Various scikit-learn extensions
Project description
scikit-ext : various scikit-learn extensions
About
The scikit_ext package contains various scikit-learn extensions, built entirely on top of sklearn base classes. The package is separated into two modules, estimators and scorers.
Estimators
MultiGridSearchCV: Extension to native sklearn GridSearchCV for multiple estimators and param_grids. Accepts a list of estimators and param_grids, iterating through each fitting a GridSearchCV model for each estimator/param_grid. Chooses the best fitted GridSearchCV model. Inherits sklearn’s BaseSearchCV class, so attributes and methods are all similar to GridSearchCV. IterRandomEstimator: Meta-Estimator intended primarily for unsupervised estimators whose fitted model can be heavily dependent on an arbitrary random initialization state. It is best used for problems where a fit_predict method is intended, so the only data used for prediction will be the same data on which the model was fitted. OptimizedEnsemble: An optimized ensemble class. Will find the optimal n_estimators parameter for the given ensemble estimator, according to the specified input parameters. OneVsRestAdjClassifier: One-Vs-Rest multiclass strategy. The adjusted version is a custom extension which overwrites the inherited predict_proba method with a more flexible method allowing custom normalization for the predicted probabilities. Any norm argument that can be passed directly to sklearn.preprocessing.normalize is allowed. Additionally, norm=None will skip the normalization step alltogeter. To mimick the inherited OneVsRestClassfier behavior, set norm=’l2’. All other methods are inherited from OneVsRestClassifier. Scorers
_TimeScorer: Score using estimated prediction latency of estimator. _MemoryScorer: Score using estimated memory of pickled estimator object. _CombinedScorer: Score combining multiple scorers by averaging their scores. cluster_distribution_score: Scoring function which scores the resulting cluster distribution accross classes. A more even distribution indicates a higher score. Authors
Evan Harris
License
This project is licensed under the MIT License - see the LICENSE file for details
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