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def gen_features(columns, classes=None, prefix='', suffix=''):
"""Generates a feature definition list which can be passed
into DataFrameMapper
Params:
columns a list of column names to generate features for.
classes a list of classes for each feature, a list of dictionaries with
transformer class and init parameters, or None.
If list of classes is provided, then each of them is
instantiated with default arguments. Example:
classes = [StandardScaler, LabelBinarizer]
If list of dictionaries is provided, then each of them should
have a 'class' key with transformer class. All other keys are
passed into 'class' value constructor. Example:
classes = [
{'class': StandardScaler, 'with_mean': False},
{'class': LabelBinarizer}
}]
If None value selected, then each feature left as is.
prefix add prefix to transformed column names
suffix add suffix to transformed column names.
"""
if classes is None:
return [(column, None) for column in columns]
feature_defs = []
for column in columns:
feature_transformers = []
arguments = {}
if prefix and prefix != "":
arguments['prefix'] = prefix
if suffix and suffix != "":
arguments['suffix'] = suffix
classes = [cls for cls in classes if cls is not None]
if not classes:
feature_defs.append((column, None, arguments))
else:
for definition in classes:
if isinstance(definition, dict):
params = definition.copy()
klass = params.pop('class')
feature_transformers.append(klass(**params))
else:
feature_transformers.append(definition())
if not feature_transformers:
feature_transformers = None
feature_defs.append((column, feature_transformers, arguments))
return feature_defs
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