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Merge pull request #597 from JohanManders/python-pandas-dtypes

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Python pandas dtypes
This commit is contained in:
Yuan (Terry) Tang 2015-11-09 18:08:41 -06:00
commit 1dd96b6cdc
2 changed files with 40 additions and 24 deletions
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54
python-package/xgboost/core.py
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@ -138,27 +138,42 @@ def c_array(ctype, values):
return (ctype * len(values))(*values)
def _maybe_from_pandas(data, feature_names, feature_types):
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, feature_names, feature_types
return data, label, feature_names, feature_types
if not isinstance(data, pd.DataFrame):
return data, feature_names, feature_types
return data, label, feature_names, feature_types
dtypes = data.dtypes
if not all(dtype.name in ('int64', 'float64', 'bool') for dtype in dtypes):
raise ValueError('DataFrame.dtypes must be int, float or bool')
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'}
data_dtypes = data.dtypes
if not all(dtype.name in (mapper.keys()) 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:
mapper = {'int64': 'int', 'float64': 'q', 'bool': 'i'}
feature_types = [mapper[dtype.name] for dtype in dtypes]
feature_types = [mapper[dtype.name] for dtype in data_dtypes]
data = data.values.astype('float')
return data, feature_names, feature_types
return data, label, feature_names, feature_types
class DMatrix(object):
"""Data Matrix used in XGBoost.
@ -192,9 +207,9 @@ class DMatrix(object):
silent : boolean, optional
Whether print messages during construction
feature_names : list, optional
Labels for features.
Set names for features.
feature_types : list, optional
Labels for features.
Set types for features.
"""
# force into void_p, mac need to pass things in as void_p
if data is None:
@ -204,8 +219,10 @@ class DMatrix(object):
klass = getattr(getattr(data, '__class__', None), '__name__', None)
if klass == 'DataFrame':
# once check class name to avoid unnecessary pandas import
data, feature_names, feature_types = _maybe_from_pandas(data, feature_names,
feature_types)
data, label, feature_names, feature_types = _maybe_from_pandas(data,
label,
feature_names,
feature_types)
if isinstance(data, STRING_TYPES):
self.handle = ctypes.c_void_p()
@ -520,10 +537,10 @@ class DMatrix(object):
if len(feature_names) != self.num_col():
msg = 'feature_names must have the same length as data'
raise ValueError(msg)
# prohibit to use symbols may affect to parse. e.g. ``[]=.``
if not all(isinstance(f, STRING_TYPES) and f.isalnum()
# prohibit to use symbols may affect to parse. e.g. []<
if not all(isinstance(f, STRING_TYPES) and not any(x in f for x in {'[', ']', '<'})
for f in feature_names):
raise ValueError('all feature_names must be alphanumerics')
raise ValueError('feature_names may not contain [, ] or <')
else:
# reset feature_types also
self.feature_types = None
@ -556,12 +573,11 @@ class DMatrix(object):
if len(feature_types) != self.num_col():
msg = 'feature_types must have the same length as data'
raise ValueError(msg)
# prohibit to use symbols may affect to parse. e.g. ``[]=.``
valid = ('q', 'i', 'int', 'float')
valid = ('int', 'float', 'i', 'q')
if not all(isinstance(f, STRING_TYPES) and f in valid
for f in feature_types):
raise ValueError('all feature_names must be {i, q, int, float}')
raise ValueError('All feature_names must be {int, float, i, q}')
self._feature_types = feature_types

10
tests/python/test_basic.py
View File

@ -48,7 +48,7 @@ class TestBasic(unittest.TestCase):
feature_names=['a', 'b', 'c', 'd', 'd'])
# contains symbol
self.assertRaises(ValueError, xgb.DMatrix, data,
feature_names=['a', 'b', 'c', 'd', 'e=1'])
feature_names=['a', 'b', 'c', 'd', 'e<1'])
dm = xgb.DMatrix(data)
dm.feature_names = list('abcde')
@ -105,7 +105,7 @@ class TestBasic(unittest.TestCase):
df = pd.DataFrame([[1, 2., True], [2, 3., False]], columns=['a', 'b', 'c'])
dm = xgb.DMatrix(df, label=pd.Series([1, 2]))
assert dm.feature_names == ['a', 'b', 'c']
assert dm.feature_types == ['int', 'q', 'i']
assert dm.feature_types == ['int', 'float', 'i']
assert dm.num_row() == 2
assert dm.num_col() == 3
@ -125,14 +125,14 @@ class TestBasic(unittest.TestCase):
df = pd.DataFrame([[1, 2., True], [2, 3., False]])
dm = xgb.DMatrix(df, label=pd.Series([1, 2]))
assert dm.feature_names == ['0', '1', '2']
assert dm.feature_types == ['int', 'q', 'i']
assert dm.feature_types == ['int', 'float', 'i']
assert dm.num_row() == 2
assert dm.num_col() == 3
df = pd.DataFrame([[1, 2., 1], [2, 3., 1]], columns=[4, 5, 6])
dm = xgb.DMatrix(df, label=pd.Series([1, 2]))
assert dm.feature_names == ['4', '5', '6']
assert dm.feature_types == ['int', 'q', 'int']
assert dm.feature_types == ['int', 'float', 'int']
assert dm.num_row() == 2
assert dm.num_col() == 3
@ -293,4 +293,4 @@ class TestBasic(unittest.TestCase):
assert isinstance(g, Digraph)
ax = xgb.plot_tree(classifier, num_trees=0)
assert isinstance(ax, Axes)
assert isinstance(ax, Axes)