recpack.metrics.DCGK
- class recpack.metrics.DCGK(K)
Computes the sum of gains of all items in a recommendation list.
Relevant items that are ranked higher in the Top-K recommendations have a higher gain.
The Discounted Cumulative Gain (DCG) is computed for every user as
\[\text{DiscountedCumulativeGain}(u) = \sum\limits_{i \in Top-K(u)} \frac{y^{true}_{u,i}}{\log_2 (\text{rank}(u,i) + 1)}\]- 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 scoresy_pred
.Attributes
The names of the columns in the results DataFrame.
Name of the metric.
Dimension of the item-space in both
y_true
andy_pred
Dimension of the user-space in both
y_true
andy_pred
after elimination of users without interactions iny_true
.Get the detailed results for this metric.
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 scoresy_pred
. Only Top-K recommendations are considered.Detailed metric results can be retrieved with
results
. Global aggregate metric value is retrieved asvalue
.- 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
andy_pred
- property num_users: int
Dimension of the user-space in both
y_true
andy_pred
after elimination of users without interactions iny_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.