nt-napi-bindings 1.0.0

//! Neural model bindings for Node.js
//!
//! Provides NAPI bindings for neural network training and inference

use napi::bindgen_prelude::*;
use napi_derive::napi;
use std::sync::Arc;
use tokio::sync::Mutex;

/// Model type enumeration
#[napi(string_enum)]
pub enum ModelType {
    NHITS,
    LSTMAttention,
    Transformer,
}

/// Model configuration
#[napi(object)]
pub struct ModelConfig {
    pub model_type: String,    // "nhits", "lstm_attention", "transformer"
    pub input_size: u32,       // Number of historical timesteps
    pub horizon: u32,          // Forecast horizon
    pub hidden_size: u32,      // Hidden layer size
    pub num_layers: u32,       // Number of layers
    pub dropout: f64,          // Dropout rate
    pub learning_rate: f64,    // Learning rate
}

impl Default for ModelConfig {
    fn default() -> Self {
        Self {
            model_type: "nhits".to_string(),
            input_size: 168,  // 1 week of hourly data
            horizon: 24,      // 24 hour forecast
            hidden_size: 512,
            num_layers: 3,
            dropout: 0.1,
            learning_rate: 0.001,
        }
    }
}

/// Training configuration
#[napi(object)]
pub struct TrainingConfig {
    pub epochs: u32,
    pub batch_size: u32,
    pub validation_split: f64,
    pub early_stopping_patience: u32,
    pub use_gpu: bool,
}

impl Default for TrainingConfig {
    fn default() -> Self {
        Self {
            epochs: 100,
            batch_size: 32,
            validation_split: 0.2,
            early_stopping_patience: 10,
            use_gpu: true,
        }
    }
}

/// Training metrics
#[napi(object)]
pub struct TrainingMetrics {
    pub epoch: u32,
    pub train_loss: f64,
    pub val_loss: f64,
    pub train_mae: f64,
    pub val_mae: f64,
}

/// Prediction result with confidence intervals
#[napi(object)]
pub struct PredictionResult {
    pub predictions: Vec<f64>,
    pub lower_bound: Vec<f64>,  // 5th percentile
    pub upper_bound: Vec<f64>,  // 95th percentile
    pub timestamp: String,
}

/// Neural model for time series forecasting
#[napi]
pub struct NeuralModel {
    config: Arc<ModelConfig>,
    model_id: Arc<Mutex<Option<String>>>,
}

#[napi]
impl NeuralModel {
    /// Create a new neural model
    #[napi(constructor)]
    pub fn new(config: ModelConfig) -> Self {
        tracing::info!(
            "Creating neural model: {} (input={}, horizon={})",
            config.model_type, config.input_size, config.horizon
        );

        Self {
            config: Arc::new(config),
            model_id: Arc::new(Mutex::new(None)),
        }
    }

    /// Train the model on historical data
    #[napi]
    pub async fn train(
        &self,
        data: Vec<f64>,
        _targets: Vec<f64>,
        training_config: TrainingConfig,
    ) -> Result<Vec<TrainingMetrics>> {
        tracing::info!(
            "Training model with {} samples, {} epochs",
            data.len(),
            training_config.epochs
        );

        // TODO: Implement actual training using nt-neural crate
        // For now, return mock metrics
        let mut metrics = Vec::new();

        for epoch in 0..training_config.epochs {
            let train_loss = 1.0 / (epoch as f64 + 1.0);
            let val_loss = train_loss * 1.1;

            metrics.push(TrainingMetrics {
                epoch,
                train_loss,
                val_loss,
                train_mae: train_loss * 0.8,
                val_mae: val_loss * 0.8,
            });
        }

        // Set model ID after training
        let mut model_id = self.model_id.lock().await;
        *model_id = Some(format!("model-{}", generate_uuid()));

        tracing::info!("Training completed. Model ID: {:?}", *model_id);

        Ok(metrics)
    }

    /// Make predictions
    #[napi]
    pub async fn predict(&self, input_data: Vec<f64>) -> Result<PredictionResult> {
        let model_id = self.model_id.lock().await;

        if model_id.is_none() {
            return Err(Error::from_reason(
                "Model not trained. Call train() first."
            ));
        }

        tracing::debug!("Making prediction with {} input points", input_data.len());

        if input_data.len() != self.config.input_size as usize {
            return Err(Error::from_reason(format!(
                "Input size mismatch. Expected {}, got {}",
                self.config.input_size,
                input_data.len()
            )));
        }

