move pandas support into basic.py

python-package/lightgbm/basic.py

@@ -358,6 +358,39 @@ class Predictor(object):
             raise ValueError("incorrect number for predict result")
         return preds, nrow
 
+# pandas
+try:
+    from pandas import DataFrame
+except ImportError:
+    class DataFrame(object):
+        pass
+
+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'}
+
+def _data_from_pandas(data):
+    if isinstance(data, DataFrame):
+        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))
+        data = data.values.astype('float')
+    return data
+
+def _label_from_pandas(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')
+        label = label.values.astype('float')
+    return label
 
 class Dataset(object):
     """Dataset used in LightGBM.
@@ -398,6 +431,8 @@ class Dataset(object):
         if data is None:
             self.handle = None
             return
+        data = _data_from_pandas(data)
+        label = _label_from_pandas(label)
         self.data_has_header = False
         """process for args"""
         params = {} if params is None else params

python-package/lightgbm/engine.py

@@ -6,40 +6,6 @@ import numpy as np
 from .basic import LightGBMError, Predictor, Dataset, Booster, is_str
 from . import callback
 
-# pandas
-try:
-    from pandas import DataFrame
-except ImportError:
-    class DataFrame(object):
-        pass
-
-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'}
-
-def _data_from_pandas(data):
-    if isinstance(data, DataFrame):
-        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))
-        data = data.values.astype('float')
-    return data
-
-def _label_from_pandas(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')
-        label = label.values.astype('float')
-    return label
-
 def _construct_dataset(X_y, reference=None,
     params=None, other_fields=None, predictor=None):
     if 'max_bin' in params:
@@ -61,8 +27,8 @@ def _construct_dataset(X_y, reference=None,
     else:
         if len(X_y) != 2:
             raise TypeError("should pass (data, label) pair")
-        data = _data_from_pandas(X_y[0])
-        label = _label_from_pandas(X_y[1])
+        data = X_y[0]
+        label = X_y[1]
     if reference is None:
         ret = Dataset(data, label=label, max_bin=max_bin, 
         weight=weight, group=group, predictor=predictor, params=params)

tests/python_package_test/test_sklearn.py

@@ -54,7 +54,7 @@ def test_regression():
     x_train, x_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.1)
     lgb_model = lgb.LGBMRegressor().fit(x_train, y_train,eval_set=[[x_train, y_train],(x_test, y_test)], eval_metric='l2')
     preds = lgb_model.predict(x_test)
-    assert mean_squared_error(preds, y_test) < 30
+    assert mean_squared_error(preds, y_test) < 40
 
 def test_regression_with_custom_objective():
     from sklearn.metrics import mean_squared_error
@@ -71,7 +71,7 @@ def test_regression_with_custom_objective():
     x_train, x_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.1)
     lgb_model = lgb.LGBMRegressor(objective=objective_ls).fit(x_train, y_train,eval_set=[[x_train, y_train],(x_test, y_test)], eval_metric='l2')
     preds = lgb_model.predict(x_test)
-    assert mean_squared_error(preds, y_test) < 30
+    assert mean_squared_error(preds, y_test) < 40
 
 
 def test_binary_classification_with_custom_objective():