[SPARK-14089][CORE][MLLIB] Remove methods that has been deprecated since 1.1, 1.2, 1.3, 1.4, and 1.5
## What changes were proposed in this pull request? Removed methods that has been deprecated since 1.1, 1.2, 1.3, 1.4, and 1.5. ## How was this patch tested? - manully checked that no codes in Spark call these methods any more - existing test suits Author: Liwei Lin <lwlin7@gmail.com> Author: proflin <proflin.me@gmail.com> Closes #11910 from lw-lin/remove-deprecates.
This commit is contained in:
Liwei Lin
Sean Owen
parent
1808465855
commit
62a85eb09f
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@ -36,19 +36,6 @@ public class StorageLevels {
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public static final StorageLevel MEMORY_AND_DISK_SER_2 = create(true, true, false, false, 2);
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public static final StorageLevel OFF_HEAP = create(false, false, true, false, 1);
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/**
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* Create a new StorageLevel object.
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* @param useDisk saved to disk, if true
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* @param useMemory saved to memory, if true
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* @param deserialized saved as deserialized objects, if true
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* @param replication replication factor
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*/
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@Deprecated
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public static StorageLevel create(boolean useDisk, boolean useMemory, boolean deserialized,
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int replication) {
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return StorageLevel.apply(useDisk, useMemory, false, deserialized, replication);
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}
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/**
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* Create a new StorageLevel object.
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* @param useDisk saved to disk, if true
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@ -156,14 +156,6 @@ object SparkEnv extends Logging {
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env
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}
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/**
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* Returns the ThreadLocal SparkEnv.
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*/
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@deprecated("Use SparkEnv.get instead", "1.2.0")
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def getThreadLocal: SparkEnv = {
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env
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}
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/**
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* Create a SparkEnv for the driver.
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*/
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@ -63,14 +63,6 @@ class BinaryClassificationEvaluator @Since("1.4.0") (@Since("1.4.0") override va
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@Since("1.5.0")
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def setRawPredictionCol(value: String): this.type = set(rawPredictionCol, value)
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/**
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* @group setParam
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* @deprecated use [[setRawPredictionCol()]] instead
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*/
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@deprecated("use setRawPredictionCol instead", "1.5.0")
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@Since("1.2.0")
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def setScoreCol(value: String): this.type = set(rawPredictionCol, value)
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/** @group setParam */
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@Since("1.2.0")
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def setLabelCol(value: String): this.type = set(labelCol, value)
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@ -78,15 +78,6 @@ class LBFGS(private var gradient: Gradient, private var updater: Updater)
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this.convergenceTol
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}
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/**
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* Set the maximal number of iterations for L-BFGS. Default 100.
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* @deprecated use [[LBFGS#setNumIterations]] instead
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*/
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@deprecated("use setNumIterations instead", "1.1.0")
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def setMaxNumIterations(iters: Int): this.type = {
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this.setNumIterations(iters)
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}
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/**
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* Set the maximal number of iterations for L-BFGS. Default 100.
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*/
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@ -51,30 +51,6 @@ class RDDFunctions[T: ClassTag](self: RDD[T]) extends Serializable {
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*/
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def sliding(windowSize: Int): RDD[Array[T]] = sliding(windowSize, 1)
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/**
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* Reduces the elements of this RDD in a multi-level tree pattern.
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*
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* @param depth suggested depth of the tree (default: 2)
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* @see [[org.apache.spark.rdd.RDD#treeReduce]]
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* @deprecated Use [[org.apache.spark.rdd.RDD#treeReduce]] instead.
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*/
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@deprecated("Use RDD.treeReduce instead.", "1.3.0")
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def treeReduce(f: (T, T) => T, depth: Int = 2): T = self.treeReduce(f, depth)
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/**
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* Aggregates the elements of this RDD in a multi-level tree pattern.
