normalized_weighted_sum.py

def weighted_sum(score, weights):
    return sum(weights[cls] * proba
        for cls, proba in score['probability'].items())

def normalized_weighted_sum(score, weights):
    return weighted_sum(score, weights) / len(weights)

enwiki_weights = {
  'Stub': 1,
  'Start': 2,
  'C': 3,
  'B': 4,
  'GA': 5,
  'FA': 6
}

normalized_weighted_sum(
    {'probability': {'Stub': 0.5, 
                     'Start': 0.3, 
                     'C': 0.1, 
                     'B': 0.05, 
                     'GA': 0.05, 
                     'FA': 0}},
    enwiki_weights)

0.308 * max(enwiki_weights.values())

output

>>> def weighted_sum(score, weights):
...     return sum(weights[cls] * proba
...         for cls, proba in score['probability'].items())
... 
>>> def normalized_weighted_sum(score, weights):
...     return weighted_sum(score, weights) / len(weights)
... 
>>> enwiki_weights = {
...   'Stub': 1,
...   'Start': 2,
...   'C': 3,
...   'B': 4,
...   'GA': 5,
...   'FA': 6
... }
>>> 
>>> normalized_weighted_sum(
...     {'probability': {'Stub': 0.5, 
...                      'Start': 0.3, 
...                      'C': 0.1, 
...                      'B': 0.05, 
...                      'GA': 0.05, 
...                      'FA': 0}},
...     enwiki_weights)
0.30833333333333335
>>> 
>>> 0.308 * max(enwiki_weights.values())
1.8479999999999999

Chatting now, Aaron pointed out my mistake: this is a one-vs-rest classifier, unlike drafttopic. The sum of probabilities.