Merge pull request #6805 from VesnaT/pls_names

PLS: Rename loadings, change U/T order
biolab · May 17, 2024 · 38c7b96 · 38c7b96
2 parents ccc6cc3 + b798933
commit 38c7b96
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Showing 3 changed files with 10 additions and 11 deletions.
diff --git a/Orange/regression/pls.py b/Orange/regression/pls.py
@@ -122,7 +122,6 @@ def trvar(i, name):
         domain = Domain(
             [trvar(i, var_names_X[i]) for i in range(n_components)],
             data.domain.class_vars,
-            list(data.domain.metas) +
             [trvar(n_components + i, var_names_Y[i]) for i in
              range(n_components)]
         )

diff --git a/Orange/widgets/model/owpls.py b/Orange/widgets/model/owpls.py
@@ -71,7 +71,7 @@ def _create_output_coeffs_loadings(self) -> Table:
         n_components = x_rotations.shape[1]
 
         names = [f"coef ({v.name})" for v in self.model.domain.class_vars]
-        names += [f"Loading {i + 1}" for i in range(n_components)]
+        names += [f"w*c {i + 1}" for i in range(n_components)]
         domain = Domain(
             [ContinuousVariable(n) for n in names],
             metas=[StringVariable("Variable name"),
@@ -98,7 +98,7 @@ def _create_output_data(self) -> Table:
         proj_domain = projection.domain
         nprobs_domain = normal_probs.domain
         dmodx_domain = dmodx.domain
-        metas = proj_domain.metas + proj_domain.attributes + \
+        metas = data_domain.metas + proj_domain.attributes + proj_domain.metas + \
             nprobs_domain.attributes + dmodx_domain.attributes
         domain = Domain(data_domain.attributes, data_domain.class_vars, metas)
         data: Table = self.data.transform(domain)

diff --git a/Orange/widgets/model/tests/test_owpls.py b/Orange/widgets/model/tests/test_owpls.py
@@ -38,7 +38,7 @@ def test_output_coefsdata(self):
         self.assertEqual(coefsdata.Y.shape, (14, 0))
         self.assertEqual(coefsdata.metas.shape, (14, 2))
 
-        self.assertEqual(["coef (MEDV)", "Loading 1", "Loading 2"],
+        self.assertEqual(["coef (MEDV)", "w*c 1", "w*c 2"],
                          [v.name for v in coefsdata.domain.attributes])
         self.assertEqual(["Variable name", "Variable role"],
                          [v.name for v in coefsdata.domain.metas])
@@ -57,7 +57,7 @@ def test_output_coefsdata_multi_target(self):
         self.assertEqual(coefsdata.Y.shape, (14, 0))
         self.assertEqual(coefsdata.metas.shape, (14, 2))
 
-        attr_names = ["coef (MEDV)", "coef (CRIM)", "Loading 1", "Loading 2"]
+        attr_names = ["coef (MEDV)", "coef (CRIM)", "w*c 1", "w*c 2"]
         self.assertEqual(attr_names,
                          [v.name for v in coefsdata.domain.attributes])
         self.assertEqual(["Variable name", "Variable role"],
@@ -78,7 +78,7 @@ def test_output_data(self):
         self.assertEqual(output.metas.shape, (506, 8))
         self.assertEqual([v.name for v in self._data.domain.variables],
                          [v.name for v in output.domain.variables])
-        metas = ["PLS U1", "PLS U2", "PLS T1", "PLS T2",
+        metas = ["PLS T1", "PLS T2", "PLS U1", "PLS U2",
                  "Sample Quantiles (MEDV)", "Theoretical Quantiles (MEDV)",
                  "DModX"]
         self.assertEqual([v.name for v in self._data.domain.metas] + metas,
@@ -93,7 +93,7 @@ def test_output_data_multi_target(self):
         orig_domain = self._data_multi_target.domain
         self.assertEqual([v.name for v in orig_domain.variables],
                          [v.name for v in output.domain.variables])
-        metas = ["PLS U1", "PLS U2", "PLS T1", "PLS T2",
+        metas = ["PLS T1", "PLS T2", "PLS U1", "PLS U2",
                  "Sample Quantiles (MEDV)", "Theoretical Quantiles (MEDV)",
                  "Sample Quantiles (CRIM)", "Theoretical Quantiles (CRIM)",
                  "DModX"]
@@ -119,10 +119,10 @@ def test_missing_target(self):
         data.Y[[0, 4]] = np.nan
         self.send_signal(self.widget.Inputs.data, data)
         output = self.get_output(self.widget.Outputs.data)
-        self.assertFalse(np.isnan(output.metas[:, 3:].astype(float)).any())
-        self.assertTrue(np.isnan(output.metas[0, 1:3].astype(float)).all())
-        self.assertTrue(np.isnan(output.metas[4, 1:3].astype(float)).all())
-        self.assertFalse(np.isnan(output.metas[1:4, 1:3].astype(float)).any())
+        self.assertFalse(np.isnan(output.metas[:, 1:3].astype(float)).any())
+        self.assertTrue(np.isnan(output.metas[0, 3:4].astype(float)).all())
+        self.assertTrue(np.isnan(output.metas[4, 3:5].astype(float)).all())
+        self.assertFalse(np.isnan(output.metas[1:4, 3:5].astype(float)).any())
 
         with data.unlocked(data.Y):
             data.Y[:] = np.nan