print(__doc__)importnumpyasnpimportmatplotlib.pyplotaspltfromsklearnimportsvm,datasetsfromsklearn.model_selectionimporttrain_test_splitfromsklearn.metricsimportconfusion_matrixfromsklearn.utils.multiclassimportunique_labels# import some data to play withiris=datasets.load_iris()X=iris.datay=iris.targetclass_names=iris.target_names# Split the data into a training set and a test setX_train,X_test,y_train,y_test=train_test_split(X,y,random_state=0)# Run classifier, using a model that is too regularized (C too low) to see# the impact on the resultsclassifier=svm.SVC(kernel='linear',C=0.01)y_pred=classifier.fit(X_train,y_train).predict(X_test)defplot_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`.    """ifnottitle:ifnormalize:title='Normalized confusion matrix'else:title='Confusion matrix, without normalization'# Compute confusion matrixcm=confusion_matrix(y_true,y_pred)# Only use the labels that appear in the dataclasses=classes[unique_labels(y_true,y_pred)]ifnormalize:cm=cm.astype('float')/cm.sum(axis=1)[:,np.newaxis]print("Normalized confusion matrix")else:print('Confusion matrix, without normalization')print(cm)fig,ax=plt.subplots()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 entriesxticklabels=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'ifnormalizeelse'd'thresh=cm.max()/2.foriinrange(cm.shape[0]):forjinrange(cm.shape[1]):ax.text(j,i,format(cm[i,j],fmt),ha="center",va="center",color="white"ifcm[i,j]>threshelse"black")fig.tight_layout()returnaxnp.set_printoptions(precision=2)# Plot non-normalized confusion matrixplot_confusion_matrix(y_test,y_pred,classes=class_names,title='Confusion matrix, without normalization')# Plot normalized confusion matrixplot_confusion_matrix(y_test,y_pred,classes=class_names,normalize=True,title='Normalized confusion matrix')plt.show()