def test_z_score(self):
domain = Domain([ContinuousVariable('A'), ContinuousVariable('B')])
table = Table.from_list(domain, [[1, 2], [3, 4]])
_table = ZScore(axis=0)(table)
np.testing.assert_array_almost_equal([[-1, -1], [1, 1]], _table.X)
_table = ZScore(axis=1)(table)
np.testing.assert_array_almost_equal([[-1, 1], [-1, 1]], _table.X)
def test_quantile_normalization(self):
domain = Domain(
[ContinuousVariable('A'), ContinuousVariable('B'), ContinuousVariable('C'), ContinuousVariable('D')]
)
table = Table.from_list(domain, [[5, 2, 3, 4], [4, 1, 4, 2], [3, 4, 6, 8]])
_table = QuantileNormalization()(table)
# expected result
result = np.array([[5.66666667, 2, 3, 4.66666667], [5.166667, 2, 5.166667, 3], [2, 3, 4.66666667, 5.66666667]])
np.testing.assert_array_almost_equal(result, _table.X)
def setUp(self) -> None:
domain = Domain(
[],
metas=[
StringVariable('Subject'),
ContinuousVariable('Time'),
DiscreteVariable('Event', values=('no', 'yes')),
DiscreteVariable('Group', values=('group1', 'group2')),
],
)
test_data = Table.from_list(
domain,
[
['B', 1, 1, 0],
['E', 2, 1, 0],
['F', 3, 1, 0],
['A', 4, 1, 0],
['D', 4.5, 1, 0],
['C', 5, 0, 0],
['U', 0.5, 1, 1],
['Z', 0.75, 1, 1],
['W', 1, 1, 1],
['V', 1.5, 0, 1],
['X', 2, 1, 1],
['Y', 3.5, 1, 1],
],
)
self.widget = self.create_widget(OWKaplanMeier)
self.send_signal(self.widget.Inputs.data, test_data)
self.widget.time_var = self.widget.data.domain['Time']
self.widget.event_var = self.widget.data.domain['Event']
self.widget.on_controls_changed()
# If we don't do this function ViewBox.mapSceneToView fails with num py.linalg.LinAlgError: Singular matrix
vb = self.widget.graph.getViewBox()
vb.resize(200, 200)
def test_log2_normalization(self):
domain = Domain([ContinuousVariable('A'), ContinuousVariable('B')])
table = Table.from_list(domain, [[0, 1], [3, 7], [15, 31]])
_table = LogarithmicScale()(table)
np.testing.assert_array_almost_equal([[0, 1], [2, 3], [4, 5]], _table.X)
