PySATL Change point detection subproject (abbreviated pysatl-cpd) is a module, designed for detecting anomalies in time series data, which refer to significant deviations from expected patterns or trends. Anomalies can indicate unusual events or changes in a system, making them crucial for monitoring and analysis in various fields such as finance, healthcare, and network security.
At the moment, the module implements the following CPD algorithms:
- Bayesian algorithm (scrubbing and online versions)
- Density based algorithms:
- KLIEP
- RuLSIF
- Graph algorithm
- k-NN based algorithm
- Algorithms, based on classifiers:
- SVM
- KNN
- Decision Tree
- Logistic Regression
- Random Forest
- Python 3.10+
- Poetry 1.8.0+
Clone the repository:
git clone https://github.com/PySATL/pysatl-cpdInstall dependencies:
poetry installfrom pathlib import Path
from pysatl_cpd.labeled_data import LabeledCpdData
# import change point detection solver
from pysatl_cpd.online_cpd_solver import OnlineCpdSolver
from pysatl_cpd.core.problem import CpdProblem
# import algorithm
from pysatl_cpd.core.algorithms.bayesian_online_algorithm import BayesianOnline
from pysatl_cpd.core.algorithms.bayesian.likelihoods.gaussian_conjugate import GaussianConjugate
from pysatl_cpd.core.algorithms.bayesian.hazards.constant import ConstantHazard
from pysatl_cpd.core.algorithms.bayesian.detectors.threshold import ThresholdDetector
from pysatl_cpd.core.algorithms.bayesian.localizers.argmax import ArgmaxLocalizer
labeled_data = LabeledCpdData.generate_cp_datasets(Path("examples/configs/test_config_exp.yml"))["example"]
# specify CPD algorithm with parameters
algorithm = BayesianOnline(
learning_sample_size=5,
likelihood=GaussianConjugate(),
hazard=ConstantHazard(rate=1.0 / (1.0 - 0.5 ** (1.0 / 500))),
detector=ThresholdDetector(threshold=0.005),
localizer=ArgmaxLocalizer(),
)
# make a solver object
solver = OnlineCpdSolver(CpdProblem(True), algorithm, labeled_data)
# then run algorithm
cpd_results = solver.run()
# print the results
print(cpd_results)
# output:
# Located change points: (200;400)
# Expected change point: (200;400)
# Difference: ()
# Computation time (sec): 0.2
# visualize data with located changepoints
cpd_results.visualize()
Install requirements
poetry install --with devInstall pre-commit hooks:
poetry run pre-commit installStarting manually:
poetry run pre-commit run --all-files --color always --verbose --show-diff-on-failureThis project is licensed under the terms of the MIT license. See the LICENSE for more information.