eeshangpt/gabil-ga

Implementation of Generic Algorithm (GABIL) for classification

GABIL-GA

Implementation of Genetic Algorithm (GABIL) for classification

Installation

Install project dependencies by running the following command

$ pip install -r requirements.txt

Execution

Execute gabil project by running the following command

$ python main.py <MANDATORY ARGS> <OPTIONAL ARGS>

Below there is a list of the mandatory and optional arguments to be provided respectively:

• Mandatory Arguments

Argument	Short Version	Long Version	Expected Value
Crossover Rate	-c	--crossover	float number
Mutation Rate	-m	--mutation	float number
Number of Generations	-g	--generations	int number
Population Size	-p	--population	int number
Dataset file Path	-d	--dataset	path to dataset file

• Optional Arguments

Argument	Specification	Expected Value	Default Value
Length Penalization (Decay)	--decay	float number	1
Max Rules at Initialization	--initrules	int number	5
Max number of Rules on each individual	--maxrules	int number	50
Elitism	--elitism	true or false	False
Parent Selection	--pselection	roulette or rank or tournament	roulette
Results Folder	--rfolder	path to folder	/gabil-runs

In case that any of the optional argument is not specified, its default value will be used instead

####Example of project invocation:

$ python main.py --crossover 0.6 -m 0.01 -g 1000 -p 8 --dataset datasets/crx.data --rfolder my-gabil-results

Note that short argument names and long argument names can be used indifferently

####Results description The following files will be created inside the result folders

• gabil-learning.txt
• hypothesis_out.txt
• input_params.txt
• test_dataset.txt
• training_dataset.txt

A description of the content of each file is summarized in the following table

Filename	Content Description	Format
gabil-learning.txt	The progress of the learning process, for each generation	comma separated values
hypothesis_out.txt	The best hypothesis found and its statistics. Accuracy and Error are computed with respect of both training and test dataset	json
input_params.txt	A summary of the input parameters provided by the user	json
training_dataset.txt	The dataset selected for training. Corresponds to 70% of the given dataset	json
test_dataset.txt	The dataset selected for testing. Corresponds to 30% of the given dataset	json