topsis-nitanshjain-102017025 0.1.0

Topsis Calculation Package

topsis_nitanshjain_102017025

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

pip install topsis-nitanshjain-102017025

Input csv format

Input file contain three or more columns
First column is the object/variable name
From 2nd to last columns contain numeric values only

How to use it

Command Prompt
topsis <InputDataFile> <Weights> <Impacts> <ResultFileName>
Example:
topsis inputfile.csv “1,1,1,1,2” “+,+,+,+,-” result.csv

Note: The weights and impacts should be ',' seperated, input file should be in pwd.

Functions, Parameters and Return Values

function = check_input()
parameters = none for this function
return values = input_file, weights (string), impacts (string), output_file

function = read_data()
parameters = input_file, weights, impacts
return values = dataframe, weights (list), impacts (list)

function = topsis_solve()
parameters = dataframe, weights, impacts, output_file
return values = dataframe, creates a csv file with the topsis rank and performance score

Sample input data

Model	P1	P2	P3	P4	P5
M1	0.62	0.38	3.8	33.8	9.65
M2	0.75	0.56	5.7	50.3	14.33
M3	0.95	0.90	6.5	65.6	18.49
M4	0.61	0.37	6.2	43.6	12.70
M5	0.60	0.36	6.4	61.2	17.14
M6	0.76	0.58	5.3	68.0	18.66
M7	0.66	0.44	6.2	47.2	13.63
M8	0.80	0.64	5.7	37.1	11.06

Sample output data

Model	P1	P2	P3	P4	P5	Performance Score	Topsis Rank
M1	0.62	0.38	3.8	33.8	9.65	0.317272185	8
M2	0.75	0.56	5.7	50.3	14.33	0.452068871	4
M3	0.95	0.90	6.5	65.6	18.49	0.689037307	1
M4	0.61	0.37	6.2	43.6	12.70	0.340383903	7
M5	0.60	0.36	6.4	61.2	17.14	0.367206376	6
M6	0.76	0.58	5.3	68.0	18.66	0.481350901	3
M7	0.66	0.44	6.2	47.2	13.63	0.372999972	5
M8	0.80	0.64	5.7	37.1	11.06	0.51226635	2