Cleanup pandas support

python-package/xgboost/compat.py

@@ -0,0 +1,47 @@
+# coding: utf-8
+# pylint: disable=unused-import, invalid-name
+"""For compatibility"""
+
+from __future__ import absolute_import
+
+import sys
+
+
+PY3 = (sys.version_info[0] == 3)
+
+if PY3:
+    # pylint: disable=invalid-name, redefined-builtin
+    STRING_TYPES = str,
+else:
+    # pylint: disable=invalid-name
+    STRING_TYPES = basestring,
+
+# pandas
+try:
+    from pandas import DataFrame
+    PANDAS_INSTALLED = True
+except ImportError:
+
+    class DataFrame(object):
+        """ dummy for pandas.DataFrame """
+        pass
+
+    PANDAS_INSTALLED = False
+
+# sklearn
+try:
+    from sklearn.base import BaseEstimator
+    from sklearn.base import RegressorMixin, ClassifierMixin
+    from sklearn.preprocessing import LabelEncoder
+    SKLEARN_INSTALLED = True
+
+    XGBModelBase = BaseEstimator
+    XGBRegressorBase = RegressorMixin
+    XGBClassifierBase = ClassifierMixin
+except ImportError:
+    SKLEARN_INSTALLED = False
+
+    # used for compatiblity without sklearn
+    XGBModelBase = object
+    XGBClassifierBase = object
+    XGBRegressorBase = object

python-package/xgboost/core.py

@@ -4,7 +4,6 @@
 from __future__ import absolute_import
 
 import os
-import sys
 import ctypes
 import collections
 
@@ -13,20 +12,12 @@ import scipy.sparse
 
 from .libpath import find_lib_path
 
+from .compat import STRING_TYPES, PY3, DataFrame
 
 class XGBoostError(Exception):
     """Error throwed by xgboost trainer."""
     pass
 
-PY3 = (sys.version_info[0] == 3)
-
-if PY3:
-    # pylint: disable=invalid-name, redefined-builtin
-    STRING_TYPES = str,
-else:
-    # pylint: disable=invalid-name
-    STRING_TYPES = basestring,
-
 
 def from_pystr_to_cstr(data):
     """Convert a list of Python str to C pointer
@@ -138,42 +129,49 @@ def c_array(ctype, values):
     return (ctype * len(values))(*values)
 
 
-def _maybe_from_pandas(data, label, feature_names, feature_types):
-    """ Extract internal data from pd.DataFrame """
-    try:
-        import pandas as pd
-    except ImportError:
-        return data, label, feature_names, feature_types
 
-    if not isinstance(data, pd.DataFrame):
-        return data, label, feature_names, feature_types
+PANDAS_DTYPE_MAPPER = {'int8': 'int', 'int16': 'int', 'int32': 'int', 'int64': 'int',
+                       'uint8': 'int', 'uint16': 'int', 'uint32': 'int', 'uint64': 'int',
+                       'float16': 'float', 'float32': 'float', 'float64': 'float',
+                       'bool': 'i'}
 
-    mapper = {'int8': 'int', 'int16': 'int', 'int32': 'int', 'int64': 'int',
-              'uint8': 'int', 'uint16': 'int', 'uint32': 'int', 'uint64': 'int',
-              'float16': 'float', 'float32': 'float', 'float64': 'float',
-              'bool': 'i'}
+
+def _maybe_pandas_data(data, feature_names, feature_types):
+    """ Extract internal data from pd.DataFrame for DMatrix data """
+
+    if not isinstance(data, DataFrame):
+        return data, feature_names, feature_types
 
     data_dtypes = data.dtypes
-    if not all(dtype.name in (mapper.keys()) for dtype in data_dtypes):
+    if not all(dtype.name in PANDAS_DTYPE_MAPPER for dtype in data_dtypes):
         raise ValueError('DataFrame.dtypes for data must be int, float or bool')
 
-    if label is not None:
-        if isinstance(label, pd.DataFrame):
-            label_dtypes = label.dtypes
-            if not all(dtype.name in (mapper.keys()) for dtype in label_dtypes):
-                raise ValueError('DataFrame.dtypes for label must be int, float or bool')
-            else:
-                label = label.values.astype('float')
-
     if feature_names is None:
         feature_names = data.columns.format()
 
     if feature_types is None:
-        feature_types = [mapper[dtype.name] for dtype in data_dtypes]
+        feature_types = [PANDAS_DTYPE_MAPPER[dtype.name] for dtype in data_dtypes]
 
     data = data.values.astype('float')
 
-    return data, label, feature_names, feature_types
+    return data, feature_names, feature_types
+
+
+def _maybe_pandas_label(label):
+    """ Extract internal data from pd.DataFrame for DMatrix label """
+
+    if isinstance(label, DataFrame):
+        if len(label.columns) > 1:
+            raise ValueError('DataFrame for label cannot have multiple columns')
+
+        label_dtypes = label.dtypes
+        if not all(dtype.name in PANDAS_DTYPE_MAPPER for dtype in label_dtypes):
+            raise ValueError('DataFrame.dtypes for label must be int, float or bool')
+        else:
+            label = label.values.astype('float')
+    # pd.Series can be passed to xgb as it is
+
+    return label
 
 class DMatrix(object):
     """Data Matrix used in XGBoost.
@@ -216,13 +214,10 @@ class DMatrix(object):
             self.handle = None
             return
 
-        klass = getattr(getattr(data, '__class__', None), '__name__', None)
-        if klass == 'DataFrame':
-            # once check class name to avoid unnecessary pandas import
-            data, label, feature_names, feature_types = _maybe_from_pandas(data,
-                                                                           label,
-                                                                           feature_names,
-                                                                           feature_types)
+        data, feature_names, feature_types = _maybe_pandas_data(data,
+                                                                feature_names,
+                                                                feature_types)
+        label = _maybe_pandas_label(label)
 
         if isinstance(data, STRING_TYPES):
             self.handle = ctypes.c_void_p()

python-package/xgboost/sklearn.py

@@ -7,23 +7,9 @@ import numpy as np
 from .core import Booster, DMatrix, XGBoostError
 from .training import train
 
-try:
-    from sklearn.base import BaseEstimator
-    from sklearn.base import RegressorMixin, ClassifierMixin
-    from sklearn.preprocessing import LabelEncoder
-    SKLEARN_INSTALLED = True
-except ImportError:
-    SKLEARN_INSTALLED = False
+from .compat import (SKLEARN_INSTALLED, XGBModelBase,
+                     XGBClassifierBase, XGBRegressorBase, LabelEncoder)
 
-# used for compatiblity without sklearn
-XGBModelBase = object
-XGBClassifierBase = object
-XGBRegressorBase = object
-
-if SKLEARN_INSTALLED:
-    XGBModelBase = BaseEstimator
-    XGBRegressorBase = RegressorMixin
-    XGBClassifierBase = ClassifierMixin
 
 class XGBModel(XGBModelBase):
     # pylint: disable=too-many-arguments, too-many-instance-attributes, invalid-name