Merge pull request #413 from SheffieldML/devel

appveyor deploy

.travis.yml

@@ -15,7 +15,7 @@ addons:
 
 env:
   - PYTHON_VERSION=2.7
-  - PYTHON_VERSION=3.3
+  #- PYTHON_VERSION=3.3
   - PYTHON_VERSION=3.4
   - PYTHON_VERSION=3.5
 

GPy/__version__.py

@@ -1 +1 @@
-__version__ = "1.0.9"
+__version__ = "1.1.3"

GPy/testing/minibatch_tests.py

@@ -127,28 +127,32 @@ class SparseGPMinibatchTest(unittest.TestCase):
     def test_sparsegp_init(self):
         # Test if the different implementations give the exact same likelihood as the full model.
         # All of the following settings should give the same likelihood and gradients as the full model:
-        np.random.seed(1234)
-        Z = self.X[np.random.choice(self.X.shape[0], replace=False, size=10)].copy()
-        Q = Z.shape[1]
-        m = GPy.models.sparse_gp_minibatch.SparseGPMiniBatch(self.X, self.Y, Z, GPy.kern.RBF(Q)+GPy.kern.Matern32(Q)+GPy.kern.Bias(Q), GPy.likelihoods.Gaussian(), missing_data=True, stochastic=False)
-        assert(m.checkgrad())
-        m.optimize('adadelta', max_iters=10)
-        assert(m.checkgrad())
-
-        m = GPy.models.sparse_gp_minibatch.SparseGPMiniBatch(self.X, self.Y, Z, GPy.kern.RBF(Q)+GPy.kern.Matern32(Q)+GPy.kern.Bias(Q), GPy.likelihoods.Gaussian(), missing_data=True, stochastic=True)
-        assert(m.checkgrad())
-        m.optimize('rprop', max_iters=10)
-        assert(m.checkgrad())
-        
-        m = GPy.models.sparse_gp_minibatch.SparseGPMiniBatch(self.X, self.Y, Z, GPy.kern.RBF(Q)+GPy.kern.Matern32(Q)+GPy.kern.Bias(Q), GPy.likelihoods.Gaussian(), missing_data=False, stochastic=False)
-        assert(m.checkgrad())
-        m.optimize('rprop', max_iters=10)
-        assert(m.checkgrad())
-        
-        m = GPy.models.sparse_gp_minibatch.SparseGPMiniBatch(self.X, self.Y, Z, GPy.kern.RBF(Q)+GPy.kern.Matern32(Q)+GPy.kern.Bias(Q), GPy.likelihoods.Gaussian(), missing_data=False, stochastic=True)
-        assert(m.checkgrad())
-        m.optimize('adadelta', max_iters=10)
-        assert(m.checkgrad())
+        try:
+            np.random.seed(1234)
+            Z = self.X[np.random.choice(self.X.shape[0], replace=False, size=10)].copy()
+            Q = Z.shape[1]
+            m = GPy.models.sparse_gp_minibatch.SparseGPMiniBatch(self.X, self.Y, Z, GPy.kern.RBF(Q)+GPy.kern.Matern32(Q)+GPy.kern.Bias(Q), GPy.likelihoods.Gaussian(), missing_data=True, stochastic=False)
+            assert(m.checkgrad())
+            m.optimize('adadelta', max_iters=10)
+            assert(m.checkgrad())
+    
+            m = GPy.models.sparse_gp_minibatch.SparseGPMiniBatch(self.X, self.Y, Z, GPy.kern.RBF(Q)+GPy.kern.Matern32(Q)+GPy.kern.Bias(Q), GPy.likelihoods.Gaussian(), missing_data=True, stochastic=True)
+            assert(m.checkgrad())
+            m.optimize('rprop', max_iters=10)
+            assert(m.checkgrad())
+            
+            m = GPy.models.sparse_gp_minibatch.SparseGPMiniBatch(self.X, self.Y, Z, GPy.kern.RBF(Q)+GPy.kern.Matern32(Q)+GPy.kern.Bias(Q), GPy.likelihoods.Gaussian(), missing_data=False, stochastic=False)
+            assert(m.checkgrad())
+            m.optimize('rprop', max_iters=10)
+            assert(m.checkgrad())
+            
+            m = GPy.models.sparse_gp_minibatch.SparseGPMiniBatch(self.X, self.Y, Z, GPy.kern.RBF(Q)+GPy.kern.Matern32(Q)+GPy.kern.Bias(Q), GPy.likelihoods.Gaussian(), missing_data=False, stochastic=True)
+            assert(m.checkgrad())
+            m.optimize('adadelta', max_iters=10)
+            assert(m.checkgrad())
+        except ImportError:
+            from nose import SkipTest
+            raise SkipTest('climin not installed, skipping stochastic gradients')
 
     def test_predict_missing_data(self):
         m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(self.Y, self.Q, X_variance=False, missing_data=True, stochastic=True, batchsize=self.Y.shape[1])

