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use gpflow in workload mapping

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This commit is contained in:
bohanjason
2020-01-22 07:38:18 -05:00
committed by Dana Van Aken
parent 25d1950e67
commit 6f0fcfd952
4 changed files with 72 additions and 78 deletions
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66
server/analysis/tests/test_gpr.py
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@@ -14,7 +14,7 @@ from analysis.gp_tf import GPR
from analysis.gp_tf import GPRGD
from analysis.gpr import gpr_models
from analysis.gpr.optimize import tf_optimize
from analysis.gpr.optimize import gpflow_predict
from analysis.gpr.predict import gpflow_predict
# test numpy version GPR
class TestGPRNP(unittest.TestCase):
@@ -31,12 +31,12 @@ class TestGPRNP(unittest.TestCase):
cls.model.fit(X_train, y_train, ridge=1.0)
cls.gpr_result = cls.model.predict(X_test)
def test_gprnp_ypreds(self):
def test_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):
def test_sigmas(self):
sigmas_round = [round(x[0], 4) for x in self.gpr_result.sigmas]
expected_sigmas = [1.4142, 1.4142, 1.4142, 1.4142, 1.4142, 1.4142]
self.assertEqual(sigmas_round, expected_sigmas)
@@ -57,23 +57,23 @@ class TestGPRTF(unittest.TestCase):
cls.model.fit(X_train, y_train)
cls.gpr_result = cls.model.predict(X_test)
def test_gprnp_ypreds(self):
def test_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):
def test_sigmas(self):
sigmas_round = [round(x[0], 4) for x in self.gpr_result.sigmas]
expected_sigmas = [1.4142, 1.4142, 1.4142, 1.4142, 1.4142, 1.4142]
self.assertEqual(sigmas_round, expected_sigmas)
# test GPFlow version GPR
class TestGPRGPF(unittest.TestCase):
class TestGPRGPFlow(unittest.TestCase):
@classmethod
def setUpClass(cls):
super(TestGPRGPF, cls).setUpClass()
super(TestGPRGPFlow, cls).setUpClass()
boston = datasets.load_boston()
data = boston['data']
X_train = data[0:500]
@@ -88,51 +88,23 @@ class TestGPRGPF(unittest.TestCase):
**model_kwargs)
cls.gpr_result = gpflow_predict(cls.m.model, X_test)
def test_gprnp_ypreds(self):
def test_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):
def test_sigmas(self):
sigmas_round = [round(x[0], 4) for x in self.gpr_result.sigmas]
expected_sigmas = [1.4142, 1.4142, 1.4142, 1.4142, 1.4142, 1.4142]
self.assertEqual(sigmas_round, expected_sigmas)
# test Tensorflow GPRGD model
class TestGPRGD(unittest.TestCase):
# test GPFlow version Gradient Descent
class TestGDGPFlow(unittest.TestCase):
@classmethod
def setUpClass(cls):
super(TestGPRGD, 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)
Xmin = np.min(X_train, 0)
Xmax = np.max(X_train, 0)
cls.model = GPRGD(length_scale=1.0, magnitude=1.0, max_iter=1, learning_rate=0, ridge=1.0)
cls.model.fit(X_train, y_train, Xmin, Xmax)
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 = [1.4142, 1.4142, 1.4142, 1.4142, 1.4142, 1.4142]
self.assertEqual(sigmas_round, expected_sigmas)
# test Gradient Descent in GPFlow model
class TestGPFGD(unittest.TestCase):
@classmethod
def setUpClass(cls):
super(TestGPFGD, cls).setUpClass()
super(TestGDGPFlow, cls).setUpClass()
boston = datasets.load_boston()
data = boston['data']
X_train = data[0:500]
@@ -158,23 +130,23 @@ class TestGPFGD(unittest.TestCase):
cls.m = gpr_models.create_model('BasicGP', X=X_train, y=y_train, **model_kwargs)
cls.gpr_result = tf_optimize(cls.m.model, X_test, **opt_kwargs)
def test_gprnp_ypreds(self):
def test_ypreds(self):
ypreds_round = [round(x[0], 4) for x in self.gpr_result.ypreds]
expected_ypreds = [0.5272]
self.assertEqual(ypreds_round, expected_ypreds)
def test_gprnp_sigmas(self):
def test_sigmas(self):
sigmas_round = [round(x[0], 4) for x in self.gpr_result.sigmas]
expected_sigmas = [1.4153]
self.assertEqual(sigmas_round, expected_sigmas)
# test Gradient Descent in Tensorflow GPRGD model
class TestGPRGDGD(unittest.TestCase):
# test Tensorflow version Gradient Descent
class TestGDTF(unittest.TestCase):
@classmethod
def setUpClass(cls):
super(TestGPRGDGD, cls).setUpClass()
super(TestGDTF, cls).setUpClass()
boston = datasets.load_boston()
data = boston['data']
X_train = data[0:500]
@@ -191,12 +163,12 @@ class TestGPRGDGD(unittest.TestCase):
cls.model.fit(X_train, y_train, Xmin, Xmax)
cls.gpr_result = cls.model.predict(X_test)
def test_gprnp_ypreds(self):
def test_ypreds(self):
ypreds_round = [round(x[0], 4) for x in self.gpr_result.ypreds]
expected_ypreds = [0.5272]
self.assertEqual(ypreds_round, expected_ypreds)
def test_gprnp_sigmas(self):
def test_sigmas(self):
sigmas_round = [round(x[0], 4) for x in self.gpr_result.sigmas]
expected_sigmas = [1.4153]
self.assertEqual(sigmas_round, expected_sigmas)