Update Test Structure to use uv (#350)

* updates test markers

* updated lock files

* update workflow to check if it works

* update workflow to check if it works

* update workflow to check if it works

* update workflow to check if it works

* update workflow to check if it works

* update --system flag

* update --sync flag

* update tabpfn marker

* updates workflows

* updates workflows

* update caching

* updated cache again

* updated cache again

* test cache again

* update uv.lock

* updated caching according to pyproject.toml and lock

Author: mmschlk

.github/dependabot.yml

@@ -8,14 +8,15 @@ updates:
   - package-ecosystem: "pip"
     directory: "/"
     schedule:
-      interval: "monthly"
+      interval: "weekly"
     open-pull-requests-limit: 12
     groups:
       pip-all-updates:
         patterns:
           - "*"
+
   - package-ecosystem: "github-actions"
     directory: "/"
     schedule:
-        interval: "monthly"
+        interval: "weekly"
     open-pull-requests-limit: 12

.github/workflows/code-quality.yml

@@ -30,8 +30,8 @@ jobs:
 
     - name: Install pre-commit
       run: |
-        uv pip install --system pre-commit ruff
-        pre-commit install
+        uv sync --only-group lint
+        uv run pre-commit install
 
     - name: Run code-quality checks
-      run: SKIP=mypy pre-commit run --all-files
+      run: uv run pre-commit run --all-files

.github/workflows/coverage.yml

@@ -30,15 +30,19 @@ jobs:
       with:
         enable-cache: true
 
+    - name: Cache UV packages
+      uses: actions/cache@v4
+      with:
+        path: ~/.cache/uv
+        key: uv-${{ runner.os }}-${{ matrix.python-version }}-${{ hashFiles('pyproject.toml', 'uv.lock') }}
+        restore-keys: |
+          uv-${{ runner.os }}-${{ matrix.python-version }}-
+
     - name: Install dependencies
-      run: |
-        uv pip install --system --no-deps .
-        uv pip install --system -r requirements.txt
-        uv pip install --system pytest pytest-cov
+      run: uv sync --all-extras --dev
 
     - name: Measure coverage
-      run: |
-        pytest --cov=shapiq --cov-report=xml
+      run: uv run pytest --cov=shapiq --cov-report=xml -n logical
 
     - name: Coveralls
       uses: coverallsapp/github-action@v2

.github/workflows/unit-tests-current.yml

@@ -21,22 +21,18 @@ jobs:
     steps:
     - uses: actions/checkout@v4
 
-    - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v5
-      with:
-        python-version: ${{ matrix.python-version }}
-
-    - name: Install uv
+    - name: Set up Python ${{ matrix.python-version }} and uv
+      id: setup-uv-python
       uses: astral-sh/setup-uv@v5
       with:
+        python-version: ${{ matrix.python-version }}
         enable-cache: true
+        cache-dependency-glob: |
+            **/pyproject.toml
+            **/uv.lock
 
     - name: Install dependencies
-      run: |
-        uv pip install --system --no-deps .
-        uv pip install --system -r tests/requirements/requirements.txt
-        uv pip install --system pytest
+      run: uv sync --all-extras --dev
 
     - name: Test with pytest
-      run: |
-        pytest tests/
+      run: uv run pytest -n logical

.github/workflows/unit-tests.yml

@@ -72,40 +72,22 @@ tracker = "https://github.com/mmschlk/shapiq/issues"
 changelog = "https://github.com/mmschlk/shapiq/blob/main/CHANGELOG.md"
 
 [project.optional-dependencies]
-dev = [
-    # Lint/format
-    "pre-commit",
-    "ruff",
-    # "mypy",  # TODO: incorporate mypy
-    # Test
-    "pytest",
-    "coverage",
-    "build",
-    "twine",
-]
-
-doc = [
-    "sphinx",
-    "sphinx-autodoc-typehints",
-    "sphinx_rtd_theme",
-    "sphinx_toolbox",
-    # "myst_nb",
-    "nbsphinx",  # for rendering jupyter notebooks
-    "pandoc",  # for rendering jupyter notebooks
-    "furo",  # theme of the docs
-    "sphinx-copybutton",  # easier copy-pasting of code snippets from docs
-    "myst-parser",  # parse md and rst files
+ml = [
+    "tabpfn",
+    "torchvision",
+    "torch",
+    "xgboost",
+    "lightgbm",
+    "transformers",
+    "scikit-image",
+    "tensorflow; python_version < '3.13'",
+    "tf-keras; python_version < '3.13'",
 ]
 
