Ejemplo n.º 1
0
    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)
Ejemplo n.º 2
0
 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)
Ejemplo n.º 3
0
 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)
Ejemplo n.º 4
0
    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()
Ejemplo n.º 5
0
 def test_FDR_no_values(self):
     self.assertIsNone(FDR(None))
     self.assertIsNone(FDR([]))
     self.assertIsNone(FDR([0.0002, 0.0004], m=0))
Ejemplo n.º 6
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)
Ejemplo n.º 7
0
 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)
Ejemplo n.º 8
0
 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)