etna.loggers.ConsoleLogger

class ConsoleLogger(table: bool = True)

Bases: BaseLogger

Log any events and metrics to stderr output. Uses loguru.

Create instance of ConsoleLogger.

Parameters:

table (bool) – Indicator for writing tables to the console

Methods

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.
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_logger

Pytorch lightning loggers.

finish_experiment(*args, **kwargs)

Finish experiment.

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

Log any event.

e.g. “Fitted segment segment_name” to stderr output.

Parameters:

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

• kwargs – Parameters for changing additional info in log message

log_backtest_metrics(ts: TSDataset, metrics_df: DataFrame, forecast_df: DataFrame, fold_info_df: DataFrame)

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

Notes

The result of logging will be different for aggregate_metrics=True and aggregate_metrics=False options in backtest().

log_backtest_run(metrics: DataFrame, forecast: DataFrame, test: DataFrame)

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

set_params(**params: dict) → Self

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 = 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_logger

Pytorch lightning loggers.