test_model.py

from keras.preprocessing.image import ImageDataGenerator
from keras.models import load_model
from moleimages import MoleImages
from sklearn.metrics import classification_report
from sklearn.metrics import roc_curve, auc

import matplotlib.pyplot as plt
import sys

def plot_roc(y_test, y_score, title='ROC Curve'):
    fpr, tpr, _ = roc_curve(y_test, y_score)
    roc_auc = auc(fpr, tpr)
    print(roc_auc)
    plt.figure()
    lw = 2
    plt.plot(fpr, tpr, color='darkorange',
             lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)
    plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.05])
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.title(title)
    plt.legend(loc="lower right")
    plt.savefig(title + '.png')
    plt.show()



if __name__ == '__main__':
    if len(sys.argv) == 3:
        mimg = MoleImages()
        X_test, y_test = mimg.load_test_images('data_scaled_test/benign',
                                                'data_scaled_test/malign')

        model = load_model(sys.argv[1])
        y_pred_proba = model.predict(X_test)
        y_pred = (y_pred_proba >0.5)*1
        print(classification_report(y_test,y_pred))
        plot_roc(y_test, y_pred_proba, title=sys.argv[1]+sys.argv[2])
    else:
        print('use python src/test_model.py models/model.h5 title')