probos_finetune = loaded_model.predict_generator(
generator_test, len(generator_test.classes), max_q_size=1)
gnd_truth = generator_test.classes
dics = list(generator_test.class_indices.keys())
dics.extend(["micro-average curve", "macro-average curve"])
skplt.plot_precision_recall_curve(y_true=gnd_truth,
y_probas=probos_finetune)
plt.legend(dics[:5], loc='lower left')
plt.savefig(args.destdir + '/precision_recall_curve.png')
plt.close()
_ = classifier_factory(RandomForestClassifier())
_.plot_confusion_matrix(probos_finetune, gnd_truth, normalize=True)
plt.savefig(args.destdir + '/confusion-matrix.png')
plt.close()
nb = GaussianNB()
classifier_factory(nb)
nb.plot_roc_curve(probos_finetune, gnd_truth, title='roc curves')
plt.legend(dics, loc='lower right')
plt.savefig(args.destdir + '/ROC.png')
plt.close()
_.plot_learning_curve(probos_finetune, gnd_truth)
plt.savefig(args.destdir + '/learning_curve.png')
plt.close()
