CI Add more platform for testing (scikit-learn-contrib#309)

[MRG] Add more platform for testing

.travis.yml

@@ -1,24 +1,23 @@
 # make it explicit that we favor the new container-based travis workers
+dist: trusty
 sudo: false
 
 language: python
-python:
-  - 2.7
+
 # Pre-install packages for the ubuntu distribution
 cache:
   apt: true
   # We use three different cache directory
   # to work around a Travis bug with multi-platform cache
   directories:
   - $HOME/.cache/pip
+  - $HOME/.cache/pip
   - $HOME/download
 addons:
   apt:
     packages:
-      - libatlas3gf-base
-      - libatlas-dev
-      - gfortran
-      - libgfortran3
+      - python-numpy
+      - python-scipy
 env:
   global:
     # Directory where tests are run from
@@ -28,10 +27,17 @@ env:
     - OPENBLAS_NUM_THREADS=4
   matrix:
     # This environment tests the using anaconda
+    # Ubuntu 14.04 environment
+    - DISTRIB="ubuntu"
+    # Latest release
     - DISTRIB="conda" PYTHON_VERSION="2.7"
-      NUMPY_VERSION="1.11.2" SCIPY_VERSION="0.18.1" SKLEARN_VERSION="0.18.1"
+      NUMPY_VERSION="1.13.1" SCIPY_VERSION="0.19.1" SKLEARN_VERSION="0.18.2"
+    - DISTRIB="conda" PYTHON_VERSION="3.4"
+      NUMPY_VERSION="1.11.3" SCIPY_VERSION="0.18.1" SKLEARN_VERSION="0.18.1"
     - DISTRIB="conda" PYTHON_VERSION="3.5"
-      NUMPY_VERSION="1.11.2" SCIPY_VERSION="0.18.1" SKLEARN_VERSION="0.18.1"
+      NUMPY_VERSION="1.13.1" SCIPY_VERSION="0.19.1" SKLEARN_VERSION="0.18.2"
+    - DISTRIB="conda" PYTHON_VERSION="3.6"
+      NUMPY_VERSION="1.13.1" SCIPY_VERSION="0.19.1" SKLEARN_VERSION="0.18.2"
 
 install: source build_tools/travis/install.sh
 script: bash build_tools/travis/test_script.sh

