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merging with the main devel branch

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Akash Kumar Dhaka 2017-07-27 21:18:44 +03:00
commit ea6ea793fc
13 changed files with 265 additions and 44 deletions
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3
.travis.yml
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@ -16,8 +16,9 @@ addons:
env:
- PYTHON_VERSION=2.7
#- PYTHON_VERSION=3.3
- PYTHON_VERSION=3.4
#- PYTHON_VERSION=3.4
- PYTHON_VERSION=3.5
- PYTHON_VERSION=3.6
before_install:
- wget https://github.com/mzwiessele/travis_scripts/raw/master/download_miniconda.sh

144
CHANGELOG.md
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@ -1,5 +1,149 @@
# Changelog
## v1.7.6 (2017-06-19)
### Fix
* Appveyor not uploading to testpypi for now. [mzwiessele]
### Other
* Bump version: 1.7.5 → 1.7.6. [mzwiessele]
## v1.7.5 (2017-06-19)
### Fix
* Splitting forecast tests into 3 to circumvent 10 minute stop of travis. [mzwiessele]
### Other
* Bump version: 1.7.4 → 1.7.5. [mzwiessele]
## v1.7.4 (2017-06-19)
### Fix
* Paramz version for parallel optimization fix. [mzwiessele]
### Other
* Bump version: 1.7.3 → 1.7.4. [mzwiessele]
## v1.7.3 (2017-06-19)
### Fix
* Appveyor build failing. [mzwiessele]
### Other
* Bump version: 1.7.2 → 1.7.3. [mzwiessele]
## v1.7.2 (2017-06-17)
### Fix
* Appveyor build python 3.6. [mzwiessele]
### Other
* Bump version: 1.7.1 → 1.7.2. [mzwiessele]
## v1.7.1 (2017-06-17)
### Fix
* Appveyor build python 3.6. [mzwiessele]
### Other
* Bump version: 1.7.0 → 1.7.1. [mzwiessele]
## v1.7.0 (2017-06-17)
### Fix
* Support for 3.5 and higher now that 3.6 is out. [mzwiessele]
### Other
* Bump version: 1.6.3 → 1.7.0. [mzwiessele]
## v1.6.3 (2017-06-17)
### Other
* Bump version: 1.6.2 → 1.6.3. [mzwiessele]
* Merge pull request #504 from rmcantin/devel. [Max Zwiessele]
* Fix python 2-3 compatibility. [Ruben Martinez-Cantin]
* Merge pull request #511 from dirmeier/devel. [Max Zwiessele]
* Added LICENSE file to MANIFEST.in. [dirmeier]
## v1.6.2 (2017-04-12)
### Fix
* Updated keywords. [mzwiessele]
### Other
* Bump version: 1.6.1 → 1.6.2. [mzwiessele]
* Merge pull request #491 from alexfeld/parallel_opt. [Max Zwiessele]
fix for parallel optimization
* Fix in sparse_gp_mpi optimizer. [Alex Feldstein]
* Fix for parallel optimization. [Alex Feldstein]
* Merge pull request #492 from pgmoren/devel. [Zhenwen Dai]
We did some benchmarking on classification. These changes should be fine. Let's merge it in.
* Changes in EP/EPDTC to fix numerical issues and increase the flexibility of the inference. [Moreno]
Changes to avoid numerical issues and improve the performance:
- Keep value of the EP parameters between calls
- Enforce positivity of tau_tilde
- Stable computation of the EP moments for the Bernoulli likelihood
- Compute marginal in the GP model without directly inverting tau_tilde
Changes to improve the flexibility:
- Add parameter for maximum number of iterations
- Distinguish between alternated/nested mode
- Distinguish between sequential/parallel updates in EP
* Merge pull request #489 from SheffieldML/linalg_cython-1. [Max Zwiessele]
cython in linalg fix #458
* Cython in linalg. [Max Zwiessele]
did set cython to working if linalg_cython was importable.
* Merge pull request #486 from SheffieldML/deploy. [Max Zwiessele]
Merge pull request #471 from SheffieldML/devel
* Merge pull request #471 from SheffieldML/devel. [Max Zwiessele]
new version
## v1.6.1 (2017-02-28)
### Fix

2
GPy/__version__.py
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@ -1 +1 @@
__version__ = "1.6.1"
__version__ = "1.7.7"

