def test_label_encoder():
y = ['one', 'one', 'one', 'zero', 'zero', 'two']
X = np.random.rand(6, 3)
pool = [DecisionTreeClassifier().fit(X, y) for _ in range(5)]
stacked = StackedClassifier(pool).fit(X, y)
pred = stacked.predict(X)
assert np.array_equal(pred, y)
def test_label_encoder_base_ensemble():
from sklearn.ensemble import RandomForestClassifier
X, y = make_classification()
y[y == 1] = 2
y = y.astype(np.float)
pool = RandomForestClassifier().fit(X, y)
st = StackedClassifier(pool)
st.fit(X, y)
pred = st.predict(X)
assert np.isin(st.classes_, pred).all()