[ENH] Table.transpose: Use heuristic to guess data type of attributes of attributes

Orange/data/io.py

@@ -101,6 +101,80 @@ def _from_file(f):
         return _from_file(filename)
 
 
+def guess_data_type(orig_values):
+    """
+    Use heuristics to guess data type.
+    """
+    valuemap, values = [], orig_values
+    is_discrete = is_discrete_values(orig_values)
+    if is_discrete:
+        valuemap = sorted(is_discrete)
+        coltype = DiscreteVariable
+    else:
+        try:
+            values = [float(i) for i in orig_values]
+        except ValueError:
+            tvar = TimeVariable('_')
+            try:
+                values = [tvar.parse(i) for i in orig_values]
+            except ValueError:
+                coltype = StringVariable
+            else:
+                coltype = TimeVariable
+        else:
+            coltype = ContinuousVariable
+    return valuemap, values, coltype
+
+
+def sanitize_variable(valuemap, values, orig_values, coltype, coltype_kwargs,
+                      domain_vars, existing_var, new_var_name, data=None):
+    if valuemap:
+        # Map discrete data to ints
+        def valuemap_index(val):
+            try:
+                return valuemap.index(val)
+            except ValueError:
+                return np.nan
+
+        values = np.vectorize(valuemap_index, otypes=[float])(orig_values)
+        coltype_kwargs.update(values=valuemap)
+
+    if coltype is StringVariable:
+        values = ['' if i is np.nan else i for i in orig_values]
+
+    var = None
+    if domain_vars is not None:
+        if existing_var:
+            # Use existing variable if available
+            var = coltype.make(existing_var.strip(), **coltype_kwargs)
+        else:
+            # Never use existing for un-named variables
+            var = coltype(new_var_name, **coltype_kwargs)
+
+        # Reorder discrete values to match existing variable
+        if var.is_discrete and not var.ordered:
+            new_order, old_order = var.values, coltype_kwargs.get('values',
+                                                                  var.values)
+            if new_order != old_order:
+                offset = len(new_order)
+                column = values if data.ndim > 1 else data
+                column += offset
+                for i, val in enumerate(var.values):
+                    try:
+                        oldval = old_order.index(val)
+                    except ValueError:
+                        continue
+                    bn.replace(column, offset + oldval, new_order.index(val))
+
+    if isinstance(var, TimeVariable) or coltype is TimeVariable:
+        # Re-parse the values because only now after coltype.make call
+        # above, variable var is the correct one
+        _var = var if isinstance(var, TimeVariable) else TimeVariable('_')
+        values = [_var.parse(i) for i in orig_values]
+
+    return values, var
+
+
 class Flags:
     """Parser for column flags (i.e. third header row)"""
     DELIMITER = ' '
@@ -522,6 +596,7 @@ def _equal_length(lst):
 
             elif (type_flag in DiscreteVariable.TYPE_HEADERS or
                   _RE_DISCRETE_LIST.match(type_flag)):
+                coltype = DiscreteVariable
                 if _RE_DISCRETE_LIST.match(type_flag):
                     valuemap = Flags.split(type_flag)
                     coltype_kwargs.update(ordered=True)
@@ -530,38 +605,7 @@ def _equal_length(lst):
 
             else:
                 # No known type specified, use heuristics
-                is_discrete = is_discrete_values(orig_values)
-                if is_discrete:
-                    valuemap = sorted(is_discrete)
-                else:
-                    try:
-                        values = [float(i) for i in orig_values]
-                    except ValueError:
-                        tvar = TimeVariable('_')
-                        try:
-                            values = [tvar.parse(i) for i in orig_values]
-                        except ValueError:
-                            coltype = StringVariable
-                        else:
-                            coltype = TimeVariable
-                    else:
-                        coltype = ContinuousVariable
-
-            if valuemap:
-                # Map discrete data to ints
-                def valuemap_index(val):
-                    try:
-                        return valuemap.index(val)
-                    except ValueError:
-                        return np.nan
-
-                values = np.vectorize(valuemap_index, otypes=[float])(orig_values)
-                coltype = DiscreteVariable
-                coltype_kwargs.update(values=valuemap)
-
-            if coltype is StringVariable:
-                values = ['' if i is np.nan else i
-                          for i in orig_values]
+                valuemap, values, coltype = guess_data_type(orig_values)
 
             if flag.m or coltype is StringVariable:
                 append_to = (Mcols, metas)
@@ -574,37 +618,20 @@ def valuemap_index(val):
 
             cols, domain_vars = append_to
             cols.append(col)
-            var = None
+
+            existing_var, new_var_name, column = None, None, None
+            if domain_vars is not None:
+                existing_var = names and names[col]
+                if not existing_var:
+                    new_var_name = next(NAMEGEN)
+
+            values, var = sanitize_variable(
+                valuemap, values, orig_values, coltype, coltype_kwargs,
+                domain_vars, existing_var, new_var_name, data)
             if domain_vars is not None:
-                if names and names[col]:
-                    # Use existing variable if available
-                    var = coltype.make(names[col].strip(), **coltype_kwargs)
-                else:
-                    # Never use existing for un-named variables
-                    var = coltype(next(NAMEGEN), **coltype_kwargs)
                 var.attributes.update(flag.attributes)
                 domain_vars.append(var)
 
