diff options
Diffstat (limited to 'mllib')
-rw-r--r-- | mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala | 22 |
1 files changed, 21 insertions, 1 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala b/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala index 7c5cfa7bd8..26eb84a8dc 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/util/MLUtils.scala @@ -64,6 +64,7 @@ object MLUtils { * feature dimensions. * @param minPartitions min number of partitions * @return labeled data stored as an RDD[LabeledPoint] + * @since 1.0.0 */ def loadLibSVMFile( sc: SparkContext, @@ -113,7 +114,10 @@ object MLUtils { } // Convenient methods for `loadLibSVMFile`. - + + /** + * @since 1.0.0 + */ @deprecated("use method without multiclass argument, which no longer has effect", "1.1.0") def loadLibSVMFile( sc: SparkContext, @@ -126,6 +130,7 @@ object MLUtils { /** * Loads labeled data in the LIBSVM format into an RDD[LabeledPoint], with the default number of * partitions. + * @since 1.0.0 */ def loadLibSVMFile( sc: SparkContext, @@ -133,6 +138,9 @@ object MLUtils { numFeatures: Int): RDD[LabeledPoint] = loadLibSVMFile(sc, path, numFeatures, sc.defaultMinPartitions) + /** + * @since 1.0.0 + */ @deprecated("use method without multiclass argument, which no longer has effect", "1.1.0") def loadLibSVMFile( sc: SparkContext, @@ -141,6 +149,9 @@ object MLUtils { numFeatures: Int): RDD[LabeledPoint] = loadLibSVMFile(sc, path, numFeatures) + /** + * @since 1.0.0 + */ @deprecated("use method without multiclass argument, which no longer has effect", "1.1.0") def loadLibSVMFile( sc: SparkContext, @@ -151,6 +162,7 @@ object MLUtils { /** * Loads binary labeled data in the LIBSVM format into an RDD[LabeledPoint], with number of * features determined automatically and the default number of partitions. + * @since 1.0.0 */ def loadLibSVMFile(sc: SparkContext, path: String): RDD[LabeledPoint] = loadLibSVMFile(sc, path, -1) @@ -181,12 +193,14 @@ object MLUtils { * @param path file or directory path in any Hadoop-supported file system URI * @param minPartitions min number of partitions * @return vectors stored as an RDD[Vector] + * @since 1.1.0 */ def loadVectors(sc: SparkContext, path: String, minPartitions: Int): RDD[Vector] = sc.textFile(path, minPartitions).map(Vectors.parse) /** * Loads vectors saved using `RDD[Vector].saveAsTextFile` with the default number of partitions. + * @since 1.1.0 */ def loadVectors(sc: SparkContext, path: String): RDD[Vector] = sc.textFile(path, sc.defaultMinPartitions).map(Vectors.parse) @@ -197,6 +211,7 @@ object MLUtils { * @param path file or directory path in any Hadoop-supported file system URI * @param minPartitions min number of partitions * @return labeled points stored as an RDD[LabeledPoint] + * @since 1.1.0 */ def loadLabeledPoints(sc: SparkContext, path: String, minPartitions: Int): RDD[LabeledPoint] = sc.textFile(path, minPartitions).map(LabeledPoint.parse) @@ -204,6 +219,7 @@ object MLUtils { /** * Loads labeled points saved using `RDD[LabeledPoint].saveAsTextFile` with the default number of * partitions. + * @since 1.1.0 */ def loadLabeledPoints(sc: SparkContext, dir: String): RDD[LabeledPoint] = loadLabeledPoints(sc, dir, sc.defaultMinPartitions) @@ -220,6 +236,7 @@ object MLUtils { * * @deprecated Should use [[org.apache.spark.rdd.RDD#saveAsTextFile]] for saving and * [[org.apache.spark.mllib.util.MLUtils#loadLabeledPoints]] for loading. + * @since 1.0.0 */ @deprecated("Should use MLUtils.loadLabeledPoints instead.", "1.0.1") def loadLabeledData(sc: SparkContext, dir: String): RDD[LabeledPoint] = { @@ -241,6 +258,7 @@ object MLUtils { * * @deprecated Should use [[org.apache.spark.rdd.RDD#saveAsTextFile]] for saving and * [[org.apache.spark.mllib.util.MLUtils#loadLabeledPoints]] for loading. + * @since 1.0.0 */ @deprecated("Should use RDD[LabeledPoint].saveAsTextFile instead.", "1.0.1") def saveLabeledData(data: RDD[LabeledPoint], dir: String) { @@ -253,6 +271,7 @@ object MLUtils { * Return a k element array of pairs of RDDs with the first element of each pair * containing the training data, a complement of the validation data and the second * element, the validation data, containing a unique 1/kth of the data. Where k=numFolds. + * @since 1.0.0 */ @Experimental def kFold[T: ClassTag](rdd: RDD[T], numFolds: Int, seed: Int): Array[(RDD[T], RDD[T])] = { @@ -268,6 +287,7 @@ object MLUtils { /** * Returns a new vector with `1.0` (bias) appended to the input vector. + * @since 1.0.0 */ def appendBias(vector: Vector): Vector = { vector match { |