arrow-graph 0.6.0

use crate::error::Result;
use crate::graph::ArrowGraph;
use crate::ml::embeddings::{NodeEmbeddings, EmbeddingModel};
use crate::ml::features::{FeatureExtractor, MLFeatureSet};
use arrow::array::Array;
use std::collections::HashMap;
use serde::{Serialize, Deserialize};

/// Bridge for PyTorch Geometric integration
/// Provides seamless conversion between Arrow-Graph and PyTorch Geometric formats
#[derive(Debug)]
pub struct PyTorchGeometricBridge {
    graph: Option<ArrowGraph>,
    node_features: Option<Vec<Vec<f64>>>,
    edge_index: Option<Vec<(usize, usize)>>,
    edge_attr: Option<Vec<Vec<f64>>>,
    node_mapping: HashMap<String, usize>,
    reverse_mapping: HashMap<usize, String>,
}

/// Graph Neural Network processor for running GNN models
#[derive(Debug)]
pub struct GNNProcessor {
    model_config: GNNConfig,
    bridge: PyTorchGeometricBridge,
    trained_embeddings: Option<NodeEmbeddings>,
}

/// Configuration for GNN models
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GNNConfig {
    pub model_type: GNNModelType,
    pub hidden_channels: usize,
    pub num_layers: usize,
    pub dropout: f64,
    pub learning_rate: f64,
    pub epochs: usize,
    pub batch_size: usize,
    pub task_type: TaskType,
}

/// Supported GNN model types
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum GNNModelType {
    GCN,        // Graph Convolutional Network
    GraphSAGE,  // GraphSAGE
    GAT,        // Graph Attention Network
    GIN,        // Graph Isomorphism Network
    Custom(String), // Custom model specification
}

/// ML task types
#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum TaskType {
    NodeClassification,
    EdgePrediction,
    GraphClassification,
    NodeRegression,
    Embedding,
}

/// Model export formats
#[derive(Debug, Clone)]
pub struct ModelExport {
    pub format: ExportFormat,
    pub model_data: Vec<u8>,
    pub metadata: ModelMetadata,
}

/// Supported export formats
#[derive(Debug, Clone)]
pub enum ExportFormat {
    PyTorchJIT,
    ONNX,
    TensorFlow,
    JSON,
    Custom(String),
}

/// Model metadata
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ModelMetadata {
    pub model_type: String,
    pub input_dim: usize,
    pub output_dim: usize,
    pub num_parameters: usize,
    pub training_accuracy: Option<f64>,
    pub validation_accuracy: Option<f64>,
    pub created_at: String,
    pub framework_version: String,
}

impl PyTorchGeometricBridge {
    pub fn new() -> Self {
        Self {
            graph: None,
            node_features: None,
            edge_index: None,
            edge_attr: None,
            node_mapping: HashMap::new(),
            reverse_mapping: HashMap::new(),
        }
    }

    /// Initialize with Arrow Graph
    pub fn initialize(&mut self, graph: ArrowGraph) -> Result<()> {
        self.graph = Some(graph);
        self.build_node_mapping()?;
        self.extract_edge_index()?;
        Ok(())
    }

    /// Build mapping between string node IDs and integer indices
    fn build_node_mapping(&mut self) -> Result<()> {
        let graph = self.graph.as_ref()
            .ok_or_else(|| crate::error::GraphError::graph_construction("Graph not initialized"))?;

        let nodes_batch = &graph.nodes;
        if nodes_batch.num_rows() > 0 {
            let node_ids = nodes_batch.column(0)
                .as_any()
                .downcast_ref::<arrow::array::StringArray>()
                .ok_or_else(|| crate::error::GraphError::graph_construction("Expected string array for node IDs"))?;

            for (i, node_id) in (0..node_ids.len()).enumerate() {
                let id = node_ids.value(i).to_string();
                self.node_mapping.insert(id.clone(), i);
                self.reverse_mapping.insert(i, id);
            }
        }

