[ENH] MI and CMI simulation module and relevant estimators from the forest

[adam2392]

Fixes #49
Fixes #48
Supersedes: #58

Changes proposed in this pull request:

• simulation for MI and CMI data

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[codecov]

Codecov Report

Patch coverage: 69.21% and project coverage change: -7.07 ⚠️

Comparison is base (2768558) 93.39% compared to head (7463904) 86.33%.

Additional details and impacted files

@@            Coverage Diff             @@
##             main      #83      +/-   ##
==========================================
- Coverage   93.39%   86.33%   -7.07%     
==========================================
  Files          16       24       +8     
  Lines        1469     1924     +455     
==========================================
+ Hits         1372     1661     +289     
- Misses         97      263     +166     

Impacted Files	Coverage Δ
sktree/experimental/mutual_info.py	20.51% <20.51%> (ø)
sktree/experimental/tests/test_mutual_info.py	62.06% <62.06%> (ø)
sktree/tree/_marginalize.py	76.74% <76.74%> (ø)
sktree/neighbors.py	86.20% <86.20%> (ø)
sktree/experimental/simulate.py	88.13% <88.13%> (ø)
sktree/__init__.py	79.16% <100.00%> (+0.90%)	⬆️
sktree/ensemble/_supervised_forest.py	100.00% <100.00%> (ø)
sktree/ensemble/_unsupervised_forest.py	70.63% <100.00%> (+0.23%)	⬆️
sktree/experimental/__init__.py	100.00% <100.00%> (ø)
sktree/experimental/tests/test_simulate.py	100.00% <100.00%> (ø)
... and 6 more

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[adam2392]

@YuxinB @PSSF23 @jovo in case you're interested, this is the method for marginalizing a fitted tree using the ideas that have been discussed.

This file and _marginal.pyx are the core files.

Specifically, I implement some ideas for traversing the tree, while "marginalizing out features".

[adam2392]

Next, I need to add now a simulation using a fitted forest and computing a bunch of quantities for MI/CMI