USER_GUIDE.md

title	Aurora AI Framework - Complete User Guide | Getting Started Tutorial
description	Complete user guide for Aurora AI Framework v1.0.0 - Step-by-step tutorials, installation guide, configuration, and usage examples for enterprise AI platform.
keywords	Aurora AI user guide, AI framework tutorial, enterprise AI getting started, machine learning guide, AI installation, AI configuration, enterprise AI platform
author	Aurora Development Team
robots	index, follow
canonical	https://aurora-ai.github.io/docs/USER_GUIDE.md

Aurora AI Framework - Complete User Guide

Getting Started

🚀 Current System Status: LIVE

• Web Interface: http://localhost:8081 - ACTIVE
• Server: Aurora AI Sci-Fi Interface - RUNNING
• Debug Mode: Enabled (PIN: 343-268-059)
• API Health: All endpoints responding
• Last Updated: 2026-05-06

📚 Related Documentation: For complete system architecture, see our Architecture Guide. For API reference, check our API Documentation.

🚀 Installation: Complete installation instructions available in our Installation Guide.

🔧 Configuration: Detailed configuration options in our Configuration Guide.

🌐 Interface Access: The Aurora AI Framework interface is currently running and accessible at http://localhost:8081

Installation

1. Clone or download the Aurora framework

2. Install dependencies:

   pip install -r requirements.txt

3. Verify installation:

   python examples/example_usage.py --mode quick

💡 Tip: For detailed installation instructions, including system requirements and troubleshooting, see our Installation Guide.

Quick Start

1. Prepare your data (CSV format) - See Data Validation Guide for data preparation
2. Configure the framework in config/config.yaml - See Configuration Guide for detailed options
3. Run the framework:

   python main.py

🔍 Monitoring: After starting, monitor your system with our Monitoring Guide.

Configuration

Main Configuration File (config/config.yaml)

app:
  name: Aurora AI Framework
  version: 1.0.0
  description: "Configuration file for the Aurora AI framework."

data_pipeline:
  data_path: "data/input.csv"
  source: "local"
  format: "csv"
  input_file: "data/input.csv"
  output_file: "data/output.csv"
  preprocessing: "standard"

model:
  architecture: "ensemble_model"
  type: classification
  algorithm: "RandomForest"
  parameters:
    learning_rate: 0.01
    num_epochs: 100
    batch_size: 32
  n_estimators: 100
  max_depth: 10
  random_state: 42
  epochs: 10
  batch_size: 32
  optimizer: "adam"

api_server:
  host: 0.0.0.0
  port: 8080
  debug: false

monitoring:
  log_interval: 5
  drift_detection: true
  alerting: true
  alert_threshold: 0.8

security:
  enable_authentication: false
  encryption_key: "L_8Hfm33ainlgyoN0t_3YsGjw-ujM15X8_VsrKrKr5U="
  api_keys:
    internal: "internal_api_key"
    external: "external_api_key"

modules:
  enabled:
    - monitoring
    - alerting
    - data_validation
    - error_tracker
  disabled:
    - emotional_core
    - eternal_art

metadata:
  author: "Aurora Development Team"
  last_updated: "2025-05-06"

drift_detection: true alerting: true alert_threshold: 0.8

api_server: host: "0.0.0.0" port: 8080

### Data Pipeline Configuration

| Parameter | Description | Default | Options |
|-----------|-------------|---------|---------|
| `data_path` | Path to your data file | Required | Valid file path |
| `format` | Data file format | "csv" | "csv", "json", "excel" |
| `missing_value_strategy` | How to handle missing values | "mean" | "mean", "median", "mode", "drop" |
| `remove_outliers` | Whether to remove outliers | false | true, false |

### Model Configuration

#### Supported Algorithms

**Classification**:
- `RandomForest` - Random Forest Classifier
- `Logistic` - Logistic Regression
- `SVM` - Support Vector Machine

**Regression**:
- `RandomForest` - Random Forest Regressor
- `Linear` - Linear Regression
- `SVM` - Support Vector Regression

#### Model Parameters

| Parameter | Description | Default |
|-----------|-------------|---------|
| `algorithm` | Algorithm to use | Required |
| `type` | Model type (classification/regression) | Required |
| `n_estimators` | Number of estimators (for ensemble methods) | 100 |
| `max_depth` | Maximum tree depth | 10 |
| `random_state` | Random seed for reproducibility | 42 |
| `cv_folds` | Cross-validation folds | 5 |

