mirror of
https://github.com/Doctorado-ML/mufs.git
synced 2025-08-16 16:15:56 +00:00
2
.github/workflows/main.yml
vendored
2
.github/workflows/main.yml
vendored
@@ -27,7 +27,7 @@ jobs:
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pip install -q cython
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pip install -q cython
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pip install -q numpy
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pip install -q numpy
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pip install -q git+git://github.com/doctorado-ml/mdlp
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pip install -q git+git://github.com/doctorado-ml/mdlp
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pip install -q -r requirements.txt
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pip install -q -r requirements/dev.txt
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pip install -q --upgrade codecov coverage black flake8 codacy-coverage
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pip install -q --upgrade codecov coverage black flake8 codacy-coverage
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- name: Lint
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- name: Lint
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run: |
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run: |
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@@ -17,3 +17,7 @@ Proceedings, Twentieth International Conference on Machine Learning. ed. / T. Fa
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### Correlation-based Feature Selection
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### Correlation-based Feature Selection
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Hall, M. A. (1999), 'Correlation-based Feature Selection for Machine Learning'.
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Hall, M. A. (1999), 'Correlation-based Feature Selection for Machine Learning'.
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### IWSS
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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.
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@@ -26,7 +26,7 @@ class MUFS:
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"""
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"""
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def __init__(self, max_features=None, discrete=True):
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def __init__(self, max_features=None, discrete=True):
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self._max_features = max_features
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self.max_features = max_features
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self._discrete = discrete
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self._discrete = discrete
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self.symmetrical_uncertainty = (
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self.symmetrical_uncertainty = (
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Metrics.symmetrical_uncertainty
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Metrics.symmetrical_uncertainty
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@@ -53,8 +53,10 @@ class MUFS:
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"""
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"""
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self.X_ = X
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self.X_ = X
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self.y_ = y
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self.y_ = y
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if self._max_features is None:
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if self.max_features is None:
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self._max_features = X.shape[1]
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self._max_features = X.shape[1]
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else:
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self._max_features = self.max_features
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self._result = None
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self._result = None
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self._scores = []
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self._scores = []
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self._su_labels = None
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self._su_labels = None
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@@ -105,7 +107,9 @@ class MUFS:
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def _compute_merit(self, features):
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def _compute_merit(self, features):
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"""Compute the merit function for cfs algorithms
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"""Compute the merit function for cfs algorithms
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"Good feature subsets contain features highly correlated with
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(predictive of) the class, yet uncorrelated with (not predictive of)
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each other"
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Parameters
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Parameters
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----------
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----------
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features : list
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features : list
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@@ -264,3 +268,58 @@ class MUFS:
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list of scores of the features selected
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list of scores of the features selected
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"""
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"""
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return self._scores if self._fitted else []
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return self._scores if self._fitted else []
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def iwss(self, X, y, threshold):
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"""Incremental Wrapper Subset Selection
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Parameters
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----------
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X : np.array
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array of features
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y : np.array
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vector of labels
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threshold : float
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threshold to select relevant features
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Returns
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-------
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self
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self
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Raises
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------
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ValueError
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if the threshold is less than a selected value of 1e-7
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or greater than .5
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"""
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if threshold < 0 or threshold > 0.5:
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raise ValueError(
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"Threshold cannot be less than 0 or greater than 0.5"
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)
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self._initialize(X, y)
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s_list = self._compute_su_labels()
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feature_order = (-s_list).argsort()
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features = feature_order.copy().tolist()
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candidates = []
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# Add first and second features to result
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first_feature = features.pop(0)
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candidates.append(first_feature)
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self._scores.append(s_list[first_feature])
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candidates.append(features.pop(0))
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merit = self._compute_merit(candidates)
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self._scores.append(merit)
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for feature in features:
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candidates.append(feature)
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merit_new = self._compute_merit(candidates)
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delta = abs(merit - merit_new) / merit if merit != 0.0 else 0.0
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if merit_new > merit or delta < threshold:
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if merit_new > merit:
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merit = merit_new
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self._scores.append(merit_new)
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else:
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candidates.pop()
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break
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if len(candidates) == self._max_features:
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break
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self._result = candidates
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return self
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@@ -1,11 +1,14 @@
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import unittest
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import unittest
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import os
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import pandas as pd
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import numpy as np
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from mdlp import MDLP
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from mdlp import MDLP
