class TestKnnMedian(unittest.TestCase):
def setUp(self):
self.n_train = 100
self.n_test = 50
self.contamination = 0.1
self.roc_floor = 0.75
self.X_train, self.y_train, self.X_test, self.y_test = generate_data(
n_train=self.n_train, n_test=self.n_test,
contamination=self.contamination, random_state=42)
self.clf = KNN(contamination=self.contamination, method='median')
def test_fit(self):
self.clf.fit(self.X_train)
def test_decision_function(self):
self.clf.fit(self.X_train)
self.clf.decision_function(self.X_train)
self.clf.decision_function(self.X_test)
def test_sklearn_estimator(self):
check_estimator(self.clf)
def tearDown(self):
pass
class TestKnnMedian(unittest.TestCase):
def setUp(self):
self.n_train = 100
self.n_test = 50
self.contamination = 0.1
self.roc_floor = 0.75
self.X_train, self.y_train, self.X_test, self.y_test = generate_data(
n_train=self.n_train,
n_test=self.n_test,
contamination=self.contamination,
random_state=42)
self.clf = KNN(contamination=self.contamination, method='median')
def test_fit(self):
self.clf.fit(self.X_train)
def test_decision_function(self):
self.clf.fit(self.X_train)
self.clf.decision_function(self.X_train)
self.clf.decision_function(self.X_test)
def test_sklearn_estimator(self):
check_estimator(self.clf)
def tearDown(self):
pass
class TestKnnMedian(unittest.TestCase):
def setUp(self):
self.n_train = 200
self.n_test = 100
self.contamination = 0.1
self.roc_floor = 0.8
self.X_train, self.X_test, self.y_train, self.y_test = generate_data(
n_train=self.n_train,
n_test=self.n_test,
contamination=self.contamination,
random_state=42)
self.clf = KNN(contamination=self.contamination, method='median')
def test_fit(self):
self.clf.fit(self.X_train)
def test_decision_function(self):
self.clf.fit(self.X_train)
self.clf.decision_function(self.X_train)
self.clf.decision_function(self.X_test)
def test_model_clone(self):
clone_clf = clone(self.clf)
def tearDown(self):
pass
def knn(X_train, y_train=None, X_test=None, y_test=None):
# train kNN detector
clf_name = 'KNN'
clf = KNN()
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,
X_test,
y_train_pred,
y_test_pred,
show_figure=True,
save_figure=False)
return y_train_pred, y_train_scores
def pyodtry():
dfwhole = df_en_all
df = dff2
X1 = reduce(dfwhole)
X2 = reduce(df)
ddf = pd.read_pickle('LogFileDfs/original')
random_state = np.random.RandomState(42)
outliers_fraction = 0.005
clf = KNN(method='mean', contamination=outliers_fraction)
xx, yy = np.meshgrid(np.linspace(0, 1, 200), np.linspace(0, 1, 200))
clf.fit(X1)
scores_pred = clf.decision_function(X2) * -1
y_pred = clf.predict(X2)
n_inliers = len(y_pred) - np.count_nonzero(y_pred)
n_outliers = np.count_nonzero(y_pred == 1)
print('OUTLIERS : ', n_outliers, 'INLIERS : ', n_inliers)
#dfx = pdf
#dfx['outlier'] = y_pred.tolist()
df['authenticated?'] = y_pred.tolist()
ddf['authenticated?'] = df['authenticated?']
