microsoftml.rx_oneclass_svm: Anomaly Detection


microsoftml.rx_oneclass_svm(formula: str,
    data: [revoscalepy.datasource.RxDataSource.RxDataSource,
    pandas.core.frame.DataFrame], cache_size: float = 100,
    kernel: [<function linear_kernel at 0x0000007156EAC8C8>,
    <function polynomial_kernel at 0x0000007156EAC950>,
    <function rbf_kernel at 0x0000007156EAC7B8>,
    <function sigmoid_kernel at 0x0000007156EACA60>] = {'Name': 'RbfKernel',
    'Settings': {}}, epsilon: float = 0.001, nu: float = 0.1,
    shrink: bool = True, normalize: ['No', 'Warn', 'Auto',
    'Yes'] = 'Auto', ml_transforms: list = None,
    ml_transform_vars: list = None, row_selection: str = None,
    transforms: dict = None, transform_objects: dict = None,
    transform_function: str = None,
    transform_variables: list = None,
    transform_packages: list = None,
    transform_environment: dict = None, blocks_per_read: int = None,
    report_progress: int = None, verbose: int = 1,
    ensemble: microsoftml.modules.ensemble.EnsembleControl = None,
    compute_context: revoscalepy.computecontext.RxComputeContext.RxComputeContext = None)


Machine Learning One Class Support Vector Machines


One-class SVM is an algorithm for anomaly detection. The goal of anomaly 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.



The formula as described in revoscalepy.rx_formula. Interaction terms and F() are not currently supported in 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 ma_kernel(). The following choices are available:

  • rbf_kernel: Radial basis function kernel. It’s 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, rbf_kernel(gamma = .1). This is the default value.

  • linear_kernel: Linear kernel.

  • polynomial_kernel: 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.

  • sigmoid_kernel: Sigmoid kernel with parameter names gamma and coef0 in the term tanh(gamma*<x,y> + coef0). gamma, defaults to to 1 divided by the number of features. The parameter coef0 defaults to 0. For example, sigmoid_kernel(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 numpy.finfo(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 True.


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 <= 0 and 0 <= b <= 1 and 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 None if no transforms are to be performed. See featurize_text, categorical, and categorical_hash, for transformations that aresupported. These transformations are performed after any specified Python transformations. The default avlue is None.


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


NOT SUPPORTED. 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:

  • row_selection = "old" will only use observations in which the value of the variable old is True.

  • row_selection = (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 transform_function). As with all expressions, row_selection can be defined outside of the function call using the expression function.


NOT SUPPORTED. An expression of the form that represents the first round of variable transformations. As with all expressions, transforms (or row_selection) can be defined outside of the function call using the expression function.


NOT SUPPORTED. A named list that contains objects that can be referenced by transforms, transform_function, and row_selection.


The variable transformation function.


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


NOT SUPPORTED. A character vector specifying additional Python packages (outside of those specified in RxOptions.get_option("transform_packages")) to be made available and preloaded for use in variable transformation functions. For example, those explicitly defined in revoscalepy functions via their transforms and transform_function arguments or those defined implicitly via their formula or row_selection arguments. The transform_packages argument may also be None, indicating that no packages outside RxOptions.get_option("transform_packages") are preloaded.


NOT SUPPORTED. A user-defined environment to serve as a parent to all environments developed internally and used for variable data transformation. If transform_environment = None, a new “hash” environment with parent revoscalepy.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 1 to 4 provide increasing amounts of information.


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


Control parameters for ensembling.


A OneClassSvm object with the trained model.


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

See also

linear_kernel, polynomial_kernel, rbf_kernel, sigmoid_kernel, rx_predict.


Wikipedia: Anomaly detection

Microsoft Azure Machine Learning Studio: One-Class Support Vector Machine

Estimating the Support of a High-Dimensional Distribution

New Support Vector Algorithms

LIBSVM: A Library for Support Vector Machines


Anomaly Detection.
import numpy
import pandas
from microsoftml import rx_oneclass_svm, rx_predict
from revoscalepy.etl.RxDataStep import rx_data_step
from microsoftml.datasets.datasets import get_dataset

iris = get_dataset("iris")

import sklearn
if sklearn.__version__ < "0.18":
    from sklearn.cross_validation import train_test_split
    from sklearn.model_selection import train_test_split

irisdf = iris.as_df()
data_train, data_test = train_test_split(irisdf)

# Estimate a One-Class SVM model
model = rx_oneclass_svm(
            formula= "~ Sepal_Length + Sepal_Width + Petal_Length + Petal_Width",

# Add additional non-iris data to the test data set
data_test["isIris"] = 1.0
not_iris = pandas.DataFrame(data=dict(Sepal_Length=[2.5, 2.6], 
        Sepal_Width=[.75, .9], Petal_Length=[2.5, 2.5], 
        Petal_Width=[.8, .7], Species=["not iris", "not iris"], 
        isIris=[0., 0.]))

merged_test = pandas.concat([data_test, not_iris])

scoresdf = rx_predict(model, data=merged_test, extra_vars_to_write=["isIris"])

# Look at the last few observations


Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
Beginning processing data.
Rows Read: 112, Read Time: 0, Transform Time: 0
Beginning processing data.
Beginning processing data.
Rows Read: 112, Read Time: 0, Transform Time: 0
Beginning processing data.
Using these libsvm parameters: svm_type=2, nu=0.1, cache_size=100, eps=0.001, shrinking=1, kernel_type=2, gamma=0.25, degree=0, coef0=0
Reconstructed gradient.
optimization finished, #iter = 15
obj = 52.905421, rho = 9.506052
nSV = 12, nBSV = 9
Not training a calibrator because it is not needed.
Elapsed time: 00:00:00.0555122
Elapsed time: 00:00:00.0212389
Beginning processing data.
Rows Read: 40, Read Time: 0, Transform Time: 0
Beginning processing data.
Elapsed time: 00:00:00.0349974
Finished writing 40 rows.
Writing completed.
    isIris     Score
35     1.0 -0.142141
36     1.0 -0.531449
37     1.0 -0.189874
38     0.0  0.635845
39     0.0  0.555602