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Begin IWSS implementation

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Update requirements
Create requirements for dev
This commit is contained in:
Ricardo Montañana Gómez
2021-10-10 19:06:57 +02:00
parent 9d74bc8a70
commit 27f8a370c5
6 changed files with 128 additions and 6 deletions
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4
README.md
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@@ -17,3 +17,7 @@ Proceedings, Twentieth International Conference on Machine Learning. ed. / T. Fa
### Correlation-based Feature Selection
Hall, M. A. (1999), 'Correlation-based Feature Selection for Machine Learning'.
### IWSS
Based on: P. Bermejo, J. A. Gamez and J. M. Puerta, "Incremental Wrapper-based subset Selection with replacement: An advantageous alternative to sequential forward selection," 2009 IEEE Symposium on Computational Intelligence and Data Mining, 2009, pp. 367-374, doi: 10.1109/CIDM.2009.4938673.

64
mufs/Selection.py
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@@ -26,7 +26,7 @@ class MUFS:
"""
def __init__(self, max_features=None, discrete=True):
self._max_features = max_features
self.max_features = max_features
self._discrete = discrete
self.symmetrical_uncertainty = (
Metrics.symmetrical_uncertainty
@@ -53,8 +53,10 @@ class MUFS:
"""
self.X_ = X
self.y_ = y
if self._max_features is None:
if self.max_features is None:
self._max_features = X.shape[1]
else:
self._max_features = self.max_features
self._result = None
self._scores = []
self._su_labels = None
@@ -105,7 +107,9 @@ class MUFS:
def _compute_merit(self, features):
"""Compute the merit function for cfs algorithms
"Good feature subsets contain features highly correlated with
(predictive of) the class, yet uncorrelated with (not predictive of)
each other"
Parameters
----------
features : list
@@ -264,3 +268,57 @@ class MUFS:
list of scores of the features selected
"""
return self._scores if self._fitted else []
def iwss(self, X, y, threshold):
"""Incremental Wrapper Subset Selection
Parameters
----------
X : np.array
array of features
y : np.array
vector of labels
threshold : float
threshold to select relevant features
Returns
-------
self
self
Raises
------
ValueError
if the threshold is less than a selected value of 1e-7
or greater than .5
"""
if threshold < 0 or threshold > 0.5:
raise ValueError(
"Threshold cannot be less than 0 or greater than 0.5"
)
self._initialize(X, y)
s_list = self._compute_su_labels()
feature_order = (-s_list).argsort()
features = feature_order.copy().tolist()
candidates = []
# Add first and second features to result
first_feature = features.pop(0)
candidates.append(first_feature)
self._scores.append(s_list[first_feature])
candidates.append(features.pop(0))
merit = self._compute_merit(candidates)
self._scores.append(merit)
for feature in features:
candidates.append(feature)
merit_new = self._compute_merit(candidates)
delta = abs(merit - merit_new) / merit if merit != 0.0 else 0.0
if merit_new > merit or delta < threshold:
if merit_new > merit:
merit = merit_new
self._scores.append(merit_new)
else:
candidates.pop()
if len(candidates) == self._max_features:
break
self._result = candidates
return self

45
mufs/tests/MUFS_test.py
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@@ -32,7 +32,7 @@ class MUFS_test(unittest.TestCase):
def test_csf_wine(self):
mufs = MUFS()
expected = [6, 12, 9, 4, 10, 0]
self.assertListAlmostEqual(
self.assertListEqual(
expected, mufs.cfs(self.X_w, self.y_w).get_results()
)
expected = [
@@ -78,7 +78,7 @@ class MUFS_test(unittest.TestCase):
mufs = MUFS()
expected = [3, 2, 0, 1]
computed = mufs.cfs(self.X_i, self.y_i).get_results()
self.assertListAlmostEqual(expected, computed)
self.assertListEqual(expected, computed)
expected = [
0.870521418179061,
0.8968651482682227,
@@ -148,3 +148,44 @@ class MUFS_test(unittest.TestCase):
0.44518278979085646,
]
self.assertListAlmostEqual(expected, mufs.get_scores())
def test_iwss_wine(self):
mufs = MUFS()
expected = [6, 9, 12]
self.assertListEqual(
expected, mufs.iwss(self.X_w, self.y_w, 0.2).get_results()
)
expected = [0.5218299405215557, 0.5947822876110085, 0.4877384978817362]
self.assertListAlmostEqual(expected, mufs.get_scores())
def test_iwss_wine_max_features(self):
mufs = MUFS(max_features=3)
expected = [6, 9, 12]
self.assertListEqual(
expected, mufs.iwss(self.X_w, self.y_w, 0.4).get_results()
)
expected = [0.5218299405215557, 0.5947822876110085, 0.4877384978817362]
self.assertListAlmostEqual(expected, mufs.get_scores())
def test_iwss_exception(self):
mufs = MUFS()
with self.assertRaises(ValueError):
mufs.iwss(self.X_w, self.y_w, 0.51)
with self.assertRaises(ValueError):
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"),
sep="\t",
index_col=0,
)
X = data.drop("clase", axis=1).to_numpy()
y = data["clase"].to_numpy()
mufs = MUFS()
expected = [0, 2, 3, 1]
self.assertListEqual(expected, mufs.iwss(X, y, 0.3).get_results())

17
mufs/tests/balloons_R.dat Executable file
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@@ -0,0 +1,17 @@
f1 f2 f3 f4 clase
1 0.968246 -0.968246 0.968246 0.968246 1
2 0.968246 -0.968246 0.968246 -0.968246 1
3 0.968246 -0.968246 -0.968246 0.968246 1
4 0.968246 -0.968246 -0.968246 -0.968246 1
5 0.968246 0.968246 0.968246 0.968246 1
6 0.968246 0.968246 0.968246 -0.968246 0
7 0.968246 0.968246 -0.968246 0.968246 0
8 0.968246 0.968246 -0.968246 -0.968246 0
9 -0.968246 -0.968246 0.968246 0.968246 1
10 -0.968246 -0.968246 0.968246 -0.968246 0
11 -0.968246 -0.968246 -0.968246 0.968246 0
12 -0.968246 -0.968246 -0.968246 -0.968246 0
13 -0.968246 0.968246 0.968246 0.968246 1
14 -0.968246 0.968246 0.968246 -0.968246 0
15 -0.968246 0.968246 -0.968246 0.968246 0
16 -0.968246 0.968246 -0.968246 -0.968246 0

3
requirements/dev.txt Normal file
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@@ -0,0 +1,3 @@
-r production.txt
mdlp
pandas

1
requirements.txt → requirements/production.txt
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@@ -1,2 +1 @@
scikit-learn>0.24
mdlp