TimeSeriesPredictor.leaderboard

TimeSeriesPredictor.leaderboard(data: TimeSeriesDataFrame | DataFrame | Path | str | None = None, extra_info: bool = False, extra_metrics: List[str | TimeSeriesScorer] | None = None, display: bool = False, use_cache: bool = True, **kwargs) → 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 are always reported 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 the 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, Path, str], optional) –

    dataset used for additional evaluation. Must include both historic and future data (i.e., length of all time series in data must be at least prediction_length + 1).

    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 a path or a pandas.DataFrame, AutoGluon will attempt to automatically convert it to a TimeSeriesDataFrame.

  • extra_info (bool, default = False) – If True, the leaderboard will contain an additional column hyperparameters with the hyperparameters used by each model during training. An empty dictionary {} means that the model was trained with default hyperparameters.

  • extra_metrics (List[Union[str, TimeSeriesScorer]], optional) –

    A list of metrics to calculate scores for and include in the output DataFrame.

    Only valid when data is specified. The scores refer to the scores on data (same data as used to calculate the score_test column).

    This list can contain any values which would also be valid for eval_metric when creating a TimeSeriesPredictor.

    For each provided metric, a column with name str(metric) will be added to the leaderboard, containing the value of the metric computed on data.

  • display (bool, default = False) – If True, 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