[SPARK-18324][ML][DOC] Update ML programming and migration guide for 2.1 release
## What changes were proposed in this pull request? Update ML programming and migration guide for 2.1 release. ## How was this patch tested? Doc change, no test. Author: Yanbo Liang <ybliang8@gmail.com> Closes #16076 from yanboliang/spark-18324.
This commit is contained in:
Yanbo Liang
Joseph K. Bradley
parent
56a503df5c
commit
2dc0d7efe3
148
docs/ml-guide.md
148
docs/ml-guide.md
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@ -60,152 +60,34 @@ MLlib is under active development.
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The APIs marked `Experimental`/`DeveloperApi` may change in future releases,
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and the migration guide below will explain all changes between releases.
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## From 1.6 to 2.0
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## From 2.0 to 2.1
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### Breaking changes
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There were several breaking changes in Spark 2.0, which are outlined below.
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**Linear algebra classes for DataFrame-based APIs**
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Spark's linear algebra dependencies were moved to a new project, `mllib-local`
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(see [SPARK-13944](https://issues.apache.org/jira/browse/SPARK-13944)).
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As part of this change, the linear algebra classes were copied to a new package, `spark.ml.linalg`.
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The DataFrame-based APIs in `spark.ml` now depend on the `spark.ml.linalg` classes,
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leading to a few breaking changes, predominantly in various model classes
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(see [SPARK-14810](https://issues.apache.org/jira/browse/SPARK-14810) for a full list).
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**Note:** the RDD-based APIs in `spark.mllib` continue to depend on the previous package `spark.mllib.linalg`.
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_Converting vectors and matrices_
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While most pipeline components support backward compatibility for loading,
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some existing `DataFrames` and pipelines in Spark versions prior to 2.0, that contain vector or matrix
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columns, may need to be migrated to the new `spark.ml` vector and matrix types.
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Utilities for converting `DataFrame` columns from `spark.mllib.linalg` to `spark.ml.linalg` types
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(and vice versa) can be found in `spark.mllib.util.MLUtils`.
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There are also utility methods available for converting single instances of
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vectors and matrices. Use the `asML` method on a `mllib.linalg.Vector` / `mllib.linalg.Matrix`
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for converting to `ml.linalg` types, and
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`mllib.linalg.Vectors.fromML` / `mllib.linalg.Matrices.fromML`
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for converting to `mllib.linalg` types.
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<div class="codetabs">
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<div data-lang="scala" markdown="1">
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{% highlight scala %}
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import org.apache.spark.mllib.util.MLUtils
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// convert DataFrame columns
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val convertedVecDF = MLUtils.convertVectorColumnsToML(vecDF)
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val convertedMatrixDF = MLUtils.convertMatrixColumnsToML(matrixDF)
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// convert a single vector or matrix
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val mlVec: org.apache.spark.ml.linalg.Vector = mllibVec.asML
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val mlMat: org.apache.spark.ml.linalg.Matrix = mllibMat.asML
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{% endhighlight %}
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Refer to the [`MLUtils` Scala docs](api/scala/index.html#org.apache.spark.mllib.util.MLUtils$) for further detail.
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</div>
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<div data-lang="java" markdown="1">
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{% highlight java %}
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import org.apache.spark.mllib.util.MLUtils;
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import org.apache.spark.sql.Dataset;
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// convert DataFrame columns
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Dataset<Row> convertedVecDF = MLUtils.convertVectorColumnsToML(vecDF);
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Dataset<Row> convertedMatrixDF = MLUtils.convertMatrixColumnsToML(matrixDF);
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// convert a single vector or matrix
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org.apache.spark.ml.linalg.Vector mlVec = mllibVec.asML();
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org.apache.spark.ml.linalg.Matrix mlMat = mllibMat.asML();
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{% endhighlight %}
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Refer to the [`MLUtils` Java docs](api/java/org/apache/spark/mllib/util/MLUtils.html) for further detail.
