def trainELMClassifier(trainData, trainLabels, testData):
print("\nTraining ELM Classifier...")
trainData = np.asarray(trainData)
trainLabels = np.asarray(trainLabels)
print(trainData.shape)
print(trainLabels.shape)
# create initialize elm activation functions
nh = 100
activation = 'tanh'
if activation == 'rbf':
act_layer = RBFRandomLayer(n_hidden=nh,
random_state=0,
rbf_width=0.001)
elif activation == 'tanh':
act_layer = MLPRandomLayer(n_hidden=nh, activation_func='tanh')
elif activation == 'tribas':
act_layer = MLPRandomLayer(n_hidden=nh, activation_func='tribas')
elif activation == 'hardlim':
act_layer = MLPRandomLayer(n_hidden=nh, activation_func='hardlim')
# initialize ELM Classifier
elm = GenELMClassifier(hidden_layer=act_layer)
t0 = time()
elm.fit(trainData, trainLabels)
print("\nTraining finished in %0.3fs \n" % (time() - t0))
t0 = time()
predictedLabels = elm.predict(testData)
print("\nTesting finished in %0.3fs" % (time() - t0))
t0 = time()
confidence_scores = elm.decision_function(testData)
print("\nTesting finished in %0.3fs" % (time() - t0))
print("\nPredicted Labels")
print("----------------------------------")
print(predictedLabels)
print("\nConfidence Scores")
print("----------------------------------")
print(confidence_scores)
params = {
'nh': nh,
'af': activation,
}
return confidence_scores, predictedLabels, params
for train_index, test_index in sKF.split(std_X, y):
i += 1
x_train = std_X[train_index]
y_train = y[train_index]
x_test = std_X[test_index]
y_test = y[test_index]
#-------------------------------------------------------------------------------
grbf = GRBFRandomLayer(n_hidden=500, grbf_lambda=0.0001)
act = MLPRandomLayer(n_hidden=500, activation_func='sigmoid')
rbf = RBFRandomLayer(n_hidden=290,
rbf_width=0.0001,
activation_func='sigmoid')
clf = GenELMClassifier(hidden_layer=rbf)
clf.fit(x_train, y_train.ravel())
y_pre = clf.predict(x_test)
y_score = clf.decision_function(x_test)
fpr, tpr, thresholds = roc_curve(y_test, y_score)
tprs.append(tpr)
fprs.append(fpr)
roc_auc = auc(fpr, tpr)
tn, fp, fn, tp = confusion_matrix(y_test, y_pre).ravel()
test_acc = (tn + tp) / (tn + fp + fn + tp)
test_Sn = tp / (fn + tp)
test_Sp = tn / (fp + tn)
mcc = (tp * tn - fp * fn) / pow(
((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn)), 0.5)
final_test_acc.append(test_acc)
final_test_Sn.append(test_Sn)
final_test_Sp.append(test_Sp)
final_mcc.append(mcc)