output = ddf[ddf['authenticated?'] == 1]
# create sqlalchemy engine
#engine = create_engine("mysql+pymysql://{user}:{pw}@172.17.0.3/{db}".format(user="******",pw="richul123",db="emss"))
# Insert whole DataFrame into MySQL
#output.to_sql('output', con = engine, if_exists = 'replace', chunksize = 1000)
with pd.ExcelWriter(
'/home/richul/Documents/EnhancingMailServerSecurity/Output/output.xlsx'
) as writer:
output.to_excel(writer, sheet_name='output')
class IForestSupervisedKNN(BaseDetector):
def __init__(self, get_top=0.8, if_params={}, knn_params={}):
super(IForestSupervisedKNN, self).__init__()
self.get_top = get_top
self.is_fitted = False
self.iforest = IForest(**if_params)
self.knn = KNN(**knn_params)
def fit(self, X, y=None):
X = check_array(X)
self._set_n_classes(y)
self.iforest.fit(X)
scores = self.iforest.predict_proba(X)[:, 1]
normal_instances = X[np.argsort(scores)[:int(len(X) * self.get_top)]]
self.knn.fit(normal_instances)
self.decision_scores_ = self.decision_function(X)
self._process_decision_scores()
self.is_fitted = True
return self
def decision_function(self, X):
check_is_fitted(self, ['is_fitted'])
return self.knn.decision_function(X)
def obj_func_kNN(params):
## objective function used in baseian optimization
outlier_fraction = params[0]
n_neighbors = params[1]
method = params[2]
radius = params[3]
# load data set to function work space
Y_train = np.load('Y_train.npy')
X_train = np.load('X_train.npy')
# create model
clf = KNN(contamination=outlier_fraction,
n_neighbors=n_neighbors,
method=method,
radius=radius)
# fit the dataset to the model
clf.fit(X_train)
scores_pred = clf.decision_function(
X_train) * -1 # predict raw anomaly score
Rprecision = Rprecision_f(Y_train, scores_pred)
if glb_verbose:
print('R Precision : ', Rprecision)
y_pred = clf.predict(
X_train) # prediction of a datapoint category outlier or inlier
objVal = objVal_f(Rprecision, y_pred, Y_train)
return objVal
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 detectarOutlierKNN(self, idmodelo, Xtodos, corteOutlier):
# Detecao Outliers 1--------------------------------------------------------------
clf = KNN()
clf.fit(Xtodos)
# get outlier scores
y_train_scores = clf.decision_scores_ # raw outlier scores
y_test_scores = clf.decision_function(Xtodos) # outlier scores
YCodigoTodosComOutilier = self.selectMatrizY(idmodelo, "ID", "TODOS")
cont = 0
amostrasRemovidas = 0
for itemOutilier in y_train_scores:
if itemOutilier > corteOutlier:
contTodos = 0
for item in YCodigoTodosComOutilier:
amostra = str(item)
amostra = amostra.replace("[", "")
amostra = amostra.replace("]", "")
if contTodos == cont:
db.execute(
" update amostra set tpamostra = 'OUTLIER' where idamostra = " + str(amostra) + " and idmodelo = " + str(
idmodelo) + "")
print(itemOutilier)
amostrasRemovidas = amostrasRemovidas + 1
break
contTodos = contTodos + 1
cont = cont + 1
session.commit()
print("Numero de Amostras Removidas: " + str(amostrasRemovidas))
return cont
def api_alert(influxdb_ip, influxdb_port, influxdb_user, influxdb_pwd,
influxdb_database, influxdb_table, apiid):
timelimit = 'time > now()-1d'
# 访问influxdb
client = InfluxDBClient(influxdb_ip, influxdb_port, influxdb_user,
influxdb_pwd, influxdb_database)
# 获取当前API一天前的数据
result = client.query('select Average, CallCount, ErrorRate from ' +
influxdb_table + ' where ApiId = \'' + apiid +
'\' and ' + timelimit + ';')
# 把resultset格式的数据转换成list格式
apis_table = list(result.get_points(measurement='apis'))
# 把要处理的数据存成DataFrame
df = pd.DataFrame(data=apis_table)
# 去掉不参与运算的列，取训练集x
x = df
x = x.drop("time", axis=1)
