semvec/pypi-packages huggingface dataset:
Topsis-AVIRAL-102016049 1.0.3
Topsis
License: MIT
Keywords: topsis

# Topsis implementation for Multiple Criteria Decision Making (MCDM)

Topsis-AVIRAL-102016049 is a Python Package that can be used as CLI tool to calculate TOPSIS performance score and ranking them according to score by taking csv file as input.

## 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.

## Installation

Install the package using command-
 sh
pip install Topsis-AVIRAL-102016049


## How to use this package?

- ### To use via CLI
   sh
  topsis input_file weights impacts output_file
  
  ##### Arguments
  ####
  | Arguments | Description |
  | ------ | ------ |
  | input_file | Input CSV file path |
  | weights | Comma seperated numbers enclosed in "" |
  | impacts | Comma seperated '+' or '-' enclosed in "" |
  | output_file | Output CSV file path |
  
  ##### Output:
  Creates a ouput_file that contains the original data with two new columns as performance score and rank.
  
  Example:
  sh
  topsis input_data.csv "1,1,1,2,1" "+,+,+,-,+" output_file.csv
  
  #
 - ### To use in .py script
   #
   python
   <br>
   import Topsis_AVIRAL_102016049 as topsis

   import pandas as pd
   
   dataset = pd.read_csv("data.csv")
   data = dataset[:,1:]
   weights = [1,1,1,2,1]
   impacts = ["+","+","+","-","+"]
   topsisscore(data,weights,impacts,output.csv)
   
   
   ### Sample Input CSV
   #
   | Fund Name | P1 | P2 | P3 | P4 | P5 |
   | ------ | ------ | ------ |------ |------ |------ |
   | M1 | 0.65 | 0.42 | 4.2 | 60.1 | 16.34 |
   | M2 | 0.67 | 0.45 | 6.8 | 69.7 | 19.41 |
   | M3 | 0.91 | 0.83 | 6.5 | 62.9 | 17.79 |
   | M4 | 0.61 | 0.37 | 3.3 | 44.1 | 12.1 |
   | M5 | 0.8 | 0.64 | 5.5 | 55.4 | 15.59 |
   | M6 | 0.79 | 0.62 | 5.5 | 56.5 | 15.85 |
   | M7 | 0.82 | 0.67 | 5.1 | 53.6 | 15.05 |
   | M8 | 0.94 | 0.88 | 5.1 | 44.5 | 12.86 |
   
   ### Sample Output CSV File
   ### For weights "1,1,1,1,1" and impacts "+,-,+,-,+"
   #
   | Fund Name | P1 | P2 | P3 | P4 | P5 | Topsis Score | Rank |
   | ------ | ------ | ------ |------ |------ |------ | ------ | ------ |
   | M1 | 0.65 | 0.42 | 4.2 | 60.1 | 16.34 | 0.53475795 | 3 |
   | M2 | 0.67 | 0.45 | 6.8 | 69.7 | 19.41 | 0.64308057 | 1 |
   | M3 | 0.91 | 0.83 | 6.5 | 62.9 | 17.79 | 0.50063048 | 6 |
   | M4 | 0.61 | 0.37 | 3.3 | 44.1 | 12.1 | 0.50478334 | 5 |
   | M5 | 0.8 | 0.64 | 5.5 | 55.4 | 15.59 | 0.53326848 | 4 |
   | M6 | 0.79 | 0.62 | 5.5 | 56.5 | 15.85 | 0.5446234 | 2 |
   | M7 | 0.82 | 0.67 | 5.1 | 53.6 | 15.05 | 0.48796329 | 7 |
   | M8 | 0.94 | 0.88 | 5.1 | 44.5 | 12.86 | 0.4227203 | 8 |