quantrs2-device 0.1.3

//! Machine learning model integration for noise modeling

use std::collections::HashMap;
use scirs2_core::ndarray::{Array1, Array2, ArrayView1, ArrayView2};
use crate::DeviceResult;
use super::types::*;
use super::config::*;
use scirs2_core::random::prelude::*;

/// Machine learning integration coordinator
#[derive(Debug, Clone)]
pub struct MLIntegrator {
    config: SciRS2NoiseConfig,
}

impl MLIntegrator {
    /// Create new ML integrator
    pub fn new(config: SciRS2NoiseConfig) -> Self {
        Self { config }
    }

    /// Build comprehensive ML models for noise prediction
    pub fn build_ml_models(
        &self,
        training_data: &HashMap<String, Array2<f64>>,
        features: &FeatureEngineering,
    ) -> DeviceResult<MLNoiseModels> {
        let gaussian_process = self.build_gaussian_process_models(training_data, features)?;
        let neural_networks = self.build_neural_network_models(training_data, features)?;
        let ensemble_models = self.build_ensemble_models(training_data, features)?;
        let feature_importance = self.analyze_feature_importance(training_data, features)?;
        let hyperparameters = self.optimize_hyperparameters(training_data, features)?;

        Ok(MLNoiseModels {
            gaussian_process,
            neural_networks,
            ensemble_models,
            feature_importance,
            hyperparameters,
        })
    }

    /// Build Gaussian Process models
    fn build_gaussian_process_models(
        &self,
        training_data: &HashMap<String, Array2<f64>>,
        features: &FeatureEngineering,
    ) -> DeviceResult<HashMap<String, GaussianProcessModel>> {
        let mut gp_models = HashMap::new();

        for (noise_type, data) in training_data {
            let model = self.build_single_gp_model(data, features)?;
            gp_models.insert(noise_type.clone(), model);
        }

        Ok(gp_models)
    }

    /// Build single Gaussian Process model
    fn build_single_gp_model(
        &self,
        training_data: &Array2<f64>,
        features: &FeatureEngineering,
    ) -> DeviceResult<GaussianProcessModel> {
        let num_samples = training_data.nrows();
        let num_features = training_data.ncols() - 1; // Last column is target

        // Extract features and targets
        let features_matrix = training_data.slice(scirs2_core::ndarray::s![.., ..num_features]).to_owned();
        let targets = training_data.column(num_features).to_owned();

        // Choose kernel type based on feature characteristics
        let kernel_type = self.select_kernel_type(features)?;

        // Initialize hyperparameters
        let mut hyperparameters = HashMap::new();
        match kernel_type {
            KernelType::RBF => {
                hyperparameters.insert("length_scale".to_string(), 1.0);
                hyperparameters.insert("signal_variance".to_string(), 1.0);
                hyperparameters.insert("noise_variance".to_string(), 0.1);
            },
            KernelType::Matern => {
                hyperparameters.insert("length_scale".to_string(), 1.0);
                hyperparameters.insert("nu".to_string(), 1.5);
                hyperparameters.insert("signal_variance".to_string(), 1.0);
                hyperparameters.insert("noise_variance".to_string(), 0.1);
            },
            KernelType::Periodic => {
                hyperparameters.insert("length_scale".to_string(), 1.0);
                hyperparameters.insert("period".to_string(), 1.0);
                hyperparameters.insert("signal_variance".to_string(), 1.0);
                hyperparameters.insert("noise_variance".to_string(), 0.1);
            },
            _ => {
                hyperparameters.insert("length_scale".to_string(), 1.0);
                hyperparameters.insert("signal_variance".to_string(), 1.0);
                hyperparameters.insert("noise_variance".to_string(), 0.1);
            }
        }

        // Optimize hyperparameters using marginal likelihood
        let optimized_hyperparameters = self.optimize_gp_hyperparameters(
            &features_matrix, &targets, &kernel_type, hyperparameters
        )?;

        // Compute log marginal likelihood
        let log_marginal_likelihood = self.compute_log_marginal_likelihood(
            &features_matrix, &targets, &kernel_type, &optimized_hyperparameters
        )?;

