Auto

Basic usage

import pathlib

import pandas as pd

from etna.auto import Auto
from etna.datasets import TSDataset
from etna.metrics import SMAPE

CURRENT_DIR_PATH = pathlib.Path(__file__).parent

if __name__ == "__main__":
   df = pd.read_csv(CURRENT_DIR_PATH / "data" / "example_dataset.csv")

   ts = TSDataset.to_dataset(df)
   ts = TSDataset(ts, freq="D")

   # Create Auto object for greedy search
   # All trials will be saved in sqlite database
   # You can use it later for analysis with ``Auto.summary``
   auto = Auto(
      target_metric=SMAPE(),
      horizon=14,
      experiment_folder="auto-example",
   )

   # Get best pipeline
   best_pipeline = auto.fit(ts, catch=(Exception,))
   print(best_pipeline)

   # Get all metrics of greedy search
   print(auto.summary())

Details and available methods

See the API documentation for further details on available methods:

etna.auto.runner.base.AbstractRunner()	Abstract class for Runner.
etna.auto.auto.Auto(target_metric, horizon)	Automatic pipeline selection via defined or custom pipeline pool.
etna.auto.runner.local.LocalRunner()	LocalRunner for one threaded run.
etna.auto.runner.local.ParallelLocalRunner([...])	ParallelLocalRunner for multiple parallel runs with joblib.
etna.auto.pool.generator.Pool(value)	Predefined pools of pipelines.
etna.auto.auto.Tune(pipeline, target_metric, ...)	Automatic tuning of custom pipeline.