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

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.

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