A Python package to find TOPSIS for multi-criteria decision analysis method
Project description
TOPSIS-Python
Submitted By: Girik Garg 102003178
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-GIRIK-GARG-102003178 can be run as in the following example:
In Command Prompt to run the code:
>> topsis data.csv "1,1,1,1" "+,+,-,+" out.csv
Sample dataset
The decision matrix (a
) should be constructed with each row representing a Model alternative, and each column representing a criterion like Fund Name , P1 ,P2 , P3 , P4 , P5.
Model | Correlation | R2 | RMSE | Accuracy |
---|---|---|---|---|
M1 | 0.8 | 0.64 | 3.5 | 37.5 |
M2 | 0.86 | 0.74 | 3.4 | 42.2 |
M3 | 0.69 | 0.48 | 5.7 | 70 |
M4 | 0.65 | 0.42 | 5.7 | 65.5 |
M5 | 0.9 | 0.81 | 6.6 | 39.1 |
M6 | 0.76 | 0.58 | 4 | 53.5 |
M7 | 0.69 | 0.48 | 6.2 | 51.3 |
M8 | 0.65 | 0.42 | 6 | 50.2 |
Output
Row_NO Performance_score Rank
1 0.436737 7
2 0.389937 8
3 0.565651 4
4 0.590487 3
5 0.522924 5
6 0.451300 6
7 0.637889 1
8 0.635536 2
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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