recpack.metrics.ReciprocalRankK

class recpack.metrics.ReciprocalRankK(K)

Computes the inverse of the rank of the first hit in the recommendation list.

The reciprocal rank for a user is calculated as

\[\begin{split}\text{ReciprocalRank}(u) = \frac{1}{\min\limits_{i \in \text{Top-K}(u), \\ i \in y^{true}_u} rank(u,i)}\end{split}\]

when there is at least one matching item between recommendations in \(\text{Top-K}(u)\) and targets in \(y^{true}_u\), otherwise it is 0.

Parameters

K (int) – Amount of top recommendations to consider in the metric calculation.

Methods

calculate(y_true, y_pred)

Computes metric given true labels y_true and predicted scores y_pred.

Attributes

col_names

The names of the columns in the results DataFrame.

name

Name of the metric.

num_items

Dimension of the item-space in both y_true and y_pred

num_users

Dimension of the user-space in both y_true and y_pred after elimination of users without interactions in y_true.

results

Get the detailed results for this metric.

value

Global metric value obtained by taking the average over all users.

calculate(y_true: scipy.sparse._csr.csr_matrix, y_pred: scipy.sparse._csr.csr_matrix) None

Computes metric given true labels y_true and predicted scores y_pred. Only Top-K recommendations are considered.

Detailed metric results can be retrieved with results. Global aggregate metric value is retrieved as value.

Parameters
  • y_true (csr_matrix) – True user-item interactions.

  • y_pred (csr_matrix) – Predicted affinity of users for items.

property col_names

The names of the columns in the results DataFrame.

property name

Name of the metric.

property num_items: int

Dimension of the item-space in both y_true and y_pred

property num_users: int

Dimension of the user-space in both y_true and y_pred after elimination of users without interactions in y_true.

property results

Get the detailed results for this metric.

Contains an entry for every user.

Returns

The results DataFrame with columns: user_id, score

Return type

pd.DataFrame

property value

Global metric value obtained by taking the average over all users.