rxOneClassSvm: OneClass SVM

Artikkel
02/28/2023

Machine Learning One Class Support Vector Machines

Usage

  rxOneClassSvm(formula = NULL, data, cacheSize = 100, kernel = rbfKernel(),
    epsilon = 0.001, nu = 0.1, shrink = TRUE, normalize = "auto",
    mlTransforms = NULL, mlTransformVars = NULL, rowSelection = NULL,
    transforms = NULL, transformObjects = NULL, transformFunc = NULL,
    transformVars = NULL, transformPackages = NULL, transformEnvir = NULL,
    blocksPerRead = rxGetOption("blocksPerRead"),
    reportProgress = rxGetOption("reportProgress"), verbose = 1,
    computeContext = rxGetOption("computeContext"),
    ensemble = ensembleControl(), ...)

Arguments

`formula`

The formula as described in rxFormula. Interaction terms and F() are not currently supported in the MicrosoftML.

`data`

A data source object or a character string specifying a .xdf file or a data frame object.

`cacheSize`

The maximal size in MB of the cache that stores the training data. Increase this for large training sets. The default value is 100 MB.

`kernel`

A character string representing the kernel used for computing inner products. For more information, see maKernel. The following choices are available:

rbfKernel(): Radial basis function kernel. Its parameter representsgamma in the term exp(-gamma|x-y|^2. If not specified, it defaults to 1 divided by the number of features used. For example, rbfKernel(gamma = .1). This is the default value.
linearKernel(): Linear kernel.
polynomialKernel(): Polynomial kernel with parameter names a, bias, and deg in the term (a*<x,y> + bias)^deg. The bias, defaults to 0. The degree, deg, defaults to 3. If a is not specified, it is set to 1 divided by the number of features. For example, maKernelPoynomial(bias = 0, deg = `` 3).
sigmoidKernel(): Sigmoid kernel with parameter names gamma and coef0 in the term tanh(gamma*<x,y> + coef0). gamma, defaults to 1 divided by the number of features. The parameter coef0 defaults to 0. For example, sigmoidKernel(gamma = .1, coef0 = 0).

`epsilon`

The threshold for optimizer convergence. If the improvement between iterations is less than the threshold, the algorithm stops and returns the current model. The value must be greater than or equal to .Machine$double.eps. The default value is 0.001.

`nu`

The trade-off between the fraction of outliers and the number of support vectors (represented by the Greek letter nu). Must be between 0 and 1, typically between 0.1 and 0.5. The default value is 0.1.

`shrink`

Uses the shrinking heuristic if TRUE. In this case, some samples will be "shrunk" during the training procedure, which may speed up training. The default value is TRUE.

`normalize`

Specifies the type of automatic normalization used:

"auto": if normalization is needed, it is performed automatically. This is the default choice.
"no": no normalization is performed.
"yes": normalization is performed.
"warn": if normalization is needed, a warning message is displayed, but normalization is not performed.
Normalization rescales disparate data ranges to a standard scale. Feature scaling insures the distances between data points are proportional and enables various optimization methods such as gradient descent to converge much faster. If normalization is performed, a MaxMin normalizer is used. It normalizes values in an interval [a, b] where -1 <= a <= 0and 0 <= b <= 1 and b - a = 1. This normalizer preserves sparsity by mapping zero to zero.

`mlTransforms`

Specifies a list of MicrosoftML transforms to be performed on the data before training or NULL if no transforms are to be performed. See featurizeText, categorical, and categoricalHash, for transformations that are supported. These transformations are performed after any specified R transformations. The default value is NULL.

`mlTransformVars`

Specifies a character vector of variable names to be used in mlTransforms or NULL if none are to be used. The default value is NULL.

`rowSelection`

Specifies the rows (observations) from the data set that are to be used by the model with the name of a logical variable from the data set (in quotes) or with a logical expression using variables in the data set. For example, rowSelection = "old" will only use observations in which the value of the variable old is TRUE. rowSelection = (age > 20) & (age < 65) & (log(income) > 10) only uses observations in which the value of the age variable is between 20 and 65 and the value of the log of the income variable is greater than 10. The row selection is performed after processing any data transformations (see the arguments transforms or transformFunc). As with all expressions, rowSelection can be defined outside of the function call using the expression function.

