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PyImpuyte-1.3.5
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مشاهده بیشترتوضیحات
Intelligent imputation using tree-based and machine learning algorithms
| ویژگی | مقدار |
|---|---|
| سیستم عامل | OS Independent |
| نام فایل | PyImpuyte-1.3.5 |
| نام | PyImpuyte |
| نسخه کتابخانه | 1.3.5 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Marcus Suresh, Ronnie Taib |
| ایمیل نویسنده | marcus.suresh@industry.gov.au, marcus.suresh@data61.csiro.au, ronnie.taib@data61.csiro.au |
| آدرس صفحه اصلی | https://bitbucket.csiro.au/projects/DDE/repos/pyimpuyte |
| آدرس اینترنتی | https://pypi.org/project/PyImpuyte/ |
| مجوز | - |
# PyImpuyte
[](https://www.python.org/)
[](https://test.pypi.org/project/PyImpuyte/1.1.5/)
[](https://pyimpuyte.readthedocs.io/en/latest/?badge=latest)
[](https://bitbucket.csiro.au/projects/DDE/repos/pyimpuyte/browse/LICENSE)
[](https://www.python.org/downloads/release/python-370/)
[]()
[](CODE_OF_CONDUCT.md)
<span style="font-size:1.5em;">`PyImpuyte` is a Python3.7+ package that simplifies the task of imputing missing values in datasets.
<p align="center">
<img width="530" height="600" src="https://s3-marcus-public.s3-eu-west-1.amazonaws.com/PyImpuyte_1.PNG">
</p>
<span style="font-size:1.5em;">`PyImpuyte` was built with a strong customer-centric focus and leverages of `scikit-learn`. It brings together various imputation strategies and harnesses <b>machine learning algorithms</b> to improve data coverage.
<span style="font-size:1.5em;">`PyImpuyte` gives the user exactly what they want - hassle free deployment of machine learning algorithms. Simply ingest your data, set your target, pass in a feature matrix and select your chosen imputation strategy. You now have machine generated imputed values appended to your dataframe.
<span style="font-size:1.5em;">To learn more about how to use `PyImpuyte`, check out our <b>[docs](https://pyimpuyte.readthedocs.io/en/latest/)</b> for a step-by-step guide.</span>
## Contents
- [Motivation](#-motivation)
- [Installation](#-installation)
- [Quick Start](#-quick-start)
- [Contribute](#-contribute)
- [Conferences and Meet-ups](#-conferences-and-meet-ups)
- [Citation](#-citation)
- [Developers and Maintainers](#-developers-and-maintainers)
- [Acknowledgements](#-acknowledgements)
- [Copyright](#-copyright)
## Motivation
Incomplete data are quite common which can deteriorate statistical inference. As such, the `PyImpuyte` team set out to develop a Python package that simplifies the task of imputing missing values in Australian Government national statistical assets and other micro-data sets.
The development of `PyImpuyte` is motivated by helping micro-data practitioners select and implement advanced imputation methods. `PyImpuyte` adds an additional tool in the toolkit of practitioners seeking to preserve their data and fight information loss that arises from droping observations with missing values.
#### Main Features
* Interfaces with `scikit-learn` to provide a customer-centric and efficient way to perform imputation using machine learning algorithms.
* Support for numerous imputation strategies and performance metrics, as specified below:
#### Imputation Strategies
| Univariate | Generalised Linear Models | Bagging and Boosted Trees | Neural Nets
| :---------------------| :-------------------------- | :----------------------------| :-----------------------
| Mean | Linear Regressions | Bagging Regressor | Multi-layer Perceptron
| Median | Lasso | Extra Trees Regressor |
| Mode | Ridge | Extreme Gradient Boosting |
| | | Random Forest Regressor |
| | | XGBoost, LightGBM, CatBoost |
#### Performance Metrics
| |
| :---------------------|
| Simple error |
| Percentage error |
| Naive forecasting |
| Relative Error |
| Bounded Relative Error |
| Geometric mean |
| Mean Squared Error |
| Normalized Root Mean Squared Error |
| Mean Error |
| Mean Absolute Error |
| Geometric Mean Absolute Error |
| Median Absolute Error |
| Mean Percentage Error |
| Mean Absolute Percentage Error |
| Median Absolute Percentage Error |
| Symmetric Mean Absolute Percentage Error |
| Symmetric Median Absolute Percentage Error |
| Mean Arctangent Absolute Percentage Error |
| Mean Absolute Scaled Error |
| Normalized Absolute Error |
| Normalized Absolute Percentage Error |
