Changing the default number of edge partitions to match spark parallelism.
Author: Joseph E. Gonzalez <joseph.e.gonzalez@gmail.com>
Closes#3006 from jegonzal/default_partitions and squashes the following commits:
a9a5c4f [Joseph E. Gonzalez] Changing the default number of edge partitions to match spark parallelism
This adds a Streaming KMeans algorithm to MLlib. It uses an update rule that generalizes the mini-batch KMeans update to incorporate a decay factor, which allows past data to be forgotten. The decay factor can be specified explicitly, or via a more intuitive "fractional decay" setting, in units of either data points or batches.
The PR includes:
- StreamingKMeans algorithm with decay factor settings
- Usage example
- Additions to documentation clustering page
- Unit tests of basic behavior and decay behaviors
tdas mengxr rezazadeh
Author: freeman <the.freeman.lab@gmail.com>
Author: Jeremy Freeman <the.freeman.lab@gmail.com>
Author: Xiangrui Meng <meng@databricks.com>
Closes#2942 from freeman-lab/streaming-kmeans and squashes the following commits:
b2e5b4a [freeman] Fixes to docs / examples
078617c [Jeremy Freeman] Merge pull request #1 from mengxr/SPARK-3254
2e682c0 [Xiangrui Meng] take discount on previous weights; use BLAS; detect dying clusters
0411bf5 [freeman] Change decay parameterization
9f7aea9 [freeman] Style fixes
374a706 [freeman] Formatting
ad9bdc2 [freeman] Use labeled points and predictOnValues in examples
77dbd3f [freeman] Make initialization check an assertion
9cfc301 [freeman] Make random seed an argument
44050a9 [freeman] Simpler constructor
c7050d5 [freeman] Fix spacing
2899623 [freeman] Use pattern matching for clarity
a4a316b [freeman] Use collect
1472ec5 [freeman] Doc formatting
ea22ec8 [freeman] Fix imports
2086bdc [freeman] Log cluster center updates
ea9877c [freeman] More documentation
9facbe3 [freeman] Bug fix
5db7074 [freeman] Example usage for StreamingKMeans
f33684b [freeman] Add explanation and example to docs
b5b5f8d [freeman] Add better documentation
a0fd790 [freeman] Merge remote-tracking branch 'upstream/master' into streaming-kmeans
9fd9c15 [freeman] Merge remote-tracking branch 'upstream/master' into streaming-kmeans
b93350f [freeman] Streaming KMeans with decay
Given the popular demand for gradient boosting and AdaBoost in MLlib, I am creating a WIP branch for early feedback on gradient boosting with AdaBoost to follow soon after this PR is accepted. This is based on work done along with hirakendu that was pending due to decision tree optimizations and random forests work.
Ideally, boosting algorithms should work with any base learners. This will soon be possible once the MLlib API is finalized -- we want to ensure we use a consistent interface for the underlying base learners. In the meantime, this PR uses decision trees as base learners for the gradient boosting algorithm. The current PR allows "pluggable" loss functions and provides least squares error and least absolute error by default.
Here is the task list:
- [x] Gradient boosting support
- [x] Pluggable loss functions
- [x] Stochastic gradient boosting support – Re-use the BaggedPoint approach used for RandomForest.
- [x] Binary classification support
- [x] Support configurable checkpointing – This approach will avoid long lineage chains.
- [x] Create classification and regression APIs
- [x] Weighted Ensemble Model -- created a WeightedEnsembleModel class that can be used by ensemble algorithms such as random forests and boosting.
- [x] Unit Tests
Future work:
+ Multi-class classification is currently not supported by this PR since it requires discussion on the best way to support "deviance" as a loss function.
