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- def plot_confusion_matrix(y_true, y_pred, classes,
- normalize=False,
- title=None,
- cmap=plt.cm.Blues):
- """
- This function prints and plots the confusion matrix.
- Normalization can be applied by setting `normalize=True`.
- """
- if not title:
- if normalize:
- title = 'Normalized confusion matrix'
- else:
- title = 'Confusion matrix, without normalization'
- # Compute confusion matrix
- cm = confusion_matrix(y_true, y_pred)
- if normalize:
- cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
- print("Normalized confusion matrix")
- else:
- print('Confusion matrix, without normalization')
- fig, ax = plt.subplots()
- fig.set_figheight(15)
- fig.set_figwidth(15)
- im = ax.imshow(cm, interpolation='nearest', cmap=cmap)
- ax.figure.colorbar(im, ax=ax)
- # We want to show all ticks...
- ax.set(xticks=np.arange(cm.shape[1]),
- yticks=np.arange(cm.shape[0]),
- # ... and label them with the respective list entries
- xticklabels=classes, yticklabels=classes,
- title=title,
- ylabel='True label',
- xlabel='Predicted label')
- # Rotate the tick labels and set their alignment.
- plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
- rotation_mode="anchor")
- # Loop over data dimensions and create text annotations.
- fmt = '.2f' if normalize else 'd'
- thresh = cm.max() / 2.
- for i in range(cm.shape[0]):
- for j in range(cm.shape[1]):
- ax.text(j, i, format(cm[i, j], fmt),
- ha="center", va="center",
- color="white" if cm[i, j] > thresh else "black")
- fig.tight_layout()
- return ax
- np.set_printoptions(precision=2)
- # Plot non-normalized confusion matrix
- plot_confusion_matrix(y_true, y_pred, classes=target_names,
- title='Confusion matrix, without normalization')
- plt.show()
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