def calculate(method, total_roc, total_prn, x_train, x_test, y_train, y_test):
if method == 'KNN':
clf = KNN()
elif method == 'CBLOF':
clf = CBLOF()
elif method == 'PCA':
clf = PCA()
else:
clf = IForest()
clf.fit(x_train) # 使用x_train训练检测器clf
# 返回训练数据x_train上的异常标签和异常分值
y_train_pred = clf.labels_ # 返回训练数据上的分类标签 (0: 正常值, 1: 异常值)
y_train_scores = clf.decision_scores_ # 返回训练数据上的异常值 (分值越大越异常)
print("On train Data:")
evaluate_print(method, y_train, y_train_scores)
# 用训练好的clf来预测未知数据中的异常值
y_test_pred = clf.predict(x_test) # 返回未知数据上的分类标签 (0: 正常值, 1: 异常值)
y_test_scores = clf.decision_function(x_test) # 返回未知数据上的异常值 (分值越大越异常)
print("On Test Data:")
evaluate_print(method, y_test, y_test_scores)
y_true = column_or_1d(y_test)
y_pred = column_or_1d(y_test_scores)
check_consistent_length(y_true, y_pred)
roc = np.round(roc_auc_score(y_true, y_pred), decimals=4),
prn = np.round(precision_n_scores(y_true, y_pred), decimals=4)
total_roc.append(roc)
total_prn.append(prn)
def model_test(model_type, y_train, y_test, X_train, X_test, model_file,
save_flag):
if model_type == 'KNN':
clf_name = 'KNN'
clf = KNN()
clf.fit(X_train)
if model_type == 'XGBOD':
clf_name = 'XGBOD'
#set this scale_pos_weight sum(negative instances) / sum(positive instances).
clf = XGBOD(random_state=42, scale_pos_weight=50)
clf.fit(X_train, y_train)
if model_type == 'SOD':
# train SOD detector
# Note that SOD is meant to work in high dimensions d > 2.
# But here we are using 2D for visualization purpose
# thus, higher precision is expected in higher dimensions
clf_name = 'SOD'
clf = SOD()
clf.fit(X_train)
if model_type == 'VAE':
# train VAE detector (Beta-VAE)
clf_name = 'VAE'
contamination = 0.01
clf = VAE(epochs=30,
contamination=contamination,
gamma=0.8,
capacity=0.2)
clf.fit(X_train)
#save model if specified
if save_flag == '1':
pickle.dump(clf, open(model_file, "wb"))
# get the prediction labels and outlier scores of the training data
y_train_pred = clf.labels_ # binary labels (0: inliers, 1: outliers)
y_train_scores = clf.decision_scores_ # raw outlier scores
# get the prediction on the test data
y_test_pred = clf.predict(X_test) # outlier labels (0 or 1)
y_test_scores = clf.decision_function(X_test) # outlier scores
# evaluate and print the results
print("\nOn Training Data:")
evaluate_print(clf_name, y_train, y_train_scores)
conf_train = confusion_matrix(y_train, y_train_pred)
print("<<<< confusion matrix for train: ", conf_train)
print("\nOn Test Data:")
evaluate_print(clf_name, y_test, y_test_scores)
conf_test = confusion_matrix(y_test, y_test_pred)
print("<<<< confusion matrix for test: ", conf_test)
# visualize the results
#todo： Input data has to be 2-d for visualization.
