optirs-learned 0.3.1

// Performance prediction for neural architectures

use scirs2_core::ndarray::{Array1, Array2};
use scirs2_core::numeric::Float;
use std::fmt::Debug;
use std::collections::HashMap;
use crate::error::Result;
use super::evaluation::EvaluationMetrics;
use super::config::ArchitectureSpec;

/// Performance predictor using surrogate models
pub struct PerformancePredictor<T: Float + Debug + Send + Sync + 'static> {
    /// Prediction model
    model: PredictionModel<T>,
    /// Training data
    training_data: Vec<(String, EvaluationMetrics)>,
    /// Model configuration
    config: PredictorConfig,
    /// Feature extractor
    feature_extractor: FeatureExtractor,
    /// Performance metrics for the predictor
    predictor_metrics: PredictorMetrics<T>,
}

impl<T: Float + Debug + Send + Sync + 'static> PerformancePredictor<T> {
    /// Create new performance predictor
    pub fn new(config: &super::config::NASConfig) -> Result<Self> {
        let predictor_config = PredictorConfig::from_nas_config(config);
        let model = PredictionModel::new(&predictor_config)?;
        let feature_extractor = FeatureExtractor::new();
        let predictor_metrics = PredictorMetrics::new();

        Ok(Self {
            model,
            training_data: Vec::new(),
            config: predictor_config,
            feature_extractor,
            predictor_metrics,
        })
    }

    /// Predict performance for architecture
    pub fn predict(&mut self, architecture: &str) -> Result<Option<EvaluationMetrics>> {
        if !self.model.is_trained() {
            return Ok(None);
        }

        let features_f64 = self.feature_extractor.extract_features(architecture)?;
        let features: Array1<T> = features_f64.mapv(|x| scirs2_core::numeric::NumCast::from(x).unwrap_or_else(|| T::zero()));
        let prediction = self.model.predict(&features)?;

        let metrics = self.prediction_to_metrics(prediction)?;
        self.predictor_metrics.record_prediction();

        Ok(Some(metrics))
    }

    /// Train predictor on new sample
    pub fn train_on_sample(&mut self, architecture: &str, metrics: &EvaluationMetrics) -> Result<()> {
        self.training_data.push((architecture.to_string(), metrics.clone()));

        // Retrain model if enough samples accumulated
        if self.training_data.len() % self.config.retrain_interval == 0 {
            self.retrain_model()?;
        }

        Ok(())
    }

    /// Retrain the prediction model
    fn retrain_model(&mut self) -> Result<()> {
        if self.training_data.len() < self.config.min_training_samples {
            return Ok(());
        }

        // Extract features and targets
        let mut features = Vec::new();
        let mut targets = Vec::new();

        for (architecture, metrics) in &self.training_data {
            let feature_vec_f64 = self.feature_extractor.extract_features(architecture)?;
            let feature_vec: Array1<T> = feature_vec_f64.mapv(|x| scirs2_core::numeric::NumCast::from(x).unwrap_or_else(|| T::zero()));
            let target_vec = self.metrics_to_target(metrics)?;

            features.push(feature_vec);
            targets.push(target_vec);
        }

        // Train model
        self.model.train(features, targets)?;
        self.predictor_metrics.record_training();

        Ok(())
    }

    /// Convert prediction to evaluation metrics
    fn prediction_to_metrics(&self, prediction: Array1<T>) -> Result<EvaluationMetrics> {
        if prediction.len() < 8 {
            return Err(crate::error::OptimError::Other(
                "Invalid prediction vector".to_string()
            ));
        }

        Ok(EvaluationMetrics {
            accuracy: prediction[0].to_f64().unwrap_or(0.0),
            training_time_seconds: prediction[1].to_f64().unwrap_or(0.0),
            inference_time_ms: prediction[2].to_f64().unwrap_or(0.0),
            memory_usage_mb: prediction[3].to_usize().unwrap_or(0),
            flops: prediction[4].to_u64().unwrap_or(0),
            parameters: prediction[5].to_usize().unwrap_or(0),
            energy_consumption: prediction[6].to_f64().unwrap_or(0.0),
            convergence_rate: prediction[7].to_f64().unwrap_or(0.0),
            robustness_score: 0.7, // Default values for non-predicted metrics
            generalization_score: prediction[0].to_f64().unwrap_or(0.0) * 0.9,
            efficiency_score: 0.6,
            valid: true,
        })
    }