# 数据处理一下，归一化，映射到[0,1]
x['CallCount'] = (x['CallCount']-x['CallCount'].min()) / \
(x['CallCount'].max()-x['CallCount'].min())
x['Average'] = (x['Average']-x['Average'].min()) / \
(x['Average'].max()-x['Average'].min())
x['ErrorRate'] = x['ErrorRate'] / 100
# 取最后十秒的数据点作为测试点
x_last = x.tail(1)
#df_last = df.tail(1)
x = x.drop(x.index[-1])
df = df.drop(df.index[-1])
# 转换成numpy格式准备计算
x = x.values
# 训练一个kNN检测器
clf_name = 'kNN'
clf = KNN() # 初始化检测器clf
clf.fit(x) # 使用X_train训练检测器clf
# 给df添加一列显示异常分数
df['score'] = clf.decision_scores_
# 排序分数
df = df.sort_values("score", ascending=False)
#print(df.head(20))
# 新数据预测
test_data = x_last
test_scores = clf.decision_function(test_data)
if (test_scores > 0.8):
print('数据点异常程度4，必须报警')
elif (test_scores > 0.5):
print('数据点异常程度3，需要报警')
elif (test_scores > 0.1):
print('数据点异常程度2，建议报警')
elif (test_scores > 0.05):
print('数据点异常程度1，可以报警')
#这个分级是根据KNN.py的图像分析出来的，0.05以上的很明显是异常点，0.1以上已经出现了离群现象，0.5以上就距离数据点很远了。
#这个值根据训练用的时间相关，一天的数据0.05比较合适。
return test_scores
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 detect_anomaly(df): clf = KNN() x_values = df.change.values.reshape(df.index.values.shape[0],1) y_values = df.change.values.reshape(df.change.values.shape[0],1) clf.fit(y_values) clf.predict(y_values) df["out_label"] = clf.predict(y_values) #fit_predict_score df["out_score"] = clf.decision_function(y_values) return df
def detect_anomaly(df): x_values = df.index.values.reshape(df.index.values.shape[0],1) y_values = df.change.values.reshape(df.change.values.shape[0],1) clf = KNN() clf.fit(y_values) clf.predict(y_values) df["label_knn"] = clf.predict(y_values) df["score_knn"] = clf.decision_function(y_values).round(4) return df
def abnormal_KNN(train_npy, test_npy):
clf_name = 'kNN'
clf = KNN()
train_npy = np.array(train_npy).reshape(-1, 1)
clf.fit(train_npy)
test_npy = np.array(test_npy).reshape(-1, 1)
y_test_pred = clf.predict(test_npy)
y_test_scores = clf.decision_function(test_npy)
return y_test_pred
class KNNPredictionHead:
def __init__(self, n_neighbors=5, method="largest", **kwargs):
self.model = KNN(n_neighbors=n_neighbors, method=method)
def fit(self, X):
return self.model.fit(X.detach().cpu().numpy())
def decision_function(self, X):
return self.model.decision_function(X.detach().cpu().numpy())
def get_KNN_scores(dataframe,
cols,
outliers_fraction=0.01,
standardize=True):
'''Takes df, a list selected column nmaes, outliers_fraction = 0.01 default
Returns:
df with KNN scores added
'''
if standardize:
#standardize selected variables
minmax = MinMaxScaler(feature_range=(0, 1))
dataframe[cols] = minmax.fit_transform(dataframe[cols])
#Convert dataframe to a numpy array in order to incorprate our algorithm
arrays = []
for row in cols:
row = dataframe[row].values.reshape(-1, 1)
arrays.append(row)
X = np.concatenate((arrays), axis=1)
#fit
clf = KNN(contamination=outliers_fraction)
clf.fit(X)
# predict raw anomaly score
scores_pred = clf.decision_function(X) * -1
# prediction of a datapoint category outlier or inlier
y_pred = clf.predict(X)
n_inliers = len(y_pred) - np.count_nonzero(y_pred)
n_outliers = np.count_nonzero(y_pred == 1)
CheckOutliers.df4 = dataframe
CheckOutliers.df4['outlier'] = y_pred.tolist()
print('OUTLIERS:', n_outliers, 'INLIERS:', n_inliers, 'found with KNN')
# In[169]: # 训练一个kNN检测器 clf_name = 'kNN' clf = KNN() # 初始化检测器 clf.fit(new_origin_all[:pos]) # 使用训练集训练检测器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) # 用训练好的clf来预测未知数据中的异常值 y_test_pred = clf.predict(new_origin_all[pos:]) # 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(new_origin_all[pos:]) # 返回未知数据上的异常值 show_scatter(clf_name, df, y_train_pred, pos) # In[170]: clf_name = 'COF' clf = COF(n_neighbors=30) clf.fit(new_origin_all[:pos]) # 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
clf.fit(trainData)
