[dask] allow parameter aliases for local_listen_port, num_threads, tree_learner (fixes #3671)

[jameslamb]

This PR closes #3671. It updates the code in lightgbm.dask so that it respects parameter aliases.

So, for example, as of this PR you can use tree_learner, tree, tree_type, or tree_learner_type to refer to the tree_learner parameter that controls which type of distributed training is performed.

[StrikerRUS]

Hmmm, it doesn't look like GPU error is related to these Dask changes:

=================================== FAILURES ===================================
_________________ TestEngine.test_missing_value_handle_more_na _________________

self = <python_package_test.test_engine.TestEngine testMethod=test_missing_value_handle_more_na>

    def test_missing_value_handle_more_na(self):
        X_train = np.ones((100, 1))
        y_train = np.ones(100)
        trues = random.sample(range(100), 80)
        for idx in trues:
            X_train[idx, 0] = np.nan
            y_train[idx] = 0
        lgb_train = lgb.Dataset(X_train, y_train)
        lgb_eval = lgb.Dataset(X_train, y_train)
    
        params = {
            'metric': 'l2',
            'verbose': -1,
            'boost_from_average': False
        }
        evals_result = {}
        gbm = lgb.train(params, lgb_train,
                        num_boost_round=20,
                        valid_sets=lgb_eval,
                        verbose_eval=False,
                        evals_result=evals_result)
        ret = mean_squared_error(y_train, gbm.predict(X_train))
>       self.assertLess(ret, 0.005)
E       AssertionError: 0.008207460416181563 not less than 0.005

[StrikerRUS]

Thanks a lot for implementing my feature request! 🙂

Could please do the same with aliases here?

LightGBM/python-package/lightgbm/basic.py

Lines 2078 to 2081 in 4007b34

	self.set_network(machines,
	local_listen_port=params.get("local_listen_port", 12400),
	listen_time_out=params.get("listen_time_out", 120),
	num_machines=params.setdefault("num_machines", num_machines))

I think it OK to use the same PR for implementing network aliases in different placed of codebase. But feel free to disagree with me and say that it requires a separate PR or/and feature request.

[jameslamb]

Could please do the same with aliases here?

I'm happy to do this soon, but I'd like to do it as a separate PR. I have a preference for keeping the dask PRs small and quick, to prevent too much pain for @ffineis on #3708, and to make sure that the Dask interface keeps progressing towards something we're comfortable releasing with 3.2.0. You only get one chance to release code as "new" 😀

[StrikerRUS]

Looks like Dask tests are quite flaky:

/home/AzDevOps_azpcontainer/miniconda/envs/test-env/lib/python3.8/asyncio/tasks.py:490: TimeoutError

but I'd like to do it as a separate PR.

Sure, thanks! 👍

[jameslamb]

Looks like Dask tests are quite flaky:

I'm realizing that too 😫 . @ffineis faced the same thing on #3708, and I think he's come up with a good solution for now on that PR. Also so see my link and comments in #3708 (comment) think we might get better reliability from switching to the @gen_cluster fixture from Dask distributed. I'm planning to test that once #3708 is merged.

[StrikerRUS]

Great, thanks!

[github-actions]

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