Set label name with parents to avoid duplicates for AstypeAnnotations (…

…#1492) - Except `nan` for label - Set label name with parents to avoid duplicate name for `AstypeAnnotations` - Add unit test when tabular dataset included missing value - Add unit test for AstypeAnnotations when label value is nan
openvinotoolkit · May 9, 2024 · 072c8a8 · 072c8a8
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Showing 6 changed files with 146 additions and 9 deletions.
diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -23,6 +23,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   (<https://github.com/openvinotoolkit/datumaro/pull/1471>)
 - Add ExtractedMask and update importers who can use it to use it
   (<https://github.com/openvinotoolkit/datumaro/pull/1480>)
+- Set label name with parents to avoid duplicates for AstypeAnnotations
+  (<https://github.com/openvinotoolkit/datumaro/pull/1492>)
 
 ### Bug fixes
 - Split the video directory into subsets to avoid overwriting

diff --git a/src/datumaro/plugins/data_formats/tabular.py b/src/datumaro/plugins/data_formats/tabular.py
@@ -115,7 +115,13 @@ def _parse(
                 target_dtype = table.dtype(target_)
                 if target_dtype in [int, float, pd.api.types.CategoricalDtype()]:
                     # 'int' can be categorical, but we don't know this unless user gives information.
-                    labels = set(table.features(target_, unique=True))
+                    labels = set(
+                        [
+                            feature
+                            for feature in table.features(target_, unique=True)
+                            if not pd.isna(feature)
+                        ]
+                    )
                     if category is None:
                         categories.add(target_, target_dtype, labels)
                     else:  # update labels if they are different.

diff --git a/src/datumaro/plugins/transforms.py b/src/datumaro/plugins/transforms.py
@@ -1495,6 +1495,8 @@ def __init__(
     ):
         super().__init__(extractor)
 
+        self._sep_token = ":"
+
         if extractor.media_type() and not issubclass(extractor.media_type(), TableRow):
             raise MediaTypeError(
                 "Media type is not table. This transform only support tabular media"
@@ -1523,6 +1525,7 @@ def __init__(
                 dst_parent = src_cat.name
                 dst_labels = sorted(src_cat.labels)
                 for dst_label in dst_labels:
+                    dst_label = dst_parent + self._sep_token + str(dst_label)
                     dst_index = dst_label_cat.add(dst_label, parent=dst_parent, attributes={})
                     self._id_mapping[dst_label] = dst_index
                 dst_label_cat.add_label_group(src_cat.name, src_cat.labels, group_type=0)
@@ -1533,12 +1536,12 @@ def categories(self):
         return self._categories
 
     def transform_item(self, item: DatasetItem):
-        annotations = []
-        for name, value in item.annotations[0].values.items():
-            dtype = self._tabular_cat_types.get(name, None)
-            if dtype == CategoricalDtype():
-                annotations.append(Label(label=self._id_mapping[value]))
-            else:
-                annotations.append(Caption(value))
+        annotations = [
+            Label(label=self._id_mapping[name + self._sep_token + str(value)])
+            if self._tabular_cat_types.get(name) == CategoricalDtype() and value is not None
+            else Caption(value)
+            for name, value in item.annotations[0].values.items()
+            if value is not None
+        ]
 
         return self.wrap_item(item, annotations=annotations)
diff --git a/tests/assets/tabular_dataset/electricity_missing.csv b/tests/assets/tabular_dataset/electricity_missing.csv
@@ -0,0 +1,25 @@
+date,day,period,nswprice,nswdemand,vicprice,vicdemand,transfer,class
+0.425556,5,0.340426,0.076108,0.392889,0.003467,0.422915,0.414912,UP
+0.425512,4,0.617021,0.060376,0.483041,0.003467,0.422915,0.414912,DOWN
+0.013982,4,0.042553,0.061967,0.521125,0.003467,0.422915,0.414912,DOWN
+0.907349,3,0.06383,0.080581,0.331003,0.00538,0.47566,0.441228,DOWN
+0.889341,0,0.361702,0.027141,0.379649,0.001624,0.248317,0.69386,DOWN
+0.433565,3,0.787234,0.082803,0.447337,0.003467,0.422915,0.414912,UP
+0.894474,4,0.787234,0.088087,0.840672,0.006012,0.752978,0.455702,UP
+0.866997,5,0.446809,0.037739,0.506992,0.002495,0.339202,0.664474,UP
+0.460909,3,0.319149,0.054672,0.585689,0.003741,0.448731,0.389912,DOWN
+0.031857,4,0.255319,0.055242,0.115739,0.003467,0.422915,0.414912,DOWN
+0.876023,5,1.0,0.028822,0.369087,0.001477,0.336872,0.769298,UP
+0.030707,6,0.042553,0.047526,0.132104,0.003467,0.422915,0.414912,DOWN
+0.500111,6,0.914894,0.06617,0.300952,0.00446,0.287416,0.420175,UP
+0.890093,3,0.744681,0.338747,0.960875,0.023332,0.857328,0.325,UP
+0.898544,6,0.531915,0.090068,0.476941,0.005544,0.35448,0.716228,UP
+0.434406,1,0.340426,0.051039,0.518596,0.003467,0.422915,0.414912,UP
+0.881023,0,0.787234,0.029302,0.409104,0.001847,0.418436,0.746053,UP
+0.872174,4,0.148936,0.019125,0.142368,0.000841,0.250388,0.875,DOWN
+0.469094,0,0.680851,0.029152,0.267034,0.001917,0.23796,0.587281,DOWN
+0.871776,2,0.595745,0.0269,0.530348,0.001687,0.682548,0.630702,UP
+0.456086,5,0.93617,0.053591,0.57468,0.003671,0.387364,0.565789,
+0.486837,3,0.446809,0.07959,0.499851,0.005501,0.493009,0.296491,
+0.009513,0,0.170213,0.041341,0.191461,0.003467,0.422915,0.414912,
+0.429052,1,0.659574,0.100546,0.512794,0.003467,0.422915,0.414912,
diff --git a/tests/unit/test_tabular_format.py b/tests/unit/test_tabular_format.py
@@ -28,6 +28,12 @@ def fxt_electricity(fxt_tabular_root):
     yield Dataset.import_from(path, "tabular")
 
