Decision Optimization notebook tutorial

Solving and analyzing a Decision Optimization model: the diet problem

This example shows you how to create and solve a Python-based model by using a sample.

Before you begin

Requirements

To edit and run Decision Optimization models, you must have the following prerequisites:

Admin or Editor roles: You must have Admin or Editor roles in the project. Viewers of shared projects can only see experiments, but cannot modify or run them

watsonx.ai Runtime service: You must have a watsonx.ai Runtime service that is associated with your project. You can add one when you create a Decision Optimization experiment.

Deployment space: You must have a deployment space that is associated with your Decision Optimization experiment. You can choose a deployment space when you create a Decision Optimization experiment.

About this task

This well-known optimization problem identifies the best mix of foodstuffs to meet dietary requirements while minimizing costs. The data inputs are the nutritional profile and price of different foods and the min and max values for nutrients in a diet. The model is expressed as the minimization of a linear program. The files that are used in this sample are available in the DO-samples.

Procedure

To create and solve a Python-based model by using a sample:

Download and extract all the DO-samples on to your computer. You can also download just the diet.zip file from the Model_Builder subfolder for your product and version, but in this case, do not extract it.
Open your project or create an empty project.
Select the Assets tab.
Select New asset > Solve optimization problems in the Work with models section.
Click Local file in the Create a Decision Optimization experiment window that opens.
Browse to find the Model_Builder folder in your downloaded DO-samples. Select the relevant product and version subfolder. Choose the Diet.zip file and click Open. Alternatively use drag and drop.
If you haven't already associated a watsonx.ai Runtime service with your project, you must first select Add a Machine Learning service to select or create one before you choose a deployment space for your experiment.
Click New deployment space, enter a name, and click Create (or select an existing space from the drop-down menu).
Click Create.
A Decision Optimization model is created with the same name as the sample.
In the Prepare data view, you can see the data assets imported.
These tables represent the min and max values for nutrients in the diet (diet_nutrients), the nutrients in different foods (diet_food_nutrients), and the price and quantity of specific foods (diet_food).
Click Build model in the sidebar to view your model.
The Python model minimizes the cost of the food in the diet while satisfying minimum nutrient and calorie requirements.

Note also how the inputs (tables in the Prepare data view) and the outputs (in this case the solution table to be displayed in the Explore solution view) are specified in this model.
Run the model by clicking the Run button in the Build model view.

Results

When the run is completed, you can see the results in the Explore solution view. You can also click Engine statistics or Log to see the solution chart and inspect the log files. The first tab in the Explore solution view shows the objective (or objectives if you have several) with its values and weights. The Solution tables tab provides you with a list of foods and their quantities, along with the nutrients that they provide.

You can also download the solution tables as csv files.

If your model had any conflicting constraints, these would be shown in the Conflicts tab with the Relaxations necessary to solve the model.

In the Visualization view, the solution is displayed as a table and a chart in the Solution page. You can add notes, different types of tables and charts to show input data, solution data or KPIs by selecting and editing the widgets. You can also create different pages in the Visualization view. For example, an Input page is also provided in this sample. For more information, see Visualization view in a Decision Optimization experiment.

You're ready to start running comparisons between different scenarios. For example, the basic solution contains a quantity of hot dog. You might want to check an alternate solution for someone who prefers a vegetarian diet.