TimeSeriesPredictor.leaderboard#

TimeSeriesPredictor.leaderboard(data: Optional[Union[TimeSeriesDataFrame, DataFrame]] = None, silent: bool = False, use_cache: bool = True) → DataFrame[source]#

Return a leaderboard showing the performance of every trained model, the output is a pandas data frame with columns:

  • model: The name of the model.

  • score_test: The test score of the model on data, if provided. Computed according to eval_metric.

  • score_val: The validation score of the model using the internal validation data. Computed according to eval_metric.

    NOTE: Metrics scores are always shown in ‘higher is better’ format. This means that metrics such as MASE or MAPE will be multiplied by -1, so their values will be negative. This is necessary to avoid the user needing to know the metric to understand if higher is better when looking at leaderboard.

  • pred_time_val: Time taken by the model to predict on the validation data set

  • fit_time_marginal: The fit time required to train the model (ignoring base models for ensembles).

  • fit_order: The order in which models were fit. The first model fit has fit_order=1, and the Nth model fit has fit_order=N.

Parameters

  • data (Union[TimeSeriesDataFrame, pd.DataFrame], optional) –

    dataset used for additional evaluation. If not provided, the validation set used during training will be used.

    If known_covariates_names were specified when creating the predictor, data must include the columns listed in known_covariates_names with the covariates values aligned with the target time series.

    If train_data used to train the predictor contained past covariates or static features, then data must also include them (with same column names and dtypes).

    If provided data is an instance of pandas DataFrame, AutoGluon will attempt to automatically convert it to a TimeSeriesDataFrame.

  • silent (bool, default = False) – If False, the leaderboard DataFrame will be printed.

  • use_cache (bool, default = True) – If True, will attempt to use the cached predictions. If False, cached predictions will be ignored. This argument is ignored if cache_predictions was set to False when creating the TimeSeriesPredictor.

Returns

leaderboard – The leaderboard containing information on all models and in order of best model to worst in terms of test performance.

Return type

pandas.DataFrame