from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier()
knn_model = knn.fit(X_train,y_train)
#Use the values for your confusion matrix
knn_y_model = knn_model.predict(X=X_test)
# Use the probabilities for your ROC and Precision-recall curves
knn_y_proba = knn_model.predict_proba(X=X_test)

from mlxtend.plotting import plot_confusion_matrix
fig, ax = plot_confusion_matrix(conf_mat=confusion_matrix(y_test,knn_y_model),
                                show_absolute=True,show_normed=True,colorbar=True)
plt.title("Confusion matrix - KNN")
plt.ylabel('True label')
plt.xlabel('Predicted label'

import scikitplot as skplt
plot = skplt.metrics.plot_roc(y_test, knn_y_proba)
plt.title("ROC Curves - K-Nearest Neighbors")