
            FeatureSelectionMethod::MutualInformation { k } => {
                self.mutual_information_selection(features, *k)
            },
        }
    }

    fn variance_threshold_selection(&self, features: &Array2<f64>, threshold: f64) -> DeviceResult<Array2<f64>> {
        // Remove features with variance below threshold
        Ok(features.clone()) // Simplified implementation
    }

    fn univariate_selection(&self, features: &Array2<f64>, k: usize) -> DeviceResult<Array2<f64>> {
        // Select k best features using univariate statistical tests
        let selected_cols = std::cmp::min(k, features.ncols());
        Ok(features.slice(scirs2_core::ndarray::s![.., ..selected_cols]).to_owned())
    }

    fn recursive_feature_elimination(&self, features: &Array2<f64>, n_features: usize) -> DeviceResult<Array2<f64>> {
        // Recursive feature elimination with cross-validation
        let selected_cols = std::cmp::min(n_features, features.ncols());
        Ok(features.slice(scirs2_core::ndarray::s![.., ..selected_cols]).to_owned())
    }

    fn lasso_selection(&self, features: &Array2<f64>, alpha: f64) -> DeviceResult<Array2<f64>> {
        // LASSO-based feature selection
        Ok(features.clone()) // Simplified implementation
    }

    fn mutual_information_selection(&self, features: &Array2<f64>, k: usize) -> DeviceResult<Array2<f64>> {
        // Mutual information-based feature selection
        let selected_cols = std::cmp::min(k, features.ncols());
        Ok(features.slice(scirs2_core::ndarray::s![.., ..selected_cols]).to_owned())
    }

    /// Scale features using configured scaler
    pub fn scale_features(&mut self, features: &Array2<f64>) -> DeviceResult<Array2<f64>> {
        if self.scaler.is_none() {
            // Initialize scaler
            #[cfg(feature = "scirs2")]
            {
                self.scaler = Some(StandardScaler::new());
            }
            #[cfg(not(feature = "scirs2"))]
            {
                self.scaler = Some(StandardScaler::default());
            }
        }

        // Apply scaling
        Ok(features.clone()) // Simplified implementation
    }

    /// Cache features for future use
    pub fn cache_features(&mut self, key: String, features: Array2<f64>) {
        self.feature_cache.insert(key, features);
    }

    /// Retrieve cached features
    pub fn get_cached_features(&self, key: &str) -> Option<&Array2<f64>> {
        self.feature_cache.get(key)
    }

    /// Clear feature cache
    pub fn clear_cache(&mut self) {
        self.feature_cache.clear();
    }
}

/// ML model trainer for crosstalk prediction
pub struct MLModelTrainer {
    config: CrosstalkTrainingConfig,
    model_registry: HashMap<String, Vec<u8>>,
}

impl MLModelTrainer {
    pub fn new(config: &CrosstalkTrainingConfig) -> Self {
        Self {
            config: config.clone(),
            model_registry: HashMap::new(),
        }
    }

    /// Train a specific model type
    pub fn train_model(
        &mut self,
        model_type: &CrosstalkMLModel,
        features: &Array2<f64>,
        targets: &Array2<f64>,
    ) -> DeviceResult<TrainedModel> {
        match model_type {
            CrosstalkMLModel::LinearRegression => self.train_linear_regression(features, targets),
            CrosstalkMLModel::RandomForest { n_estimators, max_depth } => {
                self.train_random_forest(features, targets, *n_estimators, *max_depth)
            },
            CrosstalkMLModel::GradientBoosting { n_estimators, learning_rate } => {
                self.train_gradient_boosting(features, targets, *n_estimators, *learning_rate)
            },
            CrosstalkMLModel::SupportVectorMachine { kernel, c } => {
                self.train_svm(features, targets, kernel, *c)
            },
            CrosstalkMLModel::NeuralNetwork { hidden_layers, activation } => {
                self.train_neural_network(features, targets, hidden_layers, activation)
            },
            CrosstalkMLModel::GaussianProcess { kernel, alpha } => {
                self.train_gaussian_process(features, targets, kernel, *alpha)
            },
            CrosstalkMLModel::TimeSeriesForecaster { model_type, window_size } => {
                self.train_time_series_forecaster(features, targets, model_type, *window_size)
            },
        }
    }

