def test_kernel_decompose_1d():
'''Checks that a kernel decomposes into a sum properly'''
sk = fk.repr_string_to_kernel('ScoredKernel(k_opt=ProductKernel([ MaskKernel(ndim=1, active_dimension=0, base_kernel=RQKernel(lengthscale=4.853529, output_variance=-0.648382, alpha=0.457387)), SumKernel([ ProductKernel([ MaskKernel(ndim=1, active_dimension=0, base_kernel=SqExpPeriodicKernel(lengthscale=1.395371, period=-3.990523, output_variance=0.565365)), MaskKernel(ndim=1, active_dimension=0, base_kernel=LinKernel(offset=0.000420, lengthscale=-0.120045)) ]), ProductKernel([ MaskKernel(ndim=1, active_dimension=0, base_kernel=LinKernel(offset=0.802417, lengthscale=3.350816)), SumKernel([ MaskKernel(ndim=1, active_dimension=0, base_kernel=SqExpPeriodicKernel(lengthscale=-3.899540, period=0.087011, output_variance=2.430187)), MaskKernel(ndim=1, active_dimension=0, base_kernel=RQKernel(lengthscale=3.865315, output_variance=4.028649, alpha=-5.060996)), ProductKernel([ MaskKernel(ndim=1, active_dimension=0, base_kernel=SqExpPeriodicKernel(lengthscale=3.251723, period=1.540000, output_variance=-2.497487)), MaskKernel(ndim=1, active_dimension=0, base_kernel=LinKernel(offset=-1.424416, lengthscale=-1.732677)) ]) ]) ]) ]) ]), nll=558.339977, laplace_nle=-266.580399, bic_nle=1216.076221, noise=[ 1.66059002])')
k = fk.strip_masks(sk.k_opt)
correct_answer = ['RQ \\times Per \\times Lin', 'RQ \\times Lin \\times Per', 'RQ \\times Lin \\times RQ', 'RQ \\times Lin \\times Per \\times Lin']
kd = fk.break_kernel_into_summands(k)
assert( [k.latex_print() for k in kd] == correct_answer )
def test_kernel_decompose_1d():
'''Checks that a kernel decomposes into a sum properly'''
sk = fk.repr_string_to_kernel(
'ScoredKernel(k_opt=ProductKernel([ MaskKernel(ndim=1, active_dimension=0, base_kernel=RQKernel(lengthscale=4.853529, output_variance=-0.648382, alpha=0.457387)), SumKernel([ ProductKernel([ MaskKernel(ndim=1, active_dimension=0, base_kernel=SqExpPeriodicKernel(lengthscale=1.395371, period=-3.990523, output_variance=0.565365)), MaskKernel(ndim=1, active_dimension=0, base_kernel=LinKernel(offset=0.000420, lengthscale=-0.120045)) ]), ProductKernel([ MaskKernel(ndim=1, active_dimension=0, base_kernel=LinKernel(offset=0.802417, lengthscale=3.350816)), SumKernel([ MaskKernel(ndim=1, active_dimension=0, base_kernel=SqExpPeriodicKernel(lengthscale=-3.899540, period=0.087011, output_variance=2.430187)), MaskKernel(ndim=1, active_dimension=0, base_kernel=RQKernel(lengthscale=3.865315, output_variance=4.028649, alpha=-5.060996)), ProductKernel([ MaskKernel(ndim=1, active_dimension=0, base_kernel=SqExpPeriodicKernel(lengthscale=3.251723, period=1.540000, output_variance=-2.497487)), MaskKernel(ndim=1, active_dimension=0, base_kernel=LinKernel(offset=-1.424416, lengthscale=-1.732677)) ]) ]) ]) ]) ]), nll=558.339977, laplace_nle=-266.580399, bic_nle=1216.076221, noise=[ 1.66059002])'
)
k = fk.strip_masks(sk.k_opt)
correct_answer = [
'RQ \\times Per \\times Lin', 'RQ \\times Lin \\times Per',
'RQ \\times Lin \\times RQ', 'RQ \\times Lin \\times Per \\times Lin'
]
kd = fk.break_kernel_into_summands(k)
assert ([k.latex_print() for k in kd] == correct_answer)
def plot_decomposition(kernel, X, y, figname, noise=None, X_mean=0, X_scale=1, y_mean=0, y_scale=1):
matlab_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), "matlab"))
figname = os.path.abspath(os.path.join(os.path.dirname(__file__), figname))
print "Plotting to: %s" % figname
kernel_components = fk.break_kernel_into_summands(kernel)
latex_names = [k.latex_print().strip() for k in kernel_components]
kernel_params_list = ",".join("[ %s ]" % " ".join(str(p) for p in k.param_vector()) for k in kernel_components)
if X.ndim == 1:
X = X[:, nax]
if y.ndim == 1:
y = y[:, nax]
if noise is None:
noise = np.log(np.var(y) / 10) # Just a heuristic.
