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author | Bryan Cutler <cutlerb@gmail.com> | 2016-02-22 12:48:37 +0200 |
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committer | Nick Pentreath <nick.pentreath@gmail.com> | 2016-02-22 12:48:37 +0200 |
commit | e298ac91e3f6177c6da83e2d8ee994d9037466da (patch) | |
tree | 8494149068bd94f2e2cfa46af761f4c9dcec6a25 /mllib/src | |
parent | 024482bf51e8158eed08a7dc0758f585baf86e1f (diff) | |
download | spark-e298ac91e3f6177c6da83e2d8ee994d9037466da.tar.gz spark-e298ac91e3f6177c6da83e2d8ee994d9037466da.tar.bz2 spark-e298ac91e3f6177c6da83e2d8ee994d9037466da.zip |
[SPARK-12632][PYSPARK][DOC] PySpark fpm and als parameter desc to consistent format
Part of task for [SPARK-11219](https://issues.apache.org/jira/browse/SPARK-11219) to make PySpark MLlib parameter description formatting consistent. This is for the fpm and recommendation modules.
Closes #10602
Closes #10897
Author: Bryan Cutler <cutlerb@gmail.com>
Author: somideshmukh <somilde@us.ibm.com>
Closes #11186 from BryanCutler/param-desc-consistent-fpmrecc-SPARK-12632.
Diffstat (limited to 'mllib/src')
4 files changed, 60 insertions, 66 deletions
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala index 1250bc1a07..85d609386f 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/FPGrowth.scala @@ -152,7 +152,7 @@ object FPGrowthModel extends Loader[FPGrowthModel[_]] { * [[http://dx.doi.org/10.1145/335191.335372 Han et al., Mining frequent patterns without candidate * generation]]. * - * @param minSupport the minimal support level of the frequent pattern, any pattern appears + * @param minSupport the minimal support level of the frequent pattern, any pattern that appears * more than (minSupport * size-of-the-dataset) times will be output * @param numPartitions number of partitions used by parallel FP-growth * diff --git a/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala b/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala index ed49c9492f..94a24b527b 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/fpm/PrefixSpan.scala @@ -38,9 +38,9 @@ import org.apache.spark.storage.StorageLevel * The PrefixSpan algorithm is described in J. Pei, et al., PrefixSpan: Mining Sequential Patterns * Efficiently by Prefix-Projected Pattern Growth ([[http://doi.org/10.1109/ICDE.2001.914830]]). * - * @param minSupport the minimal support level of the sequential pattern, any pattern appears - * more than (minSupport * size-of-the-dataset) times will be output - * @param maxPatternLength the maximal length of the sequential pattern, any pattern appears + * @param minSupport the minimal support level of the sequential pattern, any pattern that appears + * more than (minSupport * size-of-the-dataset) times will be output + * @param maxPatternLength the maximal length of the sequential pattern, any pattern that appears * less than maxPatternLength will be output * @param maxLocalProjDBSize The maximum number of items (including delimiters used in the internal * storage format) allowed in a projected database before local diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala index 33aaf853e5..3e619c4264 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala @@ -218,7 +218,7 @@ class ALS private ( } /** - * Run ALS with the configured parameters on an input RDD of (user, product, rating) triples. + * Run ALS with the configured parameters on an input RDD of [[Rating]] objects. * Returns a MatrixFactorizationModel with feature vectors for each user and product. */ @Since("0.8.0") @@ -279,18 +279,17 @@ class ALS private ( @Since("0.8.0") object ALS { /** - * Train a matrix factorization model given an RDD of ratings given by users to some products, - * in the form of (userID, productID, rating) pairs. We approximate the ratings matrix as the - * product of two lower-rank matrices of a given rank (number of features). To solve for these - * features, we run a given number of iterations of ALS. This is done using a level of - * parallelism given by `blocks`. + * Train a matrix factorization model given an RDD of ratings by users for a subset of products. + * The ratings matrix is approximated as the product of two lower-rank matrices of a given rank + * (number of features). To solve for these features, ALS is run iteratively with a configurable + * level of parallelism. * - * @param ratings RDD of (userID, productID, rating) pairs + * @param ratings RDD of [[Rating]] objects with userID, productID, and rating * @param rank number of features to use - * @param iterations number of iterations of ALS (recommended: 10-20) - * @param lambda regularization factor (recommended: 0.01) + * @param iterations number of