とらりもん - Error matrixno Diff

(27th/Jul/2017 Ishibashi)
{{toc}}
{{br}}After you made LULC classification,
We should validate it for making sure its reliability.

!#Script making error matrix in Python3
import itertools
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix

class_names = ['Water', 'Urban', 'Paddy', 'Cassava', 'Forest', 'Rubber', 'rockout']

predicted = np.array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 6, 7, 2, 2, 2, 2, 2, 3, 2, 6, 6, 2, 2, 2, 3, 3, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 6, 6, 6, 3, 3, 6, 3, 2, 3, 3, 3, 3, 3, 3, 2, 7, 3, 6, 3, 3, 3, 3, 2, 3, 3, 3, 6, 6, 3, 3, 3, 3, 3, 3, 3, 3, 6, 6, 4, 2, 4, 6, 3, 4, 6, 4, 5, 5, 5, 4, 5, 7, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 5, 5, 5, 7, 6, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3, 5, 5, 5, 5, 5, 5, 5, 5, 6, 3, 6, 6, 5, 6, 5, 6, 3, 6, 5, 6, 7, 6, 6, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 2, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 6, 7, 3, 6, 7, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 5, 7, 7, 5, 7, 6, 7, 7, 3, 7, 7, 7, 7, 7, 6, 7, 7, 7, 7, 7, 7, 7, 7, 6, 7, 7, 7, 7, 7, 6, 5])

reference = np.array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7])

def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):
    
     plt.imshow(cm, interpolation='nearest', cmap=cmap)
     plt.title(title)
     plt.colorbar()
     tick_marks = np.arange(len(classes))
     plt.xticks(tick_marks, classes, rotation=45)
     plt.yticks(tick_marks, classes)

     if normalize:
         cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
         print("Normalized confusion matrix")
     else:
         print('Confusion matrix, without normalization')

     thresh = cm.max() / 2.
     for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
         plt.text(j, i, cm[i, j], horizontalalignment="center", color="white" if cm[i, j] > thresh else "black")
     plt.tight_layout()
     plt.ylabel('True label')
     plt.xlabel('Predicted label')


# Compute confusion matrix
cnf_matrix = confusion_matrix(reference, predicted)
np.set_printoptions(precision=2)  


# Plot non-normalized confusion matrix
plt.figure()
plot_confusion_matrix(cnf_matrix, classes=class_names, title='Confusion matrix, without  normalization')


# Plot normalized confusion matrix
plt.figure()
plot_confusion_matrix(cnf_matrix, classes=class_names, normalize=True, title='Normalized confusion matrix')


plt.show()