semvec/pypi-packages huggingface dataset:
Topsis-Samarjeet-102103562 1.0.0
A Python package designed for performing Topsis analysis in the context of multiple-criteria decision making (MCDM). This package generates Topsis scores and ranks items accordingly.
License: MIT

## Topsis-python-package


# TOPSIS

Submitted By: **Samarjeet Singh - 102103563**.

Type: **Package**.

Title: **TOPSIS method for multiple-criteria decision making (MCDM)**.

Version: **1.0.0**.

Date: **22-01-2024**.

Author: **Samarjeet Singh**.

Maintainer: **Samarjeet Singh <singhsamarjeet09@gmail.com>**.

Description: **A Python package designed for performing Topsis analysis in the context of multiple-criteria decision making (MCDM). This package generates Topsis scores and ranks items accordingly.**.

---

## 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-Samarjeet-102103562
```

### In Command Prompt

```
>> topsis -h
>> topsis data.csv "1,1,1,1" "+,+,-,+" result.csv
```

## Input file (data.csv)


|A        |B   |C   |D  |E   |F    |
|---------|----|----|---|----|-----|
|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|

`weights` = "2,2,3,3,4"
`impacts` = "-,+,-,+,-"


<br>

## Output file (result.csv)

|Fund Name|P1 |P2  |P3  |P4 |P5  |score|rank|
|---------|---|----|----|---|----|-----|-------------------|
|M1       |0.84|0.71|6.7 |42.1|12.59|0.46648384839516|6|
|M2       |0.91|0.83|7.0 |31.7|10.11|0.5565665031302564|2|
|M3       |0.79|0.62|4.8 |46.7|13.23|0.5552632203730501|3|
|M4       |0.78|0.61|6.4 |42.4|12.55|0.5091638066448925|5|
|M5       |0.94|0.88|3.6 |62.2|16.91|0.36399291864067096|7|
|M6       |0.88|0.77|6.5 |51.5|14.91|0.26936075735436416|8|
|M7       |0.66|0.44|5.3 |48.9|13.83|0.5251983566077868|4|
|M8       |0.93|0.86|3.4 |37.0|10.55|0.6986963797749929|1|


<br>
The output file contains columns of input file along with two additional columns having **Topsis_score** and **Rank**
Topsis-Vansh-102103072 1.1.1
This is a library for implementing Topsis in command line
License: MIT
Keywords: TOPSIS,Command Line

## Topsis-Vansh-102103072
Topsis-Vansh-102103072 is a library for Multiple Criteria Decision making using TOPSIS using command line
Assignment1
By: Vansh Batra 
Roll No: 102103072
Group: 3CO3

## Installation
Use pip to install it
```sh
pip install Topsis-Vansh-102103072
```
##General Instruction
For running it you would provide input csv file, weights ,impacts and output csv file in command line and the result would be stored in output csv file as well as displayed on command line.

## Steps to use it
In command line use the format :
topsis inputcsvfile weights impact output file
1) the first argument should be a csv file containing input data
2) The second argument should be weights 
3) The third should be impacts
4) The fourth should be the output csv file

