Merge branch 'devel' into plotting_tests

2026-05-21 14:05:14 +02:00 · 2016-08-03 08:52:10 +01:00 · 2016-08-03 08:52:10 +01:00 · 218840d05c
commit 218840d05c
parent 30b34591bb 02457660a2
14 changed files with 156 additions and 46 deletions
--- a/.travis.yml
+++ b/.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

@ -61,7 +61,6 @@ deploy:
  password:
    secure: "vMEOlP7DQhFJ7hQAKtKC5hrJXFl5BkUt4nXdosWWiw//Kg8E+PPLg88XPI2gqIosir9wwgtbSBBbbwCxkM6uxRNMpoNR8Ixyv9fmSXp4rLl7bbBY768W7IRXKIBjpuEy2brQjoT+CwDDSzUkckHvuUjJDNRvUv8ab4P/qYO1LG4="
  on:
-    tags: true
    branch: deploy
  distributions: $DIST
  skip_cleanup: true
--- a/GPy/version.py
+++ b/GPy/version.py
@ -1 +1 @@
-__version__ = "1.0.9"
+__version__ = "1.2.0"
--- a/GPy/mappings/init.py
+++ b/GPy/mappings/init.py
@ -7,4 +7,6 @@ from .mlp import MLP
 from .additive import Additive
 from .compound import Compound
 from .constant import Constant
+from .identity import Identity
+from .piecewise_linear import PiecewiseLinear

--- a/GPy/models/gp_coregionalized_regression.py
+++ b/GPy/models/gp_coregionalized_regression.py
@ -17,7 +17,7 @@ class GPCoregionalizedRegression(GP):
    :type X_list: list of numpy arrays
    :param Y_list: list of observed values related to the different noise models
    :type Y_list: list of numpy arrays
-    :param kernel: a GPy kernel, defaults to RBF ** Coregionalized
+    :param kernel: a GPy kernel ** Coregionalized, defaults to RBF ** Coregionalized
    :type kernel: None | GPy.kernel defaults
    :likelihoods_list: a list of likelihoods, defaults to list of Gaussian likelihoods
    :type likelihoods_list: None | a list GPy.likelihoods
--- a/GPy/models/sparse_gp_coregionalized_regression.py
+++ b/GPy/models/sparse_gp_coregionalized_regression.py
@ -19,7 +19,7 @@ class SparseGPCoregionalizedRegression(SparseGP):
    :type Y_list: list of numpy arrays
    :param Z_list: list of inducing inputs (optional)
    :type Z_list: empty list | list of numpy arrays
-    :param kernel: a GPy kernel, defaults to RBF ** Coregionalized
+    :param kernel: a GPy kernel ** Coregionalized, defaults to RBF ** Coregionalized
    :type kernel: None | GPy.kernel defaults
    :likelihoods_list: a list of likelihoods, defaults to list of Gaussian likelihoods
    :type likelihoods_list: None | a list GPy.likelihoods
--- a/GPy/plotting/matplot_dep/variational_plots.py
+++ b/GPy/plotting/matplot_dep/variational_plots.py
@ -34,7 +34,7 @@ def plot(parameterized, fignum=None, ax=None, colors=None, figsize=(12, 6)):
        else:
            raise ValueError("Need one ax per latent dimension input_dim")
        bg_lines.append(a.plot(means, c='k', alpha=.3))
-        lines.extend(a.plot(x, means.T[i], c=colors.next(), label=r"$\mathbf{{X_{{{}}}}}$".format(i)))
+        lines.extend(a.plot(x, means.T[i], c=next(colors), label=r"$\mathbf{{X_{{{}}}}}$".format(i)))
        fills.append(a.fill_between(x,
                        means.T[i] - 2 * np.sqrt(variances.T[i]),
                        means.T[i] + 2 * np.sqrt(variances.T[i]),
@ -86,7 +86,7 @@ def plot_SpikeSlab(parameterized, fignum=None, ax=None, colors=None, side_by_sid
        # mean and variance plot
        a = fig.add_subplot(*sub1)
        a.plot(means, c='k', alpha=.3)
-        plots.extend(a.plot(x, means.T[i], c=colors.next(), label=r"$\mathbf{{X_{{{}}}}}$".format(i)))
+        plots.extend(a.plot(x, means.T[i], c=next(colors), label=r"$\mathbf{{X_{{{}}}}}$".format(i)))
        a.fill_between(x,
                        means.T[i] - 2 * np.sqrt(variances.T[i]),
                        means.T[i] + 2 * np.sqrt(variances.T[i]),
--- a/GPy/testing/minibatch_tests.py
+++ b/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])
--- a/GPy/util/config.py
+++ b/GPy/util/config.py
@ -12,7 +12,7 @@ except ImportError:
    import configparser
    config = configparser.ConfigParser()
    from configparser import NoOptionError
-    
+

