def setUp(self):
self.n_train = 100
self.n_test = 50
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 = SOD(contamination=self.contamination)
self.clf.fit(self.X_train)
def getOutlierSOD(dataset):
'''
@brief Function that executes SOD algorithm on the dataset and obtains the
labels of the dataset indicating which instance is an inlier (0) or outlier (1)
@param dataset Dataset on which to try the algorithm
@return It returns a list of labels 0 means inlier, 1 means outlier
'''
# Initializating the model
sod = SOD()
# Fits the data and obtains labels
sod.fit(dataset)
# Return labels
return sod.labels_
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
#todoļ¼ 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():
scalers = ['no', 'std', 'minmax']
root = 'Unsupervised_Anamaly_Detection_csv'
start = 0
counts = 90
CPUS = 3
CPUS_Models = 4
sklearn_models = [
'AvgKNN', 'LargestKNN', 'MedKNN', 'PCA', 'COF', 'LODA', 'LOF', 'HBOS',
'MCD', 'AvgBagging', 'MaxBagging', 'IForest', 'CBLOF', 'COPOD', 'SOD',
'LSCPwithLODA', 'AveLMDD', 'VarLMDD', 'IqrLMDD', 'SoGaal', 'MoGaal',
'VAE', 'AutoEncoder'
]
models = {
'BRM': BRM(bootstrap_sample_percent=70),
'GM': GaussianMixture(),
'IF': IsolationForest(),
'OCSVM': OneClassSVM(),
'EE': EllipticEnvelope(),
'AvgKNN': KNN(method='mean'),
'LargestKNN': KNN(method='largest'),
'MedKNN': KNN(method='median'),
'PCA': PCA(),
'COF': COF(),
'LODA': LODA(),
'LOF': LOF(),
'HBOS': HBOS(),
'MCD': MCD(),
'AvgBagging': FeatureBagging(combination='average'),
'MaxBagging': FeatureBagging(combination='max'),
'CBLOF': CBLOF(n_clusters=10, n_jobs=4),
'FactorAnalysis': FactorAnalysis(),
'KernelDensity': KernelDensity(),
'COPOD': COPOD(),
'SOD': SOD(),
'LSCPwithLODA': LSCP([LODA(), LODA()]),
'AveLMDD': LMDD(dis_measure='aad'),
'VarLMDD': LMDD(dis_measure='var'),
'IqrLMDD': LMDD(dis_measure='iqr'),
'SoGaal': SO_GAAL(),
'MoGaal': MO_GAAL(),
'VAE': VAE(encoder_neurons=[8, 4, 2]),
'AutoEncoder': AutoEncoder(hidden_neurons=[6, 3, 3, 6]),
'OCKRA': m_OCKRA(),
}
name = "30_Models"
Parallel(n_jobs=CPUS) \
(delayed(runByScaler)
(root, scaler, models, start, counts,
other_models=sklearn_models,
CPUS=CPUS_Models,
save_name=name)
for scaler in scalers)
def test_check_parameters(self):
with assert_raises(ValueError):
SOD(n_neighbors=None, ref_set=10, alpha=0.8)
with assert_raises(ValueError):
SOD(n_neighbors=20, ref_set=None, alpha=0.8)
with assert_raises(ValueError):
SOD(n_neighbors=20, ref_set=10, alpha=None)
with assert_raises(ValueError):
SOD(n_neighbors=-1, ref_set=10, alpha=0.8)
with assert_raises(ValueError):
SOD(n_neighbors=20, ref_set=-1, alpha=0.8)
with assert_raises(ValueError):
SOD(n_neighbors=20, ref_set=10, alpha=-1)
with assert_raises(ValueError):
SOD(n_neighbors=20, ref_set=25, alpha=0.8)
with assert_raises(ValueError):
SOD(n_neighbors='not int', ref_set=25, alpha=0.8)
with assert_raises(ValueError):
SOD(n_neighbors=20, ref_set='not int', alpha=0.8)
with assert_raises(ValueError):
SOD(n_neighbors=20, ref_set=25, alpha='not float')
def __init__(
self,
*,
hyperparams: Hyperparams, #
