Some quality refactoring

main.py

@@ -1,29 +0,0 @@
-import time
-from sklearn.model_selection import train_test_split
-from sklearn.datasets import load_iris
-from stree import Stree
-
-random_state = 1
-
-X, y = load_iris(return_X_y=True)
-
-Xtrain, Xtest, ytrain, ytest = train_test_split(
-    X, y, test_size=0.3, random_state=random_state
-)
-
-now = time.time()
-print("Predicting with max_features=sqrt(n_features)")
-clf = Stree(C=0.01, random_state=random_state, max_features="auto")
-clf.fit(Xtrain, ytrain)
-print(f"Took {time.time() - now:.2f} seconds to train")
-print(clf)
-print(f"Classifier's accuracy (train): {clf.score(Xtrain, ytrain):.4f}")
-print(f"Classifier's accuracy (test) : {clf.score(Xtest, ytest):.4f}")
-print("=" * 40)
-print("Predicting with max_features=n_features")
-clf = Stree(C=0.01, random_state=random_state)
-clf.fit(Xtrain, ytrain)
-print(f"Took {time.time() - now:.2f} seconds to train")
-print(clf)
-print(f"Classifier's accuracy (train): {clf.score(Xtrain, ytrain):.4f}")
-print(f"Classifier's accuracy (test) : {clf.score(Xtest, ytest):.4f}")

stree/Strees.py

@@ -144,7 +144,6 @@ class Snode:
                 f"{self._belief: .6f} impurity={self._impurity:.4f} "
                 f"counts={count_values}"
             )
-        else:
         return (
             f"{self._title} feaures={self._features} impurity="
             f"{self._impurity:.4f} "
@@ -384,9 +383,7 @@ class Splitter:
             if self._splitter_type == "random":
                 index = random.randint(0, len(features_sets) - 1)
                 return features_sets[index]
-            else:
             return self._select_best_set(dataset, labels, features_sets)
-        else:
         return features_sets[0]
 
     def get_subspace(

stree/tests/Stree_test.py

@@ -484,13 +484,13 @@ class Stree_test(unittest.TestCase):
         clf.fit(X, y)
         nodes, leaves = clf.nodes_leaves()
         self.assertEqual(25, nodes)
-        self.assertEquals(13, leaves)
+        self.assertEqual(13, leaves)
         X, y = load_wine(return_X_y=True)
         clf = Stree(random_state=self._random_state)
         clf.fit(X, y)
         nodes, leaves = clf.nodes_leaves()
         self.assertEqual(9, nodes)
-        self.assertEquals(5, leaves)
+        self.assertEqual(5, leaves)
 
     def test_nodes_leaves_artificial(self):
         n1 = Snode(None, [1, 2, 3, 4], [1, 0, 1, 1], [], 0.0, "test1")