# get the prediction labels and outlier scores of the training data
y_train_pred = clf.labels_ # binary labels (0: inliers, 1: outliers)
#print(y_train_pred)
y_train_scores = clf.decision_scores_ # raw outlier scores
#print(y_train_scores)
# get the prediction on the test data
y_test_pred = clf.predict(testData) #X_test) # outlier labels (0 or 1)
print(y_test_pred)
from sklearn.metrics import accuracy_score
accuracy_percentage = accuracy_score(testTarget, y_test_pred) * 100
print("The prediction accuracy is:", end=" ")
print(accuracy_percentage)
y_test_scores = clf.decision_function(testData) #X_test) # outlier scores
#print(y_test_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)
#isualize(clf_name, trainData, trainTarget, testData, testTarget, y_train_pred,
# y_test_pred, show_figure=True, save_figure=False)
df['score'] = clf.decision_scores_
#df['pred'] = clf.labels_
df = df.sort_values("score", ascending=False)
print(df.head(10))
print(df['CallCount'].max())
max_score = df.head(1)['score'].values
print(max_score)
df['prob'] = df['score'] / max_score
print(df.head(10))
'''
# 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)'''
# 新数据预测
testdata = {"Average": 150, "CallCount": 130, 'ErrorRate': 100}
test = pd.DataFrame(data=testdata, index=[0])
print(test)
test_scores = clf.decision_function(test)
print(test_scores)
alert = pd.DataFrame(df.loc[df['score'] > 10])
# 数据展示
plt.title(timelimit)
plt.xlabel('Average')
plt.ylabel('CallCount')
plt.scatter('Average', 'CallCount', s=1, data=df)
plt.scatter('Average', 'CallCount', s=2, c='red', data=alert)
#plt.scatter('Average', 'CallCount', s=3, c='green', data=test)
plt.show()
y_item = np.array(y_item)
contam /= len(y_item)
contam = min(0.5, contam)
# 定义模型
classifiers = {
'KNN': KNN(contamination=contam),
'LOF': LOF(contamination=contam),
'PCA': PCA(contamination=contam),
'LODA': LODA(contamination=contam)
}
for cls in classifiers:
clf = classifiers[cls]
t0 = time.time()
x_item = standardizer(x_item)
clf.fit(x_item)
y_scores = clf.decision_function(x_item)
t1 = time.time()
duration = round(t1 - t0, ndigits=4)
roc = round(roc_auc_score(y_item, y_scores), ndigits=4)
prn = round(precision_n_scores(y_item, y_scores),
ndigits=4)
results[cls].append(roc)
print(
'benchmark id:{bench_id}, model:{clf_name}, ROC:{roc}, precision @ rank n:{prn}, '
'execution time: {duration}s'.format(
bench_id=bench_id,
clf_name=cls,
roc=roc,
prn=prn,
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=1)
clf_name = 'KNN'
clf = KNN() # 初始化检测器clf
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(clf_name, 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(clf_name, 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)
knn_roc.append(roc)
knn_prn.append(prn)
clf_name = 'LOF'
clf = LOF() # 初始化检测器clf
plt.ylabel('F2')
plt.show()
'''
KNN -> K-Nearest Neighbors Detector
For an observation, its distance to its kth nearest neighbors could be viewed as the outlying scores
Method: -Largest -Average -Median
'''
clf_name = 'KNN'
clf = KNN()
clf.fit(X_train)
X_train_pred = clf.labels_
X_train_score = clf.decision_scores_
score_pred = clf.decision_function(X_train)*-1
y_pred = clf.predict(X_train)
n_errors = (y_pred != y_train).sum()
print('No of Errors:', clf_name, n_errors)
# visualization
xx, yy = np.meshgrid(np.linspace(-10, 10, 300), np.linspace(-10, 10, 300))
