infer.py 文件源码

def predict_with_glm(X, y, model):
    """ Predict number of mutation with GLM.

    Args:
        X (np.array): feature matrix.
        y (pd.df): response.
        model (dict): model meta-data.

    Returns:
        np.array: array of predictions.

    """
    # Add const. to X
    X = np.c_[X, np.ones(X.shape[0])]
    if model['model_name'] == 'Binomial':
        pred = np.array(model['model'].predict(X) * y.length * y.N)
    elif model['model_name'] == 'NegativeBinomial':
        pred = np.array(model['model'].predict(X, exposure=(y.length * y.N).values + 1))
    else:
        sys.stderr.write('Wrong model name in model info: {}. Need Binomial or NegativeBinomial.'.format(model['model_name']))
        sys.exit(1)
    return pred