semvec/pypi-packages huggingface dataset:
Topsis-gurnoor-102003069 0.1.0
A Python package for implementing TOPSIS technique.
License: MIT

# TOPSIS


Submitted By: **Gurnoor Singh**

***

## 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:
```
>> pip install TOPSIS-Gurnoor-102003069
```


### In Command Prompt
```
>> topsis data.csv "1,1,2,1" "+,+,-,+" 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 | 0.849592 | 2
M2 |  0.66 | 0.44	| 2.89 | 63.07 | 0.143187 | 5
M3 |	0.56 | 0.31	| 1.57 | 62.87 | 0.597633 | 3
M4 |	0.82 | 0.67	| 2.68 | 70.19 | 0.364575 | 4
M5 |	0.75 | 0.56	| 1.3	 | 80.39 | 0.877204 | 1

<br>
The output file contains columns of input file along with two additional columns having **Topsis_score** and **Rank**
topsis-sarvagyjain-102003553 1.0.1
A Python package to find TOPSIS for multi-criteria decision analysis method
License: MIT
Keywords: UCS654,TIET

Project description
TOPSIS-ANALYSIS
By: Sarvagy Jain

What is TOPSIS?
Technique for Order Preference by Similarity to Ideal Solution (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.

### Installation
```bash
pip install topsis-sarvagyjain-102003553==1.0.1
```


### Usage

Arguments Required:
(Assumne we have 5  attributes in dataset.)

You have to required one .csv file. (102003553-data.csv)
Pass weights to each attribute. (e.g.: [1,1,1,1,1])
Pass impacts to each attribute. (e.g.: [+,-,+,-,+])
Pass the name of the file with you want to put on .csv file. (102003553-result-1.csv)


Enter csv filename followed by .csv extension, then enter the weights string with values separated by commas, followed by the impacts string with comma separated signs (+,-) and name of file followed by -.csv- extension in which the user wants the output file

## Example
#### sample.csv
```bash
Fund Name	P1	     P2     P3	    P4	    P5
M1	        0.84	0.71	6.7	    42.1	12.59
M2	        0.91	0.83	7	    31.7	10.11
M3	        0.79	0.62	4.8	    46.7	13.23
M4	        0.78	0.61	6.4	    42.4	12.55
M5	        0.94	0.88	3.6	    62.2	16.91
M6	        0.88	0.77	6.5	    51.5	14.91
M7	        0.66	0.44	5.3	    48.9	13.83
M8	        0.93	0.86	3.4	    37	    10.55

```

### INPUT
```python
topsis 102003553-data.csv 1,1,1,1,1 +,-,+,-,+ 102003553-result-1.csv
```

### OUTPUT

```bash
Fund Name	P1	        P2	        P3	        P4	        P5	    Topsis Score	Rank
M1	    0.351077437	0.344400588	0.421433661	0.322539084	0.335992288	0.594551725	    2
M2	    0.380333891	0.402609138	0.440303825	0.24286197	0.269807945	0.566246179	    3
M3	    0.330179971	0.300744175	0.301922623	0.357780884	0.353072118	0.485394123	    6
M4	    0.326000478	0.295893463	0.402563497	0.324837462	0.334924798	0.612775882	    1
M5	    0.39287237	0.4268627	0.226441967	0.476530428	0.451281142	0.361550918	    8
M6	    0.367795411	0.373504863	0.408853551	0.394554936	0.397906673	0.538764066	    5
M7	    0.275846558	0.21343135	0.333372896	0.374635658	0.369084459	0.560458621	    4
M8	    0.388692877	0.417161275	0.213861858	0.283466653	0.281550328	0.38966293	    7
```