A package -> Calculates Topsis Score and Rank them accordingly
Project description
TOPSIS
Submitted By: Ishaan Takkar - 101803025.
Type: Package.
Title: TOPSIS method for multiple-criteria decision making (MCDM).
Version: 1.0.3.
Date: 2020-11-13.
Author: Ishaan Takkar.
Maintainer: Ishaan Takkar ishaantakkar@gmail.com.
Description: Evaluation of alternatives based on multiple criteria using TOPSIS method..
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.
How to install this package:
>> pip install Topsis_IshaanTakkar_101803025
In Command Prompt
>> topsis data.csv "1,1,1,1" "+,+,-,+" result.csv
Input file (data.csv)
The decision matrix 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 (weights
) is not already normalised will be normalised later in the code.
Information of benefit positive(+) or negative(-) impact criteria should be provided in impacts
.
Output file (result.csv)
Model | Correlation | R2 | RMSE | Accuracy | Topsis_score | Rank |
---|---|---|---|---|---|---|
M1 | 0.79 | 0.62 | 1.25 | 60.89 | 0.77 | 2 |
M2 | 0.66 | 0.44 | 2.89 | 63.07 | 0.23 | 5 |
M3 | 0.56 | 0.31 | 1.57 | 62.87 | 0.44 | 4 |
M4 | 0.82 | 0.67 | 2.68 | 70.19 | 0.52 | 3 |
M5 | 0.75 | 0.56 | 1.3 | 80.39 | 0.81 | 1 |
The output file contains columns of input file along with two additional columns having **Topsis_score** and **Rank**
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