| Commit message (Collapse) | Author | Age | Files | Lines |
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### What changes were proposed in this pull request?
Made these MLReadable and MLWritable: DecisionTreeClassifier, DecisionTreeClassificationModel, DecisionTreeRegressor, DecisionTreeRegressionModel
* The shared implementation is in treeModels.scala
* I use case classes to create a DataFrame to save, and I use the Dataset API to parse loaded files.
Other changes:
* Made CategoricalSplit.numCategories public (to use in persistence)
* Fixed a bug in DefaultReadWriteTest.testEstimatorAndModelReadWrite, where it did not call the checkModelData function passed as an argument. This caused an error in LDASuite, which I fixed.
### How was this patch tested?
Persistence is tested via unit tests. For each algorithm, there are 2 non-trivial trees (depth 2). One is built with continuous features, and one with categorical; this ensures that both types of splits are tested.
Author: Joseph K. Bradley <joseph@databricks.com>
Closes #11581 from jkbradley/dt-io.
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byte[] conversions (and remaining Coverity items)
## What changes were proposed in this pull request?
- Fixes calls to `new String(byte[])` or `String.getBytes()` that rely on platform default encoding, to use UTF-8
- Same for `InputStreamReader` and `OutputStreamWriter` constructors
- Standardizes on UTF-8 everywhere
- Standardizes specifying the encoding with `StandardCharsets.UTF-8`, not the Guava constant or "UTF-8" (which means handling `UnuspportedEncodingException`)
- (also addresses the other remaining Coverity scan issues, which are pretty trivial; these are separated into commit https://github.com/srowen/spark/commit/1deecd8d9ca986d8adb1a42d315890ce5349d29c )
## How was this patch tested?
Jenkins tests
Author: Sean Owen <sowen@cloudera.com>
Closes #11657 from srowen/SPARK-13823.
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## What changes were proposed in this pull request?
This PR removes two methods, `collectRows()` and `takeRows()`, from `Dataset[T]`. These methods were added in PR #11443, and were later considered not useful.
## How was this patch tested?
Existing tests should do the work.
Author: Cheng Lian <lian@databricks.com>
Closes #11678 from liancheng/remove-collect-rows-and-take-rows.
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## What changes were proposed in this pull request?
This PR unifies DataFrame and Dataset by migrating existing DataFrame operations to Dataset and make `DataFrame` a type alias of `Dataset[Row]`.
Most Scala code changes are source compatible, but Java API is broken as Java knows nothing about Scala type alias (mostly replacing `DataFrame` with `Dataset<Row>`).
There are several noticeable API changes related to those returning arrays:
1. `collect`/`take`
- Old APIs in class `DataFrame`:
```scala
def collect(): Array[Row]
def take(n: Int): Array[Row]
```
- New APIs in class `Dataset[T]`:
```scala
def collect(): Array[T]
def take(n: Int): Array[T]
def collectRows(): Array[Row]
def takeRows(n: Int): Array[Row]
```
Two specialized methods `collectRows` and `takeRows` are added because Java doesn't support returning generic arrays. Thus, for example, `DataFrame.collect(): Array[T]` actually returns `Object` instead of `Array<T>` from Java side.
Normally, Java users may fall back to `collectAsList` and `takeAsList`. The two new specialized versions are added to avoid performance regression in ML related code (but maybe I'm wrong and they are not necessary here).
1. `randomSplit`
- Old APIs in class `DataFrame`:
```scala
def randomSplit(weights: Array[Double], seed: Long): Array[DataFrame]
def randomSplit(weights: Array[Double]): Array[DataFrame]
```
- New APIs in class `Dataset[T]`:
```scala
def randomSplit(weights: Array[Double], seed: Long): Array[Dataset[T]]
def randomSplit(weights: Array[Double]): Array[Dataset[T]]
```
Similar problem as above, but hasn't been addressed for Java API yet. We can probably add `randomSplitAsList` to fix this one.
1. `groupBy`
Some original `DataFrame.groupBy` methods have conflicting signature with original `Dataset.groupBy` methods. To distinguish these two, typed `Dataset.groupBy` methods are renamed to `groupByKey`.
Other noticeable changes:
1. Dataset always do eager analysis now
We used to support disabling DataFrame eager analysis to help reporting partially analyzed malformed logical plan on analysis failure. However, Dataset encoders requires eager analysi during Dataset construction. To preserve the error reporting feature, `AnalysisException` now takes an extra `Option[LogicalPlan]` argument to hold the partially analyzed plan, so that we can check the plan tree when reporting test failures. This plan is passed by `QueryExecution.assertAnalyzed`.
## How was this patch tested?
Existing tests do the work.
