def test_function_sampler_reject_sparse():
X_sparse = sparse.csr_matrix(X)
sampler = FunctionSampler(accept_sparse=False)
with pytest.raises(TypeError,
match="A sparse matrix was passed, "
"but dense data is required"):
sampler.fit(X_sparse, y)
def test_function_resampler_fit():
# Check that the validation is bypass when calling `fit`
# Non-regression test for:
# https://github.com/scikit-learn-contrib/imbalanced-learn/issues/782
X = np.array([[1, np.nan], [2, 3], [np.inf, 4]])
y = np.array([0, 1, 1])
def func(X, y):
return X[:1], y[:1]
sampler = FunctionSampler(func=func, validate=False)
sampler.fit(X, y)
sampler.fit_resample(X, y)
def test_function_sampler_reject_sparse():
X_sparse = sparse.csr_matrix(X)
sampler = FunctionSampler(accept_sparse=False)
with pytest.raises(TypeError, message="A sparse matrix was passed, "
"but dense data is required"):
sampler.fit(X_sparse, y)
