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[SPARK-10264][Documentation] Added @Since to ml.recomendation #10756

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[SPARK-10264][Documentation] Added @Since to ml.recomendation #10756

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33 changes: 30 additions & 3 deletions mllib/src/main/scala/org/apache/spark/ml/recommendation/ALS.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -180,22 +180,27 @@ private[recommendation] trait ALSParams extends ALSModelParams with HasMaxIter w
* @param itemFactors a DataFrame that stores item factors in two columns: `id` and `features`
*/
@Experimental
@Since("1.3.0")
class ALSModel private[ml] (
override val uid: String,
val rank: Int,
@Since("1.4.0") override val uid: String,
@Since("1.4.0") val rank: Int,
@transient val userFactors: DataFrame,
@transient val itemFactors: DataFrame)
extends Model[ALSModel] with ALSModelParams with MLWritable {

/** @group setParam */
@Since("1.4.0")
def setUserCol(value: String): this.type = set(userCol, value)

/** @group setParam */
@Since("1.4.0")
def setItemCol(value: String): this.type = set(itemCol, value)

/** @group setParam */
@Since("1.3.0")
def setPredictionCol(value: String): this.type = set(predictionCol, value)

@Since("1.3.0")
override def transform(dataset: DataFrame): DataFrame = {
// Register a UDF for DataFrame, and then
// create a new column named map(predictionCol) by running the predict UDF.
Expand All @@ -213,13 +218,15 @@ class ALSModel private[ml] (
predict(userFactors("features"), itemFactors("features")).as($(predictionCol)))
}

@Since("1.3.0")
override def transformSchema(schema: StructType): StructType = {
validateParams()
SchemaUtils.checkColumnType(schema, $(userCol), IntegerType)
SchemaUtils.checkColumnType(schema, $(itemCol), IntegerType)
SchemaUtils.appendColumn(schema, $(predictionCol), FloatType)
}

@Since("1.5.0")
override def copy(extra: ParamMap): ALSModel = {
val copied = new ALSModel(uid, rank, userFactors, itemFactors)
copyValues(copied, extra).setParent(parent)
Expand Down Expand Up @@ -303,65 +310,83 @@ object ALSModel extends MLReadable[ALSModel] {
* preferences rather than explicit ratings given to items.
*/
@Experimental
class ALS(override val uid: String) extends Estimator[ALSModel] with ALSParams
@Since("1.3.0")
class ALS(@Since("1.4.0") override val uid: String) extends Estimator[ALSModel] with ALSParams
with DefaultParamsWritable {

import org.apache.spark.ml.recommendation.ALS.Rating

@Since("1.4.0")
def this() = this(Identifiable.randomUID("als"))

/** @group setParam */
@Since("1.3.0")
def setRank(value: Int): this.type = set(rank, value)

/** @group setParam */
@Since("1.3.0")
def setNumUserBlocks(value: Int): this.type = set(numUserBlocks, value)

/** @group setParam */
@Since("1.3.0")
def setNumItemBlocks(value: Int): this.type = set(numItemBlocks, value)

/** @group setParam */
@Since("1.3.0")
def setImplicitPrefs(value: Boolean): this.type = set(implicitPrefs, value)

/** @group setParam */
@Since("1.3.0")
def setAlpha(value: Double): this.type = set(alpha, value)

/** @group setParam */
@Since("1.3.0")
def setUserCol(value: String): this.type = set(userCol, value)

/** @group setParam */
@Since("1.3.0")
def setItemCol(value: String): this.type = set(itemCol, value)

/** @group setParam */
@Since("1.3.0")
def setRatingCol(value: String): this.type = set(ratingCol, value)

/** @group setParam */
@Since("1.3.0")
def setPredictionCol(value: String): this.type = set(predictionCol, value)

/** @group setParam */
@Since("1.3.0")
def setMaxIter(value: Int): this.type = set(maxIter, value)

/** @group setParam */
@Since("1.3.0")
def setRegParam(value: Double): this.type = set(regParam, value)

/** @group setParam */
@Since("1.3.0")
def setNonnegative(value: Boolean): this.type = set(nonnegative, value)

/** @group setParam */
@Since("1.4.0")
def setCheckpointInterval(value: Int): this.type = set(checkpointInterval, value)

/** @group setParam */
@Since("1.3.0")
def setSeed(value: Long): this.type = set(seed, value)

/**
* Sets both numUserBlocks and numItemBlocks to the specific value.
* @group setParam
*/
@Since("1.3.0")
def setNumBlocks(value: Int): this.type = {
setNumUserBlocks(value)
setNumItemBlocks(value)
this
}

@Since("1.3.0")
override def fit(dataset: DataFrame): ALSModel = {
import dataset.sqlContext.implicits._
val r = if ($(ratingCol) != "") col($(ratingCol)).cast(FloatType) else lit(1.0f)
Expand All @@ -381,10 +406,12 @@ class ALS(override val uid: String) extends Estimator[ALSModel] with ALSParams
copyValues(model)
}

@Since("1.3.0")
override def transformSchema(schema: StructType): StructType = {
validateAndTransformSchema(schema)
}

@Since("1.5.0")
override def copy(extra: ParamMap): ALS = defaultCopy(extra)
}

Expand Down