Metadata-Version: 2.1
Name: sklearn-genetic-opt
Version: 0.5.0
Summary: Scikit-lean models hyperparameters tuning, using evolutionary algorithms
Home-page: https://github.com/rodrigo-arenas/Sklearn-genetic-opt
Author: Rodrigo Arenas
Author-email: rodrigo.arenas456@gmail.com
License: MIT
Project-URL: Documentation, https://sklearn-genetic-opt.readthedocs.io/en/latest/
Project-URL: Source Code, https://github.com/rodrigo-arenas/Sklearn-genetic-opt
Description: .. -*- mode: rst -*-
        
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        Sklearn-genetic-opt
        ###################
        
        scikit-learn models hyperparameters tuning, using evolutionary algorithms.
        
        This is meant to be an alternative from popular methods inside scikit-learn such as Grid Search and Randomized Grid Search.
        
        Sklearn-genetic-opt uses evolutionary algorithms from the deap package to choose set of hyperparameters that
        optimizes (max or min) the cross validation scores, it can be used for both regression and classification problems.
        
        Documentation is available `here <https://sklearn-genetic-opt.readthedocs.io/>`_
        
        Sampled distribution of hyperparameters:
        
        .. image:: https://github.com/rodrigo-arenas/Sklearn-genetic-opt/blob/master/demo/images/density.png?raw=true
        
        Optimization progress in a regression problem:
        
        .. image:: https://github.com/rodrigo-arenas/Sklearn-genetic-opt/blob/master/demo/images/fitness.png?raw=true
        
        
        Main Features:
        ##############
        
        * **GASearchCV**: Principal class of the package, holds the evolutionary cross validation optimization routine
        * **Algorithms**: Set of different evolutionary algorithms to use as optimization procedure
        * **Callbacks**: Custom evaluation strategies to generate Early Stopping rules
        * **Plots**: Generate pre-define plots to understand the optimization process
        * **MLflow**: Build-in integration with mlflow to log all the hyperparameters and their cv-score
        
        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
        #######
        
        .. code-block:: python
        
           from sklearn_genetic import GASearchCV
           from sklearn_genetic.space import Continuous, Categorical, Integer
           from sklearn.ensemble import RandomForestClassifier
           from sklearn.model_selection import train_test_split, StratifiedKFold
           from sklearn.datasets import load_digits
           from sklearn.metrics import accuracy_score
           import matplotlib.pyplot as plt
        
           data = load_digits()
           n_samples = len(data.images)
           X = data.images.reshape((n_samples, -1))
           y = data['target']
           X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
        
           clf = RandomForestClassifier()
        
           param_grid = {'min_weight_fraction_leaf': Continuous(0.01, 0.5, distribution='log-uniform'),
                         'bootstrap': Categorical([True, False]),
                         'max_depth': Integer(2, 30),
                         'max_leaf_nodes': Integer(2, 35),
                         'n_estimators': Integer(100, 300)}
        
           cv = StratifiedKFold(n_splits=3, shuffle=True)
        
           evolved_estimator = GASearchCV(estimator=clf,
                                          cv=cv,
                                          scoring='accuracy',
                                          population_size=10,
                                          generations=35,
                                          param_grid=param_grid,
                                          n_jobs=-1,
                                          verbose=True,
                                          keep_top_k=4)
        
           # Train and optimize the estimator
           evolved_estimator.fit(X_train, y_train)
           # Best parameters found
           print(evolved_estimator.best_params_)
           # Use the model fitted with the best parameters
           y_predict_ga = evolved_estimator.predict(X_test)
           print(accuracy_score(y_test, y_predict_ga))
        
           # Saved metadata for further analysis
           print("Stats achieved in each generation: ", evolved_estimator.history)
           print("Best k solutions: ", evolved_estimator.hof)
        
        ^^^^^^^
        Results
        ^^^^^^^
        
        Log controlled by verbosity
        
        .. image:: https://github.com/rodrigo-arenas/Sklearn-genetic-opt/blob/master/demo/images/log.JPG?raw=true
        
        Changelog
        #########
        
        See the `changelog <https://sklearn-genetic-opt.readthedocs.io/en/latest/release_notes.html>`__
        for notes on the changes of Sklearn-genetic-opt
        
        Important links
        ###############
        
        - Official source code repo: https://github.com/rodrigo-arenas/Sklearn-genetic-opt/
        - Download releases: https://pypi.org/project/sklearn-genetic-opt/
        - Issue tracker: https://github.com/rodrigo-arenas/Sklearn-genetic-opt/issues
        
        Source code
        ###########
        
        You can check the latest development version with the command::
        
           git clone https://github.com/rodrigo-arenas/Sklearn-genetic-opt.git
        
        Contributing
        ############
        
        Contributions are more than welcome!
        There are lots of opportunities on the on going project, so please get in touch if you would like to help out.
        Also check the `Contribution guide <https://github.com/rodrigo-arenas/Sklearn-genetic-opt/blob/master/CONTRIBUTING.md>`_
        
        Testing
        #######
        
        After installation, you can launch the test suite from outside the source directory::
        
           pytest sklearn_genetic
        
        
        
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.7
Description-Content-Type: text/x-rst
