Advanced-Feature-Selection-Toolbox

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Advanced-Feature-Selection-Toolbox

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This toolbox offers advanced feature selection tools. Several modifications, variants, enhancements, or improvements of algorithms such as GWO, FPA, SCA, PSO and SSA are provided.

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# Jx-AFST : Advanced Feature Selection Toolbox

[![License](https://img.shields.io/badge/license-BSD_3-blue.svg)](https://github.com/JingweiToo/Advanced-Feature-Selection-Toolbox/blob/main/LICENSE)
[![GitHub release](https://img.shields.io/badge/release-pre-yellow.svg)](https://github.com/JingweiToo/Advanced-Feature-Selection-Toolbox)

---
> "Toward Talent Scientist: Sharing and Learning Together"
>  --- [Jingwei Too](https://jingweitoo.wordpress.com/)
---

![Wheel](https://github.com/JingweiToo/Advanced-Feature-Selection-Toolbox/blob/main/Capture.JPG)

## 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](/README.md#list-of-available-advanced-feature-selection-methods)
* If you wish to use improved salp swarm algorithm ( ISSA ) then you may write
```code
from AFS.issa import jfs
```
* If you want to use time varying binary salp swarm algorithm ( TVBSSA ) then you may write
```code
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 ) 
```code 
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](/Description.md)

| No. | Abbreviation | Name                                                                                                                | Year | Extra Parameters |
|-----|--------------|---------------------------------------------------------------------------------------------------------------------|------|------------------|
| 08  | `tmgwo`      | [Two-phase Mutation Grey Wolf Optimizer](Description.md#two-phase-mutation-grey-wolf-optimizer-tmgwo)               | 2020 | Yes              |
| 07  | `tvbssa`     | Time Varying Binary Salp Swarm Algorithm                                                                            | 2020 | No               |
| 06  | `issa`       | [Improved Salp Swarm Algorithm](/Description.md#improved-salp-swarm-algorithm-issa)                                 | 2020 | Yes              |
| 05  | `essa`       | Enhanced Salp Swarm Algorithm                                                                                       | 2019 | No               |
| 04  | `mgfpa`      | [Modified Global Flower Pollination Algorithm](Description.md#modified-global-flower-pollination-algorithm-mgfpa)   | 2018 | Yes              |
| 03  | `obwoa`      | [Opposition Based Whale Optimization Algorithm](Description.md#opposition-based-whale-optimization-algorithm-obwoa) | 2018 | Yes              |
| 02  | `isca`       | [Improved Sine Cosine Algorithm](Description.md#improved-sine-cosine-algorithm-isca)                                | 2017 | Yes              |
| 01  | `bbpso`      | Bare Bones Particle Swarm Optimization                                                                              | 2003 | No               |

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