def get_encoder_from_stream(stream, ml_fields, omit_cols=None):
# ensure target variable is not included as input variable
target = ml_fields['target_name']
categorical = {k: v for k, v in ml_fields['categorical'].items() if k != target}
numerical = [field for field in ml_fields['numerical'] if field != target]
encoder = OneHotEncoder(categorical, numerical, omit_cols=omit_cols)
encoder.load_from_data_stream(stream)
return encoder
def get_trained_encoder(stream, ml_fields, problem_name, write=True,
read_from_file=False, base_features_omitted=None):
file_names = get_model_files(problem_name)
encoder_file = file_names.get('encoder')
if encoder_file and os.path.exists(encoder_file) and read_from_file:
logger.info('Reading encoder from : {}'.format(encoder_file))
logger.info(base_features_omitted)
encoder_from_file = OneHotEncoder([], [], omit_cols=base_features_omitted)
encoder_from_file.load_from_file(encoder_file)
return encoder_from_file
logger.info('Building encoder')
encoder = get_encoder_from_stream(stream, ml_fields, omit_cols=base_features_omitted)
if write:
if encoder_file:
logger.info('Writing encoder to: %s' % encoder_file)
encoder.save(encoder_file)
return encoder
def load_encoder_from_package(self):
self.logger.info('loading encoder from packaging')
self.encoder = OneHotEncoder([], [])
self.encoder.load_from_packaged_data(self.packaged_encoder)