topsis-daksh-102303322 1.0.3

Educational TOPSIS implementation in Python

TOPSIS-Daksh-102303322

for: UCS654 submitted by: Daksh Garg Roll no: 102303322 Group: 3C24

topsis-daksh is a Python library for dealing with Multiple Criteria Decision Making (MCDM) problems by using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS).

Installation

Use the package manager pip to install topsis-daksh.

pip install topsis-daksh-102303322

How TOPSIS Works

The TOPSIS method evaluates each alternative by calculating:

● The shortest distance from the ideal best solution

● The farthest distance from the ideal worst solution

Each alternative is then assigned:

● A TOPSIS score

● A rank, where rank 1 represents the best alternative

Usage

Run the package using the command line interface (CLI). The syntax requires four arguments: the input data file, the weights, the impacts and the output file

topsis <InputDataFile> <Weights> <Impacts> <OutputFile>

Example:

topsis sample.csv "1,1,1,1" "+,-,+,+" output.csv

Important Notes on Syntax:

● Weights: Comma-separated numbers (e.g., 1,1,1,1).

● Impacts: Comma-separated signs (+ for beneficial criteria, - for non-beneficial criteria).

● Quotes: It is recommended to enclose your weights and impacts in double quotes (" ") to prevent errors if your input contains spaces.

Getting Help:

To view the help message and quick usage guide, run:

topsis /h

Example

sample.csv

A csv file showing data for different car models having varying features.

Model	Price (In lakhs)	Fuel Efficiency (Km/Lit)	Comfort	Looks
Car1	4	15	Good	Average
Car2	8	16	Good	Good
Car3	10	17	Excellent	Excellent
Car4	3.5	16	Average	Average

Note: Categorical values have been converted to numerical scales (Good=4, Average=3, Excellent=5).

weights vector = [ 0.25 , 0.25 , 0.25 , 0.25 ]

impacts vector = [ - , + , + , + ]

Note: Price is a cost (-) factor, while Efficiency, Comfort, and Looks are benefit (+) factors.

input:

topsis sample.csv "0.25,0.25,0.25,0.25" "-,+,+,+" output.csv

output:

Model  Price  Fuel Efficiency  Comfort  Looks  Topsis Score  Rank
 Car1    4.0               15        4      3      0.602439     1
 Car2    8.0               16        4      4      0.384213     4
 Car3   10.0               17        5      5      0.436903     3
 Car4    3.5               16        3      3      0.566388     2

Other Notes

● Input File Format: The input CSV must contain a header row. The first column is treated as the object/alternative name (e.g., Model Name, M1, M2) and will be preserved in the output but excluded from the mathematical calculations.

● Numeric Data Only: Ensure that all columns from the 2nd column onwards contain strictly numeric values. Categorical values (e.g., "Good", "Medium") must be converted to numerical scales before running the script.

● Consistency: The number of values in your weights and impacts parameters must match the number of criteria columns (total columns minus one).

Licence

MIT