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from mcfly import find_architecture |
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import numpy as np |
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from nose.tools import assert_equal, assert_equals, assert_almost_equal |
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from keras.utils.np_utils import to_categorical |
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import unittest |
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class FindArchitectureSuite(unittest.TestCase): |
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"""Basic test cases.""" |
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def test_kNN_accuracy_1(self): |
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""" |
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The accuracy for this single-point dataset should be 1. |
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""" |
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X_train = np.array([[[1]], [[0]]]) |
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y_train = np.array([[1, 0], [0, 1]]) |
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X_val = np.array([[[0.9]]]) |
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y_val = np.array([[1, 0]]) |
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acc = find_architecture.kNN_accuracy( |
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X_train, y_train, X_val, y_val, k=1) |
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assert_almost_equal(acc, 1.0) |
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def test_kNN_accuracy_0(self): |
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""" |
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The accuracy for this single-point dataset should be 0. |
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""" |
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X_train = np.array([[[1]], [[0]]]) |
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y_train = np.array([[1, 0], [0, 1]]) |
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X_val = np.array([[[0.9]]]) |
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y_val = np.array([[0, 1]]) |
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acc = find_architecture.kNN_accuracy( |
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X_train, y_train, X_val, y_val, k=1) |
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assert_almost_equal(acc, 0) |
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def test_find_best_architecture(self): |
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num_timesteps = 100 |
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num_channels = 2 |
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num_samples_train = 5 |
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num_samples_val = 3 |
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X_train = np.random.rand( |
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num_samples_train, |
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num_timesteps, |
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num_channels) |
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y_train = to_categorical(np.array([0, 0, 1, 1, 1])) |
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X_val = np.random.rand(num_samples_val, num_timesteps, num_channels) |
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y_val = to_categorical(np.array([0, 1, 1])) |
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best_model, best_params, best_model_type, knn_acc = find_architecture.find_best_architecture( |
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X_train, y_train, X_val, y_val, verbose=False, |
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number_of_models=1, nr_epochs=1) |
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assert hasattr(best_model, 'fit') |
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self.assertIsNotNone(best_params) |
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self.assertIsNotNone(best_model_type) |
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assert knn_acc >= 0 and knn_acc <= 1 |
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def setUp(self): |
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np.random.seed(1234) |
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if __name__ == '__main__': |
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unittest.main() |
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NLeSC /
mcfly
