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