threshold = stats.scoreatpercentile(score_pred, 100*outlier_fraction)
Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()]) * -1
Z = Z.reshape(xx.shape)
# fill blue colormap from minimum anomaly score to threshold value
plt.contourf(xx, yy, Z, levels=np.linspace(Z.min(), threshold, 10), cmap=plt.cm.Blues_r)
a = plt.contour(xx, yy, Z, levels=[threshold],linewidths=2, colors='red')
plt.contourf(xx, yy, Z, levels=[threshold, Z.max()],colors='orange')
b = plt.scatter(X_train[:-n_outliers, 0], X_train[:-n_outliers, 1], c='white',s=20, edgecolor='k')
c = plt.scatter(X_train[-n_outliers:, 0], X_train[-n_outliers:, 1], c='black',s=20, edgecolor='k')
class TestKnn(unittest.TestCase):
def setUp(self):
self.n_train = 100
self.n_test = 50
self.contamination = 0.1
self.roc_floor = 0.6
self.X_train, self.y_train, self.X_test, self.y_test = generate_data(
n_train=self.n_train, n_test=self.n_test,
contamination=self.contamination, random_state=42)
self.clf = KNN(contamination=self.contamination)
self.clf.fit(self.X_train)
def test_sklearn_estimator(self):
check_estimator(self.clf)
def test_parameters(self):
assert_true(hasattr(self.clf, 'decision_scores_') and
self.clf.decision_scores_ is not None)
assert_true(hasattr(self.clf, 'labels_') and
self.clf.labels_ is not None)
assert_true(hasattr(self.clf, 'threshold_') and
self.clf.threshold_ is not None)
assert_true(hasattr(self.clf, '_mu') and
self.clf._mu is not None)
assert_true(hasattr(self.clf, '_sigma') and
self.clf._sigma is not None)
def test_train_scores(self):
assert_equal(len(self.clf.decision_scores_), self.X_train.shape[0])
def test_prediction_scores(self):
pred_scores = self.clf.decision_function(self.X_test)
# check score shapes
assert_equal(pred_scores.shape[0], self.X_test.shape[0])
# check performance
assert_greater(roc_auc_score(self.y_test, pred_scores), self.roc_floor)
def test_prediction_labels(self):
pred_labels = self.clf.predict(self.X_test)
assert_equal(pred_labels.shape, self.y_test.shape)
def test_prediction_proba(self):
pred_proba = self.clf.predict_proba(self.X_test)
assert_greater_equal(pred_proba.min(), 0)
assert_less_equal(pred_proba.max(), 1)
def test_prediction_proba_linear(self):
pred_proba = self.clf.predict_proba(self.X_test, method='linear')
assert_greater_equal(pred_proba.min(), 0)
assert_less_equal(pred_proba.max(), 1)
def test_prediction_proba_unify(self):
pred_proba = self.clf.predict_proba(self.X_test, method='unify')
assert_greater_equal(pred_proba.min(), 0)
assert_less_equal(pred_proba.max(), 1)
def test_prediction_proba_parameter(self):
with assert_raises(ValueError):
self.clf.predict_proba(self.X_test, method='something')
def test_fit_predict(self):
pred_labels = self.clf.fit_predict(self.X_train)
assert_equal(pred_labels.shape, self.y_train.shape)
def test_fit_predict_score(self):
self.clf.fit_predict_score(self.X_test, self.y_test)
self.clf.fit_predict_score(self.X_test, self.y_test,
scoring='roc_auc_score')
self.clf.fit_predict_score(self.X_test, self.y_test,
scoring='prc_n_score')
with assert_raises(NotImplementedError):
self.clf.fit_predict_score(self.X_test, self.y_test,
scoring='something')
def test_predict_rank(self):
pred_socres = self.clf.decision_function(self.X_test)
pred_ranks = self.clf._predict_rank(self.X_test)
# assert the order is reserved
assert_allclose(rankdata(pred_ranks), rankdata(pred_socres), atol=2)
assert_array_less(pred_ranks, self.X_train.shape[0] + 1)
assert_array_less(-0.1, pred_ranks)
def test_predict_rank_normalized(self):
pred_socres = self.clf.decision_function(self.X_test)
