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.

CodePudding user response：

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.

Edit:

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)

CodePudding user response：

Sklearn's classification report function takes these parameters :

sklearn.metrics.classification_report(y_true, y_pred, *, labels=None, target_names=None, sample_weight=None, digits=2, output_dict=False, zero_division='warn')

It gives a classification report with true labels et predicted labels.

If you still want to get a classification report from a confusion matrix, you might need to compute it from scratch without using Sklearn.