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Convert print to function for Python 3 compatibility. This breaks compatibility for versions of Python < 2.6

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This commit is contained in:
Mike Croucher 2015-02-26 08:11:11 +00:00
parent 2ca24a88f5
commit 1521b3e260
10 changed files with 37 additions and 37 deletions
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4
GPy/core/gp.py
View file

@ -82,7 +82,7 @@ class GP(Model):
inference_method = exact_gaussian_inference.ExactGaussianInference() inference_method = exact_gaussian_inference.ExactGaussianInference()
else: else:
inference_method = expectation_propagation.EP() inference_method = expectation_propagation.EP()
print "defaulting to ", inference_method, "for latent function inference" print("defaulting to ", inference_method, "for latent function inference")
self.inference_method = inference_method self.inference_method = inference_method
logger.info("adding kernel and likelihood as parameters") logger.info("adding kernel and likelihood as parameters")
@ -441,7 +441,7 @@ class GP(Model):
try: try:
super(GP, self).optimize(optimizer, start, **kwargs) super(GP, self).optimize(optimizer, start, **kwargs)
except KeyboardInterrupt: except KeyboardInterrupt:
print "KeyboardInterrupt caught, calling on_optimization_end() to round things up" print("KeyboardInterrupt caught, calling on_optimization_end() to round things up")
self.inference_method.on_optimization_end() self.inference_method.on_optimization_end()
raise raise

18
GPy/core/model.py
View file

@ -82,7 +82,7 @@ class Model(Parameterized):
pool.close() # signal that no more data coming in pool.close() # signal that no more data coming in
pool.join() # wait for all the tasks to complete pool.join() # wait for all the tasks to complete
except KeyboardInterrupt: except KeyboardInterrupt:
print "Ctrl+c received, terminating and joining pool." print("Ctrl+c received, terminating and joining pool.")
pool.terminate() pool.terminate()
pool.join() pool.join()
@ -95,10 +95,10 @@ class Model(Parameterized):
self.optimization_runs.append(jobs[i].get()) self.optimization_runs.append(jobs[i].get())
if verbose: if verbose:
print("Optimization restart {0}/{1}, f = {2}".format(i + 1, num_restarts, self.optimization_runs[-1].f_opt)) print(("Optimization restart {0}/{1}, f = {2}".format(i + 1, num_restarts, self.optimization_runs[-1].f_opt)))
except Exception as e: except Exception as e:
if robust: if robust:
print("Warning - optimization restart {0}/{1} failed".format(i + 1, num_restarts)) print(("Warning - optimization restart {0}/{1} failed".format(i + 1, num_restarts)))
else: else:
raise e raise e
@ -237,10 +237,10 @@ class Model(Parameterized):
""" """
if self.is_fixed or self.size == 0: if self.is_fixed or self.size == 0:
print 'nothing to optimize' print('nothing to optimize')
if not self.update_model(): if not self.update_model():
print "updates were off, setting updates on again" print("updates were off, setting updates on again")
self.update_model(True) self.update_model(True)
if start == None: if start == None:
@ -305,7 +305,7 @@ class Model(Parameterized):
transformed_index = (indices - (~self._fixes_).cumsum())[transformed_index[which[0]]] transformed_index = (indices - (~self._fixes_).cumsum())[transformed_index[which[0]]]
if transformed_index.size == 0: if transformed_index.size == 0:
print "No free parameters to check" print("No free parameters to check")
return return
# just check the global ratio # just check the global ratio
@ -342,7 +342,7 @@ class Model(Parameterized):
header_string = ["{h:^{col}}".format(h=header[i], col=cols[i]) for i in range(len(cols))] header_string = ["{h:^{col}}".format(h=header[i], col=cols[i]) for i in range(len(cols))]
header_string = map(lambda x: '|'.join(x), [header_string]) header_string = map(lambda x: '|'.join(x), [header_string])
separator = '-' * len(header_string[0]) separator = '-' * len(header_string[0])
print '\n'.join([header_string[0], separator]) print('\n'.join([header_string[0], separator]))
if target_param is None: if target_param is None:
param_index = range(len(x)) param_index = range(len(x))
transformed_index = param_index transformed_index = param_index
@ -358,7 +358,7 @@ class Model(Parameterized):
transformed_index = param_index transformed_index = param_index
if param_index.size == 0: if param_index.size == 0:
print "No free parameters to check" print("No free parameters to check")
return return
gradient = self._grads(x).copy() gradient = self._grads(x).copy()
@ -392,7 +392,7 @@ class Model(Parameterized):
ng = '%.6f' % float(numerical_gradient) ng = '%.6f' % float(numerical_gradient)
df = '%1.e' % float(df_ratio) df = '%1.e' % float(df_ratio)
grad_string = "{0:<{c0}}|{1:^{c1}}|{2:^{c2}}|{3:^{c3}}|{4:^{c4}}|{5:^{c5}}".format(formatted_name, r, d, g, ng, df, c0=cols[0] + 9, c1=cols[1], c2=cols[2], c3=cols[3], c4=cols[4], c5=cols[5]) grad_string = "{0:<{c0}}|{1:^{c1}}|{2:^{c2}}|{3:^{c3}}|{4:^{c4}}|{5:^{c5}}".format(formatted_name, r, d, g, ng, df, c0=cols[0] + 9, c1=cols[1], c2=cols[2], c3=cols[3], c4=cols[4], c5=cols[5])
print grad_string print(grad_string)
self.optimizer_array = x self.optimizer_array = x
return ret return ret

