Evaluation - Nixtla

evaluate(df, metrics, models=None, train_df=None, level=None, id_col='unique_id', time_col='ds', target_col='y', cutoff_col='cutoff', agg_fn=None)

Evaluate forecast using different metrics. Parameters:

Name	Type	Description	Default
`df`	`pandas, polars, dask or spark DataFrame`	Forecasts to evaluate. Must have `id_col`, `time_col`, `target_col` and models’ predictions.	required
`metrics`	`list of callable`	Functions with arguments `df`, `models`, `id_col`, `target_col` and optionally `train_df`.	required
`models`	`list of str`	Names of the models to evaluate. If `None` will use every column in the dataframe after removing id, time and target. Defaults to None.	`None`
`train_df`	`pandas, polars, dask or spark DataFrame`	Training set. Used to evaluate metrics such as `mase`. Defaults to None.	`None`
`level`	`list of int`	Prediction interval levels. Used to compute losses that rely on quantiles. Defaults to None.	`None`
`id_col`	`str`	Column that identifies each serie. Defaults to ‘unique_id’.	`‘unique_id’`
`time_col`	`str`	Column that identifies each timestep, its values can be timestamps or integers. Defaults to ‘ds’.	`‘ds’`
`target_col`	`str`	Column that contains the target. Defaults to ‘y’.	`‘y’`
`cutoff_col`	`str`	Column that identifies the cutoff point for each forecast cross-validation fold. Defaults to ‘cutoff’.	`‘cutoff’`
`agg_fn`	`str`	Statistic to compute on the scores by id to reduce them to a single number. Defaults to None.	`None`

Returns:

Type	Description
`AnyDFType`	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.