Feature/tf probability

[benoitLebreton-perso]

This pull request is linked to #104
The changes are optional. The user will need to pip install melusine[tf-probability] to use this feature.
The base Melusine is this way not impacted.

Description

New models are available with feature for uncertainty estimation using TFP (tensorflow-probability)
The TFP based models output are no punctual estimations but distribution on probabilities.
In other words, for each prediction : a distribution on the estimated probability is computed.
We propose to use this distribution get

• a punctual estimation (like the regular melusine models do)
• an uncertainty estimation around this punctual estimation using the dispersion of the distribution

Fixes #104

Type of change

Please delete options that are not relevant.

• New feature (non-breaking change which adds functionality or feature that would cause existing functionality to not work as expected)
• This change requires a documentation update

How Has This Been Tested?

I plan to test it using an open source dataset to check if the classification performances are the same (punctual estimation)

• Test on movies dataset

Test Configuration:

• OS:
• Python version: 3.7
• Melusine version: 2.3.1

Checklist:

• My code follows the style guidelines of this project
• I have performed a self-review of my own code
• I have commented my code, particularly in hard-to-understand areas
• I have made corresponding changes to the documentation
• My changes generate no new warnings
• I have added tests that prove my fix is effective or that my feature works
• New and existing unit tests pass locally with my changes
• Any dependent changes have been merged and published in downstream modules

[benoitLebreton-perso]

I cleaned this pull request and manage to test it on a larger dataset (the movies dataset : a 100 000 movies plots to classify)
now this pull request looks good for a review please :)

[benoitLebreton-perso]

Idea to improve (as a next step)
limit the upper/lower boundaries to 0 and 1
Indeed as we use mean+2*std (gaussian) this may give a value greater than 1 or lower than 0 (but we are talking about estimated probabilities so we may limit them to [0;1]

Another idea is to change the way we compute lower/upper bounds : we also could sample the outputs thousands of times and determine a bootstrap 95%-uncertainty area