3
GPy/core/gp.py
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@ -562,11 +562,12 @@ class GP(Model):
"""
self.inference_method.on_optimization_start()
try:
super(GP, self).optimize(optimizer, start, messages, max_iters, ipython_notebook, clear_after_finish, **kwargs)
ret = super(GP, self).optimize(optimizer, start, messages, max_iters, ipython_notebook, clear_after_finish, **kwargs)
except KeyboardInterrupt:
print("KeyboardInterrupt caught, calling on_optimization_end() to round things up")
self.inference_method.on_optimization_end()
raise
return ret
def infer_newX(self, Y_new, optimize=True):
"""

5
GPy/core/sparse_gp_mpi.py
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@ -88,9 +88,9 @@ class SparseGP_MPI(SparseGP):
def optimize(self, optimizer=None, start=None, **kwargs):
self._IN_OPTIMIZATION_ = True
if self.mpi_comm==None:
super(SparseGP_MPI, self).optimize(optimizer,start,**kwargs)
ret = super(SparseGP_MPI, self).optimize(optimizer,start,**kwargs)
elif self.mpi_comm.rank==0:
super(SparseGP_MPI, self).optimize(optimizer,start,**kwargs)
ret = super(SparseGP_MPI, self).optimize(optimizer,start,**kwargs)
self.mpi_comm.Bcast(np.int32(-1),root=0)
elif self.mpi_comm.rank>0:
x = self.optimizer_array.copy()
@ -111,6 +111,7 @@ class SparseGP_MPI(SparseGP):
self._IN_OPTIMIZATION_ = False
raise Exception("Unrecognizable flag for synchronization!")
self._IN_OPTIMIZATION_ = False
return ret
def parameters_changed(self):
if isinstance(self.inference_method,VarDTC_minibatch):

