def tagAnomalies(df):
values = df.prediction.values.reshape(-1, 1)
anomalyDetector = LOCI()
anomalyDetector.fit(values)
anomalyLabels = np.asarray(anomalyDetector.labels_)
df['isAnomaly'] = anomalyLabels
return df
def loci(X):
alpha = 0.5
k = 3
clf = LOCI(alpha=alpha, k=k)
clf.fit(X)
label = clf.labels_
#return label
writeLabel(label)
return
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)
self.clf = LOCI(contamination=self.contamination)
self.clf.fit(self.X_train)
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 = LOCI(contamination=self.contamination)
self.clf.fit(self.X_train)
def choose_model(model, nnet):
""" among implemented in PyOD """
clfs = {
'AE':
AutoEncoder(hidden_neurons=nnet, contamination=0.1, epochs=15),
'VAE':
VAE(encoder_neurons=nnet[:5],
decoder_neurons=nnet[4:],
contamination=0.1,
epochs=13),
'ABOD':
ABOD(),
'FeatureBagging':
FeatureBagging(),
'HBOS':
HBOS(),
'IForest':
IForest(),
'KNN':
KNN(),
'LOF':
LOF(),
'OCSVM':
OCSVM(),
'PCA':
PCA(),
'SOS':
SOS(),
'COF':
COF(),
'CBLOF':
CBLOF(),
'SOD':
SOD(),
'LOCI':
LOCI(),
'MCD':
MCD()
}
return clfs[model]
def runMethod(self):
'''
@brief This function is the actual implementation of HICS
'''
if self.verbose:
print("Calculating the subspaces\n")
# First we obtain the high contrast subspaces
subspaces = self.hicsFramework()
if self.verbose:
print("Now calculating the scoring\n")
# We initialize the scores for each instance as 0
scores = np.zeros(len(self.dataset))
# For each subspace
for sub in subspaces:
# We place the corresponding scorer according to parameter
scorer = None
if self.outlier_rank == "lof":
scorer = LOF()
elif self.outlier_rank == "cof":
scorer = COF()
elif self.outlier_rank == "cblof":
scorer = CBLOF()
elif self.outlier_rank == "loci":
scorer = LOCI()
elif self.outlier_rank == "hbos":
scorer = HBOS()
elif self.outlier_rank == "sod":
scorer = SOD()
# Fits the scorer with the dataset projection
scorer.fit(self.dataset[:, sub])
# Adds the scores obtained to the global ones
scores = scores + scorer.decision_scores_
# Compute the average
self.outlier_score = scores / len(subspaces)
# Marks the calculations as done
self.calculations_done = True
coef = algorith.fit_predict(data[:, :-1])
coef = (coef - coef.min()) / (coef.max() - coef.min())
fpr, tpr, _ = roc_curve(data[:, -1], coef)
roc_auc = auc(fpr, tpr)
file_result.append(roc_auc)
#kDIST
algorith = kdist.KDIST(k=60, t=0.1)
coef = algorith.fit_predict(data[:, :-1])
coef = (coef - coef.min()) / (coef.max() - coef.min())
fpr, tpr, _ = roc_curve(data[:, -1], coef)
roc_auc = auc(fpr, tpr)
file_result.append(roc_auc)
#LOCI
clf = LOCI()
clf.fit(data[:, :-1])
coef = clf.decision_scores_
coef = np.abs(coef)
coef = (coef - coef.min()) / (coef.max() - coef.min())
fpr, tpr, _ = roc_curve(data[:, -1], coef)
roc_auc = auc(fpr, tpr)
file_result.append(roc_auc)
result = np.array(file_result)
print('hi')
##############################################################################
## file1
data = load.load_data(files[1], sep=',') # k = 20
file_result = []
class TestLOCI(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)
self.clf = LOCI(contamination=self.contamination)
self.clf.fit(self.X_train)
def test_sklearn_estimator(self):
# TODO: sklearn check does not support Numba optimization
# check_estimator(self.clf)
pass
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)
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=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
if __name__ == "__main__":
contamination = 0.1 # percentage of outliers
n_train = 200 # number of training points
n_test = 100 # number of testing points
# 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 LOCI detector
clf_name = 'LOCI'
clf = LOCI()
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 initialise_pyod_classifiers(self, outlier_fraction):
#Testing every query to every class and then predicting only if it belongs to the same class
classifiers = {}
#Proximity based
classifiers['K Nearest Neighbors (KNN)'] = []
classifiers['Average K Nearest Neighbors (AvgKNN)'] = []
classifiers['Median K Nearest Neighbors (MedKNN)'] = []
classifiers['Local Outlier Factor (LOF)'] = []
classifiers['Connectivity-Based Outlier Factor (COF)'] = []
#classifiers['Clustering-Based Local Outlier Factor (CBLOF)'] = []
classifiers['LOCI'] = []
#classifiers['Histogram-based Outlier Score (HBOS)'] = []