### Monitoring Configuration

| Parameter | Description | Default |
|-----------|-------------|---------|
| `log_interval` | Monitoring interval in seconds | 5 |
| `drift_detection` | Enable data drift detection | true |
| `alerting` | Enable alerting system | true |
| `alert_threshold` | Alert threshold for metrics | 0.8 |

## Usage Examples

### Basic Usage

```python
from modules.data_pipeline import DataPipeline
from modules.model_trainer import ModelTrainer

# Configure components
config = {
    'data_path': 'data/my_data.csv',
    'algorithm': 'RandomForest',
    'type': 'classification'
}

# Initialize and run pipeline
pipeline = DataPipeline(config)
pipeline.initialize()
features, target = pipeline.process()

# Train model
trainer = ModelTrainer(config)
trainer.initialize()
trainer.train(features, target)

Complete Workflow

# Run the complete example
python examples/example_usage.py --mode complete

Custom Data Processing

# Load your own data
import pandas as pd

# Preprocess your data
data = pd.read_csv('your_data.csv')
# ... preprocessing steps ...

# Use with Aurora pipeline
pipeline = DataPipeline(config)
pipeline.initialize()
features, target = pipeline.process(data)  # Pass preprocessed data

API Reference

DataPipeline Class

Methods

• initialize() - Initialize the pipeline
• process(data=None) - Process data (load if None)
• load_data() - Load data from configured path
• preprocess_data(data) - Preprocess raw data
• split_data(features, target) - Split into train/test
• get_data_summary() - Get data statistics

Example

pipeline = DataPipeline(config)
if pipeline.initialize():
    features, target = pipeline.process()
    X_train, X_test, y_train, y_test = pipeline.split_data(features, target)

ModelTrainer Class

Methods

• initialize() - Initialize the trainer
• train(X, y, optimize_hyperparameters=True) - Train model
• predict(X) - Make predictions
• predict_proba(X) - Get probabilities (classification)
• save_model(path=None) - Save trained model
• load_model(path) - Load saved model
• get_feature_importance() - Get feature importance

Example

trainer = ModelTrainer(config)
if trainer.initialize():
    results = trainer.train(X_train, y_train)
    predictions = trainer.predict(X_test)
    trainer.save_model()

ModelMonitor Class

Methods

• initialize() - Initialize monitoring
• start_monitoring(model=None) - Start continuous monitoring
• stop_monitoring() - Stop monitoring
• record_model_performance(y_true, y_pred, model_type) - Record metrics
• detect_drift(current_data, reference_data) - Detect data drift
• generate_report() - Generate monitoring report

Example

monitor = ModelMonitor(config)
if monitor.initialize():
    monitor.start_monitoring()
    performance = monitor.record_model_performance(y_test, predictions)
    report = monitor.generate_report()

InferenceService Class

Methods

• initialize() - Initialize service
• start_service() - Start REST API server
• stop_service() - Stop server
• predict_single(features) - Make single prediction
• get_service_info() - Get service information

API Endpoints

• GET /health - Health check
• POST /predict - Make predictions
• POST /predict_proba - Get probabilities
• GET /stats - Service statistics
• GET /history - Prediction history

Example

service = InferenceService(config)
if service.initialize():
    service.start_service()
    # Service now available at http://localhost:5000

Data Format Requirements

Input Data Format

1. CSV Format (recommended):

   • First row should contain column headers
   • Target column should be the last column
   • No missing values in target column

2. JSON Format:

   • Array of objects with consistent keys
   • Each object represents one data point

3. Excel Format:

   • First sheet used by default
   • First row should contain headers

Data Quality Guidelines

1. Missing Values:

   • Configure handling strategy in config
   • Avoid missing values in target column

2. Categorical Variables:

   • Automatically encoded as integers
   • Consistent encoding across train/test

3. Numerical Variables:

   • Automatically scaled using StandardScaler
   • Outliers handled if enabled

4. Target Variable:

   • For classification: integer labels (0, 1, 2...)
   • For regression: continuous values

Monitoring and Alerting

Metrics Tracked

Model Performance

• Classification: Accuracy, Precision, Recall, F1-Score
• Regression: MSE, RMSE, R²

System Metrics

• CPU usage percentage
• Memory usage percentage
• Disk usage percentage
• Network I/O

Data Drift

• Feature distribution changes
• Statistical tests for drift detection

Alert Types

System Alerts

• High CPU usage (>80%)
• High memory usage (>85%)
• Low disk space (<10%)

Performance Alerts

