example on computing the confusion matrix using sklearn

sample_cm.py

import numpy as np
from sklearn.metrics import confusion_matrix

# The true labels we have in the plot orange:positive, blue:negative
true_label = np.array(
  [0., 0., 0., 0., 0., 
   0., 0., 0., 0., 0., 
   1., 1., 1., 1., 1., 
   1., 1., 1., 1., 1.])

# The predicted score of some model
prediction = np.array(
  [-0.53844931, -1.63710635, -1.05165483, -1.39822259, -0.86465741,
   -0.82362094, -0.59935865, -0.70585773, -1.26440723, -1.45471162,
    1.46155069,  0.36289365,  0.94834517,  0.60177741,  1.13534259,
    1.17637906,  1.40064135,  1.29414227,  0.73559277,  0.54528838])
    
# The threshold to transform the continuous prediction into binary prediction
threshold = 1 

# Transform continuous predictions into binary predictions
prediction[prediction >= threshold] = 1
prediction[prediction < threshold] = 0

print(confusion_matrix(true_label, prediction))
# output:
# [[10  0]
#  [ 5  5]]