util

Value Members

1. object DataValidators extends Logging

   

   :: DeveloperApi :: A collection of methods used to validate data before applying ML algorithms.

   :: DeveloperApi :: A collection of methods used to validate data before applying ML algorithms.

   Annotations

   @DeveloperApi()

2. object KMeansDataGenerator

   

   :: DeveloperApi :: Generate test data for KMeans.

   :: DeveloperApi :: Generate test data for KMeans. This class first chooses k cluster centers from a d-dimensional Gaussian distribution scaled by factor r and then creates a Gaussian cluster with scale 1 around each center.

   Annotations

   @DeveloperApi()

3. object LinearDataGenerator

   

   :: DeveloperApi :: Generate sample data used for Linear Data.

   :: DeveloperApi :: 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 variable Y.

   Annotations

   @DeveloperApi()

4. object LogisticRegressionDataGenerator

   

   :: DeveloperApi :: Generate test data for LogisticRegression.

   :: DeveloperApi :: Generate test data for LogisticRegression. This class chooses positive labels with probability probOne and scales features for positive examples by eps.

   Annotations

   @DeveloperApi()

5. object MFDataGenerator

   

   :: DeveloperApi :: Generate RDD(s) containing data for Matrix Factorization.

   :: DeveloperApi :: 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+n-rank).

   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

   @DeveloperApi()

6. object MLUtils

   

   Helper methods to load, save and pre-process data used in ML Lib.

7. object SVMDataGenerator

   

   :: DeveloperApi :: Generate sample data used for SVM.

   :: DeveloperApi :: 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

   @DeveloperApi()