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[SPARK-15643] [Doc] [ML] Update spark.ml and spark.mllib migration guide from 1.6 to 2.0 #13378

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[SPARK-15643] [Doc] [ML] Update spark.ml and spark.mllib migration guide from 1.6 to 2.0 #13378

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182414b
Document MLlib deprecations and behavior changes in Spark 2.0
yanboliang May 28, 2016
fb610d2
fix typos
yanboliang May 28, 2016
260f3a3
update docs
yanboliang May 30, 2016
235930d
fix typos
yanboliang Jun 1, 2016
2339200
Remove SPARK-14814 which is a breaking change
yanboliang Jun 2, 2016
d2666ac
Add two deprecations
yanboliang Jun 27, 2016
5472fb9
BaseReadWrite to MLReader/MLWriter
yanboliang Jun 28, 2016
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60 changes: 41 additions & 19 deletions docs/mllib-guide.md
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Expand Up @@ -102,32 +102,54 @@ 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 1.5 to 1.6
## From 1.6 to 2.0

There are no breaking API changes in the `spark.mllib` or `spark.ml` packages, but there are
deprecations and changes of behavior.
The deprecations and changes of behavior in the `spark.mllib` or `spark.ml` packages include:

Deprecations:

* [SPARK-11358](https://issues.apache.org/jira/browse/SPARK-11358):
In `spark.mllib.clustering.KMeans`, the `runs` parameter has been deprecated.
* [SPARK-10592](https://issues.apache.org/jira/browse/SPARK-10592):
In `spark.ml.classification.LogisticRegressionModel` and
`spark.ml.regression.LinearRegressionModel`, the `weights` field has been deprecated in favor of
the new name `coefficients`. This helps disambiguate from instance (row) "weights" given to
algorithms.
* [SPARK-14984](https://issues.apache.org/jira/browse/SPARK-14984):
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@MLnick MLnick May 29, 2016

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@yanboliang there are breaking changes for removing some deprecated methods in https://issues.apache.org/jira/browse/SPARK-14089 and https://issues.apache.org/jira/browse/SPARK-14952 that we should highlight.

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@MLnick MLnick May 29, 2016

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Though I'm happy to just do that in a follow up PR once I've made a final pass through for MiMa changes.

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@yanboliang yanboliang May 30, 2016

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Good points. I forgot to record all removed deprecated methods. It's great that you can do that in a follow up PR. Thanks!

In `spark.ml.regression.LinearRegressionSummary`, the `model` field has been deprecated.
* [SPARK-13784](https://issues.apache.org/jira/browse/SPARK-13784):
In `spark.ml.regression.RandomForestRegressionModel` and `spark.ml.classification.RandomForestClassificationModel`,
the `numTrees` parameter has been deprecated in favor of `getNumTrees` method.
* [SPARK-13761](https://issues.apache.org/jira/browse/SPARK-13761):
In `spark.ml.param.Params`, the `validateParams` method has been deprecated.
We move all functionality in overridden methods to the corresponding `transformSchema`.
* [SPARK-14829](https://issues.apache.org/jira/browse/SPARK-14829):
In `spark.mllib` package, `LinearRegressionWithSGD`, `LassoWithSGD`, `RidgeRegressionWithSGD` and `LogisticRegressionWithSGD` have been deprecated.
We encourage users to use `spark.ml.regression.LinearRegresson` and `spark.ml.classification.LogisticRegresson`.
* [SPARK-14900](https://issues.apache.org/jira/browse/SPARK-14900):
In `spark.mllib.evaluation.MulticlassMetrics`, the parameters `precision`, `recall` and `fMeasure` have been deprecated in favor of `accuracy`.
* [SPARK-15644](https://issues.apache.org/jira/browse/SPARK-15644):
In `spark.ml.util.MLReader` and `spark.ml.util.MLWriter`, the `context` method has been deprecated in favor of `session`.
* In `spark.ml.feature.ChiSqSelectorModel`, the `setLabelCol` method has been deprecated since it was not used by `ChiSqSelectorModel`.

