lenskit.metrics.predict#

Prediction accuracy metrics. See eval-predict-accuracy for an overview and instructions on using these metrics.

Attributes#

`MissingDisposition`
`ScoreArray`
`PredMetric`

Classes#

`PredictMetric`	Extension to the metric function interface for prediction metrics.
`RMSE`	Compute RMSE (root mean squared error). This is computed as:
`MAE`	Compute MAE (mean absolute error). This is computed as:
`AvgErrorAccumulator`

Module Contents#

type lenskit.metrics.predict.MissingDisposition = Literal['error', 'ignore']#

type lenskit.metrics.predict.ScoreArray = NDArray[np.floating] | pd.Series#

type lenskit.metrics.predict.PredMetric = Callable[[ScoreArray, ScoreArray], float]#

Exported Aliases#

class lenskit.metrics.predict.ItemList#: Re-exported alias for lenskit.data.ItemList.

lenskit.metrics.predict.ITEM_COMPAT_COLUMN#: Re-exported alias for lenskit.data._adapt.ITEM_COMPAT_COLUMN.

lenskit.metrics.predict.normalize_columns()#: Re-exported alias for lenskit.data._adapt.normalize_columns().

class lenskit.metrics.predict.ValueStatAccumulator#: Re-exported alias for lenskit.data.accum.ValueStatAccumulator.

class lenskit.metrics.predict.AliasedColumn#: Re-exported alias for lenskit.data.types.AliasedColumn.

class lenskit.metrics.predict.Metric#: Re-exported alias for lenskit.metrics._base.Metric.