## TODO
- [ ] Fix all tests
- [ ] Re-enable MiMA check
- [ ] Update ScalaDoc (`since`, `group`, and example code)
Author: Cheng Lian <lian@databricks.com>
Author: Yin Huai <yhuai@databricks.com>
Author: Wenchen Fan <wenchen@databricks.com>
Author: Cheng Lian <liancheng@users.noreply.github.com>
Closes #11443 from liancheng/ds-to-df.
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creation in Java code.
## What changes were proposed in this pull request?
In order to make `docs/examples` (and other related code) more simple/readable/user-friendly, this PR replaces existing codes like the followings by using `diamond` operator.
```
- final ArrayList<Product2<Object, Object>> dataToWrite =
- new ArrayList<Product2<Object, Object>>();
+ final ArrayList<Product2<Object, Object>> dataToWrite = new ArrayList<>();
```
Java 7 or higher supports **diamond** operator which replaces the type arguments required to invoke the constructor of a generic class with an empty set of type parameters (<>). Currently, Spark Java code use mixed usage of this.
## How was this patch tested?
Manual.
Pass the existing tests.
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes #11541 from dongjoon-hyun/SPARK-13702.
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## What changes were proposed in this pull request?
Remove last usage of jblas, in tests
## How was this patch tested?
Jenkins tests -- the same ones that are being modified.
Author: Sean Owen <sowen@cloudera.com>
Closes #11560 from srowen/SPARK-13715.
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## What changes were proposed in this pull request?
After SPARK-6990, `dev/lint-java` keeps Java code healthy and helps PR review by saving much time.
This issue aims remove unused imports from Java/Scala code and add `UnusedImports` checkstyle rule to help developers.
## How was this patch tested?
```
./dev/lint-java
./build/sbt compile
```
Author: Dongjoon Hyun <dongjoon@apache.org>
Closes #11438 from dongjoon-hyun/SPARK-13583.
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## What changes were proposed in this pull request?
Make some cross-cutting code improvements according to static analysis. These are individually up for discussion since they exist in separate commits that can be reverted. The changes are broadly:
- Inner class should be static
- Mismatched hashCode/equals
- Overflow in compareTo
- Unchecked warnings
- Misuse of assert, vs junit.assert
- get(a) + getOrElse(b) -> getOrElse(a,b)
- Array/String .size -> .length (occasionally, -> .isEmpty / .nonEmpty) to avoid implicit conversions
- Dead code
- tailrec
- exists(_ == ) -> contains find + nonEmpty -> exists filter + size -> count
- reduce(_+_) -> sum map + flatten -> map
The most controversial may be .size -> .length simply because of its size. It is intended to avoid implicits that might be expensive in some places.
## How was the this patch tested?
Existing Jenkins unit tests.
Author: Sean Owen <sowen@cloudera.com>
Closes #11292 from srowen/SPARK-13423.
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Author: Reynold Xin <rxin@databricks.com>
Closes #10764 from rxin/SPARK-12830.
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Fix most build warnings: mostly deprecated API usages. I'll annotate some of the changes below. CC rxin who is leading the charge to remove the deprecated APIs.
Author: Sean Owen <sowen@cloudera.com>
Closes #10570 from srowen/SPARK-12618.
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Support model save/load for FPGrowthModel
Author: Yanbo Liang <ybliang8@gmail.com>
Closes #9267 from yanboliang/spark-6724.
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jira: https://issues.apache.org/jira/browse/SPARK-11605
Check Java compatibility for MLlib for this release.
fix:
1. `StreamingTest.registerStream` needs java friendly interface.
2. `GradientBoostedTreesModel.computeInitialPredictionAndError` and `GradientBoostedTreesModel.updatePredictionError` has java compatibility issue. Mark them as `developerAPI`.
TBD:
[updated] no fix for now per discussion.
`org.apache.spark.mllib.classification.LogisticRegressionModel`
`public scala.Option<java.lang.Object> getThreshold();` has wrong return type for Java invocation.
`SVMModel` has the similar issue.
Yet adding a `scala.Option<java.util.Double> getThreshold()` would result in an overloading error due to the same function signature. And adding a new function with different name seems to be not necessary.
cc jkbradley feynmanliang
Author: Yuhao Yang <hhbyyh@gmail.com>
Closes #10102 from hhbyyh/javaAPI.
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This replaces https://github.com/apache/spark/pull/9696
Invoke Checkstyle and print any errors to the console, failing the step.
Use Google's style rules modified according to
https://cwiki.apache.org/confluence/display/SPARK/Spark+Code+Style+Guide
Some important checks are disabled (see TODOs in `checkstyle.xml`) due to
multiple violations being present in the codebase.
Suggest fixing those TODOs in a separate PR(s).
More on Checkstyle can be found on the [official website](http://checkstyle.sourceforge.net/).
