| Name | wmsd JSON |
| Version |
1.0.0
JSON |
| download |
| home_page | |
| Summary | TOPSIS ranking and interpretation using WMSD-space |
| upload_time | 2023-11-29 22:28:22 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.9 |
| license | MIT License Copyright (c) 2023 Adam Ciesiółka, Bogna Kilanowska, Maciej Woś 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. |
| keywords |
visualization
ranking
multi-criteria-decision-aid
mcda
topsis
explainability
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
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| coveralls test coverage |
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|
# TOPSIS ranking and interpretation using WMSD-space
This repository hosts the code for **MSDTransformer** - a scikit-learn compatible python library, that lets you create TOPSIS rankings of alternatives, visualize rankings in WMSD space, and get suggestions for improvement actions.
<!-- Check out the project's [documentation](https://msdtransformer.readthedocs.io/en/latest/index.html) to learn more about using the MSDTransformer. -->
## Requirements
MSDTransformer uses PySCIPOpt to determine exact WMSD boundaries quickly. If you use conda, you should be fine performing the follwoing command (easier option):
```console
conda install --channel conda-forge pyscipopt
```
If you use PyPI, then you will need a working installation of the [SCIP Optimization
Suite](https://www.scipopt.org/) (harder option, but might run faster). Please, make sure that your SCIP installation works, before installing MSDTransformer. For more details on how to install SCIP, please refer to the [PySCIPOpt documentation](https://github.com/scipopt/PySCIPOpt/blob/master/INSTALL.md).
After installing PySCIPOpt (or if you already have SCIP installed), you can install the other dependencies by running:
```console
pip install -r requirements.txt
```
## Installation
<!-- ### From PyPI
This is the simplest (one-command) install method is to run the following command in your terminal:
```console
pip install MSDtransformer
```
### From source -->
Once you have all the dependencies, you can install MSDTransformer from source. To do so, clone the repository and run the following command in the root directory of the repository:
```console
pip install .
```
## Quickstart
```python
import pandas as pd
from MSDTransformer import MSDTransformer
df = pd.read_csv("../data/students.csv", sep=';', index_col= 0)
students_transformer = MSDTransformer("R") # Relative distance
students_transformer.fit(df, expert_range=[[0,100],[1,6],[1,6]])
students_transformer.transform(df)
```
```python
students_transformer.show_ranking()
```
```python
students_transformer.plot()
```
## Citation
If you use this project in your research, please consider citing the following papers:
- [Susmaga, R. *et al.* (2023) MSD-space: Visualizing the inner-workings of TOPSIS aggregations. *European Journal of Operational Research*, 308(1), 229-242.](https://doi.org/10.1016/j.ejor.2022.12.003)
- [Susmaga, R. *et al.* (2023) Towards Explainable TOPSIS: Visual Insights into the Effects of Weights and Aggregations on Rankings. Arxiv preprint.](
https://doi.org/10.48550/arXiv.2306.07706)
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"author_email": "Maciej Wo\u015b <maciejwos01@gmail.com>, Bogna Kilanowska <bogna.kilanowska@student.put.poznan.pl>, Adam Ciesi\u00f3\u0142ka <adam.ciesiolka@student.put.poznan.pl>",
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"description": "# TOPSIS ranking and interpretation using WMSD-space\r\n\r\n\r\nThis repository hosts the code for **MSDTransformer** - a scikit-learn compatible python library, that lets you create TOPSIS rankings of alternatives, visualize rankings in WMSD space, and get suggestions for improvement actions. \r\n<!-- Check out the project's [documentation](https://msdtransformer.readthedocs.io/en/latest/index.html) to learn more about using the MSDTransformer. -->\r\n\r\n## Requirements\r\n\r\nMSDTransformer uses PySCIPOpt to determine exact WMSD boundaries quickly. If you use conda, you should be fine performing the follwoing command (easier option):\r\n```console\r\nconda install --channel conda-forge pyscipopt\r\n``` \r\nIf you use PyPI, then you will need a working installation of the [SCIP Optimization\r\nSuite](https://www.scipopt.org/) (harder option, but might run faster). Please, make sure that your SCIP installation works, before installing MSDTransformer. For more details on how to install SCIP, please refer to the [PySCIPOpt documentation](https://github.com/scipopt/PySCIPOpt/blob/master/INSTALL.md).\r\n\r\nAfter installing PySCIPOpt (or if you already have SCIP installed), you can install the other dependencies by running:\r\n```console\r\npip install -r requirements.txt\r\n```\r\n## Installation\r\n\r\n<!-- ### From PyPI\r\n\r\nThis is the simplest (one-command) install method is to run the following command in your terminal:\r\n\r\n```console\r\npip install MSDtransformer\r\n```\r\n\r\n### From source -->\r\n\r\nOnce you have all the dependencies, you can install MSDTransformer from source. To do so, clone the repository and run the following command in the root directory of the repository:\r\n\r\n```console\r\npip install .\r\n```\r\n\r\n## Quickstart\r\n\r\n```python\r\nimport pandas as pd\r\nfrom MSDTransformer import MSDTransformer\r\n\r\ndf = pd.read_csv(\"../data/students.csv\", sep=';', index_col= 0)\r\nstudents_transformer = MSDTransformer(\"R\") # Relative distance\r\nstudents_transformer.fit(df, expert_range=[[0,100],[1,6],[1,6]])\r\nstudents_transformer.transform(df)\r\n```\r\n\r\n\r\n\r\n```python\r\nstudents_transformer.show_ranking()\r\n```\r\n\r\n\r\n\r\n```python\r\nstudents_transformer.plot()\r\n```\r\n\r\n\r\n\r\n## Citation\r\n\r\nIf you use this project in your research, please consider citing the following papers:\r\n\r\n- [Susmaga, R. *et al.* (2023) MSD-space: Visualizing the inner-workings of TOPSIS aggregations. *European Journal of Operational Research*, 308(1), 229-242.](https://doi.org/10.1016/j.ejor.2022.12.003)\r\n- [Susmaga, R. *et al.* (2023) Towards Explainable TOPSIS: Visual Insights into the Effects of Weights and Aggregations on Rankings. Arxiv preprint.](\r\nhttps://doi.org/10.48550/arXiv.2306.07706)\r\n\r\n",
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