topsis-chirag-102103278 1.0.4

A Python package to find TOPSIS for MCDM (Multi-Criteria Decision Analysis Method)

Topsis

TOPSIS( Technique for order for preference by similarity to Ideal solution ) for MCDM (Multiple criteria decision making) in Python compiled by Chirag Gupta, 102103278, TIET, Patiala.

Installation

Use the package manager pip to install.

pip install topsis_chirag_102103278

Usage

Enter csv filename followed by .csv extentsion, then enter the weights vector with vector values separated by commas, followed by the impacts vector with comma separated signs (+,-) and enter the output file name followed by .csv extension.

topsis_chirag_102103278 [InputDataFile as .csv] [Weights as a string] [Impacts as a string] [ResultFileName as .csv]

Example

topsis_chirag_102103278 sample.csv "1,1,1,1" "-,+,+,+" output.csv

Sample Input

Fund	P1	P2	P3	P4	P5
M1	0.72	0.52	7	56	16.06
M2	0.83	0.69	3.7	37	10.56
M3	0.81	0.66	4	30.4	8.97
M4	0.77	0.59	3.6	44.1	12.27
M5	0.82	0.67	7	34.6	10.77
M6	0.72	0.52	3	58.5	15.69
M7	0.85	0.72	4.2	50.5	14.07
M8	0.64	0.41	3	38.5	10.64

Sample Output

Fund	P1	P2	P3	P4	P5	TOPSIS Score	Rank
M1	0.72	0.52	7	56	16.06	0.6339125495800909	1
M2	0.83	0.69	3.7	37	10.56	0.3751667010618832	6
M3	0.81	0.66	4	30.4	8.97	0.4268895365063883	3
M4	0.77	0.59	3.6	44.1	12.27	0.3631574031053784	8
M5	0.82	0.67	7	34.6	10.77	0.6320591213396627	2
M6	0.72	0.52	3	58.5	15.69	0.3687064767204701	7
M7	0.85	0.72	4.2	50.5	14.07	0.3862956874804864	5
M8	0.64	0.41	3	38.5	10.64	0.4069761888395339	4

Please Note That

• The first column and first row are removed by the library before processing, in attempt to remove indices and headers. So the csv MUST follow the format as shown in sample.csv shown in the Example section.
• The input data file MUST contain three or more columns.
• The second to last columns of the data file MUST contain NUMERIC values.
• The number of weights, impacts and columns (second to last) MUST be SAME.
• Impacts MUST either be '+' or '-'.
• Impacts and Weights MUST be separated by , (comma).

License

© 2024 Chirag Gupta

This repository is licensed under MIT License. See LICENSE for details.