Add SN classification based on Random Forest

[JulienPeloton]

IMPORTANT: Please create an issue first before opening a Pull Request.
Linked to issue(s): #318

What changes were proposed in this pull request?

This PR adds a new processor rfscore to compute the probability of an alert to be a SN Ia. The classification is based on the Machine Learning Random Forest algorithm. See #318 and astrolabsoftware/fink-science#15 for the implementation. To work, this new feature requires fink-science>=0.1.8 installed.

Note that the introduction of a new science module gives rise to a new schema for alert distribution (a new field is added). After the merging, the distribution schema will be schemas/distribution_schema_0p2.avsc.

Minor:

• Add two new dependencies in the requirements: scipy and scikit-learn
• Fix numpy version in the requirements.txt to satisfy Scipy requirements.

How was this patch tested?

Manually and CI

[sonarqubecloud]

Kudos, SonarCloud Quality Gate passed!

0 Bugs
0 Vulnerabilities (and 0 Security Hotspots to review)
0 Code Smells

100.0% Coverage
0.0% Duplication

[codecov]

Codecov Report

Merging #319 into master will increase coverage by <1%.
The diff coverage is 100%.

@@          Coverage Diff           @@
##           master   #319    +/-   ##
======================================
+ Coverage      93%    93%   +<1%     
======================================
  Files          16     16            
  Lines         745    756    +11     
======================================
+ Hits          694    705    +11     
  Misses         51     51

Impacted Files	Coverage Δ
bin/raw2science.py	97% <100%> (ø)	⬆️

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