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",
)