ruvnet--RuView/crates/ruvector-graph/src/hybrid/graph_neural.rs

//! Graph Neural Network inference capabilities
//!
//! Provides GNN-based predictions: node classification, link prediction, graph embeddings.

use crate::error::{GraphError, Result};
use crate::types::{EdgeId, NodeId};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;

/// Configuration for GNN engine
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GnnConfig {
    /// Number of GNN layers
    pub num_layers: usize,
    /// Hidden dimension size
    pub hidden_dim: usize,
    /// Aggregation method
    pub aggregation: AggregationType,
    /// Activation function
    pub activation: ActivationType,
    /// Dropout rate
    pub dropout: f32,
}

impl Default for GnnConfig {
    fn default() -> Self {
        Self {
            num_layers: 2,
            hidden_dim: 128,
            aggregation: AggregationType::Mean,
            activation: ActivationType::ReLU,
            dropout: 0.1,
        }
    }
}

/// Aggregation type for message passing
#[derive(Debug, Clone, Copy, Serialize, Deserialize)]
pub enum AggregationType {
    Mean,
    Sum,
    Max,
    Attention,
}

/// Activation function type
#[derive(Debug, Clone, Copy, Serialize, Deserialize)]
pub enum ActivationType {
    ReLU,
    Sigmoid,
    Tanh,
    GELU,
}

/// Graph Neural Network engine
pub struct GraphNeuralEngine {
    config: GnnConfig,
    // In real implementation, would store model weights
    node_embeddings: HashMap<NodeId, Vec<f32>>,
}

impl GraphNeuralEngine {
    /// Create a new GNN engine
    pub fn new(config: GnnConfig) -> Self {
        Self {
            config,
            node_embeddings: HashMap::new(),
        }
    }

    /// Load pre-trained model weights
    pub fn load_model(&mut self, _model_path: &str) -> Result<()> {
        // Placeholder for model loading
        // Real implementation would:
        // 1. Load weights from file
        // 2. Initialize neural network layers
        // 3. Set up computation graph
        Ok(())
    }

    /// Classify a node based on its features and neighbors
    pub fn classify_node(&self, node_id: &NodeId, _features: &[f32]) -> Result<NodeClassification> {
        // Placeholder for GNN inference
        // Real implementation would:
        // 1. Gather neighbor features
        // 2. Apply message passing layers
        // 3. Aggregate neighbor information
        // 4. Compute final classification

        let class_probabilities = vec![0.7, 0.2, 0.1]; // Dummy probabilities
        let predicted_class = 0;

        Ok(NodeClassification {
            node_id: node_id.clone(),
            predicted_class,
            class_probabilities,
            confidence: 0.7,
        })
    }

    /// Predict likelihood of a link between two nodes
    pub fn predict_link(&self, node1: &NodeId, node2: &NodeId) -> Result<LinkPrediction> {
        // Placeholder for link prediction
        // Real implementation would:
        // 1. Get embeddings for both nodes
        // 2. Compute compatibility score (dot product, concat+MLP, etc.)
        // 3. Apply sigmoid for probability

        let score = 0.85; // Dummy score
        let exists = score > 0.5;

        Ok(LinkPrediction {
            node1: node1.clone(),
            node2: node2.clone(),
            score,
            exists,
        })
    }

    /// Generate embedding for entire graph or subgraph
    pub fn embed_graph(&self, node_ids: &[NodeId]) -> Result<GraphEmbedding> {
        // Placeholder for graph-level embedding
        // Real implementation would use graph pooling:
        // 1. Get node embeddings
        // 2. Apply pooling (mean, max, attention-based)
        // 3. Optionally apply final MLP

        let embedding = vec![0.0; self.config.hidden_dim];

        Ok(GraphEmbedding {
            embedding,
            node_count: node_ids.len(),
            method: "mean_pooling".to_string(),
        })
    }

    /// Update node embeddings using message passing
    pub fn update_embeddings(&mut self, graph_structure: &GraphStructure) -> Result<()> {
        // Placeholder for embedding update
        // Real implementation would:
        // 1. For each layer:
        //    - Aggregate neighbor features
        //    - Apply linear transformation
        //    - Apply activation
        // 2. Store final embeddings

        for node_id in &graph_structure.nodes {
            let embedding = vec![0.0; self.config.hidden_dim];
            self.node_embeddings.insert(node_id.clone(), embedding);
        }

        Ok(())
    }

    /// Get embedding for a specific node
    pub fn get_node_embedding(&self, node_id: &NodeId) -> Option<&Vec<f32>> {
        self.node_embeddings.get(node_id)
    }

