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 = {
'missing_probability': 0.1,
}
self.dataset_reader = MissingDataRawDatasetReader(dataset,
input_dict=info_dict)
def run_one_missing_prob(missing_prob, dataset, seed):
np.random.seed(seed)
info_dict = {
'missing_probability': missing_prob,
}
dataset_reader = MissingDataRawDatasetReader(dataset,
input_dict=info_dict)
subjective_model = model_class(dataset_reader)
try:
result = subjective_model.run_modeling(normalize_final=False)
except ValueError as e:
print 'Warning: {}, return result None'.format(e)
result = None
return dataset_reader, result
def test_observer_aware_subjective_model_missingdata(self):
dataset = import_python_file(self.dataset_filepath)
np.random.seed(0)
info_dict = {
'missing_probability': 0.1,
}
dataset_reader = MissingDataRawDatasetReader(dataset,
input_dict=info_dict)
subjective_model = MaximumLikelihoodEstimationModelReduced(
dataset_reader)
result = subjective_model.run_modeling()
self.assertAlmostEquals(np.sum(result['observer_bias']),
-0.18504017984241944,
places=4)
self.assertAlmostEquals(np.var(result['observer_bias']),
0.087350553292201705,
places=4)
self.assertAlmostEquals(np.sum(result['observer_inconsistency']),
15.520738471447299,
places=4)
self.assertAlmostEquals(np.var(result['observer_inconsistency']),
0.010940587327083341,
places=4)
self.assertAlmostEquals(np.sum(result['quality_scores']),
279.94975274863879,
places=4)
self.assertAlmostEquals(np.var(result['quality_scores']),
1.4325574378911554,
places=4)
np.random.seed(0)
info_dict = {
'missing_probability': 0.5,
}
dataset_reader = MissingDataRawDatasetReader(dataset,
input_dict=info_dict)
subjective_model = MaximumLikelihoodEstimationModelReduced(
dataset_reader)
result = subjective_model.run_modeling()
self.assertAlmostEquals(np.sum(result['observer_bias']),
0.057731868199093525,
places=4)
self.assertAlmostEquals(np.var(result['observer_bias']),
0.081341845650928557,
places=4)
self.assertAlmostEquals(np.sum(result['observer_inconsistency']),
14.996238224489693,
places=4)
self.assertAlmostEquals(np.var(result['observer_inconsistency']),
0.013666025579465165,
places=4)
self.assertAlmostEquals(np.sum(result['quality_scores']),
280.67100837103203,
places=4)
self.assertAlmostEquals(np.var(result['quality_scores']),
1.4637917512768972,
places=4)
def test_observer_content_aware_subjective_model_missingdata(self):
dataset = import_python_file(self.dataset_filepath)
np.random.seed(0)
info_dict = {
'missing_probability': 0.1,
}
dataset_reader = MissingDataRawDatasetReader(dataset,
input_dict=info_dict)
subjective_model = MaximumLikelihoodEstimationModel(dataset_reader)
result = subjective_model.run_modeling()
self.assertAlmostEquals(np.sum(result['content_bias']), 0, places=4)
self.assertAlmostEquals(np.var(result['content_bias']), 0, places=4)
self.assertAlmostEquals(np.sum(result['content_ambiguity']),
3.9104244772977128,
places=4)
self.assertAlmostEquals(np.var(result['content_ambiguity']),
0.0037713583509767193,
places=4)
self.assertAlmostEquals(np.sum(result['observer_bias']),
-0.21903272050455846,
places=4)
self.assertAlmostEquals(np.var(result['observer_bias']),
0.084353684687185043,
places=4)
self.assertAlmostEquals(np.sum(result['observer_inconsistency']),
9.8168943054654481,
places=4)
self.assertAlmostEquals(np.var(result['observer_inconsistency']),
0.028159236075789944,
places=4)
self.assertAlmostEquals(np.sum(result['quality_scores']),
280.05548186797336,
places=4)
self.assertAlmostEquals(np.var(result['quality_scores']),
1.4339487982797514,
places=4)
np.random.seed(0)
info_dict = {
'missing_probability': 0.5,
}
dataset_reader = MissingDataRawDatasetReader(dataset,
input_dict=info_dict)
subjective_model = MaximumLikelihoodEstimationModel(dataset_reader)
result = subjective_model.run_modeling()
self.assertAlmostEquals(np.sum(result['content_bias']), 0, places=4)
self.assertAlmostEquals(np.var(result['content_bias']), 0, places=4)
self.assertAlmostEquals(np.sum(result['content_ambiguity']),
2.63184284168883,
places=4)
self.assertAlmostEquals(np.var(result['content_ambiguity']),
0.019164097909450246,
places=4)
self.assertAlmostEquals(np.sum(result['observer_bias']),
0.2263148440748638,
places=4)
self.assertAlmostEquals(np.var(result['observer_bias']),
0.070613033112114504,
places=4)
self.assertAlmostEquals(np.sum(result['observer_inconsistency']),
12.317917502439435,
places=4)
self.assertAlmostEquals(np.var(result['observer_inconsistency']),
0.029455722248727296,
places=4)
self.assertAlmostEquals(np.sum(result['quality_scores']),
280.29962156788139,
places=4)
self.assertAlmostEquals(np.var(result['quality_scores']),
1.4717366222424826,
places=4)