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Author: JingweiToo

Demo_PSO.py

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+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
+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 using k-fold cross-validation
+xtrain, xtest, ytrain, ytest = train_test_split(feat, label, test_size=0.3, stratify=label)
+fold = {'xt':xtrain, 'yt':ytrain, 'xv':xtest, 'yv':ytest}
+
+# feature selection
+k    = 5
+opts = {'k':k, 'fold':fold, 'N':10, 'T':100, 'w':0.9, 'c1':2, 'c2':2}
+
+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)
+
+# validation accuracy
+pred      = mdl.predict(x_valid)
+correct   = 0
+for i in range(num_valid):
+    if pred[i] == y_valid[i]:
+        correct += 1
+
+accuracy = correct / num_valid
+print("Accuracy:", 100 * accuracy)
+
+# 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()
+