data = {"X": X, "y": y}
(fd1, temp_data_file) = tempfile.mkstemp(suffix=".mat")
scipy.io.savemat(temp_data_file, data)
code = MATLAB_PLOT_DECOMP_CALLER_CODE % {
"datafile": temp_data_file,
"gpml_path": config.GPML_PATH,
"matlab_script_path": matlab_dir,
"kernel_family": kernel.gpml_kernel_expression(),
"kernel_params": "[ %s ]" % " ".join(str(p) for p in kernel.param_vector()),
"kernel_family_list": "{ %s }" % ",".join(str(k.gpml_kernel_expression()) for k in kernel_components),
"kernel_params_list": "{ %s }" % kernel_params_list,
"noise": str(noise),
"latex_names": "{ ' %s ' }" % "','".join(latex_names),
"full_kernel_name": "{ '%s' }" % kernel.latex_print().strip(),
"figname": figname,
"X_mean": X_mean,
"X_scale": X_scale,
"y_mean": y_mean,
"y_scale": y_scale,
}
run_matlab_code(code, verbose=True, jvm=True)
os.close(fd1)
# os.remove(temp_data_file)
return code
def plot_decomposition(kernel,
X,
y,
figname,
noise=None,
X_mean=0,
X_scale=1,
y_mean=0,
y_scale=1):
matlab_dir = os.path.abspath(
os.path.join(os.path.dirname(__file__), 'matlab'))
figname = os.path.abspath(os.path.join(os.path.dirname(__file__), figname))
print 'Plotting to: %s' % figname
kernel_components = fk.break_kernel_into_summands(kernel)
latex_names = [k.latex_print().strip() for k in kernel_components]
kernel_params_list = ','.join('[ %s ]' %
' '.join(str(p) for p in k.param_vector())
for k in kernel_components)
if X.ndim == 1: X = X[:, nax]
if y.ndim == 1: y = y[:, nax]
if noise is None: noise = np.log(np.var(y) / 10) # Just a heuristic.
data = {'X': X, 'y': y}
(fd1, temp_data_file) = tempfile.mkstemp(dir=config.LOCAL_TEMP_PATH,
suffix='.mat')
scipy.io.savemat(temp_data_file, data)
code = MATLAB_PLOT_DECOMP_CALLER_CODE % {
'datafile':
temp_data_file,
'gpml_path':
config.GPML_PATH,
'matlab_script_path':
matlab_dir,
'kernel_family':
kernel.gpml_kernel_expression(),
'kernel_params':
'[ %s ]' % ' '.join(str(p) for p in kernel.param_vector()),
'kernel_family_list':
'{ %s }' %
','.join(str(k.gpml_kernel_expression()) for k in kernel_components),
'kernel_params_list':
'{ %s }' % kernel_params_list,
'noise':
str(noise),
'latex_names':
"{ ' %s ' }" % "','".join(latex_names),
'full_kernel_name':
"{ '%s' }" % kernel.latex_print().strip(),
'figname':
figname,
'X_mean':
X_mean,
'X_scale':
X_scale,
'y_mean':
y_mean,
'y_scale':
y_scale
}
run_matlab_code(code, verbose=True, jvm=True)
os.close(fd1)
#os.remove(temp_data_file)
return code
