Skip to main content

A Python package to implement Topsis

Project description

topsis-python

Topsis analysis of a csv file in python

About Topsis

It is a method of compensatory aggregation that compares a set of alternatives by identifying weights for each criterion, normalising scores for each criterion and calculating the geometric distance between each alternative and the ideal alternative, which is the best score in each criterion. An assumption of TOPSIS is that the criteria are monotonically increasing or decreasing. Normalisation is usually required as the parameters or criteria are often of incongruous dimensions in multi-criteria problems

Installation

pip install topsis33

Usage

Query on terminal will provide you the topsis analysis for input csv file.

topsis33 -n "dataset-name.csv" -w "w1,w2,w3,w4,..." -i "i1,i2,i3,i4,..."

w1,w2,w3,w4 represent weights, and i1,i2,i3,i4 represent impacts where 1 is used for maximize and 0 for minimize. Size of w and i is equal to number of features.

Note that the first row and first column of dataset is dropped

Rank 1 signifies best decision

License

MIT

Project details


Release history Release notifications

Download files

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

Files for topsis33, version 1.0.0
Filename, size File type Python version Upload date Hashes
Filename, size topsis33-1.0.0-py3-none-any.whl (4.2 kB) File type Wheel Python version py3 Upload date Hashes View
Filename, size topsis33-1.0.0.tar.gz (3.6 kB) File type Source Python version None Upload date Hashes View

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page