def test_normalize_one_evaluation(self):
clustering_info = {
"clustering1":{
"type":"algorithm2",
"clustering":"clustering2",
"parameters":{},
"evaluation":{
"myeval":0.5,
"myothereval":1.0
}
},
"clustering2":{
"type":"algorithm1",
"clustering":"clustering1",
"parameters":{},
"evaluation":{
"myeval":-0.5,
"myothereval":2.0
}
},
"clustering3":{
"type":"algorithm1",
"clustering":"clustering3",
"parameters":{},
"evaluation":{
"myeval":0.2,
"myothereval":0.0
}
}
}
values_for_myeval = BestClusteringSelector.get_values_for_evaluation_type("myeval", clustering_info)
self.assertDictEqual(values_for_myeval, {'clustering1': 0.5, 'clustering3': 0.2, 'clustering2': -0.5})
BestClusteringSelector.normalize_one_evaluation_type("myeval", clustering_info)
values_for_myeval = BestClusteringSelector.get_values_for_evaluation_type("Normalized_myeval", clustering_info)
self.assertDictEqual( values_for_myeval, {'clustering1': 1.0, 'clustering3': 0.7, 'clustering2': 0.0})
def test_normalize_one_evaluation(self):
clustering_info = {
"clustering1": {
"type": "algorithm2",
"clustering": "clustering2",
"parameters": {},
"evaluation": {
"myeval": 0.5,
"myothereval": 1.0
}
},
"clustering2": {
"type": "algorithm1",
"clustering": "clustering1",
"parameters": {},
"evaluation": {
"myeval": -0.5,
"myothereval": 2.0
}
},
"clustering3": {
"type": "algorithm1",
"clustering": "clustering3",
"parameters": {},
"evaluation": {
"myeval": 0.2,
"myothereval": 0.0
}
}
}
values_for_myeval = BestClusteringSelector.get_values_for_evaluation_type(
"myeval", clustering_info)
self.assertDictEqual(values_for_myeval, {
'clustering1': 0.5,
'clustering3': 0.2,
'clustering2': -0.5
})
BestClusteringSelector.normalize_one_evaluation_type(
"myeval", clustering_info)
values_for_myeval = BestClusteringSelector.get_values_for_evaluation_type(
"Normalized_myeval", clustering_info)
self.assertDictEqual(values_for_myeval, {
'clustering1': 1.0,
'clustering3': 0.7,
'clustering2': 0.0
})
