How to use the kaggler.model.automl.BaseAutoML function in Kaggler

algo=tpe.suggest, max_evals=n_eval, verbose=1,
                             rstate=self.random_state)

        hyperparams = space_eval(self.space, best)
        return hyperparams, trials

    def fit(self, X, y):
        self.model = XGBModel(n_estimators=self.n_best, **self.params)
        self.model.fit(X=X[self.features], y=y, eval_metric='mae', verbose=False)
        return self

    def predict(self, X):
        return self.model.predict(X[self.features])


class AutoLGB(BaseAutoML):

    params = {
        "bagging_freq": 1,
        "verbosity": -1,
        "seed": RANDOM_SEED,
        "num_threads": -1,
    }

    space = {
        "learning_rate": hp.loguniform("learning_rate", np.log(0.01), np.log(0.3)),
        "num_leaves": hp.choice("num_leaves", [15, 31, 63, 127, 255]),
        "max_depth": hp.choice("max_depth", [-1, 4, 6, 8, 10]),
        "feature_fraction": hp.quniform("feature_fraction", .5, .9, 0.1),
        "bagging_fraction": hp.quniform("bagging_fraction", .5, .9, 0.1),
        "min_child_samples": hp.choice('min_child_samples', [10, 25, 100]),
        "lambda_l1": hp.choice('lambda_l1', [0, .1, 1, 10]),

imp = imp.sort_values('feature_importances', ascending=False).drop_duplicates()

        if len(random_cols) == 0:
            imp = imp[imp['feature_importances'] != 0]
        else:
            th = imp.loc[imp.feature_names.isin(random_cols), 'feature_importances'].mean()
            logger.debug('feature importance (th={:.2f}):\n{}'.format(th, imp))
            imp = imp[(imp.feature_importances > th) & ~(imp.feature_names.isin(random_cols))]

        return imp['feature_names'].tolist()

    def optimize_hyperparam(self, X, y, test_size=.2, n_eval=100):
        raise NotImplementedError


class AutoXGB(BaseAutoML):

    params = {'random_state': RANDOM_SEED,
              'n_jobs': -1}

    space = {
        "learning_rate": hp.loguniform("learning_rate", np.log(0.01), np.log(0.3)),
        "max_depth": hp.choice("num_leaves", [6, 8, 10]),
        "colsample_bytree": hp.quniform("colsample_bytree", .5, .9, 0.1),
        "subsample": hp.quniform("subsample", .5, .9, 0.1),
        "min_child_weight": hp.choice('min_child_weight', [10, 25, 100]),
    }

    def __init__(self, objective='reg:linear', metric='rmse', boosting='gbtree', params=params, space=space,
                 n_est=500, n_stop=10, sample_size=SAMPLE_SIZE, feature_selection=True, n_fs=10,
                 hyperparam_opt=True, n_hpopt=100, n_random_col=10, random_state=RANDOM_SEED, shuffle=True):