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</div>
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<div data-lang="python" markdown="1">
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{% highlight python %}
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from pyspark.mllib.util import MLUtils
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# convert DataFrame columns
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convertedVecDF = MLUtils.convertVectorColumnsToML(vecDF)
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convertedMatrixDF = MLUtils.convertMatrixColumnsToML(matrixDF)
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# convert a single vector or matrix
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mlVec = mllibVec.asML()
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mlMat = mllibMat.asML()
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{% endhighlight %}
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Refer to the [`MLUtils` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.util.MLUtils) for further detail.
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</div>
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</div>
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**Deprecated methods removed**
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Several deprecated methods were removed in the `spark.mllib` and `spark.ml` packages:
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* `setScoreCol` in `ml.evaluation.BinaryClassificationEvaluator`
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* `weights` in `LinearRegression` and `LogisticRegression` in `spark.ml`
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* `setMaxNumIterations` in `mllib.optimization.LBFGS` (marked as `DeveloperApi`)
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* `treeReduce` and `treeAggregate` in `mllib.rdd.RDDFunctions` (these functions are available on `RDD`s directly, and were marked as `DeveloperApi`)
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* `defaultStategy` in `mllib.tree.configuration.Strategy`
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* `build` in `mllib.tree.Node`
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* libsvm loaders for multiclass and load/save labeledData methods in `mllib.util.MLUtils`
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A full list of breaking changes can be found at [SPARK-14810](https://issues.apache.org/jira/browse/SPARK-14810).
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* `setLabelCol` in `feature.ChiSqSelectorModel`
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* `numTrees` in `classification.RandomForestClassificationModel` (This now refers to the Param called `numTrees`)
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* `numTrees` in `regression.RandomForestRegressionModel` (This now refers to the Param called `numTrees`)
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* `model` in `regression.LinearRegressionSummary`
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* `validateParams` in `PipelineStage`
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* `validateParams` in `Evaluator`
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### Deprecations and changes of behavior
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**Deprecations**
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Deprecations in the `spark.mllib` and `spark.ml` packages include:
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* [SPARK-14984](https://issues.apache.org/jira/browse/SPARK-14984):
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In `spark.ml.regression.LinearRegressionSummary`, the `model` field has been deprecated.
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* [SPARK-13784](https://issues.apache.org/jira/browse/SPARK-13784):
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In `spark.ml.regression.RandomForestRegressionModel` and `spark.ml.classification.RandomForestClassificationModel`,
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the `numTrees` parameter has been deprecated in favor of `getNumTrees` method.
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* [SPARK-13761](https://issues.apache.org/jira/browse/SPARK-13761):
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In `spark.ml.param.Params`, the `validateParams` method has been deprecated.
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We move all functionality in overridden methods to the corresponding `transformSchema`.
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* [SPARK-14829](https://issues.apache.org/jira/browse/SPARK-14829):
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In `spark.mllib` package, `LinearRegressionWithSGD`, `LassoWithSGD`, `RidgeRegressionWithSGD` and `LogisticRegressionWithSGD` have been deprecated.
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We encourage users to use `spark.ml.regression.LinearRegresson` and `spark.ml.classification.LogisticRegresson`.
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* [SPARK-14900](https://issues.apache.org/jira/browse/SPARK-14900):
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In `spark.mllib.evaluation.MulticlassMetrics`, the parameters `precision`, `recall` and `fMeasure` have been deprecated in favor of `accuracy`.
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* [SPARK-15644](https://issues.apache.org/jira/browse/SPARK-15644):
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In `spark.ml.util.MLReader` and `spark.ml.util.MLWriter`, the `context` method has been deprecated in favor of `session`.
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* In `spark.ml.feature.ChiSqSelectorModel`, the `setLabelCol` method has been deprecated since it was not used by `ChiSqSelectorModel`.
