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.. _svm:
Support Vector Machine Classifier
=================================
.. note::
Support Vector Machine Classifier is also available with oneAPI interfaces:
- :ref:`alg_svm`
.. toctree::
:glob:
:maxdepth: 4
Support Vector Machine (SVM) is among popular classification
algorithms. It belongs to a family of generalized linear
classification problems. Because SVM covers binary classification
problems only in the multi-class case, SVM must be used in
conjunction with multi-class classifier methods.
SVM is a binary classifier. For a multi-class case, use
Multi-Class Classifier framework of the library.
Details
*******
Given :math:`n` feature vectors :math:`x_1 = (x_{11}, \ldots, x_{1p}), \ldots, x_n = (x_{n1}, \ldots, x_{np})`
of size :math:`p` and a vector
of class labels :math:`y = (y_1, \ldots, y_n)`, where :math:`y_i \in \{-1, 1\}` describes the class to which the feature vector :math:`x_i`
belongs, the problem is to build a two-class Support Vector Machine
(SVM) classifier.
Training Stage
--------------
|short_name| provides two methods to train the SVM model:
- Boser method [Boser92]_ - performance-oriented variant of Boser [Boser92]_ and Platt [Platt98]_ algorithms
- Thunder method [Wen2018]_
The SVM model is trained to solve the quadratic optimization problem
.. math::
\underset{\alpha }{\mathrm{min}}\frac{1}{2}{\alpha }^{T}Q\alpha -{e}^{T}\alpha
with :math:`0 \leq \alpha_i \leq C`, :math:`i = 1, \ldots, n`, :math:`y^T \alpha = 0`,
where :math:`e` is the vector of ones, :math:`C` is the upper bound of the
coordinates of the vector :math:`\alpha`, :math:`Q` is a symmetric matrix of size :math:`n \times n`
with :math:`Q_{ij} = y_i y_j K(x_i, x_j)`, and :math:`K(x,y)` is a kernel function.
Working subset of α updated on each iteration of the algorithm is
based on the Working Set Selection (WSS) 3 scheme [Fan05]_.
The scheme can be optimized using one of these techniques or both:
- **Cache**:
the implementation can allocate a predefined amount of memory
to store intermediate results of the kernel computation.
- **Shrinking**:
the implementation can try to decrease the amount of kernel
related computations (see [Joachims99]_).
The solution of the problem defines the separating hyperplane and
corresponding decision function :math:`D(x)= \sum_{k} {y_k \alpha_k K(x_k, x)} + b` where only those :math:`x_k` that
correspond to non-zero :math:`\alpha_k` appear in the sum, and :math:`b` is a
bias. Each non-zero :math:`\alpha_k` is called a classification
coefficient and the corresponding :math:`x_k` is called a support
vector.
Prediction Stage
----------------
Given the SVM classifier and :math:`r` feature vectors :math:`x_1, \ldots, x_r`, the problem is to calculate the signed value of the
decision function :math:`D(x_i)`, :math:`i=1, \ldots, r`. The sign of the
value defines the class of the feature vector, and the absolute
value of the function is a multiple of the distance between the
feature vector and separating hyperplane.
Usage of Training Alternative
*****************************
To build a Support Vector Machine (SVM) Classifier model using methods of the Model Builder class of SVM Classifier,
complete the following steps:
- Create an SVM Classifier model builder using a constructor with the required number of support vectors and features.
- In any sequence:
- Use the ``setSupportVectors``, ``setClassificationCoefficients``, and ``setSupportIndices`` methods to add
pre-calculated support vectors, classification coefficients, and support indices (optional), respectively, to the model.
For each method specify random access iterators to the first and the last element
of the corresponding set of values [ISO/IEC 14882:2011 § 24.2.7]_.
- Use ``setBias`` to add a bias term to the model.
- Use the ``getModel`` method to get the trained SVM Classifier model.
- Use the ``getStatus`` method to check the status of the model building process.
If ``DAAL_NOTHROW_EXCEPTIONS`` macros is defined, the status report contains the list of errors
that describe the problems API encountered (in case of API runtime failure).
.. note::
If after calling the getModel method you use the ``setBias``, ``setSupportVectors``, ``setClassificationCoefficients``, or ``setSupportIndices``
methods, coefficients, the initial model will be automatically updated with the new set of parameters.