 # This is the default configuration file that always needs to be present.
 default_file = os.path.abspath(os.path.join(os.path.dirname( __file__ ), '..', 'defaults.cfg'))
@ -23,7 +23,7 @@ local_file = os.path.abspath(os.path.join(os.path.dirname( __file__ ), '..', 'in

 # This specifies configurations specific to the user (it is found in the user home directory)
 home = os.getenv('HOME') or os.getenv('USERPROFILE')
-user_file = os.path.join(home,'.config','gpy', 'user.cfg')
+user_file = os.path.join(home,'.config','GPy', 'user.cfg')

 # Read in the given files.
 config.readfp(open(default_file))
--- a/GPy/util/pca.py
+++ b/GPy/util/pca.py
@ -131,7 +131,7 @@ class PCA(object):
        kwargs.update(dict(s=s))
        plots = list()
        for i, l in enumerate(ulabels):
-            kwargs.update(dict(color=colors.next(), marker=marker[i % len(marker)]))
+            kwargs.update(dict(color=next(colors), marker=marker[i % len(marker)]))
            plots.append(ax.scatter(*X_[labels == l, :].T, label=str(l), **kwargs))
        ax.set_xlabel(r"PC$_1$")
        ax.set_ylabel(r"PC$_2$")
--- a/README.md
+++ b/README.md
@ -9,7 +9,7 @@ The Gaussian processes framework in Python.
 * Travis-CI [unit-tests](https://travis-ci.org/SheffieldML/GPy)
 * [![licence](https://img.shields.io/badge/licence-BSD-blue.svg)](http://opensource.org/licenses/BSD-3-Clause)

-[![develstat](https://travis-ci.org/SheffieldML/GPy.svg?branch=devel)](https://travis-ci.org/SheffieldML/GPy) [![covdevel](http://codecov.io/github/SheffieldML/GPy/coverage.svg?branch=devel)](http://codecov.io/github/SheffieldML/GPy?branch=devel) [![Research software impact](http://depsy.org/api/package/pypi/GPy/badge.svg)](http://depsy.org/package/python/GPy) [![Code Health](https://landscape.io/github/SheffieldML/GPy/devel/landscape.svg?style=flat)](https://landscape.io/github/SheffieldML/GPy/devel)
+[![develstat](https://travis-ci.org/SheffieldML/GPy.svg?branch=devel)](https://travis-ci.org/SheffieldML/GPy) [![appveyor](https://ci.appveyor.com/api/projects/status/662o6tha09m2jix3/branch/deploy?svg=true)](https://ci.appveyor.com/project/mzwiessele/gpy/branch/deploy) [![covdevel](http://codecov.io/github/SheffieldML/GPy/coverage.svg?branch=devel)](http://codecov.io/github/SheffieldML/GPy?branch=devel) [![Research software impact](http://depsy.org/api/package/pypi/GPy/badge.svg)](http://depsy.org/package/python/GPy) [![Code Health](https://landscape.io/github/SheffieldML/GPy/devel/landscape.svg?style=flat)](https://landscape.io/github/SheffieldML/GPy/devel)

 ## Updated Structure

--- a/appveyor.yml
+++ b/appveyor.yml
@ -0,0 +1,89 @@
+environment:
+  pip_access:
+    secure: 8/ZjXFwtd1S7ixd7PJOpptupKKEDhm2da/q3unabJ00=
+  gpy_version: 1.2.0
+  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
--- a/codecov.yml
+++ b/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
--- a/setup.cfg
+++ b/setup.cfg
@ -1,12 +1,11 @@
 [bumpversion]
-current_version = 1.0.9
+current_version = 1.2.0
 tag = False
 commit = True

 [bumpversion:file:GPy/__version__.py]

-[bdist_wheel]
-universal = 1
+[bumpversion:file:appveyor.yml]

 [upload_docs]
 upload-dir = doc/build/html
--- a/setup.py
+++ b/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',