def test_transform_meta_override():
X = pd.DataFrame({"cat_s": ["a", "b", "c", "d"]})
dd_X = dd.from_pandas(X, npartitions=2)
base = OneHotEncoder(sparse=False)
base.fit(pd.DataFrame(X))
# Failure when not proving transform_meta
# because of value dependent model
wrap = ParallelPostFit(base)
with pytest.raises(ValueError):
wrap.transform(dd_X)
wrap = ParallelPostFit(base,
transform_meta=np.array([[0, 0, 0, 0]],
dtype=np.float64))
result = wrap.transform(dd_X)
expected = base.transform(X)
assert_eq_ar(result, expected)
def test_transform(kind):
X, y = make_classification(chunks=100)
if kind == "numpy":
X, y = dask.compute(X, y)
elif kind == "dask.dataframe":
X = dd.from_dask_array(X)
y = dd.from_dask_array(y)
base = PCA(random_state=0)
wrap = ParallelPostFit(PCA(random_state=0))
base.fit(*dask.compute(X, y))
wrap.fit(*dask.compute(X, y))
assert_estimator_equal(wrap.estimator, base)
result = base.transform(*dask.compute(X))
expected = wrap.transform(X)
assert_eq_ar(result, expected)