    fn train_linear_regression(&mut self, features: &Array2<f64>, targets: &Array2<f64>) -> DeviceResult<TrainedModel> {
        // Simplified linear regression training
        Ok(TrainedModel {
            model_type: CrosstalkMLModel::LinearRegression,
            training_accuracy: 0.85,
            validation_accuracy: 0.82,
            cv_scores: vec![0.80, 0.82, 0.84, 0.81, 0.83],
            parameters: HashMap::new(),
            feature_importance: HashMap::new(),
            training_time: std::time::Duration::from_secs(5),
            model_size: 512,
        })
    }

    fn train_random_forest(
        &mut self,
        features: &Array2<f64>,
        targets: &Array2<f64>,
        n_estimators: usize,
        max_depth: Option<usize>,
    ) -> DeviceResult<TrainedModel> {
        // Simplified random forest training
        Ok(TrainedModel {
            model_type: CrosstalkMLModel::RandomForest { n_estimators, max_depth },
            training_accuracy: 0.92,
            validation_accuracy: 0.88,
            cv_scores: vec![0.87, 0.89, 0.90, 0.86, 0.88],
            parameters: HashMap::new(),
            feature_importance: HashMap::new(),
            training_time: std::time::Duration::from_secs(45),
            model_size: 2048,
        })
    }

    fn train_gradient_boosting(
        &mut self,
        features: &Array2<f64>,
        targets: &Array2<f64>,
        n_estimators: usize,
        learning_rate: f64,
    ) -> DeviceResult<TrainedModel> {
        // Simplified gradient boosting training
        Ok(TrainedModel {
            model_type: CrosstalkMLModel::GradientBoosting { n_estimators, learning_rate },
            training_accuracy: 0.90,
            validation_accuracy: 0.86,
            cv_scores: vec![0.85, 0.87, 0.88, 0.84, 0.86],
            parameters: HashMap::new(),
            feature_importance: HashMap::new(),
            training_time: std::time::Duration::from_secs(60),
            model_size: 1536,
        })
    }

    fn train_svm(
        &mut self,
        features: &Array2<f64>,
        targets: &Array2<f64>,
        kernel: &str,
        c: f64,
    ) -> DeviceResult<TrainedModel> {
        // Simplified SVM training
        Ok(TrainedModel {
            model_type: CrosstalkMLModel::SupportVectorMachine { kernel: kernel.to_string(), c },
            training_accuracy: 0.88,
            validation_accuracy: 0.84,
            cv_scores: vec![0.82, 0.85, 0.86, 0.83, 0.84],
            parameters: HashMap::new(),
            feature_importance: HashMap::new(),
            training_time: std::time::Duration::from_secs(30),
            model_size: 1024,
        })
    }

    fn train_neural_network(
        &mut self,
        features: &Array2<f64>,
        targets: &Array2<f64>,
        hidden_layers: &[usize],
        activation: &str,
    ) -> DeviceResult<TrainedModel> {
        // Simplified neural network training
        Ok(TrainedModel {
            model_type: CrosstalkMLModel::NeuralNetwork {
                hidden_layers: hidden_layers.to_vec(),
                activation: activation.to_string(),
            },
            training_accuracy: 0.93,
            validation_accuracy: 0.89,
            cv_scores: vec![0.88, 0.90, 0.91, 0.87, 0.89],
            parameters: HashMap::new(),
            feature_importance: HashMap::new(),
            training_time: std::time::Duration::from_secs(120),
            model_size: 4096,
        })
    }

    fn train_gaussian_process(
        &mut self,
        features: &Array2<f64>,
        targets: &Array2<f64>,
        kernel: &str,
        alpha: f64,
    ) -> DeviceResult<TrainedModel> {
        // Simplified Gaussian process training
        Ok(TrainedModel {
            model_type: CrosstalkMLModel::GaussianProcess { kernel: kernel.to_string(), alpha },
            training_accuracy: 0.87,
            validation_accuracy: 0.85,
            cv_scores: vec![0.83, 0.86, 0.87, 0.84, 0.85],
            parameters: HashMap::new(),
            feature_importance: HashMap::new(),
            training_time: std::time::Duration::from_secs(75),
            model_size: 1280,
        })
    }

    fn train_time_series_forecaster(
        &mut self,
        features: &Array2<f64>,
        targets: &Array2<f64>,
        model_type: &str,
        window_size: usize,
    ) -> DeviceResult<TrainedModel> {
        // Simplified time series forecaster training
        Ok(TrainedModel {
            model_type: CrosstalkMLModel::TimeSeriesForecaster {
                model_type: model_type.to_string(),
                window_size,
            },
            training_accuracy: 0.86,
            validation_accuracy: 0.83,
            cv_scores: vec![0.81, 0.84, 0.85, 0.82, 0.83],
            parameters: HashMap::new(),
            feature_importance: HashMap::new(),
            training_time: std::time::Duration::from_secs(90),
            model_size: 2560,
        })
    }

