def commit(self):
if self.data is None or self.cont_data is None:
self.Outputs.data.send(self.data)
self.Outputs.features.send(None)
self.Outputs.correlations.send(None)
return
attrs = [ContinuousVariable("Correlation"), ContinuousVariable("FDR")]
metas = [StringVariable("Feature 1"), StringVariable("Feature 2")]
domain = Domain(attrs, metas=metas)
model = self.vizrank.rank_model
x = np.array([[
float(model.data(model.index(row, 0), role))
for role in (Qt.DisplayRole, CorrelationRank.PValRole)
] for row in range(model.rowCount())])
x[:, 1] = FDR(list(x[:, 1]))
# pylint: disable=protected-access
m = np.array([[
a.name
for a in model.data(model.index(row, 0), CorrelationRank._AttrRole)
] for row in range(model.rowCount())],
dtype=object)
corr_table = Table(domain, x, metas=m)
corr_table.name = "Correlations"
self.Outputs.data.send(self.data)
# data has been imputed; send original attributes
self.Outputs.features.send(
AttributeList(
[self.data.domain[name] for name, _ in self.selection]))
self.Outputs.correlations.send(corr_table)
def test_FDR_list(self):
p_values = [0.0002, 0.0004, 0.00001, 0.0003, 0.0001]
result = FDR(p_values)
self.assertIsInstance(result, list)
np.testing.assert_almost_equal(
np.array([0.00033, 0.0004, 0.00005, 0.00038, 0.00025]),
result, decimal=5)
def run(selected_data_transformed: Table, data: Table, result: Result,
state: TaskState) -> None:
state.set_status("Listing words")
result.words = [
i.name for i in selected_data_transformed.domain.attributes
]
state.set_status("Computing p-values")
result.p_values = hypergeom_p_values(data.X,
selected_data_transformed.X,
callback=state.set_progress_value)
state.set_status("Computing FDR values")
result.fdr_values = FDR(result.p_values)
def apply(self):
self.clear()
self.progressBarInit()
self.filter_enabled(False)
self.words = [
i.name for i in self.selected_data_transformed.domain.attributes
]
self.p_values = hypergeom_p_values(self.data.X,
self.selected_data_transformed.X,
callback=self.progress)
self.fdr_values = FDR(self.p_values)
self.filter_and_display()
self.filter_enabled(True)
self.progressBarFinished()
def test_FDR_no_values(self):
self.assertIsNone(FDR(None))
self.assertIsNone(FDR([]))
self.assertIsNone(FDR([0.0002, 0.0004], m=0))
def test_FDR_m(self):
p_values = np.array([0.0002, 0.0004, 0.00001, 0.0003, 0.0001])
np.testing.assert_almost_equal(np.array(
[0.0002, 0.00024, 0.00003, 0.000225, 0.00015]),
FDR(p_values, m=3),
decimal=5)
def test_FDR_dependent(self):
p_values = np.array([0.0002, 0.0004, 0.00001, 0.0003, 0.0001])
np.testing.assert_almost_equal(np.array(
[0.00076, 0.00091, 0.00011, 0.00086, 0.00057]),
FDR(p_values, dependent=True),
decimal=5)
def test_FDR(self):
p_values = np.array([0.0002, 0.0004, 0.00001, 0.0003, 0.0001])
np.testing.assert_almost_equal(np.array(
[0.00033, 0.0004, 0.00005, 0.00038, 0.00025]),
FDR(p_values),
decimal=5)