    /// Convert evaluation metrics to target vector
    fn metrics_to_target(&self, metrics: &EvaluationMetrics) -> Result<Array1<T>> {
        let target = Array1::from(vec![
            scirs2_core::numeric::NumCast::from(metrics.accuracy).unwrap_or_else(|| T::zero()),
            scirs2_core::numeric::NumCast::from(metrics.training_time_seconds).unwrap_or_else(|| T::zero()),
            scirs2_core::numeric::NumCast::from(metrics.inference_time_ms).unwrap_or_else(|| T::zero()),
            scirs2_core::numeric::NumCast::from(metrics.memory_usage_mb as f64).unwrap_or_else(|| T::zero()),
            scirs2_core::numeric::NumCast::from(metrics.flops as f64).unwrap_or_else(|| T::zero()),
            scirs2_core::numeric::NumCast::from(metrics.parameters as f64).unwrap_or_else(|| T::zero()),
            scirs2_core::numeric::NumCast::from(metrics.energy_consumption).unwrap_or_else(|| T::zero()),
            scirs2_core::numeric::NumCast::from(metrics.convergence_rate).unwrap_or_else(|| T::zero()),
        ]);

        Ok(target)
    }

    /// Get predictor accuracy
    pub fn get_accuracy(&self) -> f64 {
        self.predictor_metrics.get_accuracy()
    }

    /// Get predictor statistics
    pub fn get_statistics(&self) -> &PredictorMetrics<T> {
        &self.predictor_metrics
    }
}

/// Prediction model abstraction
pub struct PredictionModel<T: Float + Debug + Send + Sync + 'static> {
    /// Model type
    model_type: ModelType,
    /// Model parameters
    parameters: Option<ModelParameters<T>>,
    /// Training configuration
    config: PredictorConfig,
}

impl<T: Float + Debug + Send + Sync + 'static> PredictionModel<T> {
    pub fn new(config: &PredictorConfig) -> Result<Self> {
        Ok(Self {
            model_type: config.model_type,
            parameters: None,
            config: config.clone(),
        })
    }

    pub fn is_trained(&self) -> bool {
        self.parameters.is_some()
    }

    pub fn train(&mut self, features: Vec<Array1<T>>, targets: Vec<Array1<T>>) -> Result<()> {
        match self.model_type {
            ModelType::LinearRegression => self.train_linear_regression(features, targets),
            ModelType::RandomForest => self.train_random_forest(features, targets),
            ModelType::NeuralNetwork => self.train_neural_network(features, targets),
            ModelType::GaussianProcess => self.train_gaussian_process(features, targets),
        }
    }

    pub fn predict(&self, features: &Array1<T>) -> Result<Array1<T>> {
        match &self.parameters {
            Some(params) => match self.model_type {
                ModelType::LinearRegression => self.predict_linear_regression(features, params),
                ModelType::RandomForest => self.predict_random_forest(features, params),
                ModelType::NeuralNetwork => self.predict_neural_network(features, params),
                ModelType::GaussianProcess => self.predict_gaussian_process(features, params),
            },
            None => Err(crate::error::OptimError::Other("Model not trained".to_string())),
        }
    }

    fn train_linear_regression(&mut self, features: Vec<Array1<T>>, targets: Vec<Array1<T>>) -> Result<()> {
        // Simplified linear regression training
        if features.is_empty() || targets.is_empty() {
            return Err(crate::error::OptimError::Other("Empty training data".to_string()));
        }

        let input_dim = features[0].len();
        let output_dim = targets[0].len();

