

def launch(self, X, y, sample_weight=None, n_estimators=1, pre_warm=True, folder='.', name=None, dump_models=False):
    self.reset()
    if (name is None):
        name = uuid.uuid4().get_hex()
    if (not os.path.exists(folder)):
        os.makedirs(folder)
    data_filename = os.path.join(self, (name + '_data.pkl'))
    data_filename = os.path.abspath(data_filename)
    host_dump(self.lb_view.client, (X, y, sample_weight), data_filename, pre_warm=pre_warm)
    for i in range(n_estimators):
        base_model = clone(self.base_model)
        if dump_models:
            model_filename = os.path.join(folder, (name + ('_model_%03d.pkl' % i)))
            model_filename = os.path.abspath(model_filename)
        else:
            model_filename = None
        self.tasks.append(self.lb_view.apply(train_model, base_model, data_filename, model_filename, random_state=i))
    return self

dataset/ETHPy150Open pydata/pyrallel/pyrallel/ensemble.py EnsembleGrower.launch/VarMisuse@97/112 `folder`->`self`