• Model performance degradation
• Training failures
• Prediction errors

Data Drift Alerts

• Significant feature distribution changes
• Data quality issues

Custom Alert Callbacks

def custom_alert_handler(alert):
    print(f"ALERT: {alert['message']}")
    # Send to external system, email, etc.

monitor = ModelMonitor(config)
monitor.add_alert_callback(custom_alert_handler)

Troubleshooting

Common Issues

1. Configuration Errors

Problem: Missing required configuration keys Solution: Check config.yaml has all required sections

python examples/example_usage.py --mode quick

2. Data Loading Issues

Problem: Cannot find or read data file Solution: Verify data path and format

# Check file exists and is readable
import os
print(os.path.exists(config['data_path']))

3. Model Training Failures

Problem: Training fails with errors Solution: Check data quality and configuration

# Validate data
pipeline = DataPipeline(config)
pipeline.initialize()
features, target = pipeline.process()
print(f"Features shape: {features.shape}")
print(f"Target distribution: {target.value_counts()}")

4. Memory Issues

Problem: Out of memory errors Solution: Reduce data size or adjust batch size

# In config.yaml
model:
  batch_size: 16  # Reduce from default 32

5. Port Conflicts

Problem: API server won't start Solution: Change port in configuration

api_server:
  port: 8081  # Use different port

Debug Mode

Enable debug logging:

logging:
  level: DEBUG

Getting Help

1. Check the logs in logs/ directory
2. Run the quick test to verify installation
3. Check the example usage for reference
4. Review the architecture documentation

Best Practices

1. Data Preparation

• Clean data before processing
• Handle missing values appropriately
• Ensure consistent feature encoding

2. Model Training

• Use cross-validation for robust evaluation
• Monitor training progress
• Save models with metadata

3. Production Deployment

• Monitor model performance continuously
• Set up appropriate alerting
• Plan for model retraining

4. Configuration Management

• Use environment-specific configs
• Secure sensitive information
• Version control configuration changes

Advanced Features

Custom Components

Create custom components by inheriting from base classes:

from core.base import BaseDataProcessor

class CustomProcessor(BaseDataProcessor):
    def initialize(self):
        # Custom initialization
        return True
    
    def process(self, data):
        # Custom processing logic
        return processed_data
    
    def cleanup(self):
        # Custom cleanup
        pass

Hyperparameter Optimization

Enable advanced optimization:

model:
  optimize_hyperparameters: true
  optimization_method: "grid_search"  # or "random_search"
  cv_folds: 10

Ensemble Methods

Combine multiple models:

# Train multiple models
models = []
for algorithm in ['RandomForest', 'Logistic', 'SVM']:
    config['model']['algorithm'] = algorithm
    trainer = ModelTrainer(config)
    trainer.initialize()
    trainer.train(X_train, y_train)
    models.append(trainer)

# Ensemble predictions
predictions = []
for model in models:
    pred = model.predict(X_test)
    predictions.append(pred)

# Average predictions
ensemble_pred = np.mean(predictions, axis=0)

Performance Optimization

1. Data Optimization

• Use appropriate data types
• Remove unnecessary features
• Optimize memory usage

2. Model Optimization

• Choose appropriate algorithm
• Tune hyperparameters
• Use feature selection

3. System Optimization

• Monitor resource usage
• Optimize batch sizes
• Use caching appropriately

Integration Examples

Flask Web Application

from flask import Flask, request, jsonify
from modules.inference_service import InferenceService

app = Flask(__name__)
service = InferenceService(config)
service.initialize()

@app.route('/predict', methods=['POST'])
def predict():
    data = request.get_json()
    prediction = service.predict_single(data['features'])
    return jsonify({'prediction': prediction.tolist()})

Batch Processing

# Process multiple datasets
datasets = ['data1.csv', 'data2.csv', 'data3.csv']
results = {}

for dataset in datasets:
    config['data_pipeline']['data_path'] = dataset
    pipeline = DataPipeline(config['data_pipeline'])
    pipeline.initialize()
    features, target = pipeline.process()
    
    trainer = ModelTrainer(config['model'])
    trainer.initialize()
    results[dataset] = trainer.train(features, target)

Scheduled Retraining

import schedule
import time

def retrain_model():
    # Load latest data
    # Retrain model
    # Update production model
    pass

# Schedule daily retraining
schedule.every().day.at("02:00").do(retrain_model)

while True:
    schedule.run_pending()
    time.sleep(60)