Metadata-Version: 2.1
Name: fast-automl
Version: 0.0.1
Summary: ML for reasonable results in a reasonable amount of time
Home-page: https://dsbowen.github.io/fast-automl
Author: Dillon Bowen
Author-email: dsbowen@wharton.upenn.edu
License: UNKNOWN
Description: # Fast AutoML
        
        Most autoML packages aim for exceptional performance but need to train for an exceptional amount of time. Fast-autoML aims for reasonable performance in a reasonable amount of time.
        
        Fast-autoML includes additional utilities, such as tools for comparing model performance by repeated cross-validation.
        
        ## Installation
        
        ```
        $ pip install fast-automl
        ```
        
        ## Quickstart
        
        ```python
        from fast_automl.automl import AutoClassifier
        
        from sklearn.datasets import load_digits
        from sklearn.model_selection import cross_val_score, train_test_split
        
        X, y = load_digits(return_X_y=True)
        X_train, X_test, y_train, y_test = train_test_split(X, y, shuffle=True, stratify=y)
        
        clf = AutoClassifier(ensemble_method='stepwise', n_jobs=-1, verbose=True).fit(X_train, y_train)
        print('CV score: {:.4f}'.format(cross_val_score(clf.best_estimator_, X_train, y_train).mean()))
        print('Test score: {:.4f}'.format(clf.score(X_test, y_test)))
        ```
        
        This runs for about 6-7 minutes and typically achieves a test accuracy of 
        96-99% and ROC AUC above .999.
        
        ```python
        from fast_automl.automl import AutoRegressor
        
        from sklearn.datasets import load_diabetes
        from sklearn.model_selection import cross_val_score, train_test_split
        
        X, y = load_diabetes(return_X_y=True)
        X_train, X_test, y_train, y_test = train_test_split(X, y, shuffle=True)
        
        reg = AutoRegressor(n_jobs=-1, verbose=True).fit(X_train, y_train)
        print('CV score: {:.4f}'.format(cross_val_score(reg.best_estimator_, X_train, y_train).mean()))
        print('Test score: {:.4f}'.format(reg.score(X_test, y_test)))
        ```
        
        This runs for about 30 seconds and typically achieves a test R-squared of 
        .47-.53.
        
        ## Citation
        
        ```
        @software{bowen2021fast-automl,
          author = {Dillon Bowen},
          title = {Fast-AutoML},
          url = {https://dsbowen.github.io/fast-automl/},
          date = {2021-02-05},
        }
        ```
        
        ## License
        
        Users must cite this package in any publications which use it.
        
        It is licensed with the MIT [License](https://github.com/dsbowen/fast-automl/blob/master/LICENSE).
        
        ## Acknowledgments
        
        This package and its documentation draw heavily on [scikit-learn](https://scikit-learn.org/stable/).
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
