def test_get_score_for_criteria(self):
criteria = {
"analysis_1":{
"action": ">",
"weight": 1.0
},
"analysis_2":{
"action": "<",
"weight": 0.5
}
}
clustering_info = {
'Clustering 1': {
'evaluation': {
'Normalized_analysis_1': 1,
'Normalized_analysis_2': 0.3
}
},
'Clustering 2': {
'evaluation': {
'Normalized_analysis_1': 0.7,
'Normalized_analysis_2': 1.
}
},
'Clustering 3': {
'evaluation': {
'Normalized_analysis_1': 0.6,
'Normalized_analysis_2': 0.5
}
},
'Clustering 4': {
'evaluation': {
'Normalized_analysis_1': 0.9,
'Normalized_analysis_2': 0.0
}
}
}
self.assertEqual(BestClusteringSelector.get_score_for_criteria("Clustering 1",
clustering_info,
criteria),
0.9)
self.assertAlmostEqual(BestClusteringSelector.get_score_for_criteria("Clustering 4",
clustering_info,
criteria),
0.933333333333,12)
def test_get_score_for_criteria(self):
criteria = {
"analysis_1": {
"action": ">",
"weight": 1.0
},
"analysis_2": {
"action": "<",
"weight": 0.5
}
}
clustering_info = {
'Clustering 1': {
'evaluation': {
'Normalized_analysis_1': 1,
'Normalized_analysis_2': 0.3
}
},
'Clustering 2': {
'evaluation': {
'Normalized_analysis_1': 0.7,
'Normalized_analysis_2': 1.
}
},
'Clustering 3': {
'evaluation': {
'Normalized_analysis_1': 0.6,
'Normalized_analysis_2': 0.5
}
},
'Clustering 4': {
'evaluation': {
'Normalized_analysis_1': 0.9,
'Normalized_analysis_2': 0.0
}
}
}
self.assertEqual(
BestClusteringSelector.get_score_for_criteria(
"Clustering 1", clustering_info, criteria), 0.9)
self.assertAlmostEqual(
BestClusteringSelector.get_score_for_criteria(
"Clustering 4", clustering_info, criteria), 0.933333333333, 12)