-                # Reorder discrete values to match existing variable
-                if var.is_discrete and not var.ordered:
-                    new_order, old_order = var.values, coltype_kwargs.get('values', var.values)
-                    if new_order != old_order:
-                        offset = len(new_order)
-                        column = values if data.ndim > 1 else data
-                        column += offset
-                        for i, val in enumerate(var.values):
-                            try:
-                                oldval = old_order.index(val)
-                            except ValueError:
-                                continue
-                            bn.replace(column, offset + oldval, new_order.index(val))
-
-            if isinstance(var, TimeVariable) or coltype is TimeVariable:
-                # Re-parse the values because only now after coltype.make call
-                # above, variable var is the correct one
-                _var = var if isinstance(var, TimeVariable) else TimeVariable('_')
-                values = [_var.parse(i) for i in orig_values]
-
             # Write back the changed data. This is needeed to pass the
             # correct, converted values into Table.from_numpy below
             try:

Orange/data/table.py

@@ -13,6 +13,7 @@
 import bottleneck as bn
 from scipy import sparse as sp
 
+import Orange.data  # import for io.py
 from Orange.data import (
     _contingency, _valuecount,
     Domain, Variable, Storage, StringVariable, Unknown, Value, Instance,
@@ -1453,6 +1454,7 @@ def transpose(cls, table, feature_names_column="",
             feature names are mapped
         :return: Table - transposed table
         """
+
         self = cls()
         n_cols, self.n_rows = table.X.shape
         old_domain = table.attributes.get("old_domain")
@@ -1521,10 +1523,22 @@ def get_table_from_attributes_of_attributes(_vars, _dtype=float):
         names = chain.from_iterable(list(attr.attributes)
                                     for attr in table.domain.attributes)
         names = sorted(set(names) - {var.name for var in class_vars})
+
+        def guessed_var(i, var_name):
+            orig_vals = M[:, i]
+            val_map, vals, var_type = Orange.data.io.guess_data_type(orig_vals)
+            values, variable = Orange.data.io.sanitize_variable(
+                val_map, vals, orig_vals, var_type,
+                {}, _metas, None, var_name)
+            M[:, i] = values
+            return variable
+
         _metas = [StringVariable(n) for n in names]
         if old_domain:
             _metas = [m for m in old_domain.metas if m.name != meta_attr_name]
         M = get_table_from_attributes_of_attributes(_metas, _dtype=object)
+        if not old_domain:
+            _metas = [guessed_var(i, m.name) for i, m in enumerate(_metas)]
         if _metas:
             self.metas = np.hstack((self.metas, M))
             metas.extend(_metas)

Orange/tests/test_table.py

@@ -2342,7 +2342,7 @@ def test_transpose_continuous_metas(self):
         metas = [ContinuousVariable("m1")]
         domain = Domain(attrs, metas=metas)
         X = np.arange(8).reshape((4, 2))
-        M = np.array([0, 1, 0, 1])[:, None]
+        M = np.array([0.0, 1.0, 0.0, 1.0])[:, None]
         data = Table(domain, X, metas=M)
 
         att = [ContinuousVariable("Feature 1"), ContinuousVariable("Feature 2"),
@@ -2449,7 +2449,7 @@ def test_transpose_class_and_metas(self):
         # original should not change
         self.assertDictEqual(data.domain.attributes[0].attributes, {})
 
-    def test_transpose_attributes_of_attributes(self):
+    def test_transpose_attributes_of_attributes_discrete(self):
         attrs = [ContinuousVariable("c1"), ContinuousVariable("c2")]
         attrs[0].attributes = {"attr1": "a", "attr2": "aa"}
         attrs[1].attributes = {"attr1": "b", "attr2": "bb"}
@@ -2458,11 +2458,12 @@ def test_transpose_attributes_of_attributes(self):
 
         att = [ContinuousVariable("Feature 1"), ContinuousVariable("Feature 2"),
                ContinuousVariable("Feature 3"), ContinuousVariable("Feature 4")]
-        metas = [StringVariable("Feature name"), StringVariable("attr1"),
-                 StringVariable("attr2")]
+        metas = [StringVariable("Feature name"),
+                 DiscreteVariable("attr1", values=("a", "b")),
+                 DiscreteVariable("attr2", values=("aa", "bb"))]
         domain = Domain(att, metas=metas)
-        result = Table(domain, np.arange(8).reshape((4, 2)).T,
-                       metas=np.array([["c1", "a", "aa"], ["c2", "b", "bb"]]))
+        M = np.array([["c1", 0.0, 0.0], ["c2", 1.0, 1.0]], dtype=object)
+        result = Table(domain, np.arange(8).reshape((4, 2)).T, metas=M)
 
