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Python 3 fixes

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This commit is contained in:
Mike Croucher 2015-06-23 01:26:52 -07:00
parent e58cc51c47
commit e7d1277e62
17 changed files with 52 additions and 48 deletions
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2
GPy/core/parameterization/index_operations.py
View file

@ -5,7 +5,7 @@ import numpy
from numpy.lib.function_base import vectorize
from .lists_and_dicts import IntArrayDict
from functools import reduce
from transformations import Transformation
from .transformations import Transformation
def extract_properties_to_index(index, props):
prop_index = dict()

4
GPy/core/parameterization/parameterized.py
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@ -6,10 +6,10 @@ import numpy; np = numpy
import itertools
from re import compile, _pattern_type
from .param import ParamConcatenation
from parameter_core import HierarchyError, Parameterizable, adjust_name_for_printing
from .parameter_core import HierarchyError, Parameterizable, adjust_name_for_printing
import logging
from index_operations import ParameterIndexOperationsView
from .index_operations import ParameterIndexOperationsView
logger = logging.getLogger("parameters changed meta")
class ParametersChangedMeta(type):

38
GPy/core/parameterization/priors.py
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@ -758,12 +758,12 @@ class DGPLVM_Lamda(Prior, Parameterized):
self.sigma2 = sigma2
# self.x = x
self.lbl = lbl
self.lamda = lamda
self.lamda = lamda
self.classnum = lbl.shape[1]
self.datanum = lbl.shape[0]
self.x_shape = x_shape
self.dim = x_shape[1]
self.lamda = Param('lamda', np.diag(lamda))
self.lamda = Param('lamda', np.diag(lamda))
self.link_parameter(self.lamda)
def get_class_label(self, y):
@ -789,7 +789,7 @@ class DGPLVM_Lamda(Prior, Parameterized):
M_i = np.zeros((self.classnum, self.dim))
for i in cls:
# Mean of each class
class_i = cls[i]
class_i = cls[i]
M_i[i] = np.mean(class_i, axis=0)
return M_i
@ -899,8 +899,8 @@ class DGPLVM_Lamda(Prior, Parameterized):
#!!!!!!!!!!!!!!!!!!!!!!!!!!!
#self.lamda.values[:] = self.lamda.values/self.lamda.values.sum()
xprime = x.dot(np.diagflat(self.lamda))
x = xprime
xprime = x.dot(np.diagflat(self.lamda))
x = xprime
# print x
cls = self.compute_cls(x)
M_0 = np.mean(x, axis=0)
@ -910,14 +910,14 @@ class DGPLVM_Lamda(Prior, Parameterized):
# Sb_inv_N = np.linalg.inv(Sb + np.eye(Sb.shape[0]) * (np.diag(Sb).min() * 0.1))
#Sb_inv_N = np.linalg.inv(Sb+np.eye(Sb.shape[0])*0.1)
#Sb_inv_N = pdinv(Sb+ np.eye(Sb.shape[0]) * (np.diag(Sb).min() * 0.1))[0]
Sb_inv_N = pdinv(Sb + np.eye(Sb.shape[0])*0.1)[0]
Sb_inv_N = pdinv(Sb + np.eye(Sb.shape[0])*0.1)[0]
return (-1 / self.sigma2) * np.trace(Sb_inv_N.dot(Sw))
# This function calculates derivative of the log of prior function
def lnpdf_grad(self, x):
x = x.reshape(self.x_shape)
xprime = x.dot(np.diagflat(self.lamda))
x = xprime
xprime = x.dot(np.diagflat(self.lamda))
x = xprime
# print x
cls = self.compute_cls(x)
M_0 = np.mean(x, axis=0)
@ -934,7 +934,7 @@ class DGPLVM_Lamda(Prior, Parameterized):
# Sb_inv_N = np.linalg.inv(Sb + np.eye(Sb.shape[0]) * (np.diag(Sb).min() * 0.1))
#Sb_inv_N = np.linalg.inv(Sb+np.eye(Sb.shape[0])*0.1)
#Sb_inv_N = pdinv(Sb+ np.eye(Sb.shape[0]) * (np.diag(Sb).min() * 0.1))[0]
Sb_inv_N = pdinv(Sb + np.eye(Sb.shape[0])*0.1)[0]
Sb_inv_N = pdinv(Sb + np.eye(Sb.shape[0])*0.1)[0]
Sb_inv_N_trans = np.transpose(Sb_inv_N)
Sb_inv_N_trans_minus = -1 * Sb_inv_N_trans
Sw_trans = np.transpose(Sw)
@ -951,14 +951,14 @@ class DGPLVM_Lamda(Prior, Parameterized):
# Because of the GPy we need to transpose our matrix so that it gets the same shape as out matrix (denominator layout!!!)
DPxprim_Dx = DPxprim_Dx.T
DPxprim_Dlamda = DPx_Dx.dot(x)
DPxprim_Dlamda = DPx_Dx.dot(x)
# Because of the GPy we need to transpose our matrix so that it gets the same shape as out matrix (denominator layout!!!)
DPxprim_Dlamda = DPxprim_Dlamda.T
DPxprim_Dlamda = DPxprim_Dlamda.T
self.lamda.gradient = np.diag(DPxprim_Dlamda)
self.lamda.gradient = np.diag(DPxprim_Dlamda)
# print DPxprim_Dx
return DPxprim_Dx
return DPxprim_Dx
# def frb(self, x):
@ -1139,8 +1139,8 @@ class DGPLVM_T(Prior):
# This function calculates log of our prior
def lnpdf(self, x):
x = x.reshape(self.x_shape)
xprim = x.dot(self.vec)
x = xprim
xprim = x.dot(self.vec)
x = xprim
# print x
cls = self.compute_cls(x)
M_0 = np.mean(x, axis=0)
@ -1156,11 +1156,11 @@ class DGPLVM_T(Prior):
# This function calculates derivative of the log of prior function
def lnpdf_grad(self, x):
x = x.reshape(self.x_shape)
xprim = x.dot(self.vec)
x = xprim
x = x.reshape(self.x_shape)
xprim = x.dot(self.vec)
x = xprim
# print x
cls = self.compute_cls(x)
cls = self.compute_cls(x)
M_0 = np.mean(x, axis=0)
M_i = self.compute_Mi(cls)
Sb = self.compute_Sb(cls, M_i, M_0)

