move tests from unittest to pytest

Author: PicoCentauri

.github/copilot-instructions.md

@@ -0,0 +1,119 @@
+# scikit-matter Development Guide
+
+## Overview
+scikit-matter is a scikit-learn-compatible toolbox of methods from computational chemistry and materials science. All estimators follow sklearn API conventions (fit/transform/predict) and inherit from sklearn base classes.
+
+## Architecture
+
+### Core Selection Framework
+The codebase centers on a unique **dual-selection architecture** split across `feature_selection/` and `sample_selection/` with shared implementation in `_selection.py`:
+
+- **`_selection.py`**: Contains base classes (`GreedySelector`, `_CUR`, `_FPS`, `_PCovCUR`, `_PCovFPS`) that implement core algorithms independent of axis
+- **`feature_selection/_base.py`**: Thin wrappers that instantiate base classes with `selection_type="feature"` and inherit from `SelectorMixin` (enables `transform()`)
+- **`sample_selection/_base.py`**: Thin wrappers with `selection_type="sample"` - return indices via `selected_idx_` attribute (no `transform()`)
+
+Example: `FPS` (Farthest Point Sampling) exists as both `feature_selection.FPS` and `sample_selection.FPS`, sharing the same `_FPS` implementation but differing only in which axis they select along.
+
+### Module Organization
+- **`decomposition/`**: PCovR (Principal Covariates Regression) and variants - supervised dimensionality reduction combining PCA-like and regression objectives
+- **`linear_model/`**: `OrthogonalRegression`, `Ridge2FoldCV` (custom 2-fold CV for efficiency)
+- **`metrics/`**: Reconstruction measures (GRE, GRD, LRE) and prediction rigidities (LPR, CPR)
+- **`preprocessing/`**: `StandardFlexibleScaler`, `SparseKernelCenterer` with column-wise scaling options
+- **`utils/`**: Orthogonalizers, PCovR utilities, progress bar helpers
+- **`datasets/`**: Chemistry/materials datasets (CSD-1000r, CH4 manifolds, etc.)
+
+## Development Workflows
+
+### Testing
+```bash
+# Run all tests with coverage
+tox -e tests
+
+# Run specific test file
+tox -e tests -- tests/test_feature_simple_cur.py
+
+# Run tests against sklearn dev version
+tox -e tests-dev
+```
+
+Tests use pytest-style assertions and fixtures. Common patterns:
+- Use `@pytest.fixture` for test data setup
+- Use `assert` statements instead of `self.assertEqual()`
+- Use `pytest.raises()` for exception testing
+- Use `pytest.warns()` for warning testing
+- Use `pytest.mark.parametrize` for parameterized tests
+- Tests often load datasets via `skmatter.datasets.load_*()`
+
+### Linting & Formatting
+```bash
+# Check only (CI mode)
+tox -e lint
+
+# Auto-format code
+tox -e format
+
+# More aggressive fixes (review changes carefully)
+tox -e format-unsafe
+```
+
+Uses `ruff` for both formatting and linting. Configuration in `pyproject.toml` ignores F401 (unused imports in `__init__.py`).
+
+### Building Docs
+```bash
+tox -e docs  # Builds HTML docs, runs examples via sphinx-gallery
+```
+
+Documentation uses Sphinx with `.rst` format. Examples in `examples/` are executed during doc builds.
+
+### Building Package
+```bash
+tox -e build  # Builds wheel and sdist, runs check-manifest and twine check
+```
+
+Uses `setuptools_scm` for versioning from git tags. Version file auto-generated at `src/skmatter/_version.py`.
+
+## Key Conventions
+
+### scikit-learn Compliance
+- All estimators inherit from appropriate sklearn mixins (`RegressorMixin`, `TransformerMixin`, `SelectorMixin`)
+- Use `validate_data()` (not deprecated `check_X_y()`) for input validation
+- Implement `fit()` returning `self`, store fitted attributes with trailing underscore (`selected_idx_`, `n_selected_`)
+- Support `warm_start` parameter in selectors to continue from previous fit
+
+### Selection Methods Patterns
+```python
+# Feature selection (returns transformed X)
+from skmatter.feature_selection import CUR, FPS, PCovCUR, PCovFPS
+selector = CUR(n_to_select=10, progress_bar=True)
+X_reduced = selector.fit(X).transform(X)
+
+# Sample selection (returns indices)
+from skmatter.sample_selection import CUR
+selector = CUR(n_to_select=10)
+selector.fit(X)
+X_subset = X[selector.selected_idx_]
+```
+
+### PCovR Methods
+Always center and scale inputs (`StandardFlexibleScaler`) before using PCovR/PCovC - results change drastically near α→0 or α→1 otherwise. Use `column_wise=True` when features are comparable.
+
+### Progress Bars
+Optional `tqdm` progress bar via `progress_bar=True` parameter. Implementation uses utility functions `get_progress_bar()` / `no_progress_bar()` from `utils/`.
+
+## Dependencies & Python Support
+- **Python**: 3.11+ (as of v0.3.3)
+- **Core**: scikit-learn 1.8.x, scipy ≥1.15
+- **Optional**: matplotlib, pandas, tqdm (for examples)
+- **Testing**: Requires Python 3.11 and 3.14 on Ubuntu, macOS, Windows
+
+## Pull Request Requirements
+- Update tests for new features/bugfixes
+- Update documentation for new features  
+- Reference issue numbers in PR description
+- Reviewer updates CHANGELOG for important changes (not contributor)
+
+## Common Pitfalls
+- Don't use deprecated sklearn APIs - check sklearn version in `pyproject.toml`
+- Selection methods: feature vs sample selection use same algorithms but different interfaces
+- PCovR requires pre-scaled data - document this in examples
+- Test files use pytest - use fixtures, `assert`, `pytest.raises()`, not unittest classes

