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#!/usr/bin/env python3
"""
Comprehensive example demonstrating all AutoML Lite features.
"""
import pandas as pd
import numpy as np
from sklearn.datasets import make_classification, make_regression
from sklearn.model_selection import train_test_split
import os
from automl_lite import AutoMLite
def classification_example():
"""Run a comprehensive classification example."""
print("🤖 AutoML Lite - Comprehensive Classification Example")
print("=" * 60)
# Generate sample classification data
print("📊 Generating sample classification data...")
X, y = make_classification(
n_samples=2000,
n_features=15,
n_informative=8,
n_redundant=4,
n_clusters_per_class=1,
random_state=42
)
# Convert to DataFrame
feature_names = [f'feature_{i}' for i in range(X.shape[1])]
X_df = pd.DataFrame(X, columns=feature_names)
y_series = pd.Series(y, name='target')
print(f"Dataset shape: {X_df.shape}")
print(f"Target distribution:\n{y_series.value_counts()}")
# Split data
X_train, X_test, y_train, y_test = train_test_split(
X_df, y_series, test_size=0.2, random_state=42, stratify=y_series
)
print(f"\nTraining set: {X_train.shape}")
print(f"Test set: {X_test.shape}")
# Initialize AutoML Lite with all features enabled
print("\n🚀 Initializing AutoML Lite with all features...")
automl = AutoMLite(
time_budget=300, # 5 minutes
max_models=5, # Try 5 models
cv_folds=5, # 5-fold CV
random_state=42,
verbose=True,
enable_ensemble=True,
enable_early_stopping=True,
enable_feature_selection=True,
enable_interpretability=True,
ensemble_method="voting",
top_k_models=3,
early_stopping_patience=10
)
# Train the model
print("\n🎯 Training AutoML model...")
automl.fit(X_train, y_train)
# Results
print(f"\n✅ Training completed!")
print(f"Best model: {automl.best_model_name}")
print(f"Best CV score: {automl.best_score:.4f}")
# Make predictions
print("\n🔮 Making predictions...")
y_pred = automl.predict(X_test)
test_score = automl.score(X_test, y_test)
print(f"Test accuracy: {test_score:.4f}")
# Show leaderboard
print("\n🏆 Model Leaderboard:")
leaderboard = automl.get_leaderboard()
print(leaderboard)
# Show feature importance
print("\n🎯 Feature Importance (Top 10):")
feature_importance = automl.get_feature_importance()
print(feature_importance.head(10))
# Show ensemble info
print("\n🎯 Ensemble Information:")
ensemble_info = automl.get_ensemble_info()
print(ensemble_info)
# Show interpretability results
print("\n🔍 Interpretability Results:")
interpretability_results = automl.get_interpretability_report()
print(interpretability_results)
# Save model
print("\n💾 Saving model...")
automl.save_model("comprehensive_classification_model.pkl")
# Generate comprehensive report with test data
print("\n📋 Generating comprehensive report...")
automl.generate_report("comprehensive_classification_report.html", X_test, y_test)
print("\n🎉 Comprehensive classification example completed!")
print("📁 Files created:")
print(" - comprehensive_classification_model.pkl (saved model)")
print(" - comprehensive_classification_report.html (comprehensive report)")
return automl, X_test, y_test
def regression_example():
"""Run a comprehensive regression example."""
print("\n🤖 AutoML Lite - Comprehensive Regression Example")
print("=" * 60)
# Generate sample regression data
print("📊 Generating sample regression data...")
X, y = make_regression(
n_samples=1500,
n_features=12,
n_informative=6,
noise=0.1,
random_state=42
)
# Convert to DataFrame
feature_names = [f'feature_{i}' for i in range(X.shape[1])]
X_df = pd.DataFrame(X, columns=feature_names)
y_series = pd.Series(y, name='target')
print(f"Dataset shape: {X_df.shape}")
print(f"Target statistics:")
print(f" Mean: {y_series.mean():.2f}")
print(f" Std: {y_series.std():.2f}")
print(f" Min: {y_series.min():.2f}")
print(f" Max: {y_series.max():.2f}")
# Split data
X_train, X_test, y_train, y_test = train_test_split(
X_df, y_series, test_size=0.2, random_state=42
)
print(f"\nTraining set: {X_train.shape}")
print(f"Test set: {X_test.shape}")
# Initialize AutoML Lite with all features enabled
print("\n🚀 Initializing AutoML Lite with all features...")
automl = AutoMLite(
time_budget=300, # 5 minutes
max_models=5, # Try 5 models
cv_folds=5, # 5-fold CV
random_state=42,
verbose=True,
enable_ensemble=True,
enable_early_stopping=True,
enable_feature_selection=True,
enable_interpretability=True,
ensemble_method="voting",
top_k_models=3,
early_stopping_patience=10
)
# Train the model
print("\n🎯 Training AutoML model...")
automl.fit(X_train, y_train)
# Results
print(f"\n✅ Training completed!")
print(f"Best model: {automl.best_model_name}")
print(f"Best CV score: {automl.best_score:.4f}")
# Make predictions
print("\n🔮 Making predictions...")
y_pred = automl.predict(X_test)
test_score = automl.score(X_test, y_test)
print(f"Test R² score: {test_score:.4f}")
# Show leaderboard
print("\n🏆 Model Leaderboard:")
leaderboard = automl.get_leaderboard()
print(leaderboard)
# Show feature importance
print("\n🎯 Feature Importance (Top 10):")
feature_importance = automl.get_feature_importance()
print(feature_importance.head(10))
# Show ensemble info
print("\n🎯 Ensemble Information:")
ensemble_info = automl.get_ensemble_info()
print(ensemble_info)
# Show interpretability results
print("\n🔍 Interpretability Results:")
interpretability_results = automl.get_interpretability_report()
print(interpretability_results)
# Save model
print("\n💾 Saving model...")
automl.save_model("comprehensive_regression_model.pkl")
# Generate comprehensive report with test data
print("\n📋 Generating comprehensive report...")
automl.generate_report("comprehensive_regression_report.html", X_test, y_test)
print("\n🎉 Comprehensive regression example completed!")
print("📁 Files created:")
print(" - comprehensive_regression_model.pkl (saved model)")
print(" - comprehensive_regression_report.html (comprehensive report)")
return automl, X_test, y_test
def main():
"""Run comprehensive examples."""
print("🚀 AutoML Lite - Production Ready Package Demo")
print("=" * 80)
# Create output directory
os.makedirs("output", exist_ok=True)
# Run classification example
try:
classification_model, X_test_clf, y_test_clf = classification_example()
print("\n✅ Classification example completed successfully!")
except Exception as e:
print(f"\n❌ Classification example failed: {str(e)}")
# Run regression example
try:
regression_model, X_test_reg, y_test_reg = regression_example()
print("\n✅ Regression example completed successfully!")
except Exception as e:
print(f"\n❌ Regression example failed: {str(e)}")
print("\n🎉 All examples completed!")
print("\n📋 Summary of AutoML Lite Features:")
print(" ✅ Automated model selection and hyperparameter optimization")
print(" ✅ Ensemble learning with voting classifiers/regressors")
print(" ✅ Feature selection and importance analysis")
print(" ✅ Early stopping for efficient training")
print(" ✅ Comprehensive HTML reports with visualizations")
print(" ✅ Model interpretability analysis")
print(" ✅ Test set performance analysis")
print(" ✅ Confusion matrices, ROC curves, and residuals plots")
print(" ✅ Feature correlation analysis")
print(" ✅ Learning curves and training history")
print(" ✅ Production-ready model saving and loading")
return 0
if __name__ == "__main__":
exit(main()) |