    /// Batch node classification
    pub fn classify_nodes_batch(
        &self,
        nodes: &[(NodeId, Vec<f32>)],
    ) -> Result<Vec<NodeClassification>> {
        nodes
            .iter()
            .map(|(id, features)| self.classify_node(id, features))
            .collect()
    }

    /// Batch link prediction
    pub fn predict_links_batch(&self, pairs: &[(NodeId, NodeId)]) -> Result<Vec<LinkPrediction>> {
        pairs
            .iter()
            .map(|(n1, n2)| self.predict_link(n1, n2))
            .collect()
    }

    /// Apply attention mechanism for neighbor aggregation
    fn aggregate_with_attention(
        &self,
        _node_embedding: &[f32],
        _neighbor_embeddings: &[Vec<f32>],
    ) -> Vec<f32> {
        // Placeholder for attention-based aggregation
        // Real implementation would compute attention weights
        vec![0.0; self.config.hidden_dim]
    }

    /// Apply activation function with numerical stability
    fn activate(&self, x: f32) -> f32 {
        match self.config.activation {
            ActivationType::ReLU => x.max(0.0),
            ActivationType::Sigmoid => {
                if x > 0.0 {
                    1.0 / (1.0 + (-x).exp())
                } else {
                    let ex = x.exp();
                    ex / (1.0 + ex)
                }
            }
            ActivationType::Tanh => x.tanh(),
            ActivationType::GELU => {
                // Approximate GELU
                0.5 * x * (1.0 + (0.7978845608 * (x + 0.044715 * x.powi(3))).tanh())
            }
        }
    }
}

/// Result of node classification
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct NodeClassification {
    pub node_id: NodeId,
    pub predicted_class: usize,
    pub class_probabilities: Vec<f32>,
    pub confidence: f32,
}

/// Result of link prediction
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LinkPrediction {
    pub node1: NodeId,
    pub node2: NodeId,
    pub score: f32,
    pub exists: bool,
}

/// Graph-level embedding
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GraphEmbedding {
    pub embedding: Vec<f32>,
    pub node_count: usize,
    pub method: String,
}

/// Graph structure for GNN processing
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct GraphStructure {
    pub nodes: Vec<NodeId>,
    pub edges: Vec<(NodeId, NodeId)>,
    pub node_features: HashMap<NodeId, Vec<f32>>,
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_gnn_engine_creation() {
        let config = GnnConfig::default();
        let _engine = GraphNeuralEngine::new(config);
    }

    #[test]
    fn test_node_classification() -> Result<()> {
        let engine = GraphNeuralEngine::new(GnnConfig::default());
        let features = vec![1.0, 0.5, 0.3];

        let result = engine.classify_node(&"node1".to_string(), &features)?;

        assert_eq!(result.node_id, "node1");
        assert!(result.confidence > 0.0);
        assert!(!result.class_probabilities.is_empty());

        Ok(())
    }

    #[test]
    fn test_link_prediction() -> Result<()> {
        let engine = GraphNeuralEngine::new(GnnConfig::default());

        let result = engine.predict_link(&"node1".to_string(), &"node2".to_string())?;

        assert_eq!(result.node1, "node1");
        assert_eq!(result.node2, "node2");
        assert!(result.score >= 0.0 && result.score <= 1.0);

        Ok(())
    }

    #[test]
    fn test_graph_embedding() -> Result<()> {
        let engine = GraphNeuralEngine::new(GnnConfig::default());
        let nodes = vec!["n1".to_string(), "n2".to_string(), "n3".to_string()];

        let embedding = engine.embed_graph(&nodes)?;

        assert_eq!(embedding.node_count, 3);
        assert_eq!(embedding.embedding.len(), 128);

        Ok(())
    }

    #[test]
    fn test_batch_classification() -> Result<()> {
        let engine = GraphNeuralEngine::new(GnnConfig::default());
        let nodes = vec![
            ("n1".to_string(), vec![1.0, 0.0]),
            ("n2".to_string(), vec![0.0, 1.0]),
        ];

        let results = engine.classify_nodes_batch(&nodes)?;
        assert_eq!(results.len(), 2);

        Ok(())
    }

    #[test]
    fn test_activation_functions() {
        let engine = GraphNeuralEngine::new(GnnConfig {
            activation: ActivationType::ReLU,
            ..Default::default()
        });

        assert_eq!(engine.activate(-1.0), 0.0);
        assert_eq!(engine.activate(1.0), 1.0);
    }
}