TODO

Main:

    Generic:
        ☐ Delete unnecessary CN2 implementation
            ✔ arrivare a due implementazioni uguali @done(24-04-03 11:05)
                ✔ build_cn2 as orange_cn2(simple selector version) @done(24-03-14 20:39)
                ✔ build_base_cn2 as orange_cn2(simple selector version) @done(24-03-16 12:00)

        ☐ new section in docu
        ✔ MLJ interface for cn2 @done(24-04-29 18:23)
        ☐ Metrics  
            ✔ Entropy @done(24-04-29 18:23)
            ✔ Laplace @done(24-04-29 18:23)
            ✔ entropyMDL @done(24-04-29 18:23)
            ☐ Rule validation
            ☐ LRS
            ☐ Weighted relative accuracy
        ☐ SyntaxSearch
            # Formule di una certa grammatica e di un certo alfabeto si generan così
                # a = ExplicitAlphabet(@atoms p q r s)
                # g = SoleLogics.CompleteFlatGrammar(a, [∧, ∨])
                # formulas(g; maxdepth = 2)
                # formulas(g; maxdepth = 100, nformulas = 100)
        ☐ Define DecisionSet and corresponding apply method

    
    Utility:
        ✔ Implementing instances(PropositionalLogiset) @done(24-03-04 17:11)
            ✔ Gestione errore in istanziazione PropositionalLogiset{DataFrame}(SubDataFrame) @done(24-04-13 04:08)
            # già scritta soluzione temporanea
        ✔ add alphabet parameter @done(24-04-03 11:06)
        ✔ check empty row table in ProposiionlLogiset costruction @done(24-04-03 17:35)
        ✔ map from integer to original class names @done(24-04-10 16:31)
        ☐ semplificazione LmCF o LmDF 
            # scalarminimizer
        ✔ BoundedScalarCond -> UnivariateScalarCondion @done(24-04-10 16:31)    
    Testing:
        ✔ 100% accuracy when testing on training data ? @done(24-03-01 03:02)
            ☐ Only real attributes
                ☐ Biopsy
                ☐ Ionosphere
                ☐ Mobile
                ☐ Yeast
                ☐ Abalone
            
    Future: 
        ✔ Differenziare caso attributi discreti/categoriali
    

    MLJ-Interface:
        ✔ Constructor with keyword arguments @done(24-04-12 13:32)
        ✔ fit @done(24-06-01 13:59)
            # parlare con gio di CategoricalArrays
        ✔ predict @done(24-06-01 13:59)
        ✔ clean! @done(24-06-01 13:59)
            # https://juliaai.github.io/MLJModelInterface.jl/dev/quick_start_guide/
        ✔ MMI.metadata_pkg.MMI.metadata_pkg @done(24-06-01 13:59)



    Last:

        ☐ In RandSearch, when calling randformula, should you give it the right atompicking_mode and subalphabets_weights; right?
            ✔ :uniform working ? @done(24-06-01 18:59)
            ✔ :twostep working ? @done(24-06-01 18:59)
            ☐ subalphabets_weights working ? 
            # atompicker = ((rng,alph)->randatom(rng,alph; atompicking_mode = ..., subalphabets_weights = ...))
            ☐ was :uniform or :weighted working better...? 
            ✔ And which one are we using now? @done(24-06-02 11:56)
                ☐ Uniform

        ✔ only one expression between "evaluation function" and "quality evaluator" and "loss_function" @done(24-06-01 15:05)
            or maybe "loss function". 
        ☐ TODO Remove Italian writings (@Italian)
        ✔ Rename SequentialCoveringLearner; "learner" is a bit old-fashioned in my opinion. Maybe something like "ExtendedSequentialCovering?" @done(24-06-01 15:05)
        ☐ add documentation for SequentialCoveringLearner
        ✔ Gather all the good code in "dev" branch, and delete unnecessary branches, edo, edo-memo, edo-dec-set, etc. @done(24-06-01 15:05)
            # https://stackoverflow.com/questions/1307114/how-can-i-archive-git-branches/42232899#42232899
            ☐ keep edo ? 
            ✔ edo-memo @done(24-06-01 15:05)
            ✔ edo-dec-set @done(24-06-01 15:05)
    

    Post Experiments Patches:
        ☐ Unify names for maxinfogain
        ☐ In RandSearch default alpha and max_infogain_ratio to Nothing (after tuning phase)