Jx-AFST : Advanced Feature Selection Toolbox

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

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Introduction

• This toolbox offers several advanced wrapper feature selection methods
• The Demo_ISSA file provides an example of how to apply ISSA 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 issa in from AFS.issa import jfs to other abbreviations

• If you wish to use improved salp swarm algorithm ( ISSA ) then you may write

from AFS.issa import jfs

• If you want to use time varying binary salp swarm algorithm ( TVBSSA ) then you may write

from AFS.tvbssa 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 : Improved Salp Swarm Algorithm ( ISSA )

import numpy as np
import pandas as pd
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
from AFS.issa 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 salps
T    = 100   # maximum number of iterations
opts = {'k':k, 'fold':fold, 'N':N, 'T':T}

# 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('ISSA')
ax.grid()
plt.show()

Requirement

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

List of available advanced 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 the extra parameter(s)
• Use the opts to set the specific parameter(s)
• If you do not set extra parameters then the algorithm will use default setting in here

No.	Abbreviation	Name	Year	Extra Parameters
08	tmgwo	Two-phase Mutation Grey Wolf Optimizer	2020	Yes
07	tvbssa	Time Varying Binary Salp Swarm Algorithm	2020	No
06	issa	Improved Salp Swarm Algorithm	2020	Yes
05	essa	Enhanced Salp Swarm Algorithm	2019	No
04	mgfpa	Modified Global Flower Pollination Algorithm	2018	Yes
03	obwoa	Opposition Based Whale Optimization Algorithm	2018	Yes
02	isca	Improved Sine Cosine Algorithm	2017	Yes
01	bbpso	Bare Bones Particle Swarm Optimization	2003	No