图形展示

1、训练历史

import matplotlib.pyplot as plt
def showhist(history):
    fig,ax = plt.subplots(1,2,figsize=(8,2))
    ax[0].plot(history.history['accuracy'],label='train_acc')
    ax[0].plot(history.history['val_accuracy'],label='val_acc')
    ax[0].set_title('train and validation accuracy')
    ax[0].set_ylabel('Accuracy')
    ax[0].set_xlabel('Epoch')
    ax[0].legend( loc='lower right')    
    
    ax[1].plot(history.history['loss'],label='train_loss')
    ax[1].plot(history.history['val_loss'],label='val_loss')   
    ax[1].set_title('train and vaidation loss')
    ax[1].set_ylabel('loss')
    ax[1].set_xlabel('Epoch')
    ax[1].legend( loc='upper right')    
    plt.show()

2、结果评价

def model_assess(y_true, y_prob, roc=True, pr=False):
    '''
    pass
    '''
    re = {}
    y_true = np.array(y_true, dtype=int)
    y_pred = np.array(y_prob + 0.5, dtype=int)

    re['accuracy'] = round(metrics.accuracy_score(y_true, y_pred), 3)
    re['precision'] = round(metrics.precision_score(y_true, y_pred), 3)
    re['recall'] = round(metrics.recall_score(y_true, y_pred), 3)
    re['f1'] = round(metrics.f1_score(y_true, y_pred), 3)
    re['auc'] = round(metrics.roc_auc_score(y_true, y_prob), 3)

    fpr, tpr, thresholds = metrics.roc_curve(y_true, y_prob)
    plt.plot(fpr, tpr, label='RF:' + str(re['auc']))
    plt.title('ROC curve')
    plt.xlabel('fpr')
    plt.ylabel('tpr')

    # precision, recall, thresholds = metrics.precision_recall_curve(y_true,y_prob)
    # plt.plot(precision,recall,label='precison_and_recall')
    plt.legend()

    return re