        Ok(())
    }

    /// Extract edge index in PyTorch Geometric format
    fn extract_edge_index(&mut self) -> Result<()> {
        let graph = self.graph.as_ref()
            .ok_or_else(|| crate::error::GraphError::graph_construction("Graph not initialized"))?;

        let edges_batch = &graph.edges;
        let mut edge_index = Vec::new();
        let mut edge_attr = Vec::new();

        if edges_batch.num_rows() > 0 {
            let source_ids = edges_batch.column(0)
                .as_any()
                .downcast_ref::<arrow::array::StringArray>()
                .ok_or_else(|| crate::error::GraphError::graph_construction("Expected string array for source IDs"))?;
            let target_ids = edges_batch.column(1)
                .as_any()
                .downcast_ref::<arrow::array::StringArray>()
                .ok_or_else(|| crate::error::GraphError::graph_construction("Expected string array for target IDs"))?;
            let weights = edges_batch.column(2)
                .as_any()
                .downcast_ref::<arrow::array::Float64Array>()
                .ok_or_else(|| crate::error::GraphError::graph_construction("Expected float64 array for weights"))?;

            for i in 0..source_ids.len() {
                let source_str = source_ids.value(i);
                let target_str = target_ids.value(i);
                let weight = weights.value(i);

                if let (Some(&source_idx), Some(&target_idx)) = 
                    (self.node_mapping.get(source_str), self.node_mapping.get(target_str)) {
                    edge_index.push((source_idx, target_idx));
                    edge_attr.push(vec![weight]);

                    // Add reverse edge for undirected graphs
                    edge_index.push((target_idx, source_idx));
                    edge_attr.push(vec![weight]);
                }
            }
        }

        self.edge_index = Some(edge_index);
        self.edge_attr = Some(edge_attr);
        Ok(())
    }

    /// Set node features from feature extractor
    pub fn set_node_features(&mut self, feature_set: &MLFeatureSet) -> Result<()> {
        let mut node_features = vec![vec![0.0; feature_set.node_feature_names.len()]; self.node_mapping.len()];

        for (i, node_id) in feature_set.node_ids.iter().enumerate() {
            if let Some(&node_idx) = self.node_mapping.get(node_id) {
                if i < feature_set.node_features_matrix.len() {
                    node_features[node_idx] = feature_set.node_features_matrix[i].clone();
                }
            }
        }

        self.node_features = Some(node_features);
        Ok(())
    }

    /// Export to PyTorch Geometric format (JSON representation)
    pub fn to_pyg_format(&self) -> Result<PyGData> {
        let edge_index = self.edge_index.as_ref()
            .ok_or_else(|| crate::error::GraphError::graph_construction("Edge index not built"))?;

        let mut source_nodes = Vec::new();
        let mut target_nodes = Vec::new();

        for &(source, target) in edge_index {
            source_nodes.push(source);
            target_nodes.push(target);
        }

        Ok(PyGData {
            edge_index: vec![source_nodes, target_nodes],
            node_features: self.node_features.clone(),
            edge_attr: self.edge_attr.clone(),
            num_nodes: self.node_mapping.len(),
            num_edges: edge_index.len(),
            node_mapping: self.node_mapping.clone(),
        })
    }

    /// Create from PyTorch Geometric data
    pub fn from_pyg_format(&mut self, pyg_data: PyGData) -> Result<()> {
        // Reconstruct edge index
        if pyg_data.edge_index.len() >= 2 {
            let source_nodes = &pyg_data.edge_index[0];
            let target_nodes = &pyg_data.edge_index[1];
            
            let mut edge_index = Vec::new();
            for (i, &source) in source_nodes.iter().enumerate() {
                if i < target_nodes.len() {
                    edge_index.push((source, target_nodes[i]));
                }
            }
            self.edge_index = Some(edge_index);
        }

        self.node_features = pyg_data.node_features;
        self.edge_attr = pyg_data.edge_attr;
        self.node_mapping = pyg_data.node_mapping;

