Add pyproject.toml install information

Add __call__ method to support sklearn ensembles requirements for base estimators
Update tests

MANIFEST.in

@@ -0,0 +1 @@
+include README.md LICENSE

pyproject.toml

@@ -1,5 +1,68 @@
+[build-system]
+requires = ["setuptools", "scikit-learn>1.0", "numpy", "mufs"]
+build-backend = "setuptools.build_meta"
+
+[tool.setuptools]
+packages = ["stree"]
+license-files = ["LICENSE"]
+
+[tool.setuptools.dynamic]
+version = { attr = "stree.__version__" }
+
+[project]
+name = "STree"
+dependencies = ["scikit-learn>1.0", "numpy", "mufs"]
+license = { file = "LICENSE" }
+description = "Oblique decision tree with svm nodes."
+readme = "README.md"
+authors = [
+  { name = "Ricardo Montañana", email = "ricardo.montanana@alu.uclm.es" },
+]
+dynamic = ['version']
+requires-python = ">=3.8"
+keywords = [
+  "scikit-learn",
+  "oblique-classifier",
+  "oblique-decision-tree",
+  "decision-tree",
+  "svm",
+  "svc",
+]
+classifiers = [
+  "Development Status :: 5 - Production/Stable",
+  "Intended Audience :: Science/Research",
+  "Intended Audience :: Developers",
+  "Topic :: Software Development",
+  "Topic :: Scientific/Engineering",
+  "License :: OSI Approved :: MIT License",
+  "Natural Language :: English",
+  "Operating System :: OS Independent",
+  "Programming Language :: Python :: 3.8",
+  "Programming Language :: Python :: 3.9",
+  "Programming Language :: Python :: 3.10",
+  "Programming Language :: Python :: 3.11",
+  "Programming Language :: Python :: 3.12",
+]
+
+[project.optional-dependencies]
+dev = ["black", "flake8", "mypy", "coverage"]
+
+[project.urls]
+Code = "https://github.com/Doctorado-ML/STree"
+Documentation = "https://stree.readthedocs.io/en/latest/index.html"
+
+[tool.coverage.run]
+branch = true
+source = ["stree"]
+command_line = "-m unittest discover -s stree.tests"
+
+[tool.coverage.report]
+show_missing = true
+fail_under = 100
+
 [tool.black]
 line-length = 79
+target_version = ['py311']
 include = '\.pyi?$'
 exclude = '''
 /(
@@ -13,4 +76,4 @@ exclude = '''
   | build
   | dist
 )/
-'''
+'''

stree/Strees.py

@@ -174,6 +174,10 @@ def version() -> str:
         """Return the version of the package."""
         return __version__
 
+    def __call__(self) -> str:
+        """Only added to comply with scikit-learn base estimator for ensemble"""
+        return self.version()
+
     def _more_tags(self) -> dict:
         """Required by sklearn to supply features of the classifier
         make mandatory the labels array

stree/__init__.py

@@ -1,8 +1,9 @@
 from .Strees import Stree, Siterator
+from ._version import __version__
 
 __author__ = "Ricardo Montañana Gómez"
 __copyright__ = "Copyright 2020-2021, Ricardo Montañana Gómez"
 __license__ = "MIT License"
 __author_email__ = "ricardo.montanana@alu.uclm.es"
 
-__all__ = ["Stree", "Siterator"]
+__all__ = ["__version__", "Stree", "Siterator"]

stree/_version.py

@@ -1 +1 @@
-__version__ = "1.3.2"
+__version__ = "1.4.0"

stree/tests/Stree_test.py

@@ -289,12 +289,12 @@ def test_muticlass_dataset(self):
                 "impurity sigmoid": 0.824,
             },
             "Iris": {
-                "max_samples liblinear": 0.9550561797752809,
+                "max_samples liblinear": 0.9887640449438202,
                 "max_samples linear": 1.0,
                 "max_samples rbf": 0.6685393258426966,
                 "max_samples poly": 0.6853932584269663,
                 "max_samples sigmoid": 0.6404494382022472,
-                "impurity liblinear": 0.9550561797752809,
+                "impurity liblinear": 0.9887640449438202,
                 "impurity linear": 1.0,
                 "impurity rbf": 0.6685393258426966,
                 "impurity poly": 0.6853932584269663,
@@ -440,10 +440,10 @@ def test_multiclass_classifier_integrity(self):
         clf.fit(X, y)
         score = clf.score(X, y)
         # Check accuracy of the whole model
-        self.assertAlmostEquals(0.98, score, 5)
+        self.assertAlmostEqual(0.98, score, 5)
         svm = LinearSVC(random_state=0)
         svm.fit(X, y)
-        self.assertAlmostEquals(0.9666666666666667, svm.score(X, y), 5)
+        self.assertAlmostEqual(0.9666666666666667, svm.score(X, y), 5)
         data = svm.decision_function(X)
         expected = [
             0.4444444444444444,
@@ -455,7 +455,7 @@ def test_multiclass_classifier_integrity(self):
         ty[data > 0] = 1
         ty = ty.astype(int)
         for i in range(3):
-            self.assertAlmostEquals(
+            self.assertAlmostEqual(
                 expected[i],
                 clf.splitter_._gini(ty[:, i]),
             )
@@ -593,7 +593,7 @@ def test_score_multiclass_linear(self):
         )
         self.assertEqual(0.9526666666666667, clf2.fit(X, y).score(X, y))
         X, y = load_wine(return_X_y=True)
-        self.assertEqual(0.9831460674157303, clf.fit(X, y).score(X, y))
+        self.assertEqual(0.9887640449438202, clf.fit(X, y).score(X, y))
         self.assertEqual(1.0, clf2.fit(X, y).score(X, y))
 
     def test_zero_all_sample_weights(self):
@@ -725,6 +725,11 @@ def test_version(self):
         clf = Stree()
         self.assertEqual(__version__, clf.version())
 
+    def test_call(self) -> None:
+        """Check call method."""
+        clf = Stree()
+        self.assertEqual(__version__, clf())
+
     def test_graph(self):
         """Check graphviz representation of the tree."""
         X, y = load_wine(return_X_y=True)