Sample output (from [build 46345](https://amplab.cs.berkeley.edu/jenkins/job/SparkPullRequestBuilder/46345/consoleFull)) (duplicated because I run the build twice with different profiles):
> Checkstyle checks failed at following occurrences:
[ERROR] src/main/java/org/apache/spark/sql/execution/datasources/parquet/UnsafeRowParquetRecordReader.java:[217,7] (coding) MissingSwitchDefault: switch without "default" clause.
> [ERROR] src/main/java/org/apache/spark/sql/execution/datasources/parquet/SpecificParquetRecordReaderBase.java:[198,10] (modifier) ModifierOrder: 'protected' modifier out of order with the JLS suggestions.
> [ERROR] src/main/java/org/apache/spark/sql/execution/datasources/parquet/UnsafeRowParquetRecordReader.java:[217,7] (coding) MissingSwitchDefault: switch without "default" clause.
> [ERROR] src/main/java/org/apache/spark/sql/execution/datasources/parquet/SpecificParquetRecordReaderBase.java:[198,10] (modifier) ModifierOrder: 'protected' modifier out of order with the JLS suggestions.
> [error] running /home/jenkins/workspace/SparkPullRequestBuilder2/dev/lint-java ; received return code 1
Also fix some of the minor violations that didn't require sweeping changes.
Apologies for the previous botched PRs - I finally figured out the issue.
cr: JoshRosen, pwendell
> I state that the contribution is my original work, and I license the work to the project under the project's open source license.
Author: Dmitry Erastov <derastov@gmail.com>
Closes #9867 from dskrvk/master.
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The same as #9694, but for Java test suite. yhuai
Author: Xiangrui Meng <meng@databricks.com>
Closes #9719 from mengxr/SPARK-11672.4.
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We set `sqlContext = null` in `afterAll`. However, this doesn't change `SQLContext.activeContext` and then `SQLContext.getOrCreate` might use the `SparkContext` from previous test suite and hence causes the error. This PR calls `clearActive` in `beforeAll` and `afterAll` to avoid using an old context from other test suites.
cc: yhuai
Author: Xiangrui Meng <meng@databricks.com>
Closes #9677 from mengxr/SPARK-11672.2.
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Saw several failures on Jenkins, e.g., https://amplab.cs.berkeley.edu/jenkins/job/NewSparkPullRequestBuilder/2040/testReport/org.apache.spark.ml.util/JavaDefaultReadWriteSuite/testDefaultReadWrite/. This is the first failure in master build:
https://amplab.cs.berkeley.edu/jenkins/job/Spark-Master-SBT/3982/
I cannot reproduce it on local. So temporarily disable the tests and I will look into the issue under the same JIRA. I'm going to merge the PR after Jenkins passes compile.
Author: Xiangrui Meng <meng@databricks.com>
Closes #9641 from mengxr/SPARK-11672.
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jira: https://issues.apache.org/jira/browse/SPARK-11069
quotes from jira:
Tokenizer converts strings to lowercase automatically, but RegexTokenizer does not. It would be nice to add an option to RegexTokenizer to convert to lowercase. Proposal:
call the Boolean Param "toLowercase"
set default to false (so behavior does not change)
Actually sklearn converts to lowercase before tokenizing too
Author: Yuhao Yang <hhbyyh@gmail.com>
Closes #9092 from hhbyyh/tokenLower.
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I implemented a hierarchical clustering algorithm again. This PR doesn't include examples, documentation and spark.ml APIs. I am going to send another PRs later.
https://issues.apache.org/jira/browse/SPARK-6517
- This implementation based on a bi-sectiong K-means clustering.
- It derives from the freeman-lab 's implementation
- The basic idea is not changed from the previous version. (#2906)
- However, It is 1000x faster than the previous version through parallel processing.
Thank you for your great cooperation, RJ Nowling(rnowling), Jeremy Freeman(freeman-lab), Xiangrui Meng(mengxr) and Sean Owen(srowen).
Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Author: Xiangrui Meng <meng@databricks.com>
Author: Yu ISHIKAWA <yu-iskw@users.noreply.github.com>
Closes #5267 from yu-iskw/new-hierarchical-clustering.
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This PR implements the default save/load for non-meta estimators and transformers using the JSON serialization of param values. The saved metadata includes:
* class name
* uid
* timestamp
* paramMap
The save/load interface is similar to DataFrames. We use the current active context by default, which should be sufficient for most use cases.
~~~scala
instance.save("path")
instance.write.context(sqlContext).overwrite().save("path")
Instance.load("path")
~~~
The param handling is different from the design doc. We didn't save default and user-set params separately, and when we load it back, all parameters are user-set. This does cause issues. But it also cause other issues if we modify the default params.
TODOs:
* [x] Java test
* [ ] a follow-up PR to implement default save/load for all non-meta estimators and transformers
cc jkbradley
Author: Xiangrui Meng <meng@databricks.com>
Closes #9454 from mengxr/SPARK-11217.
