etna.loggers.LocalFileLogger#

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

Bases: 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 (Dict[str, Any] | None) – 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

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.

Attributes

This class stores its __init__ parameters as attributes.

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

Finish experiment.

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

Log any event.

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

Parameters:

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

  • kwargs – Additional parameters for particular implementation

log_backtest_metrics(ts: TSDataset, metrics_df: DataFrame, forecast_df: DataFrame, fold_info_df: DataFrame)[source]#

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

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

log_backtest_run(metrics: DataFrame, forecast: DataFrame, test: DataFrame)[source]#

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

Notes

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

set_params(**params: dict) → Self[source]#

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(job_type: str | None = None, group: str | None = 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 (str | None) – Specify the type of run, which is useful when you’re grouping runs together into larger experiments using group.

  • group (str | None) – Specify a group to organize individual runs into a larger experiment.

to_dict()[source]#

Collect all information about etna object in dict.