How to use the suod.models.parallel_processes._parallel_fit function in suod

time_cost_pred = cost_forecast_meta(clf_train, X, base_estimator_names)

# schedule the tasks
n_estimators_list, starts, n_jobs = balanced_scheduling(time_cost_pred,
                                                        n_estimators, n_jobs)

print(starts)  # this is the list of being split
start = time.time()

print('Parallel Training...')

# TODO: code cleanup. There is an existing bug for joblib on Windows:
# https://github.com/joblib/joblib/issues/806
# max_nbytes can be dropped on other OS
all_results = Parallel(n_jobs=n_jobs, max_nbytes=None, verbose=True)(
    delayed(_parallel_fit)(
        n_estimators_list[i],
        base_estimators[starts[i]:starts[i + 1]],
        X,
        n_estimators,
        rp_flags[starts[i]:starts[i + 1]],
        objective_dim,
        rp_method=rp_method,
        verbose=True)
    for i in range(n_jobs))

print('Balanced Scheduling Total Train Time:', time.time() - start)

# reformat and unfold the lists. Save the trained estimators and transformers
all_results = list(map(list, zip(*all_results)))
trained_estimators = _unfold_parallel(all_results[0], n_jobs)
jl_transformers = _unfold_parallel(all_results[1], n_jobs)

##########################################################################
    # compare with no projection, no bps, and no approximation
    print("******************************************************************")
    start = time.time()
    n_estimators = len(base_estimators)
    n_estimators_list, starts, n_jobs = _partition_estimators(n_estimators,
                                                              n_jobs)

    rp_flags = np.zeros([n_estimators, 1])
    approx_flags = np.zeros([n_estimators, 1])
    objective_dim = None
    rp_method = None

    all_results = Parallel(n_jobs=n_jobs, max_nbytes=None, verbose=True)(
        delayed(_parallel_fit)(
            n_estimators_list[i],
            base_estimators[starts[i]:starts[i + 1]],
            X_train,
            n_estimators,
            rp_flags[starts[i]:starts[i + 1]],
            objective_dim,
            rp_method=rp_method,
            verbose=True)
        for i in range(n_jobs))

    print('Orig Fit time:', time.time() - start)
    print()

    all_results = list(map(list, zip(*all_results)))
    trained_estimators = _unfold_parallel(all_results[0], n_jobs)
    jl_transformers = _unfold_parallel(all_results[1], n_jobs)

##########################################################################
    # compare with no projection, no bps, and no approximation
    print("******************************************************************")
    n_estimators = len(base_estimators)
    n_jobs = 6
    n_estimators_list, starts, n_jobs = _partition_estimators(n_estimators,
                                                              n_jobs)

    rp_flags = np.zeros([n_estimators, 1])
    approx_flags = np.zeros([n_estimators, 1])
    objective_dim = None
    rp_method = None

    start = time.time()
    all_results = Parallel(n_jobs=n_jobs, max_nbytes=None, verbose=True)(
        delayed(_parallel_fit)(
            n_estimators_list[i],
            base_estimators[starts[i]:starts[i + 1]],
            X,
            n_estimators,
            rp_flags[starts[i]:starts[i + 1]],
            objective_dim,
            rp_method=rp_method,
            verbose=True)
        for i in range(n_jobs))

    print('Orig Fit time:', time.time() - start)
    print()

    all_results = list(map(list, zip(*all_results)))
    trained_estimators = _unfold_parallel(all_results[0], n_jobs)
    jl_transformers = _unfold_parallel(all_results[1], n_jobs)

time_cost_pred, self.n_estimators, self.n_jobs)
        else:
            # use the default sklearn equal split
            n_estimators_list, starts, n_jobs = _partition_estimators(
                self.n_estimators, self.n_jobs)

        # fit the base models
        print('Parallel Training...')
        start = time.time()

        # TODO: code cleanup. There is an existing bug for joblib on Windows:
        # https://github.com/joblib/joblib/issues/806
        # a fix is on the way: https://github.com/joblib/joblib/pull/966
        # max_nbytes can be dropped on other OS
        all_results = Parallel(n_jobs=n_jobs, max_nbytes=None, verbose=True)(
            delayed(_parallel_fit)(
                n_estimators_list[i],
                self.base_estimators[starts[i]:starts[i + 1]],
                X,
                self.n_estimators,
                self.rp_flags[starts[i]:starts[i + 1]],
                self.target_dim_frac_,
                self.jl_method,
                verbose=self.verbose)
            for i in range(n_jobs))

        print('Balanced Scheduling Total Train Time:', time.time() - start)

        # reformat and unfold the lists. Save the trained estimators and transformers
        all_results = list(map(list, zip(*all_results)))

        # overwrite estimators

##########################################################################
    # compare with no projection, no bps, and no approximation
    print("******************************************************************")
    start = time.time()
    n_estimators = len(base_estimators)
    n_estimators_list, starts, n_jobs = _partition_estimators(n_estimators,
                                                              n_jobs)

    rp_flags = np.zeros([n_estimators, 1])
    approx_flags = np.zeros([n_estimators, 1])
    objective_dim = None
    rp_method = None

    all_results = Parallel(n_jobs=n_jobs, max_nbytes=None, verbose=True)(
        delayed(_parallel_fit)(
            n_estimators_list[i],
            base_estimators[starts[i]:starts[i + 1]],
            X_train,
            n_estimators,
            rp_flags[starts[i]:starts[i + 1]],
            objective_dim,
            rp_method=rp_method,
            verbose=True)
        for i in range(n_jobs))

    print('Orig Fit time:', time.time() - start)
    print()

    all_results = list(map(list, zip(*all_results)))
    trained_estimators = _unfold_parallel(all_results[0], n_jobs)
    jl_transformers = _unfold_parallel(all_results[1], n_jobs)