def test_detection(self, prepare_table):
table = prepare_table(self.table)
preprocessor = DropoutGeneSelection(n_genes=2)
zero_rate, mean_expr = preprocessor.detection(table.X)
self.assertEqual(len(zero_rate), len(table.domain.attributes))
self.assertEqual(len(mean_expr), len(table.domain.attributes))
npt.assert_allclose(
zero_rate[:5],
np.array([0.01092896, 0.02185792, 0.16120219, 0.32240437, 0.61202186]),
atol=1e-7,
)
npt.assert_allclose(
mean_expr[:5],
np.array([0.9879741, 0.77491075, 0.78471751, 0.88894012, 0.58119243]),
atol=1e-7,
)
def __get_selector(self):
kwargs = {"decay": self.decay, "y_offset": self.y_offset}
if self.filter_by_nr_of_genes:
kwargs["n_genes"] = self.n_genes
else:
kwargs["x_offset"] = self.x_offset
return DropoutGeneSelection(**kwargs)
def test_warning(self, prepare_table):
n_genes = 30
table = prepare_table(self.table)
with warnings.catch_warnings(record=True) as w:
filtered_table = DropoutGeneSelection(n_genes)(table)
self.assertIsInstance(filtered_table, Table)
self.assertNotEqual(len(filtered_table.domain.attributes), n_genes)
dropout_warnings = [warning for warning in w if
issubclass(warning.category, DropoutWarning)]
self.assertEqual(len(dropout_warnings), 1)
def createinstance(params):
n_genes = params.get("n_genes", DropoutEditor.DEFAULT_N_GENES)
return DropoutGeneSelection(n_genes)
def test_preserves_density(self, prepare_table):
table = prepare_table(self.table)
is_sparse = table.is_sparse()
filtered_table = DropoutGeneSelection(3)(table)
self.assertEqual(is_sparse, filtered_table.is_sparse())
def test_default(self, prepare_table):
n_genes = 2
table = prepare_table(self.table)
filtered_table = DropoutGeneSelection(n_genes)(table)
self.assertIsInstance(filtered_table, Table)
self.assertEqual(len(filtered_table.domain.attributes), n_genes)
def commit(self):
data = None
if self.selected is not None:
data = DropoutGeneSelection.filter_columns(self.data,
self.selected)
self.Outputs.data.send(data)
def _on_curve(self, x: float, y: float):
if self.__curve is None:
return False
return abs(DropoutGeneSelection.y(
x, self.__decay, self.__x_offset, self.__y_offset) - y) < 0.01