Topsis Calculation Package
Project description
topsis_nitanshjain_102017025
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.
Installation
pip install topsis-nitanshjain-102017025
Input csv format
Input file contain three or more columns
First column is the object/variable name
From 2nd to last columns contain numeric values only
How to use it
Python File
from topsis.topsis_nitanshjain_102017025 import solve_topsis
solve_topsis()
Command Prompt
topsis <python_file> <Input Data File> <Weights> <Impacts> <Result File Name>
Example:
topsis topsis.py inputfile.csv “1,1,1,1,2” “+,+,+,+,-” result.csv
Note: The weights and impacts should be ',' seperated, input file should be in pwd.
Functions, Parameters and Return Values
function = solve_topsis()
parameters = No input parameters
return values = Creates a csv file with the topsis rank and performance score
Sample input data
Model | P1 | P2 | P3 | P4 | P5 |
---|---|---|---|---|---|
M1 | 0.62 | 0.38 | 3.8 | 33.8 | 9.65 |
M2 | 0.75 | 0.56 | 5.7 | 50.3 | 14.33 |
M3 | 0.95 | 0.90 | 6.5 | 65.6 | 18.49 |
M4 | 0.61 | 0.37 | 6.2 | 43.6 | 12.70 |
M5 | 0.60 | 0.36 | 6.4 | 61.2 | 17.14 |
M6 | 0.76 | 0.58 | 5.3 | 68.0 | 18.66 |
M7 | 0.66 | 0.44 | 6.2 | 47.2 | 13.63 |
M8 | 0.80 | 0.64 | 5.7 | 37.1 | 11.06 |
Sample output data
Model | P1 | P2 | P3 | P4 | P5 | Performance Score | Topsis Rank |
---|---|---|---|---|---|---|---|
M1 | 0.62 | 0.38 | 3.8 | 33.8 | 9.65 | 0.317272185 | 8 |
M2 | 0.75 | 0.56 | 5.7 | 50.3 | 14.33 | 0.452068871 | 4 |
M3 | 0.95 | 0.90 | 6.5 | 65.6 | 18.49 | 0.689037307 | 1 |
M4 | 0.61 | 0.37 | 6.2 | 43.6 | 12.70 | 0.340383903 | 7 |
M5 | 0.60 | 0.36 | 6.4 | 61.2 | 17.14 | 0.367206376 | 6 |
M6 | 0.76 | 0.58 | 5.3 | 68.0 | 18.66 | 0.481350901 | 3 |
M7 | 0.66 | 0.44 | 6.2 | 47.2 | 13.63 | 0.372999972 | 5 |
M8 | 0.80 | 0.64 | 5.7 | 37.1 | 11.06 | 0.51226635 | 2 |
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