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cromp-0.0.7
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مشاهده بیشترتوضیحات
The official implementation of CROMP (Constrained Regression with Ordered and Margin-sensitive Parameters). CROMP allows user-defined order among the coefficients, user-defined minimum margins (i.e., percentage gaps) between them, and user-defined lower and upper bounds for each coefficient. In addition, CROMP also allows coefficients without any order or margin restrictions.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | cromp-0.0.7 |
| نام | cromp |
| نسخه کتابخانه | 0.0.7 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | - |
| ایمیل نویسنده | Kaushik Bar <kb.opendev@gmail.com> |
| آدرس صفحه اصلی | https://github.com/kb-open/CROMP |
| آدرس اینترنتی | https://pypi.org/project/cromp/ |
| مجوز | MIT License Copyright (c) 2022 Kaushik Bar Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
[](https://zenodo.org/badge/latestdoi/545916375)
[](https://pypi.org/project/cromp/)
[](https://github.com/kb-open/CROMP/blob/main/LICENSE.txt)
[](https://pypi.org/project/cromp/)
[](https://github.com/kb-open/CROMP/actions/)
# ✏️ CROMP (Constrained Regression with Ordered and Margin-sensitive Parameters)
This repo provides the official implementation of CROMP (Constrained Regression with Ordered and Margin-sensitive Parameters) along with the test dataset and the test pipeline code to reproduce the benchmarking results as described in the CROMP paper ("**Constrained Regression with Ordered and Margin-sensitive Parameters: Application in improving interpretability for wage models with prior knowledge**").
Author: [Kaushik Bar](kb.opendev@gmail.com)
---------------------------
## 🎯 Quick Info
CROMP allows:
* User-defined order among the coefficients
* User-defined minimum margins (i.e., percentage gaps) between the coefficients
* User-defined lower and upper bounds for each coefficient
* Coefficients without any order or margin restrictions
This package is both interpretable and accurate.
There is no other package / library available in the public domain today with these capabilities.
---------------------------
## 💻 Installation
Install using [PyPI](https://pypi.org/project/cromp/):
```bash
pip install cromp
```
---------------------------
## 💻 Dependencies
The package depends on:
* python >= 3.6
* scipy
* numpy
* pandas
The tests depend additionally on:
* scikit-learn
* [Data scientist salary data](https://www.kaggle.com/datasets/nikhilbhathi/data-scientist-salary-us-glassdoor?select=data_cleaned_2021.csv)
* [Ames House price data](https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques/data?select=train.csv)
* [SCB Sweden wage data](https://www.statistikdatabasen.scb.se/pxweb/en/ssd/START__AM__AM0103__AM0103F/SLP1t12/table/tableViewLayout1/)
---------------------------
## 🚀 Usage
Below is a minimal usage guide. This library aloows flexibility for more fine-grained controls for each coefficient separately.
Please go through the [tests](https://github.com/kb-open/cromp/blob/main/tests/cromp_tests.py) in addition to see more examples.
```python
>>> from cromp import CROMPTrain, CROMPPredict
>>> df = pd.read_csv("tests/data/ames_house_prices_data.csv")
>>> df_train = df.iloc[:-50, :]
>>> df_test = df.iloc[-50:, :]
>>> target_col = 'SalePrice'
>>> feats_in_asc_order = ['1stFlrSF', 'TotalBsmtSF', 'GrLivArea']
>>> feats_in_no_order = []
>>> # Instantiate model
>>> model = CROMPTrain()
>>> # Configure constraints
>>> ret_success = model.config_constraints(feats_in_asc_order, min_gap_pct=0.5, feats_in_no_order, lb=0.0, ub=100.0, no_intercept=True)
>>> # Train
>>> ret_success, cromp_model = model.train(df_train, target_col)
>>> # Predict
>>> model = CROMPPredict(cromp_model)
>>> result = model.predict(df_test)
```
---------------------------
## ☕ Contact
Feel free to contact [Kaushik Bar](https://www.linkedin.com/in/iamkbar/) if you have any further questions / feedback.
Author Affiliation: [Inxite Out Pvt Ltd](https://inxiteout.ai/)
---------------------------
## Citation
To cite CROMP in your work, please use the following bibtex reference:
```bibtex
@software{Bar_CROMP_Constrained_Regression_2022,
author = {Bar, Kaushik},
license = {MIT},
month = {10},
title = {{CROMP (Constrained Regression with Ordered and Margin-sensitive Parameters)}},
url = {https://github.com/kb-open/CROMP},
year = {2022}
version = {v0.0.3},
publisher = {Zenodo},
doi = {10.5281/zenodo.7152807},
url = {https://doi.org/10.5281/zenodo.7152807}
}
```
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl cromp-0.0.7:
pip install cromp-0.0.7.whl
نصب پکیج tar.gz cromp-0.0.7:
pip install cromp-0.0.7.tar.gz