Skip to main content

Implementation of TOPSIS decision making

Project description


Inspired by TOPSIS-Python (python2).

Our python 3 code follows the same structure, defining a topsis class, but uses numpy linear algebra in order to modernise, optimise, and remove redundant code.

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 farthest distance from the negative-ideal solution. More details at wikipedia. The TOPSIS algorithm is succinctly explained in this paper comparing TOPSIS and VIKOR methods

Using TOPSIS-Python

TOPSIS-Python can be run as in the following example:

>>> from topsis import topsis
>>> a = [[7, 9, 9, 8], [8, 7, 8, 7], [9, 6, 8, 9], [6, 7, 8, 6]]
>>> w = [0.1, 0.4, 0.3, 0.2]
>>> I = [1, 1, 1, 0]
>>> decision = topsis(a, w, I)

The decision matrix (a) should be constructed with each row representing an alternative, and each column representing a criterion. We have used an example given in TOPSIS Method in MADM (Dr. Farhad Faez)

Weights (w) is not already normalised will be normalised upon initialisation. Information on benefit (1) cost (0) criteria should be provided in I.

By default, the optimisation (TOPSIS calculation) does not take place. No values are stored in decision.C or decision.optimum_choice.

These can be calculated, either by calling decision.calc(), or by calling a representation of the decision (which will itself call decision.calc()):

>>> decision

Alternatives ranking C:
[0.74269409 0.40359933 0.17586999 0.44142927]

Best alternative
a[0]: [7. 9. 9. 8.]

The rankings are saved in decision.C, with the highest ranking $0.74269409$ offering us the best decision, and lowest ranking $0.17586999$ offering the worst decision making, according to TOPSIS method.

We are also then shown the best alternative index (which happens to be index 0 in this example), and the associated criteria coefficients of this alternative.


The module can be installed from pypi using pip, or downloaded and installed directly from git

pypi: git:


Installation with pip can be achieved in one line:

pip install topsis-jamesfallon


If you are using anaconda, make sure that you are using your environment's pip, and not the OS default. Your installation only if using anaconda may look like:

  1. Activate your environment
(base) $ conda activate custom_env
  1. Ensure pip is installed in your environment
(custom_env) $ conda install pip
  1. Make sure you are using your environment's pip installation. If not, you can always prepend the full path before the pip command.
(custom_env) $ which pip
  1. Now install topsis from pypi
(custom_env) $ pip install topsis-jamesfallon
Collecting topsis-jamesfallon
Installing collected packages: topsis-jamesfallon
Successfully installed topsis-jamesfallon-0.2

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-jamesfallon-0.2.3.tar.gz (4.7 kB view hashes)

Uploaded source

Built Distribution

topsis_jamesfallon-0.2.3-py3-none-any.whl (5.6 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page