iterations of ALS + * @param lambda regularization parameter * @param blocks level of parallelism to split computation into - * @param seed random seed + * @param seed random seed for initial matrix factorization model */ @Since("0.9.1") def train( @@ -305,16 +304,15 @@ object ALS { } /** - * Train a matrix factorization model given an RDD of ratings given by users to some products, - * in the form of (userID, productID, rating) pairs. We approximate the ratings matrix as the - * product of two lower-rank matrices of a given rank (number of features). To solve for these - * features, we run a given number of iterations of ALS. This is done using a level of - * parallelism given by `blocks`. + * Train a matrix factorization model given an RDD of ratings by users for a subset of products. + * The ratings matrix is approximated as the product of two lower-rank matrices of a given rank + * (number of features). To solve for these features, ALS is run iteratively with a configurable + * level of parallelism. * - * @param ratings RDD of (userID, productID, rating) pairs + * @param ratings RDD of [[Rating]] objects with userID, productID, and rating * @param rank number of features to use - * @param iterations number of iterations of ALS (recommended: 10-20) - * @param lambda regularization factor (recommended: 0.01) + * @param iterations number of iterations of ALS + * @param lambda regularization parameter * @param blocks level of parallelism to split computation into */ @Since("0.8.0") @@ -329,16 +327,15 @@ object ALS { } /** - * Train a matrix factorization model given an RDD of ratings given by users to some products, - * in the form of (userID, productID, rating) pairs. We approximate the ratings matrix as the - * product of two lower-rank matrices of a given rank (number of features). To solve for these - * features, we run a given number of iterations of ALS. The level of parallelism is determined - * automatically based on the number of partitions in `ratings`. + * Train a matrix factorization model given an RDD of ratings by users for a subset of products. + * The ratings matrix is approximated as the product of two lower-rank matrices of a given rank + * (number of features). To solve for these features, ALS is run iteratively with a level of + * parallelism automatically based on the number of partitions in `ratings`. * - * @param ratings RDD of (userID, productID, rating) pairs + * @param ratings RDD of [[Rating]] objects with userID, productID, and rating * @param rank number of features to use - * @param iterations number of iterations of ALS (recommended: 10-20) - * @param lambda regularization factor (recommended: 0.01) + * @param iterations number of iterations of ALS + * @param lambda regularization parameter */ @Since("0.8.0") def train(ratings: RDD[Rating], rank: Int, iterations: Int, lambda: Double) @@ -347,15 +344,14 @@ object ALS { } /** - * Train a matrix factorization model given an RDD of ratings given by users to some products, - * in the form of (userID, productID, rating) pairs. We approximate the ratings matrix as the - * product of two lower-rank matrices of a given rank (number of features). To solve for these - * features, we run a given number of iterations of ALS. The level of parallelism is determined - * automatically based on the number of partitions in `ratings`. + * Train a matrix factorization model given an RDD of ratings by users for a subset of products. + * The ratings matrix is approximated as the product of two lower-rank matrices of a given rank + * (number of features). To solve for these features, ALS is run iteratively with a level of + * parallelism automatically based on the number of partitions in `ratings`. * - * @param ratings RDD of (userID, productID, rating) pairs + * @param ratings RDD of [[Rating]] objects with userID, productID, and rating * @param rank number of features to use - * @param iterations number of iterations of ALS (recommended: 10-20) + * @param iterations number of iterations of ALS */ @Since("0.8.0") def train(ratings: RDD[Rating], rank: Int, iterations: Int) @@ -372,11 +368,11 @@ 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) - * @param lambda regularization factor (recommended: 0.01) + * @param iterations number of iterations of ALS + * @param lambda regularization parameter * @param blocks level of parallelism to split computation into * @param alpha confidence parameter - * @param seed random seed + * @param seed random seed for initial matrix factorization model */ @Since("0.8.1") def trainImplicit( @@ -392,16 +388,15 @@ object ALS { } /** - * Train a matrix factorization model given an RDD of 'implicit preferences' given by users - * to some products, in the form of (userID, productID, preference) pairs. We approximate the - * ratings matrix as the product of two lower-rank matrices of a given rank (number of features). - * To solve for these features, we run a given number of iterations of ALS. This is done using - * a level of parallelism given by `blocks`. + * Train a matrix factorization model given an RDD of 'implicit preferences' of users for a + * subset of products. The ratings matrix is approximated as the product of two lower-rank + * matrices of a given rank (number of features). To solve for these features, ALS is run + * iteratively with a configurable level of parallelism. * - * @param ratings RDD of (userID, productID, rating) pairs + * @param ratings RDD of [[Rating]] objects with userID, productID, and rating * @param rank number of features to use - * @param iterations number of iterations of ALS (recommended: 10-20) - * @param lambda regularization factor (recommended: 0.01) + * @param iterations number of iterations of ALS + * @param lambda regularization parameter * @param blocks level of parallelism to split computation into * @param alpha confidence parameter */ @@ -418,16 +413,16 @@ object ALS { } /** - * Train a matrix factorization model given an RDD of 'implicit preferences' given by users to - * some products, in the form of (userID, productID, preference) pairs. We approximate the - * ratings matrix as the product of two lower-rank matrices of a given rank (number of features). - * To solve for these features, we run a given number of iterations of ALS. The level of - * parallelism is determined automatically based on the number of partitions in `ratings`. + * Train a matrix factorization model given an RDD of 'implicit preferences' of users for a + * subset of products. The ratings matrix is approximated as the product of two lower-rank + * matrices of a given rank (number of features). To solve for these features, ALS is run + * iteratively with a level of parallelism determined automatically based on the number of + * partitions in `ratings`. * - * @param ratings RDD of (userID, productID, rating) pairs + * @param ratings RDD of [[Rating]] objects with userID, productID, and rating * @param rank number of features to use - * @param iterations number of iterations of ALS (recommended: 10-20) - * @param lambda regularization factor (recommended: 0.01) + * @param iterations number of iterations of ALS + * @param lambda regularization parameter * @param alpha confidence parameter */ @Since("0.8.1") @@ -437,16 +432,15 @@ object ALS { } /** - * Train a matrix factorization model given an RDD of 'implicit preferences' ratings given by - * users to some products, in the form of (userID, productID, rating) pairs. We approximate the - * ratings matrix as the product of two lower-rank matrices of a given rank (number of features). - * To solve for these features, we run a given number of iterations of ALS. The level of - * parallelism is determined automatically based on the number of partitions in `ratings`. - * Model parameters `alpha` and `lambda` are set to reasonable default values + * Train a matrix factorization model given an RDD of 'implicit preferences' of users for a + * subset of products. The ratings matrix is approximated as the product of two lower-rank + * matrices of a given rank (number of features). To solve for these features, ALS is run + * iteratively with a level of parallelism determined automatically based on the number of + * partitions in `ratings`. * - * @param ratings RDD of (userID, productID, rating) pairs + * @param ratings RDD of [[Rating]] objects with userID, productID, and rating * @param rank number of features to use - * @param iterations number of iterations of ALS (recommended: 10-20) + * @param iterations number of iterations of ALS */ @Since("0.8.1") def trainImplicit(ratings: RDD[Rating], rank: Int, iterations: Int) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala index 0dc40483dd..628cf1dd57 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala @@ -206,7 +206,7 @@ class MatrixFactorizationModel @Since("0.8.0") ( } /** - * Recommends topK products for all users. + * Recommends top products for all users. * * @param num how many products to return for every user. * @return [(Int, Array[Rating])] objects, where every tuple contains a userID and an array of @@ -224,7 +224,7 @@ class MatrixFactorizationModel @Since("0.8.0") ( /** - * Recommends topK users for all products. + * Recommends top users for all products. * * @param num how many users to return for every product. * @return [(Int, Array[Rating])] objects, where every tuple contains a productID and an array |