DOC/TST: Fix Python sklearn dep

python-package/xgboost/compat.py

@@ -48,11 +48,13 @@ try:
     from sklearn.cross_validation import KFold, StratifiedKFold
     SKLEARN_INSTALLED = True
 
-    XGBKFold = KFold
-    XGBStratifiedKFold = StratifiedKFold
     XGBModelBase = BaseEstimator
     XGBRegressorBase = RegressorMixin
     XGBClassifierBase = ClassifierMixin
+
+    XGBKFold = KFold
+    XGBStratifiedKFold = StratifiedKFold
+    XGBLabelEncoder = LabelEncoder
 except ImportError:
     SKLEARN_INSTALLED = False
 
@@ -60,5 +62,7 @@ except ImportError:
     XGBModelBase = object
     XGBClassifierBase = object
     XGBRegressorBase = object
+
     XGBKFold = None
     XGBStratifiedKFold = None
+    XGBLabelEncoder = None

python-package/xgboost/sklearn.py

@@ -7,8 +7,10 @@ import numpy as np
 from .core import Booster, DMatrix, XGBoostError
 from .training import train
 
+# Do not use class names on scikit-learn directly.
+# Re-define the classes on .compat to guarantee the behavior without scikit-learn
 from .compat import (SKLEARN_INSTALLED, XGBModelBase,
-                     XGBClassifierBase, XGBRegressorBase, LabelEncoder)
+                     XGBClassifierBase, XGBRegressorBase, XGBLabelEncoder)
 
 
 def _objective_decorator(func):
@@ -398,7 +400,7 @@ class XGBClassifier(XGBModel, XGBClassifierBase):
 
         self._features_count = X.shape[1]
 
-        self._le = LabelEncoder().fit(y)
+        self._le = XGBLabelEncoder().fit(y)
         training_labels = self._le.transform(y)
 
         if sample_weight is not None:

python-package/xgboost/testing.py

@@ -0,0 +1,22 @@
+# coding: utf-8
+
+import nose
+
+from xgboost.compat import SKLEARN_INSTALLED, PANDAS_INSTALLED
+
+
+def _skip_if_no_sklearn():
+    if not SKLEARN_INSTALLED:
+        raise nose.SkipTest()
+
+
+def _skip_if_no_pandas():
+    if not PANDAS_INSTALLED:
+        raise nose.SkipTest()
+
+
+def _skip_if_no_matplotlib():
+    try:
+        import matplotlib.pyplot as plt     # noqa
+    except ImportError:
+        raise nose.SkipTest()