How to generate confusion matrix visualization in python and how to use it in scikit-learn

Confusions matrix are quite useful to understand your classifier problems. scikit-learn allow you to retrieve easily the confusion matrix (metric.confusion_matrix(y_true, y_pred)) but it is hard to read.
An image representation is a great way to look at it like this.

From a confusion matrix, you can derive classification error, precision, recall and extract confusion highlights. mlboost has a simple util class ConfMatrix to do all of this now. Here is an example:

 from mlboost.util.confusion_matrix import ConfMatrix  
 clf.fit(X_train, y_train)  
 pred = clf.predict(X_train)  
 labels = list(set(y_train))  
 labels.sort()  
 cm = ConfMatrix(metrics.confusion_matrix(y_train, pred), labels)  
 cm.save_matrix('conf_matrix.p')  
 cm.get_classification()  
 cm.gen_conf_matrix('conf_matrix')  
 cm.gen_highlights('conf_matrix_highlights')