+ BaggedRDD caching -- Avoid repeating feature to bin mapping for each tree estimator after standard API work is completed.
cc: jkbradley hirakendu mengxr etrain atalwalkar chouqin
Author: Manish Amde <manish9ue@gmail.com>
Author: manishamde <manish9ue@gmail.com>
Closes#2607 from manishamde/gbt and squashes the following commits:
991c7b5 [Manish Amde] public api
ff2a796 [Manish Amde] addressing comments
b4c1318 [Manish Amde] removing spaces
8476b6b [Manish Amde] fixing line length
0183cb9 [Manish Amde] fixed naming and formatting issues
1c40c33 [Manish Amde] add newline, removed spaces
e33ab61 [Manish Amde] minor comment
eadbf09 [Manish Amde] parameter renaming
035a2ed [Manish Amde] jkbradley formatting suggestions
9f7359d [Manish Amde] simplified gbt logic and added more tests
49ba107 [Manish Amde] merged from master
eff21fe [Manish Amde] Added gradient boosting tests
3fd0528 [Manish Amde] moved helper methods to new class
a32a5ab [Manish Amde] added test for subsampling without replacement
781542a [Manish Amde] added support for fractional subsampling with replacement
3a18cc1 [Manish Amde] cleaned up api for conversion to bagged point and moved tests to it's own test suite
0e81906 [Manish Amde] improving caching unpersisting logic
d971f73 [Manish Amde] moved RF code to use WeightedEnsembleModel class
fee06d3 [Manish Amde] added weighted ensemble model
1b01943 [Manish Amde] add weights for base learners
9bc6e74 [Manish Amde] adding random seed as parameter
d2c8323 [Manish Amde] Merge branch 'master' into gbt
2ae97b7 [Manish Amde] added documentation for the loss classes
9366b8f [Manish Amde] minor: using numTrees instead of trees.size
3b43896 [Manish Amde] added learning rate for prediction
9b2e35e [Manish Amde] Merge branch 'master' into gbt
6a11c02 [manishamde] fixing formatting
823691b [Manish Amde] fixing RF test
1f47941 [Manish Amde] changing access modifier
5b67102 [Manish Amde] shortened parameter list
5ab3796 [Manish Amde] minor reformatting
9155a9d [Manish Amde] consolidated boosting configuration and added public API
631baea [Manish Amde] Merge branch 'master' into gbt
2cb1258 [Manish Amde] public API support
3b8ffc0 [Manish Amde] added documentation
8e10c63 [Manish Amde] modified unpersist strategy
f62bc48 [Manish Amde] added unpersist
bdca43a [Manish Amde] added timing parameters
2fbc9c7 [Manish Amde] fixing binomial classification prediction
6dd4dd8 [Manish Amde] added support for log loss
9af0231 [Manish Amde] classification attempt
62cc000 [Manish Amde] basic checkpointing
4784091 [Manish Amde] formatting
78ed452 [Manish Amde] added newline and fixed if statement
3973dd1 [Manish Amde] minor indicating subsample is double during comparison
aa8fae7 [Manish Amde] minor refactoring
1a8031c [Manish Amde] sampling with replacement
f1c9ef7 [Manish Amde] Merge branch 'master' into gbt
cdceeef [Manish Amde] added documentation
6251fd5 [Manish Amde] modified method name
5538521 [Manish Amde] disable checkpointing for now
0ae1c0a [Manish Amde] basic gradient boosting code from earlier branches
This pull request refers to issue: https://issues.apache.org/jira/browse/SPARK-3838
Python example for word2vec
mengxr
Author: Anant <anant.asty@gmail.com>
Closes#2952 from anantasty/SPARK-3838 and squashes the following commits:
87bd723 [Anant] remove stop line
4bd439e [Anant] Changes as per code review. Fized error in word2vec python example, simplified example in docs.
3d3c9ee [Anant] Added empty line after python imports
0c90c31 [Anant] Fixed erroneous code. I was still treating each line to be a single word instead of 16 words
ee4f5f6 [Anant] Fixes from code review comments
c637bcf [Anant] Added word2vec python example to docs
269f31f [Anant] added example in docs
c015b14 [Anant] Added python example for word2vec
This change replaces usages of colt with commons-math3 equivalents, and makes some minor necessary adjustments to related code and tests to match.
