test_basic_functionality.py

#!/usr/bin/env python3
"""
Basic functionality test for MLES platform.
This script tests the core components without complex imports.
"""

import sys
import os
import pandas as pd
import numpy as np
from pathlib import Path

# Add src to path
sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'src'))

def test_config():
    """Test configuration loading."""
    print("🔧 Testing configuration...")
    try:
        from config import get_config
        config = get_config()
        print(f"✅ Configuration loaded: {config.environment}")
        return True
    except Exception as e:
        print(f"❌ Configuration failed: {e}")
        return False

def test_data_loader():
    """Test data loader functionality."""
    print("📊 Testing data loader...")
    try:
        from preprocessing.data_loader import DataLoader
        loader = DataLoader()
        
        # Generate test data
        data = {
            'feature_1': np.random.randn(100),
            'feature_2': np.random.randn(100),
            'target': np.random.randint(0, 2, 100)
        }
        df = pd.DataFrame(data)
        
        # Test data splitting
        splits = loader.split_data(df, target_column='target', test_size=0.2)
        print(f"✅ Data splitting works: {len(splits['train'])} train, {len(splits['test'])} test")
        return True
    except Exception as e:
        print(f"❌ Data loader failed: {e}")
        return False

def test_data_validator():
    """Test data validator functionality."""
    print("🔍 Testing data validator...")
    try:
        from preprocessing.data_validator import DataValidator
        validator = DataValidator()
        
        # Generate test data
        data = {
            'feature_1': np.random.randn(100),
            'feature_2': np.random.randn(100),
            'target': np.random.randint(0, 2, 100)
        }
        df = pd.DataFrame(data)
        
        # Test validation
        result = validator.validate_data(df)
        print(f"✅ Data validation works: {result}")
        return True
    except Exception as e:
        print(f"❌ Data validator failed: {e}")
        return False

def test_feature_engineering():
    """Test feature engineering functionality."""
    print("⚙️  Testing feature engineering...")
    try:
        from preprocessing.feature_engineering import FeatureEngineer
        engineer = FeatureEngineer()
        
        # Generate test data
        data = {
            'feature_1': np.random.randn(100),
            'feature_2': np.random.randn(100),
            'target': np.random.randint(0, 2, 100)
        }
        df = pd.DataFrame(data)
        
        # Test feature engineering
        processed_df = engineer.engineer_features(df)
        print(f"✅ Feature engineering works: {processed_df.shape}")
        return True
    except Exception as e:
        print(f"❌ Feature engineering failed: {e}")
        return False

def test_sklearn_model():
    """Test sklearn model functionality."""
    print("🎯 Testing sklearn model...")
    try:
        from models.sklearn_models import SklearnModel
        model = SklearnModel()
        
        # Generate test data
        X = np.random.randn(100, 3)
        y = np.random.randint(0, 2, 100)
        
        # Test training
        model.train(X, y)
        print("✅ Model training works")
        
        # Test prediction
        predictions = model.predict(X[:10])
        print(f"✅ Model prediction works: {len(predictions)} predictions")
        return True
    except Exception as e:
        print(f"❌ Sklearn model failed: {e}")
        return False

def test_metrics():
    """Test metrics functionality."""
    print("📈 Testing metrics...")
    try:
        from monitoring.metrics import MetricsCollector
        metrics = MetricsCollector()
        
        # Generate test data
        y_true = np.random.randint(0, 2, 100)
        y_pred = np.random.randint(0, 2, 100)
        
        # Test metrics calculation
        results = metrics.calculate_metrics(y_true, y_pred)
        print(f"✅ Metrics calculation works: {list(results.keys())}")
        return True
    except Exception as e:
        print(f"❌ Metrics failed: {e}")
        return False

def main():
    """Run all tests."""
    print("🚀 Starting MLES Basic Functionality Tests")
    print("=" * 50)
    
    tests = [
        test_config,
        test_data_loader,
        test_data_validator,
        test_feature_engineering,
        test_sklearn_model,
        test_metrics
    ]
    
    results = []
    for test in tests:
        try:
            result = test()
            results.append(result)
        except Exception as e:
            print(f"❌ Test {test.__name__} crashed: {e}")
            results.append(False)
        print()
    
    # Summary
    print("=" * 50)
    print("📊 Test Results Summary:")
    passed = sum(results)
    total = len(results)
    
    for i, (test, result) in enumerate(zip(tests, results)):
        status = "✅ PASS" if result else "❌ FAIL"
        print(f"   {i+1}. {test.__name__}: {status}")
    
    print(f"\n🎯 Overall: {passed}/{total} tests passed")
    
    if passed == total:
        print("🎉 All tests passed! Basic functionality is working.")
        return True
    else:
        print("⚠️  Some tests failed. Check the implementation.")
        return False

if __name__ == "__main__":
    success = main()
    sys.exit(0 if success else 1)