merging with the main devel branch

.travis.yml

@@ -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

CHANGELOG.md

@@ -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

GPy/__version__.py

@@ -1 +1 @@
-__version__ = "1.6.1"
+__version__ = "1.7.7"

GPy/core/gp.py

@@ -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):
         """

GPy/core/sparse_gp_mpi.py

@@ -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):

GPy/testing/gpy_kernels_state_space_tests.py

@@ -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,):
+    def test_forecast_regular(self,):
-        """
-        Test time-series forecasting.
-        """
-
         # 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,])
-            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 = 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.lengthscale.constrain_bounded(0.25, 1000)
+        gp_kernel.std_periodic.period.constrain_bounded(0.15, 100)
-            gp_kernel.std_periodic.period.constrain_bounded(0.15, 100)
 
-            periodic_kernel = GPy.kern.sde_StdPeriodic(1,active_dims=[0,])
+        periodic_kernel = GPy.kern.sde_StdPeriodic(1,active_dims=[0,])
-            ss_kernel = GPy.kern.sde_Linear(1,X,active_dims=[0,]) + \
+        ss_kernel = GPy.kern.sde_Linear(1,X,active_dims=[0,]) + \
-                GPy.kern.sde_Bias(1, active_dims=[0,]) + periodic_kernel
+            GPy.kern.sde_Bias(1, active_dims=[0,]) + periodic_kernel
 
-            ss_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000)
+        ss_kernel.std_periodic.lengthscale.constrain_bounded(0.25, 1000)
-            ss_kernel.std_periodic.period.constrain_bounded(0.15, 100)
+        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,

GPy/util/datasets.py

@@ -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 url, files, save_names, suffices in zip_longest(*zip_urls, fillvalue=[]):
-        for f, save_name, suffix in itertools.zip_longest(files, save_names, suffices, fillvalue=None):
+        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

GPy/util/normalizer.py

@@ -1,7 +1,7 @@
 '''
 Created on Aug 27, 2014
 
-@author: t-mazwie
+@author: Max Zwiessele
 '''
 import logging
 import numpy as np

MANIFEST.in

@@ -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

README.md

@@ -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
 

appveyor.yml

@@ -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 username = maxz >> %USERPROFILE%\\.pypirc
-- echo password:%pip_access% >> %USERPROFILE%\\.pypirc
+- echo password = %pip_access% >> %USERPROFILE%\\.pypirc
 - echo[
 - echo [test] >> %USERPROFILE%\\.pypirc
-- echo repository:https://test.pypi.org/legacy/ >> %USERPROFILE%\\.pypirc
+- echo repository = https://testpypi.python.org/pypi >> %USERPROFILE%\\.pypirc
-- echo username:maxz >> %USERPROFILE%\\.pypirc
+- echo username = maxz >> %USERPROFILE%\\.pypirc
-- echo password:%pip_access% >> %USERPROFILE%\\.pypirc
+- echo password = %pip_access% >> %USERPROFILE%\\.pypirc
 - ps: >-
-    if ($env:APPVEYOR_REPO_BRANCH -eq 'devel') { 
+    If ($env:APPVEYOR_REPO_BRANCH -eq 'devel') { 
-        twine upload --skip-existing -r test dist/*
+        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
     }
  

setup.cfg

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

setup.py

@@ -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',
+                   
                    ]
       )