recpack.metrics.PrecisionK

class recpack.metrics.PrecisionK(K)

Computes the fraction of top-K recommendations that correspond to true interactions.

Different from the definition for information retrieval a recommendation algorithm is expected to always return K items when the Top-K recommendations are requested. When fewer than K items received scores, these are considered a miss. As such recommending fewer items is not beneficial for a recommendation algorithm.

Precision is computed per user as:

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

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.