test for dnn

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bohanjason 2019-10-08 21:58:19 -04:00 committed by Dana Van Aken
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#
# OtterTune - test_nn.py
#
# Copyright (c) 2017-18, Carnegie Mellon University Database Group
#
import unittest
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)
set_random_seed(1)
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]
expected_ypreds = ['20.503', '22.158', '22.158', '25.692', '24.536', '23.637']
self.assertEqual(ypreds_round, expected_ypreds)
def test_nn_yrecommend(self):
recommends_round = ['%.3f' % x[0] for x in self.nn_recommend.minl]
expected_recommends = ['14.229', '22.158', '22.158', '23.591', '23.591', '23.593']
self.assertEqual(recommends_round, expected_recommends)