#visualize(clf_name, X_train, y_train, X_test, y_test, y_train_pred,
# y_test_pred, show_figure=True, save_figure=False)
return model_file
def main():
dataset, label = pre_data()
from numpy import nan as NA
from sklearn.impute import SimpleImputer
imputer = SimpleImputer(missing_values=NA, strategy="mean")
dataset = imputer.fit_transform(dataset)
x_train, x_test, y_train, y_label = train_test_split(dataset,
label,
test_size=0.3,
random_state=44)
# x_train, x_test, y_train, y_label =[], [], [], []
# for i in range(1000):
# x_train.append(dataset[i])
# y_train.append(label[i])
# for i in range(6000,10000):
# x_train.append(dataset[i])
# y_train.append(label[i])
# x_test = dataset[1000:6000]
# y_label = label[1000:6000]
for i in range(3):
clf_name = 'IForest'
clf = IForest()
clf.fit(x_train)
# get the prediction label and outlier scores of the training data
y_train_pred = clf.labels_ # binary labels (0: inliers, 1: outliers)
y_train_scores = clf.decision_scores_ # raw outlier scores
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
print(accuracy_score(y_train, y_train_pred))
print(precision_score(y_train, y_train_pred))
print(recall_score(y_train, y_train_pred))
# get the prediction on the test data
y_test_pred = clf.predict(x_test) # outlier labels (0 or 1)
y_test_scores = clf.decision_function(x_test) # outlier scores
# evaluate and print the results
print("\nOn Training Data:")
evaluate_print(clf_name, y_train, y_train_scores)
print(accuracy_score(y_label, y_test_pred))
print(precision_score(y_train, y_train_pred))
print(recall_score(y_train, y_train_pred))
print("\nOn Test Data:")
evaluate_print(clf_name, y_label, y_test_scores)
def train(doc_list, dataset_name, clf_name):
model_roc = []
model_prc = []
if clf_name == "PCA":
clf = PCA()
elif clf_name == "MCD":
clf = MCD()
elif clf_name == "LOF":
clf = LOF()
elif clf_name == "KNN":
clf = KNN()
elif clf_name == "LODA":
clf = LODA()
for i in range(10):
data = pd.read_csv(doc_list[i], header=0, index_col=0)
train_x = data.drop(drop + ground_truth, axis=1).values
train_y = np.array([
transfor[x]
for x in list(_flatten(data[ground_truth].values.tolist()))
])
clf.fit(train_x)
predict = clf.decision_scores_
roc = roc_auc_score(train_y, predict)
prc = precision_n_scores(train_y, predict)
if ((i + 1) % 200 == 0):
print("第" + str(i + 1) + "个文件结果:")
evaluate_print(clf_name, train_y, predict)
model_roc.append(roc)
model_prc.append(prc)
model_roc_avg = np.mean(model_roc)
model_prc_avg = np.mean(model_prc)
print("模型" + clf_name + "在数据集" + dataset_name + "的平均roc_auc为" +
str(round(model_roc_avg, 4)) + ",平均prc为" +
str(round(model_prc_avg, 4)) + "。")
return model_roc_avg, model_prc_avg
def pyod_anomaly_detection(type, contamination):
X_train, y_train, X_test, y_test = data(type=type,
contamination=contamination)
if type == 'MAD':
# train MAD detector
clf_name = 'MAD'
clf = MAD(threshold=3.5)
clf.fit(X_train)
# get the prediction labels and outlier scores of the training data
y_train_pred = clf.labels_ # binary labels (0: inliers, 1: outliers)
y_train_scores = clf.decision_scores_ # raw outlier scores
# get the prediction on the test data
y_test_pred = clf.predict(X_test) # outlier labels (0 or 1)
y_test_scores = clf.decision_function(X_test) # outlier scores
# evaluate and print the results
print("\nOn Training Data:")
evaluate_print(clf_name, y_train, y_train_scores)
print("\nOn Test Data:")
evaluate_print(clf_name, y_test, y_test_scores)
