LocalFileLogger

class LocalFileLogger(experiments_folder: str, config: Optional[Dict[str, Any]] = None, gzip: bool = False)[source]

Bases: etna.loggers.file_logger.BaseFileLogger

Logger for logging files into local folder.

It writes its result into folder like experiments_folder/2021-12-12T12-12-12, where the second part is related to datetime of starting the experiment.

After every start_experiment it creates a new subfolder job_type/group. If some of these two values are None then behaviour is little different and described in start_experiment method.

Create instance of LocalFileLogger.

Parameters

  • experiments_folder (str) – path to folder to create experiment in

  • config (Optional[Dict[str, Any]]) – a dictionary-like object for saving inputs to your job, like hyperparameters for a model or settings for a data preprocessing job

  • gzip (bool) – indicator whether to use compression during saving tables or not

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

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

As a result, within self.experiment_folder subfolder job_type/group is created.

  • If job_type or group isn’t set then only one-level subfolder is created.

  • If none of job_type and group is set then experiment logs files into self.experiment_folder.

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.