Evalml: BalancedClassificationDataCVSplit produce diferentes divisiones cada vez que se llama

Repro

import joblib
from evalml.demos import load_fraud
from evalml.preprocessing.data_splitters import BalancedClassificationDataCVSplit

splitter = BalancedClassificationDataCVSplit(n_splits=3, random_seed=0, shuffle=True)

X, y = load_fraud(5000)
X = X.to_dataframe()
y = y.to_series().astype("int")

for train, test in splitter.split(X, y):
    print((joblib.hash(train), joblib.hash(test)))

# Output
('75f1b95d7ce307ac6c793055330969aa', '8c89fe1a592c50a700b6d5cbb02dba8b')
('f8c849bbfbed37c13f66c5c742e237cb', '9c4879fb550fded8be9ac03e95a1bf95')
('cdc21f0d6bbf45459c9695258f7f04dc', '5b575765bbe176e732b8eb4dc1bf2822')

for train, test in splitter.split(X, y):
    print((joblib.hash(train), joblib.hash(test)))

# Output
('bf462b82af243c552ac48acad2dfd748', '8c89fe1a592c50a700b6d5cbb02dba8b')
('b8341b536c63c7957c099b05e315f49c', '9c4879fb550fded8be9ac03e95a1bf95')
('780e74673b601790037fc0b17dde56fe', '5b575765bbe176e732b8eb4dc1bf2822')

for train, test in splitter.split(X, y):
    print((joblib.hash(train), joblib.hash(test)

# Output
('385f6c538568ad3a33cf84f61d94144c', '8c89fe1a592c50a700b6d5cbb02dba8b')
('8db65d0a3bdf87ae0f135b9766a260dd', '9c4879fb550fded8be9ac03e95a1bf95')
('2a7293fc1308b8a572091d7c76d20205', '5b575765bbe176e732b8eb4dc1bf2822')

Esto es diferente del comportamiento del divisor sklearn:

from sklearn.model_selection import StratifiedKFold

kfold = StratifiedKFold(n_splits=3, random_state=0, shuffle=True)

for train, test in kfold.split(X, y):
    print((joblib.hash(train), joblib.hash(test)))

#Output
('6c30ee6a11803927024354405389506a', '8c89fe1a592c50a700b6d5cbb02dba8b')
('df0a70e2e6ca783f12461e8c82a26ad4', '9c4879fb550fded8be9ac03e95a1bf95')
('2898e4b3d3621b436641016499f4aafb', '5b575765bbe176e732b8eb4dc1bf2822')

for train, test in kfold.split(X, y):
    print((joblib.hash(train), joblib.hash(test)))

# Output
('6c30ee6a11803927024354405389506a', '8c89fe1a592c50a700b6d5cbb02dba8b')
('df0a70e2e6ca783f12461e8c82a26ad4', '9c4879fb550fded8be9ac03e95a1bf95')
('2898e4b3d3621b436641016499f4aafb', '5b575765bbe176e732b8eb4dc1bf2822')

Creo que esto es problemático por dos razones:

1. Dado que BalancedClassificationDataCVSplit es el divisor predeterminado en automl, significa que nuestras tuberías se evalúan en diferentes divisiones
2. Dado que split modifica el estado del divisor de datos, significa que tendremos resultados diferentes entre los motores secuencial y paralelo.