ml_assisted_library_transform implementation (work in progress)

Author: PHvanLent

todos.md

@@ -25,13 +25,16 @@ Development of the ML-assisted recommendation: develop_library_transformation.ip
 Now we just need to add it the probability distribution to assign probabilities
 
 22-04-2025
-There are several parameters neccessary here. The method we will fix to XGboost, there is a beta parameter
+Afternoon :There are several parameters neccessary here. The method we will fix to XGboost, there is a beta parameter
 scheme that could be set per round. The strategy for including data in the model. Perhaps at some point in 
 the optimization "forgetting" is better than keep stacking data on top. 
 
-
 {"n_screened_strains": n_screened,
  "ml_method" : "xgboost",
  "beta": 2**(2.5),
  "data_strategy" : "all",  # there are multiple strategies here,
- "sequencing_selection_method": "best_sampling",}
+ "sequencing_selection_method": "best_sampling",}
+
+Evening: implemented probability assignment to library elements. For tomorrow, we should finish the implementation
+and do some tests. Also, I should start writing some pytests to keep everything in check, since there are starting
+to be many edge cases.