Metrics¶
Metric¶
Recall¶

class
recoder.metrics.
Recall
(k, normalize=True)[source]¶ Computes the recall @ K of the recommended items.
Parameters:  k (int) – the cut position of the recommended list
 normalize (bool, optional) – if True, normalize the value to 1 (divide by k) if k is less than the number of items in the user interactions, otherwise normalize only by number of items in the user interactions.
NDCG¶
AveragePrecision¶

class
recoder.metrics.
AveragePrecision
(k, normalize=True)[source]¶ Computes the average precision @ K of the recommended items.
Parameters:  k (int) – the cut position of the recommended list
 normalize (bool, optional) – if True, normalize the value to 1 (divide by k) if k is less than the number of items the user interacted with, otherwise normalize only by number of items the user interacted with.
RecommenderEvaluator¶

class
recoder.metrics.
RecommenderEvaluator
(recommender, metrics)[source]¶ Evaluates a
recoder.recommender.Recommender
given a set ofMetric
Parameters:  recommender (Recommender) – the Recommender to evaluate
 metrics (list) – list of metrics used to evaluate the recommender

evaluate
(eval_dataset, batch_size=1, num_users=None, num_workers=0)[source]¶ Evaluates the recommender with an evaluation dataset.
Parameters:  eval_dataset (RecommendationDataset) – the dataset to use in evaluating the model
 batch_size (int) – the size of the users batch passed to the recommender
 num_users (int, optional) – the number of users from the dataset to evaluate on. If None, evaluate on all users
 num_workers (int, optional) – the number of workers to use on evaluating the recommended items. This is useful if the recommender runs on GPU, so the evaluation can run in parallel.
Returns: A dict mapping each metric to the list of the metric values on each user in the dataset.
Return type: dict