 [tool.pytest.ini_options]
 testpaths = ["tests"]
 minversion = "8.0"
 
-
-[tool.black]
-line-length = 100
-target-version = ['py310', 'py311', 'py312', 'py313']
-
 [tool.ruff]
 lint.select = ["E", "F", "I", "UP"]  # https://beta.ruff.rs/docs/rules/
 lint.ignore = ["E501"]
@@ -118,5 +100,19 @@ extra-standard-library = ["typing_extensions"]
 combine-as-imports = true
 force-wrap-aliases = true
 
-[tool.uv.workspace]
-members = ["shapiq/shapiq"]
+[dependency-groups]
+test = [
+    "pytest>=8.3.5",
+    "pytest-cov>=6.0.0",
+    "pytest-xdist>=3.6.1",
+]
+lint = [
+    "ruff>=0.11.2",
+    "pre-commit>=4.2.0",
+]
+dev = [
+    "build>=1.2.2.post1",
+    "twine>=6.1.0",
+    {include-group = "test"},
+    {include-group = "lint"},
+]

pyproject.toml

@@ -59,11 +59,11 @@ def custom_model(background_reg_dataset) -> CustomModel:
 @pytest.fixture
 def lightgbm_basic(background_reg_dataset) -> Model:
     """Return a lgm basic booster"""
-    import lightgbm as lgb
+    lightgbm = pytest.importorskip("lightgbm")
 
     X, y = background_reg_dataset
-    train_data = lgb.Dataset(X, label=y)
-    model = lgb.train(params={}, train_set=train_data, num_boost_round=1)
+    train_data = lightgbm.Dataset(X, label=y)
+    model = lightgbm.train(params={}, train_set=train_data, num_boost_round=1)
     return model
 
 
@@ -87,7 +87,7 @@ def sequential_model_3_classes() -> Model:
 
 def _sequential_model(output_shape_nr, background_reg_dataset) -> Model:
     """Return a keras nn with specified output dimension"""
-    import keras
+    keras = pytest.importorskip("keras")
 
     model = keras.Sequential(
         [
@@ -105,10 +105,10 @@ def _sequential_model(output_shape_nr, background_reg_dataset) -> Model:
 @pytest.fixture
 def xgb_reg_model(background_reg_dataset) -> Model:
     """Return a simple xgboost regression model."""
-    from xgboost import XGBRegressor
+    xgboost = pytest.importorskip("xgboost")
 
     X, y = background_reg_dataset
-    model = XGBRegressor(random_state=42, n_estimators=3)
+    model = xgboost.XGBRegressor(random_state=42, n_estimators=3)
     model.fit(X, y)
     return model
 
@@ -125,18 +125,18 @@ def rf_clf_binary_model(background_clf_dataset_binary) -> RandomForestClassifier
 @pytest.fixture
 def xgb_clf_model(background_clf_dataset) -> Model:
     """Return a simple xgboost classification model."""
-    from xgboost import XGBClassifier
+    xgboost = pytest.importorskip("xgboost")
 
     X, y = background_clf_dataset
-    model = XGBClassifier(random_state=42, n_estimators=3)
+    model = xgboost.XGBClassifier(random_state=42, n_estimators=3)
     model.fit(X, y)
     return model
 
