save user defined data

# partial fit the new prediction data to each algorithm

def partialFitNewData(message, label):

    before = getPerformanceMetrics()

    global pla, sgd, nn, tree

    global pla, sgd, nn, tree, X, Y

    x_new = [preprocess.extract(message)]

    y_new = [0 if label == 'ham' else 1]

    print('Updating Perceptron Model...')

    pla.partial_fit(x_new, y_new)

    #dump(pla, 'pla.joblib')

    dump(pla, 'pla.joblib')

    print('Updating Stochastic Gradient Descent Model...')

    sgd.partial_fit(x_new, y_new)

    #dump(sgd, 'sgd.joblib')

    dump(sgd, 'sgd.joblib')

    print('Updating Neural Network...')

    nn.partial_fit(x_new, y_new)

    #dump(nn, 'nn.joblib')

    dump(nn, 'nn.joblib')

    print('Retraining Decision Tree...')

    X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=0)

    X = np.concatenate((X, x_new), axis=0)

    Y = np.concatenate((Y, y_new), axis=0)

    dump(X[:25000, :], 'X0.joblib')

    dump(X[25000:, :], 'X1.joblib')

    dump(Y, 'Y.joblib')

    print('Retraining Decision Tree...')

    tree = DecisionTreeClassifier()

    tree.fit(np.concatenate((X_train, x_new), axis=0), np.concatenate((Y_train, y_new), axis=0))

    #dump(tree, 'tree.joblib')

    dump(tree, 'tree.joblib')

    after = getPerformanceMetrics()

    print(np.subtract(after, before))