civisml-extensions

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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:

.. code-block:: python

    >>> from sklearn.linear_model import LogisticRegression
    >>> from sklearn.ensemble import RandomForestClassifier
    >>> from civismlext.stacking import StackedClassifier
    >>> 
    >>> # Define some Train data and labels
    >>> Xtrain, ytrain = <train_features>, <train_labels>
    >>> 
    >>> # 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)

You can learn more about stacking and see an example use of the StackedRegressor and NonNegativeLinearRegression estimators in a talk presented at PyData NYC_ in November, 2017.

See the doc strings of the various estimators for more information.

Contributing

Please see CONTRIBUTING.md for information about contributing to this project.

License

BSD-3

See LICENSE.md for details.

.. _scikit-learn: http://scikit-learn.org/ .. _pandas: http://pandas.pydata.org/ .. _Hyperband: https://arxiv.org/abs/1603.06560 .. _a talk presented at PyData NYC: https://www.youtube.com/watch?v=3gpf1lGwecA