Examples
--------
.. tabs::
.. tab:: C++ (CPU)
- :cpp_example:`svm_two_class_model_builder.cpp `
.. tab:: Python*
- :daal4py_example:`svm_two_class_model_builder.py`
Batch Processing
****************
SVM classifier follows the general workflow described in
:ref:`classification_usage_model`.
Training
--------
For a description of the input and output, refer to Usage Model:
Training and Prediction.
At the training stage, SVM classifier has the following parameters:
.. tabularcolumns:: |\Y{0.2}|\Y{0.2}|\Y{0.6}|
.. list-table:: Training Parameters for Support Vector Machine Classifier (Batch Processing)
:header-rows: 1
:widths: 20 30 60
:align: left
:class: longtable
* - Parameter
- Default Value
- Description
* - ``algorithmFPType``
- ``float``
- The floating-point type that the algorithm uses for intermediate computations. Can be ``float`` or ``double``.
* - ``method``
- ``defaultDense``
- The computation method used by the SVM classifier. Available methods for the training stage:
For CPU:
- ``defaultDense`` – Boser method [Boser92]_
- ``thunder`` - Thunder method [Wen2018]_
For GPU:
- ``thunder`` - Thunder method [Wen2018]_
* - ``nClasses``
- :math:`2`
- The number of classes.
* - ``C``
- :math:`1.0`
- The upper bound in conditions of the quadratic optimization problem.
* - ``accuracyThreshold``
- :math:`0.001`
- The training accuracy.
* - ``tau``
- :math:`1.0e-6`
- Tau parameter of the WSS scheme.
* - ``maxIterations``
- :math:`1000000`
- Maximal number of iterations for the algorithm.
* - ``cacheSize``
- :math:`8000000`
- The size of cache in bytes for storing values of the kernel matrix.
A non-zero value enables use of a cache optimization technique.
* - ``doShrinking``
- ``true``
- A flag that enables use of a shrinking optimization technique.
.. note:: This parameter is only supported for ``defaultDense`` method.
* - ``kernel``
- Pointer to an object of the KernelIface class
- The kernel function. By default, the algorithm uses a linear kernel.
Prediction
----------
For a description of the input and output, refer to Usage Model:
Training and Prediction.
At the prediction stage, SVM classifier has the following parameters:
.. tabularcolumns:: |\Y{0.2}|\Y{0.2}|\Y{0.6}|
.. list-table:: Prediction Parameters for Support Vector Machine Classifier (Batch Processing)
:header-rows: 1
:widths: 20 30 60
:align: left
:class: longtable
* - Parameter
- Default Value
- Description
* - ``algorithmFPType``
- ``float``
- The floating-point type that the algorithm uses for intermediate computations. Can be ``float`` or ``double``.
* - ``method``
- ``defaultDense``
- Performance-oriented computation method, the only prediction method supported by the algorithm.
* - ``nClasses``
- :math:`2`
- The number of classes.
* - ``kernel``
- Pointer to object of the ``KernelIface`` class
- The kernel function. By default, the algorithm uses a linear kernel.
Examples
--------
.. tabs::
.. tab:: oneAPI DPC++
Batch Processing:
- :ref:`dpc_svm_two_class_thunder_dense_batch.cpp`
.. tab:: oneAPI C++
Batch Processing:
- :ref:`cpp_svm_two_class_smo_dense_batch.cpp`
- :ref:`cpp_svm_two_class_thunder_dense_batch.cpp`
.. tab:: C++ (CPU)
Batch Processing:
- :cpp_example:`svm_two_class_boser_dense_batch.cpp `
- :cpp_example:`svm_two_class_boser_csr_batch.cpp `
- :cpp_example:`svm_two_class_thunder_dense_batch.cpp `
- :cpp_example:`svm_two_class_thunder_csr_batch.cpp `
.. tab:: Python*
Batch Processing:
- :daal4py_example:`svm.py`
Performance Considerations
**************************
For the best performance of the SVM classifier, use homogeneous
numeric tables if your input data set is homogeneous or SOA numeric
tables otherwise.
Performance of the SVM algorithm greatly depends on the cache size
cacheSize. Larger cache size typically results in greater
performance. For the best SVM algorithm performance, use cacheSize
equal to :math:`n^2 \cdot \text{sizeof(algorithmFPType)}`. However, avoid
setting the cache size to a larger value than the number of bytes
required to store :math:`n^2` data elements because the algorithm
does not fully utilize the cache in this case.
.. include:: ../../../opt-notice.rst