CI/ENH: test against an older version of statsmodels

ci/install.sh

@@ -62,19 +62,6 @@ if [ x"$FULL_DEPS" == x"true" ]; then
     echo "Installing FULL_DEPS"
     # for pytables gets the lib as well
     time sudo apt-get $APT_ARGS install libhdf5-serial-dev
-
-    # fool statsmodels into thinking pandas was already installed
-    # so it won't refuse to install itself.
-
-    SITE_PKG_DIR=$VIRTUAL_ENV/lib/python$TRAVIS_PYTHON_VERSION/site-packages
-    echo "Using SITE_PKG_DIR: $SITE_PKG_DIR"
-
-    mkdir  $SITE_PKG_DIR/pandas
-    touch $SITE_PKG_DIR/pandas/__init__.py
-    echo "version='0.10.0-phony'" >  $SITE_PKG_DIR/pandas/version.py
-    time pip install $PIP_ARGS git+git://github.com/statsmodels/statsmodels@c9062e43b8a5f7385537ca95#egg=statsmodels
-
-    rm -Rf $SITE_PKG_DIR/pandas # scrub phoney pandas
 fi
 
 # build pandas

ci/requirements-2.7.txt

@@ -17,3 +17,4 @@ scikits.timeseries==0.91.3
 MySQL-python==1.2.4
 scipy==0.10.0
 beautifulsoup4==4.2.1
+statsmodels==0.5.0

ci/requirements-2.7_LOCALE.txt

@@ -15,3 +15,4 @@ html5lib==1.0b2
 lxml==3.2.1
 scipy==0.10.0
 beautifulsoup4==4.2.1
+statsmodels==0.5.0

ci/requirements-3.2.txt

@@ -12,3 +12,4 @@ patsy==0.1.0
 lxml==3.2.1
 scipy==0.12.0
 beautifulsoup4==4.2.1
+statsmodels==0.4.3

ci/requirements-3.3.txt

@@ -13,3 +13,4 @@ patsy==0.1.0
 lxml==3.2.1
 scipy==0.12.0
 beautifulsoup4==4.2.1
+statsmodels==0.4.3

ci/speedpack/build.sh

@@ -26,6 +26,42 @@ apt-get build-dep python-lxml -y
 export PYTHONIOENCODING='utf-8'
 export VIRTUALENV_DISTRIBUTE=0
 
+
+function create_fake_pandas() {
+    local site_pkg_dir="$1"
+    rm -rf $site_pkg_dir/pandas
+    mkdir $site_pkg_dir/pandas
+    touch $site_pkg_dir/pandas/__init__.py
+    echo "version = '0.10.0-phony'" > $site_pkg_dir/pandas/version.py
+}
+
+
+function get_site_pkgs_dir() {
+    python$1 -c 'import distutils; print(distutils.sysconfig.get_python_lib())'
+}
+
+
+function create_wheel() {
+    local pip_args="$1"
+    local wheelhouse="$2"
+    local n="$3"
+    local pyver="$4"
+
+    local site_pkgs_dir="$(get_site_pkgs_dir $pyver)"
+
+
+    if [[ "$n" == *statsmodels* ]]; then
+        create_fake_pandas $site_pkgs_dir && \
+        pip wheel $pip_args --wheel-dir=$wheelhouse $n && \
+        pip install $pip_args --no-index $n && \
+        rm -Rf $site_pkgs_dir
+    else
+        pip wheel $pip_args --wheel-dir=$wheelhouse $n
+        pip install $pip_args --no-index $n
+    fi
+}
+
+
 function generate_wheels() {
     # get the requirements file
     local reqfile="$1"
@@ -62,8 +98,7 @@ function generate_wheels() {
 
     # install and build the wheels
     cat $reqfile | while read N; do
-        pip wheel $PIP_ARGS --wheel-dir=$WHEELHOUSE $N
-        pip install $PIP_ARGS --no-index $N
+        create_wheel "$PIP_ARGS" "$WHEELHOUSE" "$N" "$PY_VER"
     done
 }
 

doc/source/release.rst

@@ -128,6 +128,8 @@ Improvements to existing features
   - ``read_stata`` now accepts Stata 13 format (:issue:`4291`)
   - ``ExcelWriter`` and ``ExcelFile`` can be used as contextmanagers.
     (:issue:`3441`, :issue:`4933`)
+  - ``pandas`` is now tested with two different versions of ``statsmodels``
+    (0.4.3 and 0.5.0) (:issue:`4981`).
 
 API Changes
 ~~~~~~~~~~~

pandas/stats/tests/test_ols.py

@@ -6,6 +6,7 @@
 
 from __future__ import division
 
+from distutils.version import LooseVersion
 from datetime import datetime
 from pandas import compat
 import unittest
@@ -98,11 +99,10 @@ def testOLSWithDatasets_scotland(self):
 
     def testWLS(self):
         # WLS centered SS changed (fixed) in 0.5.0
-        v = sm.version.version.split('.')
-        if int(v[0]) >= 0 and int(v[1]) <= 5:
-            if int(v[2]) < 1:
-                raise nose.SkipTest
-        print( "Make sure you're using statsmodels 0.5.0.dev-cec4f26 or later.")
+        sm_version = sm.version.version
+        if sm_version < LooseVersion('0.5.0'):
+            raise nose.SkipTest("WLS centered SS not fixed in statsmodels"
+                                " version {0}".format(sm_version))
 
         X = DataFrame(np.random.randn(30, 4), columns=['A', 'B', 'C', 'D'])
         Y = Series(np.random.randn(30))