# changepoint
Python bindings for important functionality of the rust library [changepoint](https://crates.io/crates/changepoint), a library for doing change point detection for steams of data.
## Installation
Install via pip with
```bash
$ python3 -m pip install "changepoint"
```
__Note__: If there is no binary distribution for your OS, architecture, and Python version, you will need the Rust compiler to build the package and install a Python tool called Maturin:
```bash
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
python3 -m pip install maturin
```
See [rustup.rs](https://rustup.rs/) for instructions on installing Rust.
## Quick Docs
By convention in these docs and examples,
```python
import changepoint as cpt
```
### Models
#### Bocpd
The Bayesian change point detector, `Bocpd`, takes a prior distribution, aka one of
```python,ignore
cpt.BetaBernoulli
cpt.NormalGamma
cpt.NormalInvChiSquared
cpt.NormalInvGamma
cpt.NormalInvWishart
cpt.PoissonGamma
```
Then, a `Bocpd` may be created:
```python
cpd = cpt.Bocpd(
prior=cpt.NormalGamma(),
lam=12,
)
```
where the prior is a `NormalGamma` and the characteristic run length is `12`.
Each step of the data stream, `data`, can be processed by
```python
import random
import numpy as np
data = [random.gauss() for _ in range(30)] \
+ [random.gauss(1, 2) for _ in range(30)]
n = len(data)
change_point_history = np.zeros((n, n))
for i, x in enumerate(data):
change_point_history[i, : i + 1] = cpd.step(x)
print(cpt.map_changepoints(change_point_history))
```
#### ArgpCpd
`ArgpCpd` has an implicit prior as it is a Gaussian Process of the form `X_{i+1} = c X_{i-l-1, ..., i-1} + ε`
where `c` is the scale, `X_{i-l-1, ..., i-1}` is the previous vales in the sequence (`l` is the max-lag parameter), and `ε` is a white noise parameter.
It behaves similarity to the `Bocpd` class; for example,
```python
argp = cpt.ArgpCpd(logistic_hazard_h=-2, scale=3, noise_level=0.01)
n = len(data)
change_point_history = np.zeros((n + 1, n + 1))
xs = []
ys = []
for i, x in enumerate(data):
cps = argp.step(x)
change_point_history[i, : len(cps)] = cps
print(cpt.map_changepoints(change_point_history))
```
## Example
An example IPython notebook can be found [here](https://gitlab.com/Redpoll/changepoint/-/blob/master/changepoint-py/ChangePointExample.ipynb).
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"description": "# changepoint\n\nPython bindings for important functionality of the rust library [changepoint](https://crates.io/crates/changepoint), a library for doing change point detection for steams of data.\n\n## Installation\n\nInstall via pip with\n```bash\n$ python3 -m pip install \"changepoint\"\n```\n\n__Note__: If there is no binary distribution for your OS, architecture, and Python version, you will need the Rust compiler to build the package and install a Python tool called Maturin:\n```bash\ncurl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh\npython3 -m pip install maturin\n```\n\nSee [rustup.rs](https://rustup.rs/) for instructions on installing Rust.\n\n## Quick Docs\nBy convention in these docs and examples, \n```python\nimport changepoint as cpt\n```\n\n### Models\n#### Bocpd\n\nThe Bayesian change point detector, `Bocpd`, takes a prior distribution, aka one of\n```python,ignore\ncpt.BetaBernoulli\ncpt.NormalGamma\ncpt.NormalInvChiSquared\ncpt.NormalInvGamma\ncpt.NormalInvWishart\ncpt.PoissonGamma\n```\n\nThen, a `Bocpd` may be created:\n```python\ncpd = cpt.Bocpd(\n prior=cpt.NormalGamma(),\n lam=12,\n)\n```\nwhere the prior is a `NormalGamma` and the characteristic run length is `12`.\n\nEach step of the data stream, `data`, can be processed by\n```python\nimport random\nimport numpy as np\n\ndata = [random.gauss() for _ in range(30)] \\\n + [random.gauss(1, 2) for _ in range(30)]\n\nn = len(data)\nchange_point_history = np.zeros((n, n))\nfor i, x in enumerate(data):\n change_point_history[i, : i + 1] = cpd.step(x)\n\nprint(cpt.map_changepoints(change_point_history))\n```\n\n\n#### ArgpCpd\n\n`ArgpCpd` has an implicit prior as it is a Gaussian Process of the form `X_{i+1} = c X_{i-l-1, ..., i-1} + \u03b5`\nwhere `c` is the scale, `X_{i-l-1, ..., i-1}` is the previous vales in the sequence (`l` is the max-lag parameter), and `\u03b5` is a white noise parameter.\nIt behaves similarity to the `Bocpd` class; for example,\n\n```python\nargp = cpt.ArgpCpd(logistic_hazard_h=-2, scale=3, noise_level=0.01)\nn = len(data)\nchange_point_history = np.zeros((n + 1, n + 1))\nxs = []\nys = []\nfor i, x in enumerate(data):\n cps = argp.step(x)\n change_point_history[i, : len(cps)] = cps\n\nprint(cpt.map_changepoints(change_point_history))\n```\n\n## Example\n\nAn example IPython notebook can be found [here](https://gitlab.com/Redpoll/changepoint/-/blob/master/changepoint-py/ChangePointExample.ipynb).\n\n",
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