helper_functions.py 文件源码

def check_data(X, X_names, Y):

    #type checks
    assert type(X) is np.ndarray, "type(X) should be numpy.ndarray"
    assert type(Y) is np.ndarray, "type(Y) should be numpy.ndarray"
    assert type(X_names) is list, "X_names should be a list"

    #sizes and uniqueness
    N, P = X.shape
    assert N > 0, 'X matrix must have at least 1 row'
    assert P > 0, 'X matrix must have at least 1 column'
    assert len(Y) == N, 'len(Y) should be same as # of rows in X'
    assert len(list(set(X_names))) == len(X_names), 'X_names is not unique'
    assert len(X_names) == P, 'len(X_names) should be same as # of cols in X'

    #X_matrix values
    if '(Intercept)' in X_names:
        assert all(X[:, X_names.index('(Intercept)')] == 1.0), "'(Intercept)' column should only be composed of 1s"
    else:
        warnings.warn("there is no column named '(Intercept)' in X_names")
    assert np.all(~np.isnan(X)), 'X has nan entries'
    assert np.all(~np.isinf(X)), 'X has inf entries'

    #Y vector values
    assert all((Y == 1)|(Y == -1)), 'Y[i] should = [-1,1] for all i'
    if all(Y == 1):
        warnings.warn("all Y_i == 1 for all i")
    if all(Y == -1):
        warnings.warn("all Y_i == -1 for all i")

        #TODO (optional) collect warnings and return those?