Package to calculate the TOPSIS Score and Rank
Project description
Topsis-Sanyam-10193481
A package to implement Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) for dealing with multi-attribute or multi-criteria decision making (MADM/MCDM) problems in the real world.
How to install
This package is available through PyPi and can be installed via the following command:
$ pip install Topsis-Sanyam-101903481
Usage
To implement TOPSIS, run the following command in the terminal:
$ topsis <inputFileName> <weights> <impacts> <outputFileName>
inputFileName: CSV input file containing atleast three columns. First column must be the object/variable name and other columns must contain numeric values only.
weights : comma separated numeric values enclosed between "" or ''. Number of weights must be equal to the number of attributes/criteria (2nd to last column)
impacts : comma separated '+' or '-' values enclosed between "" or ''. Number of impacts must be equal to the number of attributes/criteria (2nd to last column)
outputFileName : CSV output file which will contain all the columns of inputs file and two additional columns having TOPSIS Score and Rank
Example
$ topsis sample.csv "0.25,0.25,0.25,0.25" "-,+,+,+" result.csv
Input File - sample.csv
| Model | Price | Storage | Camera | Looks |
|---|---|---|---|---|
| M1 | 250 | 16 | 12 | 5 |
| M2 | 200 | 16 | 8 | 3 |
| M3 | 300 | 32 | 16 | 4 |
| M4 | 275 | 32 | 8 | 4 |
| M5 | 225 | 16 | 16 | 2 |
Output File - result.csv
| Model | Price | Storage | Camera | Looks | TOPSIS Score | Rank |
|---|---|---|---|---|---|---|
| M1 | 250 | 16 | 12 | 5 | 0.5342768571 | 3 |
| M2 | 200 | 16 | 8 | 3 | 0.3083677687 | 5 |
| M3 | 300 | 32 | 16 | 4 | 0.6916322312 | 1 |
| M4 | 275 | 32 | 8 | 4 | 0.5347365844 | 2 |
| M5 | 225 | 16 | 16 | 2 | 0.4010461215 | 4 |
License
© 2022 Sanyam Sharma
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