utils.py 文件源码

def psgd_1(sgd, n_iter_per_job, n_jobs, X_train, y_train):
    """
    Parallel SGD implementation using multiprocessing. All workers sync once after running SGD independently for
    n_iter_per_job iterations.

    Parameters
    ----------
    sgd: input SGDRegression() object
    n_iter_per_job: number of iterations per worker
    n_jobs: number of parallel processes to run
    X_train: train input data
    y_train: train target data

    Returns
    -------
    sgd: the input SGDRegressor() object with updated coef_ and intercept_
    """

    sgds = Parallel(n_jobs=n_jobs)(
        delayed(psgd_method_1)(s, X_train, y_train)
        for s in [SGDRegressor(n_iter=n_iter_per_job) for _ in range(n_jobs)])
    sgd.coef_ = np.array([x.coef_ for x in sgds]).mean(axis=0)
    sgd.intercept_ = np.array([x.intercept_ for x in sgds]).mean(axis=0)
    return sgd