utils.py

from sklearn.metrics import confusion_matrix, accuracy_score, classification_report, cohen_kappa_score
import numpy as np
from operator import truediv
def AA_andEachClassAccuracy(confusion_matrix):
    counter = confusion_matrix.shape[0]
    list_diag = np.diag(confusion_matrix)
    list_raw_sum = np.sum(confusion_matrix, axis=1)
    each_acc = np.nan_to_num(truediv(list_diag, list_raw_sum))
    average_acc = np.mean(each_acc)
    return each_acc, average_acc
def reports(Prediction, Label, name):
    # start = time.time()
    # end = time.time()
    # print(end - start)

    if name == 'IP':
        target_names = ['Alfalfa', 'Corn-notill', 'Corn-mintill', 'Corn'
            , 'Grass-pasture', 'Grass-trees', 'Grass-pasture-mowed',
                        'Hay-windrowed', 'Oats', 'Soybean-notill', 'Soybean-mintill',
                        'Soybean-clean', 'Wheat', 'Woods', 'Buildings-Grass-Trees-Drives',
                        'Stone-Steel-Towers']
    elif name == 'SA':
        target_names = ['Brocoli_green_weeds_1', 'Brocoli_green_weeds_2', 'Fallow', 'Fallow_rough_plow',
                        'Fallow_smooth',
                        'Stubble', 'Celery', 'Grapes_untrained', 'Soil_vinyard_develop', 'Corn_senesced_green_weeds',
                        'Lettuce_romaine_4wk', 'Lettuce_romaine_5wk', 'Lettuce_romaine_6wk', 'Lettuce_romaine_7wk',
                        'Vinyard_untrained', 'Vinyard_vertical_trellis']
    elif name == 'PU':
        target_names = ['Asphalt', 'Meadows', 'Gravel', 'Trees', 'Painted metal sheets', 'Bare Soil', 'Bitumen',
                        'Self-Blocking Bricks', 'Shadows']

    classification = classification_report(Label, Prediction, target_names=target_names)
    oa = accuracy_score(Label, Prediction)
    confusion = confusion_matrix(Label, Prediction)
    each_acc, aa = AA_andEachClassAccuracy(confusion)
    kappa = cohen_kappa_score(Label, Prediction)
    return classification, confusion, oa * 100, each_acc * 100, aa * 100, kappa*100