recpack.metrics.RecallK

class recpack.metrics.RecallK(K)

Computes the fraction of true interactions that made it into the Top-K recommendations.

Recall per user is computed as:

\[\text{Recall}(u) = \frac{\sum\limits_{i \in \text{Top-K}(u)} y^{true}_{u,i} }{\sum\limits_{j \in I} y^{true}_{u,j}}\]

Parameters

K (int) – Size of the recommendation list consisting of the Top-K item predictions.

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.