4
GPy/core/parameterization/transformations.py
View file

@ -35,12 +35,12 @@ class Transformation(object):
"""
compute the log of the jacobian of f, evaluated at f(x)= model_param
"""
raise NotImplementedError
raise NotImplementedError
def log_jacobian_grad(self, model_param):
"""
compute the drivative of the log of the jacobian of f, evaluated at f(x)= model_param
"""
raise NotImplementedError
raise NotImplementedError
def gradfactor(self, model_param, dL_dmodel_param):
""" df(opt_param)_dopt_param evaluated at self.f(opt_param)=model_param, times the gradient dL_dmodel_param,

2
GPy/inference/optimization/optimization.py
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@ -142,7 +142,7 @@ class opt_lbfgsb(Optimizer):
#a more helpful error message is available in opt_result in the Error case
if opt_result[2]['warnflag']==2:
self.status = 'Error' + opt_result[2]['task']
self.status = 'Error' + str(opt_result[2]['task'])
class opt_simplex(Optimizer):
def __init__(self, *args, **kwargs):

2
GPy/kern/__init__.py
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@ -19,5 +19,5 @@ from ._src.splitKern import SplitKern,DEtime
from ._src.splitKern import DEtime as DiffGenomeKern
from _src.basis_funcs import LinearSlopeBasisFuncKernel, BasisFuncKernel, ChangePointBasisFuncKernel, DomainKernel
from ._src.basis_funcs import LinearSlopeBasisFuncKernel, BasisFuncKernel, ChangePointBasisFuncKernel, DomainKernel

2
GPy/kern/_src/coregionalize.py
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@ -6,7 +6,7 @@ import numpy as np
from ...core.parameterization import Param
from ...core.parameterization.transformations import Logexp
from ...util.config import config # for assesing whether to use cython
import coregionalize_cython
from . import coregionalize_cython
class Coregionalize(Kern):
"""

