class BaseLogger[source]

Bases: abc.ABC, etna.core.mixins.BaseMixin

Abstract class for implementing loggers.

Create logger instance.



finish_experiment(*args, **kwargs)

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.


Return new object instance with modified parameters.

start_experiment(*args, **kwargs)

Start experiment.


Collect all information about etna object in dict.

finish_experiment(*args, **kwargs)[source]

Finish experiment.

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

Log any event.

e.g. “Fitted segment segment_name”

  • msg (Union[str, Dict[str, Any]]) – Message or dict to log

  • kwargs – Additional parameters for particular implementation

abstract log_backtest_metrics(ts: TSDataset, metrics_df: pandas.core.frame.DataFrame, forecast_df: pandas.core.frame.DataFrame, fold_info_df: pandas.core.frame.DataFrame)[source]

Write metrics to logger.

  • 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)[source]

Backtest metrics from one fold to logger.

  • 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

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.

  • **params – Estimator parameters

  • self (etna.core.mixins.TMixin) –

  • params (dict) –


New instance with changed parameters

Return type



>>> 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.


Collect all information about etna object in dict.