mleap

import pandas as pd import numpy as np import os import shutil from sklearn.linear_model import LinearRegression from mleap.sklearn.pipeline import MLeapPipeline from mleap.bundle import Bundle # 1. Prepare sample data data = { 'feature1': np.random.rand(10), 'feature2': np.random.rand(10) } df = pd.DataFrame(data) target = np.random.rand(10) # 2. Train a scikit-learn model model_sklearn = LinearRegression() model_sklearn.fit(df[['feature1', 'feature2']], target) # 3. Wrap the scikit-learn model in an MLeapPipeline mleap_pipeline = MLeapPipeline([ ('lr', model_sklearn) ]) # 4. Define export path and clean up previous exports bundle_path = "/tmp/my_linear_regression_mleap.zip" model_name = "linear_regression_mleap_model" if os.path.exists(bundle_path): os.remove(bundle_path) if os.path.exists(f"/tmp/{model_name}"): shutil.rmtree(f"/tmp/{model_name}") # 5. Export the MLeap pipeline to a bundle file with Bundle().writer(mleap_pipeline, df[['feature1', 'feature2']], name=model_name) as writer: writer.serialize_to_zip(bundle_path) print(f"MLeap model exported to: {bundle_path}") # 6. Load the MLeap bundle back into memory loaded_bundle = Bundle.load_model(bundle_path) # 7. Make predictions with the loaded model test_data = pd.DataFrame([[0.1, 0.9]], columns=['feature1', 'feature2']) predictions = loaded_bundle.predict(test_data) print(f"Predictions: {predictions}") # 8. Clean up created files (optional) if os.path.exists(bundle_path): os.remove(bundle_path) if os.path.exists(f"/tmp/{model_name}"): shutil.rmtree(f"/tmp/{model_name}")