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@ -9,14 +9,6 @@ from sklearn.metrics import accuracy_score
@@ -9,14 +9,6 @@ from sklearn.metrics import accuracy_score
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from sklearn.metrics import confusion_matrix |
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currDir = 'C:\\Users\\Aki\\source\\repos\\rug_VS\\dialect_identification\\dialect_identification' |
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sys.path.append(os.path.join(os.path.dirname(sys.path[0]), currDir)) |
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regionLabels = ['Groningen_and_Drenthe', 'Oost_Overijsel-Gelderland', 'Limburg'] |
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regionLabels2 = ['Groningen_and_Drenthe', 'Limburg'] |
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dirOut = currDir + '\\result\\same-utterance_with_cities' |
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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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@ -56,24 +48,32 @@ def plot_confusion_matrix(cm, classes,
@@ -56,24 +48,32 @@ def plot_confusion_matrix(cm, classes,
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plt.xlabel('Predicted label', fontsize=_fontsize-4) |
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pred = np.load(dirOut + '\\pred_per_pid_3regions.npy') |
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if __name__ == "__main__": |
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currDir = 'C:\\Users\\Aki\\source\\repos\\rug_VS\\dialect_identification\\dialect_identification' |
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sys.path.append(os.path.join(os.path.dirname(sys.path[0]), currDir)) |
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regionLabels = ['Groningen_and_Drenthe', 'Oost_Overijsel-Gelderland', 'Limburg'] |
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regionLabels2 = ['Groningen_and_Drenthe', 'Limburg'] |
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dirOut = currDir + '\\result\\same-utterance_with_cities' |
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pred = np.load(dirOut + '\\pred_per_pid_3regions.npy') |
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#accuracy = accuracy_score(pred[:, 1], pred[:, 2], normalize=True, sample_weight=None) |
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#print('accuracy: {}%'.format(accuracy * 100)) |
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#accuracy = accuracy_score(pred[:, 1], pred[:, 2], normalize=True, sample_weight=None) |
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#print('accuracy: {}%'.format(accuracy * 100)) |
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# confusion matrix |
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cm = confusion_matrix(pred[:, 1], pred[:, 2], labels=regionLabels) |
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# human perception (2 regions) |
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#cm = np.array([[39, 57], [6, 104]]) |
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# human perception (3 regions) |
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#cm = np.array([[22, 14, 52], [23, 21, 52], [5, 5, 100]]) |
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print(cm) |
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# confusion matrix |
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cm = confusion_matrix(pred[:, 1], pred[:, 2], labels=regionLabels) |
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# human perception (2 regions) |
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#cm = np.array([[39, 57], [6, 104]]) |
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# human perception (3 regions) |
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#cm = np.array([[22, 14, 52], [23, 21, 52], [5, 5, 100]]) |
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print(cm) |
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np.set_printoptions(precision=2) |
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np.set_printoptions(precision=2) |
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plt.figure() |
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plot_confusion_matrix(cm, classes=['GD', 'OG', 'LB'], normalize=True) |
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#plot_confusion_matrix(cm, classes=['GD', 'LB'], normalize=True) |
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plt.figure() |
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plot_confusion_matrix(cm, classes=['GD', 'OG', 'LB'], normalize=True) |
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#plot_confusion_matrix(cm, classes=['GD', 'LB'], normalize=True) |
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#plt.show() |
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plt.savefig(dirOut + '\\cm_machine_3regions_normalized.png') |
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#plt.show() |
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plt.savefig(dirOut + '\\cm_machine_3regions_normalized.png') |