ModelTrainerService.swift

ModelTrainerService Documentation

Overview

ModelTrainerService.swift manages the training process of the machine learning model, working in conjunction with the TrainingDataManager to process data and create/update models.

Core Components

Class Structure

class ModelTrainerService {
    private let dataManager: TrainingDataManager
    private let batchSize: Int
    private var currentModel: MLModel?
}

Properties

Property	Type	Description
dataManager	TrainingDataManager	Manages training data
batchSize	Int	Size of training batches
currentModel	MLModel?	Reference to current model

Error Types

enum TrainingError: Error {
    case insufficientData
    case modelCreationFailed
    case trainingFailed
}

Primary Features

Training Process

func trainModel() async throws -> MLModel

Manages the complete training pipeline:

1. Retrieves unprocessed data

2. Processes data in batches

3. Updates model state

4. Returns trained model

Batch Processing

private func processBatch(_ batch: [TrainingDataManager.TrainingData]) async throws

Handles batch-level training operations.

Implementation Exploration Points

1. Model Architecture Definition

// TODO: Define model architecture
struct ModelArchitecture {
    // Consider including:
    - let inputDimension: Int
    - let hiddenLayers: [Int]
    - let outputDimension: Int
    - let activationFunction: ActivationType
    - let optimizer: OptimizerType
}

2. Training Configuration

// TODO: Define training parameters
struct TrainingConfiguration {
    // Consider including:
    - let learningRate: Double
    - let epochs: Int
    - let validationSplit: Double
    - let earlyStoppingPatience: Int
    - let minimumLossImprovement: Double
}

3. Batch Processing Implementation

private func processBatch(_ batch: [TrainingDataManager.TrainingData]) async throws {
    // TODO: Implement the following steps:
    1. Data Preprocessing
        - Text tokenization
        - Feature extraction
        - Normalization
    
    2. Model Update
        - Forward pass
        - Loss calculation
        - Backward pass
        - Weight updates
    
    3. Progress Tracking
        - Loss monitoring
        - Validation metrics
        - Training statistics
    
    4. Status Updates
        - Update processing flags
        - Log progress
        - Handle errors
}

4. Model Evaluation

// TODO: Implement evaluation metrics
func evaluateModel(_ model: MLModel, testData: [TrainingData]) throws -> ModelMetrics {
    struct ModelMetrics {
        let accuracy: Double
        let precision: Double
        let recall: Double
        let f1Score: Double
        let confusionMatrix: [[Int]]
    }
}

5. Model Versioning

// TODO: Implement version control
struct ModelVersion {
    let version: String
    let timestamp: Date
    let metrics: ModelMetrics
    let configuration: TrainingConfiguration
    let trainingDataHash: String
}

Usage Examples

Basic Training

let trainer = ModelTrainerService(dataManager: dataManager)

Task {
    do {
        let model = try await trainer.trainModel()
        // Use or save the model
    } catch {
        print("Training error: \(error)")
    }
}

Custom Configuration

// TODO: Example with custom configuration
let config = TrainingConfiguration(
    learningRate: 0.001,
    epochs: 10,
    validationSplit: 0.2,
    earlyStoppingPatience: 3,
    minimumLossImprovement: 0.001
)

Development Roadmap

1. Model Architecture

   • Define input/output specifications

   • Design layer architecture

   • Choose activation functions

   • Implement optimizer

2. Training Pipeline

   • Implement data preprocessing

   • Add batch processing logic

   • Create progress monitoring

   • Add validation steps

3. Evaluation System

   • Implement metrics calculation

   • Add performance logging

   • Create evaluation reports

   • Add model comparison tools

4. Version Control

   • Add model versioning

   • Implement checkpointing

   • Create rollback capability

   • Add model metadata tracking

Best Practices

1. Data Handling

   • Validate data before training

   • Implement proper batching

   • Handle imbalanced datasets

   • Use appropriate preprocessing

2. Training Management

   • Monitor resource usage

   • Implement early stopping

   • Save checkpoints

   • Log training metrics

3. Error Handling

   • Graceful failure recovery

   • Proper error reporting

   • State management

   • Data validation

4. Performance

   • Optimize batch size

   • Use appropriate threading

   • Monitor memory usage

   • Implement caching where appropriate

Related Components

• TrainingDataManager: Provides training data

• ModelPersistenceManager: Handles model storage

• MLTrainingService: Uses trained models