from sklearn.ensemble import AdaBoostClassifier
from getData import getdata, evaluate, MCC, Get_f1_score, Get_Accuracy, Get_Precision_score, Get_Recall
clf = AdaBoostClassifier()
address = ''
x_train, y_train, x_test, y_test = getdata(address)
clf.fit(x_train, y_train)
y_predict = clf.predict(x_test)
accuracy = Get_Accuracy(y_test, y_predict)
accuracy = round(accuracy, 3)
precision = Get_Precision_score(y_test, y_predict)
precision = round(precision, 3)
recall = Get_Recall(y_test, y_predict)
recall = round(recall, 3)
f1_score = Get_f1_score(y_test, y_predict)
f1_score = round(f1_score, 3)
mcc = MCC(y_test, y_predict)
mcc = round(mcc, 3)
print('\n')
evaluate(y_test, y_predict)
print("Precision = ", precision)
print("Accuracy_Score = ", accuracy)
print("F1-Score = ", f1_score)
print("MCC = ", mcc)
def IFS(address, importence_rank):
x_train, y_train, x_test, y_test = getdata(address)
for i in range(len(importence_rank)):
y_test = []
y_train = []
flag = importence_rank[i]
fileList = os.listdir(address + "\\train\positive")
flag_train = 0
for name in fileList:
x_train[flag_train].append(
np.load(address + "\\train\positive\\" + name)[flag])
y_train.append(1)
flag_train += 1
fileList = os.listdir(address + "\\train\\negitive")
for name in fileList:
x_train[flag_train].append(
np.load(address + "\\train\\negitive\\" + name)[flag])
y_train.append(0)
flag_train += 1
fileList = os.listdir(address + "\\test\positive")
flag_test = 0
for name in fileList:
x_test[flag_test].append(
np.load(address + "\\test\positive\\" + name)[flag])
y_test.append(1)
flag_test += 1
fileList = os.listdir(address + "\\test\\negitive")
for name in fileList:
x_test[flag_test].append(
np.load(address + "\\test\\negitive\\" + name)[flag])
flag_test += 1
y_test.append(0)
kf = KFold(n_splits=10)
kf.get_n_splits(x_train)
accuracy_ten = 0
precision_ten = 0
recall_ten = 0
f1_score_ten = 0
mcc_ten = 0
sen_ten = 0
spe_ten = 0
x2_train, y2_train = shuffle(x_train, y_train, random_state=0)
for train_index, test_index in kf.split(x_train):
x1_train = []
y1_train = []
x1_test = []
y1_test = []
for i in range(len(train_index)):
x1_train.append(x2_train[train_index[i]])
y1_train.append(y2_train[train_index[i]])
for i in range(len(test_index)):
x1_test.append(x2_train[test_index[i]])
y1_test.append(y2_train[test_index[i]])
x1_train = np.array(x1_train)
y1_train = np.array(y1_train)
x1_test = np.array(x1_test)
y1_test = np.array(y1_test)
clf = XGBClassifier()
clf.fit(x1_train, y1_train)
y_predict = clf.predict(x1_test)
accuracy = Get_Accuracy(y1_test, y_predict)
accuracy_ten += accuracy
precision = Get_Precision_score(y1_test, y_predict)
precision_ten += precision
recall = Get_Recall(y1_test, y_predict)
recall_ten += recall
f1_score = Get_f1_score(y1_test, y_predict)
f1_score_ten += f1_score
mcc = MCC(y1_test, y_predict)
mcc_ten += mcc
sen, spe = evaluate(y1_test, y_predict)
sen_ten += sen
spe_ten += spe
accuracy = accuracy_ten / 10
precision = precision_ten / 10
f1_score = f1_score_ten / 10
mcc = mcc_ten / 10
spe = spe_ten / 10
sen = sen_ten / 10
accuracy = round(accuracy, 3)
precision = round(precision, 3)
f1_score = round(f1_score, 3)
spe = round(spe, 3)
sen = round(sen, 3)
mcc = round(mcc, 3)
acc_list.append(accuracy)
pre_list.append(precision)
f1_list.append(f1_score)
mcc_list.append(mcc)
sen_list.append(sen)
spe_list.append(spe)
