network.strong_train(train_x, train_y)
network.save('strongtrain_test.h5')
else:
network.train(train_x, train_y)
network.save(lstm_conf['save_file_name'])
# step 5: Predict
train_pred = network.predict(train_x)
test_pred = network.predict(test_x)
# step 6: Evaluate
evaluator = Evaluator()
print('simple evaluation')
# method1
acc = evaluator.evaluate_trend_simple(y_true=train_y, y_pred=train_pred)
print(acc)
acc = evaluator.evaluate_trend_simple(y_true=test_y, y_pred=test_pred)
print(acc)
# method 2
acc_train_list = evaluator.evaluate_divided_trend(train_y, train_pred)
acc_test_list = evaluator.evaluate_divided_trend(test_y, test_pred)
print('acc_train_list = ' + str(acc_train_list))
print('acc_test_list = ' + str(acc_test_list))
# step 7: Plot
train_mid_price = mid_price[0:train_size]
test_mid_price = mid_price[train_size:len(mid_price)]
'''
plt.figure(figsize=(200, 15))
clf.fit(train_x, train_y)
clf.save_model('xgboost.m')
else:
clf.load_model('xgboost.m')
train_pred = clf.predict(train_x)
test_pred = clf.predict(test_x)
show_feature_importance(clf, conf['feature_name'])
# plot_scatter(test_y, test_pred)
plot_classification(test_y, test_pred)
evaluator = Evaluator()
print('evaluate trend')
acc = evaluator.evaluate_trend(train_y, train_pred)
print(acc)
acc = evaluator.evaluate_trend(test_y, test_pred)
print(acc)
print('evaluate trend without stay')
acc = evaluator.evaluate_trend_2(train_y, train_pred)
print(acc)
acc = evaluator.evaluate_trend_2(test_y, test_pred)
print(acc)
print('simple evaluate')
acc = evaluator.evaluate_trend_simple(train_y, train_pred)
print(acc)
acc = evaluator.evaluate_trend_simple(test_y, test_pred)
print(acc)