random_seed: int = 0,
docker_containers: Dict[str, DockerContainer] = None) -> None:
super().__init__(hyperparams=hyperparams,
random_seed=random_seed,
docker_containers=docker_containers)
self._clf = SOD(
contamination=hyperparams['contamination'],
n_neighbors=hyperparams['n_neighbors'],
ref_set=hyperparams['ref_set'],
alpha=hyperparams['alpha'],
)
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
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 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)
# 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 main():
# PART 1:
# Getting the predictions for each classifier
# SK means: The classifier is from sklearn or works like sklearn
# PY means: The classifier is from pyod or works like pyod
models = {
'SK_EE': EllipticEnvelope(),
'SK_GM': GaussianMixture(),
'SK_IF': IsolationForest(),
'SK_OCSVM': OneClassSVM(),
'SK_FA': FactorAnalysis(),
'SK_KD': KernelDensity(),
'PY_PCA': PCA(),
'PY_COF': COF(),
'PY_LODA': LODA(),
'PY_LOF': LOF(),
'PY_HBOS': HBOS(),
'PY_MCD': MCD(),
'PY_AvgKNN': KNN(method='mean'),
'PY_LargestKNN': KNN(method='largest'),
'PY_MedKNN': KNN(method='median'),
'PY_AvgBagging': FeatureBagging(combination='average'),
'PY_MaxBagging': FeatureBagging(combination='max'),
'PY_CBLOF': CBLOF(n_clusters=10, n_jobs=4),
'PY_COPOD': COPOD(),
'PY_SOD': SOD(),
'PY_LSCPwithLODA': LSCP([LODA(), LODA()]),
'PY_AveLMDD': LMDD(dis_measure='aad'),
'PY_VarLMDD': LMDD(dis_measure='var'),
'PY_IqrLMDD': LMDD(dis_measure='iqr'),
'PY_VAE': VAE(encoder_neurons=[8, 4, 2]),
'PY_AutoEncoder': AutoEncoder(hidden_neurons=[6, 3, 3, 6]),
'SK_BRM': BRM(bootstrap_sample_percent=70),
'SK_OCKRA': m_OCKRA(),
'PY_SoGaal': SO_GAAL(),
'PY_MoGaal': MO_GAAL()
}
ranker = ADRanker(data="datasets", models=models)
ranker.get_predictions()
# PART 2:
# After predictions, we can evaluate our classifiers using different scores
# You can add manually a new metric by modifying 'metrics.py'
ranker.get_scores(scores={'auc': Metrics.get_roc, 'ave': Metrics.get_ave})
# PART 3:
# Finally, it is time to summarize the results by plotting different graphs
# You can add your own graphs by modifying ' plots.py'
plot = Plots()
plot.make_plot_basic(paths=[
'results/scores/auc/no/results.csv',
'results/scores/auc/minmax/results.csv',
'results/scores/auc/std/results.csv',
'results/scores/ave/no/results.csv',
'results/scores/ave/minmax/results.csv',
'results/scores/ave/std/results.csv'
],
scalers=[
'Without scaler', 'Min max scaler',
'Standard scaler', 'Without scaler',
'Min max scaler', 'Standard scaler'
])
plot.make_cd_plot(
paths=[
'results/scores/auc/minmax/results.csv',
'results/scores/ave/no/results.csv',
'results/scores/auc/no/results.csv',
'results/scores/ave/no/results.csv',
'results/scores/auc/std/results.csv',
'results/scores/ave/std/results.csv'
],
names=[
'CD auc minmax scale', 'CD ave minmax scale', 'CD auc no scale',
'CD ave no scale', 'CD auc std scale', 'CD ave std scale'
],
titles=[
'CD diagram - AUC with min max scaling',
'CD diagram - Average precision with min max scaling',
