TOPSIS-IshaanMarjara-101853028 0.0.1

TOPSIS Implementation

TOPSIS

What is TOPSIS?

Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) originated in the 1980s as a multi-criteria decision making method. TOPSIS chooses the alternative of shortest Euclidean distance from the ideal solution, and greatest distance from the negative-ideal solution.

Installation

Use the package manager pip to install TOPSIS. Dependencies and devDependencies will be installed automatically.

pip install TOPSIS-IshaanMarjara-101853028

Usage

1) As a Library:

Import in your python File:

from TOPSIS import topsis
topsis()

Run the python file by typing in terminal/cmd:

python nameOfFile.py nameOfDataFile.csv "weights" "impacts" nameOfOutputFile.csv

2) Using Command Promt:

Command line args:

• name of input File(csv format)
• weights(as a string)
• impacts(as a string)
• name of output file(csv format) Eg.

topsis data.csv "1,1,1,1" "+,+,-,+" output.csv

Input file (data.csv)

The decision matrix should be constructed with each row representing a Model alternative, and each column representing a criterion like Accuracy, R2, Root Mean Squared Error, Correlation, and many more.

Model	Corr	Rseq	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

Weights (weights) is not already normalised will be normalised later in the code.

Information of benefit positive(+) or negative(-) impact criteria should be provided in impacts.

Output file (output.csv)

Model	Corr	Rseq	RMSE	Accuracy	Topsis_score	Rank
M1	0.79	0.62	1.25	60.89	0.7722097345612788	2
M2	0.66	0.44	2.89	63.07	0.22559875426413367	5
M3	0.56	0.31	1.57	62.87	0.43889731728018605	4
M4	0.82	0.67	2.68	70.19	0.5238778712729114	3
M5	0.75	0.56	1.3	80.39	0.8113887082429979	1

The output file contains columns of input file along with two additional columns having Topsis_score and Rank

License

MIT