Roc_curve 超过最近邻的数量
Roc_curve over number of nearest-neighbors
我正在努力重新实施并捕获其中一项无监督异常检测的结果，如下所示：
本文图片来源 Histogram-based Outlier Score (HBOS): A fast
M. Goldstein 和 A. Dengel 的无监督异常检测算法。
本文的作者，使用了 3 个数据集，这些数据集可以在 this source 中轻松地在元数据选项卡中包含一些信息。
#!pip install pyod
#from functions import auc_plot
import numpy as np
list_of_models = ['HBOS_pyod','KNN_pyod', 'KNN_sklearn','LOF_pyod', 'LOF_sklearn']
k = [5, 10, 20, 30, 40, 50, 60, 70,80, 90, 100]
#k = [3,5,6,7, 10, 20, 30, 40, 50, 60, 70]
#k = [3,5,6,7, 10,15, 25, 35, 45, 55, 65, 78, 87, 95, 99]
#k = np.arange(5, 100, step=10)
name_target = 'target'
contamination = 0.4
number_of_unique = None
auc_plot(df,name_target,contamination,number_of_unique,list_of_models,k)
我从 sklearn 下载了乳腺癌数据集，并应用了来自 sklearn and pyod（例如 HBOS）等不同包的异常值检测算法，但我仍然无法达到上图中显示的输出。
我起诉这个函数来绘制如此命名的 functions.py
def auc_plot(df,name_target,contamination,number_of_unique,list_of_models,k):
from pyod.models.hbos import HBOS
from pyod.models.knn import KNN
from pyod.models.iforest import IForest
from pyod.models.lof import LOF
from sklearn.neighbors import KNeighborsClassifier
from xgboost import XGBClassifier
from sklearn.neighbors import LocalOutlierFactor
from sklearn.svm import OneClassSVM
from sklearn import metrics
orig = df.copy()
#bins = list(range(0,k+1))
predictions_list = []
if contamination > 0.5:
contamination = 0.5
X, y = df.loc[:, df.columns!= name_target], df[name_target]
seed = 120
test_size = 0.3
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=seed,stratify=y)
#print('X_test:',X_test.shape,'y_test:',y_test.shape)
#*************************************
if 'HBOS_pyod' in list_of_models:
predictions_1_j = []
auc_1_j = []
for j in range(len(k)):
model_name_1 = 'HBOS_pyod'
# train HBOS detector
clf_name = 'HBOS_pyod'
clf = HBOS(n_bins=k[j],contamination= contamination)
#start = time.time()
clf.fit(X_train)
# get the prediction on the test data
y_test_pred = clf.predict(X_test) # outlier labels (0 or 1)
y_test_scores_hbos = clf.decision_function(X_test) # outlier scores
predictions = [round(value) for value in y_test_pred]
for i in range(0,len(predictions)):
if predictions[i] > 0.5:
predictions[i]=1
else:
predictions[i]=0
predictions_1_j.append(predictions)
# #AUC score
auc_1 = metrics.roc_auc_score(y_test, predictions)
auc_1_j.append(auc_1)
#print('auc_1_j', auc_1_j)
#***********************************************
if 'KNN_pyod' in list_of_models:
from pyod.models.knn import KNN
predictions_2_j = []
auc_2_j = []
for j in range(len(k)):
model_name_2 = 'KNN_pyod'
# train kNN detector
clf_name = 'KNN_pyod'
clf = KNN(contamination= contamination,n_neighbors=k[j])
clf.fit(X_train)
# get the prediction on the test data
y_test_pred = clf.predict(X_test) # outlier labels (0 or 1)
y_test_scores_knn = clf.decision_function(X_test) # outlier scores
predictions = [round(value) for value in y_test_pred]
for i in range(0,len(predictions)):
if predictions[i] > 0.5:
predictions[i]=1
else:
predictions[i]=0
predictions_2_j.append(predictions)
# #AUC score
auc_2 = metrics.roc_auc_score(y_test, predictions)
auc_2_j.append(auc_2)
#print('auc_2_j', auc_2_j)
#****************************************************************LOF
if 'LOF_pyod' in list_of_models:
#print('******************************************************************LOF_pyod')
from pyod.models.lof import LOF
