def test_observer_aware_subjective_model_subjreject(self):
dataset = import_python_file(self.dataset_filepath)
np.random.seed(0)
info_dict = {
'selected_subjects': range(5),
}
dataset_reader = CorruptSubjectRawDatasetReader(dataset, input_dict=info_dict)
subjective_model = MaximumLikelihoodEstimationModelReduced(dataset_reader)
with self.assertRaises(AssertionError):
subjective_model.run_modeling(subject_rejection=True)
def test_observer_aware_subjective_model_subjreject(self):
dataset = import_python_file(self.dataset_filepath)
np.random.seed(0)
info_dict = {
'selected_subjects': range(5),
}
dataset_reader = CorruptSubjectRawDatasetReader(dataset,
input_dict=info_dict)
subjective_model = MaximumLikelihoodEstimationModelReduced(
dataset_reader)
with self.assertRaises(AssertionError):
subjective_model.run_modeling(subject_rejection=True)
def test_observer_aware_subjective_model_corruptdata(self):
dataset = import_python_file(self.dataset_filepath)
np.random.seed(0)
info_dict = {
'selected_subjects': range(5),
}
dataset_reader = CorruptSubjectRawDatasetReader(dataset, input_dict=info_dict)
subjective_model = MaximumLikelihoodEstimationModelReduced(dataset_reader)
result = subjective_model.run_modeling()
self.assertAlmostEquals(np.mean(result['quality_scores']), 3.5573073781669944, places=4) # 3.5482845335713469
self.assertAlmostEquals(np.var(result['quality_scores']), 1.3559834438740614, places=4) # 1.4355485462027884
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_aware_subjective_model_corruptdata(self):
dataset = import_python_file(self.dataset_filepath)
np.random.seed(0)
info_dict = {
'selected_subjects': range(5),
}
dataset_reader = CorruptSubjectRawDatasetReader(dataset,
input_dict=info_dict)
subjective_model = MaximumLikelihoodEstimationModelReduced(
dataset_reader)
result = subjective_model.run_modeling()
self.assertAlmostEquals(np.mean(result['quality_scores']),
3.5573073781669944,
places=4) # 3.5482845335713469
self.assertAlmostEquals(np.var(result['quality_scores']),
1.3559834438740614,
places=4) # 1.4355485462027884
def test_observer_aware_subjective_model_synthetic(self):
np.random.seed(0)
dataset = import_python_file(self.dataset_filepath)
info_dict = {
'quality_scores': np.random.uniform(1, 5, 79),
'observer_bias': np.random.normal(0, 1, 26),
'observer_inconsistency': np.abs(np.random.uniform(0.4, 0.6, 26)),
'content_bias': np.zeros(9),
'content_ambiguity': np.zeros(9),
}
dataset_reader = SyntheticRawDatasetReader(dataset,
input_dict=info_dict)
subjective_model = MaximumLikelihoodEstimationModelReduced(
dataset_reader)
result = subjective_model.run_modeling()
self.assertAlmostEquals(np.sum(result['observer_bias']),
-0.90138622499935517,
places=4)
self.assertAlmostEquals(np.var(result['observer_bias']),
0.84819162765420342,
places=4)
self.assertAlmostEquals(np.sum(result['observer_inconsistency']),
12.742288471632817,
places=4)
self.assertAlmostEquals(np.var(result['observer_inconsistency']),
0.0047638169604076975,
places=4)
self.assertAlmostEquals(np.sum(result['quality_scores']),
236.78529213581052,
places=4)
self.assertAlmostEquals(np.var(result['quality_scores']),
1.3059726132293354,
places=4)
def test_observer_aware_subjective_model_synthetic(self):
np.random.seed(0)
dataset = import_python_file(self.dataset_filepath)
info_dict = {
'quality_scores': np.random.uniform(1, 5, 79),
'observer_bias': np.random.normal(0, 1, 26),
'observer_inconsistency': np.abs(np.random.uniform(0.4, 0.6, 26)),
'content_bias': np.zeros(9),
'content_ambiguity': np.zeros(9),
}
dataset_reader = SyntheticRawDatasetReader(dataset, input_dict=info_dict)
subjective_model = MaximumLikelihoodEstimationModelReduced(dataset_reader)
result = subjective_model.run_modeling()
self.assertAlmostEquals(np.sum(result['observer_bias']), -0.90138622499935517, places=4)
self.assertAlmostEquals(np.var(result['observer_bias']), 0.84819162765420342, places=4)
self.assertAlmostEquals(np.sum(result['observer_inconsistency']), 12.742288471632817, places=4)
self.assertAlmostEquals(np.var(result['observer_inconsistency']), 0.0047638169604076975, places=4)
self.assertAlmostEquals(np.sum(result['quality_scores']), 236.78529213581052, places=4)
self.assertAlmostEquals(np.var(result['quality_scores']), 1.3059726132293354, places=4)
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)