def test_model_image(sagemaker_session):
lda = LDA(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
data = RecordSet('s3://{}/{}'.format(BUCKET_NAME, PREFIX), num_records=1, feature_dim=FEATURE_DIM, channel='train')
lda.fit(data, MINI_BATCH_SZIE)
model = lda.create_model()
assert model.image == registry(REGION, 'lda') + '/lda:1'
def test_prepare_for_training_wrong_value_mini_batch_size(sagemaker_session):
lda = LDA(base_job_name='lda', 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):
lda._prepare_for_training(data, 0)
def test_predictor_type(sagemaker_session):
lda = LDA(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
data = RecordSet('s3://{}/{}'.format(BUCKET_NAME, PREFIX), num_records=1, feature_dim=FEATURE_DIM, channel='train')
lda.fit(data, MINI_BATCH_SZIE)
model = lda.create_model()
predictor = model.deploy(1, TRAIN_INSTANCE_TYPE)
assert isinstance(predictor, LDAPredictor)
def test_model_image(sagemaker_session):
lda = LDA(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
data = RecordSet('s3://{}/{}'.format(BUCKET_NAME, PREFIX),
num_records=1,
feature_dim=FEATURE_DIM,
channel='train')
lda.fit(data, MINI_BATCH_SZIE)
model = lda.create_model()
assert model.image == registry(REGION, 'lda') + '/lda:1'
def test_call_fit(base_fit, sagemaker_session):
lda = LDA(base_job_name='lda', sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
data = RecordSet('s3://{}/{}'.format(BUCKET_NAME, PREFIX), num_records=1, feature_dim=FEATURE_DIM, channel='train')
lda.fit(data, MINI_BATCH_SZIE)
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_SZIE
def test_call_fit_wrong_value_mini_batch_size(sagemaker_session):
lda = LDA(base_job_name='lda',
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):
lda.fit(data, 0)
def test_predictor_type(sagemaker_session):
lda = LDA(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
data = RecordSet('s3://{}/{}'.format(BUCKET_NAME, PREFIX),
num_records=1,
feature_dim=FEATURE_DIM,
channel='train')
lda.fit(data, MINI_BATCH_SZIE)
model = lda.create_model()
predictor = model.deploy(1, TRAIN_INSTANCE_TYPE)
assert isinstance(predictor, LDAPredictor)
def test_all_hyperparameters(sagemaker_session):
lda = LDA(sagemaker_session=sagemaker_session,
alpha0=2.2, max_restarts=3, max_iterations=10, tol=3.3,
**ALL_REQ_ARGS)
assert lda.hyperparameters() == dict(
num_topics=str(ALL_REQ_ARGS['num_topics']),
alpha0='2.2',
max_restarts='3',
max_iterations='10',
tol='3.3',
)
def test_prepare_for_training_wrong_value_mini_batch_size(sagemaker_session):
lda = LDA(base_job_name="lda", 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):
lda._prepare_for_training(data, 0)
def test_model_image(sagemaker_session):
lda = LDA(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
data = RecordSet(
"s3://{}/{}".format(BUCKET_NAME, PREFIX),
num_records=1,
feature_dim=FEATURE_DIM,
channel="train",
)
lda.fit(data, MINI_BATCH_SZIE)
model = lda.create_model()
assert image_uris.retrieve("lda", REGION) == model.image_uri
def test_all_hyperparameters(sagemaker_session):
lda = LDA(sagemaker_session=sagemaker_session,
alpha0=2.2,
max_restarts=3,
max_iterations=10,
tol=3.3,
**ALL_REQ_ARGS)
assert lda.hyperparameters() == dict(
num_topics=str(ALL_REQ_ARGS['num_topics']),
alpha0='2.2',
max_restarts='3',
max_iterations='10',
tol='3.3',
)
def test_all_hyperparameters(sagemaker_session):
lda = LDA(sagemaker_session=sagemaker_session,
alpha0=2.2,
max_restarts=3,
max_iterations=10,
tol=3.3,
**ALL_REQ_ARGS)
assert lda.hyperparameters() == dict(