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* [SPARK-18592](https://issues.apache.org/jira/browse/SPARK-18592):
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Deprecate all Param setter methods except for input/output column Params for `DecisionTreeClassificationModel`, `GBTClassificationModel`, `RandomForestClassificationModel`, `DecisionTreeRegressionModel`, `GBTRegressionModel` and `RandomForestRegressionModel`
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**Changes of behavior**
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Changes of behavior in the `spark.mllib` and `spark.ml` packages include:
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* [SPARK-7780](https://issues.apache.org/jira/browse/SPARK-7780):
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`spark.mllib.classification.LogisticRegressionWithLBFGS` directly calls `spark.ml.classification.LogisticRegresson` for binary classification now.
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This will introduce the following behavior changes for `spark.mllib.classification.LogisticRegressionWithLBFGS`:
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* The intercept will not be regularized when training binary classification model with L1/L2 Updater.
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* If users set without regularization, training with or without feature scaling will return the same solution by the same convergence rate.
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* [SPARK-13429](https://issues.apache.org/jira/browse/SPARK-13429):
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In order to provide better and consistent result with `spark.ml.classification.LogisticRegresson`,
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the default value of `spark.mllib.classification.LogisticRegressionWithLBFGS`: `convergenceTol` has been changed from 1E-4 to 1E-6.
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* [SPARK-12363](https://issues.apache.org/jira/browse/SPARK-12363):
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Fix a bug of `PowerIterationClustering` which will likely change its result.
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* [SPARK-13048](https://issues.apache.org/jira/browse/SPARK-13048):
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`LDA` using the `EM` optimizer will keep the last checkpoint by default, if checkpointing is being used.
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* [SPARK-12153](https://issues.apache.org/jira/browse/SPARK-12153):
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`Word2Vec` now respects sentence boundaries. Previously, it did not handle them correctly.
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* [SPARK-10574](https://issues.apache.org/jira/browse/SPARK-10574):
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`HashingTF` uses `MurmurHash3` as default hash algorithm in both `spark.ml` and `spark.mllib`.
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* [SPARK-14768](https://issues.apache.org/jira/browse/SPARK-14768):
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The `expectedType` argument for PySpark `Param` was removed.
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* [SPARK-14931](https://issues.apache.org/jira/browse/SPARK-14931):
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Some default `Param` values, which were mismatched between pipelines in Scala and Python, have been changed.
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* [SPARK-13600](https://issues.apache.org/jira/browse/SPARK-13600):
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`QuantileDiscretizer` now uses `spark.sql.DataFrameStatFunctions.approxQuantile` to find splits (previously used custom sampling logic).
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The output buckets will differ for same input data and params.
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* [SPARK-17870](https://issues.apache.org/jira/browse/SPARK-17870):
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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.
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* [SPARK-3261](https://issues.apache.org/jira/browse/SPARK-3261):
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`KMeans` returns potentially fewer than k cluster centers in cases where k distinct centroids aren't available or aren't selected.
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* [SPARK-17389](https://issues.apache.org/jira/browse/SPARK-17389):
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`KMeans` reduces the default number of steps from 5 to 2 for the k-means|| initialization mode.
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## Previous Spark versions
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@ -7,6 +7,153 @@ 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 1.6 to 2.0
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### Breaking changes
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There were several breaking changes in Spark 2.0, which are outlined below.
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**Linear algebra classes for DataFrame-based APIs**
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Spark's linear algebra dependencies were moved to a new project, `mllib-local`
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(see [SPARK-13944](https://issues.apache.org/jira/browse/SPARK-13944)).
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As part of this change, the linear algebra classes were copied to a new package, `spark.ml.linalg`.
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The DataFrame-based APIs in `spark.ml` now depend on the `spark.ml.linalg` classes,
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leading to a few breaking changes, predominantly in various model classes
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(see [SPARK-14810](https://issues.apache.org/jira/browse/SPARK-14810) for a full list).
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**Note:** the RDD-based APIs in `spark.mllib` continue to depend on the previous package `spark.mllib.linalg`.
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_Converting vectors and matrices_
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While most pipeline components support backward compatibility for loading,
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some existing `DataFrames` and pipelines in Spark versions prior to 2.0, that contain vector or matrix
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columns, may need to be migrated to the new `spark.ml` vector and matrix types.
