Plot a confusion matrix in scikitlearn from data not from an estimator

plot_cm.py

# This uses scikit learn internals, since the sk public API requires you to pass
# in an estimator and sometimes you just want to pass in the some data you'd
# use to calculate a raw CM

def plot_cm(y_true,y_pred,labels):
    from sklearn.metrics._plot.confusion_matrix import ConfusionMatrixDisplay

    sample_weight = None
    normalize = None
    include_values = True
    cmap='viridis'
    ax = None
    xticks_rotation='horizontal'
    values_format = None  
    
    cm = confusion_matrix(y_true, y_pred, sample_weight=sample_weight,
                          labels=labels, normalize=normalize)
    display_labels = labels
    
    disp = ConfusionMatrixDisplay(confusion_matrix=cm,
                                  display_labels=display_labels)
    return disp.plot(include_values=include_values,
                     cmap=cmap, ax=ax, xticks_rotation=xticks_rotation,
                     values_format=values_format)