classifiers['Subspace Outlier Detection (SOD)'] = []
#Linear models
classifiers['Principal Component Analysis (PCA)'] = []
#classifiers['Minimum Covariance Determinant (MCD)'] = [] #To slow
classifiers['One-Class Support Vector Machines (OCSVM)'] = []
classifiers['Deviation-based Outlier Detection (LMDD)'] = []
#Probabilistic
classifiers['Angle-Based Outlier Detection (ABOD)'] = []
classifiers['Stochastic Outlier Selection (SOS)'] = []
#Outlier Ensembles
classifiers['Isolation Forest (IForest)'] = []
classifiers['Feature Bagging'] = []
classifiers['Lightweight On-line Detector of Anomalies (LODA)'] = []
for i in range(self.k_way):
for i in range(self.k_way):
classifiers['K Nearest Neighbors (KNN)'].append(
KNN(method='largest',
n_neighbors=int(self.n_shot / 3) + 1,
contamination=outlier_fraction))
classifiers['Average K Nearest Neighbors (AvgKNN)'].append(
KNN(method='mean',
n_neighbors=int(self.n_shot / 3) + 1,
contamination=outlier_fraction))
classifiers['Median K Nearest Neighbors (MedKNN)'].append(
KNN(method='median',
n_neighbors=int(self.n_shot / 3) + 1,
contamination=outlier_fraction))
classifiers['Local Outlier Factor (LOF)'].append(
LOF(n_neighbors=int(self.n_shot / 3) + 1,
contamination=outlier_fraction))
classifiers['Connectivity-Based Outlier Factor (COF)'].append(
COF(n_neighbors=int(self.n_shot / 3) + 1,
contamination=outlier_fraction))
classifiers['LOCI'].append(
LOCI(contamination=outlier_fraction))
classifiers['Subspace Outlier Detection (SOD)'].append(
SOD(n_neighbors=int(self.n_shot / 3) + 2,
contamination=outlier_fraction,
ref_set=max(2, int((int(self.n_shot / 3) + 2) / 3))))
classifiers['Principal Component Analysis (PCA)'].append(
PCA(contamination=outlier_fraction))
classifiers[
'One-Class Support Vector Machines (OCSVM)'].append(
OCSVM(contamination=outlier_fraction))
classifiers['Deviation-based Outlier Detection (LMDD)'].append(
LMDD(contamination=outlier_fraction))
classifiers['Angle-Based Outlier Detection (ABOD)'].append(
ABOD(contamination=outlier_fraction))
classifiers['Stochastic Outlier Selection (SOS)'].append(
SOS(contamination=outlier_fraction))
classifiers['Isolation Forest (IForest)'].append(
IForest(contamination=outlier_fraction))
classifiers['Feature Bagging'].append(
FeatureBagging(contamination=outlier_fraction))
classifiers[
'Lightweight On-line Detector of Anomalies (LODA)'].append(
LODA(contamination=outlier_fraction))
self.num_different_models = len(classifiers)
return classifiers
class TestLOCI(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 = LOCI(contamination=self.contamination)
self.clf.fit(self.X_train)
def test_sklearn_estimator(self):
# TODO: sklearn check does not support Numba optimization
# check_estimator(self.clf)
pass
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)
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=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 pyod_init(model, n_features=None):
# initial model set up
if model == 'abod':
from pyod.models.abod import ABOD
clf = ABOD()
elif model == 'auto_encoder' and n_features:
#import os
#os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
from pyod.models.auto_encoder import AutoEncoder
clf = AutoEncoder(hidden_neurons=[
n_features, n_features * 5, n_features * 5, n_features
],
epochs=5,
batch_size=64,
preprocessing=False)
elif model == 'cblof':
from pyod.models.cblof import CBLOF
clf = CBLOF(n_clusters=4)
elif model == 'hbos':
from pyod.models.hbos import HBOS
clf = HBOS()
elif model == 'iforest':
from pyod.models.iforest import IForest
clf = IForest()
elif model == 'knn':
from pyod.models.knn import KNN
clf = KNN()
elif model == 'lmdd':
from pyod.models.lmdd import LMDD
clf = LMDD()
elif model == 'loci':
from pyod.models.loci import LOCI
clf = LOCI()
elif model == 'loda':
from pyod.models.loda import LODA
clf = LODA()
elif model == 'lof':
from pyod.models.lof import LOF
clf = LOF()
elif model == 'mcd':
from pyod.models.mcd import MCD
clf = MCD()
elif model == 'ocsvm':
from pyod.models.ocsvm import OCSVM
clf = OCSVM()
elif model == 'pca':
from pyod.models.pca import PCA
clf = PCA()
elif model == 'sod':
from pyod.models.sod import SOD
clf = SOD()
elif model == 'vae':
from pyod.models.vae import VAE
clf = VAE()
elif model == 'xgbod':
from pyod.models.xgbod import XGBOD
clf = XGBOD()
else:
#raise ValueError(f"unknown model {model}")
clf = PyODDefaultModel()
return clf
