Change to properties
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sinhrks
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db692a30e5
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f6f3473d17
@ -146,8 +146,8 @@ class DMatrix(object):
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You can construct DMatrix from numpy.arrays
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"""
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feature_names = None # for previous version's pickle
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feature_types = None
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_feature_names = None # for previous version's pickle
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_feature_types = None
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def __init__(self, data, label=None, missing=0.0,
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weight=None, silent=False,
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@ -200,8 +200,8 @@ class DMatrix(object):
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if weight is not None:
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self.set_weight(weight)
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self.set_feature_names(feature_names)
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self.set_feature_types(feature_types)
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self.feature_names = feature_names
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self.feature_types = feature_types
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def _init_from_csr(self, csr):
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"""
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@ -381,66 +381,6 @@ class DMatrix(object):
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c_array(ctypes.c_uint, group),
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len(group)))
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def set_feature_names(self, feature_names):
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"""Set feature names (column labels).
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Parameters
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----------
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feature_names : list or None
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Labels for features. None will reset existing feature names
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"""
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if not feature_names is None:
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# validate feature name
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if not isinstance(feature_names, list):
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feature_names = list(feature_names)
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if len(feature_names) != len(set(feature_names)):
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raise ValueError('feature_names must be unique')
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if len(feature_names) != self.num_col():
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msg = 'feature_names must have the same length as data'
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raise ValueError(msg)
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# prohibit to use symbols may affect to parse. e.g. ``[]=.``
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if not all(isinstance(f, STRING_TYPES) and f.isalnum()
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for f in feature_names):
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raise ValueError('all feature_names must be alphanumerics')
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else:
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# reset feature_types also
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self.set_feature_types(None)
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self.feature_names = feature_names
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def set_feature_types(self, feature_types):
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"""Set feature types (column types).
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This is for displaying the results and unrelated
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to the learning process.
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Parameters
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----------
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feature_types : list or None
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Labels for features. None will reset existing feature names
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"""
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if not feature_types is None:
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if self.feature_names is None:
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msg = 'Unable to set feature types before setting names'
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raise ValueError(msg)
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if isinstance(feature_types, STRING_TYPES):
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# single string will be applied to all columns
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feature_types = [feature_types] * self.num_col()
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if not isinstance(feature_types, list):
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feature_types = list(feature_types)
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if len(feature_types) != self.num_col():
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msg = 'feature_types must have the same length as data'
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raise ValueError(msg)
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# prohibit to use symbols may affect to parse. e.g. ``[]=.``
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valid = ('q', 'i', 'int', 'float')
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if not all(isinstance(f, STRING_TYPES) and f in valid
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for f in feature_types):
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raise ValueError('all feature_names must be {i, q, int, float}')
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self.feature_types = feature_types
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def get_label(self):
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"""Get the label of the DMatrix.
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@ -468,24 +408,6 @@ class DMatrix(object):
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"""
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return self.get_float_info('base_margin')
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def get_feature_names(self):
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"""Get feature names (column labels).
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Returns
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-------
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feature_names : list or None
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"""
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return self.feature_names
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def get_feature_types(self):
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"""Get feature types (column types).
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Returns
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-------
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feature_types : list or None
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"""
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return self.feature_types
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def num_row(self):
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"""Get the number of rows in the DMatrix.
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@ -531,6 +453,88 @@ class DMatrix(object):
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ctypes.byref(res.handle)))
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return res
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@property
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def feature_names(self):
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"""Get feature names (column labels).
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Returns
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-------
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feature_names : list or None
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"""
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return self._feature_names
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@property
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def feature_types(self):
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"""Get feature types (column types).
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Returns
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-------
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feature_types : list or None
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"""
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return self._feature_types
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@feature_names.setter
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def feature_names(self, feature_names):
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"""Set feature names (column labels).
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Parameters
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----------
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feature_names : list or None
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Labels for features. None will reset existing feature names
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"""
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if not feature_names is None:
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# validate feature name
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if not isinstance(feature_names, list):
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feature_names = list(feature_names)
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if len(feature_names) != len(set(feature_names)):
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raise ValueError('feature_names must be unique')
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if len(feature_names) != self.num_col():
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msg = 'feature_names must have the same length as data'
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raise ValueError(msg)
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# prohibit to use symbols may affect to parse. e.g. ``[]=.``
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if not all(isinstance(f, STRING_TYPES) and f.isalnum()
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for f in feature_names):
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raise ValueError('all feature_names must be alphanumerics')
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else:
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# reset feature_types also
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self.feature_types = None
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self._feature_names = feature_names
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@feature_types.setter
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def feature_types(self, feature_types):
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"""Set feature types (column types).
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This is for displaying the results and unrelated
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to the learning process.
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Parameters
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----------
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feature_types : list or None
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Labels for features. None will reset existing feature names
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"""
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if not feature_types is None:
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if self.feature_names is None:
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msg = 'Unable to set feature types before setting names'
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raise ValueError(msg)
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if isinstance(feature_types, STRING_TYPES):
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# single string will be applied to all columns
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feature_types = [feature_types] * self.num_col()
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if not isinstance(feature_types, list):
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feature_types = list(feature_types)
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if len(feature_types) != self.num_col():
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msg = 'feature_types must have the same length as data'
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raise ValueError(msg)
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# prohibit to use symbols may affect to parse. e.g. ``[]=.``
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valid = ('q', 'i', 'int', 'float')
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if not all(isinstance(f, STRING_TYPES) and f in valid
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for f in feature_types):
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raise ValueError('all feature_names must be {i, q, int, float}')
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self._feature_types = feature_types
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class Booster(object):
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""""A Booster of of XGBoost.
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@ -48,21 +48,23 @@ class TestBasic(unittest.TestCase):
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feature_names=['a', 'b', 'c', 'd', 'e=1'])
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dm = xgb.DMatrix(data)
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dm.set_feature_names(list('abcde'))
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assert dm.get_feature_names() == list('abcde')
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dm.feature_names = list('abcde')
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assert dm.feature_names == list('abcde')
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dm.set_feature_types('q')
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assert dm.get_feature_types() == list('qqqqq')
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dm.feature_types = 'q'
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assert dm.feature_types == list('qqqqq')
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dm.set_feature_types(list('qiqiq'))
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assert dm.get_feature_types() == list('qiqiq')
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dm.feature_types = list('qiqiq')
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assert dm.feature_types == list('qiqiq')
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self.assertRaises(ValueError, dm.set_feature_types, list('abcde'))
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def incorrect_type_set():
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dm.feature_types = list('abcde')
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self.assertRaises(ValueError, incorrect_type_set)
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# reset
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dm.set_feature_names(None)
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assert dm.get_feature_names() is None
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assert dm.get_feature_types() is None
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dm.feature_names = None
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assert dm.feature_names is None
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assert dm.feature_types is None
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def test_feature_names(self):
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data = np.random.randn(100, 5)
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