Deepak-102003483 1.1.10

Topsis Package

Deepak-102003483

What is TOPSIS

Topsis stands for Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Topsis was originated back in the 1980s and was used for making decisions which are subjected to multiple-criteria.
TOPSIS takes in the use of shortest Euclidean distance from the ideal solution, and greatest distance from the negative-ideal solution.

Package Installation

pip install Deepak-102003483==1.1.10

Input File in CSV Format

Input file must contain Three or more columns
First column contains the Object Name / Variable Name
Rest of the other columns contains only numeric values

Usage Method

Command Prompt

python <python_file> <Input_Data_File> <Weights> <Impacts> <Result_File_Name>

python_file -> Python Code file for Topsis Calculation
Input_Data_File -> CSV file name
Weights -> Weights for each Column
Impacts -> Maximaization('+'), Minimization('-')
Result_File_Name -> CSV file name to store result

Example:

python 102003483.py 102003483-data.csv “1,1,1,1,1” “+,-,+,-,+” 102003483-result-1.csv
python 102003483.py 102003483-data.csv “2,2,3,3,4” “-,+,-,+,-” 102003483-result-2.csv

Note: The Weights and Impacts should be comma (',') seperated and Input CSV file should be in pwd(Present Working Directory).

Functions and Return Values

function = topsis_102003483()
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