62 lines
2.1 KiB
Python
62 lines
2.1 KiB
Python
|
#
|
||
|
|
# OtterTune - test_gpr.py
|
||
|
|
#
|
||
|
|
# Copyright (c) 2017-18, Carnegie Mellon University Database Group
|
||
|
|
#
|
||
|
|
import unittest
|
||
|
|
from sklearn import datasets
|
||
|
|
from analysis.gp import GPRNP
|
||
|
|
from analysis.gp_tf import GPR
|
||
|
|
|
||
|
|
|
||
|
|
# test numpy version GPR
|
||
|
|
class TestGPRNP(unittest.TestCase):
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def setUpClass(cls):
|
||
|
|
super(TestGPRNP, 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)
|
||
|
|
cls.model = GPRNP(length_scale=1.0, magnitude=1.0)
|
||
|
|
cls.model.fit(X_train, y_train, ridge=1.0)
|
||
|
|
cls.gpr_result = cls.model.predict(X_test)
|
||
|
|
|
||
|
|
def test_gprnp_ypreds(self):
|
||
|
|
ypreds_round = [round(x[0], 4) for x in self.gpr_result.ypreds]
|
||
|
|
expected_ypreds = [0.0181, 0.0014, 0.0006, 0.0015, 0.0039, 0.0014]
|
||
|
|
self.assertEqual(ypreds_round, expected_ypreds)
|
||
|
|
|
||
|
|
def test_gprnp_sigmas(self):
|
||
|
|
sigmas_round = [round(x[0], 4) for x in self.gpr_result.sigmas]
|
||
|
|
expected_sigmas = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
|
||
|
|
self.assertEqual(sigmas_round, expected_sigmas)
|
||
|
|
|
||
|
|
|
||
|
|
# test Tensorflow version GPR
|
||
|
|
class TestGPRTF(unittest.TestCase):
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def setUpClass(cls):
|
||
|
|
super(TestGPRTF, 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)
|
||
|
|
cls.model = GPR(length_scale=1.0, magnitude=1.0)
|
||
|
|
cls.model.fit(X_train, y_train, ridge=1.0)
|
||
|
|
cls.gpr_result = cls.model.predict(X_test)
|
||
|
|
|
||
|
|
def test_gprnp_ypreds(self):
|
||
|
|
ypreds_round = [round(x[0], 4) for x in self.gpr_result.ypreds]
|
||
|
|
expected_ypreds = [0.0181, 0.0014, 0.0006, 0.0015, 0.0039, 0.0014]
|
||
|
|
self.assertEqual(ypreds_round, expected_ypreds)
|
||
|
|
|
||
|
|
def test_gprnp_sigmas(self):
|
||
|
|
sigmas_round = [round(x[0], 4) for x in self.gpr_result.sigmas]
|
||
|
|
expected_sigmas = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
|
||
|
|
self.assertEqual(sigmas_round, expected_sigmas)
|