TOPSIS-Python
Submitted By: Arindam Sharma
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 data.csv "1,1,1,1" "+,+,-,+" output.csv
Sample dataset
The decision matrix (a) should be provided with more than two columns and should be in .csv format.
| 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) and impacts (either + or -) should also be provided alogn with.
Output
The output comes out in a csv file as the format given below :
Model | Correlation | R<sup>2</sup> | RMSE | Accuracy | Topsis Score | Rank
------------ | ------------- | ------------ | ------------- | ------------
M1 | 0.79 | 0.62 | 1.25 | 60.89 | 0.639133 | 2
M2 | 0.66 | 0.44 | 2.89 | 63.07 | 0.212592 | 5
M3 | 0.56 | 0.31 | 1.57 | 62.87 | 0.407846 | 4
M4 | 0.82 | 0.67 | 2.68 | 70.19 | 0.519153 | 3
M5 | 0.75 | 0.56 | 1.3 | 80.39 | 0.828267 | 1
1 maintainer
Release files for TOPSIS-Arindam-101816003 0.0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| TOPSIS-Arindam-101816003-0.0.1.tar.gz | 4.0 kB | Details |
Release files / TOPSIS-Arindam-101816003-0.0.1.tar.gz
| Download URL | TOPSIS-Arindam-101816003-0.0.1.tar.gz |
|---|---|
| Size | 4.0 kB |
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