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from sklearn.datasets import load_wine, load_iris
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from sklearn.datasets import load_wine, load_iris
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from ..Selection import MUFS
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from ..Selection import MUFS
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class MUFS_test(unittest.TestCase):
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class MUFSTest(unittest.TestCase):
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def __init__(self, *args, **kwargs):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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super().__init__(*args, **kwargs)
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mdlp = MDLP(random_state=1)
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mdlp = MDLP(random_state=1)
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@@ -32,7 +35,7 @@ class MUFS_test(unittest.TestCase):
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def test_csf_wine(self):
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def test_csf_wine(self):
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mufs = MUFS()
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mufs = MUFS()
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expected = [6, 12, 9, 4, 10, 0]
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expected = [6, 12, 9, 4, 10, 0]
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self.assertListAlmostEqual(
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self.assertListEqual(
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expected, mufs.cfs(self.X_w, self.y_w).get_results()
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expected, mufs.cfs(self.X_w, self.y_w).get_results()
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)
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)
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expected = [
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expected = [
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@@ -78,7 +81,7 @@ class MUFS_test(unittest.TestCase):
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mufs = MUFS()
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mufs = MUFS()
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expected = [3, 2, 0, 1]
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expected = [3, 2, 0, 1]
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computed = mufs.cfs(self.X_i, self.y_i).get_results()
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computed = mufs.cfs(self.X_i, self.y_i).get_results()
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self.assertListAlmostEqual(expected, computed)
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self.assertListEqual(expected, computed)
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expected = [
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expected = [
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0.870521418179061,
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0.870521418179061,
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0.8968651482682227,
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0.8968651482682227,
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@@ -148,3 +151,46 @@ class MUFS_test(unittest.TestCase):
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0.44518278979085646,
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0.44518278979085646,
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]
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]
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self.assertListAlmostEqual(expected, mufs.get_scores())
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self.assertListAlmostEqual(expected, mufs.get_scores())
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def test_iwss_wine(self):
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mufs = MUFS()
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expected = [6, 9, 12]
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self.assertListEqual(
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expected, mufs.iwss(self.X_w, self.y_w, 0.2).get_results()
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)
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expected = [0.5218299405215557, 0.5947822876110085, 0.4877384978817362]
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self.assertListAlmostEqual(expected, mufs.get_scores())
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def test_iwss_wine_max_features(self):
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mufs = MUFS(max_features=3)
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expected = [6, 9, 12]
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self.assertListEqual(
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expected, mufs.iwss(self.X_w, self.y_w, 0.4).get_results()
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)
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expected = [0.5218299405215557, 0.5947822876110085, 0.4877384978817362]
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self.assertListAlmostEqual(expected, mufs.get_scores())
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def test_iwss_exception(self):
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mufs = MUFS()
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with self.assertRaises(ValueError):
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mufs.iwss(self.X_w, self.y_w, 0.51)
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with self.assertRaises(ValueError):
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mufs.iwss(self.X_w, self.y_w, -0.01)
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def test_iwss_better_merit_condition(self):
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folder = os.path.dirname(os.path.abspath(__file__))
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data = pd.read_csv(
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os.path.join(folder, "balloons_R.dat"),
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sep="\t",
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index_col=0,
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)
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X = data.drop("clase", axis=1).to_numpy()
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y = data["clase"].to_numpy()
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mufs = MUFS()
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expected = [0, 2, 3, 1]
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self.assertListEqual(expected, mufs.iwss(X, y, 0.3).get_results())
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def test_iwss_empty(self):
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mufs = MUFS()
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X = np.delete(self.X_i, [0, 1], 1)
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self.assertListEqual(mufs.iwss(X, self.y_i, 0.3).get_results(), [1, 0])
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@@ -6,7 +6,7 @@ from mdlp import MDLP
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from ..Selection import Metrics
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from ..Selection import Metrics
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class Metrics_test(unittest.TestCase):
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class MetricsTest(unittest.TestCase):
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def __init__(self, *args, **kwargs):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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super().__init__(*args, **kwargs)
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mdlp = MDLP(random_state=1)
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mdlp = MDLP(random_state=1)
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@@ -1,4 +1,4 @@
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from .MUFS_test import MUFS_test
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from .MUFS_test import MUFSTest
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from .Metrics_test import Metrics_test
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from .Metrics_test import MetricsTest
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__all__ = ["MUFS_test", "Metrics_test"]
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__all__ = ["MUFSTest", "MetricsTest"]
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17
mufs/tests/balloons_R.dat
Executable file
17
mufs/tests/balloons_R.dat
Executable file
@@ -0,0 +1,17 @@
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f1 f2 f3 f4 clase
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1 0.968246 -0.968246 0.968246 0.968246 1
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2 0.968246 -0.968246 0.968246 -0.968246 1
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3 0.968246 -0.968246 -0.968246 0.968246 1
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4 0.968246 -0.968246 -0.968246 -0.968246 1
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5 0.968246 0.968246 0.968246 0.968246 1
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6 0.968246 0.968246 0.968246 -0.968246 0
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7 0.968246 0.968246 -0.968246 0.968246 0
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8 0.968246 0.968246 -0.968246 -0.968246 0
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9 -0.968246 -0.968246 0.968246 0.968246 1
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11 -0.968246 -0.968246 -0.968246 0.968246 0
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12 -0.968246 -0.968246 -0.968246 -0.968246 0
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13 -0.968246 0.968246 0.968246 0.968246 1
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15 -0.968246 0.968246 -0.968246 0.968246 0
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16 -0.968246 0.968246 -0.968246 -0.968246 0
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3
requirements/dev.txt
Normal file
3
requirements/dev.txt
Normal file
@@ -0,0 +1,3 @@
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-r production.txt
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mdlp
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pandas
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@@ -1,2 +1 @@
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scikit-learn>0.24
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scikit-learn>0.24
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mdlp
|
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Reference in New Issue
Block a user