rngs.py

from sklearn.linear_model import SGDClassifier
from sklearn.datasets import make_classification
import numpy as np


X, y = make_classification(n_features=5, random_state=42)

rng = np.random.RandomState(10)

# According to the docs https://scikit-learn.org/stable/common_pitfalls.html#id2
# in particular the subsection on cloning for estimators these two estimators
# should influence each other. They share the `rng` instance.
sgd = SGDClassifier(random_state=rng)
sgd2 = clone(sgd)

# However the fitted coefs are the same for both `sgd` and `sgd2`
# This is surprising. I would have expected them to be different.
# With the variation being similar to what you see when you call
# sgd.fit(X, y).coef_ multiple times in a row.
print(sgd.fit(X, y).coef_)
print(sgd2.fit(X, y).coef_)

The RNG instances are not the same, which you can check with:

# %%
import numpy as np
from sklearn.base import clone

# %%
rng = np.random.RandomState(10)
rng2 = clone(rng, safe=False)
id(rng) == id(rng2)

I agree they aren't the same, but I think they should be.