Topsis-Aditi-102003512 1.0.1

Calculate TOPSIS Ranks

TOPSIS

Assignment for UCS654

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Aditi Singh

102003512

3CO20

What is TOPSIS?

TOPSIS stands for "The Technique for Order of Preference by Similarity to Ideal Solution" is a multi-criteria decision analysis method, which was originally developed by Ching-Lai Hwang and Yoon in 1981 with further developments by Yoon in 1987, and Hwang, Lai and Liu in 1993. TOPSIS chooses the alternative of shortest Euclidean distance from the ideal solution, and farthest distance from the negative-ideal solution. More details at wikipedia. The TOPSIS algorithm is succinctly explained in this [paper comparing TOPSIS and VIKOR methods]https://www.sciencedirect.com/science/article/abs/pii/S0377221703000201)

How do I use TOPSIS?

First, install the TOPSIS module using pip. pip install Topsis-Aditi-102003512

Then, run the TOPSIS file with the arguements - python Topsis-Aditi-102003512 102003512-data.csv 1,1,1,1,1 +,-,+,-,+ 102003512-result.csv

Here, the first argument after the python program is your subject dataframe is stored in csv format in data.csv. The second arguement is the weights you are assigning to each feature of the dataset. The default weights are 1. The third arguement are the assigned impacts. The default impact is '+'. The fourth argument is the filename in which the output table is to be stored. If no file name is given then '102003512-data.csv' file is chosen.

Output is the csv file of the model with TOPSIS Score and TOPSIS Rank.

Example

Running command -

python Topsis-Aditi-102003512 102003512-data.csv 1,1,1,1,1 +,-,+,-,+ 102003512-result.csv

Input Dataset - (102003512-data.csv)

Fund Name	P1	P2	P3	P4	P5
M1	0.68	0.46	5	38.5	11.16
M2	0.78	0.61	5	30.6	9.25
M3	0.79	0.62	6.9	34.9	10.8
M4	0.82	0.67	6.2	36.1	10.95
M5	0.64	0.41	6.7	46.8	13.64
M6	0.63	0.4	7	59.5	16.88
M7	0.77	0.59	4	45.3	12.67
M8	0.79	0.62	3.9	33.6	9.73

Weights -> 2,2,3,3,4 Impacts -> -,+,-,+,-

Output Dataset - (stored in 102003512-result.csv)

Fund Name	P1	P2	P3	P4	P5	Topsis Score	Rank
M1	0.68	0.46	5	38.5	11.16	0.545126218	4
M2	0.78	0.61	5	30.6	9.25	0.561913424	3
M3	0.79	0.62	6.9	34.9	10.8	0.469000051	6
M4	0.82	0.67	6.2	36.1	10.95	0.50574866	5
M5	0.64	0.41	6.7	46.8	13.64	0.405123307	7
M6	0.63	0.4	7	59.5	16.88	0.404922284	8
M7	0.77	0.59	4	45.3	12.67	0.60568518	2
M8	0.79	0.62	3.9	33.6	9.73	0.607093772	1