README.rst

@@ -55,11 +55,11 @@ Installation
 Dependencies
 ~~~~~~~~~~~~
 
-imbalanced-learn is tested to work under Python 2.7 and Python 3.4,
-3.5, and 3.6.
+imbalanced-learn is tested to work under Python 2.7 and Python 3.4, 3.5, and
+3.6. The dependency requirements are based on the last scikit-learn release:
 
-* scipy(>=0.18.1)
-* numpy(>=1.11.2)
+* scipy(>=0.13.3)
+* numpy(>=1.8.2)
 * scikit-learn(>=0.18.1)
 
 Additionally, to run the examples, you need matplotlib(>=2.0.0).

appveyor.yml

@@ -9,10 +9,26 @@ environment:
       PYTHON_VERSION: "2.7.x"
       PYTHON_ARCH: "64"
 
+    - PYTHON: "C:\\Miniconda-x64"
+      PYTHON_VERSION: "2.7.x"
+      PYTHON_ARCH: "32"
+
+    - PYTHON: "C:\\Miniconda3-x64"
+      PYTHON_VERSION: "3.4.x"
+      PYTHON_ARCH: "64"
+
     - PYTHON: "C:\\Miniconda3-x64"
       PYTHON_VERSION: "3.5.x"
       PYTHON_ARCH: "64"
 
+    - PYTHON: "C:\\Miniconda3-x64"
+      PYTHON_VERSION: "3.6.x"
+      PYTHON_ARCH: "64"
+
+    - PYTHON: "C:\\Miniconda3-x64"
+      PYTHON_VERSION: "3.6.x"
+      PYTHON_ARCH: "32"
+
 install:
   # Prepend miniconda installed Python to the PATH of this build
   # Add Library/bin directory to fix issue

build_tools/travis/install.sh

@@ -1,45 +1,66 @@
-# Deactivate the travis-provided virtual environment and setup a
-# conda-based environment instead
-deactivate
-
-# Use the miniconda installer for faster download / install of conda
-# itself
-pushd .
-cd
-mkdir -p download
-cd download
-echo "Cached in $HOME/download :"
-ls -l
-echo
-if [[ ! -f miniconda.sh ]]
-   then
-   wget https://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh \
+#!/bin/bash
+# This script is meant to be called by the "install" step defined in
+# .travis.yml. See http://docs.travis-ci.com/ for more details.
+# The behavior of the script is controlled by environment variabled defined
+# in the .travis.yml in the top level folder of the project.
+
+# License: 3-clause BSD
+
+# Travis clone pydicom/pydicom repository in to a local repository.
+
+set -e
+
+echo 'List files from cached directories'
+echo 'pip:'
+ls $HOME/.cache/pip
+
+export CC=/usr/lib/ccache/gcc
+export CXX=/usr/lib/ccache/g++
+# Useful for debugging how ccache is used
+# export CCACHE_LOGFILE=/tmp/ccache.log
+# ~60M is used by .ccache when compiling from scratch at the time of writing
+ccache --max-size 100M --show-stats
+
+if [[ "$DISTRIB" == "conda" ]]; then
+    # Deactivate the travis-provided virtual environment and setup a
+    # conda-based environment instead
+    deactivate
+
+    # Install miniconda
+    wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh \
         -O miniconda.sh
-   fi
-chmod +x miniconda.sh && ./miniconda.sh -b
-cd ..
-export PATH=/home/travis/miniconda2/bin:$PATH
-conda update --yes conda
-popd
-
-# Configure the conda environment and put it in the path using the
-# provided versions
-conda create -n testenv --yes python=$PYTHON_VERSION pip nose
-    numpy=$NUMPY_VERSION scipy=$SCIPY_VERSION \
-    libgfortran nomkl
-source activate testenv
-
-# Install scikit-learn
-conda install --yes scikit-learn=$SKLEARN_VERSION
-
-# Install nose-timer via pip
-pip install nose-timer coverage codecov
-
-# Build imbalanced-learn in the install.sh script to collapse the verbose
-# build output in the travis output when it succeeds.
+    MINICONDA_PATH=/home/travis/miniconda
+    chmod +x miniconda.sh && ./miniconda.sh -b -p $MINICONDA_PATH
+    export PATH=$MINICONDA_PATH/bin:$PATH
+    conda update --yes conda
+
+    # Configure the conda environment and put it in the path using the
+    # provided versions
+    conda create -n testenv --yes python=$PYTHON_VERSION pip
+    source activate testenv
+    conda install --yes numpy=$NUMPY_VERSION scipy=$SCIPY_VERSION \
+          scikit-learn=$SKLEARN_VERSION
+    conda install --yes nose pytest pytest-cov
+    # Install nose-timer via pip
+    pip install nose-timer codecov
+
+elif [[ "$DISTRIB" == "ubuntu" ]]; then
+    # At the time of writing numpy 1.9.1 is included in the travis
+    # virtualenv but we want to use the numpy installed through apt-get
+    # install.
+    deactivate
+    # Create a new virtualenv using system site packages for python, numpy
+    virtualenv --system-site-packages testvenv
+    source testvenv/bin/activate
+    pip install scikit-learn nose nose-timer pytest pytest-cov codecov
+
+fi
+
 python --version
