umami.configuration package#
Submodules#
umami.configuration.base module#
Base modul for process configuration (preprocessing, training, …).
umami.configuration.configuration module#
Configuration for logger of umami and tensorflow as well as reading global config.
- class umami.configuration.configuration.CustomFormatter(fmt=None, datefmt=None, style='%', validate=True)#
Bases:
Formatter
Logging Formatter to add colors and count warning / errors using implementation from https://stackoverflow.com/questions/384076/how-can-i-color-python-logging-output
- FORMATS = {10: '\x1b[38;21m%(asctime)s - %(levelname)s:%(name)s: %(message)s (%(filename)s:%(lineno)d)\x1b[0m', 20: '\x1b[32;21m%(levelname)s:%(name)s: %(message)s\x1b[0m', 30: '\x1b[33;21m%(levelname)s:%(name)s: %(message)s\x1b[0m', 40: '\x1b[31;21m%(asctime)s - %(levelname)s:%(name)s: %(message)s (%(filename)s:%(lineno)d)\x1b[0m', 50: '\x1b[31;1m%(asctime)s - %(levelname)s:%(name)s: %(message)s (%(filename)s:%(lineno)d)\x1b[0m'}#
- bold_red = '\x1b[31;1m'#
- date_format = '%(levelname)s:%(name)s: %(message)s'#
- debugformat = '%(asctime)s - %(levelname)s:%(name)s: %(message)s (%(filename)s:%(lineno)d)'#
- format(record)#
Format the specified record as text.
The record’s attribute dictionary is used as the operand to a string formatting operation which yields the returned string. Before formatting the dictionary, a couple of preparatory steps are carried out. The message attribute of the record is computed using LogRecord.getMessage(). If the formatting string uses the time (as determined by a call to usesTime(), formatTime() is called to format the event time. If there is exception information, it is formatted using formatException() and appended to the message.
- green = '\x1b[32;21m'#
- grey = '\x1b[38;21m'#
- red = '\x1b[31;21m'#
- reset = '\x1b[0m'#
- yellow = '\x1b[33;21m'#
- class umami.configuration.configuration.GlobalConfiguration#
Bases:
object
This is a global configuration to allow certain settings which are hardcoded so far.
- get_configuration()#
Assign configuration from file to class variables.
- Raises:
KeyError – if required config is not present in passed config file
- load_config_file()#
Load config file from disk.
- set_logging_level() object #
Set DebugLevel for logging.
- Returns:
Umami logger.
- Return type:
object
- set_mpl_plotting_backend()#
Setting the plotting backend of matplotlib.
- set_tf_debug_level()#
Setting the Debug level of tensorflow. For reference see https://stackoverflow.com/questions/35869137/avoid-tensorflow-print-on-standard-error
- umami.configuration.configuration.set_log_level(umami_logger, log_level: str)#
Setting log level
- Parameters:
umami_logger (logger) – logger object
log_level (str) – logging level corresponding CRITICAL, ERROR, WARNING, INFO, DEBUG, NOTSET