CI: Drop requirements.txt usage and modernize workflows

Install dev/runtime deps via `pip install .[dev]`, bump CodeQL action to v3,
extend Python matrix to 3.13/3.14, enable pip cache, and align Strees +
pyproject with scikit-learn 1.8 API (validate_data, __sklearn_tags__).

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

Author: rmontanana

.github/workflows/codeql-analysis.yml

@@ -24,11 +24,11 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v2
+        uses: actions/checkout@v4
 
       # Initializes the CodeQL tools for scanning.
       - name: Initialize CodeQL
-        uses: github/codeql-action/init@v2
+        uses: github/codeql-action/init@v3
         with:
           languages: ${{ matrix.language }}
           # If you wish to specify custom queries, you can do so here or in a config file.
@@ -39,7 +39,7 @@ jobs:
       # Autobuild attempts to build any compiled languages  (C/C++, C#, or Java).
       # If this step fails, then you should remove it and run the build manually (see below)
       - name: Autobuild
-        uses: github/codeql-action/autobuild@v2
+        uses: github/codeql-action/autobuild@v3
 
       # ℹ️ Command-line programs to run using the OS shell.
       # 📚 https://git.io/JvXDl
@@ -53,4 +53,4 @@ jobs:
       #   make release
 
       - name: Perform CodeQL Analysis
-        uses: github/codeql-action/analyze@v2
+        uses: github/codeql-action/analyze@v3

.github/workflows/main.yml

@@ -13,19 +13,20 @@ jobs:
     strategy:
       matrix:
         os: [macos-latest, ubuntu-latest, windows-latest]
-        python: [3.11, 3.12]
+        python: [3.11, 3.12, 3.13, 3.14]
 
     steps:
       - uses: actions/checkout@v4
       - name: Set up Python ${{ matrix.python }}
         uses: actions/setup-python@v5
         with:
           python-version: ${{ matrix.python }}
+          cache: 'pip'
+          cache-dependency-path: pyproject.toml
       - name: Install dependencies
         run: |
           pip install -q --upgrade pip
-          pip install -q -r requirements.txt
-          pip install -q --upgrade codecov coverage black flake8 codacy-coverage
+          pip install -q .[dev]
       - name: Lint
         run: |
           # black --check --diff stree

README.md

@@ -3,7 +3,7 @@
 ![CI](https://github.com/Doctorado-ML/STree/workflows/CI/badge.svg)
 [![CodeQL](https://github.com/Doctorado-ML/STree/actions/workflows/codeql-analysis.yml/badge.svg)](https://github.com/Doctorado-ML/STree/actions/workflows/codeql-analysis.yml)
 [![codecov](https://codecov.io/gh/doctorado-ml/stree/branch/master/graph/badge.svg)](https://codecov.io/gh/doctorado-ml/stree)
-[![Codacy Badge](https://app.codacy.com/project/badge/Grade/35fa3dfd53a24a339344b33d9f9f2f3d)](https://www.codacy.com/gh/Doctorado-ML/STree?utm_source=github.com&utm_medium=referral&utm_content=Doctorado-ML/STree&utm_campaign=Badge_Grade)
+[![Codacy Badge](https://app.codacy.com/project/badge/Grade/35fa3dfd53a24a339344b33d9f9f2f3d)](https://app.codacy.com/gh/Doctorado-ML/STree/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade)
 [![PyPI version](https://badge.fury.io/py/STree.svg)](https://badge.fury.io/py/STree)
 ![https://img.shields.io/badge/python-3.11%2B-blue](https://img.shields.io/badge/python-3.11%2B-brightgreen)
 [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/Doctorado-ML/STree)
@@ -13,7 +13,6 @@
 
 Oblique Tree classifier based on SVM nodes. The nodes are built and splitted with sklearn SVC models. Stree is a sklearn estimator and can be integrated in pipelines, grid searches, etc.
 
-
 ## Installation
 
 ```bash

pyproject.toml

@@ -4,8 +4,9 @@ build-backend = "hatchling.build"
 
 [project]
 name = "STree"
-dependencies = ["scikit-learn==1.5.2", "numpy==1.26.4", "mufs"]
-license = { file = "LICENSE" }
+dependencies = ["scikit-learn>=1.8.0", "mufs"]
+license = "MIT"
+license-files = ["LICENSE"]
 description = "Oblique decision tree with svm nodes."
 readme = "README.md"
 authors = [

stree/Strees.py

@@ -11,16 +11,16 @@
 from sklearn.preprocessing import StandardScaler
 from sklearn.utils.multiclass import check_classification_targets
 from sklearn.utils.validation import (
-    check_X_y,
-    check_array,
     check_is_fitted,
     _check_sample_weight,
+    validate_data,
 )
+from sklearn.utils._tags import (Tags, ClassifierTags, TargetTags, InputTags)
 from .Splitter import Splitter, Snode, Siterator
 from ._version import __version__
 
 
-class Stree(BaseEstimator, ClassifierMixin):
+class Stree(ClassifierMixin, BaseEstimator):
     """
     Estimator that is based on binary trees of svm nodes
     can deal with sample_weights in predict, used in boosting sklearn methods
@@ -179,15 +179,32 @@ def __call__(self) -> None:
         ensembles"""
         pass
 
-    def _more_tags(self) -> dict:
-        """Required by sklearn to supply features of the classifier
-        make mandatory the labels array
-
-        :return: the tag required
-        :rtype: dict
-        """
-        return {"requires_y": True}
-
+    def __sklearn_tags__(self):
+        return Tags(
+            estimator_type="classifier",
+            target_tags=TargetTags(
+                required=True,
+                multi_output=False,
+            ),
+            classifier_tags=ClassifierTags(
+                multi_class=True,
+                multi_label=False,
+                poor_score=False,
+            ),
+            input_tags=InputTags(
+                sparse=False,
+                one_d_array=False,
+                two_d_array=True,
+                three_d_array=False,
+                categorical=True,
+                string=True,
+                pairwise=False,
+            ),
+            requires_fit=True,
+            array_api_support=False,
+            non_deterministic=False,
+            _skip_test=False,
+        )
     def fit(
         self,
         X: np.ndarray,
@@ -245,7 +262,7 @@ def fit(
         if self.kernel not in kernels:
             raise ValueError(f"Kernel {self.kernel} not in {kernels}")
         check_classification_targets(y)
-        X, y = check_X_y(X, y)
+        X, y = validate_data(self, X, y)
         sample_weight = _check_sample_weight(
             sample_weight, X, dtype=np.float64
         )
@@ -435,12 +452,7 @@ def check_predict(self, X) -> np.array:
         """
         check_is_fitted(self, ["tree_"])
         # Input validation
-        X = check_array(X)
-        if X.shape[1] != self.n_features_:
-            raise ValueError(
-                f"Expected {self.n_features_} features but got "
-                f"({X.shape[1]})"
-            )
+        X = validate_data(self, X, reset=False)
         return X
 
     def predict_proba(self, X: np.array) -> np.array:

stree/_version.py

@@ -1 +1 @@
-__version__ = "1.4.1"
+__version__ = "1.4.2"