Setting 'user_splits_fixed' in categorical binning

Hi @guillermo-navas-palencia,

There is a problem when I try to setting value to user_splits_fixed. Suppose I have column with raito of event rate in each value like this:

value	raito
-1	0.011665
2	0
3	0.0133333
4	0.166667
7	0
8	0.0246041
9	0
10	0.025641

Then when I set user_splits = [[ 2., 7., 9., 3., 10., 4.],[8],[-1]] ,user_splits_fixed=[True, True, True], monotonic_trend=None,dtype='categorical' and the program raises error ValueError: Fixed user_splits [list([2.0, 7.0, 9.0, 3.0, 10.0, 4.0])] are removed because produce pure prebins. Provide different splits to be fixed.. What thing is wrong here?

[guillermo-navas-palencia]

I created this example to reproduce your problem:

import numpy as np

np.random.seed(0)
n = 100000

x = sum([[i] * n for i in [-1, 2, 3, 4, 7, 8, 9, 10]], [])
y = list(np.random.binomial(1, 0.011665, n))
y += list(np.zeros(n))
y += list(np.random.binomial(1, 0.0133333, n))
y += list(np.random.binomial(1, 0.166667, n))
y += list(np.zeros(n))
y += list(np.random.binomial(1, 0.0246041, n))
y += list(np.zeros(n))
y += list(np.random.binomial(1, 0.025641, n))

user_splits = [[2., 7., 9., 3., 10., 4.], [8], [-1]]
user_splits_fixed = [True, True, True]

optb1 = OptimalBinning(dtype="categorical", user_splits=user_splits)
optb2 = OptimalBinning(dtype="categorical", user_splits=user_splits,
                       user_splits_fixed=user_splits_fixed)

for optb in (optb1, optb2):
    optb.fit(x, y)
    optb.binning_table.build()
    assert optb.binning_table.iv == approx(0.09345086993827473, rel=1e-6)

After commit a6d015b2e9365ecc05cba48421972c562f7960c7, it must work as expected.

[nic9lif3]

Hi @guillermo-navas-palencia The splits result with default option of OptimalBinning is [[ 2., 7., 9., 3., 10., 4. 8],[-1]]. When I parse this splits as an option into user_splits parameter, it return the same error 😃.