import time
predictions_4_j = []
auc_4_j = []
for j in range(len(k)):
model_name_4 = 'LOF_pyod'
# train LOF detector
clf_name = 'LOF_pyod'
clf = LOF(n_neighbors=k[j],contamination= contamination)
#start = time.time()
clf.fit(X_train)
# get the prediction on the test data
y_test_pred = clf.predict(X_test) # outlier labels (0 or 1)
y_test_scores_lof = clf.decision_function(X_test) # outlier scores
#****************************************
predictions = [round(value) for value in y_test_pred]
for i in range(0,len(predictions)):
if predictions[i] > 0.5:
predictions[i]=1
else:
predictions[i]=0
predictions_4_j.append(predictions)
# #AUC score
auc_4 = metrics.roc_auc_score(y_test, predictions)
auc_4_j.append(auc_4)
#print('auc_4_j', auc_4_j)
#****************************************************************XBOS
if 'XBOS' in list_of_models:
#print('******************************************************************XBOS')
import time
#df_2_exist = False
if number_of_unique != None:
df_2 = df.copy()
#remove columns with constant numbers or those columns with unique numbers of < number_of_unique
cols = df_2.columns
for i in range(len(cols)):
if cols[i] != name_target:
m = df_2[cols[i]].value_counts()
m = np.array(m)
if len(m) < number_of_unique:
print(f'len cols {i}:',len(m), 'droped')
#print('drope')
column_name = cols[i]
df_2=df_2.drop(columns= column_name)
X_2, y_2= df_2.loc[:, df_2.columns!= name_target], df_2[name_target]
X_train_2, X_test_2, y_train_2, y_test_2 = train_test_split(X_2, y_2, test_size=0.3, random_state=120,stratify=y_2)
predictions_5_j = []
auc_5_j = []
for j in range(len(k)):
model_name_5 = 'XBOS'
#create XBOS model
clf = xbosmodel.XBOS(n_clusters=k[j],max_iter=1)
#start = time.time()
# train XBOS model
clf.fit(X_train_2)
#predict model
y_test_pred = clf.predict(X_test_2)
y_test_scores_xbos = clf.fit_predict(X_test_2)
predictions = [round(value) for value in y_test_pred]
for i in range(0,len(predictions)):
if predictions[i] > 0.5:
predictions[i]=1
else:
predictions[i]=0
predictions_5_j.append(predictions)
# #AUC score
auc_5 = metrics.roc_auc_score(y_test, predictions)
auc_5_j.append(auc_5)
else:
predictions_5_j = []
auc_5_j = []
for j in range(len(k)):
model_name_5 = 'XBOS'
#create XBOS model
clf = xbosmodel.XBOS(n_clusters=k[j],max_iter=1)
start = time.time()
# train XBOS model
clf.fit(X_train)
#predict model
y_test_pred = clf.predict(X_test)
y_test_scores_xbos = clf.fit_predict(X_test)
predictions = [round(value) for value in y_test_pred]
for i in range(0,len(predictions)):
if predictions[i] > 0.5:
predictions[i]=1
else:
predictions[i]=0
predictions_5_j.append(predictions)
# #AUC score
auc_5 = metrics.roc_auc_score(y_test, predictions)
auc_5_j.append(auc_5)
#print('auc_5_j', auc_5_j)
#**********************************************************************KNN_sklearn
if 'KNN_sklearn' in list_of_models:
#print('*****************************************************************KNN from sklearn lib')
from sklearn.neighbors import KNeighborsClassifier
import time
predictions_6_j = []
auc_6_j = []
for j in range(len(k)):
model_name_6 = 'KNN_sklearn'
# train knn detector
neigh = KNeighborsClassifier(n_neighbors=k[j])
neigh.fit(X_train,y_train)
# get the prediction on the test data
y_test_pred_6 = neigh.predict(X_test)
#*****************************************************