# visualize the results
# making dimensions = 2 for visualising purpose only. By repeating same data each dimension.
visualize(clf_name,
np.hstack((X_train, X_train)),
y_train,
np.hstack((X_test, X_test)),
y_test,
y_train_pred,
y_test_pred,
show_figure=True,
save_figure=False)
elif type == 'ABOD':
# train ABOD detector
clf_name = 'ABOD'
clf = ABOD()
clf.fit(X_train)
# get the prediction labels and outlier scores of the training data
y_train_pred = clf.labels_ # binary labels (0: inliers, 1: outliers)
y_train_scores = clf.decision_scores_ # raw outlier scores
# get the prediction on the test data
y_test_pred = clf.predict(X_test) # outlier labels (0 or 1)
y_test_scores = clf.decision_function(X_test) # outlier scores
# evaluate and print the results
print("\nOn Training Data:")
evaluate_print(clf_name, y_train, y_train_scores)
print("\nOn Test Data:")
evaluate_print(clf_name, y_test, y_test_scores)
# visualize the results
visualize(clf_name,
X_train,
y_train,
X_test,
y_test,
y_train_pred,
y_test_pred,
show_figure=True,
save_figure=False)
elif type == 'AutoEncoder':
# train AutoEncoder detector
clf_name = 'AutoEncoder'
clf = AutoEncoder(epochs=30, contamination=contamination)
clf.fit(X_train)
# get the prediction labels and outlier scores of the training data
y_train_pred = clf.labels_ # binary labels (0: inliers, 1: outliers)
y_train_scores = clf.decision_scores_ # raw outlier scores
# get the prediction on the test data
y_test_pred = clf.predict(X_test) # outlier labels (0 or 1)
y_test_scores = clf.decision_function(X_test) # outlier scores
# evaluate and print the results
print("\nOn Training Data:")
evaluate_print(clf_name, y_train, y_train_scores)
print("\nOn Test Data:")
evaluate_print(clf_name, y_test, y_test_scores)
detectors = [KNN(), LOF(), OCSVM()]
clf_name = 'LSCP'
clf = LSCP(base_estimators=detectors)
clf.fit(X_train)
# get the prediction labels and outlier scores of the training data
y_train_pred = clf.labels_ # binary labels (0: inliers, 1: outliers)
y_train_scores = clf.decision_scores_ # raw outlier scores
# get the prediction on the test data
y_test_pred = clf.predict(X_test) # outlier labels (0 or 1)
y_test_scores = clf.decision_function(X_test) # outlier scores
# evaluate and print the results
print("\nOn Training Data:")
evaluate_print('Average', y_train, y_train_scores)
print("\nOn Test Data:")
evaluate_print('Average', y_test, y_test_scores)
# visualize the results
visualize(clf_name,
X_train,
y_train,
X_test,
y_test,
y_train_pred,
y_test_pred,
show_figure=True,
save_figure=False)
print('Combining {n_clf} kNN detectors'.format(n_clf=n_clf))
for i in range(n_clf):
k = k_list[i]
clf = KNN(n_neighbors=k, method='largest')
clf.fit(X_train_norm)
train_scores[:, i] = clf.decision_scores_
test_scores[:, i] = clf.decision_function(X_test_norm)
# Decision scores have to be normalized before combination
train_scores_norm, test_scores_norm = standardizer(train_scores,
test_scores)
# Combination by average
y_by_average = average(test_scores_norm)
evaluate_print('Combination by Average', y_test, y_by_average)
# Combination by max
y_by_maximization = maximization(test_scores_norm)
evaluate_print('Combination by Maximization', y_test, y_by_maximization)
# Combination by aom
y_by_aom = aom(test_scores_norm, n_buckets=5)
evaluate_print('Combination by AOM', y_test, y_by_aom)
# Combination by moa
y_by_moa = moa(test_scores_norm, n_buckets=5)
evaluate_print('Combination by MOA', y_test, y_by_moa)
columns=["Dataset", "Dimensions", "PCA", "MCD", "LOF", "KNN", "LODA"])
result_roc
result_prc
#对全集csv文件进行训练并可视化结果
clf = PCA()
clf_name = "PCA"
read = r"D:\研一下学期\数据挖掘\作业4\pageb\meta_data\pageb.preproc.csv"
data = pd.read_csv(read, header=0, index_col=0)
train_x = data.drop(drop + ground_truth + ["original.label"], axis=1).values
train_y = np.array(
[transfor[x] for x in list(_flatten(data[ground_truth].values.tolist()))])
