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Licensed to the Apache Software Foundation (ASF) under one or more
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contributor license agreements. See the NOTICE file distributed with
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this work for additional information regarding copyright ownership.
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The ASF licenses this file to You under the Apache License, Version 2.0
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(the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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---
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* Table of contents
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{:toc}
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## Upgrading from Core 2.4 to 3.0
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- In Spark 3.0, deprecated method `TaskContext.isRunningLocally` has been removed. Local execution was removed and it always has returned `false`.
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- In Spark 3.0, deprecated method `shuffleBytesWritten`, `shuffleWriteTime` and `shuffleRecordsWritten` in `ShuffleWriteMetrics` have been removed. Instead, use `bytesWritten`, `writeTime ` and `recordsWritten` respectively.
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- In Spark 3.0, deprecated method `AccumulableInfo.apply` have been removed because creating `AccumulableInfo` is disallowed.
`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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## Upgrading from MLlib 1.6 to 2.0
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### Breaking changes
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{:.no_toc}
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There were several breaking changes in Spark 2.0, which are outlined below.
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@@ -171,6 +238,7 @@ Several deprecated methods were removed in the `spark.mllib` and `spark.ml` pack
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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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{:.no_toc}
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**Deprecations**
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@@ -221,7 +289,7 @@ Changes of behavior in the `spark.mllib` and `spark.ml` packages include:
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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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## Upgrading from MLlib 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.
@@ -248,7 +316,7 @@ Changes of behavior:
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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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## Upgrading from MLlib 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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@@ -267,7 +335,7 @@ In the `spark.ml` package, there exists one breaking API change and one behavior
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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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## Upgrading from MLlib 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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@@ -286,7 +354,7 @@ Since the `spark.ml` API was an alpha component in Spark 1.3, we do not list all
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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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## Upgrading from MLlib 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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@@ -313,7 +381,7 @@ 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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