TOPSIS
Submitted By: Priyanka 101803006
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.
How to install this package:
>> pip install TOPSIS-Priyanka-101803006
In Command Prompt
>> topsis data.csv "1,1,1,1" "+,+,-,+" result.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 | 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 |
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 (result.csv)
| Model | Correlation | R2 | RMSE | Accuracy | Topsis_score | Rank |
|---|---|---|---|---|---|---|
| M1 | 0.79 | 0.62 | 1.25 | 60.89 | 0.7722 | 2 |
| M2 | 0.66 | 0.44 | 2.89 | 63.07 | 0.2255 | 5 |
| M3 | 0.56 | 0.31 | 1.57 | 62.87 | 0.4388 | 4 |
| M4 | 0.82 | 0.67 | 2.68 | 70.19 | 0.5238 | 3 |
| M5 | 0.75 | 0.56 | 1.3 | 80.39 | 0.8113 | 1 |
The output file contains columns of input file along with two additional columns having **Topsis_score** and **Rank**