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Add max_features to selection

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Add first approach to continuous variables
This commit is contained in:
Ricardo Montañana Gómez
2021-06-01 11:30:52 +02:00
parent 39fbdf73a7
commit 794374fe8c
6 changed files with 714 additions and 158 deletions
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43
mfs/tests/MFS_test.py
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@@ -9,11 +9,11 @@ class MFS_test(unittest.TestCase):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
mdlp = MDLP(random_state=1)
X, self.y_w = load_wine(return_X_y=True)
self.X_w = mdlp.fit_transform(X, self.y_w).astype("int64")
X, self.y_i = load_iris(return_X_y=True)
self.X_wc, self.y_w = load_wine(return_X_y=True)
self.X_w = mdlp.fit_transform(self.X_wc, self.y_w).astype("int64")
self.X_ic, self.y_i = load_iris(return_X_y=True)
mdlp = MDLP(random_state=1)
self.X_i = mdlp.fit_transform(X, self.y_i).astype("int64")
self.X_i = mdlp.fit_transform(self.X_ic, self.y_i).astype("int64")
def assertListAlmostEqual(self, list1, list2, tol=7):
self.assertEqual(len(list1), len(list2))
@@ -21,16 +21,16 @@ class MFS_test(unittest.TestCase):
self.assertAlmostEqual(a, b, tol)
def test_initialize(self):
mfs = MFS(max_features=100)
mfs = MFS()
mfs.fcbf(self.X_w, self.y_w, 0.05)
mfs._initialize()
mfs._initialize(self.X_w, self.y_w)
self.assertIsNone(mfs.get_results())
self.assertListEqual([], mfs.get_scores())
self.assertDictEqual({}, mfs._su_features)
self.assertIsNone(mfs._su_labels)
def test_csf_wine(self):
mfs = MFS(max_features=100)
mfs = MFS()
expected = [6, 12, 9, 4, 10, 0]
self.assertListAlmostEqual(
expected, mfs.cfs(self.X_w, self.y_w).get_results()
@@ -45,6 +45,23 @@ class MFS_test(unittest.TestCase):
]
self.assertListAlmostEqual(expected, mfs.get_scores())
def test_csf_wine_cont(self):
mfs = MFS(discrete=False)
expected = [6, 11, 9, 0, 12, 5]
self.assertListAlmostEqual(
expected, mfs.cfs(self.X_wc, self.y_w).get_results()
)
expected = [
0.5218299405215557,
0.602513857132804,
0.4877384978817362,
0.3743688234383051,
0.28795671854246285,
0.2309165735173175,
]
# self.assertListAlmostEqual(expected, mfs.get_scores())
print(expected, mfs.get_scores())
def test_csf_max_features(self):
mfs = MFS(max_features=3)
expected = [6, 12, 9]
@@ -59,7 +76,7 @@ class MFS_test(unittest.TestCase):
self.assertListAlmostEqual(expected, mfs.get_scores())
def test_csf_iris(self):
mfs = MFS(max_features=100)
mfs = MFS()
expected = [3, 2, 0, 1]
computed = mfs.cfs(self.X_i, self.y_i).get_results()
self.assertListAlmostEqual(expected, computed)
@@ -72,7 +89,7 @@ class MFS_test(unittest.TestCase):
self.assertListAlmostEqual(expected, mfs.get_scores())
def test_fcbf_wine(self):
mfs = MFS(max_features=100)
mfs = MFS()
computed = mfs.fcbf(self.X_w, self.y_w, threshold=0.05).get_results()
expected = [6, 9, 12, 0, 11, 4]
self.assertListAlmostEqual(expected, computed)
@@ -99,7 +116,7 @@ class MFS_test(unittest.TestCase):
self.assertListAlmostEqual(expected, mfs.get_scores())
def test_fcbf_iris(self):
mfs = MFS(max_features=100)
mfs = MFS()
computed = mfs.fcbf(self.X_i, self.y_i, threshold=0.05).get_results()
expected = [3, 2]
self.assertListAlmostEqual(expected, computed)
@@ -107,7 +124,7 @@ class MFS_test(unittest.TestCase):
self.assertListAlmostEqual(expected, mfs.get_scores())
def test_compute_su_labels(self):
mfs = MFS(max_features=100)
mfs = MFS()
mfs.fcbf(self.X_i, self.y_i, threshold=0.05)
expected = [0.0, 0.0, 0.810724587460511, 0.870521418179061]
self.assertListAlmostEqual(expected, mfs._compute_su_labels().tolist())
@@ -115,12 +132,12 @@ class MFS_test(unittest.TestCase):
self.assertListAlmostEqual([1, 2, 3, 4], mfs._compute_su_labels())
def test_invalid_threshold(self):
mfs = MFS(max_features=100)
mfs = MFS()
with self.assertRaises(ValueError):
mfs.fcbf(self.X_i, self.y_i, threshold=1e-15)
def test_fcbf_exit_threshold(self):
mfs = MFS(max_features=100)
mfs = MFS()
computed = mfs.fcbf(self.X_w, self.y_w, threshold=0.4).get_results()
expected = [6, 9, 12]
self.assertListAlmostEqual(expected, computed)