# [`skchange`](https://skchange.readthedocs.io/en/latest/)
[](https://codecov.io/gh/NorskRegnesentral/skchange)
[](https://github.com/NorskRegnesentral/skchange/actions/workflows/tests.yaml)
[](https://skchange.readthedocs.io/en/latest/?badge=latest)
[](https://github.com/sktime/sktime/blob/main/LICENSE)
[](https://github.com/psf/black)
[](https://pypi.org/project/skchange/)
[`skchange`]((https://skchange.readthedocs.io/en/latest/)) provides [`sktime`](https://www.sktime.net/)-compatible change detection and changepoint-based anomaly detection algorithms.
Experimental but maturing.
## Documentation
[Docs](https://skchange.readthedocs.io/) | [Notebook tutorial](https://github.com/sktime/sktime-tutorial-pydata-global-2024)
## Installation
It is recommended to install skchange with [`numba`](https://numba.readthedocs.io/en/stable/) for faster performance:
```sh
pip install skchange[numba]
```
Alternatively, you can install `skchange` without `numba`:
```sh
pip install skchange
```
## Quickstart
### Changepoint detection / time series segmentation
```python
from skchange.change_detectors.moving_window import MovingWindow
from skchange.datasets.generate import generate_alternating_data
df = generate_alternating_data(n_segments=10, segment_length=50, mean=5, random_state=1)
detector = MovingWindow(bandwidth=10)
detector.fit_predict(df)
```
```python
ilocs
0 50
1 100
2 150
3 200
4 250
5 300
6 350
7 400
8 450
```
### 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, 120), (250, 300)]
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
ilocs labels icolumns
0 [100, 120) 1 [0]
1 [250, 300) 2 [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).
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"description": "# [`skchange`](https://skchange.readthedocs.io/en/latest/)\r\n\r\n[](https://codecov.io/gh/NorskRegnesentral/skchange)\r\n[](https://github.com/NorskRegnesentral/skchange/actions/workflows/tests.yaml)\r\n[](https://skchange.readthedocs.io/en/latest/?badge=latest)\r\n[](https://github.com/sktime/sktime/blob/main/LICENSE)\r\n[](https://github.com/psf/black)\r\n[](https://pypi.org/project/skchange/)\r\n\r\n[`skchange`]((https://skchange.readthedocs.io/en/latest/)) provides [`sktime`](https://www.sktime.net/)-compatible change detection and changepoint-based anomaly detection algorithms.\r\n\r\nExperimental but maturing.\r\n\r\n## Documentation\r\n[Docs](https://skchange.readthedocs.io/) | [Notebook tutorial](https://github.com/sktime/sktime-tutorial-pydata-global-2024)\r\n\r\n\r\n## Installation\r\nIt is recommended to install skchange with [`numba`](https://numba.readthedocs.io/en/stable/) for faster performance:\r\n```sh\r\npip install skchange[numba]\r\n```\r\n\r\nAlternatively, you can install `skchange` without `numba`:\r\n```sh\r\npip install skchange\r\n```\r\n\r\n## Quickstart\r\n\r\n### Changepoint detection / time series segmentation\r\n```python\r\nfrom skchange.change_detectors.moving_window import MovingWindow\r\nfrom skchange.datasets.generate import generate_alternating_data\r\n\r\ndf = generate_alternating_data(n_segments=10, segment_length=50, mean=5, random_state=1)\r\n\r\ndetector = MovingWindow(bandwidth=10)\r\ndetector.fit_predict(df)\r\n```\r\n```python\r\n ilocs\r\n0 50\r\n1 100\r\n2 150\r\n3 200\r\n4 250\r\n5 300\r\n6 350\r\n7 400\r\n8 450\r\n```\r\n\r\n### Multivariate anomaly detection\r\n```python\r\nimport numpy as np\r\nfrom skchange.anomaly_detectors import MVCAPA\r\nfrom skchange.datasets.generate import generate_anomalous_data\r\n\r\nn = 300\r\nanomalies = [(100, 120), (250, 300)]\r\nmeans = [[8.0, 0.0, 0.0], [2.0, 3.0, 5.0]]\r\ndf = generate_anomalous_data(n, anomalies, means, random_state=3)\r\n\r\ndetector = MVCAPA()\r\ndetector.fit_predict(df)\r\n```\r\n```python\r\n ilocs labels icolumns\r\n0 [100, 120) 1 [0]\r\n1 [250, 300) 2 [2, 1, 0]\r\n```\r\n\r\n\r\n<!-- Optional dependencies:\r\n- Penalty tuning: `optuna` >= 3.1.1\r\n- Plotting: `plotly` >= 5.13.0. -->\r\n\r\n\r\n## License\r\n\r\n`skchange` is a free and open-source software licensed under the [BSD 3-clause license](https://github.com/NorskRegnesentral/skchange/blob/main/LICENSE).\r\n",
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