voirs-spatial 0.1.0-rc.1

//! Training pipeline and loss functions for neural models

use super::models::NeuralModel;
use super::types::*;
use crate::{Error, Result};

pub use super::types::NeuralTrainingResults;

/// Neural model trainer
pub struct NeuralTrainer {
    config: TrainingConfig,
}

impl NeuralTrainer {
    /// Create a new neural trainer
    pub fn new(config: TrainingConfig) -> Self {
        Self { config }
    }

    /// Train a neural model
    pub fn train(
        &mut self,
        model: &mut dyn NeuralModel,
        training_data: &[(NeuralInputFeatures, Vec<Vec<f32>>)],
    ) -> Result<NeuralTrainingResults> {
        let start_time = std::time::Instant::now();
        let mut training_loss_history = Vec::new();
        let mut validation_loss_history = Vec::new();
        let mut best_validation_loss = f32::INFINITY;
        let mut patience_counter = 0;
        let mut early_stopped = false;

        // Split data into training and validation sets
        let split_index =
            (training_data.len() as f32 * (1.0 - self.config.validation_split)) as usize;
        let (train_data, val_data) = training_data.split_at(split_index);

        println!(
            "Starting neural model training with {} training samples, {} validation samples",
            train_data.len(),
            val_data.len()
        );

        for epoch in 0..self.config.epochs {
            let epoch_start = std::time::Instant::now();

            // Training phase
            let train_loss = self.train_epoch(model, train_data)?;
            training_loss_history.push(train_loss);

            // Validation phase
            let val_loss = self.validate_epoch(model, val_data)?;
            validation_loss_history.push(val_loss);

            println!(
                "Epoch {}/{}: train_loss={:.6}, val_loss={:.6}, time={:.2}s",
                epoch + 1,
                self.config.epochs,
                train_loss,
                val_loss,
                epoch_start.elapsed().as_secs_f32()
            );

            // Early stopping check
            if val_loss < best_validation_loss {
                best_validation_loss = val_loss;
                patience_counter = 0;
                println!("New best validation loss: {val_loss:.6}");
            } else {
                patience_counter += 1;
                if patience_counter >= self.config.early_stopping_patience {
                    println!(
                        "Early stopping triggered after {patience_counter} epochs of no improvement"
                    );
                    early_stopped = true;
                    break;
                }
            }

            // Learning rate scheduling (simple decay)
            if (epoch + 1) % 10 == 0 {
                // Would implement learning rate decay here
                println!("Learning rate decay would be applied here");
            }
        }

        let training_duration = start_time.elapsed().as_secs_f32();
        let epochs_completed = training_loss_history.len();

        // Calculate final accuracy based on final validation loss
        let final_accuracy = if !validation_loss_history.is_empty() {
            let final_val_loss = validation_loss_history[validation_loss_history.len() - 1];
            // Convert loss to accuracy estimate (simple heuristic)
            (1.0 - final_val_loss.min(1.0)).max(0.0)
        } else {
            0.0
        };

        println!(
            "Training completed: {epochs_completed} epochs, {training_duration:.2}s total, final accuracy: {final_accuracy:.3}"
        );

        Ok(NeuralTrainingResults {
            training_loss: training_loss_history,
            validation_loss: validation_loss_history,
            final_accuracy,
            training_duration_secs: training_duration,
            epochs_completed,
            early_stopped,
        })
    }

    fn train_epoch(
        &self,
        model: &mut dyn NeuralModel,
        train_data: &[(NeuralInputFeatures, Vec<Vec<f32>>)],
    ) -> Result<f32> {
        let mut total_loss = 0.0;
        let mut batch_count = 0;

        // Process in batches
        for batch_start in (0..train_data.len()).step_by(self.config.batch_size) {
            let batch_end = (batch_start + self.config.batch_size).min(train_data.len());
            let batch = &train_data[batch_start..batch_end];

            let batch_loss = self.train_batch(model, batch)?;
            total_loss += batch_loss;
            batch_count += 1;
        }

        Ok(total_loss / batch_count as f32)
    }

    fn validate_epoch(
        &self,
        model: &mut dyn NeuralModel,
        val_data: &[(NeuralInputFeatures, Vec<Vec<f32>>)],
    ) -> Result<f32> {
        let mut total_loss = 0.0;
        let mut batch_count = 0;

        // Process validation data in batches
        for batch_start in (0..val_data.len()).step_by(self.config.batch_size) {
            let batch_end = (batch_start + self.config.batch_size).min(val_data.len());
            let batch = &val_data[batch_start..batch_end];

            let batch_loss = self.validate_batch(model, batch)?;
            total_loss += batch_loss;
            batch_count += 1;
        }

        Ok(total_loss / batch_count as f32)
    }

    fn train_batch(
        &self,
        model: &mut dyn NeuralModel,
        batch: &[(NeuralInputFeatures, Vec<Vec<f32>>)],
    ) -> Result<f32> {
        let mut batch_loss = 0.0;

        for (input, target) in batch {
            // Forward pass
            let output = model.forward(input)?;

            // Compute loss
            let loss = self.compute_loss(&output.binaural_audio, target)?;
            batch_loss += loss;

            // Apply data augmentation if enabled
            if self.config.augmentation.noise_injection {
                // Would apply noise injection here
            }

            // Backward pass and parameter updates would be implemented here
            // This requires implementing automatic differentiation or using a framework
            // For now, we simulate the training process
        }

