def interactions(problem, Y, print_to_console=False):
    """Computes the second order effects

    Computes the second order effects (interactions) between
    all combinations of pairs of input factors

    Arguments
    ---------
    problem: dict
        The problem definition
    Y: numpy.array
        The NumPy array containing the model outputs
    print_to_console: bool, default=False
        Print results directly to console

    Returns
    -------
    ie_names: list
        The names of the interaction pairs
    IE: list
        The sensitivity indices for the pairwise interactions

    """

    names = problem['names']
    num_vars = problem['num_vars']

    X = generate_contrast(problem)

    ie_names = []
    IE = []

    for col in range(X.shape[1]):
        for col_2 in range(col):
            x = X[:, col] * X[:, col_2]
            var_names = (names[col_2], names[col])
            ie_names.append(var_names)
            IE.append((1. / (2 * num_vars)) * np.dot(Y, x))
    if print_to_console:
        [print('%s %f' % (n, i)) for (n, i) in zip(ie_names, IE)]

    return ie_names, IE
Computes the second order effects

    Computes the second order effects (interactions) between
    all combinations of pairs of input factors

    Arguments
    ---------
    problem: dict
        The problem definition
    Y: numpy.array
        The NumPy array containing the model outputs
    print_to_console: bool, default=False
        Print results directly to console

    Returns
    -------
    ie_names: list
        The names of the interaction pairs
    IE: list
        The sensitivity indices for the pairwise interactions