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git branchMerge branch 'devel' of github.com:SheffieldML/GPy into devel

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This commit is contained in:
Max Zwiessele 2013-06-04 15:33:53 +01:00
commit ce2edb5fff
10 changed files with 61 additions and 55 deletions
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5
.travis.yml
View file

@ -10,6 +10,11 @@ virtualenv:
before_install:
- sudo apt-get install -qq python-scipy python-pip
- sudo apt-get install -qq python-matplotlib
# Workaround for a permissions issue with Travis virtual machine images
# that breaks Python's multiprocessing:
# https://github.com/travis-ci/travis-cookbooks/issues/155
- sudo rm -rf /dev/shm
- sudo ln -s /run/shm /dev/shm
install:
- pip install --upgrade numpy==1.7.1

2
GPy/core/model.py
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@ -212,7 +212,7 @@ class model(parameterised):
currently_constrained = self.all_constrained_indices()
to_make_positive = []
for s in positive_strings:
for i in self.grep_param_names(s):
for i in self.grep_param_names(".*"+s):
if not (i in currently_constrained):
#to_make_positive.append(re.escape(param_names[i]))
to_make_positive.append(i)

71
GPy/core/parameterised.py
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@ -64,21 +64,21 @@ class parameterised(object):
m['var'] = 2. # > sets all parameters matching 'var' to 2.
m['var'] = <array-like> # > sets parameters matching 'var' to <array-like>
"""
def get(self, name):
def get(self, regexp):
warnings.warn(self._get_set_deprecation, FutureWarning, stacklevel=2)
return self[name]
return self[regexp]
def set(self, name, val):
def set(self, regexp, val):
warnings.warn(self._get_set_deprecation, FutureWarning, stacklevel=2)
self[name] = val
self[regexp] = val
def __getitem__(self, name, return_names=False):
def __getitem__(self, regexp, return_names=False):
"""
Get a model parameter by name. The name is applied as a regular
expression and all parameters that match that regular expression are
returned.
"""
matches = self.grep_param_names(name)
matches = self.grep_param_names(regexp)
if len(matches):
if return_names:
return self._get_params()[matches], np.asarray(self._get_param_names())[matches].tolist()
@ -103,8 +103,8 @@ class parameterised(object):
else:
raise AttributeError, "no parameter matches %s" % name
def tie_params(self, which):
matches = self.grep_param_names(which)
def tie_params(self, regexp):
matches = self.grep_param_names(regexp)
assert matches.size > 0, "need at least something to tie together"
if len(self.tied_indices):
assert not np.any(matches[:, None] == np.hstack(self.tied_indices)), "Some indices are already tied!"
@ -119,28 +119,23 @@ class parameterised(object):
"""Unties all parameters by setting tied_indices to an empty list."""
self.tied_indices = []
def grep_param_names(self, expr):
def grep_param_names(self, regexp):
"""
Arguments
---------
expr -- can be a regular expression object or a string to be turned into regular expression object.
:param regexp: regular expression to select parameter names
:type regexp: re | str | int
:rtype: the indices of self._get_param_names which match the regular expression.
Returns
-------
the indices of self._get_param_names which match the regular expression.
Notes
-----
Other objects are passed through - i.e. integers which weren't meant for grepping
Note:-
Other objects are passed through - i.e. integers which weren't meant for grepping
"""
if type(expr) in [str, np.string_, np.str]:
expr = re.compile(expr)
return np.nonzero([expr.search(name) for name in self._get_param_names()])[0]
elif type(expr) is re._pattern_type:
return np.nonzero([expr.search(name) for name in self._get_param_names()])[0]
if type(regexp) in [str, np.string_, np.str]:
regexp = re.compile(regexp)
return np.nonzero([regexp.match(name) for name in self._get_param_names()])[0]
elif type(regexp) is re._pattern_type:
return np.nonzero([regexp.match(name) for name in self._get_param_names()])[0]
else:
return expr
return regexp
def Nparam_transformed(self):
removed = 0
@ -152,9 +147,9 @@ class parameterised(object):
return len(self._get_params()) - removed
def unconstrain(self, which):
def unconstrain(self, regexp):
"""Unconstrain matching parameters. does not untie parameters"""
matches = self.grep_param_names(which)
matches = self.grep_param_names(regexp)
#tranformed contraints:
for match in matches:
@ -178,17 +173,17 @@ class parameterised(object):
else:
self.fixed_indices, self.fixed_values = [], []
def constrain_negative(self, which):
def constrain_negative(self, regexp):
""" Set negative constraints. """
self.constrain(which, transformations.negative_exponent())
self.constrain(regexp, transformations.negative_exponent())
def constrain_positive(self, which):
def constrain_positive(self, regexp):
""" Set positive constraints. """
self.constrain(which, transformations.logexp())
self.constrain(regexp, transformations.logexp())
def constrain_bounded(self, which,lower, upper):
def constrain_bounded(self, regexp,lower, upper):
""" Set bounded constraints. """
self.constrain(which, transformations.logistic(lower, upper))
self.constrain(regexp, transformations.logistic(lower, upper))
def all_constrained_indices(self):
if len(self.constrained_indices) or len(self.fixed_indices):
@ -196,10 +191,10 @@ class parameterised(object):
else:
return np.empty(shape=(0,))
def constrain(self,which,transform):
def constrain(self,regexp,transform):
assert isinstance(transform,transformations.transformation)
matches = self.grep_param_names(which)
matches = self.grep_param_names(regexp)
overlap = set(matches).intersection(set(self.all_constrained_indices()))
if overlap:
self.unconstrain(np.asarray(list(overlap)))
@ -214,11 +209,11 @@ class parameterised(object):
x[matches] = transform.initialize(x[matches])
self._set_params(x)
def constrain_fixed(self, which, value=None):
def constrain_fixed(self, regexp, value=None):
"""
Arguments
---------
:param which: np.array(dtype=int), or regular expression object or string
:param regexp: np.array(dtype=int), or regular expression object or string
:param value: a float to fix the matched values to. If the value is not specified,
the parameter is fixed to the current value
@ -227,7 +222,7 @@ class parameterised(object):
Fixing a parameter which is tied to another, or constrained in some way will result in an error.
To fix multiple parameters to the same value, simply pass a regular expression which matches both parameter names, or pass both of the indexes
"""
matches = self.grep_param_names(which)
matches = self.grep_param_names(regexp)
assert not np.any(matches[:, None] == self.all_constrained_indices()), "Some indices are already constrained"
self.fixed_indices.append(matches)
if value != None:

