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divik-3.2.2
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Divisive iK-means algorithm implementation
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | divik-3.2.2 |
| نام | divik |
| نسخه کتابخانه | 3.2.2 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Grzegorz Mrukwa |
| ایمیل نویسنده | g.mrukwa@gmail.com |
| آدرس صفحه اصلی | https://github.com/gmrukwa/divik |
| آدرس اینترنتی | https://pypi.org/project/divik/ |
| مجوز | Apache-2.0 |
[](https://www.codefactor.io/repository/github/gmrukwa/divik)
[](https://codeclimate.com/github/gmrukwa/divik/maintainability)
[](https://divik.readthedocs.io/en/latest/?badge=latest)
# divik
Python implementation of Divisive iK-means (DiviK) algorithm.
## Tools within this package
- Clustering at your command line with fit-clusters
- Set of algorithm implementations for unsupervised analyses
- Clustering
- DiviK - hands-free clustering method with built-in feature selection
- K-Means with Dunn method for selecting the number of clusters
- K-Means with GAP index for selecting the number of clusters
- Modular K-Means implementation with custom distance metrics and initializations
- Feature extraction
- PCA with knee-based components selection
- Locally Adjusted RBF Spectral Embedding
- Feature selection
- EXIMS
- Gaussian Mixture Model based data-driven feature selection
- High Abundance And Variance Selector - allows you to select highly variant features above noise level, based on GMM-decomposition
- Outlier based Selector
- Outlier Abundance And Variance Selector - allows you to select highly variant features above noise level, based on outlier detection
- Percentage based Selector - allows you to select highly variant features above noise level with your predefined thresholds for each
- Sampling
- StratifiedSampler - generates samples of fixed number of rows from given dataset
- UniformPCASampler - generates samples of random observations within boundaries of an original dataset, and preserving the rotation of the data
- UniformSampler - generates samples of random observations within boundaries of an original dataset
## Installation
### Docker
The recommended way to use this software is through
[Docker](https://www.docker.com/). This is the most convenient way, if you want
to use `divik` application.
To install latest stable version use:
```bash
docker pull gmrukwa/divik
```
### Python package
Prerequisites for installation of base package:
- Python 3.7 / 3.8 / 3.9
- compiler capable of compiling the native C code and OpenMP support
#### Installation of OpenMP for Ubuntu / Debian
You should have it already installed with GCC compiler, but if somehow
not, try the following:
```bash
sudo apt-get install libgomp1
```
#### Installation of OpenMP for Mac
OpenMP is available as part of LLVM. You may need to install it with conda:
```bash
conda install -c conda-forge "compilers>=1.0.4,!=1.1.0" llvm-openmp
```
#### Installation of dependencied on Mac
You may see messages that some dependencies are invalid for the platform.
It is a [known bug](https://github.com/actions/setup-python/issues/469),
with [a workaround](https://github.com/actions/setup-python/issues/469#issuecomment-1192522949).
Use:
```bash
SYSTEM_VERSION_COMPAT=0 pip install divik
```
#### DiviK Installation
Having prerequisites installed, one can install latest base version of the
package:
```bash
pip install divik
```
If you want to have compatibility with
[`gin-config`](https://github.com/google/gin-config), you can install
necessary extras with:
```bash
pip install divik[gin]
```
**Note:** Remember about `\` before `[` and `]` in `zsh` shell.
You can install all extras with:
```bash
pip install divik[all]
```
## High-Volume Data Considerations
If you are using DiviK to run the analysis that could fail to fit RAM of your
computer, consider disabling the default parallelism and switch to
[dask](https://dask.org/). It's easy to achieve through configuration:
- set all parameters named `n_jobs` to `1`;
- set all parameters named `allow_dask` to `True`.
**Note:** Never set `n_jobs>1` and `allow_dask=True` at the same time, the
computations will freeze due to how `multiprocessing` and `dask` handle
parallelism.
## Known Issues
### Segmentation Fault
It can happen if the he `gamred_native` package (part of `divik` package) was
compiled with different numpy ABI than scikit-learn. This could happen if you
used different set of compilers than the developers of the scikit-learn
package.
In such a case, a handler is defined to display the stack trace. If the trace
comes from `_matlab_legacy.py`, the most probably this is the issue.
To resolve the issue, consider following the installation instructions once
again. The exact versions get updated to avoid the issue.
## Contributing
Contribution guide will be developed soon.
Format the code with:
```bash
isort -m 3 --fgw 3 --tc .
black -t py36 .
```
## References
This software is part of contribution made by [Data Mining Group of Silesian
University of Technology](http://www.zaed.polsl.pl/), rest of which is
published [here](https://github.com/ZAEDPolSl).
- [Mrukwa, G. and Polanska, J., 2020. DiviK: Divisive intelligent K-means for
hands-free unsupervised clustering in biological big data. *arXiv preprint
arXiv:2009.10706.*][1]
[1]: https://arxiv.org/abs/2009.10706
نیازمندی
| مقدار | نام |
|---|---|
| >=0.2.0,<0.3.0 | dask-distance |
| >=2.14.0 | dask[dataframe] |
| >=0.5.0,<0.6.0) | gin-config |
| >=2.8.0 | h5py |
| >=6.0,<7.0) | importlib-metadata |
| >=1.0.0,<2.0.0 | joblib |
| >=0.5.1 | kneed |
| >=3.3.3,<4.0.0 | matplotlib |
| >=0.12.1 | numpy |
| >=0.20.3 | pandas |
| >=1.5.0,<2.0.0) | polyaxon |
| >=0.14.1 | scikit-image |
| >=0.19.0 | scikit-learn |
| >=0.19.1 | scipy |
| >=4.11.2 | tqdm |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7.1,<4.0 | Python |
نحوه نصب
نصب پکیج whl divik-3.2.2:
pip install divik-3.2.2.whl
نصب پکیج tar.gz divik-3.2.2:
pip install divik-3.2.2.tar.gz