predictions = [round(value) for value in y_test_pred_6]
for i in range(0,len(predictions)):
if predictions[i] > 0.5:
predictions[i]=1
else:
predictions[i]=0
predictions_6_j.append(predictions)
# #AUC score
auc_6 = metrics.roc_auc_score(y_test, predictions)
auc_6_j.append(auc_6)
#print('auc_6_j', auc_6_j)
#**********************************************************
if 'LOF_sklearn' in list_of_models:
#print('*****************************************************************LOF from sklearn lib')
from sklearn.neighbors import LocalOutlierFactor
import time
predictions_9_j = []
auc_9_j = []
for j in range(len(k)):
model_name_9 = 'LOF_sklearn'
# train knn detector
neigh = LocalOutlierFactor(n_neighbors=k[j],novelty=True, contamination=contamination)
start = time.time()
neigh.fit(X_train)
# get the prediction on the test data
y_test_pred_9 = neigh.predict(X_test)
#*****************************************************
predictions = [round(value) for value in y_test_pred_9]
for i in range(0,len(predictions)):
if predictions[i] > 0.5:
predictions[i]=1
else:
predictions[i]=0
predictions_9_j.append(predictions)
# #AUC score
auc_9 = metrics.roc_auc_score(y_test, predictions)
auc_9_j.append(auc_9)
#print(auc_1_j)
if 'HBOS_pyod' in list_of_models:
plt.plot(k,auc_1_j,marker='.',label="HBOS_pyod")
if 'KNN_pyod' in list_of_models:
plt.plot(k,auc_2_j,marker='.',label="KNN_pyod")
if 'LOF_pyod' in list_of_models:
plt.plot(k,auc_4_j,marker='.',label="LOF_pyod")
if 'XBOS' in list_of_models:
plt.plot(k,auc_5_j,marker='.',label="XBOS")
if 'KNN_sklearn' in list_of_models:
plt.plot(k,auc_6_j,marker='.',label="KNN_sklearn")
if 'LOF_sklearn' in list_of_models:
plt.plot(k,auc_9_j,marker='.',label="LOF_sklearn")
plt.title('ROC-Curve')
plt.ylabel('AUC')
plt.xlabel('K')
#plt.axis([0, 15, 0., 1.0])
#plt.xlim(k)
plt.xticks(np.arange(0, 100.005, 20))
plt.yticks(np.arange(0, 1.005, step=0.05)) # Set label locations
plt.ylim(0.0, 1.01)
#plt.legend(loc=0)
plt.legend(bbox_to_anchor=(1.04,1), loc="upper left")
plt.show()
从 sklearn 下载乳腺癌威斯康星数据集：
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
import time
from sklearn import metrics
from sklearn.datasets import load_breast_cancer
Bw = load_breast_cancer(
return_X_y=False,
as_frame=True)
df = Bw.frame
name_target = 'target'
#change types of feature columns
#df['duration']=df['duration'].astype(float)
#df['src_bytes']=df['src_bytes'].astype(float)
#df['dst_bytes']=df['dst_bytes'].astype(float)
num_row , num_colmn = df.shape
#calculate number of classes
classes = df[name_target].unique()
num_class = len(classes)
print(df[name_target].value_counts())
#determine which class is normal (is not anomaly)
label = np.array(df[name_target])
a,b = np.unique(label , return_counts=True)
#print("a is:",a)
#print("b is:",b)
for i in range(len(b)):
if b[i]== b.max():
normal = a[i]
#print('normal:', normal)
elif b[i] == b.min():
unnormal = a[i]
#print('unnorm:' ,unnormal)
# show anomaly classes
anomaly_class = []
for f in range(len(a)):
if a[f] != normal:
anomaly_class.append(a[f])
# convert dataset classes to 2 classe: normal and unnormal
label = np.where(label != normal, unnormal ,label)
df[name_target]=label
# showing columns's type: numerical or categorical
numeric =0
categoric = 0
for i in range(df.shape[1]):
df_col = df.iloc[:,i]
if df_col.dtype == int and df.columns[i] != name_target:
