Evolving Directed Acyclic Graph
Project description
EvoDAG
Evolving Directed Acyclic Graph (EvoDAG) is a steady-state Genetic Programming system with tournament selection. The main characteristic of EvoDAG is that the genetic operation is performed at the root. EvoDAG was inspired by the geometric semantic crossover proposed by Alberto Moraglio et al. and the implementation performed by Leonardo Vanneschi et al.
Example using command line
Let us assume one wants to create a classifier of iris dataset. The first step is to download the dataset from the UCI Machine Learning Repository
curl -O https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data
In order to train the EvoDAG using a population of 10 individuals, using early stopping to 11, sampling 100 different parameter configurations, creating an ensemble of 12, and using 4 cores then the following command is used:
~/.local/bin/EvoDAG -e 10 -p 11 -r 100 -u 4 -n 12 iris.data
The EvoDAG ensemble is stored in iris.evodag.gz.
Now that the ensemble has been initialized one can predict a test set and store the output in file called output.csv using the following command.
~/.local/bin/EvoDAG -m iris.evodag.gz -t iris.data -o output.csv
Install EvoDAG
Install using pip pip install EvoDAG
Using the source code
Clone the repository git clone https://github.com/mgraffg/EvoDAG.git
Install the package as usual python setup.py install
To install only for the use then python setup.py install --user
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