Pretty print a confusion matrix with seaborn

confusion_matrix_pretty_print.py

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

def print_confusion_matrix(confusion_matrix, class_names, figsize = (10,7), fontsize=14):
    """Prints a confusion matrix, as returned by sklearn.metrics.confusion_matrix, as a heatmap.
    
    Note that due to returning the created figure object, when this funciton is called in a
    notebook the figure willl be printed twice. To prevent this, either append ; to your
    function call, or modify the function by commenting out the return expression.
    
    Arguments
    ---------
    confusion_matrix: numpy.ndarray
        The numpy.ndarray object returned from a call to sklearn.metrics.confusion_matrix. 
        Similarly constructed ndarrays can also be used.
    class_names: list
        An ordered list of class names, in the order they index the given confusion matrix.
    figsize: tuple
        A 2-long tuple, the first value determining the horizontal size of the ouputted figure,
        the second determining the vertical size. Defaults to (10,7).
    fontsize: int
        Font size for axes labels. Defaults to 14.
        
    Returns
    -------
    matplotlib.figure.Figure
        The resulting confusion matrix figure
    """
    df_cm = pd.DataFrame(
        confusion_matrix, index=class_names, columns=class_names, 
    )
    fig = plt.figure(figsize=figsize)
    try:
        heatmap = sns.heatmap(df_cm, annot=True, fmt="d")
    except ValueError:
        raise ValueError("Confusion matrix values must be integers.")
    heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=0, ha='right', fontsize=fontsize)
    heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=45, ha='right', fontsize=fontsize)
    plt.ylabel('True label')
    plt.xlabel('Predicted label')
    # Note that due to returning the created figure object, when this funciton is called in a notebook
    # the figure willl be printed twice. To prevent this, either append ; to your function call, or
    # modify the function by commenting out this return expression.
    return fig

Thank you

Awesome

awesome, thanks.
BTW "class_names" sort alphabetically, when there are a lot of attributes it will be better to do this as @scott Boston said
https://stackoverflow.com/questions/54875846/how-to-print-labels-and-column-names-for-confusion-matrix

and the argument normalize{‘true’, ‘pred’, ‘all’}, default=None can deal with the normalization when we generate the confusion matrix @BrunoGomesCoelho
eg:
confusion_matrix_array = confusion_matrix(ture_label, predict_label, normalize='all' )

THANK YOU DEAR