[SPARK-8919] [DOCUMENTATION, MLLIB] Added @since tags to mllib.recommendation
Author: vinodkc <vinod.kc.in@gmail.com> Closes #7325 from vinodkc/add_since_mllib.recommendation and squashes the following commits: 93156f2 [vinodkc] Changed 0.8.0 to 0.9.1 c413350 [vinodkc] Added @since
This commit is contained in:
vinodkc
Xiangrui Meng
parent
ac8c549e2f
commit
4af622c855
|
|
@ -26,6 +26,7 @@ import org.apache.spark.storage.StorageLevel
|
|||
|
||||
/**
|
||||
* A more compact class to represent a rating than Tuple3[Int, Int, Double].
|
||||
* @since 0.8.0
|
||||
*/
|
||||
case class Rating(user: Int, product: Int, rating: Double)
|
||||
|
||||
|
|
@ -254,6 +255,7 @@ class ALS private (
|
|||
|
||||
/**
|
||||
* Top-level methods for calling Alternating Least Squares (ALS) matrix factorization.
|
||||
* @since 0.8.0
|
||||
*/
|
||||
object ALS {
|
||||
/**
|
||||
|
|
@ -269,6 +271,7 @@ object ALS {
|
|||
* @param lambda regularization factor (recommended: 0.01)
|
||||
* @param blocks level of parallelism to split computation into
|
||||
* @param seed random seed
|
||||
* @since 0.9.1
|
||||
*/
|
||||
def train(
|
||||
ratings: RDD[Rating],
|
||||
|
|
@ -293,6 +296,7 @@ object ALS {
|
|||
* @param iterations number of iterations of ALS (recommended: 10-20)
|
||||
* @param lambda regularization factor (recommended: 0.01)
|
||||
* @param blocks level of parallelism to split computation into
|
||||
* @since 0.8.0
|
||||
*/
|
||||
def train(
|
||||
ratings: RDD[Rating],
|
||||
|
|
@ -315,6 +319,7 @@ object ALS {
|
|||
* @param rank number of features to use
|
||||
* @param iterations number of iterations of ALS (recommended: 10-20)
|
||||
* @param lambda regularization factor (recommended: 0.01)
|
||||
* @since 0.8.0
|
||||
*/
|
||||
def train(ratings: RDD[Rating], rank: Int, iterations: Int, lambda: Double)
|
||||
: MatrixFactorizationModel = {
|
||||
|
|
@ -331,6 +336,7 @@ object ALS {
|
|||
* @param ratings RDD of (userID, productID, rating) pairs
|
||||
* @param rank number of features to use
|
||||
* @param iterations number of iterations of ALS (recommended: 10-20)
|
||||
* @since 0.8.0
|
||||
*/
|
||||
def train(ratings: RDD[Rating], rank: Int, iterations: Int)
|
||||
: MatrixFactorizationModel = {
|
||||
|
|
@ -351,6 +357,7 @@ object ALS {
|
|||
* @param blocks level of parallelism to split computation into
|
||||
* @param alpha confidence parameter
|
||||
* @param seed random seed
|
||||
* @since 0.8.1
|
||||
*/
|
||||
def trainImplicit(
|
||||
ratings: RDD[Rating],
|
||||
|
|
@ -377,6 +384,7 @@ object ALS {
|
|||
* @param lambda regularization factor (recommended: 0.01)
|
||||
* @param blocks level of parallelism to split computation into
|
||||
* @param alpha confidence parameter
|
||||
* @since 0.8.1
|
||||
*/
|
||||
def trainImplicit(
|
||||
ratings: RDD[Rating],
|
||||
|
|
@ -401,6 +409,7 @@ object ALS {
|
|||
* @param iterations number of iterations of ALS (recommended: 10-20)
|
||||
* @param lambda regularization factor (recommended: 0.01)
|
||||
* @param alpha confidence parameter
|
||||
* @since 0.8.1
|
||||
*/
|
||||
def trainImplicit(ratings: RDD[Rating], rank: Int, iterations: Int, lambda: Double, alpha: Double)
|
||||
: MatrixFactorizationModel = {
|
||||
|
|
@ -418,6 +427,7 @@ object ALS {
|
|||
* @param ratings RDD of (userID, productID, rating) pairs
|
||||
* @param rank number of features to use
|
||||
* @param iterations number of iterations of ALS (recommended: 10-20)
|
||||
* @since 0.8.1
|
||||
*/
|
||||
def trainImplicit(ratings: RDD[Rating], rank: Int, iterations: Int)
|
||||
: MatrixFactorizationModel = {
|
||||
|
|
|
|||
|
|
@ -49,6 +49,7 @@ import org.apache.spark.storage.StorageLevel
|
|||
* the features computed for this user.
|
||||
* @param productFeatures RDD of tuples where each tuple represents the productId
|
||||
* and the features computed for this product.
|
||||
* @since 0.8.0
|
||||
*/
|
||||
class MatrixFactorizationModel(
|
||||
val rank: Int,
|
||||
|
|
@ -73,7 +74,9 @@ class MatrixFactorizationModel(
|
|||
}
|
||||
}
|
||||
|
||||
/** Predict the rating of one user for one product. */
|
||||
/** Predict the rating of one user for one product.