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in ML models
Deprecated in `LogisticRegression` and `LinearRegression`
Author: vectorijk <jiangkai@gmail.com>
Closes #9311 from vectorijk/spark-10592.
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…2 regularization if the number of features is small
Author: lewuathe <lewuathe@me.com>
Author: Lewuathe <sasaki@treasure-data.com>
Author: Kai Sasaki <sasaki@treasure-data.com>
Author: Lewuathe <lewuathe@me.com>
Closes #8884 from Lewuathe/SPARK-10668.
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simplified dataframe construction
As introduced in https://issues.apache.org/jira/browse/SPARK-10630 we now have an easier way to create dataframes from local Java lists. Lets update the tests to use those.
Author: Holden Karau <holden@pigscanfly.ca>
Closes #8886 from holdenk/SPARK-10763-update-java-mllib-ml-tests-to-use-simplified-dataframe-construction.
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Add java wrapper for random vector rdd
holdenk srowen
Author: Meihua Wu <meihuawu@umich.edu>
Closes #8841 from rotationsymmetry/SPARK-10706.
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SPARK-3136 added a large number of functions for creating Java RandomRDDs, but for people that want to use custom RandomDataGenerators we should make a Java friendly method.
Author: Holden Karau <holden@pigscanfly.ca>
Closes #8782 from holdenk/SPARK-10626-create-java-friendly-method-for-randomRDD.
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minor improvements
We should document options in public API doc. Otherwise, it is hard to find out the options without looking at the code. I tried to make `DefaultSource` private and put the documentation to package doc. However, since then there exists no public class under `source.libsvm`, the Java package doc doesn't show up in the generated html file (http://bugs.java.com/bugdatabase/view_bug.do?bug_id=4492654). So I put the doc to `DefaultSource` instead. There are several minor updates in this PR:
1. Do `vectorType == "sparse"` only once.
2. Update `hashCode` and `equals`.
3. Remove inherited doc.
4. Delete temp dir in `afterAll`.
Lewuathe
Author: Xiangrui Meng <meng@databricks.com>
Closes #8699 from mengxr/SPARK-10537.
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It is convenient to implement data source API for LIBSVM format to have a better integration with DataFrames and ML pipeline API.
Two option is implemented.
* `numFeatures`: Specify the dimension of features vector
* `featuresType`: Specify the type of output vector. `sparse` is default.
Author: lewuathe <lewuathe@me.com>
Closes #8537 from Lewuathe/SPARK-10117 and squashes the following commits:
986999d [lewuathe] Change unit test phrase
11d513f [lewuathe] Fix some reviews
21600a4 [lewuathe] Merge branch 'master' into SPARK-10117
9ce63c7 [lewuathe] Rewrite service loader file
1fdd2df [lewuathe] Merge branch 'SPARK-10117' of github.com:Lewuathe/spark into SPARK-10117
ba3657c [lewuathe] Merge branch 'master' into SPARK-10117
0ea1c1c [lewuathe] LibSVMRelation is registered into META-INF
4f40891 [lewuathe] Improve test suites
5ab62ab [lewuathe] Merge branch 'master' into SPARK-10117
8660d0e [lewuathe] Fix Java unit test
b56a948 [lewuathe] Merge branch 'master' into SPARK-10117
2c12894 [lewuathe] Remove unnecessary tag
7d693c2 [lewuathe] Resolv conflict
62010af [lewuathe] Merge branch 'master' into SPARK-10117
a97ee97 [lewuathe] Fix some points
aef9564 [lewuathe] Fix
70ee4dd [lewuathe] Add Java test
3fd8dce [lewuathe] [SPARK-10117] Implement SQL data source API for reading LIBSVM data
40d3027 [lewuathe] Add Java test
7056d4a [lewuathe] Merge branch 'master' into SPARK-10117
99accaa [lewuathe] [SPARK-10117] Implement SQL data source API for reading LIBSVM data
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From Jira: We should use assertTrue, etc. instead to make sure the asserts are not ignored in tests.
Author: Holden Karau <holden@pigscanfly.ca>
Closes #8607 from holdenk/SPARK-10013-remove-java-assert-from-java-unit-tests.
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compatibility test
* Adds user guide for ml.feature.StopWordsRemovers, ran code examples on my machine
* Cleans up scaladocs for public methods
* Adds test for Java compatibility
* Follow up Python user guide code example is tracked by SPARK-10249
Author: Feynman Liang <fliang@databricks.com>
Closes #8436 from feynmanliang/SPARK-10230.
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* Replaces instances of `Lists.newArrayList` with `Arrays.asList`
* Replaces `commons.lang.StringUtils` over `com.google.collections.Strings`
* Replaces `List` interface over `ArrayList` implementations
This PR along with #8445 #8446 #8447 completely removes all `com.google.collections.Lists` dependencies within mllib's Java tests.