numeric +=1
elif df_col.dtype == float and df.columns[i] != name_target:
numeric += 1
elif df.columns[i] != name_target:
categoric += 1
#replace labels with 0 and 1
label = np.where(label == normal, 0 ,1)
df[name_target]=label
# null_check: if more than half of a column was null, then that columns will be droped
# otherwise if number of null was less than half of column, then nulls will replace with mean of that column
test = []
for i in range(df.shape[1]):
if df.iloc[:,i].isnull().sum() > df.shape[0]//2:
test.append(i)
elif df.iloc[:,i].isnull().sum() < df.shape[0]//2 and df.iloc[:,i].isnull().sum() != 0:
m = df.iloc[:,i].mean()
df.iloc[:,i] = df.iloc[:,i].replace(to_replace = np.nan, value = m)
df = df.drop(columns=df.columns[test])
#calculate anomaly rate
b = df[name_target].value_counts()
Anomaly_rate= b[1] / (b[0]+b[1])
print('=============Anomaly_rate=================')
print(Anomaly_rate)
contamination= float("{:.4f}".format(Anomaly_rate))
print('=============contamination=================')
print(contamination)
#rename labels column
df = df.rename(columns = {'labels' : 'binary_target'})
#df.to_csv(f'/content/{dataset_name}.csv', index = False)
我检查了这个 对这个问题获取情节没有用。
到目前为止，我的输出如下：
请注意，这个 ROC 图是在不同的 K（最近邻的数量）上。
更新：如果有人对 运行 代码感兴趣，我提供了 Google colab notebook 以便更快地进行故障排除。
正如我在评论中所说，主要问题之一是您没有正确创建 AUC。 ROC 曲线需要连续测量置信度，而不仅仅是硬 class 预测，因此您应该将所有 predict 调用替换为可用的 decision_function 或 predict_proba，然后删除用 0 或 1 替换预测的所有代码。
至少还有一个问题：sklearn LocalOutlierFactory 使用了一种反向的异常值：predict returns 1 表示异常值，-1 表示异常值，decision_function 给离群值更高的分数。这就是为什么您会看到 AUC 始终低于 0.5。在计算 AUC 时使用决策函数的负数，这将得到修复。
这是我通过这些更改得到的结果，以及一些调整以绘制更小的 k-values（除了 BDOS 不能接受 k<=2），以及限制 y-axis 如建议的那样（现在所有的图都将显示在该范围内）：
不是完美的复制品，但 train/test 拆分可能不同，我不确定预处理或超参数是否相同，...
我正在努力重新实施并捕获其中一项无监督异常检测的结果，如下所示：
本文的作者，使用了 3 个数据集，这些数据集可以在 this source 中轻松地在元数据选项卡中包含一些信息。
#!pip install pyod
#from functions import auc_plot
import numpy as np
list_of_models = ['HBOS_pyod','KNN_pyod', 'KNN_sklearn','LOF_pyod', 'LOF_sklearn']
k = [5, 10, 20, 30, 40, 50, 60, 70,80, 90, 100]
#k = [3,5,6,7, 10, 20, 30, 40, 50, 60, 70]
#k = [3,5,6,7, 10,15, 25, 35, 45, 55, 65, 78, 87, 95, 99]
#k = np.arange(5, 100, step=10)
name_target = 'target'
contamination = 0.4
number_of_unique = None
auc_plot(df,name_target,contamination,number_of_unique,list_of_models,k)
我从 sklearn 下载了乳腺癌数据集，并应用了来自 sklearn and pyod（例如 HBOS）等不同包的异常值检测算法，但我仍然无法达到上图中显示的输出。
我起诉这个函数来绘制如此命名的 functions.py
def auc_plot(df,name_target,contamination,number_of_unique,list_of_models,k):
from pyod.models.hbos import HBOS
from pyod.models.knn import KNN
from pyod.models.iforest import IForest
from pyod.models.lof import LOF
from sklearn.neighbors import KNeighborsClassifier
from xgboost import XGBClassifier
from sklearn.neighbors import LocalOutlierFactor
from sklearn.svm import OneClassSVM
from sklearn import metrics
orig = df.copy()
#bins = list(range(0,k+1))
predictions_list = []
if contamination > 0.5:
contamination = 0.5
X, y = df.loc[:, df.columns!= name_target], df[name_target]
seed = 120
test_size = 0.3
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=seed,stratify=y)
#print('X_test:',X_test.shape,'y_test:',y_test.shape)
#*************************************
if 'HBOS_pyod' in list_of_models:
predictions_1_j = []
auc_1_j = []
for j in range(len(k)):
model_name_1 = 'HBOS_pyod'
