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

I have questions about this formula. Are the per-class probabilities guaranteed to sum to 1? That would be strange, since these predictions aren't mutually exclusive. If they don't sum to 1, then our normalization doesn't work as we expect, for example [0, 0, 0, 0, 1.0, 1.0] would sum to 11 and normalize to 11/6. Shouldn't the denominator be the sum of all weights?

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