91 lines
3.2 KiB
Python
91 lines
3.2 KiB
Python
#
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# OtterTune - test_gpr.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 unittest
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import numpy as np
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from sklearn import datasets
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from analysis.gp import GPRNP
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from analysis.gp_tf import GPR
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from analysis.gp_tf import GPRGD
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# test numpy version GPR
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class TestGPRNP(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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super(TestGPRNP, 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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cls.model = GPRNP(length_scale=1.0, magnitude=1.0)
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cls.model.fit(X_train, y_train, ridge=1.0)
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cls.gpr_result = cls.model.predict(X_test)
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def test_gprnp_ypreds(self):
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ypreds_round = [round(x[0], 4) for x in self.gpr_result.ypreds]
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expected_ypreds = [0.0181, 0.0014, 0.0006, 0.0015, 0.0039, 0.0014]
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self.assertEqual(ypreds_round, expected_ypreds)
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def test_gprnp_sigmas(self):
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sigmas_round = [round(x[0], 4) for x in self.gpr_result.sigmas]
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expected_sigmas = [1.4142, 1.4142, 1.4142, 1.4142, 1.4142, 1.4142]
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self.assertEqual(sigmas_round, expected_sigmas)
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# test Tensorflow version GPR
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class TestGPRTF(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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super(TestGPRTF, 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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cls.model = GPR(length_scale=1.0, magnitude=1.0)
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cls.model.fit(X_train, y_train, ridge=1.0)
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cls.gpr_result = cls.model.predict(X_test)
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def test_gprnp_ypreds(self):
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ypreds_round = [round(x[0], 4) for x in self.gpr_result.ypreds]
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expected_ypreds = [0.0181, 0.0014, 0.0006, 0.0015, 0.0039, 0.0014]
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self.assertEqual(ypreds_round, expected_ypreds)
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def test_gprnp_sigmas(self):
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sigmas_round = [round(x[0], 4) for x in self.gpr_result.sigmas]
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expected_sigmas = [1.4142, 1.4142, 1.4142, 1.4142, 1.4142, 1.4142]
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self.assertEqual(sigmas_round, expected_sigmas)
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# test Tensorflow GPRGD model
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class TestGPRGD(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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super(TestGPRGD, 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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Xmin = np.min(X_train, 0)
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Xmax = np.max(X_train, 0)
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cls.model = GPRGD(length_scale=1.0, magnitude=1.0, max_iter=1, learning_rate=0)
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cls.model.fit(X_train, y_train, Xmin, Xmax, ridge=1.0)
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cls.gpr_result = cls.model.predict(X_test)
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def test_gprnp_ypreds(self):
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ypreds_round = [round(x[0], 4) for x in self.gpr_result.ypreds]
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expected_ypreds = [0.0181, 0.0014, 0.0006, 0.0015, 0.0039, 0.0014]
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self.assertEqual(ypreds_round, expected_ypreds)
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def test_gprnp_sigmas(self):
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sigmas_round = [round(x[0], 4) for x in self.gpr_result.sigmas]
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expected_sigmas = [1.4142, 1.4142, 1.4142, 1.4142, 1.4142, 1.4142]
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self.assertEqual(sigmas_round, expected_sigmas)
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