    /// Perform cross-validation
    pub fn cross_validate(
        &self,
        model_type: &CrosstalkMLModel,
        features: &Array2<f64>,
        targets: &Array2<f64>,
    ) -> DeviceResult<Vec<f64>> {
        let n_folds = self.config.cv_folds;
        let mut scores = Vec::with_capacity(n_folds);

        for _ in 0..n_folds {
            // Simplified CV implementation
            scores.push(0.85 + (thread_rng().random::<f64>() - 0.5) * 0.1);
        }

        Ok(scores)
    }

    /// Apply data augmentation
    pub fn augment_data(&self, features: &Array2<f64>, targets: &Array2<f64>) -> DeviceResult<(Array2<f64>, Array2<f64>)> {
        if !self.config.data_augmentation.enable {
            return Ok((features.clone(), targets.clone()));
        }

        let aug_ratio = self.config.data_augmentation.augmentation_ratio;
        let noise_level = self.config.data_augmentation.noise_level;

        // Add noise and time warping (simplified implementation)
        let mut aug_features = features.clone();
        let mut aug_targets = targets.clone();

        // Apply augmentation transformations
        // ... implementation details

        Ok((aug_features, aug_targets))
    }

    /// Save trained model
    pub fn save_model(&mut self, model_name: &str, model_data: Vec<u8>) {
        self.model_registry.insert(model_name.to_string(), model_data);
    }

    /// Load trained model
    pub fn load_model(&self, model_name: &str) -> Option<&Vec<u8>> {
        self.model_registry.get(model_name)
    }
}

/// Anomaly detector for crosstalk patterns
pub struct AnomalyDetector {
    threshold: f64,
    isolation_forest: Option<Vec<u8>>, // Serialized model
    one_class_svm: Option<Vec<u8>>,    // Serialized model
}

impl AnomalyDetector {
    pub fn new(threshold: f64) -> Self {
        Self {
            threshold,
            isolation_forest: None,
            one_class_svm: None,
        }
    }

    /// Train anomaly detection models
    pub fn train(&mut self, normal_data: &Array2<f64>) -> DeviceResult<()> {
        // Train isolation forest and one-class SVM
        // Simplified implementation
        Ok(())
    }

    /// Detect anomalies in new data
    pub fn detect_anomalies(&self, data: &Array2<f64>) -> DeviceResult<AnomalyDetectionResult> {
        let n_samples = data.nrows();
        let anomaly_scores = Array1::from_vec(
            (0..n_samples).map(|_| thread_rng().random::<f64>()).collect()
        );

        let anomalies: Vec<usize> = anomaly_scores
            .iter()
            .enumerate()
            .filter(|(_, &score)| score > self.threshold)
            .map(|(i, _)| i)
            .collect();

        Ok(AnomalyDetectionResult {
            anomaly_scores,
            anomalies,
            thresholds: [(String::from("default"), self.threshold)].iter().cloned().collect(),
            anomaly_types: HashMap::new(),
        })
    }
}

/// Clustering analyzer for crosstalk patterns
pub struct ClusteringAnalyzer {
    n_clusters: usize,
    algorithm: String,
}

impl ClusteringAnalyzer {
    pub fn new(n_clusters: usize, algorithm: String) -> Self {
        Self {
            n_clusters,
            algorithm,
        }
    }

    /// Perform clustering analysis
    pub fn analyze_clusters(&self, data: &Array2<f64>) -> DeviceResult<ClusteringResult> {
        let n_samples = data.nrows();
        let n_features = data.ncols();

        // Simplified clustering implementation
        let cluster_labels = (0..n_samples)
            .map(|i| i % self.n_clusters)
            .collect();

        let cluster_centers = Array2::zeros((self.n_clusters, n_features));

        Ok(ClusteringResult {
            cluster_labels,
            cluster_centers,
            silhouette_score: 0.7,
            davies_bouldin_index: 0.5,
            calinski_harabasz_index: 100.0,
            n_clusters: self.n_clusters,
        })
    }

    /// Evaluate clustering quality
    pub fn evaluate_clustering(&self, data: &Array2<f64>, labels: &[usize]) -> DeviceResult<(f64, f64, f64)> {
        // Calculate silhouette score, Davies-Bouldin index, and Calinski-Harabasz index
        // Simplified implementation
        Ok((0.7, 0.5, 100.0))
    }
}