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Utilities for converting `DataFrame` columns from `spark.mllib.linalg` to `spark.ml.linalg` types
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(and vice versa) can be found in `spark.mllib.util.MLUtils`.
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There are also utility methods available for converting single instances of
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vectors and matrices. Use the `asML` method on a `mllib.linalg.Vector` / `mllib.linalg.Matrix`
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for converting to `ml.linalg` types, and
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`mllib.linalg.Vectors.fromML` / `mllib.linalg.Matrices.fromML`
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for converting to `mllib.linalg` types.
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<div class="codetabs">
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<div data-lang="scala" markdown="1">
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{% highlight scala %}
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import org.apache.spark.mllib.util.MLUtils
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// convert DataFrame columns
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val convertedVecDF = MLUtils.convertVectorColumnsToML(vecDF)
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val convertedMatrixDF = MLUtils.convertMatrixColumnsToML(matrixDF)
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// convert a single vector or matrix
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val mlVec: org.apache.spark.ml.linalg.Vector = mllibVec.asML
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val mlMat: org.apache.spark.ml.linalg.Matrix = mllibMat.asML
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{% endhighlight %}
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Refer to the [`MLUtils` Scala docs](api/scala/index.html#org.apache.spark.mllib.util.MLUtils$) for further detail.
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</div>
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<div data-lang="java" markdown="1">
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{% highlight java %}
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import org.apache.spark.mllib.util.MLUtils;
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import org.apache.spark.sql.Dataset;
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// convert DataFrame columns
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Dataset<Row> convertedVecDF = MLUtils.convertVectorColumnsToML(vecDF);
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Dataset<Row> convertedMatrixDF = MLUtils.convertMatrixColumnsToML(matrixDF);
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// convert a single vector or matrix
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org.apache.spark.ml.linalg.Vector mlVec = mllibVec.asML();
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org.apache.spark.ml.linalg.Matrix mlMat = mllibMat.asML();
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{% endhighlight %}
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Refer to the [`MLUtils` Java docs](api/java/org/apache/spark/mllib/util/MLUtils.html) for further detail.
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</div>
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<div data-lang="python" markdown="1">
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{% highlight python %}
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from pyspark.mllib.util import MLUtils
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# convert DataFrame columns
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convertedVecDF = MLUtils.convertVectorColumnsToML(vecDF)
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convertedMatrixDF = MLUtils.convertMatrixColumnsToML(matrixDF)
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# convert a single vector or matrix
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mlVec = mllibVec.asML()
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mlMat = mllibMat.asML()
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{% endhighlight %}
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Refer to the [`MLUtils` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.util.MLUtils) for further detail.
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</div>
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</div>
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**Deprecated methods removed**
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Several deprecated methods were removed in the `spark.mllib` and `spark.ml` packages:
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* `setScoreCol` in `ml.evaluation.BinaryClassificationEvaluator`
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* `weights` in `LinearRegression` and `LogisticRegression` in `spark.ml`
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* `setMaxNumIterations` in `mllib.optimization.LBFGS` (marked as `DeveloperApi`)
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* `treeReduce` and `treeAggregate` in `mllib.rdd.RDDFunctions` (these functions are available on `RDD`s directly, and were marked as `DeveloperApi`)
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* `defaultStategy` in `mllib.tree.configuration.Strategy`
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* `build` in `mllib.tree.Node`
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* libsvm loaders for multiclass and load/save labeledData methods in `mllib.util.MLUtils`
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A full list of breaking changes can be found at [SPARK-14810](https://issues.apache.org/jira/browse/SPARK-14810).
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### Deprecations and changes of behavior
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**Deprecations**
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Deprecations in the `spark.mllib` and `spark.ml` packages include:
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* [SPARK-14984](https://issues.apache.org/jira/browse/SPARK-14984):
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In `spark.ml.regression.LinearRegressionSummary`, the `model` field has been deprecated.