 
 @pytest.fixture
 def torch_clf_model() -> Model:
     """Return a simple torch model."""
-    import torch
+    torch = pytest.importorskip("torch")
 
     model = torch.nn.Sequential(
         torch.nn.Linear(7, 10),
@@ -150,7 +150,7 @@ def torch_clf_model() -> Model:
 @pytest.fixture
 def torch_reg_model() -> Model:
     """Return a simple torch model."""
-    import torch
+    torch = pytest.importorskip("torch")
 
     model = torch.nn.Sequential(
         torch.nn.Linear(7, 10),
@@ -166,11 +166,11 @@ def tabpfn_classification_problem(
     background_clf_dataset_binary_small,
 ) -> tuple[Model, np.ndarray, np.ndarray, np.ndarray]:
     """Returns a very simple tabpfn classifier and dataset."""
-    from tabpfn import TabPFNClassifier
+    tabpfn = pytest.importorskip("tabpfn")
 
     data, labels = background_clf_dataset_binary_small
     data, x_test, labels, _ = train_test_split(data, labels, random_state=42, train_size=8)
-    model = TabPFNClassifier()
+    model = tabpfn.TabPFNClassifier()
     model.fit(data, labels)
     return model, data, labels, x_test
 
@@ -180,11 +180,11 @@ def tabpfn_regression_problem(
     background_reg_dataset_small,
 ) -> tuple[Model, np.ndarray, np.ndarray, np.ndarray]:
     """Returns a very simple tabpfn regressor and dataset."""
-    from tabpfn import TabPFNRegressor
+    tabpfn = pytest.importorskip("tabpfn")
 
     data, labels = background_reg_dataset_small
     data, x_test, labels, _ = train_test_split(data, labels, random_state=42, train_size=8)
-    model = TabPFNRegressor()
+    model = tabpfn.TabPFNRegressor()
     model.fit(data, labels)
     return model, data, labels, x_test
 
@@ -228,21 +228,21 @@ def lr_reg_model(background_reg_dataset) -> LinearRegression:
 @pytest.fixture
 def lightgbm_reg_model(background_reg_dataset) -> Model:
     """Return a simple lightgbm regression model."""
-    from lightgbm import LGBMRegressor
+    lightgbm = pytest.importorskip("lightgbm")
 
     X, y = background_reg_dataset
-    model = LGBMRegressor(random_state=42, n_estimators=3)
+    model = lightgbm.LGBMRegressor(random_state=42, n_estimators=3)
     model.fit(X, y)
     return model
 
 
 @pytest.fixture
 def lightgbm_clf_model(background_clf_dataset) -> Model:
     """Return a simple lightgbm classification model."""
-    from lightgbm import LGBMClassifier
+    lightgbm = pytest.importorskip("lightgbm")
 
     X, y = background_clf_dataset
-    model = LGBMClassifier(random_state=42, n_estimators=3)
+    model = lightgbm.LGBMClassifier(random_state=42, n_estimators=3)
     model.fit(X, y)
     return model
 

requirements.txt

@@ -1,11 +1,43 @@
 """This module contains all markers for the tests."""
 
+import importlib.util
+
 import pytest
 
-try:
-    # marker if the tabpfn library is installed
-    import tabpfn  # noqa: F401
+__all__ = [
+    "skip_if_no_tabpfn",
+    "skip_if_no_tensorflow",
+    "skip_if_no_keras",
+    "skip_if_no_xgboost",
+    "skip_if_no_lightgbm",
+]
+
+
+def is_installed(pkg_name: str) -> bool:
+    """Check if a package is installed without importing it."""
+    return importlib.util.find_spec(pkg_name) is not None
+
+
+skip_if_no_tabpfn = pytest.mark.skipif(
+    not is_installed("tabpfn"), reason="TabPFN is not available."
+)
+
+skip_if_no_tensorflow = pytest.mark.skipif(
+    not is_installed("tensorflow"),
+    reason="tensorflow is not installed"
+)
+
+skip_if_no_xgboost = pytest.mark.skipif(
+    not is_installed("xgboost"),
+    reason="xgboost is not installed"
+)
+
+skip_if_no_keras = pytest.mark.skipif(
+    not is_installed("keras"),
+    reason="keras is not installed"
+)
 
-    importorskip_tabpfn = pytest.mark.skipif(False, reason="tabpfn is installed")
-except ImportError:
-    importorskip_tabpfn = pytest.mark.skip(reason="tabpfn is not installed")
+skip_if_no_lightgbm = pytest.mark.skipif(
+    not is_installed("lightgbm"),
+    reason="lightgbm is not installed"
+)