TOPSIS
Submitted By: Sarthak Tiwari | 102183051
What is TOPSIS?
Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) originated in the 1980s as a multi-criteria decision making method.
How to install this package:
>> pip install -e .[dev]
After installation, in Command Prompt/Terminal in pwd/current dir:
>> topsis <InputDataFile> <Weights> <Impacts> <ResultFileName>
Weights (weights) may not be normalised but will be normalised in the code.
Note: To avoid errors -
Input file must contain three or more columns.
2nd to last columns must contain numeric values only.
Number of weights, number of impacts and number of columns (from 2 nd to last columns) must
be same.
Impacts must be either +ve or -ve.
Impacts and weights must be separated by ‘,’ (comma).
InputDataFile (data.csv) - an example
The decision matrix should be constructed with each row representing a Model alternative and each column representing a criterion like Correlation, R2, Root Mean Squared Error, Accuracy, etc.
| Model | Corr | Rseq | 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 |
Output file (result.csv) -
Based on the above input file and setting weights as "1,2,1,1" and impacts as "+,-,-,+".
| Model | Corr | Rseq | RMSE | Accuracy | Topsis Score | Rank |
|---|---|---|---|---|---|---|
| M1 | 0.79 | 0.62 | 1.25 | 60.89 | 0.423744391359611 | 4 |
| M2 | 0.66 | 0.44 | 2.89 | 63.07 | 0.0.467426368298297 | 3 |
| M3 | 0.56 | 0.31 | 1.57 | 62.87 | 0.760230957034903 | 1 |
| M4 | 0.82 | 0.67 | 2.68 | 70.19 | 0.207772533881566 | 5 |
| M5 | 0.75 | 0.56 | 1.3 | 80.39 | 0.504864457803718 | 2 |
The output file contains columns of input file along with two additional columns having Topsis Score and Rank.
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Metadata
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Release files for 102183051-topsis 0.0.1
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