        // Initialize weights and bias
        let mut weights = Array2::<T>::zeros((input_dim, output_dim));
        let mut bias = Array1::<T>::zeros(output_dim);

        // Simple gradient descent (placeholder implementation)
        let learning_rate = scirs2_core::numeric::NumCast::from(0.01).unwrap_or_else(|| T::zero());
        let epochs = 100;

        for _ in 0..epochs {
            for (feature, target) in features.iter().zip(targets.iter()) {
                let prediction = weights.t().dot(feature) + &bias;
                let error = target - &prediction;

                // Update weights and bias
                for i in 0..input_dim {
                    for j in 0..output_dim {
                        weights[[i, j]] = weights[[i, j]] + learning_rate * feature[i] * error[j];
                    }
                }

                for j in 0..output_dim {
                    bias[j] = bias[j] + learning_rate * error[j];
                }
            }
        }

        self.parameters = Some(ModelParameters::LinearRegression { weights, bias });
        Ok(())
    }

    fn predict_linear_regression(&self, features: &Array1<T>, params: &ModelParameters<T>) -> Result<Array1<T>> {
        match params {
            ModelParameters::LinearRegression { weights, bias } => {
                Ok(weights.t().dot(features) + bias)
            }
            _ => Err(crate::error::OptimError::Other("Invalid parameters for linear regression".to_string())),
        }
    }

    fn train_random_forest(&mut self, _features: Vec<Array1<T>>, _targets: Vec<Array1<T>>) -> Result<()> {
        // Placeholder implementation
        self.parameters = Some(ModelParameters::RandomForest);
        Ok(())
    }

    fn predict_random_forest(&self, features: &Array1<T>, _params: &ModelParameters<T>) -> Result<Array1<T>> {
        // Placeholder prediction
        Ok(Array1::from(vec![scirs2_core::numeric::NumCast::from(0.5).unwrap_or_else(|| T::zero()); 8]))
    }

    fn train_neural_network(&mut self, _features: Vec<Array1<T>>, _targets: Vec<Array1<T>>) -> Result<()> {
        // Placeholder implementation
        self.parameters = Some(ModelParameters::NeuralNetwork);
        Ok(())
    }

    fn predict_neural_network(&self, features: &Array1<T>, _params: &ModelParameters<T>) -> Result<Array1<T>> {
        // Placeholder prediction
        Ok(Array1::from(vec![scirs2_core::numeric::NumCast::from(0.6).unwrap_or_else(|| T::zero()); 8]))
    }

    fn train_gaussian_process(&mut self, _features: Vec<Array1<T>>, _targets: Vec<Array1<T>>) -> Result<()> {
        // Placeholder implementation
        self.parameters = Some(ModelParameters::GaussianProcess);
        Ok(())
    }

    fn predict_gaussian_process(&self, features: &Array1<T>, _params: &ModelParameters<T>) -> Result<Array1<T>> {
        // Placeholder prediction
        Ok(Array1::from(vec![scirs2_core::numeric::NumCast::from(0.7).unwrap_or_else(|| T::zero()); 8]))
    }
}

/// Model types for performance prediction
#[derive(Debug, Clone, Copy)]
pub enum ModelType {
    LinearRegression,
    RandomForest,
    NeuralNetwork,
    GaussianProcess,
}

/// Model parameters
pub enum ModelParameters<T: Float + Debug + Send + Sync + 'static> {
    LinearRegression {
        weights: Array2<T>,
        bias: Array1<T>,
    },
    RandomForest,
    NeuralNetwork,
    GaussianProcess,
}

/// Feature extractor for architectures
pub struct FeatureExtractor {
    feature_dim: usize,
}

impl FeatureExtractor {
    pub fn new() -> Self {
        Self {
            feature_dim: 20, // Fixed feature dimension
        }
    }

    pub fn extract_features(&self, architecture: &str) -> Result<Array1<f64>> {
        // Simple feature extraction based on architecture string
        let mut features = vec![0.0; self.feature_dim];

