def _parse_features(self,
features,
names=None,
process_one_hot=True,
**kwargs):
"""
Parse features
Parameters
----------
features : pandas.DataFrame | dict | ndarray
Features to train on or predict from
names : list, optional
List of feature names, by default None
process_one_hot : bool, optional
Check for and process one-hot variables, by default True
kwargs : dict, optional
kwargs for PreProcess.one_hot
Returns
-------
features : ndarray
Parsed features array normalized and with str columns converted
to one hot vectors if desired
"""
features, feature_names = self._parse_data(features, names=names)
if len(features.shape) != 2:
msg = ('{} can only use 2D data as input!'.format(
self.__class__.__name__))
logger.error(msg)
raise RuntimeError(msg)
if self.feature_names is not None:
if features.shape[1] != len(self.feature_names):
msg = ('data has {} features but expected {}'.format(
features.shape[1], self.feature_dims))
logger.error(msg)
raise RuntimeError(msg)
if self._feature_names is None:
self._feature_names = feature_names
elif self.feature_names != feature_names:
msg = ('Expecting features with names: {}, but was provided with: '
'{}!'.format(feature_names, self.feature_names))
logger.error(msg)
raise RuntimeError(msg)
if process_one_hot:
kwargs.update({'return_ind': True})
features, one_hot_ind = PreProcess.one_hot(features, **kwargs)
if one_hot_ind:
one_hot_features = [self.feature_names[i] for i in one_hot_ind]
self._check_one_hot_norm_params(one_hot_features)
if self.normalize_features:
features = self.normalize(features, names=feature_names)
return features
def parse_features(self, features, names=None, **kwargs):
"""Parse features - preprocessing of feature data before training or
prediction. This will do one-hot encoding based on
self.one_hot_categories, and feature normalization based on
self.normalize_features
Parameters
----------
features : pandas.DataFrame | dict | ndarray
Features to train on or predict from
names : list, optional
List of feature names, by default None
kwargs : dict, optional
kwargs for PreProcess.one_hot
Returns
-------
features : ndarray
Parsed features array normalized and with str columns converted
to one hot vectors if desired
"""
features, feature_names = self._parse_data(features, names=names)
if len(features.shape) != 2:
msg = ('{} can only use 2D data as input!'.format(
self.__class__.__name__))
logger.error(msg)
raise RuntimeError(msg)
if self.feature_names is None:
self._feature_names = feature_names
check = (self.one_hot_categories is not None
and all(np.isin(feature_names, self.input_feature_names)))
if check:
self._check_one_hot_feature_names(feature_names)
kwargs.update({
'feature_names': feature_names,
'categories': self.one_hot_categories
})
features = PreProcess.one_hot(features, **kwargs)
elif self.feature_names != feature_names:
msg = ('Expecting features with names: {}, but was provided with: '
'{}!'.format(self.feature_names, feature_names))
logger.error(msg)
raise RuntimeError(msg)
if self.normalize_features:
features = self.normalize(features, names=self.feature_names)
if features.shape[1] != self.feature_dims:
msg = ('data has {} features but expected {}'.format(
features.shape[1], self.feature_dims))
logger.error(msg)
raise RuntimeError(msg)
return features