'CD diagram - AUC without scaling',
'CD diagram - Average precision without scaling',
'CD diagram - AUC with standard scaling',
'CD diagram - Average precision with standard scaling'
])
class TestSOD(unittest.TestCase):
def setUp(self):
self.n_train = 100
self.n_test = 50
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 = SOD(contamination=self.contamination)
self.clf.fit(self.X_train)
def test_check_parameters(self):
with assert_raises(ValueError):
SOD(n_neighbors=None, ref_set=10, alpha=0.8)
with assert_raises(ValueError):
SOD(n_neighbors=20, ref_set=None, alpha=0.8)
with assert_raises(ValueError):
SOD(n_neighbors=20, ref_set=10, alpha=None)
with assert_raises(ValueError):
SOD(n_neighbors=-1, ref_set=10, alpha=0.8)
with assert_raises(ValueError):
SOD(n_neighbors=20, ref_set=-1, alpha=0.8)
with assert_raises(ValueError):
SOD(n_neighbors=20, ref_set=10, alpha=-1)
with assert_raises(ValueError):
SOD(n_neighbors=20, ref_set=25, alpha=0.8)
with assert_raises(ValueError):
SOD(n_neighbors='not int', ref_set=25, alpha=0.8)
with assert_raises(ValueError):
SOD(n_neighbors=20, ref_set='not int', alpha=0.8)
with assert_raises(ValueError):
SOD(n_neighbors=20, ref_set=25, alpha='not float')
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, 'alpha_') and self.clf.alpha_ is not None)
assert (hasattr(self.clf, 'ref_set_')
and self.clf.ref_set_ is not None)
assert (hasattr(self.clf, 'n_neighbors_')
and self.clf.n_neighbors_ 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_scores = 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_scores), 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)
# todo: fix clone issue
def test_model_clone(self):
pass
# clone_clf = clone(self.clf)
def tearDown(self):
pass
'Connectivity-Based Outlier Factor (COF)': COF(
contamination=outliers_fraction
),
'K Nearest Neighbors (KNN)': KNN(
contamination=outliers_fraction
),
'Average K Nearest Neighbors (AvgKNN)': KNN(
method='mean',
contamination=outliers_fraction
),
'Median K Nearest Neighbors (MedKNN)': KNN(
method='median',
contamination=outliers_fraction
),
'Subspace Outlier Detection (SOD)': SOD(
contamination=outliers_fraction
)
}
classifiers_indices = {
'Local Outlier Factor (LOF)': 0,
'Connectivity-Based Outlier Factor (COF)': 1,
'K Nearest Neighbors (KNN)': 2,
'Average K Nearest Neighbors (AvgKNN)': 3,
'Median K Nearest Neighbors (MedKNN)': 4,
'Subspace Outlier Detection (SOD)': 5
}
for clf_name, clf in classifiers.items():
t0 = time()
clf.fit(X_norm)
scores = clf.decision_function(X_norm)
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
f.write("Model: " + modelname + "\n")
f.write("Dataset " + str(datasetnumber) + ": " + datasetname + "\n")
f.write("Time taken: " + str(time) + " seg.\n")
f.write("Accuracy: " + str(accuracy) + "\n")
if accuracy!=None:
f.write("@scores\n")
for score in model.decision_scores_:
f.write(str(score) + "\n")
f.close()
# This is based on executing the script from the folder experiments
ROUTE = "../datasets/outlier_ground_truth/"
