foxhunt/crates/ml/src/training.rs

//! Simplified ML Training Implementation for Foxhunt HFT System
//!
//! This module provides basic ML training functionality optimized for compilation success.
//! Focus on working implementation over advanced features.
//!
//! ## New Unified Data Pipeline
//!
//! The training system now uses UnifiedDataLoader with dual data providers:
//! - DatabentoHistoricalProvider for market data
//! - BenzingaHistoricalProvider for news sentiment
//! - UnifiedFeatureExtractor for consistent feature extraction

// Sub-modules for specialized training components
pub mod orchestrator;
pub mod unified_data_loader;
pub mod unified_trainer; // NEW: Unified training trait for all models // NEW: Model-agnostic training orchestrator

// NO RE-EXPORTS - Use explicit imports: unified_data_loader::{...}

use serde::{Deserialize, Serialize};

/// Training configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TrainingConfig {
    pub learning_rate: f64,
    pub batch_size: usize,
    pub epochs: usize,
    pub validation_split: f64,
    pub early_stopping_patience: Option<usize>,
    pub random_seed: Option<u64>,
}

impl Default for TrainingConfig {
    fn default() -> Self {
        Self {
            learning_rate: 0.001,
            batch_size: 32,
            epochs: 100,
            validation_split: 0.2,
            early_stopping_patience: Some(10),
            random_seed: None,
        }
    }
}

/// Device capabilities for performance scoring
#[derive(Debug, Clone)]
pub struct DeviceCapabilities {
    pub performance_score: f64,
    pub memory_gb: f64,
    pub compute_units: u32,
}

impl DeviceCapabilities {
    pub fn cpu_default() -> Self {
        Self {
            performance_score: 1.0,
            memory_gb: 8.0,
            compute_units: num_cpus::get() as u32,
        }
    }
}

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

    #[test]
    fn test_training_config_default() {
        let config = TrainingConfig::default();
        assert_eq!(config.learning_rate, 0.001);
        assert_eq!(config.batch_size, 32);
        assert_eq!(config.epochs, 100);
        assert_relative_eq!(config.validation_split, 0.2, epsilon = 1e-10);
    }

    #[test]
    fn test_device_capabilities_cpu_default() {
        let caps = DeviceCapabilities::cpu_default();
        assert_eq!(caps.performance_score, 1.0);
        assert_eq!(caps.memory_gb, 8.0);
        assert!(caps.compute_units > 0);
    }
}