def setUp(self):
dataset_filepath = config.ROOT + '/python/test/resource/NFLX_dataset_public_raw.py'
dataset = import_python_file(dataset_filepath)
np.random.seed(0)
info_dict = {
'selected_subjects': range(5),
}
self.dataset_reader = SelectSubjectRawDatasetReader(dataset, input_dict=info_dict)
class SelectedSubjectDatasetReaderTest(unittest.TestCase):
def setUp(self):
dataset_filepath = config.ROOT + '/python/test/resource/NFLX_dataset_public_raw.py'
dataset = import_python_file(dataset_filepath)
np.random.seed(0)
info_dict = {
'selected_subjects': range(5),
}
self.dataset_reader = SelectSubjectRawDatasetReader(dataset, input_dict=info_dict)
def test_read_dataset_stats(self):
self.assertEquals(self.dataset_reader.num_ref_videos, 9)
self.assertEquals(self.dataset_reader.num_dis_videos, 79)
self.assertEquals(self.dataset_reader.num_observers, 5)
def test_opinion_score_2darray(self):
os_2darray = self.dataset_reader.opinion_score_2darray
self.assertEquals(os_2darray.shape, (79, 5))
def test_to_dataset(self):
dataset = self.dataset_reader.to_dataset()
old_scores = [dis_video['os'] for dis_video in self.dataset_reader.dataset.dis_videos]
new_scores = [dis_video['os'] for dis_video in dataset.dis_videos]
self.assertNotEquals(old_scores, new_scores)
def run_one_num_subject(num_subject, dataset, seed):
np.random.seed(seed)
total_subject = len(dataset.dis_videos[0]['os'])
info_dict = {
'selected_subjects':
np.random.permutation(total_subject)[:num_subject]
}
dataset_reader = SelectSubjectRawDatasetReader(
dataset, input_dict=info_dict)
subjective_model = model_class(dataset_reader)
result = subjective_model.run_modeling(normalize_final=False)
return dataset_reader, result