def test_all_hyperparameters(sagemaker_session): ipinsights = IPInsights(sagemaker_session=sagemaker_session, batch_metrics_publish_interval=100, epochs=10, learning_rate=0.001, num_ip_encoder_layers=3, random_negative_sampling_rate=5, shuffled_negative_sampling_rate=5, weight_decay=5.0, **ALL_REQ_ARGS) assert ipinsights.hyperparameters() == dict( num_entity_vectors=str(ALL_REQ_ARGS['num_entity_vectors']), vector_dim=str(ALL_REQ_ARGS['vector_dim']), batch_metrics_publish_interval='100', epochs='10', learning_rate='0.001', num_ip_encoder_layers='3', random_negative_sampling_rate='5', shuffled_negative_sampling_rate='5', weight_decay='5.0')
def test_all_hyperparameters(sagemaker_session): ipinsights = IPInsights(sagemaker_session=sagemaker_session, batch_metrics_publish_interval=100, epochs=10, learning_rate=0.001, num_ip_encoder_layers=3, random_negative_sampling_rate=5, shuffled_negative_sampling_rate=5, weight_decay=5.0, **ALL_REQ_ARGS) assert ipinsights.hyperparameters() == dict( num_entity_vectors=str(ALL_REQ_ARGS["num_entity_vectors"]), vector_dim=str(ALL_REQ_ARGS["vector_dim"]), batch_metrics_publish_interval="100", epochs="10", learning_rate="0.001", num_ip_encoder_layers="3", random_negative_sampling_rate="5", shuffled_negative_sampling_rate="5", weight_decay="5.0", )