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[SPARK-7654][MLlib] Migrate MLlib to the DataFrame reader/writer API.

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Author: Reynold Xin <rxin@databricks.com>

Closes #6211 from rxin/mllib-reader and squashes the following commits:

79a2cb9 [Reynold Xin] [SPARK-7654][MLlib] Migrate MLlib to the DataFrame reader/writer API.
This commit is contained in:
Reynold Xin 2015-05-16 15:03:57 -07:00
parent 1b4e710e5c
commit 161d0b4a41
13 changed files with 16 additions and 16 deletions
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2
examples/src/main/scala/org/apache/spark/examples/mllib/DatasetExample.scala
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@ -103,7 +103,7 @@ object DatasetExample {
tmpDir.deleteOnExit()
val outputDir = new File(tmpDir, "dataset").toString
println(s"Saving to $outputDir as Parquet file.")
df.saveAsParquetFile(outputDir)
df.write.parquet(outputDir)
println(s"Loading Parquet file with UDT from $outputDir.")
val newDataset = sqlContext.read.parquet(outputDir)

2
examples/src/main/scala/org/apache/spark/examples/sql/RDDRelation.scala
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@ -58,7 +58,7 @@ object RDDRelation {
df.where($"key" === 1).orderBy($"value".asc).select($"key").collect().foreach(println)
// Write out an RDD as a parquet file.
df.saveAsParquetFile("pair.parquet")
df.write.parquet("pair.parquet")
// Read in parquet file. Parquet files are self-describing so the schmema is preserved.
val parquetFile = sqlContext.read.parquet("pair.parquet")

4
mllib/src/main/scala/org/apache/spark/mllib/classification/NaiveBayes.scala
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@ -140,7 +140,7 @@ object NaiveBayesModel extends Loader[NaiveBayesModel] {
// Create Parquet data.
val dataRDD: DataFrame = sc.parallelize(Seq(data), 1).toDF()
dataRDD.saveAsParquetFile(dataPath(path))
dataRDD.write.parquet(dataPath(path))
}
def load(sc: SparkContext, path: String): NaiveBayesModel = {
@ -186,7 +186,7 @@ object NaiveBayesModel extends Loader[NaiveBayesModel] {
// Create Parquet data.
val dataRDD: DataFrame = sc.parallelize(Seq(data), 1).toDF()
dataRDD.saveAsParquetFile(dataPath(path))
dataRDD.write.parquet(dataPath(path))
}
def load(sc: SparkContext, path: String): NaiveBayesModel = {

2
mllib/src/main/scala/org/apache/spark/mllib/classification/impl/GLMClassificationModel.scala
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@ -62,7 +62,7 @@ private[classification] object GLMClassificationModel {
// Create Parquet data.
val data = Data(weights, intercept, threshold)
sc.parallelize(Seq(data), 1).toDF().saveAsParquetFile(Loader.dataPath(path))
sc.parallelize(Seq(data), 1).toDF().write.parquet(Loader.dataPath(path))
}
/**

2
mllib/src/main/scala/org/apache/spark/mllib/clustering/GaussianMixtureModel.scala
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@ -126,7 +126,7 @@ object GaussianMixtureModel extends Loader[GaussianMixtureModel] {
val dataArray = Array.tabulate(weights.length) { i =>
Data(weights(i), gaussians(i).mu, gaussians(i).sigma)
}
sc.parallelize(dataArray, 1).toDF().saveAsParquetFile(Loader.dataPath(path))
sc.parallelize(dataArray, 1).toDF().write.parquet(Loader.dataPath(path))
}
def load(sc: SparkContext, path: String): GaussianMixtureModel = {

2
mllib/src/main/scala/org/apache/spark/mllib/clustering/KMeansModel.scala
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@ -110,7 +110,7 @@ object KMeansModel extends Loader[KMeansModel] {
val dataRDD = sc.parallelize(model.clusterCenters.zipWithIndex).map { case (point, id) =>
Cluster(id, point)
}.toDF()
dataRDD.saveAsParquetFile(Loader.dataPath(path))
dataRDD.write.parquet(Loader.dataPath(path))
}
def load(sc: SparkContext, path: String): KMeansModel = {