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* [SPARK-13784](https://issues.apache.org/jira/browse/SPARK-13784):
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In `spark.ml.regression.RandomForestRegressionModel` and `spark.ml.classification.RandomForestClassificationModel`,
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the `numTrees` parameter has been deprecated in favor of `getNumTrees` method.
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* [SPARK-13761](https://issues.apache.org/jira/browse/SPARK-13761):
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In `spark.ml.param.Params`, the `validateParams` method has been deprecated.
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We move all functionality in overridden methods to the corresponding `transformSchema`.
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* [SPARK-14829](https://issues.apache.org/jira/browse/SPARK-14829):
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In `spark.mllib` package, `LinearRegressionWithSGD`, `LassoWithSGD`, `RidgeRegressionWithSGD` and `LogisticRegressionWithSGD` have been deprecated.
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We encourage users to use `spark.ml.regression.LinearRegresson` and `spark.ml.classification.LogisticRegresson`.
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* [SPARK-14900](https://issues.apache.org/jira/browse/SPARK-14900):
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In `spark.mllib.evaluation.MulticlassMetrics`, the parameters `precision`, `recall` and `fMeasure` have been deprecated in favor of `accuracy`.
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* [SPARK-15644](https://issues.apache.org/jira/browse/SPARK-15644):
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In `spark.ml.util.MLReader` and `spark.ml.util.MLWriter`, the `context` method has been deprecated in favor of `session`.
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* In `spark.ml.feature.ChiSqSelectorModel`, the `setLabelCol` method has been deprecated since it was not used by `ChiSqSelectorModel`.
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**Changes of behavior**
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Changes of behavior in the `spark.mllib` and `spark.ml` packages include:
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* [SPARK-7780](https://issues.apache.org/jira/browse/SPARK-7780):
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`spark.mllib.classification.LogisticRegressionWithLBFGS` directly calls `spark.ml.classification.LogisticRegresson` for binary classification now.
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This will introduce the following behavior changes for `spark.mllib.classification.LogisticRegressionWithLBFGS`:
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* The intercept will not be regularized when training binary classification model with L1/L2 Updater.
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* If users set without regularization, training with or without feature scaling will return the same solution by the same convergence rate.
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* [SPARK-13429](https://issues.apache.org/jira/browse/SPARK-13429):
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In order to provide better and consistent result with `spark.ml.classification.LogisticRegresson`,
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the default value of `spark.mllib.classification.LogisticRegressionWithLBFGS`: `convergenceTol` has been changed from 1E-4 to 1E-6.
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* [SPARK-12363](https://issues.apache.org/jira/browse/SPARK-12363):
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Fix a bug of `PowerIterationClustering` which will likely change its result.
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* [SPARK-13048](https://issues.apache.org/jira/browse/SPARK-13048):
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`LDA` using the `EM` optimizer will keep the last checkpoint by default, if checkpointing is being used.
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* [SPARK-12153](https://issues.apache.org/jira/browse/SPARK-12153):
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`Word2Vec` now respects sentence boundaries. Previously, it did not handle them correctly.
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* [SPARK-10574](https://issues.apache.org/jira/browse/SPARK-10574):
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`HashingTF` uses `MurmurHash3` as default hash algorithm in both `spark.ml` and `spark.mllib`.
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* [SPARK-14768](https://issues.apache.org/jira/browse/SPARK-14768):
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The `expectedType` argument for PySpark `Param` was removed.
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* [SPARK-14931](https://issues.apache.org/jira/browse/SPARK-14931):
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Some default `Param` values, which were mismatched between pipelines in Scala and Python, have been changed.
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* [SPARK-13600](https://issues.apache.org/jira/browse/SPARK-13600):
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`QuantileDiscretizer` now uses `spark.sql.DataFrameStatFunctions.approxQuantile` to find splits (previously used custom sampling logic).
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The output buckets will differ for same input data and params.
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## From 1.5 to 1.6
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There are no breaking API changes in the `spark.mllib` or `spark.ml` packages, but there are
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