from FS.pso import jfs 

from FS.de import jfs 

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() 

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()