clf.fit(train_x)
label = clf.labels_
predict = clf.decision_scores_
evaluate_print(clf_name, train_y, predict)
pca = decomposition.PCA(n_components=2)
X = pca.fit_transform(train_x)
visualize(clf_name,
X,
train_y,
X,
train_y,
label,
train_y,
show_figure=True,
save_figure=True)
clf = MCD()
clf_name = "PCA"
read = r"D:\研一下学期\数据挖掘\作业4\abalone\meta_data\abalone.preproc.csv"
X_test,
n_estimators,
# rp_flags[starts[i]:starts[i + 1]],
jl_transformers,
approx_flags[starts[i]:starts[i + 1]],
verbose=True) for i in range(n_jobs))
print('Orig decision_function time:', time.time() - start)
print()
# unfold and generate the label matrix
predicted_scores_orig = np.zeros([X_test.shape[0], n_estimators])
for i in range(n_jobs):
predicted_scores_orig[:, starts[i]:starts[i + 1]] = np.asarray(
all_results_scores[i]).T
##########################################################################
predicted_scores = standardizer(predicted_scores)
predicted_scores_orig = standardizer(predicted_scores_orig)
evaluate_print('orig', y_test, average(predicted_scores_orig))
evaluate_print('new', y_test, average(predicted_scores))
evaluate_print('orig max', y_test, maximization(predicted_scores_orig))
evaluate_print('new max', y_test, maximization(predicted_scores))
evaluate_print('orig aom', y_test, aom(predicted_scores_orig))
evaluate_print('new aom', y_test, aom(predicted_scores))
evaluate_print('orig moa', y_test, moa(predicted_scores_orig))
evaluate_print('new moa', y_test, moa(predicted_scores))
n_train = 20000 # number of training points
n_test = 2000 # number of testing points
n_features = 300 # number of features
# Generate sample data
X_train, y_train, X_test, y_test = \
generate_data(n_train=n_train,
n_test=n_test,
n_features=n_features,
contamination=contamination,
random_state=42)
# train AutoEncoder detector
clf_name = 'AutoEncoder'
clf = AutoEncoder(epochs=30, contamination=contamination)
clf.fit(X_train)
# get the prediction labels and outlier scores of the training data
y_train_pred = clf.labels_ # binary labels (0: inliers, 1: outliers)
y_train_scores = clf.decision_scores_ # raw outlier scores
# get the prediction on the test data
y_test_pred = clf.predict(X_test) # outlier labels (0 or 1)
y_test_scores = clf.decision_function(X_test) # outlier scores
# evaluate and print the results
print("\nOn Training Data:")
evaluate_print(clf_name, y_train, y_train_scores)
print("\nOn Test Data:")
evaluate_print(clf_name, y_test, y_test_scores)
def fun(dir_path):
file_list = []
total_roc = []
total_prn = []
count = 0
for home, dirs, files in os.walk("./"+dir_path+"/benchmarks"):
for filename in files:
fullname = os.path.join(home, filename)
file_list.append(fullname)cb
for file_csv in file_list:
# if count == 2:
# break
df = pd.read_csv(file_csv)
columns = df.columns
# df = df[columns].fillna('nan')
data = df.drop(columns = ['point.id', 'motherset', 'origin'])
class_mapping = {"anomaly":1, "nominal":0}
data['ground.truth'] = data['ground.truth'].map(class_mapping)
class_mapping = {"anomaly":1, "nominal":0}
y = data['ground.truth']
x = data.drop('ground.truth',axis=1)
X_train, X_test, y_train, y_test = train_test_split(
x, y, test_size=0.2, random_state=28)
random_state = np.random.RandomState(42)
outliers_fraction = 0.05
# Define seven outlier detection tools to be compared
classifiers = {
'Angle-based Outlier Detector (ABOD)': ABOD(contamination=outliers_fraction),
'Cluster-based Local Outlier Factor (CBLOF)':CBLOF(contamination=outliers_fraction,check_estimator=False, random_state=random_state),
'Feature Bagging':FeatureBagging(LOF(n_neighbors=35),contamination=outliers_fraction,check_estimator=False,random_state=random_state),
'Histogram-base Outlier Detection (HBOS)': HBOS(contamination=outliers_fraction),
'Isolation Forest': IForest(contamination=outliers_fraction,random_state=random_state),