# train HBOS detector
clf_name = 'HBOS_pyod'
clf = HBOS(n_bins=k[j],contamination= contamination)
#start = time.time()
clf.fit(X_train)
# get the prediction on the test data
y_test_pred = clf.predict(X_test) # outlier labels (0 or 1)
y_test_scores_hbos = clf.decision_function(X_test) # outlier scores
predictions = [round(value) for value in y_test_pred]
for i in range(0,len(predictions)):
if predictions[i] > 0.5:
predictions[i]=1
else:
predictions[i]=0
predictions_1_j.append(predictions)
# #AUC score
auc_1 = metrics.roc_auc_score(y_test, predictions)
auc_1_j.append(auc_1)
#print('auc_1_j', auc_1_j)
#***********************************************
if 'KNN_pyod' in list_of_models:
from pyod.models.knn import KNN
predictions_2_j = []
auc_2_j = []
for j in range(len(k)):
model_name_2 = 'KNN_pyod'
# train kNN detector
clf_name = 'KNN_pyod'
clf = KNN(contamination= contamination,n_neighbors=k[j])
clf.fit(X_train)
# get the prediction on the test data
y_test_pred = clf.predict(X_test) # outlier labels (0 or 1)
y_test_scores_knn = clf.decision_function(X_test) # outlier scores
predictions = [round(value) for value in y_test_pred]
for i in range(0,len(predictions)):
if predictions[i] > 0.5:
predictions[i]=1
else:
predictions[i]=0
predictions_2_j.append(predictions)
# #AUC score
auc_2 = metrics.roc_auc_score(y_test, predictions)
auc_2_j.append(auc_2)
#print('auc_2_j', auc_2_j)
#****************************************************************LOF
if 'LOF_pyod' in list_of_models:
#print('******************************************************************LOF_pyod')
from pyod.models.lof import LOF
import time
predictions_4_j = []
auc_4_j = []
for j in range(len(k)):
model_name_4 = 'LOF_pyod'
# train LOF detector
clf_name = 'LOF_pyod'
clf = LOF(n_neighbors=k[j],contamination= contamination)
#start = time.time()
clf.fit(X_train)
# get the prediction on the test data
y_test_pred = clf.predict(X_test) # outlier labels (0 or 1)
y_test_scores_lof = clf.decision_function(X_test) # outlier scores
#****************************************
predictions = [round(value) for value in y_test_pred]
for i in range(0,len(predictions)):
if predictions[i] > 0.5:
predictions[i]=1
else:
predictions[i]=0
predictions_4_j.append(predictions)
# #AUC score
auc_4 = metrics.roc_auc_score(y_test, predictions)
auc_4_j.append(auc_4)
#print('auc_4_j', auc_4_j)
#****************************************************************XBOS
if 'XBOS' in list_of_models:
#print('******************************************************************XBOS')
import time
#df_2_exist = False
if number_of_unique != None:
df_2 = df.copy()
#remove columns with constant numbers or those columns with unique numbers of < number_of_unique
cols = df_2.columns
for i in range(len(cols)):
if cols[i] != name_target:
m = df_2[cols[i]].value_counts()
m = np.array(m)
if len(m) < number_of_unique:
print(f'len cols {i}:',len(m), 'droped')
#print('drope')
column_name = cols[i]
df_2=df_2.drop(columns= column_name)
X_2, y_2= df_2.loc[:, df_2.columns!= name_target], df_2[name_target]
X_train_2, X_test_2, y_train_2, y_test_2 = train_test_split(X_2, y_2, test_size=0.3, random_state=120,stratify=y_2)
predictions_5_j = []
auc_5_j = []
for j in range(len(k)):
model_name_5 = 'XBOS'
#create XBOS model
clf = xbosmodel.XBOS(n_clusters=k[j],max_iter=1)
#start = time.time()
