173 lines
5.2 KiB
Python
173 lines
5.2 KiB
Python
import numpy as np
|
|
import pytest
|
|
import warnings
|
|
|
|
import pickle
|
|
|
|
from sklearn.utils.metaestimators import if_delegate_has_method
|
|
from sklearn.utils.metaestimators import available_if
|
|
|
|
|
|
class Prefix:
|
|
def func(self):
|
|
pass
|
|
|
|
|
|
class MockMetaEstimator:
|
|
"""This is a mock meta estimator"""
|
|
|
|
a_prefix = Prefix()
|
|
|
|
@if_delegate_has_method(delegate="a_prefix")
|
|
def func(self):
|
|
"""This is a mock delegated function"""
|
|
pass
|
|
|
|
|
|
@pytest.mark.filterwarnings("ignore:if_delegate_has_method was deprecated")
|
|
def test_delegated_docstring():
|
|
assert "This is a mock delegated function" in str(
|
|
MockMetaEstimator.__dict__["func"].__doc__
|
|
)
|
|
assert "This is a mock delegated function" in str(MockMetaEstimator.func.__doc__)
|
|
assert "This is a mock delegated function" in str(MockMetaEstimator().func.__doc__)
|
|
|
|
|
|
class MetaEst:
|
|
"""A mock meta estimator"""
|
|
|
|
def __init__(self, sub_est, better_sub_est=None):
|
|
self.sub_est = sub_est
|
|
self.better_sub_est = better_sub_est
|
|
|
|
@if_delegate_has_method(delegate="sub_est")
|
|
def predict(self):
|
|
pass
|
|
|
|
|
|
class MetaEstTestTuple(MetaEst):
|
|
"""A mock meta estimator to test passing a tuple of delegates"""
|
|
|
|
@if_delegate_has_method(delegate=("sub_est", "better_sub_est"))
|
|
def predict(self):
|
|
pass
|
|
|
|
|
|
class MetaEstTestList(MetaEst):
|
|
"""A mock meta estimator to test passing a list of delegates"""
|
|
|
|
@if_delegate_has_method(delegate=["sub_est", "better_sub_est"])
|
|
def predict(self):
|
|
pass
|
|
|
|
|
|
class HasPredict:
|
|
"""A mock sub-estimator with predict method"""
|
|
|
|
def predict(self):
|
|
pass
|
|
|
|
|
|
class HasNoPredict:
|
|
"""A mock sub-estimator with no predict method"""
|
|
|
|
pass
|
|
|
|
|
|
class HasPredictAsNDArray:
|
|
"""A mock sub-estimator where predict is a NumPy array"""
|
|
|
|
predict = np.ones((10, 2), dtype=np.int64)
|
|
|
|
|
|
@pytest.mark.filterwarnings("ignore:if_delegate_has_method was deprecated")
|
|
def test_if_delegate_has_method():
|
|
assert hasattr(MetaEst(HasPredict()), "predict")
|
|
assert not hasattr(MetaEst(HasNoPredict()), "predict")
|
|
assert not hasattr(MetaEstTestTuple(HasNoPredict(), HasNoPredict()), "predict")
|
|
assert hasattr(MetaEstTestTuple(HasPredict(), HasNoPredict()), "predict")
|
|
assert not hasattr(MetaEstTestTuple(HasNoPredict(), HasPredict()), "predict")
|
|
assert not hasattr(MetaEstTestList(HasNoPredict(), HasPredict()), "predict")
|
|
assert hasattr(MetaEstTestList(HasPredict(), HasPredict()), "predict")
|
|
|
|
|
|
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)
|
|
|
|
|
|
@pytest.mark.filterwarnings("ignore:if_delegate_has_method was deprecated")
|
|
def test_if_delegate_has_method_numpy_array():
|
|
"""Check that we can check for an attribute that is a NumPy array.
|
|
|
|
This is a non-regression test for:
|
|
https://github.com/scikit-learn/scikit-learn/issues/21144
|
|
"""
|
|
estimator = MetaEst(HasPredictAsNDArray())
|
|
assert hasattr(estimator, "predict")
|
|
|
|
|
|
def test_if_delegate_has_method_deprecated():
|
|
"""Check the deprecation warning of if_delegate_has_method"""
|
|
# don't warn when creating the decorator
|
|
with warnings.catch_warnings():
|
|
warnings.simplefilter("error", FutureWarning)
|
|
_ = if_delegate_has_method(delegate="predict")
|
|
|
|
# Only when calling it
|
|
with pytest.warns(FutureWarning, match="if_delegate_has_method was deprecated"):
|
|
hasattr(MetaEst(HasPredict()), "predict")
|
|
|
|
|
|
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
|