'K Nearest Neighbors (KNN)': KNN(contamination=outliers_fraction),
'Average KNN': KNN(method='mean',contamination=outliers_fraction)
}
p_prn = []
p_roc = []
for i, (clf_name, clf) in enumerate(classifiers.items()):
try:
clf.fit(X_train)
# get the prediction labels and outlier scores of the training data
y_train_pred = clf.labels_ # binary labels (0: inliers, 1: outliers)
y_train_scores = clf.decision_scores_ # raw outlier scores
# get the prediction on the test data
y_test_pred = clf.predict(X_test) # outlier labels (0 or 1)
y_test_scores = clf.decision_function(X_test) # outlier scores
# evaluate and print the results
print(str(count)+"is analysing")
print("\nOn Training Data:")
evaluate_print(clf_name, y_train, y_train_scores)
print("\nOn Test Data:")
evaluate_print(clf_name, y_test, y_test_scores)
roc=np.round(roc_auc_score(y_train, y_train_scores), decimals=4),
prn=np.round(precision_n_scores(y_test, y_test_scores), decimals=4)
p_prn.append(prn)
p_roc.append(roc[0])
except:
p_prn.append(-1)
p_roc.append(-1)
total_prn.append(p_prn)
total_roc.append(p_roc)
count += 1
total_prn = json.dumps(total_prn)
total_roc = json.dumps(total_roc)
a = open(dir_path+"_prn_list.txt", "w",encoding='UTF-8')
a.write(total_prn)
a.close()
a = open(dir_path+"_roc_list.txt", "w",encoding='UTF-8')
a.write(total_roc)
a.close()
verbose=True)
for i in range(n_jobs))
print('Orig decision_function time:', time.time() - start)
print()
# unfold and generate the label matrix
predicted_scores_orig = np.zeros([X.shape[0], n_estimators])
for i in range(n_jobs):
predicted_scores_orig[:, starts[i]:starts[i + 1]] = np.asarray(
all_results_scores[i]).T
##########################################################################
predicted_scores = standardizer(predicted_scores)
predicted_scores_orig = standardizer(predicted_scores_orig)
evaluate_print('orig', y_test, np.mean(predicted_scores_orig, axis=1))
evaluate_print('new', y_test, np.mean(predicted_scores, axis=1))
#%%
##########################################################################
start = time.time()
for i in range(n_estimators):
print(i)
trained_estimators[i].predict(X)
print('Orig decision_function time:', time.time() - start)
print()
##########################################################################
start = time.time()
X_train_add = np.zeros([X_train.shape[0], len(estimator_list)])
X_test_add = np.zeros([X_test.shape[0], len(estimator_list)])
# fit the model
for index, estimator in enumerate(estimator_list):
if normalization_list[index]:
estimator.fit(X_train_norm)
X_train_add[:, index] = estimator.decision_scores_
X_test_add[:, index] = estimator.decision_function(X_test_norm)
else:
estimator.fit(X_train)
X_train_add[:, index] = estimator.decision_scores_
X_test_add[:, index] = estimator.decision_function(X_test)
# prepare the new feature space
X_train_new = np.concatenate((X_train, X_train_add), axis=1)
X_test_new = np.concatenate((X_test, X_test_add), axis=1)
clf = XGBClassifier()
clf.fit(X_train_new, y_train)
y_test_scores = clf.predict_proba(X_test_new) # outlier scores
evaluate_print('XGBOD', y_test, y_test_scores[:, 1])
clf = XGBClassifier()
clf.fit(X_train, y_train)
y_test_scores_orig = clf.predict_proba(X_test) # outlier scores
evaluate_print('old', y_test, y_test_scores_orig[:, 1])
from pyod.models.iforest import IForest
from pyod.utils.data import generate_data
from pyod.utils.data import evaluate_print
import numpy as np
import pickle
X_train = np.loadtxt('X_train.txt', dtype=float)
y_train = np.loadtxt('y_train.txt', dtype=float)
X_test = np.loadtxt('X_test.txt', dtype=float)
y_test = np.loadtxt('y_test.txt', dtype=float)
clf = IForest()
clf.fit(X_train)
y_test_pred = clf.predict(X_test) # outlier labels (0 or 1)
y_test_scores = clf.decision_function(X_test) # outlier scores
print(y_test_pred)
print("\nOn Test Data:")
evaluate_print('IForest', y_test[:len(y_test_scores)], y_test_scores)
