Topsis-102153035 1.0

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Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)

Topsis is a method of compensatory aggregation that compares a set of alternatives, normalising scores for each criterion and calculating the geometric distance between each alternative and the ideal alternative, which is the best score in each criterion.

This package takes a csv data as input and gives a csv file as output with the Topsis scores and rankings

Installation

Use the package manager pip to install foobar.

pip install Topsis-Siddaharth-102153035

Usage

Open the Command prompt

python <input-file.csv> <weights> <impacts> <output.csv> 

Example

Consider this sample.csv file

First column of file is removed by model before processing so follow the following format.

All other columns of file should not contain any categorical values.

Model	P1	P2	P3	P4	P5
M1	0.85	0.72	4.6	41.5	11.92
M2	0.66	0.44	6.6	49.4	14.28
M3	0.9	0.81	6.7	66.5	18.73
M4	0.8	0.64	6.9	69.7	19.51
M5	0.84	0.71	4.7	36.5	10.69
M6	0.91	0.83	3.6	42.3	11.91
M7	0.65	0.42	6.9	38.1	11.52
M8	0.71	0.5	3.5	60.9	16.4

weights vector = [ 1,2,1,2,1 ]

impacts vector = [ +,-,+,+,- ]

Input

python topsis sample.csv "1,2,1,2,1" "+,-,+,+,-" result.csv

Output

result.csv file will contain the following data

Model	P1	P2	P3	P4	P5	Topsis score	Rank
M1	0.85	0.72	4.6	41.5	11.92	0.3267076760116426	6
M2	0.66	0.44	6.6	49.4	14.28	0.6230956090525585	2
M3	0.9	0.81	6.7	66.5	18.73	0.5006083702087599	5
M4	0.8	0.64	6.9	69.7	19.51	0.6275096427934269	1
M5	0.84	0.71	4.7	36.5	10.69	0.3249142875298663	7
M6	0.91	0.83	3.6	42.3	11.91	0.2715902624653612	8
M7	0.65	0.42	6.9	38.1	11.52	0.5439263412940541	4
M8	0.71	0.5	3.5	60.9	16.4	0.6166791918077927	3

License

MIT