        // TODO: Implement actual inference using nt-neural crate
        // For now, return mock predictions
        let horizon = self.config.horizon as usize;
        let last_value = input_data.last().copied().unwrap_or(0.0);

        let predictions: Vec<f64> = (0..horizon)
            .map(|i| last_value + (i as f64 * 0.01))
            .collect();

        let lower_bound: Vec<f64> = predictions.iter().map(|p| p * 0.95).collect();
        let upper_bound: Vec<f64> = predictions.iter().map(|p| p * 1.05).collect();

        Ok(PredictionResult {
            predictions,
            lower_bound,
            upper_bound,
            timestamp: chrono::Utc::now().to_rfc3339(),
        })
    }

    /// Save model to disk
    #[napi]
    pub async fn save(&self, path: String) -> Result<String> {
        let model_id = self.model_id.lock().await;

        if model_id.is_none() {
            return Err(Error::from_reason("Model not trained. Nothing to save."));
        }

        tracing::info!("Saving model to: {}", path);

        // TODO: Implement actual model saving using nt-neural crate
        // For now, just return the model ID
        Ok(model_id.as_ref().unwrap().clone())
    }

    /// Load model from disk
    #[napi]
    pub async fn load(&self, path: String) -> Result<()> {
        tracing::info!("Loading model from: {}", path);

        // TODO: Implement actual model loading using nt-neural crate
        let mut model_id = self.model_id.lock().await;
        *model_id = Some(format!("loaded-{}", generate_uuid()));

        Ok(())
    }

    /// Get model info as JSON string
    #[napi]
    pub async fn get_info(&self) -> Result<String> {
        let model_id = self.model_id.lock().await;

        let info = serde_json::json!({
            "model_id": *model_id,
            "model_type": self.config.model_type,
            "input_size": self.config.input_size,
            "horizon": self.config.horizon,
            "hidden_size": self.config.hidden_size,
            "num_layers": self.config.num_layers,
        });

        Ok(info.to_string())
    }
}

/// Batch predictor for processing multiple time series
#[napi]
pub struct BatchPredictor {
    models: Arc<Mutex<Vec<NeuralModel>>>,
}

#[napi]
impl BatchPredictor {
    /// Create a new batch predictor
    #[napi(constructor)]
    pub fn new() -> Self {
        Self {
            models: Arc::new(Mutex::new(Vec::new())),
        }
    }

    /// Add a model to the batch
    #[napi]
    pub async fn add_model(&self, model: &NeuralModel) -> Result<u32> {
        let mut models = self.models.lock().await;

        // Clone the model (simplified - in real implementation would share Arc)
        models.push(NeuralModel {
            config: model.config.clone(),
            model_id: model.model_id.clone(),
        });

        Ok((models.len() - 1) as u32)
    }

    /// Predict using all models in parallel
    #[napi]
    pub async fn predict_batch(&self, inputs: Vec<Vec<f64>>) -> Result<Vec<PredictionResult>> {
        let models = self.models.lock().await;

        if inputs.len() != models.len() {
            return Err(Error::from_reason(format!(
                "Input count ({}) doesn't match model count ({})",
                inputs.len(),
                models.len()
            )));
        }

        // TODO: Implement actual batch inference
        let mut results = Vec::new();

        for (model, input) in models.iter().zip(inputs.iter()) {
            results.push(model.predict(input.clone()).await?);
        }

        Ok(results)
    }
}

/// List available model types
#[napi]
pub fn list_model_types() -> Vec<String> {
    vec![
        "nhits".to_string(),
        "lstm_attention".to_string(),
        "transformer".to_string(),
    ]
}

// UUID generation helper
fn generate_uuid() -> String {
    use std::time::{SystemTime, UNIX_EPOCH};
    let nanos = SystemTime::now()
        .duration_since(UNIX_EPOCH)
        .unwrap()
        .as_nanos();
    format!("{:x}", nanos)
}