44 lines
1.4 KiB
Python
44 lines
1.4 KiB
Python
#
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# OtterTune - test_nn.py
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#
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# Copyright (c) 2017-18, Carnegie Mellon University Database Group
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#
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import random
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import unittest
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import numpy as np
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from tensorflow import set_random_seed
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from sklearn import datasets
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from analysis.nn_tf import NeuralNet
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# test neural network
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class TestNN(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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super(TestNN, cls).setUpClass()
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boston = datasets.load_boston()
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data = boston['data']
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X_train = data[0:500]
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X_test = data[500:]
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y_train = boston['target'][0:500].reshape(500, 1)
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random.seed(0)
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np.random.seed(0)
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set_random_seed(0)
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cls.model = NeuralNet(n_input=X_test.shape[1],
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batch_size=X_test.shape[0],
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reset_seed=True)
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cls.model.fit(X_train, y_train)
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cls.nn_result = cls.model.predict(X_test)
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cls.nn_recommend = cls.model.recommend(X_test)
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def test_nn_ypreds(self):
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ypreds_round = ['%.3f' % x[0] for x in self.nn_result]
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expected_ypreds = ['21.279', '22.668', '23.115', '27.228', '25.892', '23.967']
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self.assertEqual(ypreds_round, expected_ypreds)
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def test_nn_yrecommend(self):
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recommends_round = ['%.3f' % x[0] for x in self.nn_recommend.minl]
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expected_recommends = ['21.279', '21.279', '21.279', '21.279', '21.279', '21.279']
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self.assertEqual(recommends_round, expected_recommends)
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