 
+@pytest.fixture()
+def fxt_electricity_missing(fxt_tabular_root):
+    path = osp.join(fxt_tabular_root, "electricity_missing.csv")
+    yield Dataset.import_from(path, "tabular", target={"input": "nswprice", "output": "class"})
+
+
 @pytest.fixture()
 def fxt_buddy_target():
     yield {"input": "length(m)", "output": ["breed_category", "pet_category"]}
@@ -178,3 +184,23 @@ def test_target_dtype(self, fxt_tabular_root, target, expected_included_labels)
     )
     def test_string_to_dict(self, input_string, expected_result):
         assert string_to_dict(input_string) == expected_result
+
+    @mark_requirement(Requirements.DATUM_GENERAL_REQ)
+    def test_can_import_tabular_file_with_missing_value(self, fxt_electricity_missing) -> None:
+        import math
+
+        dataset: Type[Dataset] = fxt_electricity_missing
+        expected_categories_keys = [("class", CategoricalDtype())]
+        expected_category_labels = {"UP", "DOWN"}
+
+        result_categories = dataset.categories()[AnnotationType.tabular].items[0]
+        assert [(result_categories.name, result_categories.dtype)] == expected_categories_keys
+        assert len(dataset) == 24
+        assert result_categories.labels == expected_category_labels
+
+        num_nan_annotations = sum(
+            math.isnan(item.annotations[0].values["class"])
+            for item in dataset
+            if isinstance(item.annotations[0].values["class"], float)
+        )
+        assert num_nan_annotations == 4
diff --git a/tests/unit/test_transforms.py b/tests/unit/test_transforms.py
@@ -1271,6 +1271,37 @@ def setUp(self):
             categories={},
             media_type=TableRow,
         )
+        self.table_label_nan = Table.from_list(
+            [{"class": "DOWN"}, {"class": "UP"}, {"class": None}]
+        )
+        self.dataset_label_nan = Dataset.from_iterable(
+            [
+                DatasetItem(
+                    id="0",
+                    subset="train",
+                    media=TableRow(table=self.table_label_nan, index=0),
+                    annotations=[Tabular(values={"class": "DOWN"})],
+                ),
+                DatasetItem(
+                    id="1",
+                    subset="train",
+                    media=TableRow(table=self.table_label_nan, index=1),
+                    annotations=[Tabular(values={"class": "UP"})],
+                ),
+                DatasetItem(
+                    id="2",
+                    subset="train",
+                    media=TableRow(table=self.table_label_nan, index=2),
+                    annotations=[Tabular(values={"class": None})],
+                ),
+            ],
+            categories={
+                AnnotationType.tabular: TabularCategories.from_iterable(
+                    [("class", CategoricalDtype(), {"DOWN", "UP"})]
+                )
+            },
+            media_type=TableRow,
+        )
 
     @mark_requirement(Requirements.DATUM_GENERAL_REQ)
     def test_split_arg_valid(self):
@@ -1325,7 +1356,7 @@ def test_transform_annotation_type_label(self):
             ],
             categories={
                 AnnotationType.label: LabelCategories.from_iterable(
-                    [("DOWN", "class"), ("UP", "class")]
+                    [("class:DOWN", "class"), ("class:UP", "class")]
                 )
             },
             media_type=TableRow,
@@ -1369,3 +1400,47 @@ def test_transform_annotation_type_caption(self):
         result = transforms.AstypeAnnotations(dataset)
 
         compare_datasets(self, expected, result)
+
+    @mark_requirement(Requirements.DATUM_GENERAL_REQ)
+    def test_transform_annotation_type_label_with_nan(self):
+        table = self.table_label_nan
+        expected = Dataset.from_iterable(
+            [
+                DatasetItem(
+                    id="0",
+                    subset="train",
+                    media=TableRow(table=table, index=0),
+                    annotations=[Label(label=0)],
+                ),
+                DatasetItem(
+                    id="1",
+                    subset="train",
+                    media=TableRow(table=table, index=1),
+                    annotations=[Label(label=1)],
+                ),
+                DatasetItem(
+                    id="2",
+                    subset="train",
+                    media=TableRow(table=table, index=2),
+                    annotations=[],
+                ),
+            ],
+            categories={
+                AnnotationType.label: LabelCategories.from_iterable(
+                    [("class:DOWN", "class"), ("class:UP", "class")]
+                )
+            },
+            media_type=TableRow,
+        )
+
+        dataset = self.dataset_label_nan
+        result = transforms.AstypeAnnotations(dataset)
+
+        categories = result._categories.get(AnnotationType.label, None)
+        assert categories
+
+        # Check label_groups of categories
+        assert categories.label_groups[0].name == "class"
+        assert sorted(categories.label_groups[0].labels) == ["DOWN", "UP"]
+
+        compare_datasets(self, expected, result)