fix #200 (#312)

docs/Python-API.md

@@ -6,7 +6,7 @@
 
 * [Training API](Python-API.md#training-api)
     - [train](Python-API.md#trainparams-train_set-num_boost_round100-valid_setsnone-valid_namesnone-fobjnone-fevalnone-init_modelnone-feature_nameauto-early_stopping_roundsnone-evals_resultnone-verbose_evaltrue-learning_ratesnone-callbacksnone)
-    - [cv](Python-API.md#cvparams-train_set-num_boost_round10-nfold5-stratifiedfalse-shuffletrue-metricsnone-fobjnone-fevalnone-init_modelnone-feature_nameauto-early_stopping_roundsnone-fpreprocnone-verbose_evalnone-show_stdvtrue-seed0-callbacksnone)
+    - [cv](Python-API.md#cvparams-train_set-num_boost_round10-data_splitternone-nfold5-stratifiedfalse-shuffletrue-metricsnone-fobjnone-fevalnone-init_modelnone-feature_nameauto-early_stopping_roundsnone-fpreprocnone-verbose_evalnone-show_stdvtrue-seed0-callbacksnone)
 
 * [Scikit-learn API](Python-API.md#scikit-learn-api)
     - [Common Methods](Python-API.md#common-methods)
@@ -515,7 +515,7 @@ The methods of each Class is in alphabetical order.
     booster : a trained booster model
 
 
-####cv(params, train_set, num_boost_round=10, nfold=5, stratified=False, shuffle=True, metrics=None, fobj=None, feval=None, init_model=None, feature_name='auto', early_stopping_rounds=None, fpreproc=None, verbose_eval=None, show_stdv=True, seed=0, callbacks=None)
+####cv(params, train_set, num_boost_round=10, data_splitter=None, nfold=5, stratified=False, shuffle=True, metrics=None, fobj=None, feval=None, init_model=None, feature_name='auto', early_stopping_rounds=None, fpreproc=None, verbose_eval=None, show_stdv=True, seed=0, callbacks=None)
 
     Cross-validation with given paramaters.
 

python-package/lightgbm/basic.py

@@ -12,9 +12,8 @@
 import numpy as np
 import scipy.sparse
 
-from .compat import (DataFrame, Series, integer_types, json,
-                     json_default_with_numpy, numeric_types, range_,
-                     string_type)
+from .compat import (DataFrame, Series, integer_types, json, numeric_types,
+                     range_, string_type)
 from .libpath import find_lib_path
 
 
@@ -223,25 +222,19 @@ def c_int_array(data):
 
 def _data_from_pandas(data, feature_name):
     if isinstance(data, DataFrame):
-        if feature_name == 'auto' or feature_name is None:
+        bad_fields = [data.columns[i] for i, dtype in enumerate(data.dtypes) if dtype.name not in PANDAS_DTYPE_MAPPER]
+        if bad_fields:
+            msg = """DataFrame.dtypes for data must be int, float or bool. Did not expect the data types in fields: """
+            raise ValueError(msg + ', '.join(bad_fields))
+        if feature_name == 'auto':
             if all([isinstance(name, integer_types + (np.integer, )) for name in data.columns]):
                 msg = """Using Pandas (default) integer column names, not column indexes. You can use indexes with DataFrame.values."""
                 warnings.filterwarnings('once')
                 warnings.warn(msg, stacklevel=5)
-            data = data.rename(columns=str)
-        if feature_name == 'auto':
-            feature_name = list(data.columns)
-        data_dtypes = data.dtypes
-        if not all(dtype.name in PANDAS_DTYPE_MAPPER for dtype in data_dtypes):
-            bad_fields = [data.columns[i] for i, dtype in
-                          enumerate(data_dtypes) if dtype.name not in PANDAS_DTYPE_MAPPER]
-
-            msg = """DataFrame.dtypes for data must be int, float or bool. Did not expect the data types in fields """
-            raise ValueError(msg + ', '.join(bad_fields))
+            feature_name = [str(name) for name in data.columns]
         data = data.values.astype('float')
-    else:
-        if feature_name == 'auto':
-            feature_name = None
+    elif feature_name == 'auto':
+        feature_name = None
     return data, feature_name
 
 
@@ -366,9 +359,6 @@ def predict(self, data, num_iteration=-1,
         elif isinstance(data, np.ndarray):
             preds, nrow = self.__pred_for_np2d(data, num_iteration,
                                                predict_type)
-        elif isinstance(data, DataFrame):
-            preds, nrow = self.__pred_for_np2d(data.values, num_iteration,
-                                               predict_type)
         else:
             try:
                 csr = scipy.sparse.csr_matrix(data)

python-package/lightgbm/compat.py

@@ -39,15 +39,6 @@ def argc_(func):
     import json
 
 
-def json_default_with_numpy(obj):
-    if isinstance(obj, (np.integer, np.floating, np.bool_)):
-        return obj.item()
-    elif isinstance(obj, np.ndarray):
-        return obj.tolist()
-    else:
-        return obj
-
-
 """pandas"""
 try:
     from pandas import Series, DataFrame