Author: Feynman Liang <fliang@databricks.com>
Closes #8451 from feynmanliang/SPARK-10257.
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Fix for [JavaConverters.asJavaListConverter](http://www.scala-lang.org/api/2.10.5/index.html#scala.collection.JavaConverters$) being removed in 2.11.7 and hence the build fails with the 2.11 profile enabled. Tested with the default 2.10 and 2.11 profiles. BUILD SUCCESS in both cases.
Build for 2.10:
./build/mvn -Pyarn -Phadoop-2.6 -Dhadoop.version=2.7.1 -DskipTests clean install
and 2.11:
./dev/change-scala-version.sh 2.11
./build/mvn -Pyarn -Phadoop-2.6 -Dhadoop.version=2.7.1 -Dscala-2.11 -DskipTests clean install
Author: Jacek Laskowski <jacek@japila.pl>
Closes #8479 from jaceklaskowski/SPARK-9613-hotfix.
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JavaTests
Author: Feynman Liang <fliang@databricks.com>
Closes #8447 from feynmanliang/SPARK-10256.
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Author: Feynman Liang <fliang@databricks.com>
Closes #8446 from feynmanliang/SPARK-10255.
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* Replaces `com.google.common` dependencies with `java.util.Arrays`
* Small clean up in `JavaNormalizerSuite`
Author: Feynman Liang <fliang@databricks.com>
Closes #8445 from feynmanliang/SPARK-10254.
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to JavaConverters
Replace `JavaConversions` implicits with `JavaConverters`
Most occurrences I've seen so far are necessary conversions; a few have been avoidable. None are in critical code as far as I see, yet.
Author: Sean Owen <sowen@cloudera.com>
Closes #8033 from srowen/SPARK-9613.
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Add user guide for `VectorSlicer`, with Java test suite and Python version VectorSlicer.
Note that Python version does not support selecting by names now.
Author: Xusen Yin <yinxusen@gmail.com>
Closes #8267 from yinxusen/SPARK-9893.
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For each (document, term) pair, return top topic. Note that instances of (doc, term) pairs within a document (a.k.a. "tokens") are exchangeable, so we should provide an estimate per document-term, rather than per token.
CC: rotationsymmetry mengxr
Author: Joseph K. Bradley <joseph@databricks.com>
Closes #8329 from jkbradley/lda-topic-assignments.
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Java test suite
Otherwise, setters do not return self type. jkbradley avulanov
Author: Xiangrui Meng <meng@databricks.com>
Closes #8342 from mengxr/SPARK-10138.
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Updates FPM user guide to include Association Rules.
Author: Feynman Liang <fliang@databricks.com>
Closes #8207 from feynmanliang/SPARK-9900-arules.
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Some minor clean-ups after SPARK-9661. See my inline comments. MechCoder jkbradley
Author: Xiangrui Meng <meng@databricks.com>
Closes #8190 from mengxr/SPARK-9661-fix.
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I skimmed through the docs for various instance of Object and replaced them with Java compaible versions of the same.
1. Some methods in LDAModel.
2. runMiniBatchSGD
3. kolmogorovSmirnovTest
Author: MechCoder <manojkumarsivaraj334@gmail.com>
Closes #8126 from MechCoder/java_incop.
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Reinstated LogisticRegression.threshold Param for binary compatibility. Param thresholds overrides threshold, if set.
CC: mengxr dbtsai feynmanliang
Author: Joseph K. Bradley <joseph@databricks.com>
Closes #8079 from jkbradley/logreg-reinstate-threshold.
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I have added support for stats in LogisticRegression. The API is similar to that of LinearRegression with LogisticRegressionTrainingSummary and LogisticRegressionSummary
I have some queries and asked them inline.
Author: MechCoder <manojkumarsivaraj334@gmail.com>
Closes #7538 from MechCoder/log_reg_stats and squashes the following commits:
2e9f7c7 [MechCoder] Change defs into lazy vals
d775371 [MechCoder] Clean up class inheritance
9586125 [MechCoder] Add abstraction to handle Multiclass Metrics
40ad8ef [MechCoder] minor
640376a [MechCoder] remove unnecessary dataframe stuff and add docs
80d9954 [MechCoder] Added tests
fbed861 [MechCoder] DataFrame support for metrics
70a0fc4 [MechCoder] [SPARK-9112] [ML] Implement Stats for LogisticRegression
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This PR replaces the old "threshold" with a generalized "thresholds" Param. We keep getThreshold,setThreshold for backwards compatibility for binary classification.