# List of datasets
datasets = ["annthyroid.mat", "arrhythmia.mat", "breastw.mat", "cardio.mat", "glass.mat", "ionosphere.mat", "letter.mat", "lympho.mat", "mammography.mat", "mnist.mat", "musk.mat", "optdigits.mat", "pendigits.mat", "pima.mat", "satellite.mat", "satimage-2.mat", "speech.mat", "thyroid.mat", "vertebral.mat", "vowels.mat", "wbc.mat", "wine.mat"]
# List of models and names
models = [ABOD(), COF(), HBOS(), KNN(), LOF(), MCD(), OCSVM(), PCA(), SOD(), SOS()]
names = ["ABOD", "COF", "HBOS", "KNN", "LOF", "MCD", "OCSVM", "PCA", "SOD", "SOS"]
accuracies = []
for name, model in zip(names, models):
print("\n\n#################################################################")
print("MODEL " + name + " " + str(names.index(name)+1) + "/" + str(len(names)))
print("#################################################################")
acc = []
for dat in datasets:
if name=="ABOD" and dat in ["breastw.mat", "letter.mat", "satellite.mat"]:
result = None
else:
print("Computing dataset " + dat + " " + str(datasets.index(dat)+1) + "/" + str(len(datasets)))
# Read dataset
dataset, labels = readDataset(ROUTE + dat)
(FeatureBagging(combination='average',
random_state=rs), 'AVE_Bagging'), # n_jobs
(LMDD(dis_measure='iqr', random_state=rs), 'IQR_LMDD'),
(KNN(method='largest'), 'Largest_KNN'), # n_jobs
(LODA(), 'LODA'),
(FeatureBagging(combination='max', n_jobs=-1,
random_state=rs), 'MAX_Bagging'),
(MCD(random_state=rs), 'MCD'),
(XGBOD(random_state=rs), 'XGBOD'), # n_jobs
(GaussianMixture(random_state=rs), 'GMM'),
(LocalOutlierFactor(novelty=True), 'LOF'),
(KNN(method='median'), 'Median_KNN'), # n_jobs
(KNN(method='mean'), 'Avg_KNN'), # n_jobs
(CBLOF(n_clusters=10, random_state=rs), 'CBLOF'),
(HBOS(), 'HBOS'),
(SOD(), 'SOD'),
(PCA(random_state=rs), 'PCA'),
(VAE(encoder_neurons=[3, 4, 3],
decoder_neurons=[3, 4, 3],
random_state=rs), 'VAE'),
(AutoEncoder(hidden_neurons=[3, 4, 4, 3], verbose=0,
random_state=rs), 'AE')
]
# Start the counter of time
st = time.time()
# Initialize the pool class with the number of required CPU's
pool = mp.Pool(mp.cpu_count())
# StarMap method
pool.starmap_async(AnomalyTester, [(models[i][0], models[i][1], rootDir)
for i in range(len(models))]).get()
pool.close()
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
'LargestKNN': KNN(method='largest'),
'MedKNN': KNN(method='median'),
'PCA': PCA(),
'COF': COF(),
'LODA': LODA(),
'LOF': LOF(),
'HBOS': HBOS(),
'MCD': MCD(),
'AvgBagging': FeatureBagging(combination='average'),
'MaxBagging': FeatureBagging(combination='max'),
'IForest': IForest(),
'CBLOF': CBLOF(n_clusters=10, n_jobs=4),
'FactorAnalysis': FactorAnalysis(),
'KernelDensity': KernelDensity(),
'COPOD': COPOD(),
'SOD': SOD(),
'LSCPwithLODA': LSCP([LODA(), LODA()]),
'AveLMDD': LMDD(dis_measure='aad'),
'VarLMDD': LMDD(dis_measure='var'),
'IqrLMDD': LMDD(dis_measure='iqr'),
'SoGaal': SO_GAAL(),
#'MoGaal':MO_GAAL(),
'VAE': VAE(encoder_neurons=[8, 4, 2]),
'AutoEncoder': AutoEncoder(hidden_neurons=[6, 3, 3, 6])
}
models = {
'XGBOD': XGBOD(),
'BRM': BRM(),
'GM': GaussianMixture(),
'IF': IsolationForest(),
