Merge pull request #246 from SheffieldML/travis2

Dapid's travis changes

.travis.yml

@@ -1,27 +1,41 @@
-language: python
-python:
-  - "2.7"
+sudo: false
+
+os:
+  - linux
+#  - osx
+
+language: python
+
+#addons:
+#  apt:
+#    packages:
+#      - gfortran
+#      - libatlas-dev
+#      - libatlas-base-dev
+#      - liblapack-dev
+
+python:
+  - 2.7
+  - 3.3
+  - 3.4
 
-# command to install dependencies, e.g. pip install -r requirements.txt --use-mirrors
 before_install:
-  #Install a mini version of anaconda such that we can easily install our dependencies
   - wget http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh
   - chmod +x miniconda.sh
   - ./miniconda.sh -b
   - export PATH=/home/travis/miniconda/bin:$PATH
-  - conda update --yes conda
-  # Workaround for a permissions issue with Travis virtual machine images
-  # that breaks Python's multiprocessing:
-  # https://github.com/travis-ci/travis-cookbooks/issues/155
-  - sudo rm -rf /dev/shm
-  - sudo ln -s /run/shm /dev/shm
-
+#  - conda update --yes conda
+  
 install:
-  - conda install --yes python=$TRAVIS_PYTHON_VERSION atlas numpy=1.9 scipy=0.16 matplotlib nose sphinx pip nose
-  #- pip install . 
-  - python setup.py build_ext --inplace
-  #--use-mirrors
-  #
-# command to run tests, e.g. python setup.py test
-script: 
-  - nosetests GPy/testing
+  - conda install --yes python=$TRAVIS_PYTHON_VERSION numpy=1.9 scipy=0.16 nose pip six
+  - pip install .
+
+script:
+  - cd $HOME
+  - mkdir empty
+  - cd empty
+  - nosetests GPy.testing
+
+cache:
+  directories:
+    - $HOME/.cache/pip

GPy/plotting/__init__.py

@@ -2,10 +2,11 @@
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 try:
+    import matplotlib
     from . import matplot_dep
 except (ImportError, NameError):
     # Matplotlib not available
     import warnings
     warnings.warn(ImportWarning("Matplotlib not available, install newest version of Matplotlib for plotting"))
     #sys.modules['matplotlib'] =
-    #sys.modules[__name__+'.matplot_dep'] = ImportWarning("Matplotlib not available, install newest version of Matplotlib for plotting")
+    #sys.modules[__name__+'.matplot_dep'] = ImportWarning("Matplotlib not available, install newest version of Matplotlib for plotting")

GPy/plotting/matplot_dep/__init__.py

@@ -1,18 +1,18 @@
 # Copyright (c) 2014, GPy authors (see AUTHORS.txt).
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
-import base_plots
-import models_plots
-import priors_plots
-import variational_plots
-import kernel_plots
-import dim_reduction_plots
-import mapping_plots
-import Tango
-import visualize
-import latent_space_visualizations
-import netpbmfile
-import inference_plots
-import maps
-import img_plots
-from ssgplvm import SSGPLVM_plot   
+from . import base_plots
+from . import models_plots
+from . import priors_plots
+from . import variational_plots
+from . import kernel_plots
+from . import dim_reduction_plots
+from . import mapping_plots
+from . import Tango
+from . import visualize
+from . import latent_space_visualizations
+from . import netpbmfile
+from . import inference_plots
+from . import maps
+from . import img_plots
+from .ssgplvm import SSGPLVM_plot   

GPy/plotting/matplot_dep/base_plots.py

@@ -1,13 +1,6 @@
 # #Copyright (c) 2012, GPy authors (see AUTHORS.txt).
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
-
-
-try:
-    #import Tango
-    from matplotlib import pyplot as pb
-except:
-    pass
-import numpy as np
+from matplotlib import pyplot as pb
 
 def ax_default(fignum, ax):
     if ax is None:

GPy/testing/rv_transformation_tests.py

@@ -34,7 +34,7 @@ class RVTransformationTestCase(unittest.TestCase):
         # The PDF of the transformed variables
         p_phi = lambda phi : np.exp(-m._objective_grads(phi)[0])
         # To the empirical PDF of:
-        theta_s = prior.rvs(100000)
+        theta_s = prior.rvs(1e6)
         phi_s = trans.finv(theta_s)
         # which is essentially a kernel density estimation
         kde = st.gaussian_kde(phi_s)
@@ -56,7 +56,7 @@ class RVTransformationTestCase(unittest.TestCase):
         # The following test cannot be very accurate
         self.assertTrue(np.linalg.norm(pdf_phi - kde(phi)) / np.linalg.norm(kde(phi)) <= 1e-1)
         # Check the gradients at a few random points
-        for i in range(10):
+        for i in range(5):
             m.theta = theta_s[i]
             self.assertTrue(m.checkgrad(verbose=True))