add pandas support

python-package/lightgbm/engine.py

@@ -6,7 +6,41 @@ import numpy as np
 from .basic import LightGBMError, Predictor, Dataset, Booster, is_str
 from . import callback
 
-def _construct_dataset(data, reference=None,
+# 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:
         max_bin = int(params['max_bin'])
@@ -21,18 +55,20 @@ def _construct_dataset(data, reference=None,
         weight = None if 'weight' not in other_fields else other_fields['weight']
         group = None if 'group' not in other_fields else other_fields['group']
         init_score = None if 'init_score' not in other_fields else other_fields['init_score']
+    if is_str(X_y):
+        data = X_y
+        label = 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])
     if reference is None:
-        if is_str(data):
-            ret = Dataset(data, label=None, max_bin=max_bin, 
-            weight=weight, group=group, predictor=predictor, params=params)
-        else:
-            ret = Dataset(data[0], data[1], max_bin=max_bin, 
-                weight=weight, group=group, predictor=predictor, params=params)
+        ret = Dataset(data, label=label, max_bin=max_bin, 
+        weight=weight, group=group, predictor=predictor, params=params)
+
     else:
-        if is_str(data):
-            ret = reference.create_valid(data, label=None, weight=weight, group=group, params=params)
-        else:
-            ret = reference.create_valid(data[0], data[1], weight, group, params=params)
+        ret = reference.create_valid(data, label=label, weight=weight, group=group, params=params)
     if init_score is not None:
         ret.set_init_score(init_score)
     return ret