pickle.dump(clf, open("IForest.p", "wb"))
print('{data_file} does not exist. Use generated data'.format(
data_file=mat_file))
X, y = generate_data(train_only=True) # load data
except IOError:
print('{data_file} does not exist. Use generated data'.format(
data_file=mat_file))
X, y = generate_data(train_only=True) # load data
else:
X = mat['X']
y = mat['y'].ravel()
for t in range(ite):
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.4)
# standardizing data for processing
X_train_norm, X_test_norm = standardizer(X_train, X_test)
# initialize 20 base detectors for combination
clf = PCA()
clf.fit(X_train_norm)
train_scores = clf.decision_scores_
test_scores = clf.decision_function(X_test_norm)
print()
evaluate_print('PCA Train', y_train, train_scores)
evaluate_print('PCA Test', y_test, test_scores)
def test_evaluate_print(self):
X_train, y_train, X_test, y_test = generate_data(
n_train=self.n_train,
n_test=self.n_test,
contamination=self.contamination)
evaluate_print('dummy', y_train, y_train * 0.1)
from pyod.models.iforest import IForest
from pyod.utils.data import generate_data
from pyod.utils.data import evaluate_print
import numpy as np
import pickle
X_train = np.loadtxt('X_train.txt', dtype=float)
y_train = np.loadtxt('y_train.txt', dtype=float)
X_test = np.loadtxt('X_test.txt', dtype=float)
y_test = np.loadtxt('y_test.txt', dtype=float)
clf = IForest()
clf.fit(X_train)
# get the prediction labels and outlier scores of the training data
#y_train_pred = clf.labels_ # binary labels (0: inliers, 1: outliers)
#y_train_scores = clf.decision_scores_ # raw outlier scores
# get the prediction on the test data
y_test_pred = clf.predict(X_test) # outlier labels (0 or 1)
y_test_scores = clf.decision_function(X_test) # outlier scores
print(y_test_pred)
# evaluate and print the results
print("\nOn Test Data:")
evaluate_print('IForest', y_test, y_test_scores)
pickle.dump(clf, open("IForest.p", "wb"))
train_test_split(X, y, test_size=0.4, random_state=42)
contamination = y.sum() / len(y)
base_estimators = get_estimators_small(contamination)
model = SUOD(base_estimators=base_estimators,
n_jobs=6,
bps_flag=True,
contamination=contamination,
approx_flag_global=True)
model.fit(X_train) # fit all models with X
model.approximate(X_train) # conduct model approximation if it is enabled
predicted_labels = model.predict(X_test) # predict labels
predicted_scores = model.decision_function(X_test) # predict scores
predicted_probs = model.predict_proba(X_test) # predict scores
###########################################################################
# compared with other approaches
evaluate_print('majority vote', y_test, majority_vote(predicted_labels))
evaluate_print('average', y_test, average(predicted_scores))
evaluate_print('maximization', y_test, maximization(predicted_scores))
clf = LOF()
clf.fit(X_train)
evaluate_print('LOF', y_test, clf.decision_function(X_test))
clf = IForest()
clf.fit(X_train)
evaluate_print('IForest', y_test, clf.decision_function(X_test))
# model prediction
all_results_scores = Parallel(
n_jobs=n_jobs, max_nbytes=None,
verbose=True)(delayed(_parallel_decision_function)(
n_estimators_list[i],
trained_estimators[starts[i]:starts[i + 1]],
None,
X_test,
n_estimators,
jl_transformers,
approx_flags[starts[i]:starts[i + 1]],
verbose=True) for i in range(n_jobs))
print('Orig decision_function time:', time.time() - start)
print()
# unfold and generate the label matrix
predicted_scores_orig = np.zeros([X_test.shape[0], n_estimators])
for i in range(n_jobs):
predicted_scores_orig[:, starts[i]:starts[i + 1]] = np.asarray(
all_results_scores[i]).T
##########################################################################
predicted_scores = standardizer(predicted_scores)
predicted_scores_orig = standardizer(predicted_scores_orig)
evaluate_print('orig', y_test, average(predicted_scores_orig))