2
GPy/kern/_src/independent_outputs.py
View file

@ -105,7 +105,7 @@ class IndependentOutputs(CombinationKernel):
if X2 is None:
# TODO: make use of index_to_slices
# FIXME: Broken as X is already sliced out
print "Warning, gradients_X may not be working, I believe X has already been sliced out by the slicer!"
print("Warning, gradients_X may not be working, I believe X has already been sliced out by the slicer!")
values = np.unique(X[:,self.index_dim])
slices = [X[:,self.index_dim]==i for i in values]
[target.__setitem__(s, kern.gradients_X(dL_dK[s,s],X[s],None))

2
GPy/kern/_src/stationary.py
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@ -13,7 +13,7 @@ from ...util.config import config # for assesing whether to use cython
from ...util.caching import Cache_this
try:
import stationary_cython
from . import stationary_cython
except ImportError:
print('warning in sationary: failed to import cython module: falling back to numpy')
config.set('cython', 'working', 'false')

4
GPy/models/dpgplvm.py
View file

@ -3,7 +3,7 @@
import numpy as np
from .. import kern
from bayesian_gplvm import BayesianGPLVM
from .bayesian_gplvm import BayesianGPLVM
from ..core.parameterization.variational import NormalPosterior, NormalPrior
class DPBayesianGPLVM(BayesianGPLVM):
@ -15,5 +15,5 @@ class DPBayesianGPLVM(BayesianGPLVM):
name='bayesian gplvm', mpi_comm=None, normalizer=None,
missing_data=False, stochastic=False, batchsize=1):
super(DPBayesianGPLVM,self).__init__(Y=Y, input_dim=input_dim, X=X, X_variance=X_variance, init=init, num_inducing=num_inducing, Z=Z, kernel=kernel, inference_method=inference_method, likelihood=likelihood, mpi_comm=mpi_comm, normalizer=normalizer, missing_data=missing_data, stochastic=stochastic, batchsize=batchsize, name='dp bayesian gplvm')
self.X.mean.set_prior(X_prior)
self.X.mean.set_prior(X_prior)
self.link_parameter(X_prior)

2
GPy/models/gp_regression.py
View file

@ -31,7 +31,7 @@ class GPRegression(GP):
if kernel is None:
kernel = kern.RBF(X.shape[1])
likelihood = likelihoods.Gaussian(variance=noise_var)
likelihood = likelihoods.Gaussian(variance=noise_var)
super(GPRegression, self).__init__(X, Y, kernel, likelihood, name='GP regression', Y_metadata=Y_metadata, normalizer=normalizer)

12
GPy/models/gradient_checker.py
View file

@ -228,14 +228,14 @@ class HessianChecker(GradientChecker):
if verbose:
if block_indices:
print "\nBlock {}".format(block_indices)
print("\nBlock {}".format(block_indices))
else:
print "\nAll blocks"
print("\nAll blocks")
header = ['Checked', 'Max-Ratio', 'Min-Ratio', 'Min-Difference', 'Max-Difference']
header_string = map(lambda x: ' | '.join(header), [header])
separator = '-' * len(header_string[0])
print '\n'.join([header_string[0], separator])
print('\n'.join([header_string[0], separator]))
min_r = '%.6f' % float(numpy.min(ratio))
max_r = '%.6f' % float(numpy.max(ratio))
max_d = '%.6f' % float(numpy.max(difference))
@ -248,7 +248,7 @@ class HessianChecker(GradientChecker):
checked = "\033[91m False \033[0m"
grad_string = "{} | {} | {} | {} | {} ".format(checked, cols[0], cols[1], cols[2], cols[3])
print grad_string
print(grad_string)
if plot:
import pylab as pb
@ -348,7 +348,7 @@ class SkewChecker(HessianChecker):
numeric_hess_partial = nd.Jacobian(self._df, vectorized=True)
numeric_hess = numeric_hess_partial(x)
print "Done making numerical hessian"
print("Done making numerical hessian")
if analytic_hess.dtype is np.dtype('object'):
#Blockify numeric_hess aswell
blocksizes, pagesizes = get_block_shapes_3d(analytic_hess)
@ -365,7 +365,7 @@ class SkewChecker(HessianChecker):
#Unless super_plot is set, just plot the first one
p = True if (plot and block_ind == numeric_hess.shape[2]-1) or super_plot else False
if verbose:
print "Checking derivative of hessian wrt parameter number {}".format(block_ind)
print("Checking derivative of hessian wrt parameter number {}".format(block_ind))
check_passed[block_ind] = self.checkgrad_block(analytic_hess[:,:,block_ind], numeric_hess[:,:,block_ind], verbose=verbose, step=step, tolerance=tolerance, block_indices=block_indices, plot=p)
current_index += current_size

