TOPSIS-Radhika-102003313 1.1.1

A Python package to find TOPSIS for multi-criteria decision analysis method

Topsis

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

MCDM for algorithm selection Multiple-criteria decision methods (MCDM) can help decisions with more than one criterion. These methods can be applied to several business problems like supplier evaluation, project prioritizing, and raw material selection. They also can be used in personal situations like consumer goods choice. This work aims to use a multicriteria decision method to help on algorithm selection.

It is a multi-criteria decision analysis method that is based on the concept that the chosen alternative should have the shortest geometric distance to the Positive Ideal Solution (PIS) and the longest geometric solution from the Negative Ideal Solution (NIS).

Installation

Use the package manager pip to install topsis_102003313.

pip install topsis-102003313

-Input and output file format should be .CSV

-First column in the input file should be the object name

-Input file must have at least 2 criteria, and all criterion values should be numeric

-Weights must be numeric and comma-separated. For example, 0.25,0.25,1.0,0.25 or "0.25,0.25,1.0,0.25".

-Impacts must be comma-separated with + for criteria that are to be maximised, and - for criteria that are to be minimised.

-For example, +,-,+,- or "+, -, +, -"

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 lastly the outputFileName.

Example

Consider sample.csv:

Model	Correlation	R2	RMSE	Accuracy
M1	0.79	0.62	1.25	60.89
M2	0.66	0.44	2.89	63.07
M3	0.56	0.31	1.57	62.87
M4	0.82	0.67	2.68	70.19
M5	0.75	0.56	1.3	80.39

If we run the following command:

python topsis.py sample.csv "1,1,1,1" "+,-,+,+" outputFile.csv

we get a file named outputFile.csv in the directory with an additional 2 columns containing the TOPSIS score and the rank of each object:

Model	Correlation	R2	RMSE	Accuracy	TOPSIS Score	Rank
M1	0.79	0.62	1.25	60.89	0.7722097345612788	2.0
M2	0.66	0.44	2.89	63.07	0.22559875426413367	5.0
M3	0.56	0.31	1.57	62.87	0.43889731728018605	4.0
M4	0.82	0.67	2.68	70.19	0.5238778712729114	3.0
M5	0.75	0.56	1.3	80.39	0.8113887082429979	1.0

License

MIT