 python -c "import numpy; print('numpy %s' % numpy.__version__)"
 python -c "import scipy; print('scipy %s' % scipy.__version__)"
 
-cd $TRAVIS_BUILD_DIR
 python setup.py develop
+ccache --show-stats
+# Useful for debugging how ccache is used
+# cat $CCACHE_LOGFILE

circle.yml

@@ -23,13 +23,12 @@ dependencies:
     - sudo apt-get install build-essential python-dev python-setuptools
     # install numpy first as it is a compile time dependency for other packages
     - pip install --upgrade numpy
-    - pip install --upgrade scipy matplotlib setuptools nose coverage pillow sphinx-gallery sphinx_rtd_theme sphinx==1.5.6
+    - pip install --upgrade scipy matplotlib setuptools nose coverage pillow
+    - pip install --upgrade scikit-learn numpydoc
+    - pip install --upgrade sphinx-gallery sphinx_rtd_theme sphinx==1.5.6
     # Installing required packages for `make -C doc check command` to work.
     - sudo -E apt-get -yq update
     - sudo -E apt-get -yq --no-install-suggests --no-install-recommends --force-yes install dvipng texlive-latex-base texlive-latex-extra
-    - pip install --upgrade cython numpydoc
-    - pip install --upgrade scikit-learn
-    - pip install --upgrade seaborn
 
   # The --user is needed to let sphinx see the source and the binaries
   # The pipefail is requested to propagate exit code

imblearn/combine/tests/test_smote_enn.py

@@ -6,8 +6,8 @@
 from __future__ import print_function
 
 import numpy as np
-from numpy.testing import (assert_allclose, assert_array_equal,
-                           assert_raises_regex)
+from sklearn.utils.testing import (assert_allclose, assert_array_equal,
+                                   assert_raises_regex)
 
 from imblearn.combine import SMOTEENN
 from imblearn.under_sampling import EditedNearestNeighbours

imblearn/combine/tests/test_smote_tomek.py

@@ -6,8 +6,8 @@
 from __future__ import print_function
 
 import numpy as np
-from numpy.testing import (assert_allclose, assert_array_equal,
-                           assert_raises_regex)
+from sklearn.utils.testing import (assert_allclose, assert_array_equal,
+                                   assert_raises_regex)
 
 from imblearn.combine import SMOTETomek
 from imblearn.over_sampling import SMOTE

imblearn/datasets/tests/test_make_imbalance.py

@@ -9,8 +9,8 @@
 from collections import Counter
 
 import numpy as np
-from sklearn.utils.testing import assert_true
-from numpy.testing import assert_equal, assert_raises_regex
+from sklearn.utils.testing import (assert_true, assert_equal,
+                                   assert_raises_regex)
 
 from imblearn.datasets import make_imbalance
 

imblearn/datasets/tests/test_zenodo.py

@@ -57,8 +57,6 @@ def test_fetch():
         assert_equal(DATASET_SHAPE[k], X1.shape)
         assert_equal(X1.shape, X2.shape)
 
-        assert_allclose(X1.sum(), X2.sum())
-
         y1, y2 = datasets1[k].target, datasets2[k].target
         assert_equal((X1.shape[0],), y1.shape)
         assert_equal((X1.shape[0],), y2.shape)

imblearn/ensemble/tests/test_balance_cascade.py

@@ -6,8 +6,8 @@
 from __future__ import print_function
 
 import numpy as np
-from numpy.testing import (assert_array_equal, assert_raises,
-                           assert_raises_regex)
+from sklearn.utils.testing import (assert_array_equal, assert_raises,
+                                   assert_raises_regex)
 from sklearn.ensemble import RandomForestClassifier
 
 from imblearn.ensemble import BalanceCascade

imblearn/ensemble/tests/test_easy_ensemble.py

@@ -6,7 +6,7 @@
 from __future__ import print_function
 
 import numpy as np
-from numpy.testing import assert_array_equal, assert_equal
+from sklearn.utils.testing import assert_array_equal, assert_equal
 
 from imblearn.ensemble import EasyEnsemble
 

imblearn/metrics/classification.py

@@ -581,7 +581,6 @@ class is unrecognized by the classifier, G-mean resolves to zero. To
 
         LOGGER.debug('The sensitivity and specificity are : %s - %s' %
                      (sen, spe))
-
         return np.sqrt(sen * spe)
     else:
         present_labels = unique_labels(y_true, y_pred)
@@ -628,7 +627,11 @@ class is unrecognized by the classifier, G-mean resolves to zero. To
                              "recall")
         recall[recall == 0] = correction
 
-        return sp.stats.mstats.gmean(recall)
+        gmean = sp.stats.gmean(recall)
