TOPSIS
Project description
TOPSIS
Technique for Order of Preference by Similarity to Ideal Solution
Citation
If you use this package, please refer cite.
Installation
INSTALLING VIA PIP
inbuilt Python package management system, pip.
You can can install, update, or delete the topsis2.
install
pip install topsis2
update
pip install --upgrade topsis2
uninstall
pip uninstall topsis2
Usage
Using this TOPSIS implementation is straightforward as importing it and writing only two lines. The important thing is the decision matrix in the type of pandas data frame.
The decision matrix would be some data frame as the following example.
After building your decision matrix, you need to define the criteria types (benefit or cost). To have the type, you can define a list as the impact. For instance, we assume that the first two criteria are benefit criteria and the last is the cost.
impact = ['+', '+', '-']
The ultimate step is assigning the weight array.
weight = np.array([0.1, 0.7, 0.2])
After having the three parameters, the model produces the ranking matrix.
import numpy as np
import pandas as pd
from topsis import topsis
array = np.random.randint(10, size=(5, 3))
decision_matrix = pd.DataFrame(array, columns=[
'criterion_' + str(i) for i in range(1, 4)],
index=['option_'+str(i) for i in range(1, 6)])
impact = ['+', '+', '-']
weight = np.array([0.1, 0.7, 0.2])
tp = topsis(decision_matrix=decision_matrix,
weight=weight, impact=impact)
tp.rank()
Requirements
This package takes advantage of the following libraries, which had already imported to the TOPSIS package:
-
scipy
-
numpy
-
pandas
Keywords
TOPSIS
, MCDM
, MADM
Version
0.0.2
Updated
2022-05-19
Date-released
2022-05-18
More about TOPSIS
To read more about the TOPSIS method and the logic behind the model, please refer here.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.