Mac build fix

scripts/travis_osx_install.sh

@@ -5,15 +5,3 @@ if [ ${TRAVIS_OS_NAME} != "osx" ]; then
 fi
 
 brew update
-
-if [ ${TASK} == "python-package" ]; then
-    brew install python git graphviz
-    easy_install pip
-    pip install numpy scipy matplotlib nose
-fi
-
-if [ ${TASK} == "python-package3" ]; then
-    brew install python3 git graphviz
-    sudo pip3 install --upgrade setuptools
-    pip3 install numpy scipy matplotlib nose graphviz
-fi

scripts/travis_script.sh

@@ -33,30 +33,44 @@ if [ ${TASK} == "R-package" ]; then
     scripts/travis_R_script.sh || exit -1
 fi
 
-if [ ${TASK} == "python-package" ]; then
-    sudo apt-get install graphviz
-    sudo apt-get install python-numpy python-scipy python-matplotlib python-nose
-    sudo python -m pip install graphviz
-    make all CXX=${CXX} || exit -1
-    nosetests tests/python || exit -1
-fi
+if [ ${TASK} == "python-package" -o ${TASK} == "python-package3" ]; then
 
-if [ ${TASK} == "python-package3" ]; then
-    sudo apt-get install graphviz
-    # python3-matplotlib is unavailale on Ubuntu 12.04
-    sudo apt-get install python3-dev
-    sudo apt-get install python3-numpy python3-scipy python3-nose python3-setuptools
-
-    make all CXX=${CXX} || exit -1
-
-    if [ ${TRAVIS_OS_NAME} != "osx" ]; then
-        sudo easy_install3 pip
-        sudo easy_install3 -U distribute
-        sudo pip install graphviz matplotlib
-        nosetests3 tests/python || exit -1
+    if [ ${TRAVIS_OS_NAME} == "osx" ]; then
+        brew install graphviz
+        if [ ${TASK} == "python-package3" ]; then
+            wget -O conda.sh https://repo.continuum.io/miniconda/Miniconda3-latest-MacOSX-x86_64.sh
+        else
+            wget -O conda.sh https://repo.continuum.io/miniconda/Miniconda-latest-MacOSX-x86_64.sh
+        fi
     else
-        nosetests tests/python || exit -1
+        sudo apt-get install graphviz
+        if [ ${TASK} == "python-package3" ]; then
+            wget -O conda.sh https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
+        else
+            wget -O conda.sh https://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh
+        fi
     fi
+    bash conda.sh -b -p $HOME/miniconda
+    export PATH="$HOME/miniconda/bin:$PATH"
+    hash -r
+    conda config --set always_yes yes --set changeps1 no
+    conda update -q conda
+    # Useful for debugging any issues with conda
+    conda info -a
+
+    if [ ${TASK} == "python-package3" ]; then
+        conda create -n myenv python=3.4
+    else
+        conda create -n myenv python=2.7
+    fi
+    source activate myenv
+    conda install numpy scipy matplotlib nose
+    python -m pip install graphviz
+
+    make all CXX=${CXX} || exit -1
+
+    python -m nose tests/python || exit -1
+    python --version
 fi
 
 # only test java under linux for now

tests/python/test_basic.py

@@ -1,3 +1,4 @@
+# -*- coding: utf-8 -*-
 import numpy as np
 import xgboost as xgb
 
@@ -33,21 +34,25 @@ def test_feature_names():
     data = np.random.randn(100, 5)
     target = np.array([0, 1] * 50)
 
-    features = ['Feature1', 'Feature2', 'Feature3', 'Feature4', 'Feature5']
-    dm = xgb.DMatrix(data, label=target,
-                     feature_names=features)
-    assert dm.feature_names == features
-    assert dm.num_row() == 100
-    assert dm.num_col() == 5
+    cases = [['Feature1', 'Feature2', 'Feature3', 'Feature4', 'Feature5'],
+             [u'要因1', u'要因2', u'要因3', u'要因4', u'要因5']]
 
-    params={'objective': 'multi:softprob',
-            'eval_metric': 'mlogloss',
-            'eta': 0.3,
-            'num_class': 3}
+    for features in cases:
+        dm = xgb.DMatrix(data, label=target,
+                         feature_names=features)
+        assert dm.feature_names == features
+        assert dm.num_row() == 100
+        assert dm.num_col() == 5
+
+        params={'objective': 'multi:softprob',
+                'eval_metric': 'mlogloss',
+                'eta': 0.3,
+                'num_class': 3}
+
+        bst = xgb.train(params, dm, num_boost_round=10)
+        scores = bst.get_fscore()
+        assert list(sorted(k for k in scores)) == features
 
-    bst = xgb.train(params, dm, num_boost_round=10)
-    scores = bst.get_fscore()
-    assert list(sorted(k for k in scores)) == features
 
 def test_plotting():
     bst2 = xgb.Booster(model_file='xgb.model')