Pre-release
This release is a pre-release and may not be stable for production use.
This library is Python projects for anomaly detection. This contains these techniques.
Kullback-Leibler desity estimation
Singular spectrum analysis
Graphical lasso
CUMSUM anomaly detection
Hoteling T2
Directional data anomaly detection
REQUIREMENTS
numpy
pandas
scikit-learn
scipy
INSTALLATION
pip install pyanomUSAGE
Kullback-Leibler desity estimation
import numpy as np
from pyanom.density_ratio_estimation import KLDensityRatioEstimator
X_normal = np.loadtxt("./data/normal_data.csv", delimiter=",")
X_error = np.loadtxt("./data/error_data.csv", delimiter=",")
model = KLDensityRatioEstimator(
band_width=h, lr=0.001, max_iter=100000)
model.fit(X_normal, X_error)
anomaly_score = model.score(X_normal, X_error)Singular spectrum analysis
import numpy as np
from pyanom.subspace_methods import SSA
y_error = np.loadtxt("./data/timeseries_error2.csv", delimiter=",")
model = SSA(window_size=50, trajectory_n=25, trajectory_pattern=3, test_n=25, test_pattern=2, lag=25)
model.fit(y_error)
anomaly_score = model.score()Graphical lasso
import numpy as np
from pyanom.structure_learning import GraphicalLasso
X_normal = np.loadtxt("./data/normal_data.csv", delimiter=",")
X_error = np.loadtxt("./data/error_data.csv", delimiter=",")
model = GraphicalLasso(rho=0.1)
model.fit(X_normal)
anomaly_score = model.score(X_error)Direct learning sparse changes
from pyanom.structure_learning import DirectLearningSparseChanges
model = DirectLearningSparseChanges(
lambda1=0.1, lambda2=0.3, max_iter=10000)
model.fit(X_normal, X_error)
pmatrix_diff = model.get_sparse_changes()CUSUM anomaly detection
import numpy as np
from pyanom.outlier_detection import CAD
y_normal = np.loadtxt(
"./data/timeseries_normal.csv", delimiter=",").reshape(-1, 1)
y_error = np.loadtxt(
"./data/timeseries_error.csv", delimiter=",").reshape(-1, 1)
model = CAD(threshold=1.0)
model.fit(y_normal)
anomaly_score = model.score(y_error)Hoteling T2
import numpy as np
from pyanom.outlier_detection import HotelingT2
X_normal = np.loadtxt("./data/normal_data.csv", delimiter=",")
X_error = np.loadtxt("./data/error_data.csv", delimiter=",")
model = HotelingT2()
model.fit(X_normal)
anomaly_score = model.score(X_error)Directional data anomaly DirectionalDataAnomalyDetection
import numpy as np
from pyanom.outlier_detection import AD3
X_normal = np.loadtxt(
"./data/normal_direction_data.csv", delimiter=",")
X_error = np.loadtxt("./data/error_direction_data.csv", delimiter=",")
model = AD3()
model.fit(X_normal, normalize=True)
anomaly_score = model.score(X_error)1 maintainer
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