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python confusion matrix plot

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python confusion matrix plot

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Confusion Matrix Plot

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

from matplotlib.patches import Rectangle
from matplotlib.ticker import PercentFormatter
from sklearn.metrics import confusion_matrix, accuracy_score


def cm_analysis(y_true, y_pred, labels, classes, figsize=(16, 8), save_path=None, vmax=False):
    
    sns.set(font_scale = figsize[1] / len(classes) / 1.5)
    fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=figsize)

    ####################################################################################################
    # pred
    cm = confusion_matrix(y_true, y_pred)
    cm_sum = np.sum(cm, axis=0, keepdims=True)
    cm_perc = cm / cm_sum.astype(float) * 100
    cm = np.c_[cm, cm.sum(1)]
    cm = np.r_[cm, [cm.sum(0)]]

    annot = np.empty_like(cm).astype(str)
    nrows, ncols = cm.shape
    for i in range(len(labels)):
        for j in range(len(labels)):
            c = cm[i, j]
            p = cm_perc[i, j]
            if i == j:
                s = cm_sum[0][i]
                annot[i, j] = '%.2f%%\n%d/%d' % (p, c, s)
            elif c == 0:
                annot[i, j] = ''
            else:
                annot[i, j] = '%.2f%%\n%d' % (p, c)
                
    freq = confusion_matrix(y_true, y_pred)
    acc_1 = list(freq.sum(1) / len(y_true) * 100)
    acc_0 = list(freq.sum(0) / len(y_true) * 100)
    acc_0.append(np.sum(np.diag(freq)) / len(y_true) * 100)
    
#     for i in range(len(labels)):
#         c = cm[i, -1]
#         annot[i, -1] = '%d' % (c)
#     for j in range(len(labels)):
#         c = cm[-1, j]
#         annot[-1, j] = '%d' % (c)
    for i, p in enumerate(acc_1):
        c = cm[i, -1]
        annot[i, -1] = '%.2f%%\n%d' % (p, c)
    for j, p in enumerate(acc_0):
        c = cm[-1, j]
        annot[-1, j] = '%.2f%%\n%d' % (p, c)
    annot[-1, -1] = '%.2f%%\n%d/%d' % (acc_0[-1], np.sum(np.diag(freq)), len(y_true))
                
    cm = confusion_matrix(y_true, y_pred, normalize='pred')
    cm = pd.DataFrame(cm, columns=labels)
    cm = cm * 100
    cm["Total"] = acc_1
    cm.loc["Total"] = acc_0
    cm.index.name = 'True Label'
    cm.columns.name = 'Predicted Label'
    if vmax:
        sns.heatmap(cm, annot=annot, fmt='', ax=ax1, cbar=False, cbar_kws={'format':PercentFormatter()}, cmap="Blues", vmin=0, vmax=vmax)
    else:
        sns.heatmap(cm, annot=annot, fmt='', ax=ax1, cbar=False, cbar_kws={'format':PercentFormatter()}, cmap="Blues")
    ax1.set_title("normalize : pred")
    ax1.set_xticklabels(classes+["Total"], rotation=0)
    ax1.set_yticklabels(classes+["Total"], rotation=0)
    
    x, y, w, h = 0, len(classes), len(cm.columns), 1
    for _ in range(2):
        ax1.add_patch(Rectangle((x, y), w, h, fill=True, fc=(0,0,0,0.1), ec=(0,0,0,0.2), lw=1, clip_on=False))
        x, y = y, x 
        w, h = h, w 

    ####################################################################################################
    # true
    cm = confusion_matrix(y_true, y_pred, labels=labels)
    cm_sum = np.sum(cm, axis=1, keepdims=True)
    cm_perc = cm / cm_sum.astype(float) * 100
    cm = np.c_[cm, cm.sum(1)]
    cm = np.r_[cm, [cm.sum(0)]]
    
    annot = np.empty_like(cm).astype(str)
    nrows, ncols = cm.shape
    for i in range(len(labels)):
        for j in range(len(labels)):
            c = cm[i, j]
            p = cm_perc[i, j]
            if i == j:
                s = cm_sum[i]
                annot[i, j] = '%.2f%%\n%d/%d' % (p, c, s)
            elif c == 0:
                annot[i, j] = ''
            else:
                annot[i, j] = '%.2f%%\n%d' % (p, c)
                
    freq = confusion_matrix(y_true, y_pred)
    acc_1 = list(freq.sum(1) / len(y_true) * 100)
    acc_0 = list(freq.sum(0) / len(y_true) * 100)
    acc_0.append(np.sum(np.diag(freq)) / len(y_true) * 100)
    
    for i, p in enumerate(acc_1):
        c = cm[i, -1]
        annot[i, -1] = '%.2f%%\n%d' % (p, c)
    for j, p in enumerate(acc_0):
        c = cm[-1, j]
        annot[-1, j] = '%.2f%%\n%d' % (p, c)
    annot[-1, -1] = '%.2f%%\n%d/%d' % (acc_0[-1], np.sum(np.diag(freq)), len(y_true))
        
    cm = confusion_matrix(y_true, y_pred, labels=labels, normalize='true')
    cm = pd.DataFrame(cm, index=labels, columns=labels)
    cm = cm * 100
    cm["Total"] = acc_1
    cm.loc["Total"] = acc_0
    cm.index.name = 'True Label'
    cm.columns.name = 'Predicted Label'

    if vmax:
        sns.heatmap(cm, annot=annot, fmt='', ax=ax2, cbar=False, cbar_kws={'format':PercentFormatter()}, cmap="Blues", vmin=0, vmax=vmax)
    else:
        sns.heatmap(cm, annot=annot, fmt='', ax=ax2, cbar=False, cbar_kws={'format':PercentFormatter()}, cmap="Blues")
    ax2.set_title("normalize : true")
    ax2.set_xticklabels(classes+["Total"], rotation=0)
    ax2.set_yticklabels(classes+["Total"], rotation=0)
    
    x, y, w, h = 0, len(classes), len(cm.columns), 1
    for _ in range(2):
        ax2.add_patch(Rectangle((x, y), w, h, fill=True, fc=(0,0,0,0.1), ec=(0,0,0,0.2), lw=1, clip_on=False))
        x, y = y, x 
        w, h = h, w 

    ####################################################################################################
    plt.tight_layout()
    
    accuracy = accuracy_score(y_true, y_pred)
    plt.suptitle("Accuracy : {:>.2%}".format(accuracy))
    plt.subplots_adjust(top=0.9)


    if save_path:
        plt.savefig(save_path)
    else:
        plt.show()

    plt.close()
    sns.set(font_scale = 1)

test code

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y_true = np.random.randint(0, 8, 10000)
y_pred = np.random.randint(0, 8, 10000)
labels=list(range(8))
classes = [f"class_{i}" for i in labels]
figsize=(16, 8)

cm_analysis(
    y_true=y_true, 
    y_pred=y_pred, 
    labels=labels, 
    classes=classes,
    figsize=figsize,
    vmax=False,
)

cm_analysis(
    y_true=y_true, 
    y_pred=y_pred, 
    labels=labels, 
    classes=classes,
    figsize=figsize,
    vmax=100,
)

Referrence

  1. https://gist.github.com/hitvoice/36cf44689065ca9b927431546381a3f7#file-plot_confusion_matrix-py
  2. https://stackoverflow.com/questions/62533046/how-to-add-color-border-or-similar-highlight-to-specifc-element-of-heatmap-in-py
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