def test_numerical_default_transform():
optb = MulticlassOptimalBinning()
with raises(NotFittedError):
x_transform = optb.transform(x)
optb.fit(x, y)
x_transform = optb.transform([0.3, 2.1, 2.5, 3], metric="mean_woe")
assert x_transform == approx([0.48973998, 0.48973998, -0.00074357,
0.02189459], rel=1e-5)
def test_default_transform_multiclass():
data = load_wine()
variable_names = data.feature_names
X = data.data
y = data.target
process = BinningProcess(variable_names)
process.fit(X, y)
X_transform = process.transform(X)
optb = process.get_binned_variable(variable_names[0])
assert isinstance(optb, MulticlassOptimalBinning)
optb = MulticlassOptimalBinning()
x = X[:, 5]
optb.fit(x, y)
assert optb.transform(x) == approx(X_transform[:, 5], rel=1e-6)