Author: Sean Owen <sowen@cloudera.com>
Closes#2928 from srowen/SPARK-4022 and squashes the following commits:
61a232f [Sean Owen] Fix failure due to different sampling in JavaAPISuite.sample()
16d66b8 [Sean Owen] Simplify seeding with call to reseedRandomGenerator
a1a78e0 [Sean Owen] Use Well19937c
31c7641 [Sean Owen] Fix Python Poisson test by choosing a different seed; about 88% of seeds should work but 1 didn't, it seems
5c9c67f [Sean Owen] Additional test fixes from review
d8f88e0 [Sean Owen] Replace colt with commons-math3. Some tests do not pass yet.
Author: anant asthana <anant.asty@gmail.com>
Closes#2948 from anantasty/patch-1 and squashes the following commits:
d8fea0b [anant asthana] Just fixing comment that shows usage
This pull request is a first step towards the implementation of a stable, pull-based progress / status API for Spark (see [SPARK-2321](https://issues.apache.org/jira/browse/SPARK-2321)). For now, I'd like to discuss the basic implementation, API names, and overall interface design. Once we arrive at a good design, I'll go back and add additional methods to expose more information via these API.
#### Design goals:
- Pull-based API
- Usable from Java / Scala / Python (eventually, likely with a wrapper)
- Can be extended to expose more information without introducing binary incompatibilities.
- Returns immutable objects.
- Don't leak any implementation details, preserving our freedom to change the implementation.
#### Implementation:
- Add public methods (`getJobInfo`, `getStageInfo`) to SparkContext to allow status / progress information to be retrieved.
- Add public interfaces (`SparkJobInfo`, `SparkStageInfo`) for our API return values. These interfaces consist entirely of Java-style getter methods. The interfaces are currently implemented in Java. I decided to explicitly separate the interface from its implementation (`SparkJobInfoImpl`, `SparkStageInfoImpl`) in order to prevent users from constructing these responses themselves.
-Allow an existing JobProgressListener to be used when constructing a live SparkUI. This allows us to re-use this listeners in the implementation of this status API. There are a few reasons why this listener re-use makes sense:
- The status API and web UI are guaranteed to show consistent information.
- These listeners are already well-tested.
- The same garbage-collection / information retention configurations can apply to both this API and the web UI.
- Extend JobProgressListener to maintain `jobId -> Job` and `stageId -> Stage` mappings.
The progress API methods are implemented in a separate trait that's mixed into SparkContext. This helps to avoid SparkContext.scala from becoming larger and more difficult to read.
Author: Josh Rosen <joshrosen@databricks.com>
Author: Josh Rosen <joshrosen@apache.org>
Closes#2696 from JoshRosen/progress-reporting-api and squashes the following commits:
e6aa78d [Josh Rosen] Add tests.
b585c16 [Josh Rosen] Accept SparkListenerBus instead of more specific subclasses.
c96402d [Josh Rosen] Address review comments.
2707f98 [Josh Rosen] Expose current stage attempt id
c28ba76 [Josh Rosen] Update demo code:
646ff1d [Josh Rosen] Document spark.ui.retainedJobs.
7f47d6d [Josh Rosen] Clean up SparkUI constructors, per Andrew's feedback.
b77b3d8 [Josh Rosen] Merge remote-tracking branch 'origin/master' into progress-reporting-api
787444c [Josh Rosen] Move status API methods into trait that can be mixed into SparkContext.
f9a9a00 [Josh Rosen] More review comments:
3dc79af [Josh Rosen] Remove creation of unused listeners in SparkContext.
249ca16 [Josh Rosen] Address several review comments:
da5648e [Josh Rosen] Add example of basic progress reporting in Java.
7319ffd [Josh Rosen] Add getJobIdsForGroup() and num*Tasks() methods.
cc568e5 [Josh Rosen] Add note explaining that interfaces should not be implemented outside of Spark.
6e840d4 [Josh Rosen] Remove getter-style names and "consistent snapshot" semantics:
08cbec9 [Josh Rosen] Begin to sketch the interfaces for a stable, public status API.
ac2d13a [Josh Rosen] Add jobId->stage, stageId->stage mappings in JobProgressListener
24de263 [Josh Rosen] Create UI listeners in SparkContext instead of in Tabs:
Now graphx.SynthBenchmark example has an option of iteration number named as "niter". However, in its document, it is named as "niters". The mismatch between the implementation and document causes certain IllegalArgumentException while trying that example.
