Implement Dynamic CDI

[Omer Ronen]

Implementing a Dynamic CDIs class based on FIGS.

TODOs:

• Implement a sklearn compatible class named D-FIGS in a new file imodels/tree/dynamic_figs.py
• Write a test using the PECARN IAI dataset

More details:

• The D-FIGS class should inherit from FIGS class, and take an additional dictionary at initialization, corresponding to the features phases.
  When applying the fit or predict methods, the class should verify that the matrix $X$ is compatible with the features tiers. For example phase 2 features can be available (not NA) only if all phase 1 features are available (we may refine this logic later).
• D-FIGS should infer the phase from the matrix.
• The tests should be written in a new file named imodels/tests/dynamic_figs_test.py, using pytest (see package documentation or you can use the figs test as reference)
• Before you start writing code, please write down a short description detailing how you are going to implement the dynamic fitting algorithm. Specifically: How does the model infer the current phase of the patient? How do you store the different models for different phases and ensure these are compatible with one another?

@aagarwal1996

[Omer Ronen]

@JerryJia00 I opened a branch for you 136-implement-dynamic-cdi please write you code there