utilsforecast.evaluation
evaluate
df
(pandas, polars, dask or spark DataFrame): Forecasts to evaluate. Must have id_col
, time_col
, target_col
and models’ predictions.metrics
(list of callable): Functions with arguments df
, models
, id_col
, target_col
and optionally train_df
.models
(list of str, optional): Names of the models to evaluate. If None
will use every column in the dataframe after removing id, time and target. Defaults to None.train_df
(pandas, polars, dask or spark DataFrame, optional): Training set. Used to evaluate metrics such as mase
. Defaults to None.level
(list of int, optional): Prediction interval levels. Used to compute losses that rely on quantiles. Defaults to None.id_col
(str, optional): Column that identifies each serie. Defaults to ‘unique_id’.time_col
(str, optional): Column that identifies each timestep, its values can be timestamps or integers. Defaults to ‘ds’.target_col
(str, optional): Column that contains the target. Defaults to ‘y’.agg_fn
(str, optional): Statistic to compute on the scores by id to reduce them to a single number. Defaults to None.pandas, polars, dask or spark DataFrame
: Metrics with one row per (id, metric) combination and one column per model. If agg_fn
is not None
, there is only one row per metric.