b5d8d9ba17
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.
336 lines
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336 lines
20 KiB
Markdown
---
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layout: global
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title: Old Migration Guides - MLlib
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displayTitle: Old Migration Guides - MLlib
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description: MLlib migration guides from before Spark SPARK_VERSION_SHORT
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---
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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 2.0 to 2.1
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### Breaking changes
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**Deprecated methods removed**
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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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* [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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* [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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## 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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deprecations and changes of behavior.
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Deprecations:
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* [SPARK-11358](https://issues.apache.org/jira/browse/SPARK-11358):
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In `spark.mllib.clustering.KMeans`, the `runs` parameter has been deprecated.
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* [SPARK-10592](https://issues.apache.org/jira/browse/SPARK-10592):
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In `spark.ml.classification.LogisticRegressionModel` and
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`spark.ml.regression.LinearRegressionModel`, the `weights` field has been deprecated in favor of
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the new name `coefficients`. This helps disambiguate from instance (row) "weights" given to
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algorithms.
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Changes of behavior:
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* [SPARK-7770](https://issues.apache.org/jira/browse/SPARK-7770):
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`spark.mllib.tree.GradientBoostedTrees`: `validationTol` has changed semantics in 1.6.
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Previously, it was a threshold for absolute change in error. Now, it resembles the behavior of
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`GradientDescent`'s `convergenceTol`: For large errors, it uses relative error (relative to the
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previous error); for small errors (`< 0.01`), it uses absolute error.
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* [SPARK-11069](https://issues.apache.org/jira/browse/SPARK-11069):
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`spark.ml.feature.RegexTokenizer`: Previously, it did not convert strings to lowercase before
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tokenizing. Now, it converts to lowercase by default, with an option not to. This matches the
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behavior of the simpler `Tokenizer` transformer.
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## From 1.4 to 1.5
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In the `spark.mllib` package, there are no breaking API changes but several behavior changes:
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* [SPARK-9005](https://issues.apache.org/jira/browse/SPARK-9005):
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`RegressionMetrics.explainedVariance` returns the average regression sum of squares.
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* [SPARK-8600](https://issues.apache.org/jira/browse/SPARK-8600): `NaiveBayesModel.labels` become
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sorted.
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* [SPARK-3382](https://issues.apache.org/jira/browse/SPARK-3382): `GradientDescent` has a default
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convergence tolerance `1e-3`, and hence iterations might end earlier than 1.4.
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In the `spark.ml` package, there exists one breaking API change and one behavior change:
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* [SPARK-9268](https://issues.apache.org/jira/browse/SPARK-9268): Java's varargs support is removed
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from `Params.setDefault` due to a
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[Scala compiler bug](https://issues.scala-lang.org/browse/SI-9013).
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* [SPARK-10097](https://issues.apache.org/jira/browse/SPARK-10097): `Evaluator.isLargerBetter` is
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added to indicate metric ordering. Metrics like RMSE no longer flip signs as in 1.4.
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## From 1.3 to 1.4
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In the `spark.mllib` package, there were several breaking changes, but all in `DeveloperApi` or `Experimental` APIs:
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* Gradient-Boosted Trees
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* *(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.
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* *(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.
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* *(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.
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In the `spark.ml` package, several major API changes occurred, including:
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* `Param` and other APIs for specifying parameters
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* `uid` unique IDs for Pipeline components
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* Reorganization of certain classes
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Since the `spark.ml` API was an alpha component in Spark 1.3, we do not list all changes here.
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However, since 1.4 `spark.ml` is no longer an alpha component, we will provide details on any API
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changes for future releases.
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## From 1.2 to 1.3
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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.
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* *(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.
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* *(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`.
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* *(Breaking change)* [`StreamingLinearRegressionWithSGD`](api/scala/index.html#org.apache.spark.mllib.regression.StreamingLinearRegressionWithSGD) remains an Experimental component. In it, there were two changes:
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* The constructor taking arguments was removed in favor of a builder pattern using the default constructor plus parameter setter methods.
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* Variable `model` is no longer public.
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* *(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:
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* In `DecisionTree`, the deprecated class method `train` has been removed. (The object/static `train` methods remain.)
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* 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.
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* `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.
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* In linear regression (including Lasso and ridge regression), the squared loss is now divided by 2.
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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.
