Get Scikit-learn classification report from confusion matrix

Found this question by @CarstenWE but it had been closed with no answer: How to get classification report from the confusion matrix?

As the question is closed, I opened this question to provide an answer. The questions linked to the original all have answers to compute precision, recall, and f1-score. However, none seems to use the classification_report as the original question asked.


  • I wrote a small function to do this using a confusion matrix as input, by creating a ground-truth vector and a predicted vector, as order does not matter for these metrics:

    def classification_report_from_confusion_matrix(cm, **args):
        y_true = []
        y_pred = []
        for target in range(len(cm)):
            for pred in range(len(cm)):
                y_true += [gt]*cm[target][pred]
                y_pred += [pred]*cm[target][pred]
        return metrics.classification_report(y_true , y_pred, **args)

    This solution probably does not scale well for huge datasets, but it was enough for me.


    Here is a solution without using lists:

    def classification_report_from_confusion_matrix(confusion_matrix, **args):
        y_true = np.zeros(np.sum(confusion_matrix), dtype=int)
        y_pred = np.copy(y_true)
        i = 0
        for target in range(len(confusion_matrix)):
            for pred in range(len(confusion_matrix)):
                n = confusion_matrix[target][pred]
                y_true[i:i+n] = target
                y_pred[i:i+n] = pred
                i += n
        return metrics.classification_report(y_true, y_pred, **args)