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changeforest-0.7.2
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مشاهده بیشترتوضیحات
Classifier based non-parametric change point detection
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | changeforest-0.7.2 |
| نام | changeforest |
| نسخه کتابخانه | 0.7.2 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | - |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/changeforest/ |
| مجوز | - |
# Random Forests for Change Point Detection
Change point detection aims to identify structural breaks in the probability
distribution of a time series. Existing methods either assume a parametric model for
within-segment distributions or are based on ranks or distances and thus fail in
scenarios with a reasonably large dimensionality.
`changeforest` implements a classifier-based algorithm that consistently estimates
change points without any parametric assumptions, even in high-dimensional scenarios.
It uses the out-of-bag probability predictions of a random forest to construct a
pseudo-log-likelihood that gets optimized using a computationally feasible two-step
method.
See [1] for details.
## Installation
To install from `conda-forge` (recommended), run
```bash
conda install -c conda-forge changeforest
```
To install from `PyPI`, run
```bash
pip install changeforest
```
## Example
In the following example, we perform random forest-based change point detection on
a simulated dataset with `n=600` observations and covariance shifts at `t=200, 400`.
```python
In [1]: import numpy as np
...:
...: Sigma = np.full((5, 5), 0.7)
...: np.fill_diagonal(Sigma, 1)
...:
...: rng = np.random.default_rng(12)
...: X = np.concatenate(
...: (
...: rng.normal(0, 1, (200, 5)),
...: rng.multivariate_normal(np.zeros(5), Sigma, 200),
...: rng.normal(0, 1, (200, 5)),
...: ),
...: axis=0,
...: )
```
The simulated dataset `X` coincides with the _change in covariance_ (CIC) setup
described in [1]. Observations in the first and last segment are independently drawn
from a standard multivariate Gaussian distribution. Observations in the second segment
are i.i.d. normal with mean zero and unit variance, but with a covariance of ρ = 0.7
between coordinates. This is a challenging scenario.
```python
In [2]: from changeforest import changeforest
...:
...: result = changeforest(X, "random_forest", "bs")
...: result
Out[2]:
best_split max_gain p_value
(0, 600] 412 19.603 0.005
¦--(0, 412] 201 62.981 0.005
¦ ¦--(0, 201] 194 -12.951 0.76
¦ °--(201, 412] 211 -9.211 0.545
°--(412, 600] 418 -37.519 0.915
In [3]: result.split_points()
Out[3]: [201, 412]
```
`changeforest` correctly identifies the change point around `t=200` but is slightly
off at `t=412`. The `changeforest` function returns a `BinarySegmentationResult`.
We use its `plot` method to investigate the gain curves maximized by the change point estimates:
```
In [4]: result.plot().show()
```
<p align="center">
<img src="../docs/py_cic_rf_binary_segmentation_result_plot.png" />
</p>
Change point estimates are marked in red.
For `method="random_forest"` and `method="knn"`, the `changeforest` algorithm uses a two-step approach to
find an optimizer of the gain. This fits a classifier for three split candidates
at the segment's 1/4, 1/2 and 3/4 quantiles, computes approximate gain curves using
the resulting pseudo-log-likelihoods and selects the overall optimizer as a second guess.
We can investigate the gain curves from the optimizer using the `plot` method of `OptimizerResult`.
The initial guesses are marked in blue.
```
In [5]: result.optimizer_result.plot().show()
```
<p align="center">
<img src="../docs/py_cic_rf_optimizer_result_plot.png" />
</p>
One can observe that the approximate gain curves are piecewise linear, with maxima
around the true underlying change points.
The `BinarySegmentationResult` returned by `changeforest` is a tree-like object with attributes
`start`, `stop`, `best_split`, `max_gain`, `p_value`, `is_significant`, `optimizer_result`, `model_selection_result`, `left`, `right` and `segments`.
These can be interesting to investigate the output of the algorithm further.
The `changeforest` algorithm can be tuned with hyperparameters. See
[here](https://github.com/mlondschien/changeforest/blob/287ac0f10728518d6a00bf698a4d5834ae98715d/src/control.rs#L3-L30)
for their descriptions and default values. In Python, the parameters can
be specified with the [`Control` class](https://github.com/mlondschien/changeforest/blob/b33533fe0ddf64c1ea60d0d2203e55b117811667/changeforest-py/changeforest/control.py#L1-L26),
which can be passed to `changeforest`. The following will build random forests with
20 trees:
```python
In [6]: from changeforest import Control
...: changeforest(X, "random_forest", "bs", Control(random_forest_n_estimators=20))
Out[6]:
best_split max_gain p_value
(0, 600] 592 -11.786 0.01
¦--(0, 592] 121 -6.26 0.015
¦ ¦--(0, 121] 13 -14.219 0.615
¦ °--(121, 592] 416 21.272 0.005
¦ ¦--(121, 416] 201 37.157 0.005
¦ ¦ ¦--(121, 201] 192 -17.54 0.65
¦ ¦ °--(201, 416] 207 -6.701 0.74
¦ °--(416, 592] 584 -44.054 0.935
°--(592, 600]
```
The `changeforest` algorithm still detects change points around `t=200, 400` but also
returns two false positives.
Due to the nature of the change, `method="change_in_mean"` is unable to detect any
change points at all:
```python
In [7]: changeforest(X, "change_in_mean", "bs")
Out[7]:
best_split max_gain p_value
(0, 600] 589 8.318
```
## References
[1] M. Londschien, S. Kovács and P. Bühlmann (2022), "Random Forests for Change Point Detection", working paper.
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7 | Python |
نحوه نصب
نصب پکیج whl changeforest-0.7.2:
pip install changeforest-0.7.2.whl
نصب پکیج tar.gz changeforest-0.7.2:
pip install changeforest-0.7.2.tar.gz