_Logger

class _Logger

Bases: etna.loggers.base.BaseLogger

Composite for loggers.

Create instance for composite of loggers.

Inherited-members

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

pl_loggers

Pytorch lightning loggers.

add(logger: etna.loggers.base.BaseLogger) → int

Add new logger.

Parameters

logger (etna.loggers.base.BaseLogger) – logger to be added

Returns

result – identifier of added logger

Return type

int

disable()

Context manager for local logging disabling.

finish_experiment()

Finish experiment.

log(msg: Union[str, Dict[str, Any]], **kwargs)

Log any event.

Parameters

msg (Union[str, Dict[str, Any]]) –

log_backtest_metrics(ts: TSDataset, metrics_df: pandas.core.frame.DataFrame, forecast_df: pandas.core.frame.DataFrame, fold_info_df: pandas.core.frame.DataFrame)

Write metrics to logger.

Parameters

• ts (TSDataset) – TSDataset to with backtest data

• metrics_df (pandas.core.frame.DataFrame) – Dataframe produced with etna.pipeline.Pipeline._get_backtest_metrics()

• forecast_df (pandas.core.frame.DataFrame) – Forecast from backtest

• fold_info_df (pandas.core.frame.DataFrame) – Fold information from backtest

log_backtest_run(metrics: pandas.core.frame.DataFrame, forecast: pandas.core.frame.DataFrame, test: pandas.core.frame.DataFrame)

Backtest metrics from one fold to logger.

Parameters

• metrics (pandas.core.frame.DataFrame) – Dataframe with metrics from backtest fold

• forecast (pandas.core.frame.DataFrame) – Dataframe with forecast

• test (pandas.core.frame.DataFrame) – Dataframe with ground truth

remove(idx: int)

Remove logger by identifier.

Parameters

idx (int) – identifier of added logger

set_params(**params: dict) → etna.core.mixins.TMixin

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 – Estimator parameters

• self (etna.core.mixins.TMixin) –

• params (dict) –

Returns

New instance with changed parameters

Return type

etna.core.mixins.TMixin

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)

Start experiment.

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

to_dict()

Collect all information about etna object in dict.

property pl_loggers

Pytorch lightning loggers.