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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.spark.ml.source.libsvm
import com.google.common.base.Objects
import org.apache.spark.Logging
import org.apache.spark.annotation.Since
import org.apache.spark.mllib.linalg.{Vector, VectorUDT}
import org.apache.spark.mllib.util.MLUtils
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.{DataFrame, DataFrameReader, Row, SQLContext}
import org.apache.spark.sql.sources._
import org.apache.spark.sql.types.{DoubleType, StructField, StructType}
/**
* LibSVMRelation provides the DataFrame constructed from LibSVM format data.
* @param path File path of LibSVM format
* @param numFeatures The number of features
* @param vectorType The type of vector. It can be 'sparse' or 'dense'
* @param sqlContext The Spark SQLContext
*/
private[libsvm] class LibSVMRelation(val path: String, val numFeatures: Int, val vectorType: String)
(@transient val sqlContext: SQLContext)
extends BaseRelation with TableScan with Logging with Serializable {
override def schema: StructType = StructType(
StructField("label", DoubleType, nullable = false) ::
StructField("features", new VectorUDT(), nullable = false) :: Nil
)
override def buildScan(): RDD[Row] = {
val sc = sqlContext.sparkContext
val baseRdd = MLUtils.loadLibSVMFile(sc, path, numFeatures)
val sparse = vectorType == "sparse"
baseRdd.map { pt =>
val features = if (sparse) pt.features.toSparse else pt.features.toDense
Row(pt.label, features)
}
}
override def hashCode(): Int = {
Objects.hashCode(path, Double.box(numFeatures), vectorType)
}
override def equals(other: Any): Boolean = other match {
case that: LibSVMRelation =>
path == that.path &&
numFeatures == that.numFeatures &&
vectorType == that.vectorType
case _ =>
false
}
}
/**
* `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 [[Vector]]s.
*
* To use LIBSVM data source, you need to set "libsvm" as the format in [[DataFrameReader]] and
* optionally specify options, for example:
* {{{
* // Scala
* val df = sqlContext.read.format("libsvm")
* .option("numFeatures", "780")
* .load("data/mllib/sample_libsvm_data.txt")
*
* // Java
* DataFrame df = sqlContext.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".
*
* @see [[https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/ LIBSVM datasets]]
*/
@Since("1.6.0")
class DefaultSource extends RelationProvider with DataSourceRegister {
@Since("1.6.0")
override def shortName(): String = "libsvm"
@Since("1.6.0")
override def createRelation(sqlContext: SQLContext, parameters: Map[String, String])
: BaseRelation = {
val path = parameters.getOrElse("path",
throw new IllegalArgumentException("'path' must be specified"))
val numFeatures = parameters.getOrElse("numFeatures", "-1").toInt
val vectorType = parameters.getOrElse("vectorType", "sparse")
new LibSVMRelation(path, numFeatures, vectorType)(sqlContext)
}
}
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