|
||||
* @since 0.8.0
|
||||
*/
|
||||
def predict(user: Int, product: Int): Double = {
|
||||
val userVector = userFeatures.lookup(user).head
|
||||
val productVector = productFeatures.lookup(product).head
|
||||
|
|
@ -111,6 +114,7 @@ class MatrixFactorizationModel(
|
|||
*
|
||||
* @param usersProducts RDD of (user, product) pairs.
|
||||
* @return RDD of Ratings.
|
||||
* @since 0.9.0
|
||||
*/
|
||||
def predict(usersProducts: RDD[(Int, Int)]): RDD[Rating] = {
|
||||
// Previously the partitions of ratings are only based on the given products.
|
||||
|
|
@ -142,6 +146,7 @@ class MatrixFactorizationModel(
|
|||
|
||||
/**
|
||||
* Java-friendly version of [[MatrixFactorizationModel.predict]].
|
||||
* @since 1.2.0
|
||||
*/
|
||||
def predict(usersProducts: JavaPairRDD[JavaInteger, JavaInteger]): JavaRDD[Rating] = {
|
||||
predict(usersProducts.rdd.asInstanceOf[RDD[(Int, Int)]]).toJavaRDD()
|
||||
|
|
@ -157,6 +162,7 @@ class MatrixFactorizationModel(
|
|||
* by score, decreasing. The first returned is the one predicted to be most strongly
|
||||
* recommended to the user. The score is an opaque value that indicates how strongly
|
||||
* recommended the product is.
|
||||
* @since 1.1.0
|
||||
*/
|
||||
def recommendProducts(user: Int, num: Int): Array[Rating] =
|
||||
MatrixFactorizationModel.recommend(userFeatures.lookup(user).head, productFeatures, num)
|
||||
|
|
@ -173,6 +179,7 @@ class MatrixFactorizationModel(
|
|||
* by score, decreasing. The first returned is the one predicted to be most strongly
|
||||
* recommended to the product. The score is an opaque value that indicates how strongly
|
||||
* recommended the user is.
|
||||
* @since 1.1.0
|
||||
*/
|
||||
def recommendUsers(product: Int, num: Int): Array[Rating] =
|
||||
MatrixFactorizationModel.recommend(productFeatures.lookup(product).head, userFeatures, num)
|
||||
|
|
@ -180,6 +187,20 @@ class MatrixFactorizationModel(
|
|||
|
||||
protected override val formatVersion: String = "1.0"
|
||||
|
||||
/**
|
||||
* Save this model to the given path.
|
||||
*
|
||||
* This saves:
|
||||
* - human-readable (JSON) model metadata to path/metadata/
|
||||
* - Parquet formatted data to path/data/
|
||||
*
|
||||
* The model may be loaded using [[Loader.load]].
|
||||
*
|
||||
* @param sc Spark context used to save model data.
|
||||
* @param path Path specifying the directory in which to save this model.
|
||||
* If the directory already exists, this method throws an exception.
|
||||
* @since 1.3.0
|
||||
*/
|
||||
override def save(sc: SparkContext, path: String): Unit = {
|
||||
MatrixFactorizationModel.SaveLoadV1_0.save(this, path)
|
||||
}
|
||||
|
|
@ -191,6 +212,7 @@ class MatrixFactorizationModel(
|
|||
* @return [(Int, Array[Rating])] objects, where every tuple contains a userID and an array of
|
||||
* rating objects which contains the same userId, recommended productID and a "score" in the
|
||||
* rating field. Semantics of score is same as recommendProducts API
|
||||
* @since 1.4.0
|
||||
*/
|
||||
def recommendProductsForUsers(num: Int): RDD[(Int, Array[Rating])] = {
|
||||
MatrixFactorizationModel.recommendForAll(rank, userFeatures, productFeatures, num).map {
|
||||
|
|
@ -208,6 +230,7 @@ class MatrixFactorizationModel(
|
|||
* @return [(Int, Array[Rating])] objects, where every tuple contains a productID and an array
|
||||
* of rating objects which contains the recommended userId, same productID and a "score" in the
|
||||
* rating field. Semantics of score is same as recommendUsers API
|
||||
* @since 1.4.0
|
||||
*/
|
||||
def recommendUsersForProducts(num: Int): RDD[(Int, Array[Rating])] = {
|
||||
MatrixFactorizationModel.recommendForAll(rank, productFeatures, userFeatures, num).map {
|
||||
|
|
@ -218,6 +241,9 @@ class MatrixFactorizationModel(
|
|||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* @since 1.3.0
|
||||
*/
|
||||
object MatrixFactorizationModel extends Loader[MatrixFactorizationModel] {
|
||||
|
||||
import org.apache.spark.mllib.util.Loader._
|
||||
|
|
@ -292,6 +318,16 @@ object MatrixFactorizationModel extends Loader[MatrixFactorizationModel] {
|
|||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Load a model from the given path.
|
||||
*
|
||||
* The model should have been saved by [[Saveable.save]].
|
||||
*
|
||||
* @param sc Spark context used for loading model files.
|
||||
* @param path Path specifying the directory to which the model was saved.
|
||||
* @return Model instance
|
||||
* @since 1.3.0
|
||||
*/
|
||||
override def load(sc: SparkContext, path: String): MatrixFactorizationModel = {
|
||||
val (loadedClassName, formatVersion, _) = loadMetadata(sc, path)
|
||||
val classNameV1_0 = SaveLoadV1_0.thisClassName
|
||||
|
|
|
|||
Loading…
Reference in a new issue