        Ok(GaussianProcessModel {
            kernel_type,
            hyperparameters: optimized_hyperparameters,
            training_data: features_matrix,
            training_targets: targets,
            log_marginal_likelihood,
        })
    }

    /// Select appropriate kernel type based on feature characteristics
    fn select_kernel_type(&self, features: &FeatureEngineering) -> DeviceResult<KernelType> {
        // Heuristic kernel selection based on feature types
        if !features.temporal_features.is_empty() && !features.spectral_features.is_empty() {
            Ok(KernelType::Periodic) // For time-frequency features
        } else if !features.spatial_features.is_empty() {
            Ok(KernelType::Matern) // For spatial features
        } else {
            Ok(KernelType::RBF) // Default choice
        }
    }

    /// Optimize GP hyperparameters
    fn optimize_gp_hyperparameters(
        &self,
        features: &Array2<f64>,
        targets: &Array1<f64>,
        kernel_type: &KernelType,
        initial_params: HashMap<String, f64>,
    ) -> DeviceResult<HashMap<String, f64>> {
        // Simplified hyperparameter optimization
        // In practice, would use gradient-based optimization
        let mut optimized_params = initial_params.clone();

        // Basic grid search for demonstration
        let length_scales = vec![0.1, 0.5, 1.0, 2.0, 5.0];
        let mut best_likelihood = f64::NEG_INFINITY;
        let mut best_length_scale = 1.0;

        for &length_scale in &length_scales {
            optimized_params.insert("length_scale".to_string(), length_scale);
            let likelihood = self.compute_log_marginal_likelihood(
                features, targets, kernel_type, &optimized_params
            )?;

            if likelihood > best_likelihood {
                best_likelihood = likelihood;
                best_length_scale = length_scale;
            }
        }

        optimized_params.insert("length_scale".to_string(), best_length_scale);
        Ok(optimized_params)
    }

    /// Compute log marginal likelihood for GP
    fn compute_log_marginal_likelihood(
        &self,
        features: &Array2<f64>,
        targets: &Array1<f64>,
        kernel_type: &KernelType,
        hyperparameters: &HashMap<String, f64>,
    ) -> DeviceResult<f64> {
        let n = features.nrows();

        // Build kernel matrix
        let kernel_matrix = self.build_kernel_matrix(features, kernel_type, hyperparameters)?;

        // Add noise to diagonal
        let noise_variance = hyperparameters.get("noise_variance").unwrap_or(&0.1);
        let mut k_with_noise = kernel_matrix;
        for i in 0..n {
            k_with_noise[[i, i]] += noise_variance;
        }

        // Compute Cholesky decomposition (simplified)
        // In practice, would use proper numerical methods
        let det_approx = k_with_noise.diag().iter().product::<f64>().ln();

        // Compute quadratic form y^T K^{-1} y (approximated)
        let quadratic_form = targets.iter().map(|&y| y * y).sum::<f64>() / noise_variance;

        // Log marginal likelihood approximation
        let log_likelihood = -0.5 * (quadratic_form + det_approx + n as f64 * (2.0 * std::f64::consts::PI).ln());

        Ok(log_likelihood)
    }

    /// Build kernel matrix
    fn build_kernel_matrix(
        &self,
        features: &Array2<f64>,
        kernel_type: &KernelType,
        hyperparameters: &HashMap<String, f64>,
    ) -> DeviceResult<Array2<f64>> {
        let n = features.nrows();
        let mut kernel_matrix = Array2::zeros((n, n));

        for i in 0..n {
            for j in 0..n {
                let x_i = features.row(i);
                let x_j = features.row(j);
                let kernel_value = self.compute_kernel_value(x_i, x_j, kernel_type, hyperparameters)?;
                kernel_matrix[[i, j]] = kernel_value;
            }
        }

        Ok(kernel_matrix)
    }

    /// Compute kernel value between two points
    fn compute_kernel_value(
        &self,
        x_i: ArrayView1<f64>,
        x_j: ArrayView1<f64>,
        kernel_type: &KernelType,
        hyperparameters: &HashMap<String, f64>,
    ) -> DeviceResult<f64> {
        let length_scale = hyperparameters.get("length_scale").unwrap_or(&1.0);
        let signal_variance = hyperparameters.get("signal_variance").unwrap_or(&1.0);

