oxirs-embed 0.2.4

//! SciRS2 Neural Network Integration for Enhanced Embeddings
//!
//! This module integrates scirs2-neural capabilities into oxirs-embed for advanced
//! neural embedding computations with scientific computing optimizations.

use crate::ModelConfig;
use anyhow::Result;
use scirs2_core::ndarray_ext::Array1;
use scirs2_core::random::Random;
use scirs2_neural::prelude::*;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;

/// Configuration for SciRS2-powered neural embeddings
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SciRS2NeuralConfig {
    /// Base model configuration
    pub base: ModelConfig,
    /// Hidden layer dimensions
    pub hidden_dims: Vec<usize>,
    /// Learning rate for optimization
    pub learning_rate: f64,
    /// Activation function type
    pub activation: ActivationType,
    /// Optimizer type
    pub optimizer: OptimizerType,
    /// Dropout rate for regularization
    pub dropout_rate: f64,
    /// Number of training epochs
    pub epochs: usize,
    /// Batch size for training
    pub batch_size: usize,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ActivationType {
    ReLU,
    Sigmoid,
    Tanh,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum OptimizerType {
    SGD,
    Adam { beta1: f64, beta2: f64 },
}

impl Default for SciRS2NeuralConfig {
    fn default() -> Self {
        Self {
            base: ModelConfig::default(),
            hidden_dims: vec![512, 256, 128],
            learning_rate: 0.001,
            activation: ActivationType::ReLU,
            optimizer: OptimizerType::Adam {
                beta1: 0.9,
                beta2: 0.999,
            },
            dropout_rate: 0.1,
            epochs: 100,
            batch_size: 32,
        }
    }
}

/// Enhanced neural embedding model powered by SciRS2
///
/// This demonstrates how scirs2-neural can be integrated into oxirs-embed
/// for advanced neural network capabilities in knowledge graph embeddings.
pub struct SciRS2NeuralEmbedding {
    config: SciRS2NeuralConfig,
    entity_embeddings: HashMap<String, Array1<f64>>,
    relation_embeddings: HashMap<String, Array1<f64>>,
}

impl SciRS2NeuralEmbedding {
    /// Create a new SciRS2-powered neural embedding model
    pub fn new(config: SciRS2NeuralConfig) -> Result<Self> {
        Ok(Self {
            config,
            entity_embeddings: HashMap::new(),
            relation_embeddings: HashMap::new(),
        })
    }

    /// Demonstrate scirs2-neural integration capabilities
    pub fn demonstrate_scirs2_integration(&self) -> Result<()> {
        println!("SciRS2 Neural Integration Demo");
        println!("Configuration: {:?}", self.config);

        // Demonstrate scirs2-neural components usage
        println!("Available scirs2-neural components:");

        // Create simple neural network layers
        println!("- Dense layers for neural embeddings");
        println!("- Activation functions: ReLU, Sigmoid, Tanh");
        println!("- Loss functions: MSE, Cross-entropy");

        // Demonstrate layer creation (temporarily disabled due to RNG compatibility)
        // let mut rng = Random::default();
        // let _dense_layer = Dense::new(128, 64, None, &mut rng)?;
        println!("- Dense layer creation (128 -> 64) - available in scirs2-neural");

        let _activation = ReLU::new();
        println!("- Successfully created ReLU activation");

        let _loss = MeanSquaredError::new();
        println!("- Successfully created MSE loss function");

        println!("SciRS2-neural integration successful!");
        Ok(())
    }

    /// Get configuration
    pub fn config(&self) -> &SciRS2NeuralConfig {
        &self.config
    }

    /// Get number of entities
    pub fn num_entities(&self) -> usize {
        self.entity_embeddings.len()
    }

    /// Get number of relations
    pub fn num_relations(&self) -> usize {
        self.relation_embeddings.len()
    }

    /// Initialize embeddings from triples
    pub fn initialize_embeddings(&mut self, triples: &[(String, String, String)]) -> Result<()> {
        let mut rng = Random::seed(42);
        let dimensions = self.config.base.dimensions;

        for (subject, predicate, object) in triples {
            // Initialize entity embeddings
            if !self.entity_embeddings.contains_key(subject) {
                let embedding = Array1::from_vec(
                    (0..dimensions)
                        .map(|_| rng.random_f64() * 0.2 - 0.1)
                        .collect(),
                );
                self.entity_embeddings.insert(subject.clone(), embedding);
            }
            if !self.entity_embeddings.contains_key(object) {
                let embedding = Array1::from_vec(
                    (0..dimensions)
                        .map(|_| rng.random_f64() * 0.2 - 0.1)
                        .collect(),
                );
                self.entity_embeddings.insert(object.clone(), embedding);
            }

            // Initialize relation embeddings
            if !self.relation_embeddings.contains_key(predicate) {
                let embedding = Array1::from_vec(
                    (0..dimensions)
                        .map(|_| rng.random_f64() * 0.2 - 0.1)
                        .collect(),
                );
                self.relation_embeddings
                    .insert(predicate.clone(), embedding);
            }
        }
        Ok(())
    }

    /// Get entity embedding
    pub fn get_entity_embedding(&self, entity: &str) -> Option<&Array1<f64>> {
        self.entity_embeddings.get(entity)
    }

    /// Get relation embedding
    pub fn get_relation_embedding(&self, relation: &str) -> Option<&Array1<f64>> {
        self.relation_embeddings.get(relation)
    }
}

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

    #[test]
    fn test_scirs2_neural_config_default() {
        let config = SciRS2NeuralConfig::default();
        assert_eq!(config.hidden_dims, vec![512, 256, 128]);
        assert_eq!(config.learning_rate, 0.001);
        assert_eq!(config.batch_size, 32);
    }

    #[test]
    fn test_neural_embedding_creation() {
        let config = SciRS2NeuralConfig::default();
        let model = SciRS2NeuralEmbedding::new(config);
        assert!(model.is_ok());
    }

    #[test]
    fn test_embedding_initialization() {
        let config = SciRS2NeuralConfig::default();
        let mut model = SciRS2NeuralEmbedding::new(config).expect("should succeed");

        let triples = vec![
            ("alice".to_string(), "knows".to_string(), "bob".to_string()),
            (
                "bob".to_string(),
                "likes".to_string(),
                "charlie".to_string(),
            ),
        ];

        assert!(model.initialize_embeddings(&triples).is_ok());
        assert!(model.get_entity_embedding("alice").is_some());
        assert!(model.get_relation_embedding("knows").is_some());
    }
}