         # transpose and compare
         self._compare_tables(result, Table.transpose(data))
@@ -2475,6 +2476,33 @@ def test_transpose_attributes_of_attributes(self):
         self.assertDictEqual(data.domain.attributes[0].attributes,
                              {"attr1": "a", "attr2": "aa"})
 
+    def test_transpose_attributes_of_attributes_continuous(self):
+        attrs = [ContinuousVariable("c1"), ContinuousVariable("c2")]
+        attrs[0].attributes = {"attr1": "1.100", "attr2": "1.300"}
+        attrs[1].attributes = {"attr1": "2.200", "attr2": "2.300"}
+        domain = Domain(attrs)
+        data = Table(domain, np.arange(8).reshape((4, 2)))
+
+        att = [ContinuousVariable("Feature 1"), ContinuousVariable("Feature 2"),
+               ContinuousVariable("Feature 3"), ContinuousVariable("Feature 4")]
+        metas = [StringVariable("Feature name"), ContinuousVariable("attr1"),
+                 ContinuousVariable("attr2")]
+        domain = Domain(att, metas=metas)
+        result = Table(domain, np.arange(8).reshape((4, 2)).T,
+                       metas=np.array([["c1", 1.1, 1.3],
+                                       ["c2", 2.2, 2.3]], dtype=object))
+
+        # transpose and compare
+        self._compare_tables(result, Table.transpose(data))
+
+        # transpose of transpose
+        t = Table.transpose(Table.transpose(data), "Feature name")
+        self._compare_tables(data, t)
+
+        # original should not change
+        self.assertDictEqual(data.domain.attributes[0].attributes,
+                             {"attr1": "1.100", "attr2": "1.300"})
+
     def test_transpose_attributes_of_attributes_missings(self):
         attrs = [ContinuousVariable("c1"), ContinuousVariable("c2")]
         attrs[0].attributes = {"attr1": "a", "attr2": "aa"}
@@ -2484,11 +2512,12 @@ def test_transpose_attributes_of_attributes_missings(self):
 
         att = [ContinuousVariable("Feature 1"), ContinuousVariable("Feature 2"),
                ContinuousVariable("Feature 3"), ContinuousVariable("Feature 4")]
-        metas = [StringVariable("Feature name"), StringVariable("attr1"),
-                 StringVariable("attr2")]
+        metas = [StringVariable("Feature name"),
+                 DiscreteVariable("attr1", values=("a", "b")),
+                 DiscreteVariable("attr2", values=("aa",))]
         domain = Domain(att, metas=metas)
-        result = Table(domain, np.arange(8).reshape((4, 2)).T,
-                       metas=np.array([["c1", "a", "aa"], ["c2", "b", ""]]))
+        M = np.array([["c1", 0.0, 0.0], ["c2", 1.0, np.nan]], dtype=object)
+        result = Table(domain, np.arange(8).reshape((4, 2)).T, metas=M)
 
         # transpose and compare
         self._compare_tables(result, Table.transpose(data))
@@ -2517,10 +2546,11 @@ def test_transpose_class_metas_attributes(self):
         att[1].attributes = {"cls": "2.000", "m1": "bb", "m2": "bbb"}
         att[2].attributes = {"cls": "3.000", "m1": "cc", "m2": "ccc"}
         att[3].attributes = {"cls": "4.000", "m1": "dd", "m2": "ddd"}
-        metas = [StringVariable("Feature name"), StringVariable("attr1"),
-                 StringVariable("attr2")]
+        metas = [StringVariable("Feature name"),
+                 DiscreteVariable("attr1", values=("a1", "b1")),
+                 DiscreteVariable("attr2", values=("aa1", "bb1"))]
         domain = Domain(att, metas=metas)
-        M = np.array([["c1", "a1", "aa1"], ["c2", "b1", "bb1"]])
+        M = np.array([["c1", 0.0, 0.0], ["c2", 1.0, 1.0]], dtype=object)
         result = Table(domain, np.arange(8).reshape((4, 2)).T, metas=M)
 
         # transpose and compare
@@ -2546,7 +2576,8 @@ def _compare_tables(self, table1, table2):
         self.assertEqual(table1.n_rows, table2.n_rows)
         np.testing.assert_array_equal(table1.X, table2.X)
         np.testing.assert_array_equal(table1.Y, table2.Y)
-        np.testing.assert_array_equal(table1.metas, table2.metas)
+        np.testing.assert_array_equal(table1.metas.astype(str),
+                                      table2.metas.astype(str))
         np.testing.assert_array_equal(table1.W, table2.W)
 
         self.assertEqual([(type(x), x.name, x.attributes)