4
mllib/src/main/scala/org/apache/spark/mllib/clustering/PowerIterationClustering.scala
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@ -74,7 +74,7 @@ object PowerIterationClusteringModel extends Loader[PowerIterationClusteringMode
sc.parallelize(Seq(metadata), 1).saveAsTextFile(Loader.metadataPath(path))
val dataRDD = model.assignments.toDF()
dataRDD.saveAsParquetFile(Loader.dataPath(path))
dataRDD.write.parquet(Loader.dataPath(path))
}
def load(sc: SparkContext, path: String): PowerIterationClusteringModel = {
@ -86,7 +86,7 @@ object PowerIterationClusteringModel extends Loader[PowerIterationClusteringMode
assert(formatVersion == thisFormatVersion)
val k = (metadata \ "k").extract[Int]
val assignments = sqlContext.parquetFile(Loader.dataPath(path))
val assignments = sqlContext.read.parquet(Loader.dataPath(path))
Loader.checkSchema[PowerIterationClustering.Assignment](assignments.schema)
val assignmentsRDD = assignments.map {

2
mllib/src/main/scala/org/apache/spark/mllib/feature/Word2Vec.scala
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@ -580,7 +580,7 @@ object Word2VecModel extends Loader[Word2VecModel] {
sc.parallelize(Seq(metadata), 1).saveAsTextFile(Loader.metadataPath(path))
val dataArray = model.toSeq.map { case (w, v) => Data(w, v) }
sc.parallelize(dataArray.toSeq, 1).toDF().saveAsParquetFile(Loader.dataPath(path))
sc.parallelize(dataArray.toSeq, 1).toDF().write.parquet(Loader.dataPath(path))
}
}

4
mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala
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@ -281,8 +281,8 @@ object MatrixFactorizationModel extends Loader[MatrixFactorizationModel] {
val metadata = compact(render(
("class" -> thisClassName) ~ ("version" -> thisFormatVersion) ~ ("rank" -> model.rank)))
sc.parallelize(Seq(metadata), 1).saveAsTextFile(metadataPath(path))
model.userFeatures.toDF("id", "features").saveAsParquetFile(userPath(path))
model.productFeatures.toDF("id", "features").saveAsParquetFile(productPath(path))
model.userFeatures.toDF("id", "features").write.parquet(userPath(path))
model.productFeatures.toDF("id", "features").write.parquet(productPath(path))
}
def load(sc: SparkContext, path: String): MatrixFactorizationModel = {

2
mllib/src/main/scala/org/apache/spark/mllib/regression/IsotonicRegression.scala
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@ -184,7 +184,7 @@ object IsotonicRegressionModel extends Loader[IsotonicRegressionModel] {
sqlContext.createDataFrame(
boundaries.toSeq.zip(predictions).map { case (b, p) => Data(b, p) }
).saveAsParquetFile(dataPath(path))
).write.parquet(dataPath(path))
}
def load(sc: SparkContext, path: String): (Array[Double], Array[Double]) = {

2
mllib/src/main/scala/org/apache/spark/mllib/regression/impl/GLMRegressionModel.scala
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@ -60,7 +60,7 @@ private[regression] object GLMRegressionModel {
val data = Data(weights, intercept)
val dataRDD: DataFrame = sc.parallelize(Seq(data), 1).toDF()
// TODO: repartition with 1 partition after SPARK-5532 gets fixed
dataRDD.saveAsParquetFile(Loader.dataPath(path))
dataRDD.write.parquet(Loader.dataPath(path))
}
/**

2
mllib/src/main/scala/org/apache/spark/mllib/tree/model/DecisionTreeModel.scala
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@ -223,7 +223,7 @@ object DecisionTreeModel extends Loader[DecisionTreeModel] with Logging {
val dataRDD: DataFrame = sc.parallelize(nodes)
.map(NodeData.apply(0, _))
.toDF()
dataRDD.saveAsParquetFile(Loader.dataPath(path))
dataRDD.write.parquet(Loader.dataPath(path))
}
def load(sc: SparkContext, path: String, algo: String, numNodes: Int): DecisionTreeModel = {

2
mllib/src/main/scala/org/apache/spark/mllib/tree/model/treeEnsembleModels.scala
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@ -414,7 +414,7 @@ private[tree] object TreeEnsembleModel extends Logging {
val dataRDD = sc.parallelize(model.trees.zipWithIndex).flatMap { case (tree, treeId) =>
tree.topNode.subtreeIterator.toSeq.map(node => NodeData(treeId, node))
}.toDF()
dataRDD.saveAsParquetFile(Loader.dataPath(path))
dataRDD.write.parquet(Loader.dataPath(path))
}
/**