Topsis Calculator
Project description
TOPSIS-Python
Submitted By: YASH MITTAL 101803457
pypi: https://pypi.org/project/TOPSIS-Yash--101803457
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. More details at wikipedia.
How to use this package:
TOPSIS-YASH-101803457 can be run as in the following example:
In Command Prompt
>> pip install TOPSIS-Yash--101803457==1.0.0
python
from topsis_gen.topsis_cal import topsis topsis("data.csv","1,1,1,2","+,+,-,+")
Sample dataset
The decision matrix (a) 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 (w) is not already normalised will be normalised later in the code.
Information of benefit positive(+) or negative(-) impact criteria should be provided in I.
Output
Model Score Rank
----- -------- ----
1 0.639133 2
2 0.212592 5
3 0.407846 4
4 0.519153 3
5 0.828267 1
The rankings are displayed in the form of a table using a package 'tabulate', with the 1st rank offering us the best decision, and last rank offering the worst decision making, according to TOPSIS method.
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