import numpy as np
"""
    Desc: average_precision_score returns overestimated AUC of precision-recall curve
"""
# pathological example
p = [0.833, 0.800] # precision
r = [0.294, 0.235] # recall

# computation of average_precision_score()
print("AUC       = {:3f}".format(-np.sum(np.diff(r) * np.array(p)[:-1]))) # _binary_uninterpolated_average_precision()

# computation of auc() with trapezoid interpolation
print("AUC TRAP. = {:3f}".format(-np.trapz(p, r)))

# possible fix in _binary_uninterpolated_average_precision() **(edited)**
print("AUC FIX   = {:3f}".format(-np.sum(np.diff(r) * np.minimum(p[:-1], p[1:])))

#>> AUC       = 0.049147
#>> AUC TRAP. = 0.048174
#>> AUC FIX   = 0.047200

sum((r[i] - r[i+1]) * p[i] for i in range(len(p)-1))
>> 0.049147

sum((r[i] - r[i+1]) * p[i+1] for i in range(len(p)-1))
>> 0.047200

sum((r[i] - r[i+1]) * min(p[i] + p[i+1]) for i in range(len(p)-1)) 
>> 0.047200

p = [0.3, 1.0]
r = [1.0, 0.0]

#Results:
>> 0.30    # sklearn's _binary_uninterpolated_average_precision()
>> 0.30    # my consistent _binary_uninterpolated_average_precision()
>> 0.65    # np.trapz() (trapezoidal interpolation)

p = [1.0, 0.3]
r = [1.0, 0.0]

#Results:
>> 1.00    # sklearn's _binary_uninterpolated_average_precision()
>> 0.30    # my consistent _binary_uninterpolated_average_precision()
>> 0.65    # np.trapz() (trapezoidal interpolation)

p = [0.4, 0.1, 1.0]
r = [1.0, 0.9, 0.0]

#Results:
>> 0.13      # sklearn's _binary_uninterpolated_average_precision()
>> 0.10      # my consistent _binary_uninterpolated_average_precision()
>> 0.52      # np.trapz() (trapezoidal interpolation)