Note that the primary author of this PR is holdenk
Author: Holden Karau <holden@pigscanfly.ca>
Author: Joseph K. Bradley <joseph@databricks.com>
Closes #7909 from jkbradley/holdenk-SPARK-8069-add-cutoff-aka-threshold-to-random-forest and squashes the following commits:
3952977 [Joseph K. Bradley] fixed pyspark doc test
85febc8 [Joseph K. Bradley] made python unit tests a little more robust
7eb1d86 [Joseph K. Bradley] small cleanups
6cc2ed8 [Joseph K. Bradley] Fixed remaining merge issues.
0255e44 [Joseph K. Bradley] Many cleanups for thresholds, some more tests
7565a60 [Holden Karau] fix pep8 style checks, add a getThreshold method similar to our LogisticRegression.scala one for API compat
be87f26 [Holden Karau] Convert threshold to thresholds in the python code, add specialized support for Array[Double] to shared parems codegen, etc.
6747dad [Holden Karau] Override raw2prediction for ProbabilisticClassifier, fix some tests
25df168 [Holden Karau] Fix handling of thresholds in LogisticRegression
c02d6c0 [Holden Karau] No default for thresholds
5e43628 [Holden Karau] CR feedback and fixed the renamed test
f3fbbd1 [Holden Karau] revert the changes to random forest :(
51f581c [Holden Karau] Add explicit types to public methods, fix long line
f7032eb [Holden Karau] Fix a java test bug, remove some unecessary changes
adf15b4 [Holden Karau] rename the classifier suite test to ProbabilisticClassifierSuite now that we only have it in Probabilistic
398078a [Holden Karau] move the thresholding around a bunch based on the design doc
4893bdc [Holden Karau] Use numtrees of 3 since previous result was tied (one tree for each) and the switch from different max methods picked a different element (since they were equal I think this is ok)
638854c [Holden Karau] Add a scala RandomForestClassifierSuite test based on corresponding python test
e09919c [Holden Karau] Fix return type, I need more coffee....
8d92cac [Holden Karau] Use ClassifierParams as the head
3456ed3 [Holden Karau] Add explicit return types even though just test
a0f3b0c [Holden Karau] scala style fixes
6f14314 [Holden Karau] Since hasthreshold/hasthresholds is in root classifier now
ffc8dab [Holden Karau] Update the sharedParams
0420290 [Holden Karau] Allow us to override the get methods selectively
978e77a [Holden Karau] Move HasThreshold into classifier params and start defining the overloaded getThreshold/getThresholds functions
1433e52 [Holden Karau] Revert "try and hide threshold but chainges the API so no dice there"
1f09a2e [Holden Karau] try and hide threshold but chainges the API so no dice there
efb9084 [Holden Karau] move setThresholds only to where its used
6b34809 [Holden Karau] Add a test with thresholding for the RFCS
74f54c3 [Holden Karau] Fix creation of vote array
1986fa8 [Holden Karau] Setting the thresholds only makes sense if the underlying class hasn't overridden predict, so lets push it down.
2f44b18 [Holden Karau] Add a global default of null for thresholds param
f338cfc [Holden Karau] Wait that wasn't a good idea, Revert "Some progress towards unifying threshold and thresholds"
634b06f [Holden Karau] Some progress towards unifying threshold and thresholds
85c9e01 [Holden Karau] Test passes again... little fnur
099c0f3 [Holden Karau] Move thresholds around some more (set on model not trainer)
0f46836 [Holden Karau] Start adding a classifiersuite
f70eb5e [Holden Karau] Fix test compile issues
a7d59c8 [Holden Karau] Move thresholding into Classifier trait
5d999d2 [Holden Karau] Some more progress, start adding a test (maybe try and see if we can find a better thing to use for the base of the test)
1fed644 [Holden Karau] Use thresholds to scale scores in random forest classifcation
31d6bf2 [Holden Karau] Start threading the threshold info through
0ef228c [Holden Karau] Add hasthresholds
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warnings, 1.5.0 edition
Enable most javac lint warnings; fix a lot of build warnings. In a few cases, touch up surrounding code in the process.
I'll explain several of the changes inline in comments.
Author: Sean Owen <sowen@cloudera.com>
Closes #7862 from srowen/SPARK-9534 and squashes the following commits:
ea51618 [Sean Owen] Enable most javac lint warnings; fix a lot of build warnings. In a few cases, touch up surrounding code in the process.
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Regressor
Added featureImportance to RandomForestClassifier and Regressor.
This follows the scikit-learn implementation here: [https://github.com/scikit-learn/scikit-learn/blob/a95203b249c1cf392f86d001ad999e29b2392739/sklearn/tree/_tree.pyx#L3341]
CC: yanboliang Would you mind taking a look? Thanks!
