Metadata-Version: 2.1
Name: skchange
Version: 0.8.1
Summary: Sktime-compatible change and anomaly detection
Author-email: Martin Tveten <tveten@nr.no>
Maintainer-email: Martin Tveten <tveten@nr.no>, Johannes Voll Kolstø <jvkolsto@nr.no>
Project-URL: Homepage, https://github.com/NorskRegnesentral/skchange
Keywords: data-science,machine-learning,statistics,scikit-learn,time-series,change-detection,anomaly-detection
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Python: <3.13,>=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy >=1.21
Requires-Dist: pandas >=1.1
Requires-Dist: numba >=0.56
Requires-Dist: sktime >=0.33
Provides-Extra: all_extras
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Provides-Extra: dev
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Requires-Dist: pytest ; extra == 'dev'
Requires-Dist: pytest-cov ; extra == 'dev'
Requires-Dist: ipykernel ; extra == 'dev'
Provides-Extra: docs
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# [skchange](https://skchange.readthedocs.io/en/latest/)

[![codecov](https://codecov.io/gh/NorskRegnesentral/skchange/graph/badge.svg?token=QSS3AY45KY)](https://codecov.io/gh/NorskRegnesentral/skchange)
[![tests](https://github.com/NorskRegnesentral/skchange/actions/workflows/tests.yaml/badge.svg)](https://github.com/NorskRegnesentral/skchange/actions/workflows/tests.yaml)
[![docs](https://readthedocs.org/projects/skchange/badge/?version=latest)](https://skchange.readthedocs.io/en/latest/?badge=latest)
[![BSD 3-clause](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://github.com/sktime/sktime/blob/main/LICENSE)
[![!black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

`skchange` provides sktime-compatible change detection and changepoint-based anomaly detection algorithms.

Experimental but maturing.

## [Documentation](https://skchange.readthedocs.io/en/latest/)
Now available.


## Installation
```sh
pip install skchange
```
Requires Python >= 3.9, < 3.13.

## Quickstart

### Changepoint detection / time series segmentation
```python
from skchange.change_detectors.moscore import Moscore
from skchange.datasets.generate import generate_alternating_data

df = generate_alternating_data(n_segments=10, segment_length=50, mean=5, random_state=1)

detector = Moscore(bandwidth=10)
detector.fit_predict(df)
```
```python
0     49
1     99
2    149
3    199
4    249
5    299
6    349
7    399
8    449
Name: changepoint, dtype: int64
```

### Multivariate anomaly detection
```python
import numpy as np
from skchange.anomaly_detectors import Mvcapa
from skchange.datasets.generate import generate_anomalous_data

n = 300
anomalies = [(100, 119), (250, 299)]
means = [[8.0, 0.0, 0.0], [2.0, 3.0, 5.0]]
df = generate_anomalous_data(n, anomalies, means, random_state=3)

detector = Mvcapa()
detector.fit_predict(df)
```
```python
  anomaly_interval anomaly_columns
0       [100, 119]             [0]
1       [250, 299]       [2, 1, 0]
```


<!-- Optional dependencies:
- Penalty tuning: `optuna` >= 3.1.1
- Plotting: `plotly` >= 5.13.0. -->


## License

`skchange` is a free and open-source software licensed under the [BSD 3-clause license](https://github.com/NorskRegnesentral/skchange/blob/main/LICENSE).