# train XBOS model
clf.fit(X_train_2)
#predict model
y_test_pred = clf.predict(X_test_2)
y_test_scores_xbos = clf.fit_predict(X_test_2)
predictions = [round(value) for value in y_test_pred]
for i in range(0,len(predictions)):
if predictions[i] > 0.5:
predictions[i]=1
else:
predictions[i]=0
predictions_5_j.append(predictions)
# #AUC score
auc_5 = metrics.roc_auc_score(y_test, predictions)
auc_5_j.append(auc_5)
else:
predictions_5_j = []
auc_5_j = []
for j in range(len(k)):
model_name_5 = 'XBOS'
#create XBOS model
clf = xbosmodel.XBOS(n_clusters=k[j],max_iter=1)
start = time.time()
# train XBOS model
clf.fit(X_train)
#predict model
y_test_pred = clf.predict(X_test)
y_test_scores_xbos = clf.fit_predict(X_test)
predictions = [round(value) for value in y_test_pred]
for i in range(0,len(predictions)):
if predictions[i] > 0.5:
predictions[i]=1
else:
predictions[i]=0
predictions_5_j.append(predictions)
# #AUC score
auc_5 = metrics.roc_auc_score(y_test, predictions)
auc_5_j.append(auc_5)
#print('auc_5_j', auc_5_j)
#**********************************************************************KNN_sklearn
if 'KNN_sklearn' in list_of_models:
#print('*****************************************************************KNN from sklearn lib')
from sklearn.neighbors import KNeighborsClassifier
import time
predictions_6_j = []
auc_6_j = []
for j in range(len(k)):
model_name_6 = 'KNN_sklearn'
# train knn detector
neigh = KNeighborsClassifier(n_neighbors=k[j])
neigh.fit(X_train,y_train)
# get the prediction on the test data
y_test_pred_6 = neigh.predict(X_test)
#*****************************************************
predictions = [round(value) for value in y_test_pred_6]
for i in range(0,len(predictions)):
if predictions[i] > 0.5:
predictions[i]=1
else:
predictions[i]=0
predictions_6_j.append(predictions)
# #AUC score
auc_6 = metrics.roc_auc_score(y_test, predictions)
auc_6_j.append(auc_6)
#print('auc_6_j', auc_6_j)
#**********************************************************
if 'LOF_sklearn' in list_of_models:
#print('*****************************************************************LOF from sklearn lib')
from sklearn.neighbors import LocalOutlierFactor
import time
predictions_9_j = []
auc_9_j = []
for j in range(len(k)):
model_name_9 = 'LOF_sklearn'
# train knn detector
neigh = LocalOutlierFactor(n_neighbors=k[j],novelty=True, contamination=contamination)
start = time.time()
neigh.fit(X_train)
# get the prediction on the test data
y_test_pred_9 = neigh.predict(X_test)
#*****************************************************
predictions = [round(value) for value in y_test_pred_9]
for i in range(0,len(predictions)):
if predictions[i] > 0.5:
predictions[i]=1
else:
predictions[i]=0
predictions_9_j.append(predictions)
# #AUC score
auc_9 = metrics.roc_auc_score(y_test, predictions)
auc_9_j.append(auc_9)
#print(auc_1_j)
if 'HBOS_pyod' in list_of_models:
plt.plot(k,auc_1_j,marker='.',label="HBOS_pyod")
if 'KNN_pyod' in list_of_models:
plt.plot(k,auc_2_j,marker='.',label="KNN_pyod")
if 'LOF_pyod' in list_of_models:
plt.plot(k,auc_4_j,marker='.',label="LOF_pyod")
if 'XBOS' in list_of_models:
plt.plot(k,auc_5_j,marker='.',label="XBOS")
if 'KNN_sklearn' in list_of_models:
plt.plot(k,auc_6_j,marker='.',label="KNN_sklearn")
if 'LOF_sklearn' in list_of_models:
plt.plot(k,auc_9_j,marker='.',label="LOF_sklearn")
plt.title('ROC-Curve')