        // Rebuild reverse mapping
        self.reverse_mapping.clear();
        for (node_id, &idx) in &self.node_mapping {
            self.reverse_mapping.insert(idx, node_id.clone());
        }

        Ok(())
    }

    /// Get node features in tensor-ready format
    pub fn get_node_features_tensor(&self) -> Option<Vec<Vec<f64>>> {
        self.node_features.clone()
    }

    /// Get edge index in tensor format
    pub fn get_edge_index_tensor(&self) -> Option<Vec<Vec<usize>>> {
        self.edge_index.as_ref().map(|edges| {
            let mut sources = Vec::new();
            let mut targets = Vec::new();
            
            for &(source, target) in edges {
                sources.push(source);
                targets.push(target);
            }
            
            vec![sources, targets]
        })
    }

    /// Get node mapping
    pub fn get_node_mapping(&self) -> &HashMap<String, usize> {
        &self.node_mapping
    }

    /// Get reverse node mapping
    pub fn get_reverse_mapping(&self) -> &HashMap<usize, String> {
        &self.reverse_mapping
    }
}

/// PyTorch Geometric data structure
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PyGData {
    pub edge_index: Vec<Vec<usize>>,
    pub node_features: Option<Vec<Vec<f64>>>,
    pub edge_attr: Option<Vec<Vec<f64>>>,
    pub num_nodes: usize,
    pub num_edges: usize,
    pub node_mapping: HashMap<String, usize>,
}

impl GNNProcessor {
    pub fn new(config: GNNConfig) -> Self {
        Self {
            model_config: config,
            bridge: PyTorchGeometricBridge::new(),
            trained_embeddings: None,
        }
    }

    /// Initialize with graph and features
    pub fn initialize(&mut self, graph: ArrowGraph, feature_set: Option<MLFeatureSet>) -> Result<()> {
        self.bridge.initialize(graph)?;
        
        if let Some(features) = feature_set {
            self.bridge.set_node_features(&features)?;
        }

        Ok(())
    }

    /// Train GNN model (simplified simulation)
    pub fn train_model(&mut self) -> Result<TrainingResult> {
        // This is a simplified simulation of GNN training
        // In a real implementation, this would interface with PyTorch

        let num_nodes = self.bridge.node_mapping.len();
        let hidden_dim = self.model_config.hidden_channels;

        // Simulate training process
        let mut training_losses = Vec::new();
        let mut validation_accuracies = Vec::new();

        for epoch in 0..self.model_config.epochs {
            // Simulate training step
            let loss = 1.0 / (epoch + 1) as f64; // Decreasing loss
            training_losses.push(loss);

            // Simulate validation
            let accuracy = 0.5 + 0.4 * (1.0 - (-epoch as f64 / 100.0).exp());
            validation_accuracies.push(accuracy);
        }

        // Generate mock embeddings
        let mut embeddings = HashMap::new();
        let mut rng = rand::thread_rng();
        use rand::Rng;

        for (node_id, _) in &self.bridge.node_mapping {
            let embedding: Vec<f64> = (0..hidden_dim)
                .map(|_| rng.gen::<f64>() - 0.5)
                .collect();
            embeddings.insert(node_id.clone(), embedding);
        }

        self.trained_embeddings = Some(NodeEmbeddings {
            embeddings,
            dimension: hidden_dim,
        });

        Ok(TrainingResult {
            final_loss: training_losses.last().copied().unwrap_or(1.0),
            final_accuracy: validation_accuracies.last().copied().unwrap_or(0.5),
            training_losses,
            validation_accuracies,
            num_epochs: self.model_config.epochs,
            model_type: self.model_config.model_type.clone(),
        })
    }

    /// Get trained embeddings
    pub fn get_embeddings(&self) -> Option<&NodeEmbeddings> {
        self.trained_embeddings.as_ref()
    }

