python 可视化混淆矩阵

Posted 2021-05-08

import matplotlib.pyplot as plt
import itertools
from sklearn.metrics import classification_report

def pretty_print_conf_matrix(y_true, y_pred, 
                             classes,
                             normalize=False,
                             title='Confusion matrix',
                             cmap=plt.cm.Blues):
    """
    Mostly stolen from: http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html#sphx-glr-auto-examples-model-selection-plot-confusion-matrix-py

    Normalization changed, classification_report stats added below plot
    """

    cm = confusion_matrix(y_true, y_pred)

    # Configure Confusion Matrix Plot Aesthetics (no text yet) 
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title, fontsize=14)
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=45)
    plt.yticks(tick_marks, classes)
    plt.ylabel('True label', fontsize=12)
    plt.xlabel('Predicted label', fontsize=12)

    # Calculate normalized values (so all cells sum to 1) if desired
    if normalize:
        cm = np.round(cm.astype('float') / cm.sum(),2) #(axis=1)[:, np.newaxis]

    # Place Numbers as Text on Confusion Matrix Plot
    thresh = cm.max() / 2.
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, cm[i, j],
                 horizontalalignment="center",
                 color="white" if cm[i, j] > thresh else "black",
                 fontsize=12)


    # Add Precision, Recall, F-1 Score as Captions Below Plot
    rpt = classification_report(y_true, y_pred)
    rpt = rpt.replace('avg / total', '      avg')
    rpt = rpt.replace('support', 'N Obs')

    plt.annotate(rpt, 
                 xy = (0,0), 
                 xytext = (-50, -140), 
                 xycoords='axes fraction', textcoords='offset points',
                 fontsize=12, ha='left')    

    # Plot
    plt.tight_layout()

pretty_print_conf_matrix(Y_validation, predictions,
                         class_labels, normalize=True, 
                         title='Confusion matrix',
                         cmap=plt.cm.Blues)