2
GPy/core/parameterization/parameter_core.py
View file

@ -558,7 +558,7 @@ class Indexable(Nameable, Updateable):
""" """
if warning and reconstrained.size > 0: if warning and reconstrained.size > 0:
# TODO: figure out which parameters have changed and only print those # TODO: figure out which parameters have changed and only print those
print "WARNING: reconstraining parameters {}".format(self.hierarchy_name() or self.name) print("WARNING: reconstraining parameters {}".format(self.hierarchy_name() or self.name))
index = self._raveled_index() index = self._raveled_index()
which.add(what, index) which.add(what, index)
return index return index

2
GPy/core/parameterization/parameterized.py
View file

@ -325,7 +325,7 @@ class Parameterized(Parameterizable):
self._notify_parent_change() self._notify_parent_change()
self.parameters_changed() self.parameters_changed()
except Exception as e: except Exception as e:
print "WARNING: caught exception {!s}, trying to continue".format(e) print("WARNING: caught exception {!s}, trying to continue".format(e))
def copy(self, memo=None): def copy(self, memo=None):
if memo is None: if memo is None:

8
GPy/core/parameterization/ties_and_remappings.py
View file

@ -98,7 +98,7 @@ class Tie(Parameterized):
if np.all(self.label_buf[idx]==0): if np.all(self.label_buf[idx]==0):
# None of p has been tied before. # None of p has been tied before.
tie_idx = self._expandTieParam(1) tie_idx = self._expandTieParam(1)
print tie_idx print(tie_idx)
tie_id = self.label_buf.max()+1 tie_id = self.label_buf.max()+1
self.label_buf[tie_idx] = tie_id self.label_buf[tie_idx] = tie_id
else: else:
@ -189,14 +189,14 @@ class Tie(Parameterized):
b0 = self.label_buf==self.label_buf[self.buf_idx[i]] b0 = self.label_buf==self.label_buf[self.buf_idx[i]]
b = self._highest_parent_.param_array[b0]!=self.tied_param[i] b = self._highest_parent_.param_array[b0]!=self.tied_param[i]
if b.sum()==0: if b.sum()==0:
print 'XXX' print('XXX')
continue continue
elif b.sum()==1: elif b.sum()==1:
print '!!!' print('!!!')
val = self._highest_parent_.param_array[b0][b][0] val = self._highest_parent_.param_array[b0][b][0]
self._highest_parent_.param_array[b0] = val self._highest_parent_.param_array[b0] = val
else: else:
print '@@@' print('@@@')
self._highest_parent_.param_array[b0] = self.tied_param[i] self._highest_parent_.param_array[b0] = self.tied_param[i]
changed = True changed = True
return changed return changed