    /// Predict on new data
    pub fn predict(&self, node_features: Vec<Vec<f64>>) -> Result<Vec<f64>> {
        // Simplified prediction simulation
        let output_dim = match self.model_config.task_type {
            TaskType::NodeClassification => 5, // 5 classes
            TaskType::NodeRegression => 1,
            TaskType::Embedding => self.model_config.hidden_channels,
            _ => 1,
        };

        let mut predictions = Vec::new();
        let mut rng = rand::thread_rng();
        use rand::Rng;

        for _ in 0..node_features.len() {
            let prediction: Vec<f64> = (0..output_dim)
                .map(|_| rng.gen::<f64>())
                .collect();
            predictions.extend(prediction);
        }

        Ok(predictions)
    }

    /// Export model in specified format
    pub fn export_model(&self, format: ExportFormat) -> Result<ModelExport> {
        let metadata = ModelMetadata {
            model_type: format!("{:?}", self.model_config.model_type),
            input_dim: self.bridge.node_features.as_ref()
                .and_then(|features| features.first().map(|f| f.len()))
                .unwrap_or(0),
            output_dim: self.model_config.hidden_channels,
            num_parameters: self.estimate_num_parameters(),
            training_accuracy: None, // Would be filled from training results
            validation_accuracy: None,
            created_at: chrono::Utc::now().to_rfc3339(),
            framework_version: "arrow-graph-0.5.0".to_string(),
        };

        let model_data = match format {
            ExportFormat::JSON => {
                let pyg_data = self.bridge.to_pyg_format()?;
                serde_json::to_vec(&pyg_data)
                    .map_err(|e| crate::error::GraphError::graph_construction(&format!("JSON serialization error: {}", e)))?
            }
            ExportFormat::PyTorchJIT => {
                // Placeholder for PyTorch JIT export
                b"# PyTorch JIT model placeholder".to_vec()
            }
            ExportFormat::ONNX => {
                // Placeholder for ONNX export
                b"# ONNX model placeholder".to_vec()
            }
            ExportFormat::TensorFlow => {
                // Placeholder for TensorFlow export
                b"# TensorFlow model placeholder".to_vec()
            }
            ExportFormat::Custom(name) => {
                format!("# Custom export for {}", name).into_bytes()
            }
        };

        Ok(ModelExport {
            format,
            model_data,
            metadata,
        })
    }

    /// Get PyTorch Geometric data
    pub fn get_pyg_data(&self) -> Result<PyGData> {
        self.bridge.to_pyg_format()
    }

    /// Create GNN processor from embeddings
    pub fn from_embeddings(embeddings: NodeEmbeddings, config: GNNConfig) -> Self {
        let mut processor = Self::new(config);
        processor.trained_embeddings = Some(embeddings);
        processor
    }

    fn estimate_num_parameters(&self) -> usize {
        // Simplified parameter count estimation
        let input_dim = self.bridge.node_features.as_ref()
            .and_then(|features| features.first().map(|f| f.len()))
            .unwrap_or(64);
        
        let hidden_dim = self.model_config.hidden_channels;
        let num_layers = self.model_config.num_layers;

        // Rough estimate for typical GNN architectures
        let layer_params = input_dim * hidden_dim + hidden_dim * hidden_dim * (num_layers - 1);
        layer_params + hidden_dim // Add bias terms
    }
}

/// Training result information
#[derive(Debug, Clone)]
pub struct TrainingResult {
    pub final_loss: f64,
    pub final_accuracy: f64,
    pub training_losses: Vec<f64>,
    pub validation_accuracies: Vec<f64>,
    pub num_epochs: usize,
    pub model_type: GNNModelType,
}

impl Default for GNNConfig {
    fn default() -> Self {
        Self {
            model_type: GNNModelType::GCN,
            hidden_channels: 64,
            num_layers: 2,
            dropout: 0.5,
            learning_rate: 0.01,
            epochs: 100,
            batch_size: 32,
            task_type: TaskType::NodeClassification,
        }
    }
}

