File / Domain Editor: Construct domains with unique names

Author: janezd

Orange/widgets/data/owfile.py

@@ -124,16 +124,16 @@ class Warning(widget.OWWidget.Warning):
         file_too_big = Msg("The file is too large to load automatically."
                            " Press Reload to load.")
         load_warning = Msg("Read warning:\n{}")
-        performance_warning = widget.Msg(
+        performance_warning = Msg(
             "Categorical variables with >100 values may decrease performance.")
         renamed_vars = Msg("Some variables have been renamed "
                            "to avoid duplicates.\n{}")
 
     class Error(widget.OWWidget.Error):
-        file_not_found = widget.Msg("File not found.")
-        missing_reader = widget.Msg("Missing reader.")
-        sheet_error = widget.Msg("Error listing available sheets.")
-        unknown = widget.Msg("Read error:\n{}")
+        file_not_found = Msg("File not found.")
+        missing_reader = Msg("Missing reader.")
+        sheet_error = Msg("Error listing available sheets.")
+        unknown = Msg("Read error:\n{}")
 
     class NoFileSelected:
         pass
@@ -484,7 +484,9 @@ def apply_domain_edit(self):
         if self.data is None:
             table = None
         else:
-            domain, cols = self.domain_editor.get_domain(self.data.domain, self.data)
+            domain, cols, renamed = \
+                self.domain_editor.get_domain(self.data.domain, self.data,
+                                              deduplicate=True)
             if not (domain.variables or domain.metas):
                 table = None
             elif domain is self.data.domain:
@@ -496,8 +498,8 @@ def apply_domain_edit(self):
                 table.ids = np.array(self.data.ids)
                 table.attributes = getattr(self.data, 'attributes', {})
                 self._inspect_discrete_variables(domain)
-        if self.domain_editor.renamed_variables:
-            self.Warning.renamed_vars(', '.join(self.domain_editor.renamed_variables))
+            if renamed:
+                self.Warning.renamed_vars(', '.join(renamed))
 
         self.Outputs.data.send(table)
         self.apply_button.setEnabled(False)

Orange/widgets/utils/domaineditor.py

@@ -220,8 +220,6 @@ def __init__(self, widget):
         self.place_delegate = PlaceDelegate(self, VarTableModel.places)
         self.setItemDelegateForColumn(Column.place, self.place_delegate)
 
-        self.renamed_variables = []
-
     @staticmethod
     def _is_missing(x):
         return str(x) in ("nan", "")
@@ -253,21 +251,27 @@ def _merge(cols, force_dense=False):
         sparse_cols = [c if sp.issparse(c) else sp.csc_matrix(c) for c in cols]
         return sp.hstack(sparse_cols).tocsr()
 
-    def get_domain(self, domain, data):
-        """Create domain (and dataset) from changes made in the widget.
-
-        Parameters
-        ----------
-        domain : old domain
-        data : source data
+    def get_domain(self, domain, data, deduplicate=False):
+        """
+        Create domain (and dataset) from changes made in the widget.
 
         Returns
         -------
-        (new_domain, [attribute_columns, class_var_columns, meta_columns])
+
+        Args:
+            domain (Domain): original domain
+            data (Table): original data
+            deduplicate (bool): if True, variable names are deduplicated and
+               the result contains an additional list with names of renamed
+               variables
+
+        Returns:
+            (new_domain, [attribute_columns, class_var_columns, meta_columns])
+            or
+            (new_domain, [attribute_columns, class_var_columns, meta_columns], renamed)
         """
         # Allow type-checking with type() instead of isinstance() for exact comparison
         # pylint: disable=unidiomatic-typecheck
-        self.renamed_variables = []
         variables = self.model().variables
         places = [[], [], []]  # attributes, class_vars, metas
         cols = [[], [], []]  # Xcols, Ycols, Mcols
@@ -286,15 +290,17 @@ def numbers_are_round(var, col_data):
                    chain(((at, Place.feature) for at in domain.attributes),
                          ((cl, Place.class_var) for cl in domain.class_vars),
                          ((mt, Place.meta) for mt in domain.metas)))):
-            return domain, [data.X, data.Y, data.metas]
-
-        unique_names = get_unique_names_duplicates([var[0] for  var in variables])
-        for var, u in zip(variables, unique_names):
-            if var[0] != u:
-                self.renamed_variables.append(var[0])
-                var[0] = u
-        self.model().set_variables(variables)
+            if deduplicate:
+                return domain, [data.X, data.Y, data.metas], []
+            else:
+                return domain, [data.X, data.Y, data.metas]
 
+        relevant_names = [var[0] for var in variables if var[2] != Place.skip]
+        if deduplicate:
+            renamed_iter = iter(get_unique_names_duplicates(relevant_names))
+        else:
+            renamed_iter = iter(relevant_names)
+        renamed = []
         for (name, tpe, place, _, may_be_numeric), (orig_var, orig_plc) in \
                 zip(variables,
                         chain([(at, Place.feature) for at in domain.attributes],
@@ -303,24 +309,28 @@ def numbers_are_round(var, col_data):
             if place == Place.skip:
                 continue
 
+            new_name = next(renamed_iter)
+            if new_name != name:
+                renamed.append(name)
+
             col_data = self._get_column(data, orig_var, orig_plc)
             is_sparse = sp.issparse(col_data)
 
-            if name == orig_var.name and tpe == type(orig_var):
+            if new_name == orig_var.name and tpe == type(orig_var):
                 var = orig_var
             elif tpe == type(orig_var):
-                var = orig_var.copy(name=name)
+                var = orig_var.copy(name=new_name)
             elif tpe == DiscreteVariable:
                 values = list(str(i) for i in unique(col_data) if not self._is_missing(i))
                 round_numbers = numbers_are_round(orig_var, col_data)
                 col_data = [np.nan if self._is_missing(x) else values.index(str(x))
                             for x in self._iter_vals(col_data)]
                 if round_numbers:
                     values = [str(int(float(v))) for v in values]
-                var = tpe(name, values)
+                var = tpe(new_name, values)
                 col_data = self._to_column(col_data, is_sparse)
             elif tpe == StringVariable:
-                var = tpe.make(name)
+                var = tpe.make(new_name)
                 if type(orig_var) in [DiscreteVariable, TimeVariable]:
                     col_data = [orig_var.repr_val(x) if not np.isnan(x) else ""
                                 for x in self._iter_vals(col_data)]
@@ -334,13 +344,13 @@ def numbers_are_round(var, col_data):
                 # in metas which are transformed to dense below
                 col_data = self._to_column(col_data, False, dtype=object)
             elif tpe == ContinuousVariable and type(orig_var) == DiscreteVariable:
-                var = tpe.make(name)
+                var = tpe.make(new_name)
                 if may_be_numeric:
                     col_data = [np.nan if self._is_missing(x) else float(orig_var.values[int(x)])
                                 for x in self._iter_vals(col_data)]
                 col_data = self._to_column(col_data, is_sparse)
             else:
-                var = tpe(name)
+                var = tpe(new_name)
             places[place].append(var)
             cols[place].append(col_data)
 
@@ -350,7 +360,10 @@ def numbers_are_round(var, col_data):
         Y = self._merge(cols[Place.class_var], force_dense=True)
         m = self._merge(cols[Place.meta], force_dense=True)
         domain = Domain(*places)
-        return domain, [X, Y, m]
+        if deduplicate:
+            return domain, [X, Y, m], renamed
+        else:
+            return domain, [X, Y, m]
 
     def _get_column(self, data, source_var, source_place):
         """ Extract column from data and preserve sparsity. """