Complete iwss based implementation (#2)

mufs/Selection.py

@@ -318,6 +318,7 @@ class MUFS:
                 self._scores.append(merit_new)
             else:
                 candidates.pop()
+                break
             if len(candidates) == self._max_features:
                 break
         self._result = candidates

mufs/tests/MUFS_test.py

@@ -1,11 +1,14 @@
 import unittest
+import os
+import pandas as pd
+import numpy as np
 from mdlp import MDLP
 from sklearn.datasets import load_wine, load_iris
 
 from ..Selection import MUFS
 
 
-class MUFS_test(unittest.TestCase):
+class MUFSTest(unittest.TestCase):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
         mdlp = MDLP(random_state=1)
@@ -175,9 +178,6 @@ class MUFS_test(unittest.TestCase):
             mufs.iwss(self.X_w, self.y_w, -0.01)
 
     def test_iwss_better_merit_condition(self):
-        import pandas as pd
-        import os
-
         folder = os.path.dirname(os.path.abspath(__file__))
         data = pd.read_csv(
             os.path.join(folder, "balloons_R.dat"),
@@ -189,3 +189,8 @@ class MUFS_test(unittest.TestCase):
         mufs = MUFS()
         expected = [0, 2, 3, 1]
         self.assertListEqual(expected, mufs.iwss(X, y, 0.3).get_results())
+
+    def test_iwss_empty(self):
+        mufs = MUFS()
+        X = np.delete(self.X_i, [0, 1], 1)
+        self.assertListEqual(mufs.iwss(X, self.y_i, 0.3).get_results(), [1, 0])

mufs/tests/Metrics_test.py

@@ -6,7 +6,7 @@ from mdlp import MDLP
 from ..Selection import Metrics
 
 
-class Metrics_test(unittest.TestCase):
+class MetricsTest(unittest.TestCase):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
         mdlp = MDLP(random_state=1)

mufs/tests/__init__.py

@@ -1,4 +1,4 @@
-from .MUFS_test import MUFS_test
-from .Metrics_test import Metrics_test
+from .MUFS_test import MUFSTest
+from .Metrics_test import MetricsTest
 
-__all__ = ["MUFS_test", "Metrics_test"]
+__all__ = ["MUFSTest", "MetricsTest"]