101
GPy/testing/gpy_kernels_state_space_tests.py
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@ -306,11 +306,7 @@ class StateSpaceKernelsTests(np.testing.TestCase):
gp_kernel=gp_kernel,
mean_compare_decimal=2, var_compare_decimal=2)
def test_forecast(self,):
"""
Test time-series forecasting.
"""
def test_forecast_regular(self,):
# Generate data ->
np.random.seed(339) # seed the random number generator
#import pdb; pdb.set_trace()
@ -334,37 +330,102 @@ class StateSpaceKernelsTests(np.testing.TestCase):
#import pdb; pdb.set_trace()
def get_new_kernels():
periodic_kernel = GPy.kern.StdPeriodic(1,active_dims=[0,])
gp_kernel = GPy.kern.Linear(1, active_dims=[0,]) + GPy.kern.Bias(1, active_dims=[0,]) + periodic_kernel
gp_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000)
gp_kernel.std_periodic.period.constrain_bounded(0.15, 100)
periodic_kernel = GPy.kern.StdPeriodic(1,active_dims=[0,])
gp_kernel = GPy.kern.Linear(1, active_dims=[0,]) + GPy.kern.Bias(1, active_dims=[0,]) + periodic_kernel
gp_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000)
gp_kernel.std_periodic.period.constrain_bounded(0.15, 100)
periodic_kernel = GPy.kern.sde_StdPeriodic(1,active_dims=[0,])
ss_kernel = GPy.kern.sde_Linear(1,X,active_dims=[0,]) + \
GPy.kern.sde_Bias(1, active_dims=[0,]) + periodic_kernel
periodic_kernel = GPy.kern.sde_StdPeriodic(1,active_dims=[0,])
ss_kernel = GPy.kern.sde_Linear(1,X,active_dims=[0,]) + \
GPy.kern.sde_Bias(1, active_dims=[0,]) + periodic_kernel
ss_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000)
ss_kernel.std_periodic.period.constrain_bounded(0.15, 100)
ss_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000)
ss_kernel.std_periodic.period.constrain_bounded(0.15, 100)
return ss_kernel, gp_kernel
ss_kernel, gp_kernel = get_new_kernels()
self.run_for_model(X_train, Y_train, ss_kernel, kalman_filter_type = 'regular',
use_cython=False, optimize_max_iters=30, check_gradients=True,
predict_X=X_test,
gp_kernel=gp_kernel,
mean_compare_decimal=2, var_compare_decimal=2)
def test_forecast_svd(self,):
# Generate data ->
np.random.seed(339) # seed the random number generator
#import pdb; pdb.set_trace()
(X,Y) = generate_sine_data(x_points=None, sin_period=5.0, sin_ampl=5.0, noise_var=2.0,
plot = False, points_num=100, x_interval = (0, 40), random=True)
(X1,Y1) = generate_linear_data(x_points=X, tangent=1.0, add_term=20.0, noise_var=0.0,
plot = False, points_num=100, x_interval = (0, 40), random=True)
Y = Y + Y1
X_train = X[X <= 20]
Y_train = Y[X <= 20]
X_test = X[X > 20]
Y_test = Y[X > 20]
X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1)
X_train.shape = (X_train.shape[0],1); Y_train.shape = (Y_train.shape[0],1)
X_test.shape = (X_test.shape[0],1); Y_test.shape = (Y_test.shape[0],1)
# Generate data <-
#import pdb; pdb.set_trace()
periodic_kernel = GPy.kern.StdPeriodic(1,active_dims=[0,])
gp_kernel = GPy.kern.Linear(1, active_dims=[0,]) + GPy.kern.Bias(1, active_dims=[0,]) + periodic_kernel
gp_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000)
gp_kernel.std_periodic.period.constrain_bounded(0.15, 100)
periodic_kernel = GPy.kern.sde_StdPeriodic(1,active_dims=[0,])
ss_kernel = GPy.kern.sde_Linear(1,X,active_dims=[0,]) + \
GPy.kern.sde_Bias(1, active_dims=[0,]) + periodic_kernel
ss_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000)
ss_kernel.std_periodic.period.constrain_bounded(0.15, 100)
ss_kernel, gp_kernel = get_new_kernels()
self.run_for_model(X_train, Y_train, ss_kernel, kalman_filter_type = 'svd',
use_cython=False, optimize_max_iters=30, check_gradients=False,
predict_X=X_test,
gp_kernel=gp_kernel,
mean_compare_decimal=2, var_compare_decimal=2)
ss_kernel, gp_kernel = get_new_kernels()
def test_forecast_svd_cython(self,):
# Generate data ->
np.random.seed(339) # seed the random number generator
#import pdb; pdb.set_trace()
(X,Y) = generate_sine_data(x_points=None, sin_period=5.0, sin_ampl=5.0, noise_var=2.0,
plot = False, points_num=100, x_interval = (0, 40), random=True)
(X1,Y1) = generate_linear_data(x_points=X, tangent=1.0, add_term=20.0, noise_var=0.0,
plot = False, points_num=100, x_interval = (0, 40), random=True)
Y = Y + Y1
X_train = X[X <= 20]
Y_train = Y[X <= 20]
X_test = X[X > 20]
Y_test = Y[X > 20]
X.shape = (X.shape[0],1); Y.shape = (Y.shape[0],1)
X_train.shape = (X_train.shape[0],1); Y_train.shape = (Y_train.shape[0],1)
X_test.shape = (X_test.shape[0],1); Y_test.shape = (Y_test.shape[0],1)
# Generate data <-
#import pdb; pdb.set_trace()
periodic_kernel = GPy.kern.StdPeriodic(1,active_dims=[0,])
gp_kernel = GPy.kern.Linear(1, active_dims=[0,]) + GPy.kern.Bias(1, active_dims=[0,]) + periodic_kernel
gp_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000)
gp_kernel.std_periodic.period.constrain_bounded(0.15, 100)
periodic_kernel = GPy.kern.sde_StdPeriodic(1,active_dims=[0,])
ss_kernel = GPy.kern.sde_Linear(1,X,active_dims=[0,]) + \
GPy.kern.sde_Bias(1, active_dims=[0,]) + periodic_kernel
ss_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000)
ss_kernel.std_periodic.period.constrain_bounded(0.15, 100)
self.run_for_model(X_train, Y_train, ss_kernel, kalman_filter_type = 'svd',
use_cython=True, optimize_max_iters=30, check_gradients=False,
predict_X=X_test,

9
GPy/util/datasets.py
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@ -206,7 +206,10 @@ def authorize_download(dataset_name=None):
def download_data(dataset_name=None):
"""Check with the user that the are happy with terms and conditions for the data set, then download it."""
import itertools
try:
from itertools import zip_longest
except ImportError:
from itertools import izip_longest as zip_longest
dr = data_resources[dataset_name]
if not authorize_download(dataset_name):
@ -220,8 +223,8 @@ def download_data(dataset_name=None):
if 'suffices' in dr: zip_urls += (dr['suffices'], )
else: zip_urls += ([],)
for url, files, save_names, suffices in itertools.zip_longest(*zip_urls, fillvalue=[]):
for f, save_name, suffix in itertools.zip_longest(files, save_names, suffices, fillvalue=None):
for url, files, save_names, suffices in zip_longest(*zip_urls, fillvalue=[]):
for f, save_name, suffix in zip_longest(files, save_names, suffices, fillvalue=None):
download_url(os.path.join(url,f), dataset_name, save_name, suffix=suffix)
return True

2
GPy/util/normalizer.py
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@ -1,7 +1,7 @@
'''
Created on Aug 27, 2014
@author: t-mazwie
@author: Max Zwiessele
'''
import logging
import numpy as np

