text: # Copyright 2017-2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License"). You
  # may not use this file except in compliance with the License. A copy of
  # the License is located at
  #
  #     http://aws.amazon.com/apache2.0/
  #
  # or in the "license" file accompanying this file. This file is
  # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
  # ANY KIND, either express or implied. See the License for the specific
  # language governing permissions and limitations under the License.
  from __future__ import absolute_import
  
  import os
  
  import numpy as np
  import pytest
  
  from sagemaker import NTM, NTMModel
  from sagemaker.amazon.common import read_records
  from sagemaker.utils import unique_name_from_base
  from tests.integ import DATA_DIR, TRAINING_DEFAULT_TIMEOUT_MINUTES
  from tests.integ.timeout import timeout, timeout_and_delete_endpoint_by_name
  from tests.integ.record_set import prepare_record_set_from_local_files
  
  
  @pytest.mark.canary_quick
  def test_ntm(sagemaker_session):
      job_name = unique_name_from_base('ntm')
  
      with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
          data_path = os.path.join(DATA_DIR, 'ntm')
          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])
  
          ntm = NTM(role='SageMakerRole', train_instance_count=1, train_instance_type='ml.c4.xlarge',
                    num_topics=10,
                    sagemaker_session=sagemaker_session)
  
          record_set = prepare_record_set_from_local_files(data_path, ntm.data_location,
                                                           len(all_records), feature_num,
                                                           sagemaker_session)
          ntm.fit(records=record_set, job_name=job_name)
  
      with timeout_and_delete_endpoint_by_name(job_name, sagemaker_session):
          model = NTMModel(ntm.model_data, role='SageMakerRole', sagemaker_session=sagemaker_session)
          predictor = model.deploy(1, 'ml.c4.xlarge', 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_weights"] is not None
  