`transforms`

An expression of the form list(name = expression, ``...) that represents the first round of variable transformations. As with all expressions, transforms (or rowSelection) can be defined outside of the function call using the expression function.

`transformObjects`

A named list that contains objects that can be referenced by transforms, transformsFunc, and rowSelection.

`transformFunc`

The variable transformation function. See rxTransform for details.

`transformVars`

A character vector of input data set variables needed for the transformation function. See rxTransform for details.

`transformPackages`

A character vector specifying additional R packages (outside of those specified in rxGetOption("transformPackages")) to be made available and preloaded for use in variable transformation functions. For example, those explicitly defined in RevoScaleR functions via their transforms and transformFunc arguments or those defined implicitly via their formula or rowSelection arguments. The transformPackages argument may also be NULL, indicating that no packages outside rxGetOption("transformPackages") are preloaded.

`transformEnvir`

A user-defined environment to serve as a parent to all environments developed internally and used for variable data transformation. If transformEnvir = NULL, a new "hash" environment with parent baseenv() is used instead.

`blocksPerRead`

Specifies the number of blocks to read for each chunk of data read from the data source.

`reportProgress`

An integer value that specifies the level of reporting on the row processing progress:

0: no progress is reported.
1: the number of processed rows is printed and updated.
2: rows processed and timings are reported.
3: rows processed and all timings are reported.

`verbose`

An integer value that specifies the amount of output wanted. If 0, no verbose output is printed during calculations. Integer values from 1 to 4 provide increasing amounts of information.

`computeContext`

Sets the context in which computations are executed, specified with a valid RxComputeContext. Currently local and RxInSqlServer compute contexts are supported.

`ensemble`

Control parameters for ensembling.

`...`

Additional arguments to be passed directly to the Microsoft Compute Engine.

Details

detection is to identify outliers that do not belong to some target class. This type of SVM is one-class because the training set contains only examples from the target class. It infers what properties are normal for the objects in the target class and from these properties predicts which examples are unlike the normal examples. This is useful for anomaly detection because the scarcity of training examples is the defining character of anomalies: typically there are very few examples of network intrusion, fraud, or other types of anomalous behavior.

Value

rxOneClassSvm: A rxOneClassSvm object with the trained model.

OneClassSvm: A learner specification object of class maml for the OneClass Svm trainer.

Notes

This algorithm is single-threaded and will always attempt to load the entire dataset into memory.

Author(s)

Microsoft Corporation Microsoft Technical Support

References

Anomaly detection

Azure Machine Learning Studio (classic): One-Class Support Vector Machine

Support of a High-Dimensional Distribution

Support Vector Algorithms

for Support Vector Machines

Examples


 # Estimate a One-Class SVM model
 trainRows <- c(1:30, 51:80, 101:130)
 testRows = !(1:150 %in% trainRows)
 trainIris <- iris[trainRows,]
 testIris <- iris[testRows,]

 svmModel <- rxOneClassSvm(
     formula = ~Sepal.Length + Sepal.Width + Petal.Length + Petal.Width,
     data = trainIris)

 # Add additional non-iris data to the test data set
 testIris$isIris <- 1
 notIris <- data.frame(
     Sepal.Length = c(2.5, 2.6),
     Sepal.Width = c(.75, .9),
     Petal.Length = c(2.5, 2.5),
     Petal.Width = c(.8, .7),
     Species = c("not iris", "not iris"),
     isIris = 0)
 testIris <- rbind(testIris, notIris)  

 scoreDF <- rxPredict(svmModel, 
      data = testIris, extraVarsToWrite = "isIris")

 # Look at the last few observations
 tail(scoreDF)
 # Look at average scores conditioned by 'isIris'
 rxCube(Score ~ F(isIris), data = scoreDF)

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