3
MANIFEST.in
View file

@ -16,6 +16,9 @@ recursive-include GPy *.c
recursive-include GPy *.h
recursive-include GPy *.pyx
# LICENSE
include LICENSE.txt
# Testing
#include GPy/testing/baseline/*.png
#include GPy/testing/pickle_test.pickle

2
README.md
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@ -76,7 +76,7 @@ If that is the case, it is best to clean the repo and reinstall.
[<img src="https://upload.wikimedia.org/wikipedia/commons/8/8e/OS_X-Logo.svg" height=40px>](http://www.apple.com/osx/)
[<img src="https://upload.wikimedia.org/wikipedia/commons/3/35/Tux.svg" height=40px>](https://en.wikipedia.org/wiki/List_of_Linux_distributions)
Python 2.7, 3.4 and higher
Python 2.7, 3.5 and higher
## Citation

22
appveyor.yml
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@ -3,12 +3,14 @@ environment:
secure: 8/ZjXFwtd1S7ixd7PJOpptupKKEDhm2da/q3unabJ00=
COVERALLS_REPO_TOKEN:
secure: d3Luic/ESkGaWnZrvWZTKrzO+xaVwJWaRCEP0F+K/9DQGPSRZsJ/Du5g3s4XF+tS
gpy_version: 1.6.1
gpy_version: 1.7.7
matrix:
- PYTHON_VERSION: 2.7
MINICONDA: C:\Miniconda-x64
- PYTHON_VERSION: 3.5
MINICONDA: C:\Miniconda35-x64
- PYTHON_VERSION: 3.6
MINICONDA: C:\Miniconda36-x64
#configuration:
# - Debug
@ -62,21 +64,21 @@ deploy_script:
- echo test >> %USERPROFILE%\\.pypirc
- echo[
- echo [pypi] >> %USERPROFILE%\\.pypirc
- echo username:maxz >> %USERPROFILE%\\.pypirc
- echo password:%pip_access% >> %USERPROFILE%\\.pypirc
- echo username = maxz >> %USERPROFILE%\\.pypirc
- echo password = %pip_access% >> %USERPROFILE%\\.pypirc
- echo[
- echo [test] >> %USERPROFILE%\\.pypirc
- echo repository:https://test.pypi.org/legacy/ >> %USERPROFILE%\\.pypirc
- echo username:maxz >> %USERPROFILE%\\.pypirc
- echo password:%pip_access% >> %USERPROFILE%\\.pypirc
- echo repository = https://testpypi.python.org/pypi >> %USERPROFILE%\\.pypirc
- echo username = maxz >> %USERPROFILE%\\.pypirc
- echo password = %pip_access% >> %USERPROFILE%\\.pypirc
- ps: >-
if ($env:APPVEYOR_REPO_BRANCH -eq 'devel') {
twine upload --skip-existing -r test dist/*
If ($env:APPVEYOR_REPO_BRANCH -eq 'devel') {
echo not deploying on devel # twine upload --skip-existing -r test dist/*
}
elseif ($env:APPVEYOR_REPO_BRANCH -eq 'deploy') {
ElseIf ($env:APPVEYOR_REPO_BRANCH -eq 'deploy') {
twine upload --skip-existing dist/*
}
else {
Else {
echo not deploying on other branches
}

2
setup.cfg
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@ -1,5 +1,5 @@
[bumpversion]
current_version = 1.6.1
current_version = 1.7.7
tag = True
commit = True

11
setup.py
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@ -150,7 +150,7 @@ setup(name = 'GPy',
py_modules = ['GPy.__init__'],
test_suite = 'GPy.testing',
setup_requires = ['numpy>=1.7'],
install_requires = ['numpy>=1.7', 'scipy>=0.16', 'six', 'paramz>=0.6.9'],
install_requires = ['numpy>=1.7', 'scipy>=0.16', 'six', 'paramz>=0.7.4'],
extras_require = {'docs':['sphinx'],
'optional':['mpi4py',
'ipython>=4.0.0',
@ -169,9 +169,14 @@ setup(name = 'GPy',
'Operating System :: Microsoft :: Windows',
'Operating System :: POSIX :: Linux',
'Programming Language :: Python :: 2.7',
'Programming Language :: Python :: 3.3',
'Programming Language :: Python :: 3.4',
'Programming Language :: Python :: 3.5',
'Programming Language :: Python :: 3.6',
'Framework :: IPython',
'Intended Audience :: Science/Research',
'Intended Audience :: Developers',
'Topic :: Software Development',
'Topic :: Software Development :: Libraries :: Python Modules',
]
)