def __init__(self, config_file):
"""
@param config_file: File like object containing the configuration.
"""
self.__config = Configuration(config_file)
self.__transformer = Transformer(self.__config)
self.__cleaner = Cleaner(self.__config)
def __init__(self, config_file):
"""
@param config_file: File like object containing the configuration.
"""
self.__config = Configuration(config_file)
self.__transformer = Transformer(self.__config)
self.__cleaner = Cleaner(self.__config)
class Abris(object):
"""
Main entry class for the whole preprocessing engine (and probably the only one that needs to be used
if no more features are needed).
"""
def __init__(self, config_file):
"""
@param config_file: File like object containing the configuration.
"""
self.__config = Configuration(config_file)
self.__transformer = Transformer(self.__config)
self.__cleaner = Cleaner(self.__config)
def prepare(self, data_source):
"""
Called with the training data.
@param data_source: Either a pandas.DataFrame or a file-like object.
"""
dataframe = self.__get_dataframe(data_source, use_target=True)
self.__config.get_data_model().set_features_types_from_dataframe(dataframe)
dataframe = self.__cleaner.prepare(dataframe)
return self.__transformer.prepare(dataframe)
def apply(self, data_source):
"""
Called with the predict data (new information).
@param data_source: Either a pandas.DataFrame or a file-like object.
"""
dataframe = self.__get_dataframe(data_source, use_target=False)
dataframe = self.__cleaner.apply(dataframe)
dataframe = self.__transformer.apply(dataframe)
return dataframe
def __get_dataframe(self, input, use_target):
if isinstance(input, pd.DataFrame):
return input
return prepare_csv_to_dataframe(input, self.__config, use_target)
class Abris(object):
"""
Main entry class for the whole preprocessing engine (and probably the only one that needs to be used
if no more features are needed).
"""
def __init__(self, config_file):
"""
@param config_file: File like object containing the configuration.
"""
self.__config = Configuration(config_file)
self.__transformer = Transformer(self.__config)
self.__cleaner = Cleaner(self.__config)
def prepare(self, data_source):
"""
Called with the training data.
@param data_source: Either a pandas.DataFrame or a file-like object.
"""
dataframe = self.__get_dataframe(data_source, use_target=True)
self.__config.get_data_model().set_features_types_from_dataframe(
dataframe)
dataframe = self.__cleaner.prepare(dataframe)
return self.__transformer.prepare(dataframe)
def apply(self, data_source):
"""
Called with the predict data (new information).
@param data_source: Either a pandas.DataFrame or a file-like object.
"""
dataframe = self.__get_dataframe(data_source, use_target=False)
dataframe = self.__cleaner.apply(dataframe)
dataframe = self.__transformer.apply(dataframe)
return dataframe
def __get_dataframe(self, input, use_target):
if isinstance(input, pd.DataFrame):
return input
return prepare_csv_to_dataframe(input, self.__config, use_target)
