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  • package root
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    root
  • package org
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    root
  • package apache
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    org
  • package spark

    Core Spark functionality.

    Core Spark functionality. org.apache.spark.SparkContext serves as the main entry point to Spark, while org.apache.spark.rdd.RDD is the data type representing a distributed collection, and provides most parallel operations.

    In addition, org.apache.spark.rdd.PairRDDFunctions contains operations available only on RDDs of key-value pairs, such as groupByKey and join; org.apache.spark.rdd.DoubleRDDFunctions contains operations available only on RDDs of Doubles; and org.apache.spark.rdd.SequenceFileRDDFunctions contains operations available on RDDs that can be saved as SequenceFiles. These operations are automatically available on any RDD of the right type (e.g. RDD[(Int, Int)] through implicit conversions.

    Java programmers should reference the org.apache.spark.api.java package for Spark programming APIs in Java.

    Classes and methods marked with Experimental are user-facing features which have not been officially adopted by the Spark project. These are subject to change or removal in minor releases.

    Classes and methods marked with Developer API are intended for advanced users want to extend Spark through lower level interfaces. These are subject to changes or removal in minor releases.

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    apache
  • package ml

    DataFrame-based machine learning APIs to let users quickly assemble and configure practical machine learning pipelines.

    DataFrame-based machine learning APIs to let users quickly assemble and configure practical machine learning pipelines.

    Definition Classes
    spark
  • package source
    Definition Classes
    ml
  • package libsvm
    Definition Classes
    source
  • LibSVMDataSource
c

org.apache.spark.ml.source.libsvm

LibSVMDataSource

class LibSVMDataSource extends AnyRef

libsvm package implements Spark SQL data source API for loading LIBSVM data as DataFrame. The loaded DataFrame has two columns: label containing labels stored as doubles and features containing feature vectors stored as Vectors.

To use LIBSVM data source, you need to set "libsvm" as the format in DataFrameReader and optionally specify options, for example:

// Scala
val df = spark.read.format("libsvm")
  .option("numFeatures", "780")
  .load("data/mllib/sample_libsvm_data.txt")

// Java
Dataset<Row> df = spark.read().format("libsvm")
  .option("numFeatures, "780")
  .load("data/mllib/sample_libsvm_data.txt");

LIBSVM data source supports the following options:

  • "numFeatures": number of features. If unspecified or nonpositive, the number of features will be determined automatically at the cost of one additional pass. This is also useful when the dataset is already split into multiple files and you want to load them separately, because some features may not present in certain files, which leads to inconsistent feature dimensions.
  • "vectorType": feature vector type, "sparse" (default) or "dense".
Source
LibSVMDataSource.scala
Note

This class is public for documentation purpose. Please don't use this class directly. Rather, use the data source API as illustrated above.

See also

LIBSVM datasets

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