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Automatic fallback to Numpy if Cython modules not available

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This commit is contained in:
Mike Croucher 2015-09-07 13:22:51 +01:00
parent 276330d1d1
commit e7a9b53506
5 changed files with 53 additions and 31 deletions
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44
GPy/testing/kernel_tests.py
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@ -6,9 +6,16 @@ import numpy as np
import GPy
import sys
from GPy.core.parameterization.param import Param
from ..util.config import config
verbose = 0
try:
from . import linalg_cython
config.set('cython', 'working', 'True')
except ImportError:
config.set('cython', 'working', 'False')
class Kern_check_model(GPy.core.Model):
"""
@ -312,12 +319,12 @@ class KernelGradientTestsContinuous(unittest.TestCase):
k = GPy.kern.LinearFull(self.D, self.D-1)
k.randomize()
self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
def test_standard_periodic(self):
k = GPy.kern.StdPeriodic(self.D, self.D-1)
k.randomize()
self.assertTrue(check_kernel_gradient_functions(k, X=self.X, X2=self.X2, verbose=verbose))
class KernelTestsMiscellaneous(unittest.TestCase):
def setUp(self):
N, D = 100, 10
@ -371,6 +378,7 @@ class KernelTestsNonContinuous(unittest.TestCase):
X2 = self.X2[self.X2[:,-1]!=2]
self.assertTrue(check_kernel_gradient_functions(kern, X=X, X2=X2, verbose=verbose, fixed_X_dims=-1))
@unittest.skipIf(not config.getboolean('cython', 'working'),"Cython modules have not been built on this machine")
class Coregionalize_cython_test(unittest.TestCase):
"""
Make sure that the coregionalize kernel work with and without cython enabled
@ -438,28 +446,28 @@ class KernelTestsProductWithZeroValues(unittest.TestCase):
"Gradient resulted in NaN")
class Kernel_Psi_statistics_GradientTests(unittest.TestCase):
def setUp(self):
from GPy.core.parameterization.variational import NormalPosterior
N,M,Q = 100,20,3
X = np.random.randn(N,Q)
X_var = np.random.rand(N,Q)+0.01
self.Z = np.random.randn(M,Q)
self.qX = NormalPosterior(X, X_var)
self.w1 = np.random.randn(N)
self.w2 = np.random.randn(N,M)
self.w3 = np.random.randn(M,M)
self.w3 = np.random.randn(M,M)
self.w3 = self.w3+self.w3.T
self.w3n = np.random.randn(N,M,M)
self.w3n = np.random.randn(N,M,M)
self.w3n = self.w3n+np.swapaxes(self.w3n, 1,2)
def test_kernels(self):
from GPy.kern import RBF,Linear
Q = self.Z.shape[1]
kernels = [RBF(Q,ARD=True), Linear(Q,ARD=True)]
for k in kernels:
k.randomize()
self._test_kernel_param(k)
@ -476,12 +484,12 @@ class Kernel_Psi_statistics_GradientTests(unittest.TestCase):
psi0 = kernel.psi0(self.Z, self.qX)
psi1 = kernel.psi1(self.Z, self.qX)
if not psi2n:
psi2 = kernel.psi2(self.Z, self.qX)
psi2 = kernel.psi2(self.Z, self.qX)
return (self.w1*psi0).sum() + (self.w2*psi1).sum() + (self.w3*psi2).sum()
else:
psi2 = kernel.psi2n(self.Z, self.qX)
return (self.w1*psi0).sum() + (self.w2*psi1).sum() + (self.w3n*psi2).sum()
def df(p):
kernel.param_array[:] = p
kernel.update_gradients_expectations(self.w1, self.w2, self.w3 if not psi2n else self.w3n, self.Z, self.qX)
@ -492,39 +500,39 @@ class Kernel_Psi_statistics_GradientTests(unittest.TestCase):
self.assertTrue(m.checkgrad())
def _test_Z(self, kernel, psi2n=False):
def f(p):
psi0 = kernel.psi0(p, self.qX)
psi1 = kernel.psi1(p, self.qX)
psi2 = kernel.psi2(p, self.qX)
if not psi2n:
psi2 = kernel.psi2(p, self.qX)
psi2 = kernel.psi2(p, self.qX)
return (self.w1*psi0).sum() + (self.w2*psi1).sum() + (self.w3*psi2).sum()
else:
psi2 = kernel.psi2n(p, self.qX)
return (self.w1*psi0).sum() + (self.w2*psi1).sum() + (self.w3n*psi2).sum()
def df(p):
return kernel.gradients_Z_expectations(self.w1, self.w2, self.w3 if not psi2n else self.w3n, p, self.qX)
from GPy.models import GradientChecker
m = GradientChecker(f, df, self.Z.copy())
self.assertTrue(m.checkgrad())
def _test_qX(self, kernel, psi2n=False):
def f(p):
self.qX.param_array[:] = p
self.qX._trigger_params_changed()
psi0 = kernel.psi0(self.Z, self.qX)
psi1 = kernel.psi1(self.Z, self.qX)
if not psi2n:
psi2 = kernel.psi2(self.Z, self.qX)
psi2 = kernel.psi2(self.Z, self.qX)
return (self.w1*psi0).sum() + (self.w2*psi1).sum() + (self.w3*psi2).sum()
else:
psi2 = kernel.psi2n(self.Z, self.qX)
return (self.w1*psi0).sum() + (self.w2*psi1).sum() + (self.w3n*psi2).sum()
def df(p):
self.qX.param_array[:] = p
self.qX._trigger_params_changed()