def Ada(method, norm, selection):
print(
'---------------------------------------------------------------------------------------------------'
)
print('AdaBoost')
acc_save = []
f1_save = []
auc_save = []
dataset = np.loadtxt('new.csv', delimiter=',')
[a, b] = dataset.shape
dataset_label = dataset[:, b - 1]
dataset_train = dataset[:, 0:b - 1]
skf = StratifiedKFold(n_splits=5, shuffle=True)
for train_index, dev_index in skf.split(dataset_train, dataset_label):
X_cv_train, X_cv_dev = dataset_train[train_index], dataset_train[
dev_index]
y_cv_train, y_cv_dev = dataset_label[train_index], dataset_label[
dev_index]
if norm == 3:
if method == 1:
proba_noise.pre_select_data(selection, 1)
ratio = 1
if method == 2:
[ratio, sw] = proba_weight.pre_select_data(selection, 1)
else:
if method == 1:
noise.pre_select_data(dataset, 1, selection)
ratio = 1
if method == 2:
[ratio, sw] = weight.pre_select_data(dataset, 1, selection)
train = np.loadtxt('B_Trainset_data.csv', delimiter=',')
label_train = np.loadtxt('B_Trainset_label.csv', delimiter=',')
test = np.loadtxt('Test_data.csv', delimiter=',')
label_test = np.loadtxt('Test_label.csv', delimiter=',')
AdaB = AdaBoostClassifier(n_estimators=100, learning_rate=0.7)
if method == 1:
AdaB.fit(train, label_train)
if method == 2:
AdaB.fit(train, label_train, sw)
y_pred_train = AdaB.predict(train)
acc_train = accuracy_score(y_pred_train, label_train)
print('acc_train = ', acc_train)
# f1
target = ['class 1', 'class 2']
print(
classification_report(label_train,
y_pred_train,
target_names=target))
# auc
# fpr, tpr, thresholds = metrics.roc_curve(label_train, y_pred_train, pos_label=2)
print('auc = ',
roc_auc_score(label_train, y_pred_train, average='weighted'))
y_pred_test = AdaB.predict(test)
y_prob_pred_test = AdaB.predict_proba(test)
acc_test = accuracy_score(y_pred_test, label_test)
print('acc_test = ', acc_test)
acc_save.append(acc_test)
target = ['class 1', 'class 2']
print(
classification_report(label_test, y_pred_test,
target_names=target))
f1 = f1_score(label_test, y_pred_test, average='weighted')
print('f1 score =', f1)
f1_save.append(f1)
auc = roc_auc_score(label_test, y_pred_test, average='weighted')
# auc roc_curve
auc = roc_auc_score(label_test,
y_prob_pred_test[:, 1],
average='weighted')
print('auc = ', auc)
auc_save.append(auc)
fpr, tpr, thresholds = roc_curve(label_test, y_prob_pred_test[:, 1])
plt.plot(fpr, tpr)
plt.xlabel('fpr')
plt.ylabel('tpr')
plt.show()
print(
'----------------------------------------------------------------------------------------------------'
)
print('5 iteration average results')
print('test_acc = ', np.mean(acc_save))
print('f1_score = ', np.mean(f1_save))
print('auc = ', np.mean(auc_save))
def KClassifier(method, norm, selection):
print(
'---------------------------------------------------------------------------------------------------'
)
print('KNN')
acc_save = []
f1_save = []
auc_save = []
dataset = np.loadtxt('new.csv', delimiter=',')
[a, b] = dataset.shape
dataset_label = dataset[:, b - 1]
dataset_train = dataset[:, 0:b - 1]
skf = StratifiedKFold(n_splits=5, shuffle=True)
for train_index, dev_index in skf.split(dataset_train, dataset_label):
X_cv_train, X_cv_dev = dataset_train[train_index], dataset_train[
dev_index]
y_cv_train, y_cv_dev = dataset_label[train_index], dataset_label[
dev_index]
if norm == 3:
proba_noise.pre_select_data(selection, 1)
else:
noise.pre_select_data(dataset, 1, selection)
train = np.loadtxt('B_Trainset_data.csv', delimiter=',')
label_train = np.loadtxt('B_Trainset_label.csv', delimiter=',')
test = np.loadtxt('Test_data.csv', delimiter=',')
label_test = np.loadtxt('Test_label.csv', delimiter=',')
KNN = KNeighborsClassifier(n_neighbors=100)
KNN.fit(train, label_train)
