topsis-Pranav-102203059 1.0

A Python package to implement the TOPSIS method

TOPSIS Implementation Package

This package implements the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), a multi-criteria decision-making method widely used in ranking and evaluation problems.

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What is TOPSIS?

TOPSIS is a decision-making method that ranks alternatives based on their closeness to the ideal solution and distance from the worst solution. It is commonly used in scenarios involving multiple criteria and alternatives.

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Installation

To install the package from PyPI, run the following command:

pip install topsis-Pranav-102203059

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Usage

After installation, you can use the package via the command line.

Command Line Syntax:

topsis <InputDataFile> <Weights> <Impacts> <ResultFileName>

Parameters:

• <InputDataFile>: Path to the input file (e.g., data.csv or data.xlsx) containing the dataset.

  • The file must have:
    • At least 3 columns.
    • First column for the names/identifiers (e.g., alternatives like M1, M2, etc.).
    • Remaining columns with numeric values for criteria.

• <Weights>: Comma-separated weights for the criteria (e.g., "1,1,1,2").

• <Impacts>: Comma-separated impacts for the criteria. Use + for positive impact and - for negative impact (e.g., "+,+,-,+").

• <ResultFileName>: Path for the output file to save the result (e.g., result.csv).

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Example Usage:

1. Input File: 102203059-data.xlsx

   Fund Name, P1, P2, P3, P4, P5
   M1, 0.84, 0.71, 6.7, 42.1, 12.59
   M2, 0.91, 0.83, 7.0, 31.7, 10.11
   M3, 0.79, 0.62, 4.8, 46.7, 13.23
   

2. Command:

   topsis 102203059-data.xlsx "1,1,1,1,2" "+,+,-,+,+" 102203059-result.csv
   

3. Output File: 102203059-result.csv

   Fund Name, P1, P2, P3, P4, P5, Topsis Score, Rank
   M1, 0.84, 0.71, 6.7, 42.1, 12.59, 0.7523, 1
   M2, 0.91, 0.83, 7.0, 31.7, 10.11, 0.6321, 2
   M3, 0.79, 0.62, 4.8, 46.7, 13.23, 0.5809, 3
   

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How It Works

1. Normalization:

   • The criteria values are normalized using vector normalization

2. Weight Application:

   • Each normalized value is multiplied by its respective weight

3. Determine Ideal Best and Worst:

   • Ideal Best (A⁺):
     • Maximum value for positive impact (+).
     • Minimum value for negative impact (-).
   • Ideal Worst (A⁻):
     • Minimum value for positive impact (+).
     • Maximum value for negative impact (-).

4. Calculate Distances:

   • Euclidean distance from the ideal best and worst

5. Calculate TOPSIS Score:

   • Calculate closeness to the ideal solution

6. Rank Alternatives:

   • Rank alternatives based on the TOPSIS score in descending order.

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Dependencies

Ensure the following Python libraries are installed:

• pandas
• numpy