semvec/pypi-packages huggingface dataset:
GeneticFeatureSelection 1.0.6
GeneticFeatureSelection
License: MIT
Requires Python: >=3.6
Keywords: Genetic Feature Selection

GeneticFeatureSelection aims to provide a simple implementation for the users to perform sequential feature selection with genetic algorithm.

## 1) Initial Population 
   a) Heuristic 
   
   b) Randomized 
    
## 2) Selection
   a) Roulette Wheel Selection 
   
   b) Rank Selection 
   
   c) Steady State Selection 
   
   d) Tournament Selection 
   
   e) Elitism Selection 
   
   f) Boltzmann Selection 
   

## 3) Reproduction
   a) One-point crossover 
   
   b) k-point crossover 
   
   c) Uniform crossover 
   

## 4) Mutation
   a) Bit string mutation ?   


Quick Start:

	!pip install GeneticFeatureSelection
	
	from GeneticFeatureSelection import GeneticFeatureSelection
	
	gfs = GeneticFeatureSelection()

	gfs.sequential_selection(
    		X=X_train, 
   		y=y_train, 
    		pop_size=12, 
    		estimator=xgbr, 
    		scoring='neg_mean_absolute_error', 
    		cv=5, 
    		select_method='R', 
    		offspring_size=20, 
   		c_pt=1, 
    		epsilon=.1, 
    		tolerance=1, 
    		verbose=1
		)

It requires at least six arguments to run:

- estimators: machine learning model
- X (array): features space
- y (array): target
- cv (int): number of folds in a (Stratified)KFold
- scoring (str): see https://scikit-learn.org/stable/modules/model_evaluation.html
- estimator: machine learning models
- pop_size (int): population size
- select_method (str): 'RW' (Roulette Wheel)
		        'SS' (Steady State/Stratify)
		        'T' (Tournament)
		        'SUS' (Stochastic Universal Sampling)
		        'R' (Rank)
- offspring_size (int): offspring size
- c_pt (int): 'crossover point'
- epsilon (float): mutation rate
- tolerance (int): number of fails that GFS can tolerate
- verbose (int): level of verbosity