def test_model_n_trees_invalid():
case_kwargs = copy.deepcopy(toy_kwargs)
case_kwargs.update({"n_trees": [42]})
model = CascadeForestClassifier(**case_kwargs)
with pytest.raises(ValueError) as excinfo:
model._set_n_trees(0)
assert "Invalid value for n_trees." in str(excinfo.value)
def test_model_n_trees_non_positive():
case_kwargs = copy.deepcopy(toy_kwargs)
case_kwargs.update({"n_trees": 0})
model = CascadeForestClassifier(**case_kwargs)
with pytest.raises(ValueError) as excinfo:
model._set_n_trees(0)
assert "should be strictly positive." in str(excinfo.value)
def test_model_n_trees_auto():
case_kwargs = copy.deepcopy(toy_kwargs)
case_kwargs.update({"n_trees": "auto"})
model = CascadeForestClassifier(**case_kwargs)
n_trees = model._set_n_trees(0)
assert n_trees == 100
n_trees = model._set_n_trees(2)
assert n_trees == 300
n_trees = model._set_n_trees(10)
assert n_trees == 500