pred_ranks = self.clf._predict_rank(self.X_test, normalized=True)
# assert the order is reserved
assert_allclose(rankdata(pred_ranks), rankdata(pred_socres), atol=2)
assert_array_less(pred_ranks, 1.01)
assert_array_less(-0.1, pred_ranks)
def tearDown(self):
pass
n_features=2,
contamination=contamination,
random_state=42)
# train kNN detector
clf_name = 'KNN'
clf = KNN()
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,
class TestKnn(unittest.TestCase):
def setUp(self):
self.n_train = 200
self.n_test = 100
self.contamination = 0.1
self.roc_floor = 0.8
self.X_train, self.X_test, self.y_train, self.y_test = generate_data(
n_train=self.n_train,
n_test=self.n_test,
contamination=self.contamination,
random_state=42)
self.clf = KNN(contamination=self.contamination)
self.clf.fit(self.X_train)
def test_parameters(self):
assert (hasattr(self.clf, 'decision_scores_')
and self.clf.decision_scores_ is not None)
assert (hasattr(self.clf, 'labels_') and self.clf.labels_ is not None)
assert (hasattr(self.clf, 'threshold_')
and self.clf.threshold_ is not None)
assert (hasattr(self.clf, '_mu') and self.clf._mu is not None)
assert (hasattr(self.clf, '_sigma') and self.clf._sigma is not None)
def test_train_scores(self):
assert_equal(len(self.clf.decision_scores_), self.X_train.shape[0])
def test_prediction_scores(self):
pred_scores = self.clf.decision_function(self.X_test)
# check score shapes
assert_equal(pred_scores.shape[0], self.X_test.shape[0])
# check performance
assert (roc_auc_score(self.y_test, pred_scores) >= self.roc_floor)
def test_prediction_labels(self):
pred_labels = self.clf.predict(self.X_test)
assert_equal(pred_labels.shape, self.y_test.shape)
def test_prediction_proba(self):
pred_proba = self.clf.predict_proba(self.X_test)
assert (pred_proba.min() >= 0)
assert (pred_proba.max() <= 1)
def test_prediction_proba_linear(self):
pred_proba = self.clf.predict_proba(self.X_test, method='linear')
assert (pred_proba.min() >= 0)
assert (pred_proba.max() <= 1)
def test_prediction_proba_unify(self):
pred_proba = self.clf.predict_proba(self.X_test, method='unify')
assert (pred_proba.min() >= 0)
assert (pred_proba.max() <= 1)
def test_prediction_proba_parameter(self):
with assert_raises(ValueError):
self.clf.predict_proba(self.X_test, method='something')
def test_prediction_labels_confidence(self):
pred_labels, confidence = self.clf.predict(self.X_test,
return_confidence=True)
assert_equal(pred_labels.shape, self.y_test.shape)
assert_equal(confidence.shape, self.y_test.shape)
assert (confidence.min() >= 0)
assert (confidence.max() <= 1)
def test_prediction_proba_linear_confidence(self):
pred_proba, confidence = self.clf.predict_proba(self.X_test,
method='linear',
return_confidence=True)
assert (pred_proba.min() >= 0)
assert (pred_proba.max() <= 1)
assert_equal(confidence.shape, self.y_test.shape)
assert (confidence.min() >= 0)
assert (confidence.max() <= 1)
def test_fit_predict(self):
pred_labels = self.clf.fit_predict(self.X_train)
assert_equal(pred_labels.shape, self.y_train.shape)
def test_fit_predict_score(self):
self.clf.fit_predict_score(self.X_test, self.y_test)
self.clf.fit_predict_score(self.X_test,
self.y_test,
scoring='roc_auc_score')
self.clf.fit_predict_score(self.X_test,
self.y_test,
scoring='prc_n_score')
with assert_raises(NotImplementedError):
self.clf.fit_predict_score(self.X_test,
self.y_test,
scoring='something')
def test_predict_rank(self):
pred_socres = self.clf.decision_function(self.X_test)
pred_ranks = self.clf._predict_rank(self.X_test)
# assert the order is reserved
assert_allclose(rankdata(pred_ranks), rankdata(pred_socres), atol=2)