10
GPy/likelihoods/Gaussian.py
View file

@ -51,11 +51,15 @@ class Gaussian(likelihood):
return ["noise_variance"]
def _set_params(self, x):
x = float(x)
x = np.float64(x)
if self._variance != x:
self.precision = 1. / x
if x == 0.:
self.precision = None
self.V = None
else:
self.precision = 1. / x
self.V = (self.precision) * self.Y
self.covariance_matrix = np.eye(self.N) * x
self.V = (self.precision) * self.Y
self._variance = x
def predictive_values(self, mu, var, full_cov):

2
GPy/testing/kernel_tests.py
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@ -8,7 +8,7 @@ import GPy
class KernelTests(unittest.TestCase):
def test_kerneltie(self):
K = GPy.kern.rbf(5, ARD=True)
K.tie_params('[01]')
K.tie_params('.*[01]')
K.constrain_fixed('2')
X = np.random.rand(5,5)
Y = np.ones((5,1))

2
GPy/testing/mrd_tests.py
View file

@ -23,7 +23,7 @@ class MRDTests(unittest.TestCase):
Ylist = [np.random.multivariate_normal(np.zeros(N), K, D).T for _ in range(num_m)]
likelihood_list = [GPy.likelihoods.Gaussian(Y) for Y in Ylist]
m = GPy.models.MRD(*likelihood_list, Q=Q, kernel=k, M=M)
m = GPy.models.MRD(likelihood_list, Q=Q, kernels=k, M=M)
m.ensure_default_constraints()
self.assertTrue(m.checkgrad())

