ottertune/server/analysis/tests/test_nn.py

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#
# OtterTune - test_nn.py
#
# Copyright (c) 2017-18, Carnegie Mellon University Database Group
#
import random
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import unittest
import numpy as np
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from tensorflow import set_random_seed
from sklearn import datasets
from analysis.nn_tf import NeuralNet
# test neural network
class TestNN(unittest.TestCase):
@classmethod
def setUpClass(cls):
super(TestNN, cls).setUpClass()
boston = datasets.load_boston()
data = boston['data']
X_train = data[0:500]
X_test = data[500:]
y_train = boston['target'][0:500].reshape(500, 1)
random.seed(0)
np.random.seed(0)
set_random_seed(0)
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cls.model = NeuralNet(n_input=X_test.shape[1],
batch_size=X_test.shape[0])
cls.model.fit(X_train, y_train)
cls.nn_result = cls.model.predict(X_test)
cls.nn_recommend = cls.model.recommend(X_test)
def test_nn_ypreds(self):
ypreds_round = ['%.3f' % x[0] for x in self.nn_result]
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expected_ypreds = ['20.021', '22.578', '22.722', '26.889', '24.362', '23.258']
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self.assertEqual(ypreds_round, expected_ypreds)
def test_nn_yrecommend(self):
recommends_round = ['%.3f' % x[0] for x in self.nn_recommend.minl]
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expected_recommends = ['13.321', '15.482', '15.621', '18.648', '16.982', '15.986']
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self.assertEqual(recommends_round, expected_recommends)