jstypka

from itertools import compress

import numpy as np
from sklearn.metrics import auc

def compute_precision_recall_auc(y_true, y_pred, precision_recall_fun):
    thresholds = np.append(np.unique(y_pred), [-1])
    thresholds.sort()

    precision = np.zeros(len(y_true))  # reuse them for every binarisation

jstypka

def count_ones(n):
  # How many ones are there in a numbers binary representation a.k.a. how many keywords are relevant to a sample
  count = 0
  while n != 0:
      n &= n - 1
      count += 1

  return count

def compute_descendant_metric(y_true, y_pred, labels, ontology):