/// Helper function to create PyTorch Geometric bridge from Arrow Graph
pub fn create_pyg_bridge(graph: ArrowGraph) -> Result<PyTorchGeometricBridge> {
    let mut bridge = PyTorchGeometricBridge::new();
    bridge.initialize(graph)?;
    Ok(bridge)
}

/// Helper function to create GNN processor with default config
pub fn create_gnn_processor(
    graph: ArrowGraph, 
    task_type: TaskType
) -> Result<GNNProcessor> {
    let config = GNNConfig {
        task_type,
        ..Default::default()
    };
    
    let mut processor = GNNProcessor::new(config);
    processor.initialize(graph, None)?;
    Ok(processor)
}

/// Integration with existing embedding models
pub fn gnn_from_node2vec<T: EmbeddingModel>(
    mut embedding_model: T,
    graph: &ArrowGraph,
    task_type: TaskType,
) -> Result<GNNProcessor> {
    // Generate embeddings first
    embedding_model.initialize(graph)?;
    let embeddings = embedding_model.generate_embeddings()?;
    
    // Create GNN config based on embedding dimension
    let config = GNNConfig {
        hidden_channels: embeddings.dimension,
        task_type,
        ..Default::default()
    };
    
    Ok(GNNProcessor::from_embeddings(embeddings, config))
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::graph::ArrowGraph;
    use crate::ml::features::FeatureExtractor;
    use arrow::array::{StringArray, Float64Array};
    use arrow::record_batch::RecordBatch;
    use arrow::datatypes::{Schema, Field, DataType};
    use std::sync::Arc;

    fn create_test_graph() -> Result<ArrowGraph> {
        let nodes_schema = Arc::new(Schema::new(vec![
            Field::new("id", DataType::Utf8, false),
        ]));
        let node_ids = StringArray::from(vec!["A", "B", "C", "D"]);
        let nodes_batch = RecordBatch::try_new(
            nodes_schema,
            vec![Arc::new(node_ids)],
        )?;

        let edges_schema = Arc::new(Schema::new(vec![
            Field::new("source", DataType::Utf8, false),
            Field::new("target", DataType::Utf8, false),
            Field::new("weight", DataType::Float64, false),
        ]));
        let sources = StringArray::from(vec!["A", "B", "C"]);
        let targets = StringArray::from(vec!["B", "C", "D"]);
        let weights = Float64Array::from(vec![1.0, 1.0, 1.0]);
        let edges_batch = RecordBatch::try_new(
            edges_schema,
            vec![Arc::new(sources), Arc::new(targets), Arc::new(weights)],
        )?;

        ArrowGraph::new(nodes_batch, edges_batch)
    }

    #[test]
    fn test_pyg_bridge_initialization() {
        let graph = create_test_graph().unwrap();
        let mut bridge = PyTorchGeometricBridge::new();
        
        bridge.initialize(graph).unwrap();
        
        assert_eq!(bridge.node_mapping.len(), 4);
        assert_eq!(bridge.reverse_mapping.len(), 4);
        assert!(bridge.edge_index.is_some());
    }

    #[test]
    fn test_pyg_data_conversion() {
        let graph = create_test_graph().unwrap();
        let mut bridge = PyTorchGeometricBridge::new();
        
        bridge.initialize(graph).unwrap();
        let pyg_data = bridge.to_pyg_format().unwrap();
        
        assert_eq!(pyg_data.num_nodes, 4);
        assert!(pyg_data.num_edges > 0);
        assert_eq!(pyg_data.edge_index.len(), 2); // [sources, targets]
    }

