43 lines
1.4 KiB
Python
43 lines
1.4 KiB
Python
#
|
|
# OtterTune - test_nn.py
|
|
#
|
|
# Copyright (c) 2017-18, Carnegie Mellon University Database Group
|
|
#
|
|
import random
|
|
import unittest
|
|
import numpy as np
|
|
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)
|
|
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 = ['20.021', '22.578', '22.722', '26.889', '24.362', '23.258']
|
|
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 = ['13.321', '15.482', '15.621', '18.648', '16.982', '15.986']
|
|
self.assertEqual(recommends_round, expected_recommends)
|