plt.ylabel('AUC')
plt.xlabel('K')
#plt.axis([0, 15, 0., 1.0])
#plt.xlim(k)
plt.xticks(np.arange(0, 100.005, 20))
plt.yticks(np.arange(0, 1.005, step=0.05)) # Set label locations
plt.ylim(0.0, 1.01)
#plt.legend(loc=0)
plt.legend(bbox_to_anchor=(1.04,1), loc="upper left")
plt.show()
从 sklearn 下载乳腺癌威斯康星数据集：
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
import time
from sklearn import metrics
from sklearn.datasets import load_breast_cancer
Bw = load_breast_cancer(
return_X_y=False,
as_frame=True)
df = Bw.frame
name_target = 'target'
#change types of feature columns
#df['duration']=df['duration'].astype(float)
#df['src_bytes']=df['src_bytes'].astype(float)
#df['dst_bytes']=df['dst_bytes'].astype(float)
num_row , num_colmn = df.shape
#calculate number of classes
classes = df[name_target].unique()
num_class = len(classes)
print(df[name_target].value_counts())
#determine which class is normal (is not anomaly)
label = np.array(df[name_target])
a,b = np.unique(label , return_counts=True)
#print("a is:",a)
#print("b is:",b)
for i in range(len(b)):
if b[i]== b.max():
normal = a[i]
#print('normal:', normal)
elif b[i] == b.min():
unnormal = a[i]
#print('unnorm:' ,unnormal)
# show anomaly classes
anomaly_class = []
for f in range(len(a)):
if a[f] != normal:
anomaly_class.append(a[f])
# convert dataset classes to 2 classe: normal and unnormal
label = np.where(label != normal, unnormal ,label)
df[name_target]=label
# showing columns's type: numerical or categorical
numeric =0
categoric = 0
for i in range(df.shape[1]):
df_col = df.iloc[:,i]
if df_col.dtype == int and df.columns[i] != name_target:
numeric +=1
elif df_col.dtype == float and df.columns[i] != name_target:
numeric += 1
elif df.columns[i] != name_target:
categoric += 1
#replace labels with 0 and 1
label = np.where(label == normal, 0 ,1)
df[name_target]=label
# null_check: if more than half of a column was null, then that columns will be droped
# otherwise if number of null was less than half of column, then nulls will replace with mean of that column
test = []
for i in range(df.shape[1]):
if df.iloc[:,i].isnull().sum() > df.shape[0]//2:
test.append(i)
elif df.iloc[:,i].isnull().sum() < df.shape[0]//2 and df.iloc[:,i].isnull().sum() != 0:
m = df.iloc[:,i].mean()
df.iloc[:,i] = df.iloc[:,i].replace(to_replace = np.nan, value = m)
df = df.drop(columns=df.columns[test])
#calculate anomaly rate
b = df[name_target].value_counts()
Anomaly_rate= b[1] / (b[0]+b[1])
print('=============Anomaly_rate=================')
print(Anomaly_rate)
contamination= float("{:.4f}".format(Anomaly_rate))
print('=============contamination=================')
print(contamination)
#rename labels column
df = df.rename(columns = {'labels' : 'binary_target'})
#df.to_csv(f'/content/{dataset_name}.csv', index = False)
我检查了这个
请注意，这个 ROC 图是在不同的 K（最近邻的数量）上。
更新：如果有人对 运行 代码感兴趣，我提供了 Google colab notebook 以便更快地进行故障排除。
正如我在评论中所说，主要问题之一是您没有正确创建 AUC。 ROC 曲线需要连续测量置信度，而不仅仅是硬 class 预测，因此您应该将所有 predict 调用替换为可用的 decision_function 或 predict_proba，然后删除用 0 或 1 替换预测的所有代码。
至少还有一个问题：sklearn LocalOutlierFactory 使用了一种反向的异常值：predict returns 1 表示异常值，-1 表示异常值，decision_function 给离群值更高的分数。这就是为什么您会看到 AUC 始终低于 0.5。在计算 AUC 时使用决策函数的负数，这将得到修复。
这是我通过这些更改得到的结果，以及一些调整以绘制更小的 k-values（除了 BDOS 不能接受 k<=2），以及限制 y-axis 如建议的那样（现在所有的图都将显示在该范围内）：
不是完美的复制品，但 train/test 拆分可能不同，我不确定预处理或超参数是否相同，...