implicit_model_trainer module¶
-
class
openrec.legacy.implicit_model_trainer.
ImplicitModelTrainer
(batch_size, test_batch_size, train_dataset, model, sampler, item_serving_size=None, eval_save_prefix=None)¶ Bases:
object
The ImplicitModelTrainer class implements logics for basic recommender training and evaluation using users’ implicit feedback.
Parameters: - batch_size (int) – Training batch size.
- test_batch_size (int) – Test/Evaluation batch size (number of users per testing batch).
- train_dataset (Dataset) – Dataset for model training.
- model (Recommender) – The target recommender.
- sampler (Sampler) – The sampler for model training.
- item_serving_size (int, optional) – Test/Evaluation batch size (number of items per testing batch).
Notes
The function
train
should be called for model training and evaluation.-
train
(num_itr, display_itr, eval_datasets=[], evaluators=[], num_negatives=None, seed=10)¶ Train and evaluate a recommender.
Parameters: - num_itr (int) – total number of training iterations.
- display_itr (int) – Evaluation/testing period.
- eval_datasets (list of Dataset) – A list of datasets for evaluation/testing.
- evaluators (list of Evaluator) – A list of evaluators for evaluation/testing.
- num_negatives (int, optional) – If specified, a given number of items NOT interacted with each user will be sampled (as negative items) for evaluations.