Author: Joseph K. Bradley <joseph@databricks.com>
Author: Feynman Liang <fliang@databricks.com>
Closes #7838 from jkbradley/dt-feature-importance and squashes the following commits:
72a167a [Joseph K. Bradley] fixed unit test
86cea5f [Joseph K. Bradley] Modified RF featuresImportances to return Vector instead of Map
5aa74f0 [Joseph K. Bradley] finally fixed unit test for real
33df5db [Joseph K. Bradley] fix unit test
42a2d3b [Joseph K. Bradley] fix unit test
fe94e72 [Joseph K. Bradley] modified feature importance unit tests
cc693ee [Feynman Liang] Add classifier tests
79a6f87 [Feynman Liang] Compare dense vectors in test
21d01fc [Feynman Liang] Added failing SKLearn test
ac0b254 [Joseph K. Bradley] Added featureImportance to RandomForestClassifier/Regressor. Need to add unit tests
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1. Use `PrefixSpanModel` to wrap the frequent sequences.
2. Define `FreqSequence` to wrap each frequent sequence, which contains a Java-friendly method `javaSequence`
3. Overload `run` for Java users.
4. Added a unit test in Java to check Java compatibility.
zhangjiajin feynmanliang
Author: Xiangrui Meng <meng@databricks.com>
Closes #7869 from mengxr/SPARK-9527 and squashes the following commits:
4345594 [Xiangrui Meng] add PrefixSpanModel and make PrefixSpan Java friendly
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support ml.NaiveBayes for Python
Author: Yanbo Liang <ybliang8@gmail.com>
Closes #7568 from yanboliang/spark-9214 and squashes the following commits:
5ee3fd6 [Yanbo Liang] fix typos
3ecd046 [Yanbo Liang] fix typos
f9c94d1 [Yanbo Liang] change lambda_ to smoothing and fix other issues
180452a [Yanbo Liang] fix typos
7dda1f4 [Yanbo Liang] support ml.NaiveBayes for Python
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Implements `logPerplexity` in `OnlineLDAOptimizer`. Also refactors inference code into companion object to enable future reuse (e.g. `predict` method).
Author: Feynman Liang <fliang@databricks.com>
Closes #7705 from feynmanliang/SPARK-6793-perplexity and squashes the following commits:
6da2c99 [Feynman Liang] Remove get* from LDAModel public API
8381da6 [Feynman Liang] Code review comments
17f7000 [Feynman Liang] Documentation typo fixes
2f452a4 [Feynman Liang] Remove auxillary DistributedLDAModel constructor
a275914 [Feynman Liang] Prevent empty counts calls to variationalInference
06d02d9 [Feynman Liang] Remove deprecated LocalLDAModel constructor
afecb46 [Feynman Liang] Fix regression bug in sstats accumulator
5a327a0 [Feynman Liang] Code review quick fixes
998c03e [Feynman Liang] Fix style
1cbb67d [Feynman Liang] Fix access modifier bug
4362daa [Feynman Liang] Organize imports
4f171f7 [Feynman Liang] Fix indendation
2f049ce [Feynman Liang] Fix failing save/load tests
7415e96 [Feynman Liang] Pick changes from big PR
11e7c33 [Feynman Liang] Merge remote-tracking branch 'apache/master' into SPARK-6793-perplexity
f8adc48 [Feynman Liang] Add logPerplexity, refactor variationalBound into a method
cd521d6 [Feynman Liang] Refactor methods into companion class
7f62a55 [Feynman Liang] --amend
c62cb1e [Feynman Liang] Outer product for stats, revert Range slicing
aead650 [Feynman Liang] Range slice, in-place update, reduce transposes
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multiple params
Removed varargs annotation from Params.setDefault taking multiple params.
Though varargs is technically correct, it often requires that developers do clean assembly, rather than (not clean) assembly, which is a nuisance during development.
CC: mengxr
Author: Joseph K. Bradley <joseph@databricks.com>
Closes #7604 from jkbradley/params-setdefault-varargs and squashes the following commits:
6016dc6 [Joseph K. Bradley] removed varargs annotation from Params.setDefault taking multiple params
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I Implemented the KMeans API for spark.ml Pipelines. But it doesn't include clustering abstractions for spark.ml (SPARK-7610). It would fit for another issues. And I'll try it later, since we are trying to add the hierarchical clustering algorithms in another issue. Thanks.
[SPARK-7879] KMeans API for spark.ml Pipelines - ASF JIRA https://issues.apache.org/jira/browse/SPARK-7879
Author: Yu ISHIKAWA <yuu.ishikawa@gmail.com>
Closes #6756 from yu-iskw/SPARK-7879 and squashes the following commits:
be752de [Yu ISHIKAWA] Add assertions
a14939b [Yu ISHIKAWA] Fix the dashed line's length in pyspark.ml.rst
4c61693 [Yu ISHIKAWA] Remove the test about whether "features" and "prediction" columns exist or not in Python
fb2417c [Yu ISHIKAWA] Use getInt, instead of get
f397be4 [Yu ISHIKAWA] Switch the comparisons.
ca78b7d [Yu ISHIKAWA] Add the Scala docs about the constraints of each parameter.
