etna.loggers._Logger#

class _Logger[source]#

Bases: BaseLogger

Composite for loggers.

Create instance for composite of loggers.

Methods

`add`(logger)	Add new logger.
`disable`()	Context manager for local logging disabling.
`finish_experiment`()	Finish experiment.
`log`(msg, **kwargs)	Log any event.
`log_backtest_metrics`(ts, metrics_df, ...)	Write metrics to logger.
`log_backtest_run`(metrics, forecast, test)	Backtest metrics from one fold to logger.
`remove`(idx)	Remove logger by identifier.
`set_params`(**params)	Return new object instance with modified parameters.
`start_experiment`(args, *kwargs)	Start experiment.
`to_dict`()	Collect all information about etna object in dict.

Attributes

This class stores its __init__ parameters as attributes.

pl_loggers

Pytorch lightning loggers.

add(logger: BaseLogger) → int[source]#

Add new logger.

Parameters:: logger (BaseLogger) – logger to be added
Returns:: result – identifier of added logger
Return type:: int

disable()[source]#: Context manager for local logging disabling.

finish_experiment()[source]#: Finish experiment.

log(msg: str | Dict[str, Any], **kwargs)[source]#

Log any event.

Parameters:: msg (str | Dict[str, Any]) –

log_backtest_metrics(ts: TSDataset, metrics_df: DataFrame, forecast_df: DataFrame, fold_info_df: DataFrame)[source]#

Write metrics to logger.

Parameters:

ts (TSDataset) – TSDataset to with backtest data
metrics_df (DataFrame) – Dataframe produced with etna.pipeline.Pipeline._get_backtest_metrics()
forecast_df (DataFrame) – Forecast from backtest
fold_info_df (DataFrame) – Fold information from backtest

log_backtest_run(metrics: DataFrame, forecast: DataFrame, test: DataFrame)[source]#

Backtest metrics from one fold to logger.

Parameters:

metrics (DataFrame) – Dataframe with metrics from backtest fold
forecast (DataFrame) – Dataframe with forecast
test (DataFrame) – Dataframe with ground truth

remove(idx: int)[source]#

Remove logger by identifier.

Parameters:: idx (int) – identifier of added logger

set_params(**params: dict) → Self[source]#

Return new object instance with modified parameters.

Method also allows to change parameters of nested objects within the current object. For example, it is possible to change parameters of a model in a Pipeline.

Nested parameters are expected to be in a <component_1>.<...>.<parameter> form, where components are separated by a dot.

Parameters:: **params (dict) – Estimator parameters
Returns:: New instance with changed parameters
Return type:: Self

Examples

>>> from etna.pipeline import Pipeline
>>> from etna.models import NaiveModel
>>> from etna.transforms import AddConstTransform
>>> model = model=NaiveModel(lag=1)
>>> transforms = [AddConstTransform(in_column="target", value=1)]
>>> pipeline = Pipeline(model, transforms=transforms, horizon=3)
>>> pipeline.set_params(**{"model.lag": 3, "transforms.0.value": 2})
Pipeline(model = NaiveModel(lag = 3, ), transforms = [AddConstTransform(in_column = 'target', value = 2, inplace = True, out_column = None, )], horizon = 3, )

start_experiment(*args, **kwargs)[source]#

Start experiment.

Complete logger initialization or reinitialize it before the next experiment with the same name.

to_dict()[source]#: Collect all information about etna object in dict.

property pl_loggers[source]#: Pytorch lightning loggers.