:py:mod:`sklearn_pmml_model.linear_model.base`
==============================================

.. py:module:: sklearn_pmml_model.linear_model.base


Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   sklearn_pmml_model.linear_model.base.PMMLGeneralizedLinearRegressor
   sklearn_pmml_model.linear_model.base.PMMLGeneralizedLinearClassifier



Functions
~~~~~~~~~

.. autoapisummary::

   sklearn_pmml_model.linear_model.base._get_coefficients
   sklearn_pmml_model.linear_model.base._get_intercept



.. py:class:: PMMLGeneralizedLinearRegressor(pmml)


   Bases: :py:obj:`sklearn_pmml_model.base.OneHotEncodingMixin`, :py:obj:`sklearn_pmml_model.base.PMMLBaseRegressor`

   
   Abstract class for Generalized Linear Models (GLMs).

   The PMML model consists out of a <GeneralRegressionModel> element,
   containing a <ParamMatrix> element that contains zero or more <PCell>
   elements describing the coefficients for each parameter. Parameters
   are described in the <PPMatrix> element, that maps parameters to fields in
   the data.

   :Parameters:

       **pmml** : str, object
           Filename or file object containing PMML data.









   .. rubric:: Notes

   Specification: http://dmg.org/pmml/v4-3/GeneralRegression.html





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.. py:class:: PMMLGeneralizedLinearClassifier(pmml)


   Bases: :py:obj:`sklearn_pmml_model.base.OneHotEncodingMixin`, :py:obj:`sklearn_pmml_model.base.PMMLBaseClassifier`

   
   Abstract class for Generalized Linear Models (GLMs).

   The PMML model consists out of a <GeneralRegressionModel> element,
   containing a <ParamMatrix> element that contains zero or more <PCell>
   elements describing the coefficients for each parameter. Parameters
   are described in the <PPMatrix> element, that maps parameters to fields in
   the data.

   :Parameters:

       **pmml** : str, object
           Filename or file object containing PMML data.









   .. rubric:: Notes

   Specification: http://dmg.org/pmml/v4-3/GeneralRegression.html





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.. py:function:: _get_coefficients(linear_model, model)

   
   Obtain the coefficients for the GLM regression.

   Raises an exception when we notice non linear parameter configurations.

   :Parameters:

       **linear_model** : PMMLGeneralizedLinearRegressor, PMMLGeneralizedLinearClassifier
           The PMML class representing the classifier. Should contain at least target_field,
           fields and field_mapping properties.

       **model** : eTree.Element
           The <GeneralRegressionModel> element that is assumed to contains a
           <PPMatrix> and <ParamMatrix> element.

   :Returns:

       coefficients: numpy.ndarray
           Coefficient value for every field. Zero if not present.













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.. py:function:: _get_intercept(model)

   
   Find all parameters that are not included in the <ParamMatrix>.

   These constitute the intercept. In the very unlikely case there are multiple
   parameters fitting this criteria, we sum the result.

   :Parameters:

       **model** : eTree.Element
           The <GeneralRegressionModel> element that is assumed to contains a
           <PPMatrix> and <ParamMatrix> element.

   :Returns:

       **intercept** : float
           Value of the intercept of the method.













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