The analytic hierarchy process (AHP) is a structured technique for organizing and analyzing complex decisions, based on mathematics and psychology. It was developed by Thomas L. Saaty in the 1970s and has been extensively studied and refined since then.[1]

The Wikipedia [2] page on AHP references two full examples of AHP and many more can be found on the internet.

pyAHP provides a flexible interface to build AHP models and solve them using a plethora of methods. Checkout the documentation here.

To install pyAHP, simply:

pip install pyahp

import json
from pyahp import parse

with open('model.json') as json_model:
    # model can also be a python dictionary
    model = json.load(json_model)

ahp_model = parse(model)
priorities = ahp_model.get_priorities()

$> python -m pyahp -f examples/television.json
    [+] Television Model
	    Method: eigenvalue
	    Results:
		    Samsung: 0.243
		    Sony: 0.106
		    Panasonic: 0.27
		    Toshiba: 0.38
	    Recommended is Toshiba

The models supplied to the library are in JSON format. The model has to follow a specific schema and a number of errors are raised in case the schema validation fails. A very simple model with three criteria and one criteria with two subcriteria and three alternatives is as follows:

{
  "name": "Sample Model",
  "method": "approximate",
  "criteria": ["critA", "critB", "critC"],
  "subCriteria": {
    "critA": ["subCritA", "subCritB"]
  },
  "alternatives": ["altA", "altB", "altC"],
  "preferenceMatrices": {
    "criteria": [
      [1, 1, 1],
      [1, 1, 1],
      [1, 1, 1]
    ],
    "subCriteria:critA": [
      [1, 1],
      [1, 1]
    ],
    "alternatives:subCritA": [
      [1, 1, 1],
      [1, 1, 1],
      [1, 1, 1]
    ],
    "alternatives:subCritB": [
      [1, 1, 1],
      [1, 1, 1],
      [1, 1, 1]
    ],
    "alternatives:critB": [
      [1, 1, 1],
      [1, 1, 1],
      [1, 1, 1]
    ],
    "alternatives:critC": [
      [1, 1, 1],
      [1, 1, 1],
      [1, 1, 1]
    ]
  }
}

There are a wide variety of methods available for calculating the priorities from preference matrices. This library currently supports the following methods:

• Approximate (approximate)
• Geometric (geometric)
• Eigenvalue (eigenvalue)

In the sample model above, due to the design of the model and hierarchy, critA has two sub-criteria. Hence, we need to provide a preference matrix for the sub-criteria of critA, named subCriteria:critA, and two alternative preferences matrices with the name alternatives:subCritA and alternatives:subCritB. All the other criteria have corresponding preference matrices.

• Abhinav Mishra @mishrabhinav