assert_array_less(pred_ranks, self.X_train.shape[0] + 1)
assert_array_less(-0.1, pred_ranks)
def test_predict_rank_normalized(self):
pred_socres = self.clf.decision_function(self.X_test)
pred_ranks = self.clf._predict_rank(self.X_test, normalized=True)
# assert the order is reserved
assert_allclose(rankdata(pred_ranks), rankdata(pred_socres), atol=2)
assert_array_less(pred_ranks, 1.01)
assert_array_less(-0.1, pred_ranks)
def test_model_clone(self):
clone_clf = clone(self.clf)
def tearDown(self):
pass
class TestKnnMahalanobis(unittest.TestCase):
def setUp(self):
self.n_train = 200
self.n_test = 100
self.contamination = 0.1
self.roc_floor = 0.8
self.X_train, self.y_train, self.X_test, self.y_test = generate_data(
n_train=self.n_train,
n_test=self.n_test,
contamination=self.contamination,
random_state=42)
# calculate covariance for mahalanobis distance
X_train_cov = np.cov(self.X_train, rowvar=False)
self.clf = KNN(algorithm='auto',
metric='mahalanobis',
metric_params={'V': X_train_cov})
self.clf.fit(self.X_train)
def test_parameters(self):
assert (hasattr(self.clf, 'decision_scores_')
and self.clf.decision_scores_ is not None)
assert (hasattr(self.clf, 'labels_') and self.clf.labels_ is not None)
assert (hasattr(self.clf, 'threshold_')
and self.clf.threshold_ is not None)
assert (hasattr(self.clf, '_mu') and self.clf._mu is not None)
assert (hasattr(self.clf, '_sigma') and self.clf._sigma is not None)
def test_train_scores(self):
assert_equal(len(self.clf.decision_scores_), self.X_train.shape[0])
def test_prediction_scores(self):
pred_scores = self.clf.decision_function(self.X_test)
# check score shapes
assert_equal(pred_scores.shape[0], self.X_test.shape[0])
# check performance
assert (roc_auc_score(self.y_test, pred_scores) >= self.roc_floor)
def test_prediction_labels(self):
pred_labels = self.clf.predict(self.X_test)
assert_equal(pred_labels.shape, self.y_test.shape)
def test_prediction_proba(self):
pred_proba = self.clf.predict_proba(self.X_test)
assert (pred_proba.min() >= 0)
assert (pred_proba.max() <= 1)
def test_prediction_proba_linear(self):
pred_proba = self.clf.predict_proba(self.X_test, method='linear')
assert (pred_proba.min() >= 0)
assert (pred_proba.max() <= 1)
def test_prediction_proba_unify(self):
pred_proba = self.clf.predict_proba(self.X_test, method='unify')
assert (pred_proba.min() >= 0)
assert (pred_proba.max() <= 1)
def test_prediction_proba_parameter(self):
with assert_raises(ValueError):
self.clf.predict_proba(self.X_test, method='something')
def test_fit_predict(self):
pred_labels = self.clf.fit_predict(self.X_train)
assert_equal(pred_labels.shape, self.y_train.shape)
def test_fit_predict_score(self):
self.clf.fit_predict_score(self.X_test, self.y_test)
self.clf.fit_predict_score(self.X_test,
self.y_test,
scoring='roc_auc_score')
self.clf.fit_predict_score(self.X_test,
self.y_test,
scoring='prc_n_score')
with assert_raises(NotImplementedError):
self.clf.fit_predict_score(self.X_test,
self.y_test,
scoring='something')
def test_predict_rank(self):
pred_socres = self.clf.decision_function(self.X_test)
pred_ranks = self.clf._predict_rank(self.X_test)
# assert the order is reserved
assert_allclose(rankdata(pred_ranks), rankdata(pred_socres), atol=3)
assert_array_less(pred_ranks, self.X_train.shape[0] + 1)
assert_array_less(-0.1, pred_ranks)
def test_predict_rank_normalized(self):
pred_socres = self.clf.decision_function(self.X_test)
pred_ranks = self.clf._predict_rank(self.X_test, normalized=True)
# assert the order is reserved
assert_allclose(rankdata(pred_ranks), rankdata(pred_socres), atol=3)
