scikit-learn: error in average_precision_score

I believe there is an error in sklearn.metrics.average_precision_score. Here is a script to show the result:

from sklearn.metrics import average_precision_score

y_true = [
       1, 1,
       1, 1,
       1, 1,
       1, -1,      # Single negative here, pos 8
       1, 1
       ]
y_score = list(range(len(y_true)))

for average in ('micro', 'macro', 'weighted', 'samples'):
    print( "Average:", average)
    print(average_precision_score(y_true, y_score, average=average))

What we have are essentially ten documents, nine of which are Positive. The example at position eight is negative. According to my understanding (and Wikipedia, see https://en.wikipedia.org/wiki/Information_retrieval#Average_precision third equation for AveP down the screen), the average precision score should be:

| Pos | Value | Precision |                                 
| === |   === | ========  |                                 
|   1 |     1 | 1/1       |                                 
|   2 |     1 | 2/2       |                                 
|   3 |     1 | 3/3       |                                 
|   4 |     1 | 4/4       |                                 
|   5 |     1 | 5/5       |                                 
|   6 |     1 | 6/6       |                                 
|   7 |     1 | 7/7       |                                 
|   8 |     0 | 7/8       | Contributes zero since negative example
|   9 |     1 | 8/9       |                                 
|  10 |     1 | 9/10      |                                 

The True Positive (TP) value here is 9. The average precision = (1*7 + 8/9 + 9/10) / TP = 0.976 The value produced by the script (for all averaging schemes) = 0.865 I believe a_p_s is producing this value because it is dividing by N instead of TP.

Regards, -Tom