def __init__(self, output_path: str):
"""Initialise the objects and constants.
:param output_path:
"""
self.__logger = logging.getLogger(CONSTANTS.app_name)
self.__logger.debug(__name__)
self.__output_path = output_path
self.__readers_writers = ReadersWriters()
def __init__(self, variables_settings: PandasDataFrame, output_path: str,
output_table: str):
"""Initialise the objects and constants.
:param variables_settings:
:param output_path: the output path.
:param output_table: the output table name.
"""
self.__logger = logging.getLogger(CONSTANTS.app_name)
self.__logger.debug(__name__)
self.__variables_settings = variables_settings
self.__output_path = output_path
self.__output_table = output_table
self.__readers_writers = ReadersWriters()
self.__FeatureParserThread = FeatureParserThread()
def __init__(self, model_features_table: str, input_path: str,
output_path: str, input_features_configs: str,
output_table: str):
"""Initialise the objects and constants.
:param model_features_table: the feature table name.
:param input_path: the input path.
:param output_path: the output path.
:param input_features_configs: the input features' configuration file.
:param output_table: the output table name.
"""
self.__logger = logging.getLogger(CONSTANTS.app_name)
self.__logger.debug(__name__)
self.__model_features_table = model_features_table
self.__output_path = output_path
self.__output_table = output_table
self.__readers_writers = ReadersWriters()
# initialise settings
self.__variables_settings = self.__init_settings(
input_path, input_features_configs)
self.__features_dic_names = self.__init_features_names()
self.__features_dic_dtypes = self.__init_features_dtypes()
self.__init_output(output_path, output_table)
def __init__(self, method_name: str, path: str = None, title: str = None):
"""Initialise the objects and constants.
:param method_name: the training method that will be used
(options: {'lr': Logistic Regression, 'lr_cv': Logistic Regression with Cross-Validation,
'mlm': Mixed Linear Model, 'rfc': Random Forest Classifier, 'gbc': Gradient Boosting Classifier,
'dtc' Decision Tree Classifier, 'knc': K-Nearest Neighbors Classifier, 'nb': Multinomial Naive Bayes,
'nn': Multi-Layer Perceptron (MLP) Neural Network}).
:param path: the directory path of the saved trained model file, using this application (if applicable).
:param title: the file name of the saved trained model file, using this application
"""
self.__logger = logging.getLogger(CONSTANTS.app_name)
self.__logger.debug(__name__)
self.__readers_writers = ReadersWriters()
self.__method = None
self.method_name = method_name
self.model_labels = None
self.model_train = None
self.model_predict = dict()
self.model_cross_validate = None
if method_name is not None:
self.__init__method(method_name)
else:
self.load(path, title)
os.makedirs(io_path, exist_ok=True)
logger = Logger(path=io_path, app_name=app_name, ext="log")
logger = logging.getLogger(app_name)
# Initialise constants and some of classes
# In[ ]:
# Initialise constants
CONSTANTS.set(io_path, app_name)
# In[ ]:
# Initialise other classes
readers_writers = ReadersWriters()
preprocess = PreProcess(io_path)
# In[ ]:
# Set print settings
pd.set_option('display.width', 1600, 'display.max_colwidth', 800)
pp = pprint.PrettyPrinter(indent=4)
# ### 1.2. Initialise Features Metadata
# Read the input features' confugration file & store the features metadata
# In[ ]:
# variables settings