Complete source comments

Change docstring type to numpy
update hyperameters table and explanation

.github/workflows/codeql-analysis.yml

@@ -1,56 +0,0 @@
-name: "CodeQL"
-
-on:
-  push:
-    branches: [ master ]
-  pull_request:
-    # The branches below must be a subset of the branches above
-    branches: [ master ]
-  schedule:
-    - cron: '16 17 * * 3'
-
-jobs:
-  analyze:
-    name: Analyze
-    runs-on: ubuntu-latest
-
-    strategy:
-      fail-fast: false
-      matrix:
-        language: [ 'python' ]
-        # CodeQL supports [ 'cpp', 'csharp', 'go', 'java', 'javascript', 'python' ]
-        # Learn more:
-        # https://docs.github.com/en/free-pro-team@latest/github/finding-security-vulnerabilities-and-errors-in-your-code/configuring-code-scanning#changing-the-languages-that-are-analyzed
-
-    steps:
-    - name: Checkout repository
-      uses: actions/checkout@v2
-
-    # Initializes the CodeQL tools for scanning.
-    - name: Initialize CodeQL
-      uses: github/codeql-action/init@v1
-      with:
-        languages: ${{ matrix.language }}
-        # If you wish to specify custom queries, you can do so here or in a config file.
-        # By default, queries listed here will override any specified in a config file.
-        # Prefix the list here with "+" to use these queries and those in the config file.
-        # queries: ./path/to/local/query, your-org/your-repo/queries@main
-
-    # 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@v1
-
-    # ℹ️ Command-line programs to run using the OS shell.
-    # 📚 https://git.io/JvXDl
-
-    # ✏️ If the Autobuild fails above, remove it and uncomment the following three lines
-    #    and modify them (or add more) to build your code if your project
-    #    uses a compiled language
-
-    #- run: |
-    #   make bootstrap
-    #   make release
-
-    - name: Perform CodeQL Analysis
-      uses: github/codeql-action/analyze@v1

requirements.txt

@@ -1,4 +1,4 @@
 numpy
-scikit-learn
+scikit-learn==0.23.2
 pandas
 ipympl

setup.py

@@ -1,6 +1,6 @@
 import setuptools
 
-__version__ = "1.0rc1"
+__version__ = "0.9rc6"
 __author__ = "Ricardo Montañana Gómez"
 
 
@@ -30,7 +30,7 @@ setuptools.setup(
         "Topic :: Scientific/Engineering :: Artificial Intelligence",
         "Intended Audience :: Science/Research",
     ],
-    install_requires=["scikit-learn", "numpy", "ipympl"],
+    install_requires=["scikit-learn==0.23.2", "numpy", "ipympl"],
     test_suite="stree.tests",
     zip_safe=False,
 )

stree/Strees.py

@@ -629,12 +629,6 @@ class Stree(BaseEstimator, ClassifierMixin):
         """
         if depth > self.__max_depth:
             return None
-        # Mask samples with 0 weight
-        if any(sample_weight == 0):
-            indices_zero = sample_weight == 0
-            X = X[~indices_zero, :]
-            y = y[~indices_zero]
-            sample_weight = sample_weight[~indices_zero]
         if np.unique(y).shape[0] == 1:
             # only 1 class => pure dataset
             return Snode(
@@ -649,6 +643,14 @@ class Stree(BaseEstimator, ClassifierMixin):
         # Train the model
         clf = self._build_clf()
         Xs, features = self.splitter_.get_subspace(X, y, self.max_features_)
+        # solve WARNING: class label 0 specified in weight is not found
+        # in bagging
+        if any(sample_weight == 0):
+            indices = sample_weight == 0
+            y_next = y[~indices]
+            # touch weights if removing any class
+            if np.unique(y_next).shape[0] != self.n_classes_:
+                sample_weight += 1e-5
         clf.fit(Xs, y, sample_weight=sample_weight)
         impurity = self.splitter_.partition_impurity(y)
         node = Snode(clf, X, y, features, impurity, title, sample_weight)

stree/tests/Stree_test.py

@@ -413,29 +413,39 @@ class Stree_test(unittest.TestCase):
         with self.assertRaises(ValueError):
             Stree().fit(X, y, np.zeros(len(y)))
 
-    def test_mask_samples_weighted_zero(self):
+    def test_weights_removing_class(self):
+        # This patch solves an stderr message from sklearn svm lib
+        # "WARNING: class label x specified in weight is not found"
         X = np.array(
             [
-                [1, 1],
+                [0.1, 0.1],
-                [1, 1],
+                [0.1, 0.2],
-                [1, 1],
+                [0.2, 0.1],
-                [2, 2],
+                [5, 6],
-                [2, 2],
+                [8, 9],
-                [2, 2],
+                [6, 7],
-                [3, 3],
+                [0.2, 0.2],
-                [3, 3],
-                [3, 3],
             ]
         )
-        y = np.array([1, 1, 1, 2, 2, 2, 5, 5, 5])
+        y = np.array([0, 0, 0, 1, 1, 1, 0])
-        yw = np.array([1, 1, 1, 5, 5, 5, 5, 5, 5])
+        epsilon = 1e-5
-        w = [1, 1, 1, 0, 0, 0, 1, 1, 1]
+        weights = [1, 1, 1, 0, 0, 0, 1]
-        model1 = Stree().fit(X, y)
+        weights = np.array(weights, dtype="float64")
-        model2 = Stree().fit(X, y, w)
+        weights_epsilon = [x + epsilon for x in weights]
-        predict1 = model1.predict(X)
+        weights_no_zero = np.array([1, 1, 1, 0, 0, 2, 1])
-        predict2 = model2.predict(X)
+        original = weights_no_zero.copy()
-        self.assertListEqual(y.tolist(), predict1.tolist())
+        clf = Stree()
-        self.assertListEqual(yw.tolist(), predict2.tolist())
+        clf.fit(X, y)
-        self.assertEqual(model1.score(X, y), 1)
+        node = clf.train(
-        self.assertAlmostEqual(model2.score(X, y), 0.66666667)
+            X,
-        self.assertEqual(model2.score(X, y, w), 1)
+            y,
+            weights,
+            1,
+            "test",
+        )
+        # if a class is lost with zero weights the patch adds epsilon
+        self.assertListEqual(weights.tolist(), weights_epsilon)
+        self.assertListEqual(node._sample_weight.tolist(), weights_epsilon)
+        # zero weights are ok when they don't erase a class
+        _ = clf.train(X, y, weights_no_zero, 1, "test")
+        self.assertListEqual(weights_no_zero.tolist(), original.tolist())