class Transformer(object):
"""
The purpose of this class is to take a dataframe and transform it into
a numpy array compatible format.
"""
def __init__(self, config):
self.__config = config
self.__mapper = None
self.__label_encoder_adapter = TransformerAdapter(
LabelEncoderMissingValuesTransformer())
def prepare(self, dataframe):
"""
Takes the already cleaned dataframe, splits it into train and test
and returns the train and test as numpy arrays.
If the problem is supervised, the target column will be that last one
of the returned arrays.
"""
mapping = DataFrameMapCreator().get_mapping_from_config(self.__config)
self.__mapper = DataFrameMapper(mapping)
train, test = split_dataframe_train_test(
dataframe,
self.__config.get_option_parameter("split", "train_percentage"))
return self.__get_correct_return_parameters(train, test)
def __get_correct_return_parameters(self, train, test):
model = self.__config.get_data_model()
train_transformed = self.__mapper.fit_transform(train)
test_transformed = self.__mapper.transform(test)
if model.has_target():
return self.__add_target_data(train_transformed, train), \
self.__add_target_data(test_transformed, test)
else:
return train_transformed, test_transformed
def __add_target_data(self, transformed_data, original_data):
"""
Picks up the target data from the original_data and appends it as a
column to the transformed_data.
Both arguments are expected to be np.array's.
"""
model = self.__config.get_data_model()
target_feature = model.find_target_feature()
name = target_feature.get_name()
if target_feature.is_categorical():
target_row = original_data[name]
target = self.__label_encoder_adapter.transform(target_row)
else:
target = original_data[name].values.astype(
type_name_to_data_type("float"))
target = target[..., None]
return np.hstack((transformed_data, target))
def apply(self, dataframe):
return self.__mapper.transform(dataframe)
class Transformer(object):
"""
The purpose of this class is to take a dataframe and transform it into
a numpy array compatible format.
"""
def __init__(self, config):
self.__config = config
self.__mapper = None
self.__label_encoder_adapter = TransformerAdapter(LabelEncoderMissingValuesTransformer())
def prepare(self, dataframe):
"""
Takes the already cleaned dataframe, splits it into train and test
and returns the train and test as numpy arrays.
If the problem is supervised, the target column will be that last one
of the returned arrays.
"""
mapping = DataFrameMapCreator().get_mapping_from_config(self.__config)
self.__mapper = DataFrameMapper(mapping)
train, test = split_dataframe_train_test(dataframe, self.__config.get_option_parameter("split", "train_percentage"))
return self.__get_correct_return_parameters(train, test)
def __get_correct_return_parameters(self, train, test):
model = self.__config.get_data_model()
train_transformed = self.__mapper.fit_transform(train)
test_transformed = self.__mapper.transform(test)
if model.has_target():
return self.__add_target_data(train_transformed, train), \
self.__add_target_data(test_transformed, test)
else:
return train_transformed, test_transformed
def __add_target_data(self, transformed_data, original_data):
"""
Picks up the target data from the original_data and appends it as a
column to the transformed_data.
Both arguments are expected to be np.array's.
"""
model = self.__config.get_data_model()
target_feature = model.find_target_feature()
name = target_feature.get_name()
if target_feature.is_categorical():
target_row = original_data[name]
target = self.__label_encoder_adapter.transform(target_row)
else:
target = original_data[name].values.astype(type_name_to_data_type("float"))
target = target[..., None]
return np.hstack((transformed_data, target))
def apply(self, dataframe):
return self.__mapper.transform(dataframe)
def __init__(self, config):
self.__config = config
self.__mapper = None
self.__label_encoder_adapter = TransformerAdapter(
LabelEncoderMissingValuesTransformer())
def __init__(self, config):
self.__config = config
self.__mapper = None
self.__label_encoder_adapter = TransformerAdapter(LabelEncoderMissingValuesTransformer())