evaluate_print('new', y_test, average(predicted_scores))
evaluate_print('orig moa', y_test, moa(predicted_scores_orig))
evaluate_print('new moa', y_test, moa(predicted_scores))
train_scores = np.zeros([X_train.shape[0], n_clf])
test_scores = np.zeros([X_test.shape[0], n_clf])
for i in range(n_clf):
k = k_list[i]
clf = KNN(n_neighbors=k, method='largest')
clf.fit(X_train_norm)
train_scores[:, i] = clf.decision_scores_
test_scores[:, i] = clf.decision_function(X_test_norm)
# decision scores have to be normalized before combination
train_scores_norm, test_scores_norm = standardizer(train_scores,
test_scores)
# combination by average
y_by_average = average(test_scores_norm)
evaluate_print('Combination by Average', y_test, y_by_average)
# combination by max
y_by_maximization = maximization(test_scores_norm)
evaluate_print('Combination by Maximization', y_test, y_by_maximization)
# combination by aom
y_by_aom = aom(test_scores_norm, n_buckets=5)
evaluate_print('Combination by AOM', y_test, y_by_aom)
# combination by moa
y_by_moa = moa(test_scores_norm, n_buckets=5)
evaluate_print('Combination by MOA', y_test, y_by_moa)
from pyod.models.auto_encoder import AutoEncoder
from pyod.utils.data import generate_data
from pyod.utils.data import evaluate_print
import numpy as np
import pickle
if __name__ == '__main__':
X_train = np.loadtxt('X_train.txt', dtype=float)
y_train = np.loadtxt('y_train.txt', dtype=float)
X_test = np.loadtxt('X_test.txt', dtype=float)
y_test = np.loadtxt('y_test.txt', dtype=float)
clf = AutoEncoder(epochs=30, contamination=0.2)
clf.fit(X_train)
# get the prediction labels and outlier scores of the training data
#y_train_pred = clf.labels_ # binary labels (0: inliers, 1: outliers)
#y_train_scores = clf.decision_scores_ # raw outlier scores
# get the prediction on the test data
y_test_pred = clf.predict(X_test) # outlier labels (0 or 1)
y_test_scores = clf.decision_function(X_test) # outlier scores
print(y_test_pred)
# evaluate and print the results
print("\nOn Test Data:")
evaluate_print('AutoEncoder', y_test, y_test_scores)
pickle.dump(clf, open("autoencoder.p", "wb"))
# train IForest detector
clf_name = 'IForest'
clf = IForest()
clf.fit(X_train)
# get the prediction label and decision_scores_ on the training data
y_train_pred = clf.labels_ # binary labels (0: inliers, 1: outliers)
y_train_scores = clf.decision_scores_ # raw outlier scores
# get the prediction on the test data
y_test_pred = clf.predict(X_test) # outlier labels (0 or 1)
y_test_scores = clf.decision_function(X_test) # outlier scores
# evaluate and print the results
print("\nOn Training Data:")
evaluate_print(clf_name, y_train, y_train_scores)
print("\nOn Test Data:")
evaluate_print(clf_name, y_test, y_test_scores)
# visualize the results
visualize(clf_name,
X_train,
y_train,
X_test,
y_test,
y_train_pred,
y_test_pred,
show_figure=True,
save_figure=False)
X_train, X_test, y_train, y_test = \
train_test_split(X, y, test_size=0.4, random_state=42)
contamination = y.sum() / len(y)
base_estimators = get_estimators_small(contamination)
model = SUOD(base_estimators=base_estimators,
n_jobs=6,
bps_flag=True,
contamination=contamination,
approx_flag_global=True)
model.fit(X_train) # fit all models with X
model.approximate(X_train) # conduct model approximation if it is enabled
# save the model
dump(model, 'model.joblib')
# load the model
model = load('model.joblib')
predicted_labels = model.predict(X_test) # predict labels
predicted_scores = model.decision_function(X_test) # predict scores
predicted_probs = model.predict_proba(X_test) # predict scores
###########################################################################
# model evaluation with the loaded model
evaluate_print('majority vote', y_test, majority_vote(predicted_labels))
evaluate_print('average', y_test, average(predicted_scores))
evaluate_print('maximization', y_test, maximization(predicted_scores))
