For multi class problems decide how the model performed in a binary situation

plot_correct_quadrant_classification.py

# for multi class problems decide whether the model was correct going long or short
def correct_quadrant_classifications(cls_labels, cm):
    #Find nuumber of correct classifications
    correct_clf = 0
    if len(cls_labels) % 2 == 0:
        for i in range(0, len(cls_labels)):
            correct_clf = correct_clf + cm[i][i]
    # calculate the rate of correct predictions
    correct_clf_rate =  correct_clf/cm.sum()
    #print('\n{:.0f} out of {:.0f} predictions were correct'.format(correct_clf, cm.sum()))

    # Find how many predictions were in the correct quadrant
    correct_quad = 0
    if len(cls_labels) % 2 == 0:
        for i in range(0, int(len(cls_labels)/2)):
            for j in range(0, int(len(cls_labels)/2)):
                correct_quad = correct_quad + cm[i][j]

        for i in range(int(len(cls_labels)/2) ,len(cls_labels)):
            for j in range(int(len(cls_labels)/2) ,len(cls_labels)):
                correct_quad = correct_quad + cm[i][j]
            print('''\nTotal number of predictions in the correct quadrant was {:.0f} out of {:.0f},
                  this corresponds to a {:.1f}% classification accuracy rate\n'''.format(
                                                correct_quad, cm.sum(), correct_quad/cm.sum()*100))
        # calculate the rate of predictions in the correct quadrant, e.g. long or short
        correct_quad_rate = correct_quad/cm.sum()
    else:
        print('cannot calculate correct quadrant classification on odd number of classes')
        
    return correct_clf_rate, correct_quad_rate