        Ok(batch_loss / batch.len() as f32)
    }

    fn validate_batch(
        &self,
        model: &mut dyn NeuralModel,
        batch: &[(NeuralInputFeatures, Vec<Vec<f32>>)],
    ) -> Result<f32> {
        let mut batch_loss = 0.0;

        for (input, target) in batch {
            // Forward pass only (no gradient computation)
            let output = model.forward(input)?;

            // Compute loss
            let loss = self.compute_loss(&output.binaural_audio, target)?;
            batch_loss += loss;
        }

        Ok(batch_loss / batch.len() as f32)
    }

    fn compute_loss(&self, predicted: &[Vec<f32>], target: &[Vec<f32>]) -> Result<f32> {
        if predicted.len() != target.len() {
            return Err(Error::LegacyProcessing(
                "Predicted and target channel counts don't match".to_string(),
            ));
        }

        let mut total_loss = 0.0;
        let mut sample_count = 0;

        match self.config.loss_function {
            LossFunction::MSE => {
                for (pred_channel, target_channel) in predicted.iter().zip(target.iter()) {
                    let min_len = pred_channel.len().min(target_channel.len());
                    for i in 0..min_len {
                        let diff = pred_channel[i] - target_channel[i];
                        total_loss += diff * diff;
                        sample_count += 1;
                    }
                }
            }
            LossFunction::MAE => {
                for (pred_channel, target_channel) in predicted.iter().zip(target.iter()) {
                    let min_len = pred_channel.len().min(target_channel.len());
                    for i in 0..min_len {
                        let diff = (pred_channel[i] - target_channel[i]).abs();
                        total_loss += diff;
                        sample_count += 1;
                    }
                }
            }
            LossFunction::SpectralLoss => {
                // Simplified spectral loss - would implement FFT-based comparison
                for (pred_channel, target_channel) in predicted.iter().zip(target.iter()) {
                    let min_len = pred_channel.len().min(target_channel.len());
                    for i in 0..min_len {
                        let diff = pred_channel[i] - target_channel[i];
                        total_loss += diff * diff; // Simplified spectral approximation
                        sample_count += 1;
                    }
                }
                total_loss *= 1.2; // Weight spectral loss slightly higher
            }
            LossFunction::PerceptualLoss => {
                // Simplified perceptual loss based on psychoacoustic principles
                for (pred_channel, target_channel) in predicted.iter().zip(target.iter()) {
                    let min_len = pred_channel.len().min(target_channel.len());
                    for i in 0..min_len {
                        let diff = pred_channel[i] - target_channel[i];
                        // Apply perceptual weighting (simplified)
                        let perceptual_weight = 1.0 + 0.5 * (i as f32 / min_len as f32);
                        total_loss += diff * diff * perceptual_weight;
                        sample_count += 1;
                    }
                }
            }
            LossFunction::MultiScaleSpectralLoss => {
                // Multi-scale analysis at different time scales
                for (pred_channel, target_channel) in predicted.iter().zip(target.iter()) {
                    let min_len = pred_channel.len().min(target_channel.len());
                    // Multiple scales: full, half, quarter
                    for scale in [1, 2, 4] {
                        for i in (0..min_len).step_by(scale) {
                            let diff = pred_channel[i] - target_channel[i];
                            total_loss += diff * diff / (scale as f32);
                            sample_count += 1;
                        }
                    }
                }
            }
            LossFunction::Combined => {
                // Combination of MSE and spectral loss
                let mse_loss = self.compute_mse_loss(predicted, target)?;
                let spectral_loss = self.compute_spectral_loss(predicted, target)?;
                total_loss = 0.7 * mse_loss + 0.3 * spectral_loss;
                sample_count = 1; // Already normalized
            }
        }

        if sample_count > 0 {
            Ok(total_loss / sample_count as f32)
        } else {
            Ok(0.0)
        }
    }

    fn compute_mse_loss(&self, predicted: &[Vec<f32>], target: &[Vec<f32>]) -> Result<f32> {
        let mut total_loss = 0.0;
        let mut sample_count = 0;

        for (pred_channel, target_channel) in predicted.iter().zip(target.iter()) {
            let min_len = pred_channel.len().min(target_channel.len());
            for i in 0..min_len {
                let diff = pred_channel[i] - target_channel[i];
                total_loss += diff * diff;
                sample_count += 1;
            }
        }

        Ok(if sample_count > 0 {
            total_loss / sample_count as f32
        } else {
            0.0
        })
    }

    fn compute_spectral_loss(&self, predicted: &[Vec<f32>], target: &[Vec<f32>]) -> Result<f32> {
        // Simplified spectral loss computation
        // In a full implementation, this would use FFT to compare frequency domain representations
        let mut total_loss = 0.0;
        let mut sample_count = 0;

        for (pred_channel, target_channel) in predicted.iter().zip(target.iter()) {
            let min_len = pred_channel.len().min(target_channel.len());

            // Simple approximation: compare signal energy at different scales
            for window_size in [16, 32, 64, 128] {
                for start in (0..min_len).step_by(window_size / 2) {
                    let end = (start + window_size).min(min_len);
                    if end > start {
                        let pred_energy: f32 = pred_channel[start..end].iter().map(|x| x * x).sum();
                        let target_energy: f32 =
                            target_channel[start..end].iter().map(|x| x * x).sum();
                        let diff = pred_energy - target_energy;
                        total_loss += diff * diff;
                        sample_count += 1;
                    }
                }
            }
        }

        Ok(if sample_count > 0 {
            total_loss / sample_count as f32
        } else {
            0.0
        })
    }
}