Prediction Accuracy Metrics¶
The lenskit.metrics.predict module contains prediction accuracy metrics.
These are intended to be used as a part of a Pandas split-apply-combine operation
on a data frame that contains both predictions and ratings; for convenience, the
lenskit.batch.predict() function will include ratings in the prediction
frame when its input user-item pairs contains ratings. So you can perform the
following to compute per-user RMSE over some predictions:
preds = predict(algo, pairs)
user_rmse = preds.groupby('user').apply(lambda df: rmse(df.prediction, df.rating))
Metric Functions¶
Prediction metric functions take two series, predictions and truth.
-
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 nearest-neighbor 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)