16
GPy/core/parameterization/transformations.py
View file

@ -72,7 +72,7 @@ class Logexp(Transformation):
return np.einsum('i,i->i', df, np.where(f>_lim_val, 1., 1. - np.exp(-f))) return np.einsum('i,i->i', df, np.where(f>_lim_val, 1., 1. - np.exp(-f)))
def initialize(self, f): def initialize(self, f):
if np.any(f < 0.): if np.any(f < 0.):
print "Warning: changing parameters to satisfy constraints" print("Warning: changing parameters to satisfy constraints")
return np.abs(f) return np.abs(f)
def __str__(self): def __str__(self):
return '+ve' return '+ve'
@ -130,7 +130,7 @@ class NormalTheta(Transformation):
def initialize(self, f): def initialize(self, f):
if np.any(f[self.var_indices] < 0.): if np.any(f[self.var_indices] < 0.):
print "Warning: changing parameters to satisfy constraints" print("Warning: changing parameters to satisfy constraints")
f[self.var_indices] = np.abs(f[self.var_indices]) f[self.var_indices] = np.abs(f[self.var_indices])
return f return f
@ -177,7 +177,7 @@ class NormalNaturalAntti(NormalTheta):
def initialize(self, f): def initialize(self, f):
if np.any(f[self.var_indices] < 0.): if np.any(f[self.var_indices] < 0.):
print "Warning: changing parameters to satisfy constraints" print("Warning: changing parameters to satisfy constraints")
f[self.var_indices] = np.abs(f[self.var_indices]) f[self.var_indices] = np.abs(f[self.var_indices])
return f return f
@ -220,7 +220,7 @@ class NormalEta(Transformation):
def initialize(self, f): def initialize(self, f):
if np.any(f[self.var_indices] < 0.): if np.any(f[self.var_indices] < 0.):
print "Warning: changing parameters to satisfy constraints" print("Warning: changing parameters to satisfy constraints")
f[self.var_indices] = np.abs(f[self.var_indices]) f[self.var_indices] = np.abs(f[self.var_indices])
return f return f
@ -360,7 +360,7 @@ class LogexpNeg(Transformation):
return np.einsum('i,i->i', df, np.where(f>_lim_val, -1, -1 + np.exp(-f))) return np.einsum('i,i->i', df, np.where(f>_lim_val, -1, -1 + np.exp(-f)))
def initialize(self, f): def initialize(self, f):
if np.any(f < 0.): if np.any(f < 0.):
print "Warning: changing parameters to satisfy constraints" print("Warning: changing parameters to satisfy constraints")
return np.abs(f) return np.abs(f)
def __str__(self): def __str__(self):
return '+ve' return '+ve'
@ -412,7 +412,7 @@ class LogexpClipped(Logexp):
return np.einsum('i,i->i', df, gf) # np.where(f < self.lower, 0, gf) return np.einsum('i,i->i', df, gf) # np.where(f < self.lower, 0, gf)
def initialize(self, f): def initialize(self, f):
if np.any(f < 0.): if np.any(f < 0.):
print "Warning: changing parameters to satisfy constraints" print("Warning: changing parameters to satisfy constraints")
return np.abs(f) return np.abs(f)
def __str__(self): def __str__(self):
return '+ve_c' return '+ve_c'
@ -428,7 +428,7 @@ class Exponent(Transformation):
return np.einsum('i,i->i', df, f) return np.einsum('i,i->i', df, f)
def initialize(self, f): def initialize(self, f):
if np.any(f < 0.): if np.any(f < 0.):
print "Warning: changing parameters to satisfy constraints" print("Warning: changing parameters to satisfy constraints")
return np.abs(f) return np.abs(f)
def __str__(self): def __str__(self):
return '+ve' return '+ve'
@ -486,7 +486,7 @@ class Logistic(Transformation):
return np.einsum('i,i->i', df, (f - self.lower) * (self.upper - f) / self.difference) return np.einsum('i,i->i', df, (f - self.lower) * (self.upper - f) / self.difference)
def initialize(self, f): def initialize(self, f):
if np.any(np.logical_or(f < self.lower, f > self.upper)): if np.any(np.logical_or(f < self.lower, f > self.upper)):
print "Warning: changing parameters to satisfy constraints" print("Warning: changing parameters to satisfy constraints")
#return np.where(np.logical_or(f < self.lower, f > self.upper), self.f(f * 0.), f) #return np.where(np.logical_or(f < self.lower, f > self.upper), self.f(f * 0.), f)
#FIXME: Max, zeros_like right? #FIXME: Max, zeros_like right?
return np.where(np.logical_or(f < self.lower, f > self.upper), self.f(np.zeros_like(f)), f) return np.where(np.logical_or(f < self.lower, f > self.upper), self.f(np.zeros_like(f)), f)

