Topsis-Divyansh-102083048 1.0.0

TOPSIS implementation for Multi Criteria Decision Making (MCDM)

Topsis implementation for Multiple Criteria Decision Making (MCDM)

Topsis-Divyansh-102083048 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?

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

Install the package using command-

pip install Topsis-Divyansh-102083048

How to use this package?

• To use via CLI

  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:

  topsis input_data.csv "1,1,1,2,1" "+,+,+,-,+" output_file.csv
  

• To use in .py script

  from Topsis-Divyansh-102083048 import topsis
  import pandas as pd
  
  dataset = pd.read_csv("data.csv")
  data = dataset[:,1:]
  weights = [1,1,1,2,1]
  impacts = ["+","+","+","-","+"]
  topsis(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