Error: Fixed user_splits are removed because produce pure prebins

[ben-herbst]

I need to fix the bins for certain categorical values. Your example in the tutorial is the following:

user_splits = np.array([
               ['Businessman'],
               ['Working'],
               ['Commercial associate'],
               ['Pensioner', 'Maternity leave'],
               ['State servant'],
               ['Unemployed', 'Student']], dtype=object)
optb = OptimalBinning(name=variable_cat, dtype="categorical", solver="cp",
                      user_splits=user_splits,
                      user_splits_fixed=[False, True, True, True, True, True])

optb.fit(x_cat, y_cat)

Then

opt.splits

gives

[['Businessman', 'Pensioner', 'Maternity leave'],
 ['State servant'],
 ['Commercial associate'],
 ['Working'],
 ['Unemployed', 'Student']]

This is not what I need. I need the bins to remain exactly the same as specified. If I change the following

optb = OptimalBinning(name=variable_cat, dtype="categorical", solver="cp",
                      user_splits=user_splits,
                      user_splits_fixed=[True, True, True, True, True, True])

optb.fit(x_cat, y_cat)

I get the error:

ValueError: Fixed user_splits [list(['Businessman'])] are removed because produce pure prebins. Provide different splits to be fixed.

I get the same error if I try

user_splits = np.array([
               ['Businessman'],
               ['Working'],
               ['Commercial associate'],
               ['Pensioner', 'Maternity leave'],
               ['State servant'],
               ['Unemployed'], 
               ['Student']], dtype=object)
optb = OptimalBinning(name=variable_cat, dtype="categorical", solver="cp",
                      user_splits=user_splits,
                      user_splits_fixed=[False, True, True, True, True, True, True])

optb.fit(x_cat, y_cat)

but the following seemingly similar change does not give the error

user_splits = np.array([
               ['Businessman'],
               ['Working'],
               ['Commercial associate'],
               ['Pensioner'], 
               ['Maternity leave'],
               ['State servant'],
               ['Unemployed', 'Student']], dtype=object)
optb = OptimalBinning(name=variable_cat, dtype="categorical", solver="cp",
                      user_splits=user_splits,
                      user_splits_fixed=[False, True, True, True, True, True, True])

optb.fit(x_cat, y_cat)

[guillermo-navas-palencia]

Hi @ben-herbst.

The reason is simple, both categories "Businessman" and "Student" are pure bins, in this case, the average target is zero. In the latter example, the first bin ["Businessman"] is not fixed, and "Student" is merged with "Unemployed".

[ben-herbst]

Thanks, much appreciated!

[image: Praelexis] https://praelexis.com/ Ben Herbst Data Scientist | Machine Learning Engineer

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On Fri, Dec 1, 2023 at 10:26 PM Guillermo @.***> wrote:

Closed #285 https://github.com/guillermo-navas-palencia/optbinning/issues/285 as completed.

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