rxOneClassSvm: OneClass SVM
Machine Learning One Class Support Vector Machines
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(), ...)
The formula as described in rxFormula. Interaction terms and
F() are not currently supported in the MicrosoftML.
A data source object or a character string specifying a .xdf file or a data frame object.
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.
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. It's parameter represents
gammain the term
exp(-gamma|x-y|^2. If not specified, it defaults to
1divided 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
degin the term
(a*<x,y> + bias)^deg. The
bias, defaults to
0. The degree,
deg, defaults to
ais not specified, it is set to
1divided by the number of features. For example,
maKernelPoynomial(bias = 0, deg = `` 3).
sigmoidKernel(): Sigmoid kernel with parameter names
coef0in the term
tanh(gamma*<x,y> + coef0).
gamma, defaults to to
1divided by the number of features. The parameter
0. For example,
sigmoidKernel(gamma = .1, coef0 = 0).
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.
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.
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
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
MaxMinnormalizer is used. It normalizes values in an interval [a, b] where
-1 <= a <= 0and
0 <= b <= 1and
b - a = 1. This normalizer preserves sparsity by mapping zero to zero.
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
Specifies a character vector of variable names to be used in
NULL if none are to be used. The default value is
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
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
transformFunc). As with all expressions,
rowSelection can be defined outside of the function call using the expression function.
An expression of the form
list(name = expression, ``...) that represents the first round of variable transformations. As with all expressions,
rowSelection) can be defined outside of the function call using the expression function.
A named list that contains objects that can be referenced by
The variable transformation function. See rxTransform for details.
A character vector of input data set variables needed for the transformation function. See rxTransform for details.
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 exmple, those explicitly defined in RevoScaleR functions via their
transformFunc arguments or those defined implicitly via their
rowSelection arguments. The
transformPackages argument may also be
NULL, indicating that no packages outside
rxGetOption("transformPackages") are preloaded.
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.
Specifies the number of blocks to read for each chunk of data read from the data source.
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.
An integer value that specifies the amount of output wanted. If
0, no verbose output is printed during calculations. Integer values from
4 provide increasing amounts of information.
Sets the context in which computations are executed, specified with a valid RxComputeContext. Currently local and RxInSqlServer compute contexts are supported.
Control parameters for ensembling.
Additional arguments to be passed directly to the Microsoft Compute Engine.
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.
rxOneClassSvmobject with the trained model.
OneClassSvm: A learner specification object of class
mamlfor the OneClass Svm trainer.
This algorithm is single-threaded and will always attempt to load the entire dataset into memory.
Microsoft Technical Support
# 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)