12
GPy/testing/unit_tests.py
View file

@ -22,7 +22,7 @@ class GradientTests(unittest.TestCase):
self.X2D = np.random.uniform(-3.,3.,(40,2))
self.Y2D = np.sin(self.X2D[:,0:1]) * np.sin(self.X2D[:,1:2])+np.random.randn(40,1)*0.05
def check_model_with_white(self, kern, model_type='GP_regression', dimension=1, constraint=''):
def check_model_with_white(self, kern, model_type='GP_regression', dimension=1):
#Get the correct gradients
if dimension == 1:
X = self.X1D
@ -37,7 +37,7 @@ class GradientTests(unittest.TestCase):
noise = GPy.kern.white(dimension)
kern = kern + noise
m = model_fit(X, Y, kernel=kern)
m.constrain_positive(constraint)
m.ensure_default_constraints()
m.randomize()
# contrain all parameters to be positive
self.assertTrue(m.checkgrad())
@ -135,12 +135,12 @@ class GradientTests(unittest.TestCase):
def test_sparse_GP_regression_rbf_white_kern_1d(self):
''' Testing the sparse GP regression with rbf kernel with white kernel on 1d data '''
rbf = GPy.kern.rbf(1)
self.check_model_with_white(rbf, model_type='sparse_GP_regression', dimension=1, constraint='(variance|lengthscale|precision)')
self.check_model_with_white(rbf, model_type='sparse_GP_regression', dimension=1)
def test_sparse_GP_regression_rbf_white_kern_2D(self):
''' Testing the sparse GP regression with rbf and white kernel on 2d data '''
rbf = GPy.kern.rbf(2)
self.check_model_with_white(rbf, model_type='sparse_GP_regression', dimension=2, constraint='(variance|lengthscale|precision)')
self.check_model_with_white(rbf, model_type='sparse_GP_regression', dimension=2)
def test_GPLVM_rbf_bias_white_kern_2D(self):
""" Testing GPLVM with rbf + bias and white kernel """
@ -150,7 +150,7 @@ class GradientTests(unittest.TestCase):
K = k.K(X)
Y = np.random.multivariate_normal(np.zeros(N),K,D).T
m = GPy.models.GPLVM(Y, Q, kernel = k)
m.constrain_positive('(rbf|bias|white)')
m.ensure_default_constraints()
self.assertTrue(m.checkgrad())
def test_GPLVM_rbf_linear_white_kern_2D(self):
@ -161,7 +161,7 @@ class GradientTests(unittest.TestCase):
K = k.K(X)
Y = np.random.multivariate_normal(np.zeros(N),K,D).T
m = GPy.models.GPLVM(Y, Q, init = 'PCA', kernel = k)
m.constrain_positive('(linear|bias|white)')
m.ensure_default_constraints()
self.assertTrue(m.checkgrad())
def test_GP_EP_probit(self):

2
GPy/util/plot.py
View file

@ -1,4 +1,4 @@
# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
# #Copyright (c) 2012, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)

4
MANIFEST.in Normal file
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@ -0,0 +1,4 @@
include *.txt
recursive-include docs *.txt
include *.md
recursive-include docs *.md

6
setup.py
View file

@ -23,14 +23,12 @@ setup(name = 'GPy',
package_data = {'GPy': ['GPy/examples']},
py_modules = ['GPy.__init__'],
long_description=read('README.md'),
#ext_modules = [Extension(name = 'GPy.kern.lfmUpsilonf2py',
# sources = ['GPy/kern/src/lfmUpsilonf2py.f90'])],
install_requires=['sympy', 'numpy>=1.6', 'scipy>=0.9','matplotlib>=1.1', 'nose'],
extras_require = {
'docs':['Sphinx', 'ipython'],
},
#setup_requires=['sphinx'],
#cmdclass = {'build_sphinx': BuildDoc},
classifiers=[
"License :: OSI Approved :: BSD License"],
#ext_modules = [Extension(name = 'GPy.kern.lfmUpsilonf2py',
# sources = ['GPy/kern/src/lfmUpsilonf2py.f90'])],
)