        // Basic features from architecture string
        features[0] = architecture.len() as f64; // Architecture complexity
        features[1] = architecture.matches("Dense").count() as f64;
        features[2] = architecture.matches("Conv").count() as f64;
        features[3] = architecture.matches("LSTM").count() as f64;
        features[4] = architecture.matches("Attention").count() as f64;

        // Parse numeric values if possible
        if let Ok(spec) = serde_json::from_str::<ArchitectureSpec>(architecture) {
            features[5] = spec.layers.len() as f64;
            features[6] = (spec.estimated_params as f64).log10();
            features[7] = (spec.estimated_flops as f64).log10();
            features[8] = spec.estimated_memory_mb as f64;
        }

        // Fill remaining features with derived values
        for i in 9..self.feature_dim {
            features[i] = (features[i % 9] * (i as f64 + 1.0)).sin();
        }

        Ok(Array1::from(features))
    }
}

/// Predictor configuration
#[derive(Debug, Clone)]
pub struct PredictorConfig {
    pub model_type: ModelType,
    pub min_training_samples: usize,
    pub retrain_interval: usize,
    pub validation_split: f64,
    pub enable_cross_validation: bool,
}

impl PredictorConfig {
    pub fn from_nas_config(config: &super::config::NASConfig) -> Self {
        Self {
            model_type: if config.enable_performance_prediction {
                ModelType::LinearRegression
            } else {
                ModelType::LinearRegression
            },
            min_training_samples: 20,
            retrain_interval: 10,
            validation_split: 0.2,
            enable_cross_validation: false,
        }
    }
}

/// Performance metrics for the predictor itself
#[derive(Debug)]
pub struct PredictorMetrics<T: Float + Debug + Send + Sync + 'static> {
    total_predictions: usize,
    total_trainings: usize,
    prediction_errors: Vec<T>,
    training_times: Vec<std::time::Duration>,
}

impl<T: Float + Debug + Send + Sync + 'static> PredictorMetrics<T> {
    pub fn new() -> Self {
        Self {
            total_predictions: 0,
            total_trainings: 0,
            prediction_errors: Vec::new(),
            training_times: Vec::new(),
        }
    }

    pub fn record_prediction(&mut self) {
        self.total_predictions += 1;
    }

    pub fn record_training(&mut self) {
        self.total_trainings += 1;
    }

    pub fn record_prediction_error(&mut self, error: T) {
        self.prediction_errors.push(error);
        if self.prediction_errors.len() > 1000 {
            self.prediction_errors.remove(0);
        }
    }

    pub fn get_accuracy(&self) -> f64 {
        if self.prediction_errors.is_empty() {
            return 0.0;
        }

        let avg_error: T = self.prediction_errors.iter().cloned().fold(T::zero(), |acc, x| acc + x)
            / T::from(self.prediction_errors.len()).expect("unwrap failed");

        (T::one() - avg_error).to_f64().unwrap_or(0.0).max(0.0).min(1.0)
    }
}

/// Performance metrics structure
pub struct PerformanceMetrics {
    pub accuracy: f64,
    pub latency: f64,
    pub memory: f64,
    pub energy: f64,
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::learned_optimizers::neural_architecture_search::config::NASConfig;

    #[test]
    fn test_performance_predictor_creation() {
        let config = NASConfig::new();
        let predictor = PerformancePredictor::<f32>::new(&config);
        assert!(predictor.is_ok());
    }

    #[test]
    fn test_feature_extractor() {
        let extractor = FeatureExtractor::new();
        let features = extractor.extract_features("test_architecture");
        assert!(features.is_ok());

        let feature_vec = features.expect("unwrap failed");
        assert_eq!(feature_vec.len(), 20);
    }

    #[test]
    fn test_prediction_model() {
        let config = PredictorConfig {
            model_type: ModelType::LinearRegression,
            min_training_samples: 5,
            retrain_interval: 2,
            validation_split: 0.2,
            enable_cross_validation: false,
        };

        let mut model = PredictionModel::<f32>::new(&config).expect("unwrap failed");
        assert!(!model.is_trained());
    }
}