doc(smac): Update link for get_smac_object_callback (#1610)

* doc(smac): Update link for `get_smac_object_callback`

* doc(links): Update more smac links

Author: eddiebergman

autosklearn/estimators.py

@@ -276,12 +276,12 @@ def __init__(
 
         smac_scenario_args : dict, optional (None)
             Additional arguments inserted into the scenario of SMAC. See the
-            `SMAC documentation <https://automl.github.io/SMAC3/main/api/smac.scenario.scenario.html#module-smac.scenario.scenario>`_
+            `SMAC documentation <https://automl.github.io/SMAC3/main/api/smac.scenario.html#smac.scenario.Scenario>`_
             for a list of available arguments.
 
         get_smac_object_callback : callable
             Callback function to create an object of class
-            `smac.optimizer.smbo.SMBO <https://automl.github.io/SMAC3/main/api/smac.optimizer.smbo.html>`_.
+            `smac.facade.AbstractFacade <https://automl.github.io/SMAC3/main/api/smac.facade.html>`_.
             The function must accept the arguments ``scenario_dict``,
             ``instances``, ``num_params``, ``runhistory``, ``seed`` and ``ta``.
             This is an advanced feature. Use only if you are familiar with

autosklearn/experimental/askl2.py

@@ -264,7 +264,7 @@ def __init__(
 
         smac_scenario_args : dict, optional (None)
             Additional arguments inserted into the scenario of SMAC. See the
-            `SMAC documentation <https://automl.github.io/SMAC3/main/api/smac.scenario.scenario.html#module-smac.scenario.scenario>`_
+            `SMAC documentation <https://automl.github.io/SMAC3/main/api/smac.scenario.html#smac.scenario.Scenario>`_
             for a list of available arguments.
 
         logging_config : dict, optional (None)

examples/40_advanced/example_multi_objective.py

@@ -8,7 +8,7 @@
 competing metrics: `precision` and `recall` (read more on this tradeoff
 in the `scikit-learn docs <https://scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html>`_.
 
-Auto-sklearn uses `SMAC3's implementation of ParEGO <https://automl.github.io/SMAC3/main/details/multi_objective.html>`_.
+Auto-sklearn uses `SMAC3's implementation of ParEGO <https://automl.github.io/SMAC3/main/examples/3_multi_objective/2_parego.html#parego>`_.
 Multi-objective ensembling and proper access to the full Pareto set will be added in the near
 future.
 """