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50 lines
1.6 KiB
Python
50 lines
1.6 KiB
Python
# Copyright (c) Microsoft. All rights reserved.
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# Licensed under the MIT license.
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from sklearn.datasets import load_diabetes
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from sklearn.linear_model import Ridge
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from sklearn.metrics import mean_squared_error
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from sklearn.model_selection import train_test_split
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from azureml.core.run import Run
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import os
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import numpy as np
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import mylib
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# sklearn.externals.joblib is removed in 0.23
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try:
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from sklearn.externals import joblib
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except ImportError:
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import joblib
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os.makedirs('./outputs', exist_ok=True)
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X, y = load_diabetes(return_X_y=True)
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run = Run.get_context()
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X_train, X_test, y_train, y_test = train_test_split(X, y,
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test_size=0.2,
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random_state=0)
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data = {"train": {"X": X_train, "y": y_train},
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"test": {"X": X_test, "y": y_test}}
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# list of numbers from 0.0 to 1.0 with a 0.05 interval
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alphas = mylib.get_alphas()
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for alpha in alphas:
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# Use Ridge algorithm to create a regression model
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reg = Ridge(alpha=alpha)
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reg.fit(data["train"]["X"], data["train"]["y"])
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preds = reg.predict(data["test"]["X"])
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mse = mean_squared_error(preds, data["test"]["y"])
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run.log('alpha', alpha)
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run.log('mse', mse)
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model_file_name = 'ridge_{0:.2f}.pkl'.format(alpha)
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# save model in the outputs folder so it automatically get uploaded
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with open(model_file_name, "wb") as file:
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joblib.dump(value=reg, filename=os.path.join('./outputs/',
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model_file_name))
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print('alpha is {0:.2f}, and mse is {1:0.2f}'.format(alpha, mse))
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