package util
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Type Members

trait
Loader[M <: Saveable] extends AnyRef
Trait for classes which can load models and transformers from files.
Trait for classes which can load models and transformers from files. This should be inherited by an object paired with the model class.
 Annotations
 @Since( "1.3.0" )

trait
Saveable extends AnyRef
Trait for models and transformers which may be saved as files.
Trait for models and transformers which may be saved as files. This should be inherited by the class which implements model instances.
 Annotations
 @Since( "1.3.0" )
Value Members

object
DataValidators extends Logging
A collection of methods used to validate data before applying ML algorithms.
A collection of methods used to validate data before applying ML algorithms.
 Annotations
 @Since( "0.8.0" )

object
KMeansDataGenerator
Generate test data for KMeans.
Generate test data for KMeans. This class first chooses k cluster centers from a ddimensional Gaussian distribution scaled by factor r and then creates a Gaussian cluster with scale 1 around each center.
 Annotations
 @Since( "0.8.0" )

object
LinearDataGenerator
Generate sample data used for Linear Data.
Generate sample data used for Linear Data. This class generates uniformly random values for every feature and adds Gaussian noise with mean
eps
to the response variableY
. Annotations
 @Since( "0.8.0" )

object
LogisticRegressionDataGenerator
Generate test data for LogisticRegression.
Generate test data for LogisticRegression. This class chooses positive labels with probability
probOne
and scales features for positive examples byeps
. Annotations
 @Since( "0.8.0" )

object
MFDataGenerator
Generate RDD(s) containing data for Matrix Factorization.
Generate RDD(s) containing data for Matrix Factorization.
This method samples training entries according to the oversampling factor 'trainSampFact', which is a multiplicative factor of the number of degrees of freedom of the matrix: rank*(m+nrank).
It optionally samples entries for a testing matrix using 'testSampFact', the percentage of the number of training entries to use for testing.
This method takes the following inputs: sparkMaster (String) The master URL. outputPath (String) Directory to save output. m (Int) Number of rows in data matrix. n (Int) Number of columns in data matrix. rank (Int) Underlying rank of data matrix. trainSampFact (Double) Oversampling factor. noise (Boolean) Whether to add gaussian noise to training data. sigma (Double) Standard deviation of added gaussian noise. test (Boolean) Whether to create testing RDD. testSampFact (Double) Percentage of training data to use as test data.
 Annotations
 @Since( "0.8.0" )

object
MLUtils extends Logging
Helper methods to load, save and preprocess data used in MLLib.
Helper methods to load, save and preprocess data used in MLLib.
 Annotations
 @Since( "0.8.0" )

object
SVMDataGenerator
Generate sample data used for SVM.
Generate sample data used for SVM. This class generates uniform random values for the features and adds Gaussian noise with weight 0.1 to generate labels.
 Annotations
 @Since( "0.8.0" )