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[SPARK-20506][DOCS] 2.2 migration guide

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Update ML guide for migration `2.1` -> `2.2` and the previous version migration guide section.

## How was this patch tested?

Build doc locally.

Author: Nick Pentreath <nickp@za.ibm.com>

Closes #17996 from MLnick/SPARK-20506-2.2-migration-guide.
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Nick Pentreath 2017-05-19 20:51:56 +02:00
parent 7f203a248f
commit b5d8d9ba17
2 changed files with 66 additions and 19 deletions
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56
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@ -26,7 +26,7 @@ The primary Machine Learning API for Spark is now the [DataFrame](sql-programmin
* MLlib will still support the RDD-based API in `spark.mllib` with bug fixes.
* MLlib will not add new features to the RDD-based API.
* In the Spark 2.x releases, MLlib will add features to the DataFrames-based API to reach feature parity with the RDD-based API.
* After reaching feature parity (roughly estimated for Spark 2.2), the RDD-based API will be deprecated.
* After reaching feature parity (roughly estimated for Spark 2.3), the RDD-based API will be deprecated.
* The RDD-based API is expected to be removed in Spark 3.0.
*Why is MLlib switching to the DataFrame-based API?*
@ -66,41 +66,59 @@ To use MLlib in Python, you will need [NumPy](http://www.numpy.org) version 1.4
[^1]: To learn more about the benefits and background of system optimised natives, you may wish to
watch Sam Halliday's ScalaX talk on [High Performance Linear Algebra in Scala](http://fommil.github.io/scalax14/#/).
# Highlights in 2.2
The list below highlights some of the new features and enhancements added to MLlib in the `2.2`
release of Spark:
* `ALS` methods for _top-k_ recommendations for all users or items, matching the functionality
in `mllib` ([SPARK-19535](https://issues.apache.org/jira/browse/SPARK-19535)). Performance
was also improved for both `ml` and `mllib`
([SPARK-11968](https://issues.apache.org/jira/browse/SPARK-11968) and
[SPARK-20587](https://issues.apache.org/jira/browse/SPARK-20587))
* `Correlation` and `ChiSquareTest` stats functions for `DataFrames`
([SPARK-19636](https://issues.apache.org/jira/browse/SPARK-19636) and
[SPARK-19635](https://issues.apache.org/jira/browse/SPARK-19635))
* `FPGrowth` algorithm for frequent pattern mining
([SPARK-14503](https://issues.apache.org/jira/browse/SPARK-14503))
* `GLM` now supports the full `Tweedie` family
([SPARK-18929](https://issues.apache.org/jira/browse/SPARK-18929))
* `Imputer` feature transformer to impute missing values in a dataset
([SPARK-13568](https://issues.apache.org/jira/browse/SPARK-13568))
* `LinearSVC` for linear Support Vector Machine classification
([SPARK-14709](https://issues.apache.org/jira/browse/SPARK-14709))
* Logistic regression now supports constraints on the coefficients during training
([SPARK-20047](https://issues.apache.org/jira/browse/SPARK-20047))
# Migration guide
MLlib is under active development.
The APIs marked `Experimental`/`DeveloperApi` may change in future releases,
and the migration guide below will explain all changes between releases.
## From 2.0 to 2.1
## From 2.1 to 2.2
### Breaking changes
**Deprecated methods removed**
* `setLabelCol` in `feature.ChiSqSelectorModel`
* `numTrees` in `classification.RandomForestClassificationModel` (This now refers to the Param called `numTrees`)
* `numTrees` in `regression.RandomForestRegressionModel` (This now refers to the Param called `numTrees`)
* `model` in `regression.LinearRegressionSummary`
* `validateParams` in `PipelineStage`
* `validateParams` in `Evaluator`
There are no breaking changes.
### Deprecations and changes of behavior
**Deprecations**
* [SPARK-18592](https://issues.apache.org/jira/browse/SPARK-18592):
Deprecate all Param setter methods except for input/output column Params for `DecisionTreeClassificationModel`, `GBTClassificationModel`, `RandomForestClassificationModel`, `DecisionTreeRegressionModel`, `GBTRegressionModel` and `RandomForestRegressionModel`
There are no deprecations.
**Changes of behavior**
* [SPARK-17870](https://issues.apache.org/jira/browse/SPARK-17870):
Fix a bug of `ChiSqSelector` which will likely change its result. Now `ChiSquareSelector` use pValue rather than raw statistic to select a fixed number of top features.
* [SPARK-3261](https://issues.apache.org/jira/browse/SPARK-3261):
`KMeans` returns potentially fewer than k cluster centers in cases where k distinct centroids aren't available or aren't selected.
* [SPARK-17389](https://issues.apache.org/jira/browse/SPARK-17389):
`KMeans` reduces the default number of steps from 5 to 2 for the k-means|| initialization mode.
* [SPARK-19787](https://issues.apache.org/jira/browse/SPARK-19787):
Default value of `regParam` changed from `1.0` to `0.1` for `ALS.train` method (marked `DeveloperApi`).
**Note** this does _not affect_ the `ALS` Estimator or Model, nor MLlib's `ALS` class.
* [SPARK-14772](https://issues.apache.org/jira/browse/SPARK-14772):
Fixed inconsistency between Python and Scala APIs for `Param.copy` method.
* [SPARK-11569](https://issues.apache.org/jira/browse/SPARK-11569):
`StringIndexer` now handles `NULL` values in the same way as unseen values. Previously an exception
would always be thrown regardless of the setting of the `handleInvalid` parameter.
## Previous Spark versions
Earlier migration guides are archived [on this page](ml-migration-guides.html).

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@ -7,6 +7,35 @@ description: MLlib migration guides from before Spark SPARK_VERSION_SHORT
The migration guide for the current Spark version is kept on the [MLlib Guide main page](ml-guide.html#migration-guide).
## From 2.0 to 2.1
### Breaking changes
**Deprecated methods removed**
* `setLabelCol` in `feature.ChiSqSelectorModel`
* `numTrees` in `classification.RandomForestClassificationModel` (This now refers to the Param called `numTrees`)
* `numTrees` in `regression.RandomForestRegressionModel` (This now refers to the Param called `numTrees`)
* `model` in `regression.LinearRegressionSummary`
* `validateParams` in `PipelineStage`
* `validateParams` in `Evaluator`
### Deprecations and changes of behavior
**Deprecations**
* [SPARK-18592](https://issues.apache.org/jira/browse/SPARK-18592):
Deprecate all Param setter methods except for input/output column Params for `DecisionTreeClassificationModel`, `GBTClassificationModel`, `RandomForestClassificationModel`, `DecisionTreeRegressionModel`, `GBTRegressionModel` and `RandomForestRegressionModel`
**Changes of behavior**
* [SPARK-17870](https://issues.apache.org/jira/browse/SPARK-17870):
Fix a bug of `ChiSqSelector` which will likely change its result. Now `ChiSquareSelector` use pValue rather than raw statistic to select a fixed number of top features.
* [SPARK-3261](https://issues.apache.org/jira/browse/SPARK-3261):
`KMeans` returns potentially fewer than k cluster centers in cases where k distinct centroids aren't available or aren't selected.
* [SPARK-17389](https://issues.apache.org/jira/browse/SPARK-17389):
`KMeans` reduces the default number of steps from 5 to 2 for the k-means|| initialization mode.
## From 1.6 to 2.0
### Breaking changes