BaseFileLogger

class BaseFileLogger

Bases: etna.loggers.base.BaseLogger

Base logger for logging files.

Create logger instance.

Inherited-members

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([job_type, group])	Start experiment within current experiment, it is used for separate different folds during backtest.
to_dict()	Collect all information about etna object in dict.

finish_experiment(*args, **kwargs)

Finish experiment.

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

Log any event.

This class does nothing with it, use other loggers to do it.

Parameters

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

• kwargs – Additional parameters for particular implementation

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

Notes

If some exception during saving is raised, then it becomes a warning.

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

Notes

If some exception during saving is raised, then it becomes a warning.

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, )

abstract start_experiment(job_type: Optional[str] = None, group: Optional[str] = None, *args, **kwargs)

Start experiment within current experiment, it is used for separate different folds during backtest.

Parameters

• job_type (Optional[str]) – Specify the type of run, which is useful when you’re grouping runs together into larger experiments using group.

• group (Optional[str]) – Specify a group to organize individual runs into a larger experiment.

to_dict()

Collect all information about etna object in dict.