scikit-learn-compatible estimators from Civis Analytics
Project description
scikit-learn-compatible estimators from Civis Analytics
Installation
Installation with pip is recommended:
$ pip install civisml-extensions
For development, a few additional dependencies are needed:
$ pip install -r dev-requirements.txt
Contents and Usage
This package contains scikit-learn-compatible estimators for stacking ( StackedClassifier, StackedRegressor), non-negative linear regression ( NonNegativeLinearRegression), preprocessing pandas DataFrames ( DataFrameETL), and using Hyperband for cross-validating hyperparameters ( HyperbandSearchCV).
Usage of these estimators follows the standard sklearn conventions. Here is an example of using the StackedClassifier:
>>> from sklearn.linear_model import LogisticRegression >>> from sklearn.ensemble import RandomForestClassifier >>> from civismlext.stacking import StackedClassifier >>> # Note that the final estimator 'metalr' is the meta-estimator >>> estlist = [('rf', RandomForestClassifier()), >>> ('lr', LogisticRegression()), >>> ('metalr', LogisticRegression())] >>> mysm = StackedClassifier(estlist) >>> # Set some parameters, if you didn't set them at instantiation >>> mysm.set_params(rf__random_state=7, lr__random_state=8, >>> metalr__random_state=9, metalr__C=10**7) >>> # Fit >>> mysm.fit(Xtrain, ytrain) >>> # Predict! >>> ypred = mysm.predict_proba(Xtest)
See the doc strings of the various estimators for more information.
Contributing
See CONTIBUTING.md for information about contributing to this project.
License
BSD-3
See LICENSE.md for details.
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