Topsis-AVIRAL-102016049 is a Python Package that can be used as CLI tool to calculate TOPSIS performance score and ranking them according to score by taking csv file as input.
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.
Install the package using command- sh pip install Topsis-AVIRAL-102016049
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sh topsis input_file weights impacts output_file
Arguments Description input_file Input CSV file path weights Comma seperated numbers enclosed in "" impacts Comma seperated '+' or '-' enclosed in "" output_file Output CSV file path Creates a ouput_file that contains the original data with two new columns as performance score and rank.
Example: sh topsis input_data.csv "1,1,1,2,1" "+,+,+,-,+" output_file.csv
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python
import Topsis_AVIRAL_102016049 as topsisimport pandas as pd
dataset = pd.read_csv("data.csv") data = dataset[:,1:] weights = [1,1,1,2,1] impacts = ["+","+","+","-","+"] topsisscore(data,weights,impacts,output.csv)
Fund Name P1 P2 P3 P4 P5 M1 0.65 0.42 4.2 60.1 16.34 M2 0.67 0.45 6.8 69.7 19.41 M3 0.91 0.83 6.5 62.9 17.79 M4 0.61 0.37 3.3 44.1 12.1 M5 0.8 0.64 5.5 55.4 15.59 M6 0.79 0.62 5.5 56.5 15.85 M7 0.82 0.67 5.1 53.6 15.05 M8 0.94 0.88 5.1 44.5 12.86 Fund Name P1 P2 P3 P4 P5 Topsis Score Rank M1 0.65 0.42 4.2 60.1 16.34 0.53475795 3 M2 0.67 0.45 6.8 69.7 19.41 0.64308057 1 M3 0.91 0.83 6.5 62.9 17.79 0.50063048 6 M4 0.61 0.37 3.3 44.1 12.1 0.50478334 5 M5 0.8 0.64 5.5 55.4 15.59 0.53326848 4 M6 0.79 0.62 5.5 56.5 15.85 0.5446234 2 M7 0.82 0.67 5.1 53.6 15.05 0.48796329 7 M8 0.94 0.88 5.1 44.5 12.86 0.4227203 8 
