def test_entropy_constant(self):
X = np.zeros((100, 1))
domain = Domain([ContinuousVariable('v1')],
[DiscreteVariable('c1', values=["1"])])
table = data.Table(domain, X, X)
disc = discretize.EntropyMDL()
dvar = disc(table, table.domain[0])
self.assertEqual(len(dvar.values), 1)
self.assertEqual(dvar.compute_value.points, [])
def test_entropy(self):
X = np.array([0] * 25 + [1] * 25 + [2] * 25 + [3] * 25
).reshape((100, 1))
Y = np.array([0] * 25 + [1] * 75)
table = data.Table.from_numpy(None, X, Y)
disc = discretize.EntropyMDL()
dvar = disc(table, table.domain[0])
self.assertEqual(len(dvar.values), 2)
self.assertEqual(dvar.compute_value.points, [0.5])
def test_entropy_with_two_values(self):
s = [0] * 50 + [1] * 50
random.shuffle(s)
X = np.array(s).reshape((100, 1))
table = data.Table.from_numpy(None, X, X)
disc = discretize.EntropyMDL()
dvar = disc(table, table.domain[0])
self.assertEqual(len(dvar.values), 2)
self.assertEqual(dvar.compute_value.points, [0.5])
MDL = namedtuple("MDL", [])
EqualFreq = namedtuple("EqualFreq", ["k"])
EqualWidth = namedtuple("EqualWidth", ["k"])
Remove = namedtuple("Remove", [])
Custom = namedtuple("Custom", ["points"])
METHODS = [(Default, ), (Leave, ), (MDL, ), (EqualFreq, ), (EqualWidth, ),
(Remove, ), (Custom, )]
_dispatch = {
Default:
lambda m, data, var: _dispatch[type(m.method)](m.method, data, var),
Leave:
lambda m, data, var: var,
MDL:
lambda m, data, var: disc.EntropyMDL()(data, var),
EqualFreq:
lambda m, data, var: disc.EqualFreq(m.k)(data, var),
EqualWidth:
lambda m, data, var: disc.EqualWidth(m.k)(data, var),
Remove:
lambda m, data, var: None,
Custom:
lambda m, data, var: disc.Discretizer.create_discretized_var(
var, m.points)
}
# Variable discretization state
DState = namedtuple(
"DState",
[
def _mdl_discretization(
data: Table,
var: Union[ContinuousVariable, str, int]) -> Union[DiscreteVariable, str]:
if not data.domain.has_discrete_class:
return "no discrete class"
return disc.EntropyMDL()(data, var)
