Change point detection module (abbreviated CPD module) 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/Lesh79/PySATL-CPD-ModuleInstall dependencies:
poetry install# import needed CPD algorithm from pysatl_cpd.core
from pysatl_cpd.core.algorithms.graph_algorithm import GraphAlgorithm
from pysatl_cpd.core.problem import CpdProblem
from pysatl_cpd.core.scrubber.linear import LinearScrubber
from pysatl_cpd.core.scrubber.data_providers import ListUnivariateProvider
# import solver
from pysatl_cpd.cpd_solver import CpdSolver
# specify data scrubber
scrubber = LinearScrubber(ListUnivariateProvider([1] * 100 + [50] * 100 + [100] * 100))
# specify CPD algorithm with parameters
algorithm = GraphAlgorithm(lambda a, b: abs(a - b) < 5, 3)
# make a solver object
solver = CpdSolver(CpdProblem(True), algorithm, scrubber)
# then run algorithm
cpd_results = solver.run()
# print the results
print(cpd_results)
# output:
# Located change points: (100;200)
# Computation time (ms): 0.03
# 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.