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In the `spark.ml` package, the main API changes are from Spark SQL. We list the most important changes here:
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* The old [SchemaRDD](http://spark.apache.org/docs/1.2.1/api/scala/index.html#org.apache.spark.sql.SchemaRDD) has been replaced with [DataFrame](api/scala/index.html#org.apache.spark.sql.DataFrame) with a somewhat modified API. All algorithms in `spark.ml` which used to use SchemaRDD now use DataFrame.
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* In Spark 1.2, we used implicit conversions from `RDD`s of `LabeledPoint` into `SchemaRDD`s by calling `import sqlContext._` where `sqlContext` was an instance of `SQLContext`. These implicits have been moved, so we now call `import sqlContext.implicits._`.
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* Java APIs for SQL have also changed accordingly. Please see the examples above and the [Spark SQL Programming Guide](sql-programming-guide.html) for details.
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Other changes were in `LogisticRegression`:
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* The `scoreCol` output column (with default value "score") was renamed to be `probabilityCol` (with default value "probability"). The type was originally `Double` (for the probability of class 1.0), but it is now `Vector` (for the probability of each class, to support multiclass classification in the future).
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* In Spark 1.2, `LogisticRegressionModel` did not include an intercept. In Spark 1.3, it includes an intercept; however, it will always be 0.0 since it uses the default settings for [spark.mllib.LogisticRegressionWithLBFGS](api/scala/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS). The option to use an intercept will be added in the future.
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## From 1.1 to 1.2
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The only API changes in MLlib v1.2 are in
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[`DecisionTree`](api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree),
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which continues to be an experimental API in MLlib 1.2:
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1. *(Breaking change)* The Scala API for classification takes a named argument specifying the number
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of classes. In MLlib v1.1, this argument was called `numClasses` in Python and
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`numClassesForClassification` in Scala. In MLlib v1.2, the names are both set to `numClasses`.
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This `numClasses` parameter is specified either via
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[`Strategy`](api/scala/index.html#org.apache.spark.mllib.tree.configuration.Strategy)
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or via [`DecisionTree`](api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree)
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static `trainClassifier` and `trainRegressor` methods.
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2. *(Breaking change)* The API for
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[`Node`](api/scala/index.html#org.apache.spark.mllib.tree.model.Node) has changed.
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This should generally not affect user code, unless the user manually constructs decision trees
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(instead of using the `trainClassifier` or `trainRegressor` methods).
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The tree `Node` now includes more information, including the probability of the predicted label
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(for classification).
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3. Printing methods' output has changed. The `toString` (Scala/Java) and `__repr__` (Python) methods used to print the full model; they now print a summary. For the full model, use `toDebugString`.
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Examples in the Spark distribution and examples in the
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[Decision Trees Guide](mllib-decision-tree.html#examples) have been updated accordingly.
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## From 1.0 to 1.1
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The only API changes in MLlib v1.1 are in
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[`DecisionTree`](api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree),
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which continues to be an experimental API in MLlib 1.1:
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1. *(Breaking change)* The meaning of tree depth has been changed by 1 in order to match
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the implementations of trees in
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[scikit-learn](http://scikit-learn.org/stable/modules/classes.html#module-sklearn.tree)
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and in [rpart](http://cran.r-project.org/web/packages/rpart/index.html).
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In MLlib v1.0, a depth-1 tree had 1 leaf node, and a depth-2 tree had 1 root node and 2 leaf nodes.
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In MLlib v1.1, a depth-0 tree has 1 leaf node, and a depth-1 tree has 1 root node and 2 leaf nodes.
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This depth is specified by the `maxDepth` parameter in
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[`Strategy`](api/scala/index.html#org.apache.spark.mllib.tree.configuration.Strategy)
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or via [`DecisionTree`](api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree)
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static `trainClassifier` and `trainRegressor` methods.
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2. *(Non-breaking change)* We recommend using the newly added `trainClassifier` and `trainRegressor`
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methods to build a [`DecisionTree`](api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree),
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rather than using the old parameter class `Strategy`. These new training methods explicitly
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separate classification and regression, and they replace specialized parameter types with
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simple `String` types.
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Examples of the new, recommended `trainClassifier` and `trainRegressor` are given in the
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[Decision Trees Guide](mllib-decision-tree.html#examples).
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## From 0.9 to 1.0
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In MLlib v1.0, we support both dense and sparse input in a unified way, which introduces a few
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breaking changes. If your data is sparse, please store it in a sparse format instead of dense to
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take advantage of sparsity in both storage and computation. Details are described below.
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