sklearn_pmml_model.svm._base
============================

.. py:module:: sklearn_pmml_model.svm._base


Classes
-------

.. autoapisummary::

   sklearn_pmml_model.svm._base.PMMLBaseSVM


Functions
---------

.. autoapisummary::

   sklearn_pmml_model.svm._base.get_vectors
   sklearn_pmml_model.svm._base.get_alt_svms
   sklearn_pmml_model.svm._base.get_overlapping_vectors
   sklearn_pmml_model.svm._base.get_coefficients


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

.. py:class:: PMMLBaseSVM

   
   Abstract class for Support Vector Machines.

   The PMML model consists out of a <SupportVectorMachineModel> element,
   containing a <SupportVectorMachine> element that contains a <SupportVectors>
   element describing support vectors, and a <Coefficients> element describing
   the coefficients for each support vector. Support vectors are referenced from
   a <VectorDictionary> element, in which the true support vectors are described
   using <VectorInstance> elements. Furthermore, the model contains one out of
   <LinearKernelType>, <PolynomialKernelType>, <RadialBasisKernelType> or
   <SigmoidKernelType> describing the kernel function used.

   :Parameters:

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











   .. rubric:: Notes

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



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   .. py:attribute:: shape_fit_


   .. py:attribute:: support_


   .. py:attribute:: _n_support


   .. py:attribute:: support_vectors_


   .. py:attribute:: _probA


   .. py:attribute:: _probB


.. py:function:: get_vectors(vector_dictionary, s)

   
   Return support vector values, parsed as a numpy array.
















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.. py:function:: get_alt_svms(svms, classes, target_class)

   
   Find alternative SVMs (e.g., for target class 0, find the svms classifying 0 against 1, and 0 against 2).


   :Parameters:

       **svms** : list
           List of eTree.Element objects describing the different one-to-one support vector machines in the PMML.

       **classes** : numpy.array
           The classes to be predicted by the model.

       **target_class** : str
           The target class.



   :Returns:

       **alt_svms** : list
           List of eTree.Elements filtered to only include SVMs comparing the target class against alternate classes.











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.. py:function:: get_overlapping_vectors(svms)

   
   Return support vector ids that are present in all provided SVM elements.


   :Parameters:

       **svms** : list
           List of eTree.Element objects describing the different one-to-one support vector machines in the PMML.



   :Returns:

       **output** : set
           Set containing all integer vector ids that are present in all provided SVM elements.











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.. py:function:: get_coefficients(classes, n_support, support_ids, svms)

   
   Return support vector coefficients.


   :Parameters:

       **classes** : numpy.array
           The classes to be predicted by the model.

       **n_support** : numpy.array
           Numpy array describing the number of support vectors for each class.

       **support_ids: list**
           A list describing the ids of all support vectors in the model.

       **svms** : list
           List of eTree.Element objects describing the different one-to-one support vector machines in the PMML.














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