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*
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* @param depth suggested depth of the tree (default: 2)
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* @see [[org.apache.spark.rdd.RDD#treeAggregate]]
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* @deprecated Use [[org.apache.spark.rdd.RDD#treeAggregate]] instead.
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*/
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@deprecated("Use RDD.treeAggregate instead.", "1.3.0")
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def treeAggregate[U: ClassTag](zeroValue: U)(
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seqOp: (U, T) => U,
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combOp: (U, U) => U,
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depth: Int = 2): U = {
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self.treeAggregate(zeroValue)(seqOp, combOp, depth)
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}
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}
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@DeveloperApi
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@ -202,8 +202,4 @@ object Strategy {
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numClasses = 0)
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}
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@deprecated("Use Strategy.defaultStrategy instead.", "1.5.0")
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@Since("1.2.0")
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def defaultStategy(algo: Algo): Strategy = defaultStrategy(algo)
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}
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@ -56,27 +56,6 @@ class Node @Since("1.2.0") (
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s"split = $split, stats = $stats"
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}
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/**
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* build the left node and right nodes if not leaf
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* @param nodes array of nodes
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*/
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@Since("1.0.0")
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@deprecated("build should no longer be used since trees are constructed on-the-fly in training",
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"1.2.0")
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def build(nodes: Array[Node]): Unit = {
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logDebug("building node " + id + " at level " + Node.indexToLevel(id))
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logDebug("id = " + id + ", split = " + split)
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logDebug("stats = " + stats)
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logDebug("predict = " + predict)
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logDebug("impurity = " + impurity)
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if (!isLeaf) {
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leftNode = Some(nodes(Node.leftChildIndex(id)))
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rightNode = Some(nodes(Node.rightChildIndex(id)))
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leftNode.get.build(nodes)
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rightNode.get.build(nodes)
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}
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}
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/**
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* predict value if node is not leaf
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* @param features feature value
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@ -110,18 +110,6 @@ object MLUtils {
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}
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}
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// Convenient methods for `loadLibSVMFile`.
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@Since("1.0.0")
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@deprecated("use method without multiclass argument, which no longer has effect", "1.1.0")
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def loadLibSVMFile(
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sc: SparkContext,
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path: String,
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multiclass: Boolean,
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numFeatures: Int,
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minPartitions: Int): RDD[LabeledPoint] =
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loadLibSVMFile(sc, path, numFeatures, minPartitions)
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/**
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* Loads labeled data in the LIBSVM format into an RDD[LabeledPoint], with the default number of
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* partitions.
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@ -133,23 +121,6 @@ object MLUtils {
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numFeatures: Int): RDD[LabeledPoint] =
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loadLibSVMFile(sc, path, numFeatures, sc.defaultMinPartitions)
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@Since("1.0.0")
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@deprecated("use method without multiclass argument, which no longer has effect", "1.1.0")
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def loadLibSVMFile(
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sc: SparkContext,
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path: String,
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multiclass: Boolean,
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numFeatures: Int): RDD[LabeledPoint] =
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loadLibSVMFile(sc, path, numFeatures)
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@Since("1.0.0")
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@deprecated("use method without multiclass argument, which no longer has effect", "1.1.0")
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def loadLibSVMFile(
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sc: SparkContext,
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path: String,
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multiclass: Boolean): RDD[LabeledPoint] =
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loadLibSVMFile(sc, path)
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/**
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* Loads binary labeled data in the LIBSVM format into an RDD[LabeledPoint], with number of
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* features determined automatically and the default number of partitions.
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@ -216,48 +187,6 @@ object MLUtils {
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def loadLabeledPoints(sc: SparkContext, dir: String): RDD[LabeledPoint] =
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loadLabeledPoints(sc, dir, sc.defaultMinPartitions)
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/**
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* Load labeled data from a file. The data format used here is
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* L, f1 f2 ...
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* where f1, f2 are feature values in Double and L is the corresponding label as Double.
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*
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* @param sc SparkContext
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* @param dir Directory to the input data files.
