def test_observer_content_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 = MaximumLikelihoodEstimationModel(dataset_reader)
with self.assertRaises(AssertionError):
subjective_model.run_modeling(subject_rejection=True)
def test_observer_content_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 = MaximumLikelihoodEstimationModel(dataset_reader)
with self.assertRaises(AssertionError):
subjective_model.run_modeling(subject_rejection=True)
def test_observer_content_aware_subjective_model_nosubject(self):
subjective_model = MaximumLikelihoodEstimationModel.from_dataset_file(
self.dataset_filepath)
result = subjective_model.run_modeling(mode='NO_SUBJECT')
self.assertAlmostEquals(np.sum(result['observer_bias']), 0.0, places=4)
self.assertAlmostEquals(np.var(result['observer_bias']), 0.0, places=4)
self.assertAlmostEquals(np.sum(result['observer_inconsistency']),
0.0,
places=4)
self.assertAlmostEquals(np.var(result['observer_inconsistency']),
0.0,
places=4)
self.assertAlmostEquals(np.sum(result['quality_scores']),
280.0384615384616,
places=4)
self.assertAlmostEquals(np.var(result['quality_scores']),
1.4012220200639218,
places=4)
self.assertAlmostEquals(np.sum(result['content_bias']), 0.0, places=4)
self.assertAlmostEquals(np.var(result['content_bias']), 0.0, places=4)
self.assertAlmostEquals(np.sum(result['content_ambiguity']),
6.06982228334157,
places=4)
self.assertAlmostEquals(np.var(result['content_ambiguity']),
0.0045809756997836721,
places=4)
def test_observer_content_aware_subjective_model_nocontent(self):
subjective_model = MaximumLikelihoodEstimationModel.from_dataset_file(
self.dataset_filepath)
result = subjective_model.run_modeling(mode='NO_CONTENT')
self.assertAlmostEquals(np.sum(result['observer_bias']),
-0.090840910829083799,
places=4)
self.assertAlmostEquals(np.var(result['observer_bias']),
0.089032585621095089,
places=4)
self.assertAlmostEquals(np.sum(result['observer_inconsistency']),
15.681766163430936,
places=4)
self.assertAlmostEquals(np.var(result['observer_inconsistency']),
0.012565584832977776,
places=4)
self.assertAlmostEquals(np.sum(result['quality_scores']),
280.31447815213642,
places=4)
self.assertAlmostEquals(np.var(result['quality_scores']),
1.4355485462027884,
places=4)
self.assertAlmostEquals(np.sum(result['content_bias']), 0.0, places=4)
self.assertAlmostEquals(np.var(result['content_bias']), 0.0, places=4)
self.assertAlmostEquals(np.sum(result['content_ambiguity']),
0.0,
places=4)
self.assertAlmostEquals(np.var(result['content_ambiguity']),
0.0,
places=4)
def test_observer_content_aware_subjective_model(self):
subjective_model = MaximumLikelihoodEstimationModel.from_dataset_file(
self.dataset_filepath)
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']),
3.8972884776604402,
places=4)
self.assertAlmostEquals(np.var(result['content_ambiguity']),
0.0041122094732031289,
places=4)
self.assertAlmostEquals(np.sum(result['observer_bias']),
-0.055712761348815837,
places=4)
self.assertAlmostEquals(np.var(result['observer_bias']),
0.085842891905121704,
places=4)
self.assertAlmostEquals(np.sum(result['observer_inconsistency']),
10.164665557559516,
places=4)
self.assertAlmostEquals(np.var(result['observer_inconsistency']),
0.028749990587721687,
places=4)
self.assertAlmostEquals(np.sum(result['quality_scores']),
280.20774261173619,
places=4)
self.assertAlmostEquals(np.var(result['quality_scores']),
1.4351342153719635,
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_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_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)
def test_observer_content_aware_subjective_model_numerical(self):
subjective_model = MaximumLikelihoodEstimationModel.from_dataset_file(
self.dataset_filepath)
result = subjective_model.run_modeling(gradient_method='numerical')
self.assertAlmostEquals(np.sum(result['content_ambiguity']),
3.8972884776604402,
places=4)
self.assertAlmostEquals(np.var(result['content_ambiguity']),
0.0041122094732031289,
places=4)
self.assertAlmostEquals(np.sum(result['observer_bias']),
-0.055712761348815837,
places=4)
self.assertAlmostEquals(np.var(result['observer_bias']),
0.085842891905121704,
places=4)
self.assertAlmostEquals(np.sum(result['observer_inconsistency']),
10.164665557559516,
places=3)
self.assertAlmostEquals(np.var(result['observer_inconsistency']),
0.028749990587721687,
places=4)
self.assertAlmostEquals(np.sum(result['quality_scores']),
280.20774261173619,
places=4)
self.assertAlmostEquals(np.var(result['quality_scores']),
1.4351342153719635,
places=4)
self.assertAlmostEquals(np.sum(result['content_ambiguity_std']),
0.30465244947706538,
places=4)
self.assertAlmostEquals(np.sum(result['observer_bias_std']),
1.7392847550878989,
places=4)
self.assertAlmostEquals(np.sum(result['observer_inconsistency_std']),
12.393285044624955,
places=4)
self.assertAlmostEquals(np.sum(result['quality_scores_std']),
8.8863877635750423,
places=4)
def test_observer_content_aware_subjective_model_numerical(self):
subjective_model = MaximumLikelihoodEstimationModel.from_dataset_file(
self.dataset_filepath)
result = subjective_model.run_modeling(gradient_method='numerical')
self.assertAlmostEquals(np.sum(result['content_ambiguity']), 3.8972884776604402, places=4)
self.assertAlmostEquals(np.var(result['content_ambiguity']), 0.0041122094732031289, places=4)
self.assertAlmostEquals(np.sum(result['observer_bias']), -0.055712761348815837, places=4)
self.assertAlmostEquals(np.var(result['observer_bias']), 0.085842891905121704, places=4)
self.assertAlmostEquals(np.sum(result['observer_inconsistency']), 10.164665557559516, places=3)
self.assertAlmostEquals(np.var(result['observer_inconsistency']), 0.028749990587721687, places=4)
self.assertAlmostEquals(np.sum(result['quality_scores']), 280.20774261173619, places=4)
self.assertAlmostEquals(np.var(result['quality_scores']), 1.4351342153719635, places=4)
self.assertAlmostEquals(np.sum(result['content_ambiguity_std']), 0.30465244947706538, places=4)
self.assertAlmostEquals(np.sum(result['observer_bias_std']), 1.7392847550878989, places=4)
self.assertAlmostEquals(np.sum(result['observer_inconsistency_std']), 12.393285044624955, places=4)
self.assertAlmostEquals(np.sum(result['quality_scores_std']), 8.8863877635750423, 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)