pyproject.toml

@@ -122,3 +122,6 @@ convention = "numpy"
     "D205",
     "D400",
 ]
+"tests/**" = [
+    "D103",
+]

tests/test_clustering.py

@@ -1,59 +1,73 @@
-import unittest
-
 import numpy as np
+import pytest
 
 from skmatter.clustering import QuickShift
 
 
-class QuickShiftTests(unittest.TestCase):
-    @classmethod
-    def setUpClass(cls) -> None:
-        cls.points = np.array(
-            [
-                [-1.72779275, -1.32763554],
-                [-4.44991964, -2.13474901],
-                [0.54817734, -2.43319467],
-                [3.19881307, -0.49547222],
-                [-1.1335991, 2.33478428],
-                [0.55437388, 0.18745963],
-            ]
-        )
-        cls.cuts = np.array(
-            [6.99485011, 8.80292681, 7.68486852, 9.5115009, 8.07736919, 6.22057056]
-        )
-        cls.weights = np.array(
-            [
-                -3.94008092,
-                -12.68095664,
-                -7.07512499,
-                -9.03064023,
-                -8.26529849,
-                -2.61132267,
-            ]
-        )
-        cls.qs_labels_ = np.array([0, 0, 0, 5, 5, 5])
-        cls.qs_cluster_centers_idx_ = np.array([0, 5])
-        cls.gabriel_labels_ = np.array([5, 5, 5, 5, 5, 5])
-        cls.gabriel_cluster_centers_idx_ = np.array([5])
-        cls.cell = [3, 3]
-        cls.gabriel_shell = 2
-
-    def test_fit_qs(self):
-        model = QuickShift(dist_cutoff_sq=self.cuts)
-        model.fit(self.points, samples_weight=self.weights)
-        self.assertTrue(np.all(model.labels_ == self.qs_labels_))
-        self.assertTrue(
-            np.all(model.cluster_centers_idx_ == self.qs_cluster_centers_idx_)
-        )
-
-    def test_fit_garbriel(self):
-        model = QuickShift(gabriel_shell=self.gabriel_shell)
-        model.fit(self.points, samples_weight=self.weights)
-        self.assertTrue(np.all(model.labels_ == self.gabriel_labels_))
-        self.assertTrue(
-            np.all(model.cluster_centers_idx_ == self.gabriel_cluster_centers_idx_)
-        )
-
-    def test_dimension_check(self):
-        model = QuickShift(self.cuts, metric_params={"cell_length": self.cell})
-        self.assertRaises(ValueError, model.fit, np.array([[2]]))
+@pytest.fixture(scope="module")
+def test_data():
+    points = np.array(
+        [
+            [-1.72779275, -1.32763554],
+            [-4.44991964, -2.13474901],
+            [0.54817734, -2.43319467],
+            [3.19881307, -0.49547222],
+            [-1.1335991, 2.33478428],
+            [0.55437388, 0.18745963],
+        ]
+    )
+    cuts = np.array(
+        [6.99485011, 8.80292681, 7.68486852, 9.5115009, 8.07736919, 6.22057056]
+    )
+    weights = np.array(
+        [
+            -3.94008092,
+            -12.68095664,
+            -7.07512499,
+            -9.03064023,
+            -8.26529849,
+            -2.61132267,
+        ]
+    )
+    qs_labels_ = np.array([0, 0, 0, 5, 5, 5])
+    qs_cluster_centers_idx_ = np.array([0, 5])
+    gabriel_labels_ = np.array([5, 5, 5, 5, 5, 5])
+    gabriel_cluster_centers_idx_ = np.array([5])
+    cell = [3, 3]
+    gabriel_shell = 2
+
+    return {
+        "points": points,
+        "cuts": cuts,
+        "weights": weights,
+        "qs_labels_": qs_labels_,
+        "qs_cluster_centers_idx_": qs_cluster_centers_idx_,
+        "gabriel_labels_": gabriel_labels_,
+        "gabriel_cluster_centers_idx_": gabriel_cluster_centers_idx_,
+        "cell": cell,
+        "gabriel_shell": gabriel_shell,
+    }
+
+
+def test_fit_qs(test_data):
+    model = QuickShift(dist_cutoff_sq=test_data["cuts"])
+    model.fit(test_data["points"], samples_weight=test_data["weights"])
+    assert np.all(model.labels_ == test_data["qs_labels_"])
+    assert np.all(model.cluster_centers_idx_ == test_data["qs_cluster_centers_idx_"])
+
+
+def test_fit_garbriel(test_data):
+    model = QuickShift(gabriel_shell=test_data["gabriel_shell"])
+    model.fit(test_data["points"], samples_weight=test_data["weights"])
+    assert np.all(model.labels_ == test_data["gabriel_labels_"])
+    assert np.all(
+        model.cluster_centers_idx_ == test_data["gabriel_cluster_centers_idx_"]
+    )
+
+
+def test_dimension_check(test_data):
+    model = QuickShift(
+        test_data["cuts"], metric_params={"cell_length": test_data["cell"]}
+    )
+    with pytest.raises(ValueError):
+        model.fit(np.array([[2]]))