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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 foobar.

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
>>> from topsis_python.topsis 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


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topsis-ppruthi-101883058-1.0.4.tar.gz (3.3 kB view hashes)

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