topsis-kashish-102316021 1.0.0

TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) implementation in Python

topsis_kashish_102316021

TOPSIS - Technique for Order of Preference by Similarity to Ideal Solution

TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) is a multi-criteria decision-making method used to rank alternatives based on multiple criteria.

The best alternative is the one that:

• Has the shortest distance from the ideal best solution
• Has the farthest distance from the ideal worst solution

This project provides a command-line implementation of the TOPSIS method in Python.

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Installation

Install from PyPI

pip install topsis_kashish_102316021

Install Locally (from project directory)

pip install .

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Usage

Command Line

topsis_kashish_102316021 <input_file> <weights> <impacts> <output_file>

Parameters

• input_file → CSV file containing alternatives and criteria
• weights → Comma-separated numeric weights (e.g., "1,1,1,1,1")
• impacts → Comma-separated impacts:
  • + for benefit criteria
  • - for cost criteria
• output_file → Output CSV file containing results

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Example

topsis_kashish_102316021 data.csv "1,1,1,1,1" "+,+,-,+,+" result.csv

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Python API Usage

You can also use the package inside Python:

from topsis_kashish_102316021 import topsis

topsis(
    "data.csv",
    ["1", "1", "1", "1", "1"],
    ["+", "+", "-", "+", "+"],
    "result.csv"
)

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Input Format

• The first column must contain alternative names.
• Remaining columns must contain numeric criteria values.
• No missing values are allowed.

Example Input File

Object,Criteria1,Criteria2,Criteria3,Criteria4,Criteria5
Option1,25,50,12,200,45
Option2,30,60,10,180,50
Option3,20,45,15,220,40

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Output Format

The output CSV file includes:

• Original data
• Topsis Score (value between 0 and 1; higher is better)
• Rank (1 indicates the best alternative)

Example Output

Object,Criteria1,Criteria2,Criteria3,Criteria4,Criteria5,Topsis Score,Rank
Option1,25,50,12,200,45,0.534,3
Option2,30,60,10,180,50,0.782,1
Option3,20,45,15,220,40,0.654,2

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Validation Checks

The program validates:

• Number of weights must match number of criteria
• Number of impacts must match number of criteria
• Impacts must be either + or -
• Criteria columns must contain numeric values only

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Technologies Used

• Python
• NumPy
• Pandas
• setuptools

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