Author: Grace <jie.huang@intel.com>
Closes#2888 from GraceH/synthbenchmark and squashes the following commits:
f101ee1 [Grace] Modify option name according to example doc
Thare are some inconsistent spellings 'MLlib' and 'MLLib' in some documents and source codes.
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes#2903 from sarutak/SPARK-4055 and squashes the following commits:
b031640 [Kousuke Saruta] Fixed inconsistent spelling "MLlib and MLLib"
Changed the usage string to correctly reflect the file name.
Author: Karthik <karthik.gomadam@gmail.com>
Closes#2699 from namelessnerd/patch-1 and squashes the following commits:
8570e33 [Karthik] Update JavaCustomReceiver.java
Author: Sandy Ryza <sandy@cloudera.com>
Closes#789 from sryza/sandy-spark-1813 and squashes the following commits:
48b05e9 [Sandy Ryza] Simplify
b824932 [Sandy Ryza] Allow both spark.kryo.classesToRegister and spark.kryo.registrator at the same time
6a15bb7 [Sandy Ryza] Small fix
a2278c0 [Sandy Ryza] Respond to review comments
6ef592e [Sandy Ryza] SPARK-1813. Add a utility to SparkConf that makes using Kryo really easy
Having Python examples in Streaming Programming Guide.
Also add RecoverableNetworkWordCount example.
Author: Davies Liu <davies.liu@gmail.com>
Author: Davies Liu <davies@databricks.com>
Closes#2808 from davies/pyguide and squashes the following commits:
8d4bec4 [Davies Liu] update readme
26a7e37 [Davies Liu] fix format
3821c4d [Davies Liu] address comments, add missing file
7e4bb8a [Davies Liu] add Python examples in Streaming Programming Guide
SPARK-3934: When run with a mix of unordered categorical and continuous features, on multiclass classification, RandomForest fails. The bug is in the sanity checks in getFeatureOffset and getLeftRightFeatureOffsets, which use the wrong indices for checking whether features are unordered.
Fix: Remove the sanity checks since they are not really needed, and since they would require DTStatsAggregator to keep track of an extra set of indices (for the feature subset).
Added test to RandomForestSuite which failed with old version but now works.
SPARK-3918: Added baggedInput.unpersist at end of training.
Also:
* I removed DTStatsAggregator.isUnordered since it is no longer used.
* DecisionTreeMetadata: Added logWarning when maxBins is automatically reduced.
* Updated DecisionTreeRunner to explicitly fix the test data to have the same number of features as the training data. This is a temporary fix which should eventually be replaced by pre-indexing both datasets.
* RandomForestModel: Updated toString to print total number of nodes in forest.
* Changed Predict class to be public DeveloperApi. This was necessary to allow users to create their own trees by hand (for testing).
CC: mengxr manishamde chouqin codedeft Just notifying you of these small bug fixes.
Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com>
Closes#2785 from jkbradley/dtrunner-update and squashes the following commits:
9132321 [Joseph K. Bradley] merged with master, fixed imports
9dbd000 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dtrunner-update
e116473 [Joseph K. Bradley] Changed Predict class to be public DeveloperApi.
f502e65 [Joseph K. Bradley] bug fix for SPARK-3934
7f3d60f [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dtrunner-update
ba567ab [Joseph K. Bradley] Changed DTRunner to load test data using same number of features as in training data.
4e88c1f [Joseph K. Bradley] changed RF toString to print total number of nodes
Author: Daoyuan Wang <daoyuan.wang@intel.com>
Closes#2834 from adrian-wang/sqlpypath and squashes the following commits:
da7aa95 [Daoyuan Wang] fix file path using path.join
Previously, when the val partitionStrategy was created it called a function in the Analytics object which was a copy of the PartitionStrategy.fromString() method. This function has been removed, and the assignment of partitionStrategy now uses the PartitionStrategy.fromString method instead. In this way, it better matches the declarations of edge/vertex StorageLevel variables.
