Anomaly detection package.
Project description
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 pyanom
USAGE
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)
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
pyanom-0.0.2b1.tar.gz
(10.4 kB
view hashes)
Built Distribution
pyanom-0.0.2b1-py3-none-any.whl
(12.4 kB
view hashes)