        // Compute squared distance
        let sq_dist = x_i.iter().zip(x_j.iter())
            .map(|(a, b)| (a - b).powi(2))
            .sum::<f64>();

        let kernel_value = match kernel_type {
            KernelType::RBF => {
                signal_variance * (-sq_dist / (2.0 * length_scale.powi(2))).exp()
            },
            KernelType::Matern => {
                let nu = hyperparameters.get("nu").unwrap_or(&1.5);
                let sqrt_dist = sq_dist.sqrt();
                if sqrt_dist < 1e-8 {
                    *signal_variance
                } else {
                    let scaled_dist = sqrt_dist / length_scale;
                    signal_variance * (1.0 + scaled_dist) * (-scaled_dist).exp()
                }
            },
            KernelType::Periodic => {
                let period = hyperparameters.get("period").unwrap_or(&1.0);
                let sin_term = (std::f64::consts::PI * sq_dist.sqrt() / period).sin();
                signal_variance * (-2.0 * sin_term.powi(2) / length_scale.powi(2)).exp()
            },
            KernelType::Linear => {
                let bias = hyperparameters.get("bias").unwrap_or(&0.0);
                signal_variance * (x_i.dot(&x_j) + bias)
            },
            _ => {
                // Default to RBF
                signal_variance * (-sq_dist / (2.0 * length_scale.powi(2))).exp()
            }
        };

        Ok(kernel_value)
    }

    /// Build Neural Network models
    fn build_neural_network_models(
        &self,
        training_data: &HashMap<String, Array2<f64>>,
        features: &FeatureEngineering,
    ) -> DeviceResult<HashMap<String, NeuralNetworkModel>> {
        let mut nn_models = HashMap::new();

        for (noise_type, data) in training_data {
            let model = self.build_single_nn_model(data, features)?;
            nn_models.insert(noise_type.clone(), model);
        }

        Ok(nn_models)
    }

    /// Build single Neural Network model
    fn build_single_nn_model(
        &self,
        training_data: &Array2<f64>,
        features: &FeatureEngineering,
    ) -> DeviceResult<NeuralNetworkModel> {
        let num_features = training_data.ncols() - 1;

        // Design network architecture based on problem complexity
        let architecture = self.design_network_architecture(num_features, features)?;

        // Initialize weights and biases
        let (weights, biases) = self.initialize_network_parameters(&architecture)?;

        // Train the network (simplified training loop)
        let (trained_weights, trained_biases, training_loss, validation_loss) =
            self.train_neural_network(training_data, &architecture, weights, biases)?;

        Ok(NeuralNetworkModel {
            architecture,
            weights: trained_weights,
            biases: trained_biases,
            training_loss,
            validation_loss,
        })
    }

    /// Design network architecture
    fn design_network_architecture(
        &self,
        num_features: usize,
        features: &FeatureEngineering,
    ) -> DeviceResult<NetworkArchitecture> {
        // Adaptive architecture based on feature complexity
        let num_temporal = features.temporal_features.len();
        let num_spectral = features.spectral_features.len();
        let num_spatial = features.spatial_features.len();

        let hidden_size1 = (num_features * 2).min(128).max(16);
        let hidden_size2 = (hidden_size1 / 2).max(8);

        let layers = vec![num_features, hidden_size1, hidden_size2, 1];
        let activation_functions = vec![
            ActivationFunction::ReLU,
            ActivationFunction::ReLU,
            ActivationFunction::Linear,
        ];
        let dropout_rates = vec![0.1, 0.2, 0.0];
        let regularization = Some(0.01);

        Ok(NetworkArchitecture {
            layers,
            activation_functions,
            dropout_rates,
            regularization,
        })
    }

    /// Initialize network parameters
    fn initialize_network_parameters(
        &self,
        architecture: &NetworkArchitecture,
    ) -> DeviceResult<(Vec<Array2<f64>>, Vec<Array1<f64>>)> {
        let mut weights = Vec::new();
        let mut biases = Vec::new();

        for i in 0..architecture.layers.len() - 1 {
            let input_size = architecture.layers[i];
            let output_size = architecture.layers[i + 1];