2
GPy/testing/link_function_tests.py
View file

@ -49,7 +49,7 @@ class LinkFunctionTests(np.testing.TestCase):
self.assertTrue(grad3.checkgrad(verbose=True))
if test_lim:
print "Testing limits"
print("Testing limits")
#Remove some otherwise we are too close to the limit for gradcheck to work effectively
lim_of_inf = lim_of_inf - 1e-4
grad = GradientChecker(link_func.transf, link_func.dtransf_df, x0=lim_of_inf)

4
GPy/util/block_matrices.py
View file

@ -100,10 +100,10 @@ def block_dot(A, B, diagonal=False):
Dshape = D.shape
if diagonal and (len(Cshape) == 1 or len(Dshape) == 1\
or C.shape[0] != C.shape[1] or D.shape[0] != D.shape[1]):
print "Broadcasting, C: {} D:{}".format(C.shape, D.shape)
print("Broadcasting, C: {} D:{}".format(C.shape, D.shape))
return C*D
else:
print "Dotting, C: {} C:{}".format(C.shape, D.shape)
print("Dotting, C: {} C:{}".format(C.shape, D.shape))
return np.dot(C,D)
dot = np.vectorize(f, otypes = [np.object])
return dot(A,B)

10
GPy/util/choleskies.py
View file

@ -5,7 +5,7 @@ import numpy as np
from . import linalg
from .config import config
import choleskies_cython
from . import choleskies_cython
def safe_root(N):
i = np.sqrt(N)
@ -59,12 +59,12 @@ def _backprop_gradient_pure(dL, L):
"""
dL_dK = np.tril(dL).copy()
N = L.shape[0]
for k in xrange(N - 1, -1, -1):
for j in xrange(k + 1, N):
for i in xrange(j, N):
for k in range(N - 1, -1, -1):
for j in range(k + 1, N):
for i in range(j, N):
dL_dK[i, k] -= dL_dK[i, j] * L[j, k]
dL_dK[j, k] -= dL_dK[i, j] * L[i, k]
for j in xrange(k + 1, N):
for j in range(k + 1, N):
dL_dK[j, k] /= L[k, k]
dL_dK[k, k] -= L[j, k] * dL_dK[j, k]
dL_dK[k, k] /= (2 * L[k, k])

2
GPy/util/linalg.py
View file

@ -15,7 +15,7 @@ import warnings
import os
from .config import config
import logging
import linalg_cython
from . import linalg_cython
_scipyversion = np.float64((scipy.__version__).split('.')[:2])

6
README.md
View file

@ -13,7 +13,6 @@ Continuous integration status: ![CI status](https://travis-ci.org/SheffieldML/GP
### Python 3 Compatibility
Work is underway to make GPy run on Python 3.
* Python 2.x compatibility is currently broken in this fork
* All tests in the testsuite now run on Python3.
To see this for yourself, in Ubuntu 14.04, you can do
@ -21,12 +20,17 @@ To see this for yourself, in Ubuntu 14.04, you can do
git clone https://github.com/mikecroucher/GPy.git
cd GPy
git checkout devel
python3 setup.py build_ext --inplace
nosetests3 GPy/testing
nosetests3 is Ubuntu's way of reffering to the Python 3 version of nosetests. You install it with
sudo apt-get install python3-nose
The command `python3 setup.py build_ext --inplace` builds the Cython extensions. IF it doesn't work, you may need to install this:
sudo apt-get install python3-dev
* Test coverage is less than 100% so it is expected that there is still more work to be done. We need more tests and examples to try out.
* All weave functions not covered by the test suite are *simply commented out*. Can add equivalents later as test functions become available
* A set of benchmarks would be useful!