Metadata-Version: 2.1
Name: sklearn-genetic-opt
Version: 0.1.0
Summary: Sklearn models hyperparameters tuning using genetic algorithms
Home-page: https://github.com/rodrigo-arenas/Sklearn-genetic
Author: Rodrigo Arenas
Author-email: rodrigo.arenas456@gmail.com
License: MIT
Description: [![Build Status](https://www.travis-ci.com/rodrigo-arenas/Sklearn-genetic-opt.svg?branch=master)](https://www.travis-ci.com/rodrigo-arenas/Sklearn-genetic-opt)
        [![Codecov](https://codecov.io/gh/rodrigo-arenas/Sklearn-genetic-opt/branch/main/graphs/badge.svg?branch=master&service=github)](https://codecov.io/github/rodrigo-arenas/Sklearn-genetic-opt?branch=master)
        [![PyPI Version](https://badge.fury.io/py/sklearn-genetic-opt.svg)](https://badge.fury.io/py/sklearn-genetic-opt)
        [![Python Version](https://img.shields.io/badge/python-3.6%20%7C%203.7%20%7C%203.8%20%7C%203.9-blue)](https://www.python.org/downloads/)
        
        # Sklearn-genetic-opt
        Sklearn models hyperparameters tuning using genetic algorithms
        
        # Usage:
        Install sklearn-genetic-opt
        
        It's advised to install sklearn-genetic using a virtual env, inside the env use:
        
        ```
        pip install sklearn-genetic-opt
        ```
        
        ## Example
        
        ```python
        from sklearn_genetic import GASearchCV
        from sklearn.tree import DecisionTreeClassifier
        from sklearn.model_selection import train_test_split
        from sklearn.datasets import load_digits
        from sklearn.metrics import accuracy_score
        
        
        data = load_digits() 
        y = data['target']
        X = data['data'] 
        
        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
        
        clf = DecisionTreeClassifier()
        
        evolved_estimator = GASearchCV(clf,
                                       cv=3,
                                       scoring='accuracy',
                                       population_size=16,
                                       generations=30,
                                       tournament_size=3,
                                       elitism=True,
                                       crossover_probability=0.9,
                                       mutation_probability=0.05,
                                       continuous_parameters={'min_weight_fraction_leaf': (0, 0.5)},
                                       categorical_parameters={'criterion': ['gini', 'entropy']},
                                       integer_parameters={'max_depth': (2, 20), 'max_leaf_nodes': (2, 30)},
                                       encoding_length=10,
                                       n_jobs=-1)
                            
        evolved_estimator.fit(X_train,y_train)
        print(evolved_estimator.best_params_)
        y_predict_ga = evolved_estimator.predict(X_test)
        print(accuracy_score(y_test,y_predict_ga))
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6
Description-Content-Type: text/markdown
