[MRG+1] Update version dependencies to numpy 1.8.2, scipy 1.13.3 (scikit-learn#8855)

Author: jnothman

.travis.yml

@@ -15,6 +15,7 @@ addons:
       - python-scipy
       - libatlas3gf-base
       - libatlas-dev
+dist: trusty
 env:
   global:
     # Directory where tests are run from
@@ -23,17 +24,17 @@ env:
     - OPENBLAS_NUM_THREADS=4
   matrix:
     # This environment tests that scikit-learn can be built against
-    # versions of numpy, scipy with ATLAS that comes with Ubuntu Precise 12.04
+    # versions of numpy, scipy with ATLAS that comes with Ubuntu Trusty 14.04
     - DISTRIB="ubuntu" PYTHON_VERSION="2.7" CYTHON_VERSION="0.23.4"
       COVERAGE=true
     # This environment tests the oldest supported anaconda env
     - DISTRIB="conda" PYTHON_VERSION="2.7" INSTALL_MKL="false"
-      NUMPY_VERSION="1.6.2" SCIPY_VERSION="0.11.0" CYTHON_VERSION="0.23"
+      NUMPY_VERSION="1.8.1" SCIPY_VERSION="0.13.3" CYTHON_VERSION="0.23"
       COVERAGE=true
     # This environment tests the newest supported anaconda env
     # It also runs tests requiring Pandas.
     - DISTRIB="conda" PYTHON_VERSION="3.6" INSTALL_MKL="true"
-      NUMPY_VERSION="1.11.2" SCIPY_VERSION="0.18.1" PANDAS_VERSION="0.19.1"
+      NUMPY_VERSION="1.11.2" SCIPY_VERSION="0.18.1" PANDAS_VERSION="0.19.2"
       CYTHON_VERSION="0.25.2" COVERAGE=true
     # This environment use pytest to run the tests. It uses the newest
     # supported anaconda env. It also runs tests requiring Pandas.

README.rst

@@ -50,8 +50,8 @@ Dependencies
 scikit-learn requires:
 
 - Python (>= 2.7 or >= 3.3)
-- NumPy (>= 1.6.1)
-- SciPy (>= 0.9)
+- NumPy (>= 1.8.1)
+- SciPy (>= 0.13.3)
 
 For running the examples Matplotlib >= 1.1.1 is required.
 

build_tools/travis/install.sh

@@ -45,7 +45,11 @@ if [[ "$DISTRIB" == "conda" ]]; then
             ${PANDAS_VERSION+pandas=$PANDAS_VERSION}
 
     else
+        # libgfortran=1 needs to be specified explicitly. This is a work-around for
+        # https://github.com/ContinuumIO/anaconda-issues/issues/445
+        # with numpy=1.8.1, python=2.7
         conda create -n testenv --yes python=$PYTHON_VERSION pip nose pytest \
+            libgfortran=1 \
             numpy=$NUMPY_VERSION scipy=$SCIPY_VERSION \
             nomkl cython=$CYTHON_VERSION \
             ${PANDAS_VERSION+pandas=$PANDAS_VERSION}

doc/conf.py

@@ -239,8 +239,8 @@
     'reference_url': {
         'sklearn': None,
         'matplotlib': 'http://matplotlib.org',
-        'numpy': 'http://docs.scipy.org/doc/numpy-1.6.0',
-        'scipy': 'http://docs.scipy.org/doc/scipy-0.11.0/reference'},
+        'numpy': 'http://docs.scipy.org/doc/numpy-1.8.1',
+        'scipy': 'http://docs.scipy.org/doc/scipy-0.13.3/reference'},
     'expected_failing_examples': [
         '../examples/applications/plot_stock_market.py']
 }

doc/developers/advanced_installation.rst

@@ -36,8 +36,8 @@ Installing an official release
 Scikit-learn requires:
 
 - Python (>= 2.7 or >= 3.3),
-- NumPy (>= 1.6.1),
-- SciPy (>= 0.9).
+- NumPy (>= 1.8.1),
+- SciPy (>= 0.13.3).
 
 
 Mac OSX

doc/install.rst

@@ -16,8 +16,8 @@ Installing the latest release
 Scikit-learn requires:
 
 - Python (>= 2.7 or >= 3.3),
-- NumPy (>= 1.6.1),
-- SciPy (>= 0.9).
+- NumPy (>= 1.8.1),
+- SciPy (>= 0.13.3).
 
 If you already have a working installation of numpy and scipy,
 the easiest way to install scikit-learn is using ``pip`` ::

setup.py

@@ -40,8 +40,8 @@
 
 VERSION = sklearn.__version__
 
-SCIPY_MIN_VERSION = '0.9'
-NUMPY_MIN_VERSION = '1.6.1'
+SCIPY_MIN_VERSION = '0.13.3'
+NUMPY_MIN_VERSION = '1.8.1'
 
 
 # Optional setuptools features