Module datarush.analytics
Package com.pervasive.datarush.analytics.svm.learner

Class SVMLearner

    • Constructor Detail

      • SVMLearner

        public SVMLearner()
        Learns an SVM model from a training dataset.

    • Method Detail

      • getModel

        public PMMLPort getModel()
        Returns a dataflow variable that will contain the PMML model.
        Returns:
        a dataflow variable that will contain the PMML model.

      • setSilent

        public static void setSilent​(boolean quiet)
        Our default SVM library will send regular status reports to System.out. However, it has a static property that can suppress console output. If console output is desired, setSilent(false) This method has side effects in the static variables of libsvm.svm
        Parameters:
        quiet - true if this SVMLearner will suppress libSVM's console output; false if it shouldn't.

      • isSilent

        public static boolean isSilent()
        Our default SVM library will send regular status reports to System.out. However, it has a static property that can suppress console output. By default, we suppress it, but it may be switched on at runtime.
        Returns:
        whether the SVM trainer's console output is suppressed.

      • getIncludedColumns

        public List<String> getIncludedColumns()
        Returns the list of columns to include for the purpose of building the model. An empty list means all columns of type double.
        Returns:
        The list of columns to include

      • setIncludedColumns

        public void setIncludedColumns​(List<String> includedColumns)
        Sets the list of columns to include. An empty list means all columns of type double.
        Parameters:
        includedColumns - The list of columns to include

      • getKernelType

        public final KernelType getKernelType()
        Returns the kernel and associated parameters to use.
        Returns:
        the kernel and associated parameters to use.

      • setKernelType

        public final void setKernelType​(KernelType kernelType)
        Sets the kernel and associated parameters to use.
        Parameters:
        kernelType - the kernel and associated parameters to use.

      • getEpsilon

        public double getEpsilon()
        Returns the tolerance for termination criteria. Defaults to 0.001. Larger values will terminate early but provide less precise results. Directly maps to the LIBSVM "-e" command line flag.
        Returns:
        the tolerance for termination criteria

      • setEpsilon

        public void setEpsilon​(double epsilon)
        Sets the tolerance for termination criteria. Defaults to 0.001. Larger values will terminate early but provide less precise results. Directly maps to the LIBSVM "-e" command line flag.
        Parameters:
        epsilon - the tolerance for termination criteria

      • getType

        public SVMType getType()
        Returns the type of SVM model to build. Defaults to SVMTypeOneClass.
        Returns:
        the type of SVM model to build.

      • setType

        public void setType​(SVMType type)
        Sets the type of SVM model to build. Defaults to SVMTypeOneClass.
        Parameters:
        type - the type of SVM model to build.

      • getSvmCacheSizeMB

        public double getSvmCacheSizeMB()
        Returns the cache size. Directly maps to the LIBSVM "-m" command line flag.
        Returns:
        the cache size.

      • setSvmCacheSizeMB

        public void setSvmCacheSizeMB​(double svmCacheSizeMB)
        Sets the cache size. Directly maps to the LIBSVM "-m" command line flag.
        Parameters:
        svmCacheSizeMB - the cache size.

      • computeMetadata

        protected void computeMetadata​(StreamingMetadataContext ctx)
        Description copied from class: StreamingOperator
        Implementations must adhere to the following contracts

        General

        Regardless of input ports/output port types, all implementations must do the following:

        1. Validation. Validation of configuration should always be performed first.
        2. Declare parallelizability.. Implementations must declare parallelizability by calling StreamingMetadataContext.parallelize(ParallelismStrategy).

        Input record ports

        Implementations with input record ports must declare the following:
        1. Required data ordering:
          2. Implementations that have data ordering requirements must declare them by calling RecordPort#setRequiredDataOrdering, otherwise data may arrive in any order.
        2. Required data distribution (only applies to parallelizable operators):
          3. Implementations that have data distribution requirements must declare them by calling RecordPort#setRequiredDataDistribution, otherwise data will arrive in an unspecified partial distribution.
        Note that if the upstream operator's output distribution/ordering is compatible with those required, we avoid a re-sort/re-distribution which is generally a very large savings from a performance standpoint. In addition, some operators may chose to query the upstream output distribution/ordering by calling RecordPort#getSourceDataDistribution and RecordPort#getSourceDataOrdering. These should be viewed as a hints to help chose a more efficient algorithm. In such cases, though, operators must still declare data ordering and data distribution requirements; otherwise there is no guarantee that data will arrive sorted/distributed as required.

        Output record ports

        Implementations with output record ports must declare the following:
        1. Type: Implementations must declare their output type by calling RecordPort#setType.
        Implementations with output record ports may declare the following:
        1. Output data ordering: Implementations that can make guarantees as to their output ordering may do so by calling RecordPort#setOutputDataOrdering
        2. Output data distribution (only applies to parallelizable operators): Implementations that can make guarantees as to their output distribution may do so by calling RecordPort#setOutputDataDistribution
        Note that both of these properties are optional; if unspecified, performance may suffer since the framework may unnecessarily re-sort/re-distributed the data.

        Input model ports

        In general, there is nothing special to declare for input model ports. Models are implicitly duplicated to all partitions when going from non-parallel to parallel operators. The case of a model going from a parallel to a non-parallel node is a special case of a "model reducer" operator. In the case of a model reducer, the downstream operator, must declare the following:
        1. Merge handler: Model reducers must declare a merge handler by calling AbstractModelPort#setMergeHandler.
        Note that MergeModel is a convenient, re-usable model reducer, parameterized with a merge-handler.

        Output model ports

        SimpleModelPort's have no associated metadata and therefore there is never any output metadata to declare. PMMLPort's, on the other hand, do have associated metadata. For all PMMLPorts, implementations must declare the following:
        1. pmmlModelSpec: Implementations must declare the PMML model spec by calling PMMLPort.setPMMLModelSpec.
        Specified by:
        computeMetadata in class StreamingOperator
        Parameters:
        ctx - the context

      • execute

        protected void execute​(ExecutionContext ctx)
        Description copied from class: ExecutableOperator
        Executes the operator. Implementations should adhere to the following contracts:
        1. Following execution, all input ports must be at end-of-data.
        2. Following execution, all output ports must be at end-of-data.
        Specified by:
        execute in class ExecutableOperator
        Parameters:
        ctx - context in which to lookup physical ports bound to logical ports