Confusion Matrix を美しくプロットする関数

plot_confusion_matrix.py

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
from sklearn.metrics import confusion_matrix
from sklearn.utils.multiclass import unique_labels


def plot_confusion_matrix(y_true, y_pred,
                          save_path,
                          normalize=True,
                          title=None,
                          cmap=plt.cm.YlOrRd):

    if not title:
        if normalize:
            title = 'Normalized confusion matrix'
        else:
            title = 'Confusion matrix, without normalization'

    # Compute confusion matrix
    cm = confusion_matrix(y_true, y_pred)
    # Only use the labels that appear in the data
    classes = unique_labels(y_true, y_pred).tolist()
    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        # print("Normalized confusion matrix")
    # else:
        # print('Confusion matrix, without normalization')

    # print(cm)

    fig, ax = plt.subplots(figsize=(7, 7))
    im = ax.imshow(cm, interpolation='nearest', cmap=cmap)
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, fontsize=12)
    plt.yticks(tick_marks, fontsize=12)
    plt.xlabel('Predicted label', fontsize=12)
    plt.ylabel('True label', fontsize=12)
    plt.title(title, fontsize=15)

    divider = make_axes_locatable(ax)
    cax = divider.append_axes('right', size="5%", pad=0.15)
    cbar = ax.figure.colorbar(im, ax=ax, cax=cax)
    cbar.ax.tick_params(labelsize=10)

    # We want to show all ticks...
    ax.set(xticks=np.arange(cm.shape[1]),
           yticks=np.arange(cm.shape[0]),
           # ... and label them with the respective list entries
           xticklabels=classes, yticklabels=classes,
           # title=title,
           ylabel='True label',
           xlabel='Predicted label')

    # Rotate the tick labels and set their alignment.
    plt.setp(ax.get_xticklabels(), ha="right",
             rotation_mode="anchor")

    # Loop over data dimensions and create text annotations.
    fmt = '.2f' if normalize else 'd'
    thresh = cm.max() / 2.
    for i in range(cm.shape[0]):
        for j in range(cm.shape[1]):
            ax.text(j, i, format(cm[i, j], fmt),
                    fontsize=10,
                    ha="center", va="center",
                    color="white" if cm[i, j] > thresh else "black")
    fig.tight_layout()
    plt.savefig(save_path)
    plt.close()