Assignment 6: UCS538
Submitted By: DIVYAM JAIN-101803128
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.
TOPSIS-Divyam-101803128 can be run as in the following example:
from topsis_101803128.topsis import CalcTopsisScore
filename = "input.csv"
weight = "1,1,1,2"
impact = "+,+,-,+"
CalcTopsisScore(filename, weight, impact )
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.
| Model | Correlation | R2 | RMSE | Accuracy | Score | Rank |
|---|---|---|---|---|---|---|
| M1 | 0.79 | 0.62 | 1.25 | 60.89 | 0.77221 | 2 |
| M2 | 0.66 | 0.44 | 2.89 | 63.07 | 0.225599 | 5 |
| M3 | 0.56 | 0.31 | 1.57 | 62.87 | 0.438897 | 4 |
| M4 | 0.82 | 0.67 | 2.68 | 70.19 | 0.523878 | 3 |
| M5 | 0.75 | 0.56 | 1.3 | 80.39 | 0.811389 | 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.
© 2020 Divyam Jain
This repository is licensed under the MIT license. See LICENSE for details.
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