Update version number and sample

Add iwss (#2)

.github/workflows/main.yml

@@ -27,7 +27,7 @@ jobs:
           pip install -q cython
           pip install -q numpy
           pip install -q git+git://github.com/doctorado-ml/mdlp
-          pip install -q -r requirements.txt
+          pip install -q -r requirements/dev.txt
           pip install -q --upgrade codecov coverage black flake8 codacy-coverage
       - name: Lint
         run: |

README.md

@@ -1,6 +1,8 @@
 ![CI](https://github.com/Doctorado-ML/mufs/workflows/CI/badge.svg)
 [![Codacy Badge](https://app.codacy.com/project/badge/Grade/66ad727eb13e4c7a8816db1e44d994a7)](https://www.codacy.com/gh/Doctorado-ML/mufs/dashboard?utm_source=github.com&utm_medium=referral&utm_content=Doctorado-ML/mufs&utm_campaign=Badge_Grade)
 [![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/Doctorado-ML/mufs.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/Doctorado-ML/mufs/context:python)
+[![PyPI version](https://badge.fury.io/py/MUFS.svg)](https://badge.fury.io/py/MUFS)
+![https://img.shields.io/badge/python-3.8%2B-blue](https://img.shields.io/badge/python-3.8%2B-brightgreen)
 
 # MUFS
 
@@ -15,3 +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.

mufs/Selection.py

@@ -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 +268,58 @@ 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()
+                break
+            if len(candidates) == self._max_features:
+                break
+        self._result = candidates
+        return self

mufs/__init__.py

@@ -1,6 +1,6 @@
 from .Selection import MUFS
 
-__version__ = "0.1.1"
+__version__ = "0.1.2"
 __author__ = "Ricardo Montañana Gómez"
 __author_email__ = "Ricardo.Montanana@alu.uclm.es"
 __copyright__ = "Copyright 2021, Ricardo Montañana Gómez"

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)
@@ -32,7 +35,7 @@ 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 +81,7 @@ 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 +151,46 @@ 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):
+        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())
+
+    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 .MUFS_test import MUFSTest
-from .Metrics_test import Metrics_test
+from .Metrics_test import MetricsTest
 
-__all__ = ["MUFS_test", "Metrics_test"]
+__all__ = ["MUFSTest", "MetricsTest"]

mufs/tests/balloons_R.dat

@@ -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

requirements/dev.txt

@@ -0,0 +1,3 @@
+-r production.txt
+mdlp
+pandas

requirements/production.txt

@@ -1,2 +1 @@
 scikit-learn>0.24
-mdlp

sample.py

@@ -1,4 +1,5 @@
 import warnings
+import time
 from mufs import MUFS
 from mufs.Metrics import Metrics
 from stree import Stree
@@ -26,16 +27,26 @@ for i in range(n):
 # Classification
 warnings.filterwarnings("ignore")
 print("CFS")
+now = time.time()
 cfs_f = mufsc.cfs(X, y).get_results()
-print(cfs_f)
+time_cfs = time.time() - now
+print(cfs_f, "items: ", len(cfs_f), f"time: {time_cfs:.3f} seconds")
 print("FCBF")
-fcfb_f = mufsc.fcbf(X, y, 5e-2).get_results()
+now = time.time()
-print(fcfb_f, len(fcfb_f))
+fcbf_f = mufsc.fcbf(X, y, 0.07).get_results()
+time_fcbf = time.time() - now
+print(fcbf_f, "items: ", len(fcbf_f), f"time: {time_fcbf:.3f} seconds")
+now = time.time()
+print("IWSS")
+iwss_f = mufsc.iwss(X, y, 0.5).get_results()
+time_iwss = time.time() - now
+print(iwss_f, "items: ", len(iwss_f), f"time: {time_iwss:.3f} seconds")
 print("X.shape=", X.shape)
 clf = Stree(random_state=0)
 print("Accuracy whole dataset", clf.fit(X, y).score(X, y))
 clf = Stree(random_state=0)
 print("Accuracy cfs", clf.fit(X[:, cfs_f], y).score(X[:, cfs_f], y))
 clf = Stree(random_state=0)
-subf = fcfb_f
+print("Accuracy fcfb", clf.fit(X[:, fcbf_f], y).score(X[:, fcbf_f], y))
-print("Accuracy fcfb", clf.fit(X[:, subf], y).score(X[:, subf], y))
+clf = Stree(random_state=0)
+print("Accuracy iwss", clf.fit(X[:, iwss_f], y).score(X[:, iwss_f], y))