2
GPy/core/parameterization/updateable.py
View file

@ -36,7 +36,7 @@ class Updateable(Observable):
self.trigger_update() self.trigger_update()
def toggle_update(self): def toggle_update(self):
print "deprecated: toggle_update was renamed to update_toggle for easier access" print("deprecated: toggle_update was renamed to update_toggle for easier access")
self.update_toggle() self.update_toggle()
def update_toggle(self): def update_toggle(self):
self.update_model(not self.update_model()) self.update_model(not self.update_model())

2
GPy/core/sparse_gp.py
View file

@ -49,7 +49,7 @@ class SparseGP(GP):
else: else:
#inference_method = ?? #inference_method = ??
raise NotImplementedError, "what to do what to do?" raise NotImplementedError, "what to do what to do?"
print "defaulting to ", inference_method, "for latent function inference" print("defaulting to ", inference_method, "for latent function inference")
self.Z = Param('inducing inputs', Z) self.Z = Param('inducing inputs', Z)
self.num_inducing = Z.shape[0] self.num_inducing = Z.shape[0]

2
GPy/core/sparse_gp_mpi.py
View file

@ -56,7 +56,7 @@ class SparseGP_MPI(SparseGP):
self.N_range = (N_start, N_end) self.N_range = (N_start, N_end)
self.N_list = np.array(N_list) self.N_list = np.array(N_list)
self.Y_local = self.Y[N_start:N_end] self.Y_local = self.Y[N_start:N_end]
print 'MPI RANK '+str(self.mpi_comm.rank)+' with the data range '+str(self.N_range) print('MPI RANK '+str(self.mpi_comm.rank)+' with the data range '+str(self.N_range))
mpi_comm.Bcast(self.param_array, root=0) mpi_comm.Bcast(self.param_array, root=0)
self.update_model(True) self.update_model(True)

18
GPy/core/verbose_optimization.py
View file

@ -1,7 +1,7 @@
# Copyright (c) 2012-2014, Max Zwiessele. # Copyright (c) 2012-2014, Max Zwiessele.
# Licensed under the BSD 3-clause license (see LICENSE.txt) # Licensed under the BSD 3-clause license (see LICENSE.txt)
from __future__ import print_function
import numpy as np import numpy as np
import sys import sys
import time import time
@ -65,8 +65,8 @@ class VerboseOptimization(object):
#self.progress.add_class('box-flex1') #self.progress.add_class('box-flex1')
else: else:
self.exps = exponents(self.fnow, self.current_gradient) self.exps = exponents(self.fnow, self.current_gradient)
print 'Running {} Code:'.format(self.opt_name) print('Running {} Code:'.format(self.opt_name))
print ' {3:7s} {0:{mi}s} {1:11s} {2:11s}'.format("i", "f", "|g|", "secs", mi=self.len_maxiters) print(' {3:7s} {0:{mi}s} {1:11s} {2:11s}'.format("i", "f", "|g|", "secs", mi=self.len_maxiters))
def __enter__(self): def __enter__(self):
self.start = time.time() self.start = time.time()
@ -107,11 +107,11 @@ class VerboseOptimization(object):
b = np.any(n_exps < self.exps) b = np.any(n_exps < self.exps)
if a or b: if a or b:
self.p_iter = self.iteration self.p_iter = self.iteration
print '' print('')
if b: if b:
self.exps = n_exps self.exps = n_exps
print '\r', print('\r', end=' ')
print '{3:> 7.2g} {0:>0{mi}g} {1:> 12e} {2:> 12e}'.format(self.iteration, float(self.fnow), float(self.current_gradient), time.time()-self.start, mi=self.len_maxiters), # print 'Iteration:', iteration, ' Objective:', fnow, ' Scale:', beta, '\r', print('{3:> 7.2g} {0:>0{mi}g} {1:> 12e} {2:> 12e}'.format(self.iteration, float(self.fnow), float(self.current_gradient), time.time()-self.start, mi=self.len_maxiters), end=' ') # print 'Iteration:', iteration, ' Objective:', fnow, ' Scale:', beta, '\r',
sys.stdout.flush() sys.stdout.flush()
def print_status(self, me, which=None): def print_status(self, me, which=None):
@ -140,6 +140,6 @@ class VerboseOptimization(object):
self.print_out() self.print_out()
if not self.ipython_notebook: if not self.ipython_notebook:
print print()
print 'Optimization finished in {0:.5g} Seconds'.format(self.stop-self.start) print('Optimization finished in {0:.5g} Seconds'.format(self.stop-self.start))
print print()