Jostar, from the Farsi word جستار meaning finder, is a Python-based feature selection module comprised of nine different feature selection approaches from single objective to multi-objective methods, for regression and classification tasks. The algorithms, to this date, are:
- Ant Colony Optimization (ACO)
- Differential Evolution (DE)
- Genetic Algorithm (GA)
- Plus-L Minus-R (LRS)
- Non-dominated Sorting Genetic Algorithm (NSGAII)
- Particle Swarm Optimization (PSO)
- Simulated Annealing (SA)
- Sequential Forward Selection (SFS)
- Sequential Backward Selection (SBS)
- User-friendly, Sklearn-like interface (just call fit )
- Thorough documentation and explanation of hyperparameters and their ranges
- Tune hyperparameters easily
- Generate rankings of the selected features
- Display the results of your classification or regression task
With only few lines of code:
from sklearn.cross_decomposition import PLSRegression
from jostar.algorithms import ACO
from sklearn.metrics import r2_score
import pandas as pd
data = pd.read_csv(r"F:\SnapbeanSummer2020\regression_data_v2.csv").to_numpy()
x = data[:,1:]
y = data[:,0]
model = PLSRegression()
# # optimizing
aco = ACO(model=model, n_f=5, weight=1, scoring=r2_score, n_iter=30)
aco.fit(x,y,decor=0.95, scale = True)Evolution of created pareto front via NSGAII:
Use pip as below to install jostar.
pip install jostarto test if your installation was successful, change path to the directory and run pytest.
pytestJostar comes with a powerful documentation. Below is the in-line documentation for Genetic Algorithm.
class GA(BaseFeatureSelector):
def __init__(self, model, n_f, weight, scoring, n_gen=1000, n_pop=20 , cv=None,
cross_perc = 0.5, mut_perc = 0.3, mut_rate= 0.02, beta = 5,
verbose= True, random_state=None,**kwargs):
"""
Genetic Algorithms or GA is a widely used global optimization algorithm
which was first introduced by Holland. GA is based on the natural selection
in the evolution theory. Properties of GA such as probability of mutation and
cross over determines the specifics of the search done in each iteration.
Additionally, we can also set the proportion of the population we want to
perform cross over or mutation for.
Parameters
----------
model : class
Instantiated Sklearn regression or classification estimator.
n_f : int
Number of features needed to be extracted.
weight : int
Maximization or minimization objective, for maximization use +1
and for mimization use -1.
scoring : callable
Callable sklearn score or customized score function that takes in y_pred and y_true
n_gen : int, optional
Maximum number of generations or iterations. For more complex
problems it is better to set this parameter larger.
The default is 1000.
n_pop : int, optional
Number of population size at each iteration. Typically, this
parameter is set to be 10*n_f, but it is dependent on the complexity
of the model and it is advised that user tune this parameter based
on their problem. The default is 20.
cv : class, optional
Instantiated sklearn cross-validation class. The default is None.
cross_perc : float,
The percentage of the population to perform cross over on. A common
choice for this parameter is 0.5. The larger cross_perc is chosen,
the more exploition of the current population. The default is 0.5.
mut_perc : float, optional
The percentage of the population to perform mutation on. This is
usually chosen a small percentage (smaller than cross_perc). As
mut_perc is set larger, the model explorates more.
The default is 0.1.
mut_rate : float, optional
The mutation rate. This parameter determines the probability of
mutation for each individual in a population. It is often chosen
a small number to maintain the current good solutions.
The default is 0.1.
beta : int, optional
Selection Pressure for cross-over. The higher this parameter the
stricter the selection of parents for cross-over. This value
could be an integer [1,10]. The default value
is 5.
verbose : bool, optional
Wether to print out progress messages. The default is True.
random_state : int, optional
Determines the random state for random number generation,
cross-validation, and classifier/regression model setting.
The default is None.
Returns
-------
Instantiated Optimziation model class.
"""If you would like to develop and run tests for Jostar, run the following command in you virtual environment to install dev dependencies:
$ pip install -e .[dev]Jostar is an extended Python version of YPEA developed in MATALB. If you found this project useful in your research, pelase consider citing our paper.
@article{Hassanzadeh_2021, title={Broadacre Crop Yield Estimation Using Imaging Spectroscopy from
Unmanned Aerial Systems (UAS): A Field-Based Case Study with Snap Bean}, volume={13},
ISSN={2072-4292}, url={http://dx.doi.org/10.3390/rs13163241}, DOI={10.3390/rs13163241},
number={16}, journal={Remote Sensing}, publisher={MDPI AG},
author={Hassanzadeh, Amirhossein and Zhang, Fei and van Aardt, Jan and Murphy,
Sean P. and Pethybridge, Sarah J.}, year={2021}, month={Aug}, pages={3241}}
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