+        # old version of scipy return MaskedConstant instead of 0.0
+        if isinstance(gmean, np.ma.core.MaskedConstant):
+            return 0.0
+        return gmean
 
 
 def make_index_balanced_accuracy(alpha=0.1, squared=True):

imblearn/metrics/tests/test_classification.py

@@ -10,18 +10,17 @@
 
 import numpy as np
 
-from numpy.testing import (assert_allclose, assert_array_equal,
-                           assert_no_warnings, assert_equal,
-                           assert_raises)
-from sklearn.utils.testing import assert_warns_message, ignore_warnings
-
 from sklearn import datasets
 from sklearn import svm
 
 from sklearn.preprocessing import label_binarize
 from sklearn.utils.fixes import np_version
-from sklearn.utils.testing import assert_not_equal, assert_raise_message
 from sklearn.utils.validation import check_random_state
+from sklearn.utils.testing import (assert_allclose, assert_array_equal,
+                                   assert_no_warnings, assert_equal,
+                                   assert_raises, assert_warns_message,
+                                   ignore_warnings, assert_not_equal,
+                                   assert_raise_message)
 from sklearn.metrics import (accuracy_score, average_precision_score,
                              brier_score_loss, cohen_kappa_score,
                              jaccard_similarity_score, precision_score,
@@ -230,6 +229,7 @@ def test_geometric_mean_multiclass():
 
     y_true = [0, 0, 0, 0]
     y_pred = [1, 1, 1, 1]
+    print(geometric_mean_score(y_true, y_pred))
     assert_allclose(geometric_mean_score(y_true, y_pred), 0.0, rtol=R_TOL)
 
     cor = 0.001

imblearn/metrics/tests/test_score_objects.py

@@ -3,13 +3,12 @@
 #          Christos Aridas
 # License: MIT
 
-from numpy.testing import assert_allclose
-
 import sklearn
 
 from sklearn.datasets import make_blobs
 from sklearn.metrics import make_scorer
 from sklearn.svm import LinearSVC
+from sklearn.utils.testing import assert_allclose
 
 from imblearn.metrics import (sensitivity_score, specificity_score,
                               geometric_mean_score,

imblearn/over_sampling/tests/test_adasyn.py

@@ -6,8 +6,8 @@
 from __future__ import print_function
 
 import numpy as np
-from numpy.testing import (assert_allclose, assert_array_equal,
-                           assert_equal, assert_raises_regex)
+from sklearn.utils.testing import (assert_allclose, assert_array_equal,
+                                   assert_equal, assert_raises_regex)
 from sklearn.neighbors import NearestNeighbors
 
 from imblearn.over_sampling import ADASYN

imblearn/over_sampling/tests/test_random_over_sampler.py

@@ -8,7 +8,7 @@
 from collections import Counter
 
 import numpy as np
-from numpy.testing import assert_array_equal, assert_equal
+from sklearn.utils.testing import assert_array_equal, assert_equal
 
 from imblearn.over_sampling import RandomOverSampler
 

imblearn/over_sampling/tests/test_smote.py

@@ -6,8 +6,8 @@
 from __future__ import print_function
 
 import numpy as np
-from numpy.testing import (assert_allclose, assert_array_equal,
-                           assert_raises_regex)
+from sklearn.utils.testing import (assert_allclose, assert_array_equal,
+                                   assert_raises_regex)
 from sklearn.neighbors import NearestNeighbors
 from sklearn.svm import SVC
 

imblearn/under_sampling/prototype_generation/tests/test_cluster_centroids.py

@@ -4,8 +4,8 @@
 from collections import Counter
 
 import numpy as np
-from numpy.testing import (assert_allclose, assert_array_equal,
-                           assert_equal, assert_raises_regex)
+from sklearn.utils.testing import (assert_allclose, assert_array_equal,
+                                   assert_equal, assert_raises_regex)
 from sklearn.cluster import KMeans
 
 from imblearn.under_sampling import ClusterCentroids

imblearn/under_sampling/prototype_selection/tests/test_allknn.py

@@ -6,8 +6,8 @@
 from __future__ import print_function
 
 import numpy as np
-from numpy.testing import (assert_allclose, assert_array_equal,
-                           assert_raises)
+from sklearn.utils.testing import (assert_allclose, assert_array_equal,
+                                   assert_raises)
 from sklearn.neighbors import NearestNeighbors
 
 from imblearn.under_sampling import AllKNN