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[SPARK-10117] Implement SQL data source API for reading LIBSVM data
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104 changes: 104 additions & 0 deletions
104
mllib/src/main/scala/org/apache/spark/ml/source/libsvm/LibSVMRelation.scala
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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. | ||
*/ | ||
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package org.apache.spark.ml.source.libsvm | ||
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import com.google.common.base.Objects | ||
import org.apache.spark.Logging | ||
import org.apache.spark.mllib.linalg.Vector | ||
import org.apache.spark.mllib.regression.LabeledPoint | ||
import org.apache.spark.mllib.util.MLUtils | ||
import org.apache.spark.rdd.RDD | ||
import org.apache.spark.sql.types._ | ||
import org.apache.spark.sql.{Row, SQLContext} | ||
import org.apache.spark.sql.sources.{DataSourceRegister, PrunedScan, BaseRelation, RelationProvider} | ||
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class LibSVMRelation(val path: String, val numFeatures: Int, val featuresType: String) | ||
(@transient val sqlContext: SQLContext) | ||
extends BaseRelation with PrunedScan with Logging { | ||
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private final val vectorType: DataType | ||
= classOf[Vector].getAnnotation(classOf[SQLUserDefinedType]).udt().newInstance() | ||
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override def schema: StructType = StructType( | ||
StructField("label", DoubleType, nullable = false) :: | ||
StructField("features", vectorType, nullable = false) :: Nil | ||
) | ||
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override def buildScan(requiredColumns: Array[String]): RDD[Row] = { | ||
val sc = sqlContext.sparkContext | ||
val baseRdd = MLUtils.loadLibSVMFile(sc, path, numFeatures) | ||
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val rowBuilders = requiredColumns.map { | ||
case "label" => (pt: LabeledPoint) => Seq(pt.label) | ||
case "features" if featuresType == "sparse" => (pt: LabeledPoint) => Seq(pt.features.toSparse) | ||
case "features" if featuresType == "dense" => (pt: LabeledPoint) => Seq(pt.features.toDense) | ||
} | ||
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baseRdd.map(pt => { | ||
Row.fromSeq(rowBuilders.map(_(pt)).reduceOption(_ ++ _).getOrElse(Seq.empty)) | ||
}) | ||
} | ||
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override def hashCode(): Int = { | ||
Objects.hashCode(path, schema) | ||
} | ||
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override def equals(other: Any): Boolean = other match { | ||
case that: LibSVMRelation => (this.path == that.path) && this.schema.equals(that.schema) | ||
case _ => false | ||
} | ||
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} | ||
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class DefaultSource extends RelationProvider with DataSourceRegister { | ||
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/** | ||
* The string that represents the format that this data source provider uses. This is | ||
* overridden by children to provide a nice alias for the data source. For example: | ||
* | ||
* {{{ | ||
* override def format(): String = "parquet" | ||
* }}} | ||
* | ||
* @since 1.5.0 | ||
*/ | ||
override def shortName(): String = "libsvm" | ||
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private def checkPath(parameters: Map[String, String]): String = { | ||
parameters.getOrElse("path", sys.error("'path' must be specified")) | ||
} | ||
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/** | ||
* Returns a new base relation with the given parameters. | ||
* Note: the parameters' keywords are case insensitive and this insensitivity is enforced | ||
* by the Map that is passed to the function. | ||
*/ | ||
override def createRelation(sqlContext: SQLContext, parameters: Map[String, String]): | ||
BaseRelation = { | ||
val path = checkPath(parameters) | ||
val numFeatures = parameters.getOrElse("numFeatures", "-1").toInt | ||
/** | ||
* featuresType can be selected "dense" or "sparse". | ||
* This parameter decides the type of returned feature vector. | ||
*/ | ||
val featuresType = parameters.getOrElse("featuresType", "sparse") | ||
new LibSVMRelation(path, numFeatures, featuresType)(sqlContext) | ||
} | ||
} |
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mllib/src/main/scala/org/apache/spark/ml/source/libsvm/package.scala
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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. | ||
*/ | ||
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package org.apache.spark.ml.source | ||
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import org.apache.spark.sql.{DataFrame, DataFrameReader} | ||
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package object libsvm { | ||
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/** | ||
* Implicit declaration in order to be used from SQLContext. | ||
* It is necessary to import org.apache.spark.ml.source.libsvm._ | ||
* @param read | ||
*/ | ||
implicit class LibSVMReader(read: DataFrameReader) { | ||
def libsvm(filePath: String): DataFrame | ||
= read.format(classOf[DefaultSource].getName).load(filePath) | ||
} | ||
} |
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mllib/src/test/scala/org/apache/spark/ml/source/LibSVMRelationSuite.scala
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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. | ||
*/ | ||
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package org.apache.spark.ml.source | ||
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import java.io.File | ||
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import com.google.common.base.Charsets | ||
import com.google.common.io.Files | ||
import org.apache.spark.SparkFunSuite | ||
import org.apache.spark.ml.source.libsvm._ | ||
import org.apache.spark.mllib.linalg.{SparseVector, Vectors, DenseVector} | ||
import org.apache.spark.mllib.util.MLlibTestSparkContext | ||
import org.apache.spark.util.Utils | ||
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class LibSVMRelationSuite extends SparkFunSuite with MLlibTestSparkContext { | ||
var path: String = _ | ||
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override def beforeAll(): Unit = { | ||
super.beforeAll() | ||
val lines = | ||
""" | ||
|1 1:1.0 3:2.0 5:3.0 | ||
|0 | ||
|0 2:4.0 4:5.0 6:6.0 | ||
""".stripMargin | ||
val tempDir = Utils.createTempDir() | ||
val file = new File(tempDir.getPath, "part-00000") | ||
Files.write(lines, file, Charsets.US_ASCII) | ||
path = tempDir.toURI.toString | ||
} | ||
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test("select as sparse vector") { | ||
val df = sqlContext.read.options(Map("numFeatures" -> "6")).libsvm(path) | ||
assert(df.columns(0) == "label") | ||
assert(df.columns(1) == "features") | ||
val row1 = df.first() | ||
assert(row1.getDouble(0) == 1.0) | ||
assert(row1.getAs[SparseVector](1) == Vectors.sparse(6, Seq((0, 1.0), (2, 2.0), (4, 3.0)))) | ||
} | ||
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test("select as dense vector") { | ||
val df = sqlContext.read.options(Map("numFeatures" -> "6", "featuresType" -> "dense")) | ||
.libsvm(path) | ||
assert(df.columns(0) == "label") | ||
assert(df.columns(1) == "features") | ||
assert(df.count() == 3) | ||
val row1 = df.first() | ||
assert(row1.getDouble(0) == 1.0) | ||
assert(row1.getAs[DenseVector](1) == Vectors.dense(1.0, 0.0, 2.0, 0.0, 3.0, 0.0)) | ||
} | ||
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test("select without any option") { | ||
val df = sqlContext.read.libsvm(path) | ||
val row1 = df.first() | ||
assert(row1.getAs[SparseVector](1) == Vectors.sparse(6, Seq((0, 1.0), (2, 2.0), (4, 3.0)))) | ||
} | ||
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} |