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\subsubsection{Binary-class Support Vector Machines}
\label{l2svm}

\noindent{\bf Description}

Support Vector Machines are used to model the relationship between a categorical 
dependent variable y and one or more explanatory variables denoted X. This 
implementation learns (and predicts with) a binary class support vector machine 
(y with domain size 2).
\\

\noindent{\bf Usage}

\begin{tabbing}
\texttt{-f} \textit{path}/\texttt{l2-svm.dml -nvargs} 
\=\texttt{X=}\textit{path}/\textit{file} 
  \texttt{Y=}\textit{path}/\textit{file} 
  \texttt{icpt=}\textit{int} 
  \texttt{tol=}\textit{double}\\
\>\texttt{reg=}\textit{double} 
  \texttt{maxiter=}\textit{int} 
  \texttt{model=}\textit{path}/\textit{file}\\
\>\texttt{Log=}\textit{path}/\textit{file}
  \texttt{fmt=}\textit{csv}$\vert$\textit{text}
\end{tabbing}

\begin{tabbing}
\texttt{-f} \textit{path}/\texttt{l2-svm-predict.dml -nvargs} 
\=\texttt{X=}\textit{path}/\textit{file} 
  \texttt{Y=}\textit{path}/\textit{file} 
  \texttt{icpt=}\textit{int} 
  \texttt{model=}\textit{path}/\textit{file}\\
\>\texttt{scores=}\textit{path}/\textit{file}
  \texttt{accuracy=}\textit{path}/\textit{file}\\
\>\texttt{confusion=}\textit{path}/\textit{file}
  \texttt{fmt=}\textit{csv}$\vert$\textit{text}
\end{tabbing}

%%\begin{verbatim}
%%-f path/l2-svm.dml -nvargs X=path/file Y=path/file icpt=int tol=double
%%                      reg=double maxiter=int model=path/file
%%\end{verbatim}

\noindent{\bf Arguments}

\begin{itemize}
\item X: Location (on HDFS) to read the matrix of feature vectors; 
each row constitutes one feature vector.
\item Y: Location to read the one-column matrix of (categorical) 
labels that correspond to feature vectors in X. Binary class labels 
can be expressed in one of two choices: $\pm 1$ or $1/2$. Note that,
this argument is optional for prediction.
\item icpt (default: {\tt 0}): If set to 1 then a constant bias column is 
added to X. 
\item tol (default: {\tt 0.001}): Procedure terminates early if the reduction
in objective function value is less than tolerance times the initial objective
function value.
\item reg (default: {\tt 1}): Regularization constant. See details to find 
out where lambda appears in the objective function. If one were interested 
in drawing an analogy with the C parameter in C-SVM, then C = 2/lambda. 
Usually, cross validation is employed to determine the optimum value of 
lambda.
\item maxiter (default: {\tt 100}): The maximum number of iterations.
\item model: Location (on HDFS) that contains the learnt weights.
\item Log: Location (on HDFS) to collect various metrics (e.g., objective 
function value etc.) that depict progress across iterations while training.
\item fmt (default: {\tt text}): Specifies the output format. Choice of 
comma-separated values (csv) or as a sparse-matrix (text).
\item scores: Location (on HDFS) to store scores for a held-out test set.
Note that, this is an optional argument.
\item accuracy: Location (on HDFS) to store the accuracy computed on a
held-out test set. Note that, this is an optional argument.
\item confusion: Location (on HDFS) to store the confusion matrix
computed using a held-out test set. Note that, this is an optional 
argument.
\end{itemize}

\noindent{\bf Details}

Support vector machines learn a classification function by solving the
following optimization problem ($L_2$-SVM):
\begin{eqnarray*}
&\textrm{argmin}_w& \frac{\lambda}{2} ||w||_2^2 + \sum_i \xi_i^2\\
&\textrm{subject to:}& y_i w^{\top} x_i \geq 1 - \xi_i ~ \forall i
\end{eqnarray*}
where $x_i$ is an example from the training set with its label given by $y_i$, 
$w$ is the vector of parameters and $\lambda$ is the regularization constant 
specified by the user.

To account for the missing bias term, one may augment the data with a column
of constants which is achieved by setting intercept argument to 1 (C-J Hsieh 
et al, 2008).

This implementation optimizes the primal directly (Chapelle, 2007). It uses 
nonlinear conjugate gradient descent to minimize the objective function 
coupled with choosing step-sizes by performing one-dimensional Newton 
minimization in the direction of the gradient.
\\

\noindent{\bf Returns}

The learnt weights produced by l2-svm.dml are populated into a single column matrix 
and written to file on HDFS (see model in section Arguments). The number of rows in 
this matrix is ncol(X) if intercept was set to 0 during invocation and ncol(X) + 1 
otherwise. The bias term, if used, is placed in the last row. Depending on what arguments
are provided during invocation, l2-svm-predict.dml may compute one or more of scores, 
accuracy and confusion matrix in the output format specified. 
\\

%%\noindent{\bf See Also}
%%
%%In case of multi-class classification problems (y with domain size greater than 2), 
%%please consider using a multi-class classifier learning algorithm, e.g., multi-class
%%support vector machines (see Section \ref{msvm}). To model the relationship between 
%%a scalar dependent variable y and one or more explanatory variables X, consider 
%%Linear Regression instead (see Section \ref{linreg-solver} or Section 
%%\ref{linreg-iterative}).
%%\\
%%
\noindent{\bf Examples}

\begin{verbatim}
hadoop jar SystemML.jar -f l2-svm.dml -nvargs X=/user/biadmin/X.mtx 
                                              Y=/user/biadmin/y.mtx 
                                              icpt=0 tol=0.001 fmt=csv
                                              reg=1.0 maxiter=100 
                                              model=/user/biadmin/weights.csv
                                              Log=/user/biadmin/Log.csv
\end{verbatim}

\begin{verbatim}
hadoop jar SystemML.jar -f l2-svm-predict.dml -nvargs X=/user/biadmin/X.mtx 
                                                      Y=/user/biadmin/y.mtx 
                                                      icpt=0 fmt=csv
                                                      model=/user/biadmin/weights.csv
                                                      scores=/user/biadmin/scores.csv
                                                      accuracy=/user/biadmin/accuracy.csv
                                                      confusion=/user/biadmin/confusion.csv
\end{verbatim}

\noindent{\bf References}

\begin{itemize}
\item W. T. Vetterling and B. P. Flannery. \newblock{\em Conjugate Gradient Methods in Multidimensions in 
Numerical Recipes in C - The Art in Scientific Computing}. \newblock W. H. Press and S. A. Teukolsky
(eds.), Cambridge University Press, 1992.
\item J. Nocedal and  S. J. Wright. Numerical Optimization, Springer-Verlag, 1999.
\item C-J Hsieh, K-W Chang, C-J Lin, S. S. Keerthi and S. Sundararajan. \newblock{\em A Dual Coordinate 
Descent Method for Large-scale Linear SVM.} \newblock International Conference of Machine Learning
(ICML), 2008.
\item Olivier Chapelle. \newblock{\em Training a Support Vector Machine in the Primal}. \newblock Neural 
Computation, 2007.
\end{itemize}