num_topics=str(ALL_REQ_ARGS["num_topics"]),
alpha0="2.2",
max_restarts="3",
max_iterations="10",
tol="3.3",
)
def test_init_required_named(sagemaker_session):
lda = LDA(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
assert lda.role == COMMON_TRAIN_ARGS["role"]
assert lda.instance_count == INSTANCE_COUNT
assert lda.instance_type == COMMON_TRAIN_ARGS["instance_type"]
assert lda.num_topics == ALL_REQ_ARGS["num_topics"]
def test_call_fit(base_fit, sagemaker_session):
lda = LDA(base_job_name='lda',
sagemaker_session=sagemaker_session,
**ALL_REQ_ARGS)
data = RecordSet('s3://{}/{}'.format(BUCKET_NAME, PREFIX),
num_records=1,
feature_dim=FEATURE_DIM,
channel='train')
lda.fit(data, MINI_BATCH_SZIE)
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_SZIE
def test_init_required_named(sagemaker_session):
lda = LDA(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
assert lda.role == COMMON_TRAIN_ARGS['role']
assert lda.train_instance_count == TRAIN_INSTANCE_COUNT
assert lda.train_instance_type == COMMON_TRAIN_ARGS['train_instance_type']
assert lda.num_topics == ALL_REQ_ARGS['num_topics']
def test_init_required_positional(sagemaker_session):
lda = LDA(ROLE,
INSTANCE_TYPE,
NUM_TOPICS,
sagemaker_session=sagemaker_session)
assert lda.role == ROLE
assert lda.instance_count == INSTANCE_COUNT
assert lda.instance_type == INSTANCE_TYPE
assert lda.num_topics == NUM_TOPICS
def test_predictor_custom_serialization(sagemaker_session):
lda = LDA(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
data = RecordSet(
"s3://{}/{}".format(BUCKET_NAME, PREFIX),
num_records=1,
feature_dim=FEATURE_DIM,
channel="train",
)
lda.fit(data, MINI_BATCH_SZIE)
model = lda.create_model()
custom_serializer = Mock()
custom_deserializer = Mock()
predictor = model.deploy(
1,
INSTANCE_TYPE,
serializer=custom_serializer,
deserializer=custom_deserializer,
)
assert isinstance(predictor, LDAPredictor)
assert predictor.serializer is custom_serializer
assert predictor.deserializer is custom_deserializer
def test_image(sagemaker_session):
lda = LDA(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
assert lda.train_image() == registry(REGION, 'lda') + '/lda:1'
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})
LDA(sagemaker_session=sagemaker_session, **test_params)
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
LDA(sagemaker_session=sagemaker_session, **test_params)
def test_image(sagemaker_session):
lda = LDA(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
assert image_uris.retrieve("lda", REGION) == lda.training_image_uri()
def test_image(sagemaker_session):
lda = LDA(sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
assert lda.train_image() == registry(REGION, 'lda') + '/lda:1'
def test_alpha0_validation_fail_type(sagemaker_session):
with pytest.raises(ValueError):
LDA(alpha0='other',
sagemaker_session=sagemaker_session,
**ALL_REQ_ARGS)
def test_max_restarts_validation_fail_type2(sagemaker_session):
with pytest.raises(ValueError):
LDA(max_restarts=0.1,
sagemaker_session=sagemaker_session,
**ALL_REQ_ARGS)
def test_max_iterations_validation_fail_value(sagemaker_session):
with pytest.raises(ValueError):
LDA(max_iterations=0,
sagemaker_session=sagemaker_session,
**ALL_REQ_ARGS)
def test_tol_validation_fail_value(sagemaker_session):
with pytest.raises(ValueError):
LDA(tol=0, sagemaker_session=sagemaker_session, **ALL_REQ_ARGS)
def test_num_topics_validation_fail_value(sagemaker_session):
with pytest.raises(ValueError):
LDA(num_topics=0,
sagemaker_session=sagemaker_session,
**COMMON_TRAIN_ARGS)