y_pred_train = KNN.predict(train)
acc_train = accuracy_score(y_pred_train, label_train)
print('acc_train = ', acc_train)
target = ['class 1', 'class 2']
print(
classification_report(label_train,
y_pred_train,
target_names=target))
print('auc = ',
roc_auc_score(label_train, y_pred_train, average='weighted'))
y_pred_test = KNN.predict(test)
# y_prob_pred_test = suppvec.predict_proba(test)
acc_test = accuracy_score(y_pred_test, label_test)
print('acc_test = ', acc_test)
acc_save.append(acc_test)
target = ['class 1', 'class 2']
print(
classification_report(label_test, y_pred_test,
target_names=target))
f1 = f1_score(label_test, y_pred_test, average='weighted')
print('f1 score =', f1)
f1_save.append(f1)
auc = roc_auc_score(label_test, y_pred_test, average='weighted')
print('auc = ', auc)
auc_save.append(auc)
fpr, tpr, thresholds = roc_curve(label_test, y_pred_test)
plt.plot(fpr, tpr)
plt.xlabel('fpr')
plt.ylabel('tpr')
plt.show()
print(
'----------------------------------------------------------------------------------------------------'
)
print('5 iteration average results')
print('test_acc = ', np.mean(acc_save))
print('f1_score = ', np.mean(f1_save))
print('auc = ', np.mean(auc_save))
def support(method, norm, selection):
print(
'---------------------------------------------------------------------------------------------------'
)
print('SVM')
acc_save = []
f1_save = []
auc_save = []
dataset = np.loadtxt('new.csv', delimiter=',')
[a, b] = dataset.shape
dataset_label = dataset[:, b - 1]
dataset_train = dataset[:, 0:b - 1]
skf = StratifiedKFold(n_splits=5, shuffle=True)
for train_index, dev_index in skf.split(dataset_train, dataset_label):
X_cv_train, X_cv_dev = dataset_train[train_index], dataset_train[
dev_index]
y_cv_train, y_cv_dev = dataset_label[train_index], dataset_label[
dev_index]
if norm == 3:
if method == 1:
proba_noise.pre_select_data(selection, 1)
ratio = 1
if method == 2:
[ratio, sw] = proba_weight.pre_select_data(selection, 1)
else:
if method == 1:
noise.pre_select_data(dataset, 1, selection)
ratio = 1
if method == 2:
[ratio, sw] = weight.pre_select_data(dataset, 1, selection)
train = np.loadtxt('B_Trainset_data.csv', delimiter=',')
label_train = np.loadtxt('B_Trainset_label.csv', delimiter=',')
test = np.loadtxt('Test_data.csv', delimiter=',')
label_test = np.loadtxt('Test_label.csv', delimiter=',')
suppvec = SVC(kernel='rbf',
probability=True,
class_weight={
0: 1,
1: int(ratio)
})
suppvec.fit(train, label_train)
y_pred_train = suppvec.predict(train)
acc_train = accuracy_score(y_pred_train, label_train)
print('acc_train = ', acc_train)
target = ['class 1', 'class 2']
print(
classification_report(label_train,
y_pred_train,
target_names=target))
print('auc = ',
roc_auc_score(label_train, y_pred_train, average='weighted'))
y_pred_test = suppvec.predict(test)
y_prob_pred_test = suppvec.predict_proba(test)
acc_test = accuracy_score(y_pred_test, label_test)
print('acc_test = ', acc_test)
acc_save.append(acc_test)
target = ['class 1', 'class 2']
print(
classification_report(label_test, y_pred_test,
target_names=target))
f1 = f1_score(label_test, y_pred_test, average='weighted')
print('f1 score =', f1)
f1_save.append(f1)
auc = roc_auc_score(label_test, y_pred_test)
# auc roc_curve
# auc = roc_auc_score(label_test, y_pred_test)
print('auc = ', auc)
auc_save.append(auc)
fpr, tpr, thresholds = roc_curve(label_test, y_prob_pred_test[:, 1])
plt.plot(fpr, tpr)
plt.xlabel('fpr')
plt.ylabel('tpr')
plt.show()
print(
'----------------------------------------------------------------------------------------------------'
)
print('5 iteration average results')
print('test_acc = ', np.mean(acc_save))
print('f1_score = ', np.mean(f1_save))
print('auc = ', np.mean(auc_save))