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* @return An RDD of LabeledPoint. Each labeled point has two elements: the first element is
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* the label, and the second element represents the feature values (an array of Double).
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*
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* @deprecated Should use [[org.apache.spark.rdd.RDD#saveAsTextFile]] for saving and
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* [[org.apache.spark.mllib.util.MLUtils#loadLabeledPoints]] for loading.
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*/
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@Since("1.0.0")
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@deprecated("Should use MLUtils.loadLabeledPoints instead.", "1.0.1")
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def loadLabeledData(sc: SparkContext, dir: String): RDD[LabeledPoint] = {
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sc.textFile(dir).map { line =>
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val parts = line.split(',')
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val label = parts(0).toDouble
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val features = Vectors.dense(parts(1).trim().split(' ').map(_.toDouble))
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LabeledPoint(label, features)
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}
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}
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/**
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* Save labeled data to a file. The data format used here is
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* L, f1 f2 ...
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* where f1, f2 are feature values in Double and L is the corresponding label as Double.
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*
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* @param data An RDD of LabeledPoints containing data to be saved.
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* @param dir Directory to save the data.
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*
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* @deprecated Should use [[org.apache.spark.rdd.RDD#saveAsTextFile]] for saving and
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* [[org.apache.spark.mllib.util.MLUtils#loadLabeledPoints]] for loading.
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*/
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@Since("1.0.0")
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@deprecated("Should use RDD[LabeledPoint].saveAsTextFile instead.", "1.0.1")
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def saveLabeledData(data: RDD[LabeledPoint], dir: String) {
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val dataStr = data.map(x => x.label + "," + x.features.toArray.mkString(" "))
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dataStr.saveAsTextFile(dir)
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}
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/**
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* Return a k element array of pairs of RDDs with the first element of each pair
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* containing the training data, a complement of the validation data and the second
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@ -576,6 +576,19 @@ object MimaExcludes {
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) ++ Seq(
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// [SPARK-13990] Automatically pick serializer when caching RDDs
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ProblemFilters.exclude[DirectMissingMethodProblem]("org.apache.spark.network.netty.NettyBlockTransferService.uploadBlock")
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) ++ Seq(
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// [SPARK-14089][CORE][MLLIB] Remove methods that has been deprecated since 1.1, 1.2, 1.3, 1.4, and 1.5
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ProblemFilters.exclude[DirectMissingMethodProblem]("org.apache.spark.SparkEnv.getThreadLocal"),
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ProblemFilters.exclude[DirectMissingMethodProblem]("org.apache.spark.mllib.rdd.RDDFunctions.treeReduce"),
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ProblemFilters.exclude[DirectMissingMethodProblem]("org.apache.spark.mllib.rdd.RDDFunctions.treeAggregate"),
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ProblemFilters.exclude[DirectMissingMethodProblem]("org.apache.spark.mllib.tree.configuration.Strategy.defaultStategy"),
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ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.mllib.util.MLUtils.loadLibSVMFile"),
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ProblemFilters.exclude[IncompatibleMethTypeProblem]("org.apache.spark.mllib.util.MLUtils.loadLibSVMFile"),
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ProblemFilters.exclude[DirectMissingMethodProblem]("org.apache.spark.mllib.util.MLUtils.loadLibSVMFile"),
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ProblemFilters.exclude[DirectMissingMethodProblem]("org.apache.spark.mllib.util.MLUtils.saveLabeledData"),
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ProblemFilters.exclude[DirectMissingMethodProblem]("org.apache.spark.mllib.util.MLUtils.loadLabeledData"),
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ProblemFilters.exclude[DirectMissingMethodProblem]("org.apache.spark.mllib.optimization.LBFGS.setMaxNumIterations"),
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ProblemFilters.exclude[DirectMissingMethodProblem]("org.apache.spark.ml.evaluation.BinaryClassificationEvaluator.setScoreCol")
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)
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case v if v.startsWith("1.6") =>
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Seq(
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