Logger

Logger

class Logger(logger='sktime', logger_backend='logging', log_methods='all', level=None, log_fitted_params=False)[source]

Logging transformer, writes data to logging, and otherwise leaves it unchanged.

In methods, logs X and y to logger. The logger can us as logger_backend a python logging instance, primarily for printing, with data logged as extra, or a custom DataLog instance to retrieve full objects and not just printouts.

Parameters:

loggerstr optional, default=”sktime”

logger name to use, passed to logger_backend to identify the unique logger instance referenced by get_logger.

logger_backendstr, one of “logging” (default), “datalog”

Backend to use for logging.

• “logging”: uses the standard Python logging module, logs to logging.getLogger(logger)

• “datalog”: uses a multiton logger class for easy retrieval of data, logs to DataLog(logger), with DataLog from the transformations.compose module.

In either case, a reference to the logger can be retrieved by calling obj.get_logger, where obj is an instance of Logger.

log_methodsstr or list of str, default=``”transform”``

if "all", will log fit, transform, inverse_transform; if str or list of str, all strings must be from among the above, and will log exactly the methods that are passed as str; can also be "off"" to disable logging entirely.

levellogging level, optional, default=logging.INFO

logging level, one of logging.INFO, logging.DEBUG, logging.WARNING, logging.ERROR

log_fitted_paramsbool, optional, default=False

if True, will also write X and y seen in fit to self as X_ and y_, these can be retrieved by calling get_fitted_params. If False, get_fitted_params will return an empty dict.

Attributes:

get_logger

is_fitted

Whether fit has been called.

Examples

>>> from sktime.transformations.compose import DataLog, Logger
>>> from sktime.datasets import load_airline
>>> from sktime.forecasting.naive import NaiveForecaster
>>> from sktime.transformations.detrend import Detrender
>>>
>>> # create a logger
>>> logger = Logger(logger="foo", log_methods="all", logger_backend="datalog")
>>> # create a pipeline that logs after detrending and before forecasting
>>> pipe = Detrender() * logger * NaiveForecaster(sp=12)
>>> pipe.fit(load_airline(), fh=[1, 2, 3])
TransformedTargetForecaster(...)
>>> # get the log
>>> log = DataLog("foo").get_log()

Methods

check_is_fitted([method_name])	Check if the estimator has been fitted.
clone()	Obtain a clone of the object with same hyper-parameters and config.
clone_tags(estimator[, tag_names])	Clone tags from another object as dynamic override.
create_test_instance([parameter_set])	Construct an instance of the class, using first test parameter set.
create_test_instances_and_names([parameter_set])	Create list of all test instances and a list of names for them.
fit(X[, y])	Fit transformer to X, optionally to y.
fit_transform(X[, y])	Fit to data, then transform it.
get_class_tag(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
get_class_tags()	Get class tags from class, with tag level inheritance from parent classes.
get_config()	Get config flags for self.
get_fitted_params([deep])	Get fitted parameters.
get_param_defaults()	Get object's parameter defaults.
get_param_names([sort])	Get object's parameter names.
get_params([deep])	Get a dict of parameters values for this object.
get_tag(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
get_tags()	Get tags from instance, with tag level inheritance and overrides.
get_test_params([parameter_set])	Return testing parameter settings for the estimator.
inverse_transform(X[, y])	Inverse transform X and return an inverse transformed version.
is_composite()	Check if the object is composed of other BaseObjects.
load_from_path(serial)	Load object from file location.
load_from_serial(serial)	Load object from serialized memory container.
reset()	Reset the object to a clean post-init state.
save([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
set_config(**config_dict)	Set config flags to given values.
set_params(**params)	Set the parameters of this object.
set_random_state([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
set_tags(**tag_dict)	Set instance level tag overrides to given values.
transform(X[, y])	Transform X and return a transformed version.
update(X[, y, update_params])	Update transformer with X, optionally y.