Sehajbir-Singh-Mann-102003478 1.1.2

Topsis Package

Topsis-Sehajbir_Singh_Mann-102003478

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 Sehajbir-Singh-Mann-102003478==1.1.2

Input File in CSV Format

Input file must contain Three or more columns
First column contains the Object Name / Variable Name
Columns from 2nd to last should have numeric values.

How to use it

Python File

which includes complete code for topsis calculation

Command Prompt

python <python_file> <Input Data File> <Weights> <Impacts> <Result File Name>

Example:

python 102003478.py 102003478-data.csv “1,1,1,1,1” “+,-,+,-,+” 102003478-result-1.csv
python 102003478.py 102003478-data.csv “2,2,3,3,4” “-,+,-,+,-” 102003478-result-2.csv

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

Functions and Return Values

function = topsis_102003478()
return values = Creates a csv file with the topsis rank and performance score

Sample input data

Fund Name	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

Fund Name	P1	P2	P3	P4	P5	Topsis Score	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