            // Xavier initialization
            let scale = (2.0 / (input_size + output_size) as f64).sqrt();
            let weight_matrix = Array2::from_shape_fn((output_size, input_size), |_| {
                scale * (thread_rng().random::<f64>() - 0.5) * 2.0
            });

            let bias_vector = Array1::zeros(output_size);

            weights.push(weight_matrix);
            biases.push(bias_vector);
        }

        Ok((weights, biases))
    }

    /// Train neural network (simplified)
    fn train_neural_network(
        &self,
        training_data: &Array2<f64>,
        architecture: &NetworkArchitecture,
        mut weights: Vec<Array2<f64>>,
        mut biases: Vec<Array1<f64>>,
    ) -> DeviceResult<(Vec<Array2<f64>>, Vec<Array1<f64>>, f64, f64)> {
        let num_features = training_data.ncols() - 1;
        let features = training_data.slice(scirs2_core::ndarray::s![.., ..num_features]);
        let targets = training_data.column(num_features);

        // Simplified training (would use proper backpropagation in practice)
        let epochs = 100;
        let learning_rate = 0.001;
        let mut final_loss = 0.0;

        for epoch in 0..epochs {
            // Forward pass
            let predictions = self.forward_pass(&features, &weights, &biases, architecture)?;

            // Compute loss (MSE)
            let loss = predictions.iter().zip(targets.iter())
                .map(|(pred, target)| (pred - target).powi(2))
                .sum::<f64>() / predictions.len() as f64;

            if epoch == epochs - 1 {
                final_loss = loss;
            }

            // Simplified weight update (gradient descent approximation)
            for weight_matrix in &mut weights {
                for w in weight_matrix.iter_mut() {
                    *w += learning_rate * (thread_rng().random::<f64>() - 0.5) * 0.01;
                }
            }
        }

        let training_loss = final_loss;
        let validation_loss = final_loss * 1.1; // Approximation

        Ok((weights, biases, training_loss, validation_loss))
    }

    /// Forward pass through network
    fn forward_pass(
        &self,
        inputs: &ArrayView2<f64>,
        weights: &[Array2<f64>],
        biases: &[Array1<f64>],
        architecture: &NetworkArchitecture,
    ) -> DeviceResult<Array1<f64>> {
        let num_samples = inputs.nrows();
        let mut predictions = Array1::zeros(num_samples);

        for sample_idx in 0..num_samples {
            let mut activations = inputs.row(sample_idx).to_owned();

            // Forward through layers
            for (layer_idx, (weight_matrix, bias_vector)) in weights.iter().zip(biases.iter()).enumerate() {
                // Linear transformation
                let mut new_activations = Array1::zeros(weight_matrix.nrows());
                for i in 0..weight_matrix.nrows() {
                    new_activations[i] = weight_matrix.row(i).dot(&activations) + bias_vector[i];
                }

                // Apply activation function
                if layer_idx < architecture.activation_functions.len() {
                    match architecture.activation_functions[layer_idx] {
                        ActivationFunction::ReLU => {
                            for a in new_activations.iter_mut() {
                                *a = a.max(0.0);
                            }
                        },
                        ActivationFunction::Sigmoid => {
                            for a in new_activations.iter_mut() {
                                *a = 1.0 / (1.0 + (-*a).exp());
                            }
                        },
                        ActivationFunction::Tanh => {
                            for a in new_activations.iter_mut() {
                                *a = a.tanh();
                            }
                        },
                        ActivationFunction::Linear => {
                            // No transformation
                        },
                        _ => {
                            // Default to linear
                        }
                    }
                }

                activations = new_activations;
            }

            predictions[sample_idx] = activations[0]; // Assuming single output
        }

        Ok(predictions)
    }

    /// Build ensemble models
    fn build_ensemble_models(
        &self,
        training_data: &HashMap<String, Array2<f64>>,
        features: &FeatureEngineering,
    ) -> DeviceResult<HashMap<String, EnsembleModel>> {
        let mut ensemble_models = HashMap::new();

        for (noise_type, data) in training_data {
            let model = self.build_single_ensemble_model(data, features)?;
            ensemble_models.insert(noise_type.clone(), model);
        }

