README.md

Jx-WFST : Wrapper Feature Selection Toolbox

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"Toward Talent Scientist: Sharing and Learning Together" --- Jingwei Too

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Introduction

• This toolbox offers 13 wrapper feature selection methods
• The Demo_PSO provides an example of how to apply PSO on benchmark dataset
• Source code of these methods are written based on pseudocode & paper

Usage

The main function jfs is adopted to perform feature selection. You may switch the algorithm by changing the pso in from FS.pso import jfs to other abbreviations

• If you wish to use particle swarm optimization ( PSO ) then you may write

from FS.pso import jfs 

• If you want to use differential evolution ( DE ) then you may write

from FS.de import jfs 

Input

• feat : feature vector matrix ( Instance x Features )
• label : label matrix ( Instance x 1 )
• opts : parameter settings
  • N : number of solutions / population size ( for all methods )
  • T : maximum number of iterations ( for all methods )
  • k : k-value in k-nearest neighbor

Output

• Acc : accuracy of validation model
• fmdl : feature selection model ( It contains several results )
  • sf : index of selected features
  • nf : number of selected features
  • c : convergence curve

Example 1 : Particle Swarm Optimization ( PSO )

import numpy as np import pandas as pd from sklearn.neighbors import KNeighborsClassifier from sklearn.model_selection import train_test_split from FS.pso import jfs # change this to switch algorithm import matplotlib.pyplot as plt # load data data = pd.read_csv('ionosphere.csv') data = data.values feat = np.asarray(data[:, 0:-1]) # feature vector label = np.asarray(data[:, -1]) # label vector # split data into train & validation (70 -- 30) xtrain, xtest, ytrain, ytest = train_test_split(feat, label, test_size=0.3, stratify=label) fold = {'xt':xtrain, 'yt':ytrain, 'xv':xtest, 'yv':ytest} # parameter k = 5 # k-value in KNN N = 10 # number of particles T = 100 # maximum number of iterations w = 0.9 c1 = 2 c2 = 2 opts = {'k':k, 'fold':fold, 'N':N, 'T':T, 'w':w, 'c1':c1, 'c2':c2} # perform feature selection fmdl = jfs(feat, label, opts) sf = fmdl['sf'] # model with selected features num_train = np.size(xtrain, 0) num_valid = np.size(xtest, 0) x_train = xtrain[:, sf] y_train = ytrain.reshape(num_train) # Solve bug x_valid = xtest[:, sf] y_valid = ytest.reshape(num_valid) # Solve bug mdl = KNeighborsClassifier(n_neighbors = k) mdl.fit(x_train, y_train) # accuracy y_pred = mdl.predict(x_valid) Acc = np.sum(y_valid == y_pred) / num_valid print("Accuracy:", 100 * Acc) # number of selected features num_feat = fmdl['nf'] print("Feature Size:", num_feat) # plot convergence curve = fmdl['c'] curve = curve.reshape(np.size(curve,1)) x = np.arange(0, opts['T'], 1.0) + 1.0 fig, ax = plt.subplots() ax.plot(x, curve, 'o-') ax.set_xlabel('Number of Iterations') ax.set_ylabel('Fitness') ax.set_title('PSO') ax.grid() plt.show() 

Example 2 : Genetic Algorithm ( GA )

import numpy as np import pandas as pd from sklearn.neighbors import KNeighborsClassifier from sklearn.model_selection import train_test_split from FS.ga import jfs # change this to switch algorithm import matplotlib.pyplot as plt # load data data = pd.read_csv('ionosphere.csv') data = data.values feat = np.asarray(data[:, 0:-1]) label = np.asarray(data[:, -1]) # split data into train & validation (70 -- 30) xtrain, xtest, ytrain, ytest = train_test_split(feat, label, test_size=0.3, stratify=label) fold = {'xt':xtrain, 'yt':ytrain, 'xv':xtest, 'yv':ytest} # parameter k = 5 # k-value in KNN N = 10 # number of chromosomes T = 100 # maximum number of generations CR = 0.8 MR = 0.01 opts = {'k':k, 'fold':fold, 'N':N, 'T':T, 'CR':CR, 'MR':MR} # perform feature selection fmdl = jfs(feat, label, opts) sf = fmdl['sf'] # model with selected features num_train = np.size(xtrain, 0) num_valid = np.size(xtest, 0) x_train = xtrain[:, sf] y_train = ytrain.reshape(num_train) # Solve bug x_valid = xtest[:, sf] y_valid = ytest.reshape(num_valid) # Solve bug mdl = KNeighborsClassifier(n_neighbors = k) mdl.fit(x_train, y_train) # accuracy y_pred = mdl.predict(x_valid) Acc = np.sum(y_valid == y_pred) / num_valid print("Accuracy:", 100 * Acc) # number of selected features num_feat = fmdl['nf'] print("Feature Size:", num_feat) # plot convergence curve = fmdl['c'] curve = curve.reshape(np.size(curve,1)) x = np.arange(0, opts['T'], 1.0) + 1.0 fig, ax = plt.subplots() ax.plot(x, curve, 'o-') ax.set_xlabel('Number of Iterations') ax.set_ylabel('Fitness') ax.set_title('GA') ax.grid() plt.show() 

Requirement

• Python 3
• Numpy
• Pandas
• Scikit-learn
• Matplotlib

List of available wrapper feature selection methods

• Note that the methods are altered so that they can be used in feature selection tasks
• The extra parameters represent the parameter(s) other than population size and maximum number of iterations
• Click on the name of method to view how to set the extra parameter(s)
• Use the opts to set the specific parameters
• If you do not set extra parameters then the algorithm will use default setting in here

No.	Abbreviation	Name	Year	Extra Parameters
13	hho	Harris Hawk Optimization	2019	No
12	ssa	Salp Swarm Algorithm	2017	No
11	woa	Whale Optimization Algorithm	2016	Yes
10	sca	Sine Cosine Algorithm	2016	Yes
09	ja	Jaya Algorithm	2016	No
08	gwo	Grey Wolf Optimizer	2014	No
07	fpa	Flower Pollination Algorithm	2012	Yes
06	ba	Bat Algorithm	2010	Yes
05	fa	Firefly Algorithm	2010	Yes
04	cs	Cuckoo Search Algorithm	2009	Yes
03	de	Differential Evolution	1997	Yes
02	pso	Particle Swarm Optimization	1995	Yes
01	ga	Genetic Algorithm	-	Yes