| Root Mean Squared Percentage Error |
| Root Median Squared Percentage Error |
| Root Mean Squared Scaled Error |
| Integral Normalized Root Squared Error |
| Root Relative Squared Error |
| Mean Relative Error |
| Median Relative Absolute Error |
| Geometric Mean Relative Absolute Error |
| Mean Bounded Relative Absolute Error |
| Unscaled Mean Bounded Relative Absolute Error |
| Mean Directional Accuracy |
#### Versions and Dependencies
* Python 3.7+
* Dependencies:
- `missingno` >= 0.4.1
- `numpy` >= 1.15.4
- `pandas` >= 0.20.3
- `scikit-learn` >= 0.20.2
- `xgboost` >= 0.83
## Installation
There are two ways to install the `PyImpuyte` package:
- Install `PyImpuyte` from PyPI (recommended):
```
pip install PyImpuyte==1.3.5
```
- Install `PyImpuyte` from the Bitbucket source:
```
git clone https://bitbucket.csiro.au/scm/dde/pyimpuyte.git
cd pyimpuyte
python setup.py install
```
## Quick Start
To start imputing missing values with `PyImpuyte`, a `config.json` file must be passed. The following workflow can be used:
```config.json
{
"pyimpuyte": {
"input": [
"data/synth_data_test.csv"
],
"feature_list": ["TURNOVER", "WAGES", "SALES"],
"target": "FTE",
"skip_columns": null,
"nrows": 1000,
"drop_duplicates": true,
"output": "out/synth_data_test.csv",
"evaluation": "out/evaluation.csv"
}
}
```
For more information about how to configure `PyImpuyte`, see our suggested **[template](https://bitbucket.csiro.au/projects/DDE/repos/pyimpuyte/browse/config.md)**.
## Contribute
We welcome all kinds of contributions that improve the performance of the currently published pacakge. See the [Contribution Guide](https://bitbucket.csiro.au/projects/DDE/repos/pyimpuyte/browse/CONTRIBUTING.md) for more details.
## Conferences and Meet-ups
* We presented our research at the **[2019 Australasian Joint Conference on Artificial Intelligence](http://nugget.unisa.edu.au/AI2019/index.php)** which lead to the development of `PyImpuyte`.
* We will be presenting at the next Canberra Data Scientists Meet-up on 28 July 2020.
## Citation
Please cite our work in your publications if it helps your research.
* Conference Paper - Chapter 18 of **[AI2019: Advances in Artificial Intelligence](https://link.springer.com/chapter/10.1007/978-3-030-35288-2_18)**.
```BibTeX
@inbook{inbook,
author = {Suresh, Marcus and Taib, Ronnie and Zhao, Yanchang and Jin, Warren},
year = {2019},
month = {11},
pages = {215-227},
title = {Sharpening the BLADE: Missing Data Imputation Using Supervised Machine Learning},
isbn = {978-3-030-35287-5},
doi = {10.1007/978-3-030-35288-2_18}
}
```
* Python Package - **[PyImpuyte](https://bitbucket.csiro.au/projects/DDE/repos/pyimpuyte)**.
```BibTeX
@misc{Suresh2020_PyImpuyte,
title={PyImpuyte},
author={Suresh, Marcus et al.},
year={2020},
howpublished={\url{https://bitbucket.csiro.au/projects/DDE/repos/pyimpuyte}},
}
```
## Developers and Maintainers
* The developers began work to bring `PyImpuyte` into production in October 2019. `PyImpuyte` is actively maintained and there will be incremental improvements scheduled on a regular basis. The lead developers and maintainers are:
* <b>Marcus Suresh</b>, Bitbucket: [sur033](https://bitbucket.csiro.au/users/sur033) and GitHub: [marcus-suresh](https://github.com/marcus-suresh)
* <b>Ronnie Taib</b>, GitHub: [rtaib](https://github.com/rtaib)
* See the [Developers](https://bitbucket.csiro.au/projects/DDE/repos/pyimpuyte/browse/DEVELOPERS.rst) page to get in touch with the `PyImpuyte` team.
## Acknowledgements
* This research was funded by the Australian Government through the [Department of Industry, Science, Energy and Resources (DISER)](https://www.industry.gov.au/) and the [Data Integration Partnership for Australia (DIPA)](https://www.pmc.gov.au/public-data/data-integration-partnership-australia).
* The developers would like to extend their gratitude to Dr. Abrie Swanepoel (Branch Manager) and Dr. Tala Talgasawatta (Director) from DISER for their ongoing support in `PyImpuyte`.
## Copyright
`PyImpuyte` is distributed under the MIT license. See [LICENSE](https://bitbucket.csiro.au/projects/DDE/repos/pyimpuyte/browse/LICENSE) for details.
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl PyImpuyte-1.3.5:
pip install PyImpuyte-1.3.5.whl
نصب پکیج tar.gz PyImpuyte-1.3.5:
pip install PyImpuyte-1.3.5.tar.gz