Phenix project - Happy meal algorithm
The main goal of this repository is to create balanced meals from a list of products
Understanding our algorithm
Overview of the balanced meal generation process
In the following workflow, EAN means European Article Number and OFF Open Food Facts.
(The source code of this workflow can be found here)
We used the Naive Bayes method in order to automatically assign categories to articles according to their names.
We used the Random Forest method (40 trees) to automatically assign categories to articles according to their nutrients (6 features).
Defining weights in grams
This algorithm works as follow:
- If the quantity is mentioned in OFF, then converts the quantity in grams
- if not, then compute the average of article's category quantities
- If the article is rice, semolina, pasta or instant mashed potatoes, then multiply by a specific coefficient
Description can be found here.
Reallocating remaining articles
add a description here
The numpy and openfoodfacts packages are required to properly use the repo. Tested on the following version:
import sys import numpy, openfoodfacts print('Python %s' % '.'.join(map(str, sys.version_info[:3]))) print('Numpy %s, Openfoodfacts %s' % (numpy.__version__, openfoodfacts.__version__))
Python 3.6.5 Numpy 1.15.0, Openfoodfacts 0.1.0
How to use it
Depending on the feature you wish to test, you may need an Internet connection (product information)
Pass a barcode (EAN) as argument of the demo script:
python product_info_demo.py 3392460480827
Run a simulation with the demo script similarly as below:
python tetris_demo.py 1000 --item_max_qty 100. --portion_size 500. --overflow_thresh 0.2 --underflow_thresh 0.1
which should return the result of the algorithm:
------------ RESULT ------------ 40 batches for 1 persons (portion of 500.0): 502 items 1 batches for 3 persons (portion of 500.0): 27 items 3 batches for 2 persons (portion of 500.0): 65 items Average batch loss: 41.91587070338677 Number of remaining items: 201 portioned, 4 unportioned Number of large items: 201 Number of unindentified items: 0
Many parameters can be adjusted with the previous arguments. To check the full list of arguments and their meaning, use the help of the parser:
python tetris_demo.py -h
- Implement an EAN - product information function
- Implement a naive meal balancing algorithm
- Explore brute force and smart optimisation techniques