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diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md index de7d66fb2d..d2d1cc93fe 100644 --- a/docs/mllib-guide.md +++ b/docs/mllib-guide.md @@ -7,7 +7,19 @@ description: MLlib machine learning library overview for Spark SPARK_VERSION_SHO MLlib is Spark's scalable machine learning library consisting of common learning algorithms and utilities, including classification, regression, clustering, collaborative -filtering, dimensionality reduction, as well as underlying optimization primitives, as outlined below: +filtering, dimensionality reduction, as well as underlying optimization primitives. +Guides for individual algorithms are listed below. + +The API is divided into 2 parts: + +* [The original `spark.mllib` API](mllib-guide.html#mllib-types-algorithms-and-utilities) is the primary API. +* [The "Pipelines" `spark.ml` API](mllib-guide.html#sparkml-high-level-apis-for-ml-pipelines) is a higher-level API for constructing ML workflows. + +We list major functionality from both below, with links to detailed guides. + +# MLlib types, algorithms and utilities + +This lists functionality included in `spark.mllib`, the main MLlib API. * [Data types](mllib-data-types.html) * [Basic statistics](mllib-statistics.html) @@ -49,8 +61,8 @@ and the migration guide below will explain all changes between releases. Spark 1.2 introduced a new package called `spark.ml`, which aims to provide a uniform set of high-level APIs that help users create and tune practical machine learning pipelines. -It is currently an alpha component, and we would like to hear back from the community about -how it fits real-world use cases and how it could be improved. + +*Graduated from Alpha!* The Pipelines API is no longer an alpha component, although many elements of it are still `Experimental` or `DeveloperApi`. Note that we will keep supporting and adding features to `spark.mllib` along with the development of `spark.ml`. @@ -58,7 +70,11 @@ Users should be comfortable using `spark.mllib` features and expect more feature Developers should contribute new algorithms to `spark.mllib` and can optionally contribute to `spark.ml`. -See the **[spark.ml programming guide](ml-guide.html)** for more information on this package. +More detailed guides for `spark.ml` include: + +* **[spark.ml programming guide](ml-guide.html)**: overview of the Pipelines API and major concepts +* [Feature transformers](ml-features.html): Details on transformers supported in the Pipelines API, including a few not in the lower-level `spark.mllib` API +* [Ensembles](ml-ensembles.html): Details on ensemble learning methods in the Pipelines API # Dependencies @@ -90,21 +106,14 @@ version 1.4 or newer. For the `spark.ml` package, please see the [spark.ml Migration Guide](ml-guide.html#migration-guide). -## From 1.2 to 1.3 - -In the `spark.mllib` package, there were several breaking changes. The first change (in `ALS`) is the only one in a component not marked as Alpha or Experimental. - -* *(Breaking change)* In [`ALS`](api/scala/index.html#org.apache.spark.mllib.recommendation.ALS), the extraneous method `solveLeastSquares` has been removed. The `DeveloperApi` method `analyzeBlocks` was also removed. -* *(Breaking change)* [`StandardScalerModel`](api/scala/index.html#org.apache.spark.mllib.feature.StandardScalerModel) remains an Alpha component. In it, the `variance` method has been replaced with the `std` method. To compute the column variance values returned by the original `variance` method, simply square the standard deviation values returned by `std`. -* *(Breaking change)* [`StreamingLinearRegressionWithSGD`](api/scala/index.html#org.apache.spark.mllib.regression.StreamingLinearRegressionWithSGD) remains an Experimental component. In it, there were two changes: - * The constructor taking arguments was removed in favor of a builder patten using the default constructor plus parameter setter methods. - * Variable `model` is no longer public. -* *(Breaking change)* [`DecisionTree`](api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree) remains an Experimental component. In it and its associated classes, there were several changes: - * In `DecisionTree`, the deprecated class method `train` has been removed. (The object/static `train` methods remain.) - * In `Strategy`, the `checkpointDir` parameter has been removed. Checkpointing is still supported, but the checkpoint directory must be set before calling tree and tree ensemble training. -* `PythonMLlibAPI` (the interface between Scala/Java and Python for MLlib) was a public API but is now private, declared `private[python]`. This was never meant for external use. -* In linear regression (including Lasso and ridge regression), the squared loss is now divided by 2. - So in order to produce the same result as in 1.2, the regularization parameter needs to be divided by 2 and the step size needs to be multiplied by 2. +## From 1.3 to 1.4 + +In the `spark.mllib` package, there were several breaking changes, but all in `DeveloperApi` or `Experimental` APIs: + +* Gradient-Boosted Trees + * *(Breaking change)* The signature of the [`Loss.gradient`](api/scala/index.html#org.apache.spark.mllib.tree.loss.Loss) method was changed. This is only an issues for users who wrote their own losses for GBTs. + * *(Breaking change)* The `apply` and `copy` methods for the case class [`BoostingStrategy`](api/scala/index.html#org.apache.spark.mllib.tree.configuration.BoostingStrategy) have been changed because of a modification to the case class fields. This could be an issue for users who use `BoostingStrategy` to set GBT parameters. +* *(Breaking change)* The return value of [`LDA.run`](api/scala/index.html#org.apache.spark.mllib.clustering.LDA) has changed. It now returns an abstract class `LDAModel` instead of the concrete class `DistributedLDAModel`. The object of type `LDAModel` can still be cast to the appropriate concrete type, which depends on the optimization algorithm. ## Previous Spark Versions |