TopN Accuracy Metrics¶
The lenskit.metrics.topn
module contains metrics for evaluating topN
recommendation lists.
Classification Metrics¶
These metrics treat the recommendation list as a classification of relevant items.

lenskit.metrics.topn.
precision
(recs, relevant)¶ Compute the precision of a set of recommendations.
Parameters:  recs (arraylike) – a sequence of recommended items
 relevant (setlike) – the set of relevant items
Returns: the fraction of recommended items that are relevant
Return type: double

lenskit.metrics.topn.
recall
(recs, relevant)¶ Compute the recall of a set of recommendations.
Parameters:  recs (arraylike) – a sequence of recommended items
 relevant (setlike) – the set of relevant items
Returns: the fraction of relevant items that were recommended.
Return type: double
Ranked List Metrics¶
These metrics treat the recommendation list as a ranked list of items that may or may not be relevant.

lenskit.metrics.topn.
recip_rank
(recs, relevant)¶ Compute the reciprocal rank of the first relevant item in a recommendation list. This is used to compute MRR.
Parameters:  recs (arraylike) – a sequence of recommended items
 relevant (setlike) – the set of relevant items
Returns: the reciprocal rank of the first relevant item.
Return type: double
Utility Metrics¶
The DCG function estimates a utility score for a ranked list of recommendations. The results can be combined with ideal DCGs to compute nDCG.

lenskit.metrics.topn.
dcg
(scores, discount=<ufunc 'log2'>)¶ Compute the Discounted Cumulative Gain of a series of recommended items with rating scores. These should be relevance scores; they can be \({0,1}\) for binary relevance data.
Discounted cumultative gain is computed as:
\[\begin{align*} \mathrm{DCG}(L,u) & = \sum_{i=1}^{L} \frac{r_{ui}}{d(i)} \end{align*}\]You will usually want normalized discounted cumulative gain; this is
\[\begin{align*} \mathrm{nDCG}(L, u) & = \frac{\mathrm{DCG}(L,u)}{\mathrm{DCG}(L_{\mathrm{ideal}}, u)} \end{align*}\]Compute that by computing the DCG of the recommendations & the test data, then merge the results and divide. The :py:fun:`compute_ideal_dcgs` function is helpful for preparing that data.
Parameters:  scores (arraylike) – The utility scores of a list of recommendations, in recommendation order.
 discount (ufunc) – the rank discount function. Each item’s score will be divided the discount of its rank, if the discount is greater than 1.
Returns: the DCG of the scored items.
Return type: double

lenskit.metrics.topn.
compute_ideal_dcgs
(ratings, discount=<ufunc 'log2'>)¶ Compute the ideal DCG for rating data. This groups the rating data by everything except its
item
andrating
columns, sorts each group by rating, and computes the DCG.Parameters: ratings (pandas.DataFrame) – A rating data frame with item
,rating
, and other columns.Returns:  The data frame of DCG values. The
item
andrating
columns in ratings
are replaced by anideal_dcg
column.
Return type: pandas.DataFrame  The data frame of DCG values. The