Changes of behavior:

* [SPARK-7770](https://issues.apache.org/jira/browse/SPARK-7770):
`spark.mllib.tree.GradientBoostedTrees`: `validationTol` has changed semantics in 1.6.
Previously, it was a threshold for absolute change in error. Now, it resembles the behavior of
`GradientDescent`'s `convergenceTol`: For large errors, it uses relative error (relative to the
previous error); for small errors (`< 0.01`), it uses absolute error.
* [SPARK-11069](https://issues.apache.org/jira/browse/SPARK-11069):
`spark.ml.feature.RegexTokenizer`: Previously, it did not convert strings to lowercase before
tokenizing. Now, it converts to lowercase by default, with an option not to. This matches the
behavior of the simpler `Tokenizer` transformer.
* [SPARK-7780](https://issues.apache.org/jira/browse/SPARK-7780):
`spark.mllib.classification.LogisticRegressionWithLBFGS` directly calls `spark.ml.classification.LogisticRegresson` for binary classification now.
This will introduce the following behavior changes for `spark.mllib.classification.LogisticRegressionWithLBFGS`:
* The intercept will not be regularized when training binary classification model with L1/L2 Updater.
* If users set without regularization, training with or without feature scaling will return the same solution by the same convergence rate.
* [SPARK-13429](https://issues.apache.org/jira/browse/SPARK-13429):
In order to provide better and consistent result with `spark.ml.classification.LogisticRegresson`,
the default value of `spark.mllib.classification.LogisticRegressionWithLBFGS`: `convergenceTol` has been changed from 1E-4 to 1E-6.
* [SPARK-12363](https://issues.apache.org/jira/browse/SPARK-12363):
Fix a bug of `PowerIterationClustering` which will likely change its result.
* [SPARK-13048](https://issues.apache.org/jira/browse/SPARK-13048):
`LDA` using the `EM` optimizer will keep the last checkpoint by default, if checkpointing is being used.
* [SPARK-12153](https://issues.apache.org/jira/browse/SPARK-12153):
`Word2Vec` now respects sentence boundaries. Previously, it did not handle them correctly.
* [SPARK-10574](https://issues.apache.org/jira/browse/SPARK-10574):
`HashingTF` uses `MurmurHash3` as default hash algorithm in both `spark.ml` and `spark.mllib`.
* [SPARK-14768](https://issues.apache.org/jira/browse/SPARK-14768):
The `expectedType` argument for PySpark `Param` was removed.
* [SPARK-14931](https://issues.apache.org/jira/browse/SPARK-14931):
Some default `Param` values, which were mismatched between pipelines in Scala and Python, have been changed.
* [SPARK-13600](https://issues.apache.org/jira/browse/SPARK-13600):
`QuantileDiscretizer` now uses `spark.sql.DataFrameStatFunctions.approxQuantile` to find splits (previously used custom sampling logic).
The output buckets will differ for same input data and params.

## Previous Spark versions

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27 changes: 27 additions & 0 deletions docs/mllib-migration-guides.md
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Expand Up @@ -7,6 +7,33 @@ description: MLlib migration guides from before Spark SPARK_VERSION_SHORT

The migration guide for the current Spark version is kept on the [MLlib Programming Guide main page](mllib-guide.html#migration-guide).

## From 1.5 to 1.6

There are no breaking API changes in the `spark.mllib` or `spark.ml` packages, but there are
deprecations and changes of behavior.

Deprecations:

* [SPARK-11358](https://issues.apache.org/jira/browse/SPARK-11358):
In `spark.mllib.clustering.KMeans`, the `runs` parameter has been deprecated.
* [SPARK-10592](https://issues.apache.org/jira/browse/SPARK-10592):
In `spark.ml.classification.LogisticRegressionModel` and
`spark.ml.regression.LinearRegressionModel`, the `weights` field has been deprecated in favor of
the new name `coefficients`. This helps disambiguate from instance (row) "weights" given to
algorithms.

Changes of behavior:

* [SPARK-7770](https://issues.apache.org/jira/browse/SPARK-7770):
`spark.mllib.tree.GradientBoostedTrees`: `validationTol` has changed semantics in 1.6.
Previously, it was a threshold for absolute change in error. Now, it resembles the behavior of
`GradientDescent`'s `convergenceTol`: For large errors, it uses relative error (relative to the
previous error); for small errors (`< 0.01`), it uses absolute error.
* [SPARK-11069](https://issues.apache.org/jira/browse/SPARK-11069):
`spark.ml.feature.RegexTokenizer`: Previously, it did not convert strings to lowercase before
tokenizing. Now, it converts to lowercase by default, with an option not to. This matches the
behavior of the simpler `Tokenizer` transformer.

## From 1.4 to 1.5

In the `spark.mllib` package, there are no breaking API changes but several behavior changes:
Expand Down