assert_array_less(pred_ranks, 1.01)
assert_array_less(-0.1, pred_ranks)
def tearDown(self):
pass
def get_out_score(X, observers):
print('>> internal check : r = ', observers.shape[0]/X.shape[0]*100)
clf = KNN()
clf.fit(observers)
return clf.decision_function(X)
# # kNN
# In[3]:
#kNN
clf_name = 'kNN'
clf = KNN(method='median')
# In[4]:
#用训练集训练
clf.fit(X_train)
y_train_pred = clf.labels_
y_train_scores = clf.decision_scores_
y_test_pred = clf.predict(X_test)
y_test_scores = clf.decision_function(X_test)
#评价性能
roc_train = round(roc_auc_score(y_train, y_train_scores), 4)
prn_train = round(precision_n_scores(y_train, y_train_scores), ndigits=4)
roc_test = round(roc_auc_score(y_test, y_test_scores), 4)
prn_test = round(precision_n_scores(y_test, y_test_scores), ndigits=4)
# In[5]:
#输出计算得到的roc_auc和precision @ rank n
print("\nOn Train Data:")
print(clf_name, 'roc:', roc_train, 'precision @ rank n:', prn_train)
print("\nOn Test Data:")
print(clf_name, 'roc:', roc_test, 'precision @ rank n:', prn_test)
# In[6]:
Hence, we use a library called **pyod** which hosts a number of outlier detection algorithms. """ pip install pyod """### KNN Classifier (Proximity-Based)""" from pyod.models.knn import KNN # kNN detector # train kNN detector clf_name = 'KNN' clf = KNN() clf.fit(X) y_pred = clf.predict(X) # outlier labels (0 or 1) y_scores = clf.decision_function(X) # outlier scores y_pred """0 means normal value while 1 means anomalous value.""" colors = np.array(['#377eb8', '#ff7f00']) plt.scatter(X[:, 0], X[:, 1], s=10, color=colors[(y_pred - 1) // 2]) """Finding the ROC Accuracy score for the prediction label.""" clf.fit_predict_score(X[:, 0].reshape(-1,1), y_pred, scoring='roc_auc_score') """### Angle-based Outlier Detector (Probabilistic Based Model)""" from pyod.models import abod
#划分测试集和训练集
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.33)
#使用pyod中的KNN算法拟合数据
clf_name = 'KNN'
clf = KNN()
clf.fit(X_train)
#预测得到由0和1组成的数组，1表示离群点，0表示飞离群点
y_train_pred = clf.labels_ # binary labels (0: inliers, 1: outliers)
y_train_scores = clf.decision_scores_ # raw outlier scores，The outlier scores of the training data.
#预测样本是不是离群点，返回0和1 的数组
y_test_pred = clf.predict(X_test)
y_test_scores = clf.decision_function(
X_test) # outlier scores，The anomaly score of the input samples.
#使用sklearn中的roc_auc_score方法得到auc值，即roc曲线下面的面积
try:
sumAuc_train += sklearn.metrics.roc_auc_score(y_train,
y_train_scores,
average='macro')
sumAuc_test += sklearn.metrics.roc_auc_score(y_test,
y_test_scores,
average='macro')
#s=precision_score(y_train, y_train_scores, average='macro')
i += 1
print(sumAuc_train, sumAuc_test)
except ValueError:
pass
#得到ROC值和精确度 prn
k_list = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140,
150, 160, 170, 180, 190, 200]
train_scores = np.zeros([X_train.shape[0], n_clf])
test_scores = np.zeros([X_test.shape[0], n_clf])
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)
# Generate sample data
X_train, y_train, X_test, y_test = \
generate_data(n_train=n_train,
n_test=n_test,
n_features=2,
contamination=contamination,
random_state=42)
# train kNN detector
clf_name = 'KNN'
clf = KNN()
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=True)
k_list = [
10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160,
170, 180, 190, 200
]
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)
# In[2]:
# train kNN detector
clf_name = 'KNN'
clf = KNN()
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
# In[3]:
# 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)
# In[4]:
