lenskit.metrics.predict.PredictMetric#

class lenskit.metrics.predict.PredictMetric(missing_scores='error', missing_truth='error')#

Bases: lenskit.metrics._base.Metric

Extension to the metric function interface for prediction metrics.

In addition to the general metric interface, predict metrics can be called with a single item list (or item list collection) that has both scores and a rating field.

Parameters:

  • missing_scores (MissingDisposition) – The action to take when a test item has not been scored. The default throws an exception, avoiding situations where non-scored items are silently excluded from overall statistics.

  • missing_truth (MissingDisposition) – The action to take when no test items are available for a scored item. The default is to also to fail; if you are scoring a superset of the test items for computational efficiency, set this to "ignore".

Stability:
Caller (see Stability Levels).
default = None#
missing_scores: MissingDisposition#
missing_truth: MissingDisposition#
align_scores(predictions, truth=None)#

Align prediction scores and rating values, applying the configured missing dispositions. The result is two Pandas series, predictions and truth, that are aligned and checked for missing data in accordance with the configured options.

Parameters:
Return type:

tuple[pandas.Series[float], pandas.Series[float]]