def score_data(self, data, feature):
if len(data.domain.class_vars) != 1:
raise ValueError('RReliefF requires one single class')
if not data.domain.class_var.is_continuous:
raise ValueError('RReliefF supports regression; use ReliefF '
'for classification')
from Orange.preprocess._relieff import rrelieff
weights = np.asarray(rrelieff(data.X, data.Y,
self.n_iterations, self.k_nearest,
np.array([a.is_discrete for a in data.domain.attributes])))
if feature:
return weights[0]
return weights
def score_data(self, data, feature):
if len(data.domain.class_vars) != 1:
raise ValueError('RReliefF requires one single class')
if not data.domain.class_var.is_continuous:
raise ValueError('RReliefF supports regression; use ReliefF '
'for classification')
from Orange.preprocess._relieff import rrelieff
weights = np.asarray(rrelieff(data.X, data.Y,
self.n_iterations, self.k_nearest,
np.array([a.is_discrete for a in data.domain.attributes])))
if feature:
return weights[0]
return weights
def score_data(self, data, feature):
if len(data.domain.class_vars) != 1:
raise ValueError("RReliefF requires one single class")
if not data.domain.class_var.is_continuous:
raise ValueError("RReliefF supports regression; use ReliefF "
"for classification")
if isinstance(self.random_state, np.random.RandomState):
rstate = self.random_state
else:
rstate = np.random.RandomState(self.random_state)
from Orange.preprocess._relieff import rrelieff
weights = np.asarray(
rrelieff(
data.X,
data.Y,
self.n_iterations,
self.k_nearest,
np.array([a.is_discrete for a in data.domain.attributes]),
rstate,
))
if feature:
return weights[0]
return weights
