Package with basic implementations of mono and multi-objective genetic algorithms for feature selection.
Python package with lightweight implementations of genetic algorithms for classification/regression tasks.
The pywinEA module is a native python implementation of some of the most widely used genetic algorithms. This package has been developed on the top of scikit-learn which allows to use any model already implemented. This module aims to provide a good alternative to other feature selection techniques with full scikit-learn compatibility.
Why evolutionary algorithms?
One of the first stages in the development of any machine learning model is to filter out redundant and/or irrelevant attributes. However, the complexity of finding the best combination of attributes is most often an NP problem.
Among the most frequent feature selection strategies are embedded methods. These methods combine a heuristic search strategy with a classification/regression model. This is where genetic algorithms come into play. This type of strategy represents one of the best alternatives to address the immense search space generally reaching good solutions.
- Python (>= 3.6)
- NumPy (>= 1.13.3)
- SciPy (>= 0.19.1)
- Scikit-learn (>= 0.20.0)
- tqdm (>= 4.42.1)
- matplotlib (>= 3.1.3)
- pandas (>= 1.0.1)
pip install pywinEA
It is possible that older versions of the packages listed above may work. However, full compatibility is not guaranteed.
Examples of the basic use of the package can be found in the notebooks directory. A diagram of the module structure is also shown below. For more advanced use it is recommended to look at the documentation.
Additionally by using the classes defined in the interface subpackage it is possible to implement new operators, algorithms, etc. Feel free to add things.
The following is an example of the most basic implementation of a genetic algorithm.
# Basic GA from pywinEAt.algorithm import GA from sklearn.naive_bayes import GaussianNB # Fitness function # Data loading and processing... POPULATION_SIZE = 50 GENERATIONS = 200 FITNESS = GaussianNB() CV = 5 ANNHILATION = 0.1 ELITISM = 0.2 MUTATION = 0.1 ga_basic = GA( population_size=POPULATION_SIZE, generations=GENERATIONS, cv=CV, fitness=FITNESS, annihilation=ANNHILATION, elitism=ELITISM, mutation_rate=MUTATION, positive_class=1, id="BasicGA" ) ga_basic.set_features(feature_names) # Selection of the feature names (optional) # Fit algorithm ga_basic.fit(x_data, y_data) # Get the names of the most relevant features ga_basic.best_features
This type of algorithm usually works well, however we may be interested in maximizing two objectives, for example the performance of the classifier (maximization) and the number of characteristics (minimization). In this case the multi-target algorithms (NSGA2 and SPEA2) are the best alternative.
# Multi-objective from pywinEA.algorithm import NSGA2 from sklearn.naive_bayes import GaussianNB # Fitness function # Data loading and processing... POPULATION_SIZE = 50 GENERATIONS = 200 FITNESS = GaussianNB() CV = 5 MUTATION = 0.1 nsga = NSGA2( population_size=POPULATION_SIZE, generations=GENERATIONS, fitness=FITNESS, cv=CV, mutation_rate=MUTATION, optimize_features=True, positive_class=1, id="NSGA2" ) nsga.set_features(feature_names) # Selection of the feature names (optional) # Fit algorithm nsga.fit(x_data, y_data) # Get the names of the most relevant features nsga.best_features
The result of the multi-objective algorithms is a non-dominant front of solutions. For example:
For more examples it is recommended to take a look at the notebooks.
The package is still in testing, it is possible to find some unexpected errors. Any problem 👉 issues
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
|Filename, size||File type||Python version||Upload date||Hashes|
|Filename, size pywinEA-0.0.2-py3-none-any.whl (116.4 kB)||File type Wheel||Python version py3||Upload date||Hashes View|