lenskit.metrics.RMSE#

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

Bases: PredictMetric, ListMetric, DecomposedMetric

Compute RMSE (root mean squared error). This is computed as:

\[\sum_{r_{ui} \in R} \left(r_{ui} - s(i|u)\right)^2\]

This metric does not do any fallbacks; if you want to compute RMSE with fallback predictions (e.g. usign a bias model when a collaborative filter cannot predict), generate predictions with FallbackScorer.

Parameters:

  • missing_scores (Literal['error', 'ignore'])

  • missing_truth (Literal['error', 'ignore'])

__init__(missing_scores='error', missing_truth='error')#
Parameters:

  • missing_scores (Literal['error', 'ignore'])

  • missing_truth (Literal['error', 'ignore'])

Methods

__init__([missing_scores, missing_truth])

align_scores(predictions[, truth])

Align prediction scores and rating values, applying the configured missing dispositions.

compute_list_data(output, test)

Compute measurements for a single list.

extract_list_metric(metric)

Extract a single-list metric from the per-list measurement result (if applicable).

global_aggregate(values)

Aggregate list metrics to compute a global value.

measure_list(predictions[, test])

Compute the metric value for a single result list.

Attributes

default

The default value to infer when computing statistics over missing values.

label

The metric's default label in output.

missing_scores

missing_truth

measure_list(predictions, test=None, /)#

Compute the metric value for a single result list.

Individual metric classes need to implement this method.

Parameters:
Return type:

float

compute_list_data(output, test)#

Compute measurements for a single list.

extract_list_metric(metric)#

Extract a single-list metric from the per-list measurement result (if applicable).

Returns:

The per-list metric, or None if this metric does not compute per-list metrics.

global_aggregate(values)#

Aggregate list metrics to compute a global value.