```sh
 topsis input.csv "1,2,1,1" "+,-,+,-" output.csv
```
## Example
| Fund Name |   P1   |   P2   |  P3  |   P4   |   P5   |
|-----------|--------|--------|------|--------|--------|
|    M1     |  0.88  |  0.77  | 3.1  |  42.5  | 11.81  |
|    M2     |  0.74  |  0.55  | 3.6  |  67.6  | 18.12  |
|    M3     |  0.73  |  0.53  | 3.3  |  39.3  | 10.97  |
|    M4     |  0.75  |  0.56  |  5   |  30.8  |  9.28  |
|    M5     |   0.7  |  0.49  |  5   |  34.2  |  10.1  |
|    M6     |   0.6  |  0.36  | 6.4  |  49.1  | 14.12  |
|    M7     |  0.84  |  0.71  | 5.6  |  69.3  | 19.11  |
|    M8     |   0.9  |  0.81  | 6.4  |  61.6  | 17.43  |

Weights "1,1,1,2,2"
Impacts "+,-,+,+,+"
## OUTPUT
Output file: output.csv is written succesfully
 | Fund Name | P1   | P2   | P3   | P4   | P5   | Topsis Score | Rank |
|-----------|------|------|------|------|------|--------------|------|
| M1        | 0.88 | 0.77 | 3.1  | 42.5 | 11.81| 0.282550     | 5    |
| M2        | 0.74 | 0.55 | 3.6  | 67.6 | 18.12| 0.737680     | 2    |
| M3        | 0.73 | 0.53 | 3.3  | 39.3 | 10.97| 0.262740     | 7    |
| M4        | 0.75 | 0.56 | 5.0  | 30.8 | 9.28 | 0.220834     | 8    |
| M5        | 0.70 | 0.49 | 5.0  | 34.2 | 10.10| 0.263293     | 6    |
| M6        | 0.60 | 0.36 | 6.4  | 49.1 | 14.12| 0.554459     | 4    |
| M7        | 0.84 | 0.71 | 5.6  | 69.3 | 19.11| 0.780545     | 1    |
| M8        | 0.90 | 0.81 | 6.4  | 61.6 | 17.43| 0.688456     | 3    |
The result is also stored in output.csv file
## License

MIT
topsis-arryuann-102103062 1.0.2
It returns the csv file with topsis score as well as the rank to the provided csv file.
License: MIT

## topsis-Arryuann-102103062
for: Assignment01-Topsis (UCS633) submitted by: Arryuann Khanna Roll no: 102103062 Group: 3COE3
This Python library helps with Multiple Criteria Decision Making(MCDM) problems by using Technique for Order of Preference by Similarity to Ideal Solution(TOPSIS).

## Installation
```pip install topsis-Arryuann-102103062```

## How to use it?
Open terminal and enter a valid csv filename followed by .csv extentsion, then enter the weights vector with vector values separated by commas, then impacts vector with comma separated signs (+,-) and finally the output file name in csv format.

```topsis 102103062-data.csv "1,1,1,1" "+,-,+,+" 102103062-result-1.csv```

## Example

If you have this content inside the 102103062-data.csv-

| Fund Name  | P1    |    P2 |    P3 |    P4 |    P5 | 
|------------|-------|-------|-------|-------|-------|
| M1         | 0.88  | 0.77  | 3.0   | 57.7  | 15.59 |
| M2         | 0.87  | 0.76  | 4.9   | 39.4  | 11.48 |
| M3         | 0.91  | 0.83  | 5.5   | 59.4  | 16.66 |
| M4         | 0.61  | 0.37  | 3.2   | 39.6  | 10.95 |
| M5         | 0.60  | 0.36  | 5.6   | 62.3  | 17.22 |
| M6         | 0.77  | 0.59  | 6.1   | 51.5  | 14.74 |
| M7         | 0.61  | 0.37  | 4.7   | 43.3  | 12.25 |
| M7         | 0.71  | 0.50  | 6.1   | 42.6  | 12.48 |

and you ran the command-

`topsis 102103062-data.csv "1,1,1,1,1" "+,-,+,-,+" 102103062-result-1.csv`

you get this as output-
```
 Fund Name    P1    P2   P3    P4     P5  Topsis Score  Rank
0        M1  0.88  0.77  3.0  57.7  15.59      0.626255     1
1        M2  0.87  0.76  4.9  39.4  11.48      0.535066     3
2        M3  0.91  0.83  5.5  59.4  16.66      0.460473     4
3        M4  0.61  0.37  3.2  39.6  10.95      0.559417     2
4        M5  0.60  0.36  5.6  62.3  17.22      0.363836     7
5        M6  0.77  0.59  6.1  51.5  14.74      0.353200     8
6        M7  0.61  0.37  4.7  43.3  12.25      0.455437     5
7        M8  0.71  0.50  6.1  42.6  12.48      0.378622     6
```

and also saved in 102103062-result-1.csv in current working directory

## License

© 2024 Arryuann Khanna

This repository is licensed under the MIT license. See LICENSE for details.
Topsis-Suvidha-102103019 0.1.0
Python package for implementing TOPSIS technique.
License: MIT

# TOPSIS


Submitted By: Suvidha Srivastava

***

## What is TOPSIS?

The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) emerged
in the 1980s as a method for making decisions involving multiple criteria. TOPSIS
selects an alternative based on its proximity to the ideal solution, measured by the 
shortest Euclidean distance, and its distance from the negative-ideal solution, 
measured by the greatest distance.
<br>

## How to install this package:
```
>> pip install TOPSIS-Hasika-102003542
```


### 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**