Metadata-Version: 1.1
Name: ml_insights
Version: 1.0.0
Summary: Package to calibrate and understand ML Models
Home-page: http://ml-insights.readthedocs.io/en/latest/
Author: Brian Lucena / Ramesh Sampath
Author-email: brianlucena@gmail.com
License: MIT license
Description: ML Insights
        ===========
        
        Welcome to ML-Insights!
        
        This package contains two core sets of functions:
        
        1) Calibration
        2) Interpreting Models
        
        For probability calibration, the main class is `SplineCalib`.  Given a set of model outputs and the "true" classes, you can `fit` a SplineCalib object.  That object can then be used to `calibrate` future model predictions post-hoc.
        
        .. code-block:: python
        
            >>> model.fit(X_train, y_train)
            >>> sc = mli.SplineCalib()
            >>> sc.fit(X_valid, y_valid)
            >>> uncalib_preds = model.predict_proba(X_test)
            >>> calib_preds = sc.calibrate(uncalib_preds)
        
        
        .. code-block:: python
        
            >>> cv_preds = mli.cv_predictions(model, X_train, y_train)
            >>> model.fit(X_train, y_train)
            >>> sc = mli.SplineCalib()
            >>> sc.fit(cv_preds, y_train)
            >>> uncalib_preds = model.predict_proba(X_test)
            >>> calib_preds = sc.calibrate(uncalib_preds)
        
        
        
        For model interpretability, we provide the `ice_plot` and `histogram_pair` functions as well as other tools.
        
        
        .. code-block:: python
        
            >>> rd = mli.get_range_dict(X_train)
            >>> mli.ice_plot(model, X_test.sample(3), X_train.columns, rd)
        
        .. code-block:: python
        
            >>> mli.histogram_pair(df.outcome, df.feature, bins=np.linspace(0,100,11))
        
        Please see the documentation and examples at the links below.
        
        
        - `Documentation <https://ml-insights.readthedocs.io>`_
        - `Notebook Examples and Usage <https://github.com/numeristical/introspective/tree/master/examples>`_
        
        
        Python
        ------
        Python 3.4+
        
        
        Disclaimer
        ==========
        
        We have tested this tool to the best of our ability, but understand that it may have bugs.  It was most recently developed on Python 3.7.3.  Use at your own risk, but feel free to report any bugs to our github. <https://github.com/numeristical/introspective>
        
        
        Installation
        =============
        
        .. code-block:: bash
        
            $ pip install ml_insights
        
        
        Usage
        ======
        
        .. code-block:: python
        
            >>> import ml_insights as mli
            >>> xray = mli.ModelXRay(model, data)
        
        .. code-block:: python
        
        	>>> rfm = RandomForestClassifier(n_estimators = 500, class_weight='balanced_subsample')
        	>>> rfm_cv = mli.SplineCalibratedClassifierCV(rfm)
        	>>> rfm_cv.fit(X_train,y_train)
        	>>> test_res_calib_cv = rfm_cv.predict_proba(X_test)[:,1]
        	>>> log_loss(y_test,test_res_calib_cv)
        
        Source
        ======
        
        Find the latest version on github: https://github.com/numeristical/introspective
        
        Feel free to fork and contribute!
        
        License
        =======
        
        Free software: `MIT license <LICENSE>`_
        
        Developed By
        ============
        
        - Brian Lucena
        - Ramesh Sampath
        
        References
        ==========
        
        Alex Goldstein, Adam Kapelner, Justin Bleich, and Emil Pitkin. 2014. Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation. Journal of Computational and Graphical Statistics (March 2014)
Keywords: ml_insights
Platform: UNKNOWN
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
