[SPARK-23112][DOC] Add highlights and migration guide for 2.3
Update ML user guide with highlights and migration guide for `2.3`. ## How was this patch tested? Doc only. Author: Nick Pentreath <nickp@za.ibm.com> Closes #20363 from MLnick/SPARK-23112-ml-guide.
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@ -72,32 +72,31 @@ To use MLlib in Python, you will need [NumPy](http://www.numpy.org) version 1.4
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[^1]: To learn more about the benefits and background of system optimised natives, you may wish to
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watch Sam Halliday's ScalaX talk on [High Performance Linear Algebra in Scala](http://fommil.github.io/scalax14/#/).
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# Highlights in 2.2
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# Highlights in 2.3
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The list below highlights some of the new features and enhancements added to MLlib in the `2.2`
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The list below highlights some of the new features and enhancements added to MLlib in the `2.3`
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release of Spark:
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* [`ALS`](ml-collaborative-filtering.html) methods for _top-k_ recommendations for all
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users or items, matching the functionality in `mllib`
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([SPARK-19535](https://issues.apache.org/jira/browse/SPARK-19535)).
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Performance was also improved for both `ml` and `mllib`
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([SPARK-11968](https://issues.apache.org/jira/browse/SPARK-11968) and
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[SPARK-20587](https://issues.apache.org/jira/browse/SPARK-20587))
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* [`Correlation`](ml-statistics.html#correlation) and
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[`ChiSquareTest`](ml-statistics.html#hypothesis-testing) stats functions for `DataFrames`
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([SPARK-19636](https://issues.apache.org/jira/browse/SPARK-19636) and
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[SPARK-19635](https://issues.apache.org/jira/browse/SPARK-19635))
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* [`FPGrowth`](ml-frequent-pattern-mining.html#fp-growth) algorithm for frequent pattern mining
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([SPARK-14503](https://issues.apache.org/jira/browse/SPARK-14503))
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* `GLM` now supports the full `Tweedie` family
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([SPARK-18929](https://issues.apache.org/jira/browse/SPARK-18929))
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* [`Imputer`](ml-features.html#imputer) feature transformer to impute missing values in a dataset
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([SPARK-13568](https://issues.apache.org/jira/browse/SPARK-13568))
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* [`LinearSVC`](ml-classification-regression.html#linear-support-vector-machine)
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for linear Support Vector Machine classification
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([SPARK-14709](https://issues.apache.org/jira/browse/SPARK-14709))
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* Logistic regression now supports constraints on the coefficients during training
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([SPARK-20047](https://issues.apache.org/jira/browse/SPARK-20047))
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* Built-in support for reading images into a `DataFrame` was added
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([SPARK-21866](https://issues.apache.org/jira/browse/SPARK-21866)).
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* [`OneHotEncoderEstimator`](ml-features.html#onehotencoderestimator) was added, and should be
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used instead of the existing `OneHotEncoder` transformer. The new estimator supports
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transforming multiple columns.
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* Multiple column support was also added to `QuantileDiscretizer` and `Bucketizer`
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([SPARK-22397](https://issues.apache.org/jira/browse/SPARK-22397) and
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[SPARK-20542](https://issues.apache.org/jira/browse/SPARK-20542))
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* A new [`FeatureHasher`](ml-features.html#featurehasher) transformer was added
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([SPARK-13969](https://issues.apache.org/jira/browse/SPARK-13969)).
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* Added support for evaluating multiple models in parallel when performing cross-validation using
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[`TrainValidationSplit` or `CrossValidator`](ml-tuning.html)
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([SPARK-19357](https://issues.apache.org/jira/browse/SPARK-19357)).
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* Improved support for custom pipeline components in Python (see
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[SPARK-21633](https://issues.apache.org/jira/browse/SPARK-21633) and
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[SPARK-21542](https://issues.apache.org/jira/browse/SPARK-21542)).
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* `DataFrame` functions for descriptive summary statistics over vector columns
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([SPARK-19634](https://issues.apache.org/jira/browse/SPARK-19634)).
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* Robust linear regression with Huber loss
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([SPARK-3181](https://issues.apache.org/jira/browse/SPARK-3181)).
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# Migration guide
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@ -115,36 +114,17 @@ There are no breaking changes.
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**Deprecations**
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There are no deprecations.
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* `OneHotEncoder` has been deprecated and will be removed in `3.0`. It has been replaced by the
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new [`OneHotEncoderEstimator`](ml-features.html#onehotencoderestimator)
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(see [SPARK-13030](https://issues.apache.org/jira/browse/SPARK-13030)). **Note** that
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`OneHotEncoderEstimator` will be renamed to `OneHotEncoder` in `3.0` (but
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`OneHotEncoderEstimator` will be kept as an alias).
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**Changes of behavior**
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* [SPARK-21027](https://issues.apache.org/jira/browse/SPARK-21027):
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We are now setting the default parallelism used in `OneVsRest` to be 1 (i.e. serial), in 2.2 and earlier version,
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the `OneVsRest` parallelism would be parallelism of the default threadpool in scala.
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## From 2.1 to 2.2
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### Breaking changes
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There are no breaking changes.
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### Deprecations and changes of behavior
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**Deprecations**
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There are no deprecations.
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**Changes of behavior**
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* [SPARK-19787](https://issues.apache.org/jira/browse/SPARK-19787):
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Default value of `regParam` changed from `1.0` to `0.1` for `ALS.train` method (marked `DeveloperApi`).
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**Note** this does _not affect_ the `ALS` Estimator or Model, nor MLlib's `ALS` class.
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* [SPARK-14772](https://issues.apache.org/jira/browse/SPARK-14772):
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Fixed inconsistency between Python and Scala APIs for `Param.copy` method.
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* [SPARK-11569](https://issues.apache.org/jira/browse/SPARK-11569):
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`StringIndexer` now handles `NULL` values in the same way as unseen values. Previously an exception
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would always be thrown regardless of the setting of the `handleInvalid` parameter.
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We are now setting the default parallelism used in `OneVsRest` to be 1 (i.e. serial). In 2.2 and
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earlier versions, the level of parallelism was set to the default threadpool size in Scala.
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## Previous Spark versions
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@ -7,6 +7,29 @@ description: MLlib migration guides from before Spark SPARK_VERSION_SHORT
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The migration guide for the current Spark version is kept on the [MLlib Guide main page](ml-guide.html#migration-guide).
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## From 2.1 to 2.2
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### Breaking changes
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There are no breaking changes.
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### Deprecations and changes of behavior
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**Deprecations**
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There are no deprecations.
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**Changes of behavior**
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* [SPARK-19787](https://issues.apache.org/jira/browse/SPARK-19787):
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Default value of `regParam` changed from `1.0` to `0.1` for `ALS.train` method (marked `DeveloperApi`).
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**Note** this does _not affect_ the `ALS` Estimator or Model, nor MLlib's `ALS` class.
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* [SPARK-14772](https://issues.apache.org/jira/browse/SPARK-14772):
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Fixed inconsistency between Python and Scala APIs for `Param.copy` method.
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* [SPARK-11569](https://issues.apache.org/jira/browse/SPARK-11569):
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`StringIndexer` now handles `NULL` values in the same way as unseen values. Previously an exception
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would always be thrown regardless of the setting of the `handleInvalid` parameter.
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## From 2.0 to 2.1
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### Breaking changes
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