https://github.com/magier/halga
An educational tool showing how an evolutionary algorithm can be use to learn the structure of Bayesian network
https://github.com/magier/halga
Last synced: 10 months ago
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An educational tool showing how an evolutionary algorithm can be use to learn the structure of Bayesian network
- Host: GitHub
- URL: https://github.com/magier/halga
- Owner: Magier
- License: mit
- Created: 2021-04-22T12:13:42.000Z (about 5 years ago)
- Default Branch: main
- Last Pushed: 2021-04-23T19:04:48.000Z (about 5 years ago)
- Last Synced: 2024-01-26T11:45:48.264Z (over 2 years ago)
- Language: Python
- Size: 86.9 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# HalGA
An educational tool showing how an evolutionary algorithm can be use to learn the structure of a Bayesian network.
The approach is heavily inspired by the example provided in the documentation of [Halerium](https://hal.erium.io/examples/04_causal_structure/03-causal_structure_calschool.html), where the causal structure is defined for the [California School](https://rdrr.io/cran/Ecdat/man/Caschool.html) data set.
### Core Idea:
Learn a Bayesian Network purely from observational data. The resulting model should do well on predicting `mathscr`, `readscr` and `testscr`.
This task can be broken down into the subtasks of:
- **learning the causal structure** → use a **Genetic Algorithm**
- learning the parameters → train Bayesian Network on data using Halerium for proposed proposed structure
## Dataset
#### Description
a cross-section from 1998-1999
number of observations: 420
observation: schools
country: United States
#### Columns
- `distcod`: district code
- `county`: county
- `district`: district
- `grspan`: grade span of district
- `enrltot`: total enrollment
- `teachers`: number of teachers
- `calwpct`: percent qualifying for CalWORKS
- `mealpct`: percent qualifying for reduced-price lunch
- `computer`: number of computers
- `testscr`: average test score (read.scr+math.scr)/2
- `compstu`: computer per student
- `expnstu`: expenditure per student
- `str`: student teacher ratio
- `avginc`: district average income
- `elpct`: percent of English learners
- `readscr`: average reading score
- `mathscr`: average math score
#### Source
California Department of Education https://www.cde.ca.gov.
## Used Tools
- [DEAP](https://github.com/deap/deap) as framework for genetic algorithms
- [HALerium](https://hal.erium.io/) to evaluate fitness of causal structure
- [Streamlit](https://streamlit.io/) for the web UI
## Usage
### Install
All dependencies are stored in the `requirements.txt` and can be installed using `pip install -r requirements.txt`.
### Run UI
To run the web UI run `streamlit run src/main.py` from to root folder of this project from to root folder of this project.
### Run Genetic Algorithm
The execution of the genetic algorithm can be a fairly long-running process. Because of this, the UI does not support interactive updates on the progress of the run.
Instead, it's recommended to run the optimization process from the terminal by executing `python src/ga_library.py`
## Note
This is an _educational tool_ intended as showcase how a **Genetic Algorithm** can be applied to a real problem.
It does **not** try to challenge the state-of-the-art nor does it use any novel operations.