Prediction Accuracy Metrics¶
The lenskit.metrics.predict
module containins prediction accuracy metrics.
Metric Functions¶

lenskit.metrics.predict.
rmse
(predictions, truth, missing='error')¶ Compute RMSE (root mean squared error).
Parameters:  predictions (pandas.Series) – the predictions
 truth (pandas.Series) – the ground truth ratings from data
 missing (string) – how to handle predictions without truth. Can be one of
'error'
or'ignore'
.
Returns: the root mean squared approximation error
Return type: double

lenskit.metrics.predict.
mae
(predictions, truth, missing='error')¶ Compute MAE (mean absolute error).
Parameters:  predictions (pandas.Series) – the predictions
 truth (pandas.Series) – the ground truth ratings from data
 missing (string) – how to handle predictions without truth. Can be one of
'error'
or'ignore'
.
Returns: the mean absolute approximation error
Return type: double
Working with Missing Data¶
LensKit rating predictors do not report predictions when their core model is unable
to predict. For example, a nearestneighbor recommender will not score an item if
it cannot find any suitable neighbors. Following the Pandas convention, these items
are given a score of NaN (when Pandas implements better missing data handling, it will
use that, so use pandas.Series.isna()
/pandas.Series.notna()
, not the
isnan
versions.
However, this causes problems when computing predictive accuracy: recommenders are not being tested on the same set of items. If a recommender only scores the easy items, for example, it could do much better than a recommender that is willing to attempt more difficult items.
A good solution to this is to use a fallback predictor so that every item has a
prediction. In LensKit, lenskit.algorithms.basic.Fallback
implements
this functionality; it wraps a sequence of recommenders, and for each item, uses
the first one that generates a score.
You set it up like this:
cf = ItemItem(20)
base = Bias(damping=5)
algo = Fallback(cf, base)