    #[test]
    fn test_gnn_processor_initialization() {
        let graph = create_test_graph().unwrap();
        let config = GNNConfig::default();
        let mut processor = GNNProcessor::new(config);
        
        processor.initialize(graph, None).unwrap();
        
        assert_eq!(processor.bridge.node_mapping.len(), 4);
    }

    #[test]
    fn test_gnn_with_features() {
        let graph = create_test_graph().unwrap();
        let mut extractor = FeatureExtractor::default();
        
        extractor.initialize(graph.clone()).unwrap();
        let features = extractor.extract_features().unwrap();
        
        let config = GNNConfig::default();
        let mut processor = GNNProcessor::new(config);
        processor.initialize(graph, Some(features)).unwrap();
        
        assert!(processor.bridge.node_features.is_some());
    }

    #[test]
    fn test_gnn_training_simulation() {
        let graph = create_test_graph().unwrap();
        let mut processor = create_gnn_processor(graph, TaskType::NodeClassification).unwrap();
        
        let result = processor.train_model().unwrap();
        
        assert_eq!(result.num_epochs, 100);
        assert!(!result.training_losses.is_empty());
        assert!(!result.validation_accuracies.is_empty());
        assert!(processor.get_embeddings().is_some());
    }

    #[test]
    fn test_model_export() {
        let graph = create_test_graph().unwrap();
        let mut processor = create_gnn_processor(graph, TaskType::Embedding).unwrap();
        
        processor.train_model().unwrap();
        let export = processor.export_model(ExportFormat::JSON).unwrap();
        
        assert!(!export.model_data.is_empty());
        assert!(export.metadata.num_parameters > 0);
        assert_eq!(export.metadata.model_type, "GCN");
    }

    #[test]
    fn test_prediction() {
        let graph = create_test_graph().unwrap();
        let mut processor = create_gnn_processor(graph, TaskType::NodeRegression).unwrap();
        
        processor.train_model().unwrap();
        
        let node_features = vec![
            vec![1.0, 2.0, 3.0],
            vec![4.0, 5.0, 6.0],
        ];
        
        let predictions = processor.predict(node_features).unwrap();
        assert!(!predictions.is_empty());
    }

    #[test]
    fn test_different_model_types() {
        let graph = create_test_graph().unwrap();
        
        for model_type in [GNNModelType::GCN, GNNModelType::GraphSAGE, GNNModelType::GAT] {
            let config = GNNConfig {
                model_type: model_type.clone(),
                ..Default::default()
            };
            
            let mut processor = GNNProcessor::new(config);
            processor.initialize(graph.clone(), None).unwrap();
            
            let result = processor.train_model().unwrap();
            assert_eq!(result.model_type, model_type);
        }
    }

    #[test]
    fn test_different_task_types() {
        let graph = create_test_graph().unwrap();
        
        for task_type in [TaskType::NodeClassification, TaskType::EdgePrediction, TaskType::GraphClassification] {
            let processor = create_gnn_processor(graph.clone(), task_type.clone()).unwrap();
            assert_eq!(processor.model_config.task_type, task_type);
        }
    }

    #[test]
    fn test_node_mapping_consistency() {
        let graph = create_test_graph().unwrap();
        let mut bridge = PyTorchGeometricBridge::new();
        
        bridge.initialize(graph).unwrap();
        
        // Check that forward and reverse mappings are consistent
        for (node_id, &idx) in &bridge.node_mapping {
            assert_eq!(bridge.reverse_mapping.get(&idx), Some(node_id));
        }
        
        for (&idx, node_id) in &bridge.reverse_mapping {
            assert_eq!(bridge.node_mapping.get(node_id), Some(&idx));
        }
    }

    #[test]
    fn test_edge_index_format() {
        let graph = create_test_graph().unwrap();
        let mut bridge = PyTorchGeometricBridge::new();
        
        bridge.initialize(graph).unwrap();
        let tensor_format = bridge.get_edge_index_tensor().unwrap();
        
        assert_eq!(tensor_format.len(), 2); // [sources, targets]
        assert_eq!(tensor_format[0].len(), tensor_format[1].len()); // Same length
        
        // Check that all indices are valid
        let num_nodes = bridge.node_mapping.len();
        for &source in &tensor_format[0] {
            assert!(source < num_nodes);
        }
        for &target in &tensor_format[1] {
            assert!(target < num_nodes);
        }
    }
}