def __call__(self, data):
"""
Apply randomization of the given data. Returns a new
data table.
Parameters
----------
data : Orange.data.Table
A data table to be randomized.
Returns
-------
data : Orange.data.Table
Randomized data table.
"""
new_data = Table(data)
new_data.ensure_copy()
if self.rand_type & Randomize.RandomizeClasses:
new_data.Y = self.randomize(new_data.Y)
if self.rand_type & Randomize.RandomizeAttributes:
new_data.X = self.randomize(new_data.X)
if self.rand_type & Randomize.RandomizeMetas:
new_data.metas = self.randomize(new_data.metas)
return new_data
def test_Y_setter_2d_single_instance(self):
iris = Table('iris')[:1]
# Convert iris.Y to (1, 1) shape
new_y = iris.Y[:, np.newaxis]
iris.Y = np.hstack((new_y, new_y))
iris.Y = csr_matrix(iris.Y)
# We expect the Y shape to match the X shape, which is (1, 4) in iris
self.assertEqual(iris.Y.shape, (1, 2))
def test_Y_setter_2d_single_instance(self):
iris = Table('iris')[:1]
# Convert iris.Y to (1, 1) shape
new_y = iris.Y[:, np.newaxis]
iris.Y = np.hstack((new_y, new_y))
iris.Y = csr_matrix(iris.Y)
# We expect the Y shape to match the X shape, which is (1, 4) in iris
self.assertEqual(iris.Y.shape, (1, 2))
def test_Y_setter_2d(self):
iris = Table("iris")
assert iris.Y.shape == (150, )
# Convert iris.Y to (150, 1) shape
new_y = iris.Y[:, np.newaxis]
iris.Y = np.hstack((new_y, new_y))
iris.Y = csr_matrix(iris.Y)
# We expect the Y shape to match the X shape, which is (150, 4) in iris
self.assertEqual(iris.Y.shape, (150, 2))
def test_Y_setter_1d(self):
iris = Table('iris')
assert iris.Y.shape == (150, )
with iris.unlocked():
iris.Y = csr_matrix(iris.Y)
# We expect the Y shape to match the X shape, which is (150, 4) in iris
self.assertEqual(iris.Y.shape, (150, ))
def prepare_data():
data = Table("iris")
values = list(range(15))
class_var = DiscreteVariable("iris5", values=[str(v) for v in values])
data = data.transform(Domain(attributes=data.domain.attributes, class_vars=[class_var]))
data.Y = np.array(values * 10, dtype=float)
return data
def prepare_data():
data = Table("iris")
values = list(range(15))
class_var = DiscreteVariable("iris5", values=[str(v) for v in values])
data = data.transform(Domain(attributes=data.domain.attributes, class_vars=[class_var]))
data.Y = np.array(values * 10, dtype=float)
return data
def test_sparse(self):
"""
Test sparse data.
GH-2152
GH-2157
"""
table = Table("iris")
table.X = sp.csr_matrix(table.X)
self.assertTrue(sp.issparse(table.X))
table.Y = sp.csr_matrix(table._Y) # pylint: disable=protected-access
self.assertTrue(sp.issparse(table.Y))
self.send_signal("Data", table)
self.widget.set_subset_data(table[:30])
data = self.get_output("Data")
self.assertTrue(data.is_sparse())
self.assertEqual(len(data.domain), 5)
def test_Y_setter_1d(self):
iris = Table('iris')
assert iris.Y.shape == (150,)
iris.Y = csr_matrix(iris.Y)
# We expect the Y shape to match the X shape, which is (150, 4) in iris
self.assertEqual(iris.Y.shape, (150, 1))
def test_Y_setter_1d(self):
iris = Table("iris")
assert iris.Y.shape == (150, )
iris.Y = csr_matrix(iris.Y)
# We expect the Y shape to match the X shape, which is (150, 4) in iris
self.assertEqual(iris.Y.shape, (150, 1))
def test_sparse(self):
"""
Test sparse data.
GH-2152
GH-2157
"""
table = Table("iris")
table.X = sp.csr_matrix(table.X)
self.assertTrue(sp.issparse(table.X))
table.Y = sp.csr_matrix(table._Y) # pylint: disable=protected-access
self.assertTrue(sp.issparse(table.Y))
self.send_signal(self.widget.Inputs.data, table)
self.widget.set_subset_data(table[:30])
data = self.get_output("Data")
self.assertTrue(data.is_sparse())
self.assertEqual(len(data.domain), 5)