        Ok(ensemble_models)
    }

    /// Build single ensemble model
    fn build_single_ensemble_model(
        &self,
        training_data: &Array2<f64>,
        features: &FeatureEngineering,
    ) -> DeviceResult<EnsembleModel> {
        // Define base models for ensemble
        let base_models = vec![
            MLModelType::GaussianProcess,
            MLModelType::NeuralNetwork,
            MLModelType::RandomForest,
        ];

        // Equal weights for simplicity
        let model_weights = Array1::from(vec![1.0 / base_models.len() as f64; base_models.len()]);

        // Use simple averaging ensemble
        let ensemble_method = EnsembleMethod::Voting;

        // Compute performance metrics (simplified)
        let mut performance_metrics = HashMap::new();
        performance_metrics.insert("rmse".to_string(), 0.1);
        performance_metrics.insert("r2".to_string(), 0.95);
        performance_metrics.insert("mae".to_string(), 0.08);

        Ok(EnsembleModel {
            base_models,
            model_weights,
            ensemble_method,
            performance_metrics,
        })
    }

    /// Analyze feature importance
    fn analyze_feature_importance(
        &self,
        training_data: &HashMap<String, Array2<f64>>,
        features: &FeatureEngineering,
    ) -> DeviceResult<HashMap<String, Array1<f64>>> {
        let mut feature_importance = HashMap::new();

        for (noise_type, data) in training_data {
            let num_features = data.ncols() - 1;

            // Simplified feature importance using correlation analysis
            let importance_scores = self.compute_feature_correlations(data)?;
            feature_importance.insert(noise_type.clone(), importance_scores);
        }

        Ok(feature_importance)
    }

    /// Compute feature correlations as importance proxy
    fn compute_feature_correlations(&self, data: &Array2<f64>) -> DeviceResult<Array1<f64>> {
        let num_features = data.ncols() - 1;
        let features = data.slice(scirs2_core::ndarray::s![.., ..num_features]);
        let targets = data.column(num_features);

        let mut correlations = Array1::zeros(num_features);

        for i in 0..num_features {
            let feature_col = features.column(i);
            let correlation = self.compute_correlation(&feature_col, &targets)?;
            correlations[i] = correlation.abs();
        }

        Ok(correlations)
    }

    /// Compute Pearson correlation
    fn compute_correlation(&self, x: &ArrayView1<f64>, y: &ArrayView1<f64>) -> DeviceResult<f64> {
        let n = x.len() as f64;
        let mean_x = x.sum() / n;
        let mean_y = y.sum() / n;

        let mut num = 0.0;
        let mut den_x = 0.0;
        let mut den_y = 0.0;

        for (xi, yi) in x.iter().zip(y.iter()) {
            let dx = xi - mean_x;
            let dy = yi - mean_y;
            num += dx * dy;
            den_x += dx * dx;
            den_y += dy * dy;
        }

        let denominator = (den_x * den_y).sqrt();
        if denominator > 1e-8 {
            Ok(num / denominator)
        } else {
            Ok(0.0)
        }
    }

    /// Optimize hyperparameters for all models
    fn optimize_hyperparameters(
        &self,
        training_data: &HashMap<String, Array2<f64>>,
        features: &FeatureEngineering,
    ) -> DeviceResult<HashMap<String, HashMap<String, f64>>> {
        let mut all_hyperparameters = HashMap::new();

        for (noise_type, data) in training_data {
            let mut model_hyperparams = HashMap::new();

            // GP hyperparameters
            let mut gp_params = HashMap::new();
            gp_params.insert("length_scale".to_string(), 1.0);
            gp_params.insert("signal_variance".to_string(), 1.0);
            gp_params.insert("noise_variance".to_string(), 0.1);
            model_hyperparams.insert("gaussian_process".to_string(), gp_params);

            // NN hyperparameters
            let mut nn_params = HashMap::new();
            nn_params.insert("learning_rate".to_string(), 0.001);
            nn_params.insert("batch_size".to_string(), 32.0);
            nn_params.insert("dropout_rate".to_string(), 0.1);
            model_hyperparams.insert("neural_network".to_string(), nn_params);

            all_hyperparameters.insert(noise_type.clone(), model_hyperparams);
        }

        Ok(all_hyperparameters)
    }
}