Skip to main content

A Python package implementing TOPSIS technique.

Project description

TOPSIS

Submitted By: Harsh 101803327


What is TOPSIS?

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.


How to install this package:

>> pip install TOPSIS-Harsh-101803327

In Command Prompt

>> topsis data.csv "1,1,1,1" "+,+,-,+" result.csv

Input file (data.csv)

The decision matrix should be constructed with each row representing a Model alternative, and each column representing a criterion like Accuracy, R2, Root Mean Squared Error, Correlation, and many more.

Model Correlation R2 RMSE Accuracy
M1 0.79 0.62 1.25 60.89
M2 0.66 0.44 2.89 63.07
M3 0.56 0.31 1.57 62.87
M4 0.82 0.67 2.68 70.19
M5 0.75 0.56 1.3 80.39

Weights (weights) is not already normalised will be normalised later in the code.

Information of benefit positive(+) or negative(-) impact criteria should be provided in impacts.


Output file (result.csv)

Model Correlation R2 RMSE Accuracy Topsis_score Rank
M1 0.79 0.62 1.25 60.89 0.7722 2
M2 0.66 0.44 2.89 63.07 0.2255 5
M3 0.56 0.31 1.57 62.87 0.4388 4
M4 0.82 0.67 2.68 70.19 0.5238 3
M5 0.75 0.56 1.3 80.39 0.8113 1

The output file contains columns of input file along with two additional columns having **Topsis_score** and **Rank**

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_Harsh-101803327-1.0.1.tar.gz (3.2 kB view details)

Uploaded Source

File details

Details for the file TOPSIS_Harsh-101803327-1.0.1.tar.gz.

File metadata

  • Download URL: TOPSIS_Harsh-101803327-1.0.1.tar.gz
  • Upload date:
  • Size: 3.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.2.0 pkginfo/1.6.1 requests/2.24.0 setuptools/40.8.0 requests-toolbelt/0.9.1 tqdm/4.50.2 CPython/3.7.3

File hashes

Hashes for TOPSIS_Harsh-101803327-1.0.1.tar.gz
Algorithm Hash digest
SHA256 8bc236eecf0cf5d49427556b7d81d1b01c1391444f2198125ee5ef68ebc7d7f0
MD5 0256a1700ba928ed2d892a9a273db32f
BLAKE2b-256 128f7027d2a4c416354ef164c878980df7bf237dc5ecb347f2bd7a9a376b27a4

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