semvec/pypi-packages huggingface dataset:
Topsis-AdityaVegesina-102017171 0.1
TOPSIS method for multi-criteria decision making
License: MIT
Keywords: TOPSIS,102017171,KEYWORDS

# Topsis_AdityaVegesina_102017171
Topsis_AdityaVegesina_102017171 is a Python Package implementing Topsis.  It is method used for multi-criteria decision analysis. Topsis stands for 'Technique for Order of Preference by Similarity to Ideal Solution'.

## Installation
Install the Package using the command - 
```s
$ pip install Topsis_AdityaVegesina_102017171
```
## How to use:
This package can be used via the command line using the following command:

python -m Topsis_AdityaVegesina_102017171  input-file-path  weights-in-string-format  impacts-in-the-format-of + and -  output-file-path

## Example:
python -m  Topsis_AdityaVegesina_102017171.topsis102017171 "C:\Users\aditya\OneDrive\Desktop\102017171-data.csv" "1,1,1,1,1" "+,-,-,-,+" "C:\Users\aditya\OneDrive\Desktop\102017171-result.csv"


## License

MIT

**Free Software, Hell Yeah!**
Topsis-Aparna-102016091 0.3
A python package implementing topsis
License: MIT
Keywords: topsis


  Submitted By: Aparna Sood

## 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.

It is a package for Multiple-criteria decision-making using TOPSIS. Requires input file,weights and impacts. Returns a data frame which has score and rank of every label. This package helps improve decision-making.

## Installation
```sh
pip install Topsis-Aparna-102016091
```

## Usage

```sh
import Topsis_Aparna_102016091 as ap 
ap.topsisscore(inputfilename.csv , weights , impacts , outputfilename.csv)
```
- weights and impacts provided as parameters should be separated by comma(,) and equal to number of columns.
- weights should only contain numeric values
- the categorical column should either be dropped or enginnered into a numerical column

Take care of these and you are ready to go.

## Result

The output (outputfilename.csv) is saved as csv file, with extra two columns of topsis score and rank.
Rank 1 signifies best decision.
## Contribution

Pull requests are welcome!!

## Example

inputfilename.csv

|Fund Name|P1  |P2  |P3 |P4  |P5   |
|---------|----|----|---|----|-----|
|M1       |0.8 |0.64|6.9|44.5|13.21|
|M2       |0.81|0.66|7  |33.1|10.39|
|M3       |0.89|0.79|4.1|49.1|13.72|
|M4       |0.8 |0.64|4.8|45.1|12.84|
|M5       |0.79|0.62|7  |65.1|18.38|
|M6       |0.8 |0.64|4.1|51.2|14.19|
|M7       |0.61|0.37|4.2|48  |13.3 |
|M8       |0.71|0.5 |4.1|43.1|12.1 |

weights = "1,1,1,1,1"

impacts = "+,-,+,-,+"

outputfilename.csv 

|FIELD1|Fund Name|P1  |P2 |P3  |P4   |P5   |topsis score       |rank|
|------|---------|----|---|----|-----|-----|-------------------|----|
|0     |M1       |0.8 |0.64|6.9 |44.5 |13.21|0.5513405258358909 |1.0 |
|1     |M2       |0.81|0.66|7.0 |33.1 |10.39|0.5474324527004779 |2.0 |
|2     |M3       |0.89|0.79|4.1 |49.1 |13.72|0.356585782293044  |8.0 |
|3     |M4       |0.8 |0.64|4.8 |45.1 |12.84|0.4305717292651279 |6.0 |
|4     |M5       |0.79|0.62|7.0 |65.1 |18.38|0.5240978466600755 |3.0 |
|5     |M6       |0.8 |0.64|4.1 |51.2 |14.19|0.38297613208032333|7.0 |
|6     |M7       |0.61|0.37|4.2 |48.0 |13.3 |0.5010998980377374 |4.0 |
|7     |M8       |0.71|0.5|4.1 |43.1 |12.1 |0.45991281671067175|5.0 |
Topsis-Jashan-102017034 0.3
This python package helps to implement a multiple criteria decision analysis process called TOPSIS
License: MIT
Keywords: TOPSIS,MCDM,102017034

# Topsis_Jashan_102017034

_for: **TOPSIS PACKAGE**_
_submitted by: **Jashanveer Kaur Dhillon**_
_Roll no: **102017034**_
_Group: **CS2**_


Topsis_Jashan_102017034 is a Python library for dealing with Multiple Criteria Decision Making(MCDM) problems.

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install Topsis-Jashan-102017034.

```bash
pip install Topsis_Jashan_102017034
```

## Usage

Firstly, install the Topsis_Jashan_102017034 package. After installation of the package, write the following command :-
```
from Topsis_Jashan_102017034.Topsis import topsis
```
topsis() function has four parameters ; The first parameter is for the input csv file , the second parameter is for weights , next parameter is for impacts and the last parameter is for the output csv file.

Example :-
topsis(input.csv,"1,1,1,1","+,-,+,+",output.csv)

Make sure that number of weights and impact is equal to the number of columns in input.csv

You can also run topsis from command line as follows :-
$ python [package name] [path of csv as string] [list of weights as string] [list of sign as string] [path of output file csv]



## Example

#### sample.csv

A csv file showing data for different mobile handsets having varying features.

| Model  | Storage space(in gb) | Camera(in MP)| Price(in $)  | Looks(out of 5) |
| :----: |:--------------------:|:------------:|:------------:|:---------------:|
| M1 | 16 | 12 | 250 | 5 |
| M2 | 16 | 8  | 200 | 3 |
| M3 | 32 | 16 | 300 | 4 |
| M4 | 32 | 8  | 275 | 4 |
| M5 | 16 | 16 | 225 | 2 |

weights vector = [ 1 , 1 , 1 , 1 ]

impacts vector = [ "+" , "+" , "-" , "+" ]

### input:

```python
topsis(input.csv,weights,impacts,result.csv)
```

### output:
```
      TOPSIS RESULTS
-----------------------------

    P-Score  Rank
1  0.534277     3
2  0.308368     5
3  0.691632     1
4  0.534737     2
5  0.401046     4

``` 

## Other notes
* Make sure the csv does not contain categorical values


## License
[MIT](https://choosealicense.com/licenses/mit/)