Metadata-Version: 2.1
Name: Topsis-Mohit-102197021
Version: 0.0.1
Summary: multiple criteria decision making using topsis
Author: Mohit
Author-email: singlamohit444@gmail.com
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: License :: OSI Approved :: GNU General Public License v2 or later (GPLv2+)
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
Provides-Extra: dev
License-File: LICENSE


# TOPSIS

Submitted By: **Mohit - 102197021**.

Type: **Package**.

Title: **TOPSIS method for multiple-criteria decision making (MCDM)**.

Version: **0.0.1**.

Date: **2023-01-23**.

Author: **Mohit**.

Maintainer: **Mohit <singlamohit444@gmail.com>**.

Description: **Evaluation of alternatives based on multiple criteria using TOPSIS method.**.

---

## What is TOPSIS?

**T**echnique for **O**rder **P**reference by **S**imilarity to **I**deal **S**olution
(TOPSIS) originated in the 1980s as a multi-criteria decision making method.
TOPSIS chooses the alternative of shortest Euclidean distance from the ideal solution,
and greatest distance from the negative-ideal solution.

<br>

## How to install this package:

```bash
>> pip install -e .[dev]
```

### In Command Prompt

```
>> topsis data.csv "1,1,1,1" "+,+,-,+" result.csv
```

## Input file (data.csv)

The decision matrix should be constructed with each row representing a Model alternative, and each column representing a criterion like Accuracy, R<sup>2</sup>, Root Mean Squared Error, Correlation, and many more.

| Model | Correlation | R<sup>2</sup> | RMSE | Accuracy |
| ----- | ----------- | ------------- | ---- | -------- |
| M1    | 0.79        | 0.62          | 1.25 | 60.89    |
| M2    | 0.66        | 0.44          | 2.89 | 63.07    |
| M3    | 0.56        | 0.31          | 1.57 | 62.87    |
| M4    | 0.82        | 0.67          | 2.68 | 70.19    |
| M5    | 0.75        | 0.56          | 1.3  | 80.39    |

Weights (`weights`) is not already normalised will be normalised later in the code.

Information of benefit positive(+) or negative(-) impact criteria should be provided in `impacts`.

<br>

## Output file (result.csv)

| Model | Correlation | R<sup>2</sup> | RMSE | Accuracy | Topsis_score | Rank |
| ----- | ----------- | ------------- | ---- | -------- | ------------ | ---- |
| M1    | 0.79        | 0.62          | 1.25 | 60.89    | 0.77         | 2    |
| M2    | 0.66        | 0.44          | 2.89 | 63.07    | 0.23         | 5    |
| M3    | 0.56        | 0.31          | 1.57 | 62.87    | 0.44         | 4    |
| M4    | 0.82        | 0.67          | 2.68 | 70.19    | 0.52         | 3    |
| M5    | 0.75        | 0.56          | 1.3  | 80.39    | 0.81         | 1    |

<br>
The output file contains columns of input file along with two additional columns having **Topsis_score** and **Rank**