effc650 [Yu ISHIKAWA] Using expertSetParam and expertGetParam
c8dc6e6 [Yu ISHIKAWA] Remove an unnecessary test
19a9d63 [Yu ISHIKAWA] Include spark.ml.clustering to python tests
1abb19c [Yu ISHIKAWA] Add the statements about spark.ml.clustering into pyspark.ml.rst
f8338bc [Yu ISHIKAWA] Add the placeholders in Python
4a03003 [Yu ISHIKAWA] Test for contains in Python
6566c8b [Yu ISHIKAWA] Use `get`, instead of `apply`
288e8d5 [Yu ISHIKAWA] Using `contains` to check the column names
5a7d574 [Yu ISHIKAWA] Renamce `validateInitializationMode` to `validateInitMode` and remove throwing exception
97cfae3 [Yu ISHIKAWA] Fix the type of return value of `KMeans.copy`
e933723 [Yu ISHIKAWA] Remove the default value of seed from the Model class
978ee2c [Yu ISHIKAWA] Modify the docs of KMeans, according to mllib's KMeans
2ec80bc [Yu ISHIKAWA] Fit on 1 line
e186be1 [Yu ISHIKAWA] Make a few variables, setters and getters be expert ones
b2c205c [Yu ISHIKAWA] Rename the method `getInitializationSteps` to `getInitSteps` and `setInitializationSteps` to `setInitSteps` in Scala and Python
f43f5b4 [Yu ISHIKAWA] Rename the method `getInitializationMode` to `getInitMode` and `setInitializationMode` to `setInitMode` in Scala and Python
3cb5ba4 [Yu ISHIKAWA] Modify the description about epsilon and the validation
4fa409b [Yu ISHIKAWA] Add a comment about the default value of epsilon
2f392e1 [Yu ISHIKAWA] Make some variables `final` and Use `IntParam` and `DoubleParam`
19326f8 [Yu ISHIKAWA] Use `udf`, instead of callUDF
4d2ad1e [Yu ISHIKAWA] Modify the indentations
0ae422f [Yu ISHIKAWA] Add a test for `setParams`
4ff7913 [Yu ISHIKAWA] Add "ml.clustering" to `javacOptions` in SparkBuild.scala
11ffdf1 [Yu ISHIKAWA] Use `===` and the variable
220a176 [Yu ISHIKAWA] Set a random seed in the unit testing
92c3efc [Yu ISHIKAWA] Make the points for a test be fewer
c758692 [Yu ISHIKAWA] Modify the parameters of KMeans in Python
6aca147 [Yu ISHIKAWA] Add some unit testings to validate the setter methods
687cacc [Yu ISHIKAWA] Alias mllib.KMeans as MLlibKMeans in KMeansSuite.scala
a4dfbef [Yu ISHIKAWA] Modify the last brace and indentations
5bedc51 [Yu ISHIKAWA] Remve an extra new line
444c289 [Yu ISHIKAWA] Add the validation for `runs`
e41989c [Yu ISHIKAWA] Modify how to validate `initStep`
7ea133a [Yu ISHIKAWA] Change how to validate `initMode`
7991e15 [Yu ISHIKAWA] Add a validation for `k`
c2df35d [Yu ISHIKAWA] Make `predict` private
93aa2ff [Yu ISHIKAWA] Use `withColumn` in `transform`
d3a79f7 [Yu ISHIKAWA] Remove the inhefited docs
e9532e1 [Yu ISHIKAWA] make `parentModel` of KMeansModel private
8559772 [Yu ISHIKAWA] Remove the `paramMap` parameter of KMeans
6684850 [Yu ISHIKAWA] Rename `initializationSteps` to `initSteps`
99b1b96 [Yu ISHIKAWA] Rename `initializationMode` to `initMode`
79ea82b [Yu ISHIKAWA] Modify the parameters of KMeans docs
6569bcd [Yu ISHIKAWA] Change how to set the default values with `setDefault`
20a795a [Yu ISHIKAWA] Change how to set the default values with `setDefault`
11c2a12 [Yu ISHIKAWA] Limit the imports
badb481 [Yu ISHIKAWA] Alias spark.mllib.{KMeans, KMeansModel}
f80319a [Yu ISHIKAWA] Rebase mater branch and add copy methods
85d92b1 [Yu ISHIKAWA] Add `KMeans.setPredictionCol`
aa9469d [Yu ISHIKAWA] Fix a python test suite error caused by python 3.x
c2d6bcb [Yu ISHIKAWA] ADD Java test suites of the KMeans API for spark.ml Pipeline
598ed2e [Yu ISHIKAWA] Implement the KMeans API for spark.ml Pipelines in Python
63ad785 [Yu ISHIKAWA] Implement the KMeans API for spark.ml Pipelines in Scala
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