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import pickle
import pytest
from sklearn.utils.metaestimators import available_if
class AvailableParameterEstimator:
"""This estimator's `available` parameter toggles the presence of a method"""
def __init__(self, available=True, return_value=1):
self.available = available
self.return_value = return_value
@available_if(lambda est: est.available)
def available_func(self):
"""This is a mock available_if function"""
return self.return_value
def test_available_if_docstring():
assert "This is a mock available_if function" in str(
AvailableParameterEstimator.__dict__["available_func"].__doc__
)
assert "This is a mock available_if function" in str(
AvailableParameterEstimator.available_func.__doc__
)
assert "This is a mock available_if function" in str(
AvailableParameterEstimator().available_func.__doc__
)
def test_available_if():
assert hasattr(AvailableParameterEstimator(), "available_func")
assert not hasattr(AvailableParameterEstimator(available=False), "available_func")
def test_available_if_unbound_method():
# This is a non regression test for:
# https://github.com/scikit-learn/scikit-learn/issues/20614
# to make sure that decorated functions can be used as an unbound method,
# for instance when monkeypatching.
est = AvailableParameterEstimator()
AvailableParameterEstimator.available_func(est)
est = AvailableParameterEstimator(available=False)
with pytest.raises(
AttributeError,
match="This 'AvailableParameterEstimator' has no attribute 'available_func'",
):
AvailableParameterEstimator.available_func(est)
def test_available_if_methods_can_be_pickled():
"""Check that available_if methods can be pickled.
Non-regression test for #21344.
"""
return_value = 10
est = AvailableParameterEstimator(available=True, return_value=return_value)
pickled_bytes = pickle.dumps(est.available_func)
unpickled_func = pickle.loads(pickled_bytes)
assert unpickled_func() == return_value
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