Python Evaluator.evaluate_trend_simple Exemples

Exemple #1

Fichier : test_lstm_mean.py Projet : yashyennam/Analysis-of-Stock-High-Frequent-Data-with-LSTM

    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))

Exemple #2

Fichier : test_xgboost.py Projet : yashyennam/Analysis-of-Stock-High-Frequent-Data-with-LSTM

    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)