Support using Hyperopt, Optuna as tuning backend; Support the use of different algorithms

[ItsYikunJiang]

Problem

• Causaltune currently only supports FLAML as its hyperparamter optimization library. However, there are two widely used hyperparameter optimization libraries that causaltune does not support: Hyperopt and Optuna.

Proposed changes

• Introduce HierTuneHub as the unified interface for parsing search space and dealing with different hyperparamter optimization libraries.
• Add support for Hyperopt and Optuna as optimization backends for causaltune.
• Enable users to choose which algorithm they want to use during the hyperparameter optimization process.

Note that it will bring breaking change to causaltune. CausalTune.fit() can not resume and init config can not be passed into the tuning backend (it can be fixed later).

Types of changes

What types of changes does your code introduce to causaltune?
Put an x in the boxes that apply

• Bugfix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)
• Documentation Update (if none of the other choices apply)

Checklist

• I have read the CONTRIBUTING doc
• Description above provides context of the change
• I have added tests that prove my fix is effective or that my feature works
• Unit tests for changes (not needed for documentation changes)
• Bumping version in setup.py is an individual PR and not mixed with feature or bugfix PRs
• Commits follow "How to write a good git commit message"
• Relevant documentation is updated including usage instructions