Skip to main content

A Python package for implementing the Topsis method

Project description

Topsis-Vivek-102203871

This Python package implements the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) decision-making method. It helps evaluate and rank alternatives based on multiple criteria, supporting both CSV and Excel file formats.

Features

  • Simple command-line interface.
  • Supports both .csv and .xlsx input formats.
  • Allows specifying weights and impacts for decision criteria.
  • Outputs the Topsis score and rank for each alternative.

Installation

To install the package, use pip (Python package manager). Run the following command in your terminal:

pip install Topsis-Vivek-102203871

STEPS

Step 1: Prepare Your Input Data The input file should have at least 3 columns:

The first column should represent the alternatives/objects (e.g., M1, M2, M3, etc.). The second column and beyond should contain numeric values representing the criteria for each alternative. Example Input Data (CSV format) 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 M4, 0.78, 0.61, 6.4, 42.4, 12.55 M5, 0.94, 0.88, 3.6, 62.2, 16.91 Example Input Data (Excel format) Alternatively, you can provide the data in Excel (.xlsx) format.

Step 2: Define Weights and Impacts Weights: The relative importance of each criterion (must be comma-separated). Impacts: The direction of preference for each criterion (+ for benefit, - for cost). Example Weights and Impacts Weights: "0.25, 0.25, 0.25, 0.25" Impacts: "+,+,-,+" Step 3: Running the Command Once your input data is ready, you can run the package using the command line. The format is:

python -m Topsis_Vivek_102203871 <input_file> <weights> <impacts> <result_file>

Example Command

python -m Topsis_Vivek_102203871 102203871-data.xlsx "0.25, 0.25, 0.25, 0.25" "+,+,-,+" 102203871-result.csv

Where:

102203871-data.xlsx is the input file (can be .csv or .xlsx). "0.25, 0.25, 0.25, 0.25" represents the weights of each criterion. "+,+,-,+" represents the impacts of each criterion. 102203871-result.csv is the output file where results will be saved. Step 4: Understanding the Output The output file will contain:

The original data. Two additional columns: Topsis Score: The computed score for each alternative. Rank: The rank based on the Topsis score (lower score = better rank). Example Output (CSV):

Fund Name, P1, P2, P3, P4, P5, Topsis Score, Rank M1, 0.84, 0.71, 6.7, 42.1, 12.59, 0.865, 2 M2, 0.91, 0.83, 7.0, 31.7, 10.11, 0.799, 3 M3, 0.79, 0.62, 4.8, 46.7, 13.23, 0.935, 1 M4, 0.78, 0.61, 6.4, 42.4, 12.55, 0.876, 4 M5, 0.94, 0.88, 3.6, 62.2, 16.91, 0.980, 5 The Topsis Score column represents how close each alternative is to the ideal solution, and the Rank column indicates the rank based on the Topsis score.

How it works

How the Package Works Normalization: The decision matrix is normalized using vector normalization. Weighted Matrix: The normalized matrix is weighted according to the user-defined weights. Ideal Solutions: The positive and negative ideal solutions are determined based on the direction of the impacts. Topsis Score: The Topsis score is calculated using the distance between the alternatives and the ideal solutions. Rank: The alternatives are ranked based on the calculated Topsis score. Error Handling The package performs the following validations:

File Not Found: Checks if the input file exists. Correct Number of Parameters: Ensures that the number of command-line arguments is correct. Valid Weights: Ensures that the weights are numeric and properly formatted. Valid Impacts: Ensures that the impacts contain only + or -. Valid Input Data: Ensures that the data columns contain only numeric values (except the first column).

License

This package is distributed under the MIT License. See LICENSE.txt for more details.

Contact

For any questions or issues, feel free to contact me at atrivivek001@gmail.com.

Summary

  • This README.md provides a full guide on how to install, use, and understand the Topsis-Vivek-102203871 package.
  • It includes clear instructions, example inputs/outputs, and error handling.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

topsis_vivek_102203871-1.0.4.tar.gz (5.1 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

Topsis_Vivek_102203871-1.0.4-py3-none-any.whl (8.6 kB view details)

Uploaded Python 3

File details

Details for the file topsis_vivek_102203871-1.0.4.tar.gz.

File metadata

  • Download URL: topsis_vivek_102203871-1.0.4.tar.gz
  • Upload date:
  • Size: 5.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.0.1 CPython/3.12.7

File hashes

Hashes for topsis_vivek_102203871-1.0.4.tar.gz
Algorithm Hash digest
SHA256 2255f8fd36893cc68ef41c346bfa676aaaf7986ca0fbbdf3a3486c82f6a43a3a
MD5 ee2dc4eabd6c1a0d44f3ca670e38dfaf
BLAKE2b-256 c974b85d4fdceba8135f76165bb32bc99bc8f0e492ff2af589aa418536f219ba

See more details on using hashes here.

File details

Details for the file Topsis_Vivek_102203871-1.0.4-py3-none-any.whl.

File metadata

File hashes

Hashes for Topsis_Vivek_102203871-1.0.4-py3-none-any.whl
Algorithm Hash digest
SHA256 789dc139c5f442cd43ae20a315309cf5d220e77d230474cbf8048fed9121b7d3
MD5 631c83d236ab92c83b501f92911c9ab5
BLAKE2b-256 02311af1658027520705be582a219577dfc2c0d739b332a0d193682a16d11df0

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page