housekeeping

.github/workflows/tests.yml

@@ -21,9 +21,3 @@ jobs:
 
       - name: Measure coverage.
         run: tox run -e coverage
-
-      - name: Upload
-        uses: codecov/codecov-action@v3
-        with:
-          token: ${{ secrets.CODECOV_TOKEN }}
-          files: ./coverage.xml, coverage.xml

.github/workflows/type-lint.yml

@@ -1,4 +1,4 @@
-# .github/workflows/tests.yml
+# .github/workflows/type-lint.yml
 name: Type-lint
 on: push
 jobs:
@@ -7,7 +7,7 @@ jobs:
     steps:
       - uses: actions/checkout@v3
 
-      - name: Setup python 3.10
+      - name: Setup python 3.11
         uses: actions/setup-python@v4
         with:
           cache: 'pip'

.gitignore

@@ -1,10 +1,54 @@
-*/*.egg-info/
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
 *.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
 
 /.idea/
 /.tox/
-/.coverage
 
-.pytest_cache
-*.DS_Store
-__pycache__/
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# IPython
+profile_default/
+ipython_config.py
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/

Makefile

@@ -5,5 +5,4 @@ SHELL = /bin/bash
 .PHONY: style
 style:
 	black .
-	flake8
 	python3 -m isort .

pyproject.toml

@@ -18,6 +18,12 @@ line_length = 79
 multi_line_output = 3
 include_trailing_comma = true
 
+[tool.mypy]
+python_version = "3.11"
+warn_return_any = false
+warn_unused_configs = true
+ignore_errors = true # TODO: change this
+
 # Black formatting
 [tool.black]
 line-length = 120
@@ -43,11 +49,14 @@ testpaths = ["tests"]
 python_files = "test_*.py"
 
 [project.optional-dependencies]
-tests = [
+dev = [ # Install wtih pip install .[dev] or pip install -e '.[dev]' in zsh
     "coverage>=7.2.3",
     "flake8>=6.0.0",
     "pytest<=7.4.4",
     "pytest-cov>=4.0.0",
     "pytest-mock==3.10.0",
-    "pytest_xdist",
+    "pre-commit>=3.2.0",
+    "mypy>=1.8.0",
+    "black[d]>=23.0.0",
+    "isort>=5.0.0",
 ]

src/utils/datasets/group_label_dataset.py

@@ -4,15 +4,6 @@
 class GroupLabelDataset(Dataset):
     class_group_by2 = [[0, 1], [2, 3], [4, 5], [6, 7], [8, 9]]
 
-    @staticmethod
-    def check_class_groups(groups):
-        vals = [[] for _ in range(10)]
-        for g, group in enumerate(groups):
-            for i in group:
-                vals[i].append(g)
-        for v in vals:
-            assert len(v) == 1  # Check that this is the first time i is encountered
-
     def __init__(self, dataset, class_groups=None):
         self.dataset = dataset
         self.class_labels = [i for i in range(len(class_groups))]
@@ -33,3 +24,12 @@ def __getitem__(self, item):
 
     def __len__(self):
         return len(self.dataset)
+
+    @staticmethod
+    def check_class_groups(groups):
+        vals = [[] for _ in range(10)]
+        for g, group in enumerate(groups):
+            for i in group:
+                vals[i].append(g)
+        for v in vals:
+            assert len(v) == 1  # Check that this is the first time i is encountered

src/utils/datasets/mark_dataset.py

@@ -5,18 +5,6 @@
 
 
 class MarkDataset(Dataset):
-    def get_mark_sample_ids(self):
-        indices = []
-        cls = self.cls_to_mark
-        prob = self.mark_prob
-        for i in range(len(self.dataset)):
-            x, y = self.dataset[i]
-            if y == cls:
-                rnd = torch.rand(1)
-                if rnd < prob:
-                    indices.append(i)
-        return torch.tensor(indices, dtype=torch.int)
-
     def __init__(self, dataset, p=0.3, cls_to_mark=2, only_train=False):
         super().__init__()
         self.class_labels = dataset.class_labels
@@ -54,6 +42,18 @@ def __getitem__(self, item):
             else:
                 return x, y
 
+    def get_mark_sample_ids(self):
+        indices = []
+        cls = self.cls_to_mark
+        prob = self.mark_prob
+        for i in range(len(self.dataset)):
+            x, y = self.dataset[i]
+            if y == cls:
+                rnd = torch.rand(1)
+                if rnd < prob:
+                    indices.append(i)
+        return torch.tensor(indices, dtype=torch.int)
+
     def mark_image_contour(self, x):
         x = self.dataset.inverse_transform(x)
         mask = torch.zeros_like(x[0])

tests/utils/conftest.py

@@ -3,7 +3,7 @@
 
 
 @pytest.fixture()
-def dataset():
+def load_dataset():
     x = torch.stack([torch.rand(2, 2), torch.rand(2, 2), torch.rand(2, 2)])
     y = torch.tensor([0, 1, 0]).long()
     return torch.utils.data.TensorDataset(x, y)

tests/utils/test_corrupt_label_dataset.py

@@ -4,6 +4,13 @@
 
 
 @pytest.mark.utils
-def test_corrupt_label_dataset(dataset):
+@pytest.mark.parametrize(
+    "dataset, n_expected",
+    [
+        ("load_dataset", 2),
+    ],
+)
+def test_corrupt_label_dataset(dataset, n_expected, request):
+    dataset = request.getfixturevalue(dataset)
     # cl_dataset = CorruptLabelDataset(dataset)
-    assert 2 == 2
+    assert 2 == n_expected

tox.ini

@@ -33,6 +33,6 @@ description = Run type checking
 base_python = py311
 deps =
     {[testenv]deps}
-    mypy==0.982
+    mypy==1.9.0
 commands =
     python3 -m mypy src