appveyor.yml

@@ -0,0 +1,89 @@
+environment:
+  pip_access:
+    secure: 8/ZjXFwtd1S7ixd7PJOpptupKKEDhm2da/q3unabJ00=
+  gpy_version: 1.1.3
+  matrix:
+    - PYTHON_VERSION: 2.7
+      MINICONDA: C:\Miniconda-x64
+    - PYTHON_VERSION: 3.5
+      MINICONDA: C:\Miniconda35-x64
+
+#configuration:
+#  - Debug
+#  - Release
+  
+install:
+ - "set PATH=%MINICONDA%;%MINICONDA%\\Scripts;%PATH%"
+ - conda config --set always_yes yes --set changeps1 no
+ - conda update -q conda
+ - conda info -a
+ - "conda create -q -n build-environment python=%PYTHON_VERSION% numpy scipy matplotlib"
+ - activate build-environment
+ # We need wheel installed to build wheels
+ - python -m pip install wheel
+ # GPy needs paramz
+ - python -m pip install paramz
+ - python -m pip install nose-show-skipped
+ - python -m pip install coverage
+ - python -m pip install codecov
+ - python -m pip install twine
+ - "python setup.py develop"
+ 
+build: off
+
+test_script:
+  # Put your test command here.
+  # If you don't need to build C extensions on 64-bit Python 3.3 or 3.4,
+  # you can remove "build.cmd" from the front of the command, as it's
+  # only needed to support those cases.
+  # Note that you must use the environment variable %PYTHON% to refer to
+  # the interpreter you're using - Appveyor does not do anything special
+  # to put the Python evrsion you want to use on PATH.
+  #- "build.cmd %PYTHON%\\python.exe setup.py test"
+  - "coverage run travis_tests.py"
+
+after_test:
+  # This step builds your wheels.
+  - "python setup.py bdist_wheel bdist_wininst"
+  - codecov
+  
+artifacts:
+  # bdist_wheel puts your built wheel in the dist directory
+  - path: dist\*
+
+
+deploy_script:
+- echo [distutils] > %USERPROFILE%\\.pypirc
+- echo index-servers = >> %USERPROFILE%\\.pypirc
+- echo     pypi >> %USERPROFILE%\\.pypirc
+- echo     test >> %USERPROFILE%\\.pypirc
+- echo[
+- echo [pypi] >> %USERPROFILE%\\.pypirc
+- echo username:maxz >> %USERPROFILE%\\.pypirc
+- echo password:%pip_access% >> %USERPROFILE%\\.pypirc
+- echo[
+- echo [test] >> %USERPROFILE%\\.pypirc
+- echo repository:https://testpypi.python.org/pypi >> %USERPROFILE%\\.pypirc
+- echo username:maxz >> %USERPROFILE%\\.pypirc
+- echo password:%pip_access% >> %USERPROFILE%\\.pypirc
+- ps: >-
+    if ($env:APPVEYOR_REPO_BRANCH -eq 'devel') { 
+        twine upload -r test dist/*
+    }
+    elseif ($env:APPVEYOR_REPO_BRANCH -eq 'deploy') {  
+        twine upload dist/*
+    }
+    else {
+        echo not deploying on other branches
+    }
+ 
+# deploy:
+#   - provider: GitHub
+#     release: GPy-v$(gpy_version)
+#     description: 'GPy windows install'
+#     artifact: dist/*.exe               # upload wininst to GitHub
+#     draft: false
+#     prerelease: false
+#     on:
+#         branch: deploy                 # release from deploy branch only
+#         appveyor_repo_tag: true        # deploy on tag push only

codecov.yml

@@ -0,0 +1,24 @@
+codecov:
+  branch: devel
+
+coverage:
+  precision: 2
+  round: down
+  range: "40...100"
+
+  status:
+    project:
+      default:
+        target: auto
+        if_no_uploads: error
+
+    patch:
+      default:
+        if_no_uploads: error
+
+    changes: true
+
+
+comment:
+  layout: "header, diff, changes, suggestions"
+  behavior: default

setup.cfg

@@ -1,12 +1,11 @@
 [bumpversion]
-current_version = 1.0.9
+current_version = 1.1.3
 tag = False
 commit = True
 
 [bumpversion:file:GPy/__version__.py]
 
-[bdist_wheel]
-universal = 1
+[bumpversion:file:appveyor.yml]
 
 [upload_docs]
 upload-dir = doc/build/html

setup.py

@@ -59,14 +59,7 @@ def read_to_rst(fname):
 
 desc = """
 
-- `GPy homepage <http://sheffieldml.github.io/GPy/>`_
-- `Tutorial notebooks <http://nbviewer.ipython.org/github/SheffieldML/notebook/blob/master/GPy/index.ipynb>`_
-- `User mailing-list <https://lists.shef.ac.uk/sympa/subscribe/gpy-users>`_
-- `Developer documentation <http://gpy.readthedocs.org/en/devel/>`_
-- `Travis-CI unit-tests <https://travis-ci.org/SheffieldML/GPy>`_
-- `License <https://opensource.org/licenses/BSD-3-Clause>`_
-
-For full description and installation instructions please refer to the github page.
+Please refer to the github homepage for detailed instructions on installation and usage.
 
 """
 
@@ -116,6 +109,7 @@ setup(name = 'GPy',
       author = read_to_rst('AUTHORS.txt'),
       author_email = "gpy.authors@gmail.com",
       description = ("The Gaussian Process Toolbox"),
+      long_description = desc,
       license = "BSD 3-clause",
       keywords = "machine-learning gaussian-processes kernels",
       url = "http://sheffieldml.github.com/GPy/",
@@ -154,8 +148,7 @@ setup(name = 'GPy',
       include_package_data = True,
       py_modules = ['GPy.__init__'],
       test_suite = 'GPy.testing',
-      long_description=desc,
-      install_requires=['numpy>=1.7', 'scipy>=0.16', 'six', 'paramz>=0.5.2'],
+      install_requires = ['numpy>=1.7', 'scipy>=0.16', 'six', 'paramz>=0.5.2'],
       extras_require = {'docs':['sphinx'],
                         'optional':['mpi4py',
                                     'ipython>=4.0.0',