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)