Delete train_diabetes.py

how-to-use-azureml/using-mlflow/train-remote/train_diabetes.py

@@ -1,46 +0,0 @@
-# Copyright (c) Microsoft. All rights reserved.
-# Licensed under the MIT license.
-
-import numpy as np
-from sklearn.datasets import load_diabetes
-from sklearn.linear_model import Ridge
-from sklearn.metrics import mean_squared_error
-from sklearn.model_selection import train_test_split
-import mlflow
-import mlflow.sklearn
-
-import matplotlib
-matplotlib.use('Agg')
-import matplotlib.pyplot as plt
-
-with mlflow.start_run():
-    X, y = load_diabetes(return_X_y=True)
-    columns = ['age', 'gender', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']
-    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
-    data = {
-        "train": {"X": X_train, "y": y_train},
-        "test": {"X": X_test, "y": y_test}}
-
-    mlflow.log_metric("Training samples", len(data['train']['X']))
-    mlflow.log_metric("Test samples", len(data['test']['X']))
-
-    # Log the algorithm parameter alpha to the run
-    mlflow.log_metric('alpha', 0.03)
-    # Create, fit, and test the scikit-learn Ridge regression model
-    regression_model = Ridge(alpha=0.03)
-    regression_model.fit(data['train']['X'], data['train']['y'])
-    preds = regression_model.predict(data['test']['X'])
-
-    # Log mean squared error
-    print('Mean Squared Error is', mean_squared_error(data['test']['y'], preds))
-    mlflow.log_metric('mse', mean_squared_error(data['test']['y'], preds))
-
-    # Save the model to the outputs directory for capture
-    mlflow.sklearn.log_model(regression_model, "model")
-
-    # Plot actuals vs predictions and save the plot within the run
-    fig = plt.figure(1)
-    idx = np.argsort(data['test']['y'])
-    plt.plot(data['test']['y'][idx], preds[idx])
-    fig.savefig("actuals_vs_predictions.png")
-    mlflow.log_artifact("actuals_vs_predictions.png")