def test_lda(sagemaker_session):
with timeout(minutes=15):
data_path = os.path.join(DATA_DIR, 'lda')
data_filename = 'nips-train_1.pbr'
with open(os.path.join(data_path, data_filename), 'rb') as f:
all_records = read_records(f)
# all records must be same
feature_num = int(all_records[0].features['values'].float32_tensor.shape[0])
lda = LDA(role='SageMakerRole', train_instance_type='ml.c4.xlarge', num_topics=10,
sagemaker_session=sagemaker_session, base_job_name='test-lda')
record_set = prepare_record_set_from_local_files(data_path, lda.data_location,
len(all_records), feature_num, sagemaker_session)
lda.fit(record_set, 100)
endpoint_name = name_from_base('lda')
with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
model = LDAModel(lda.model_data, role='SageMakerRole', sagemaker_session=sagemaker_session)
predictor = model.deploy(1, 'ml.c4.xlarge', endpoint_name=endpoint_name)
predict_input = np.random.rand(1, feature_num)
result = predictor.predict(predict_input)
assert len(result) == 1
for record in result:
assert record.label["topic_mixture"] is not None
def test_lda(sagemaker_session, cpu_instance_type):
job_name = unique_name_from_base("lda")
with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
data_path = os.path.join(DATA_DIR, "lda")
data_filename = "nips-train_1.pbr"
with open(os.path.join(data_path, data_filename), "rb") as f:
all_records = read_records(f)
# all records must be same
feature_num = int(all_records[0].features["values"].float32_tensor.shape[0])
lda = LDA(
role="SageMakerRole",
instance_type=cpu_instance_type,
num_topics=10,
sagemaker_session=sagemaker_session,
)
record_set = prepare_record_set_from_local_files(
data_path, lda.data_location, len(all_records), feature_num, sagemaker_session
)
lda.fit(records=record_set, mini_batch_size=100, job_name=job_name)
with timeout_and_delete_endpoint_by_name(job_name, sagemaker_session):
model = LDAModel(lda.model_data, role="SageMakerRole", sagemaker_session=sagemaker_session)
predictor = model.deploy(1, cpu_instance_type, endpoint_name=job_name)
predict_input = np.random.rand(1, feature_num)
result = predictor.predict(predict_input)
assert len(result) == 1
for record in result:
assert record.label["topic_mixture"] is not None