Skip to main content

A python package to identify the best model out of different models using TOPSIS

Project description

Ranking System Using Topsis

Project 1 : UCS633

Submitted By: Pritpal Singh Pruthi 101883058


pypi: https://pypi.org/project/topsis-ppruthi-101883058/


Installation

Use the package manager pip to install Ranking system.

pip install topsis-ppruthi-101883058

How to use this package:

topsis-ppruthi-101883058 can be run as done below:

In Command Prompt

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

In Python IDLE:

>>> import pandas as pd
>>> import topsis
>>> data = pd.read_csv('data.csv').values
>>> data = data[:,1:]
>>> w = [1,1,1,1]
>>> impacts = ["+" , "+" , "-" , "+" ]
>>> topsis.topsis(data,w,impacts)

Sample dataset

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 list 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

Model   Score    Rank
-----  --------  ----
  1    0.77221     2
  2    0.225599    5
  3    0.438897    4
  4    0.523878    3
  5    0.811389    1

The rankings are displayed in the form of a table using a package 'tabulate', with the 1st rank offering us the best decision, and last rank offering the worst decision making, according to TOPSIS method.

License

MIT

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-ppruthi-101883058-2.0.0.tar.gz (3.3 kB view details)

Uploaded Source

Built Distribution

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

topsis_ppruthi_101883058-2.0.0-py3-none-any.whl (4.3 kB view details)

Uploaded Python 3

File details

Details for the file topsis-ppruthi-101883058-2.0.0.tar.gz.

File metadata

  • Download URL: topsis-ppruthi-101883058-2.0.0.tar.gz
  • Upload date:
  • Size: 3.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.2.0 requests-toolbelt/0.9.1 tqdm/4.41.1 CPython/3.8.1

File hashes

Hashes for topsis-ppruthi-101883058-2.0.0.tar.gz
Algorithm Hash digest
SHA256 d3103841c978e2e74c961c87a6a66be7bbc2b6c6f59c0f5344d1d790e15321dc
MD5 4e4e3656915bb6f5a9914c9783ecb4ca
BLAKE2b-256 fbf6f4bde45bd8907fb4c3ed89d179956033079c2d55293768688ef5d68dedb2

See more details on using hashes here.

File details

Details for the file topsis_ppruthi_101883058-2.0.0-py3-none-any.whl.

File metadata

  • Download URL: topsis_ppruthi_101883058-2.0.0-py3-none-any.whl
  • Upload date:
  • Size: 4.3 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.2.0 requests-toolbelt/0.9.1 tqdm/4.41.1 CPython/3.8.1

File hashes

Hashes for topsis_ppruthi_101883058-2.0.0-py3-none-any.whl
Algorithm Hash digest
SHA256 5953d41d2ccbac1d4992ab74f02bd8ab7a7cad80576c3bf9eee720b0583bf695
MD5 05945c611756c1941e6aef321afbe9cd
BLAKE2b-256 dd3085f01212df247de2f6f051edf222c31276bc1308fa0eab55175355d3e6d5

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