75 lines
2.3 KiB
Python
75 lines
2.3 KiB
Python
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import joblib
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import numpy as np
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def load_data(filename):
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"""
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Loads the data from a saved .npz file.
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### YOU CAN NOT EDIT THIS FUNCTION ###
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:param filename: string, path to the .npz file storing the data.
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:return: two numpy arrays:
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- x, a Numpy array of shape (n_samples, n_features) with the inputs;
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- y, a Numpy array of shape (n_samples, ) with the targets.
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"""
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data = np.load(filename)
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x = data['x']
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y = data['y']
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return x, y
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def evaluate_predictions(y_true, y_pred):
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"""
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Evaluates the mean squared error between the values in y_true and the values
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in y_pred.
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### YOU CAN NOT EDIT THIS FUNCTION ###
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:param y_true: Numpy array, the true target values from the test set;
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:param y_pred: Numpy array, the values predicted by your model.
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:return: float, the the mean squared error between the two arrays.
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"""
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assert y_true.shape == y_pred.shape
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return ((y_true - y_pred) ** 2).mean()
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def load_model(filename):
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"""
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Loads a Scikit-learn model saved with joblib.dump.
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This is just an example, you can write your own function to load the model.
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Some examples can be found in src/utils.py.
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:param filename: string, path to the file storing the model.
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:return: the model.
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"""
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model = joblib.load(filename)
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return model
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if __name__ == '__main__':
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# Load the data
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# This will be replaced with the test data when grading the assignment
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data_path = '../data/data.npz'
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x, y = load_data(data_path)
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############################################################################
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# EDITABLE SECTION OF THE SCRIPT: if you need to edit the script, do it here
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############################################################################
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# Load the trained model
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baseline_model_path = './baseline_model.pickle'
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baseline_model = load_model(baseline_model_path)
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# Predict on the given samples
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y_pred = baseline_model.predict(x)
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############################################################################
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# STOP EDITABLE SECTION: do not modify anything below this point.
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############################################################################
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# Evaluate the prediction using MSE
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mse = evaluate_predictions(y_pred, y)
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print('MSE: {}'.format(mse))
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