def test_model_image(sagemaker_session):
ntm = NTM(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
data = RecordSet("s3://{}/{}".format(BUCKET_NAME, PREFIX), num_records=1, feature_dim=FEATURE_DIM, channel='train')
ntm.fit(data, MINI_BATCH_SIZE)
model = ntm.create_model()
assert model.image == registry(REGION, "ntm") + '/ntm:1'
def test_prepare_for_training_wrong_value_upper_mini_batch_size(sagemaker_session):
ntm = NTM(base_job_name="ntm", sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
data = RecordSet("s3://{}/{}".format(BUCKET_NAME, PREFIX), num_records=1, feature_dim=FEATURE_DIM,
channel='train')
with pytest.raises(ValueError):
ntm._prepare_for_training(data, 10001)
def test_predictor_type(sagemaker_session):
ntm = NTM(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
data = RecordSet("s3://{}/{}".format(BUCKET_NAME, PREFIX), num_records=1, feature_dim=FEATURE_DIM, channel='train')
ntm.fit(data, MINI_BATCH_SIZE)
model = ntm.create_model()
predictor = model.deploy(1, TRAIN_INSTANCE_TYPE)
assert isinstance(predictor, NTMPredictor)
def test_call_fit(base_fit, sagemaker_session):
ntm = NTM(base_job_name="ntm", sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
data = RecordSet("s3://{}/{}".format(BUCKET_NAME, PREFIX), num_records=1, feature_dim=FEATURE_DIM, channel='train')
ntm.fit(data, MINI_BATCH_SIZE)
base_fit.assert_called_once()
assert len(base_fit.call_args[0]) == 2
assert base_fit.call_args[0][0] == data
assert base_fit.call_args[0][1] == MINI_BATCH_SIZE
def test_all_hyperparameters(sagemaker_session):
ntm = NTM(sagemaker_session=sagemaker_session,
encoder_layers=[1, 2, 3], epochs=3, encoder_layers_activation='tanh', optimizer='sgd',
tolerance=0.05, num_patience_epochs=2, batch_norm=False, rescale_gradient=0.5, clip_gradient=0.5,
weight_decay=0.5, learning_rate=0.5, **ALL_REQ_ARGS)
assert ntm.hyperparameters() == dict(
num_topics=str(ALL_REQ_ARGS['num_topics']),
encoder_layers='[1, 2, 3]',
epochs='3',
encoder_layers_activation='tanh',
optimizer='sgd',
tolerance='0.05',
num_patience_epochs='2',
batch_norm='False',
rescale_gradient='0.5',
clip_gradient='0.5',
weight_decay='0.5',
learning_rate='0.5'
)
def test_required_hyper_parameters_value(sagemaker_session,
required_hyper_parameters, value):
with pytest.raises(ValueError):
test_params = ALL_REQ_ARGS.copy()
test_params[required_hyper_parameters] = value
NTM(sagemaker_session=sagemaker_session, **test_params)
def test_weight_decay_fail_type(sagemaker_session):
with pytest.raises(ValueError):
NTM(weight_decay='other',
sagemaker_session=sagemaker_session,
**ALL_REQ_ARGS)
def test_weight_decay_validation_fail_value_lower(sagemaker_session):
with pytest.raises(ValueError):
NTM(weight_decay=-1,
sagemaker_session=sagemaker_session,
**ALL_REQ_ARGS)
def test_clip_gradient_fail_type(sagemaker_session):
with pytest.raises(ValueError):
NTM(clip_gradient='other',
sagemaker_session=sagemaker_session,
**ALL_REQ_ARGS)
def test_clip_gradient_validation_fail_value(sagemaker_session):
with pytest.raises(ValueError):
NTM(clip_gradient=0,
sagemaker_session=sagemaker_session,
**ALL_REQ_ARGS)
def test_num_patience_epochs_validation_fail_value_upper(sagemaker_session):
with pytest.raises(ValueError):
NTM(num_patience_epochs=100,
sagemaker_session=sagemaker_session,
**ALL_REQ_ARGS)
def test_rescale_gradient_validation_fail_value_upper(sagemaker_session):
with pytest.raises(ValueError):
NTM(rescale_gradient=10,
sagemaker_session=sagemaker_session,
**ALL_REQ_ARGS)
def test_call_fit_none_mini_batch_size(sagemaker_session):
ntm = NTM(base_job_name="ntm", sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
data = RecordSet("s3://{}/{}".format(BUCKET_NAME, PREFIX), num_records=1, feature_dim=FEATURE_DIM,
channel='train')
ntm.fit(data)
def test_image(sagemaker_session):
ntm = NTM(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
assert ntm.train_image() == registry(REGION, "ntm") + '/ntm:1'
def test_optimizer_validation_fail_value(sagemaker_session):
with pytest.raises(ValueError):
NTM(optimizer='other',
sagemaker_session=sagemaker_session,
**ALL_REQ_ARGS)
def test_iterable_hyper_parameters_type(sagemaker_session,
iterable_hyper_parameters, value):
with pytest.raises(TypeError):
test_params = ALL_REQ_ARGS.copy()
test_params.update({iterable_hyper_parameters: value})
NTM(sagemaker_session=sagemaker_session, **test_params)
def test_encoder_layers_activation_validation_fail_value(sagemaker_session):
with pytest.raises(ValueError):
NTM(encoder_layers_activation='other',
sagemaker_session=sagemaker_session,
**ALL_REQ_ARGS)
def test_epochs_validation_fail_value_lower(sagemaker_session):
with pytest.raises(ValueError):
NTM(epochs=0, sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
def test_encoder_layers_validation_fail_type(sagemaker_session):
with pytest.raises(TypeError):
NTM(encoder_layers=0,
sagemaker_session=sagemaker_session,
**ALL_REQ_ARGS)
def test_num_topics_validation_fail_value_upper(sagemaker_session):
with pytest.raises(ValueError):
NTM(num_topics=10000,
sagemaker_session=sagemaker_session,
**COMMON_TRAIN_ARGS)
def test_num_topics_validation_fail_type(sagemaker_session):
with pytest.raises(ValueError):
NTM(num_topics='other',
sagemaker_session=sagemaker_session,
**COMMON_TRAIN_ARGS)
def test_tolerance_validation_fail_type(sagemaker_session):
with pytest.raises(ValueError):
NTM(tolerance='other',
sagemaker_session=sagemaker_session,
**ALL_REQ_ARGS)
def test_optional_hyper_parameters_value(sagemaker_session,
optional_hyper_parameters, value):
with pytest.raises(ValueError):
test_params = ALL_REQ_ARGS.copy()
test_params.update({optional_hyper_parameters: value})
NTM(sagemaker_session=sagemaker_session, **test_params)
def test_tolerance_validation_fail_value_upper(sagemaker_session):
with pytest.raises(ValueError):
NTM(tolerance=0.5, sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
def test_num_patience_epochs_validation_fail_type(sagemaker_session):
with pytest.raises(ValueError):
NTM(num_patience_epochs='other',
sagemaker_session=sagemaker_session,
**ALL_REQ_ARGS)
def test_image(sagemaker_session):
ntm = NTM(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
assert ntm.train_image() == registry(REGION, "ntm") + '/ntm:1'
def test_image(sagemaker_session):
ntm = NTM(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
assert image_uris.retrieve("ntm", REGION) == ntm.training_image_uri()
