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import itertools |
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import matplotlib.pyplot as plt |
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import numpy as np |
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import matplotlib.pyplot as plt |
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plt.rcParams['font.sans-serif'] = ['SimHei'] |
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plt.rcParams['axes.unicode_minus'] = False |
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def plot_confusion_matrix(cm, classes, |
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normalize=False, |
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title='Confusion matrix', |
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cmap=plt.cm.Blues): |
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""" |
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This function prints and plots the confusion matrix. |
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Normalization can be applied by setting `normalize=True`. |
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""" |
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if normalize: |
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cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] |
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print("Normalized confusion matrix") |
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else: |
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print('Confusion matrix, without normalization') |
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print(cm) |
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plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues) |
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plt.title(title) |
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plt.colorbar() |
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tick_marks = np.arange(len(classes)) |
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plt.xticks(tick_marks, classes, fontsize=16) |
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plt.yticks(tick_marks, classes, fontsize=16) |
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fmt = '.2f' if normalize else 'd' |
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thresh = cm.max() / 2. |
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for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])): |
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plt.text(j, i, format(cm[i, j], fmt), |
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horizontalalignment="center", |
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color="white" if cm[i, j] > thresh else "black") |
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plt.tight_layout() |
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plt.ylabel('True Label',fontsize=12) |
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plt.xlabel('Predicted Label',fontsize=12) |
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plt.show() |
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cnf_matrix = np.array([[ 299 , 6 , 5 , 3 , 1 , 4, 11], |
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[ 9, 51 , 0, 2 , 8, 2 , 2], |
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[ 2 , 1 ,120 , 6 ,13 , 9 , 9], |
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[ 5 , 1 , 7 ,1148 , 2 , 4 , 18], |
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[ 0 , 0 , 9 , 4 ,442 , 1 , 22], |
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[ 2 ,0 , 7 , 3 , 0 ,145 , 5], |
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[ 10 ,0, 6 ,11, 29 , 0, 624]]) |
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class_names = ["SU", 'FE', 'AN', 'HA', 'SA', 'DI', 'NE'] |
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plt.figure(dpi=200) |
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plot_confusion_matrix(cnf_matrix, classes=class_names, normalize=True, |
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title=None) |
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