from keras.models import Sequential
from keras.layers import Dense
import numpy as np
from sklearn.metrics import f1_score

if __name__ == '__main__':
    print('Hello')
    dataset = np.loadtxt('data.csv', delimiter=',')

    np.random.shuffle(dataset)

    X = dataset[:,0:8]
    y = dataset[:,8]

    X_train = X[0:650]
    y_train = y[0:650]

    X_test = X[651:]
    y_test = y[651:]

    model = Sequential()
    model.add(Dense(12, input_dim=8, activation='relu'))
    model.add(Dense(8, activation='relu'))
    model.add(Dense(1, activation='sigmoid'))

    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
    model.fit(X_train, y_train, epochs=15, batch_size=10)
    
    
    
    _, accuracy = model.evaluate(X_test, y_test)

    print('Accuracy: %.2f' % (accuracy*100))        
    predictions = (model.predict(X_test) > 0.5).astype(int)
    fscore = f1_score(y_test, predictions, average='macro')
    print('fscore: ', fscore)