recpack.metrics.HitK

class recpack.metrics.HitK(K)

Computes the number of hits in a list of Top-K recommendations.

A hit is counted when a recommended item in the top K for this user was interacted with.

Detailed results show which of the items in the list of Top-K recommended items were hits and which were not.

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 summing up scores for every user then 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: pandas.core.frame.DataFrame

Get the detailed results for this metric.

Contains an entry for every user-item pair in the Top-K recommendations list of every user.

If there is a user with no recommendations, the results DataFrame will contain K rows for that user with item_id = NaN and score = 0.

property value: Global metric value obtained by summing up scores for every user then taking the average over all users.