Author: NamelessAnalyst <NamelessAnalyst@users.noreply.github.com>
Closes#2569 from NamelessAnalyst/branch-1.1 and squashes the following commits:
c24ff51 [NamelessAnalyst] Update Analytics.scala
(cherry picked from commit 5a21e3e7e9)
Signed-off-by: Ankur Dave <ankurdave@gmail.com>
Provide a parent class for the Params case classes used in many MLlib examples, where the parent class pretty-prints the case class fields:
Param1Name Param1Value
Param2Name Param2Value
...
Using this class will make it easier to print test settings to logs.
Also, updated DecisionTreeRunner to print a little more info.
CC: mengxr
Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com>
Closes#2700 from jkbradley/dtrunner-update and squashes the following commits:
cff873f [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dtrunner-update
7a08ae4 [Joseph K. Bradley] code review comment updates
b4d2043 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dtrunner-update
d8228a7 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dtrunner-update
0fc9c64 [Joseph K. Bradley] Added abstract TestParams class for mllib example parameters
12b7798 [Joseph K. Bradley] Added abstract class TestParams for pretty-printing Params values
5f84f03 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dtrunner-update
f7441b6 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dtrunner-update
19eb6fc [Joseph K. Bradley] Updated DecisionTreeRunner to print training time.
This patch forces use of commons http client 4.2 in Kinesis-asl profile so that the AWS SDK does not run into dependency conflicts
Author: aniketbhatnagar <aniket.bhatnagar@gmail.com>
Closes#2535 from aniketbhatnagar/Kinesis-HttpClient-Dep-Fix and squashes the following commits:
aa2079f [aniketbhatnagar] Merge branch 'Kinesis-HttpClient-Dep-Fix' of https://github.com/aniketbhatnagar/spark into Kinesis-HttpClient-Dep-Fix
73f55f6 [aniketbhatnagar] SPARK-3638 | Forced a compatible version of http client in kinesis-asl profile
70cc75b [aniketbhatnagar] deleted merge files
725dbc9 [aniketbhatnagar] Merge remote-tracking branch 'origin/Kinesis-HttpClient-Dep-Fix' into Kinesis-HttpClient-Dep-Fix
4ed61d8 [aniketbhatnagar] SPARK-3638 | Forced a compatible version of http client in kinesis-asl profile
9cd6103 [aniketbhatnagar] SPARK-3638 | Forced a compatible version of http client in kinesis-asl profile
SQL example code for Python, as shown on [SQL Programming Guide](https://spark.apache.org/docs/1.0.2/sql-programming-guide.html)
Author: jyotiska <jyotiska123@gmail.com>
Closes#2521 from jyotiska/sql_example and squashes the following commits:
1471dcb [jyotiska] added imports for sql
b25e436 [jyotiska] pep 8 compliance
43fd10a [jyotiska] lines broken to maintain 80 char limit
b4fdf4e [jyotiska] removed blank lines
83d5ab7 [jyotiska] added inferschema and applyschema to the demo
306667e [jyotiska] replaced blank line with end line
c90502a [jyotiska] fixed new line
4939a70 [jyotiska] added new line at end for python style
0b46148 [jyotiska] fixed appname for python sql example
8f67b5b [jyotiska] added python sql example
topicpMap to topicMap
Author: Gaspar Munoz <munozs.88@gmail.com>
Closes#2614 from gasparms/patch-1 and squashes the following commits:
00aab2c [Gaspar Munoz] Typo error in KafkaWordCount example
Call SparkContext.stop() in all examples (and touch up minor nearby code style issues while at it)
Author: Sean Owen <sowen@cloudera.com>
Closes#2575 from srowen/SPARK-2626 and squashes the following commits:
5b2baae [Sean Owen] Call SparkContext.stop() in all examples (and touch up minor nearby code style issues while at it)
DecisionTreeRunner functionality additions:
* Allow user to pass in a test dataset
* Do not print full model if the model is too large.
As part of this, modify DecisionTreeModel and RandomForestModel to allow printing less info. Proposed updates:
* toString: prints model summary
* toDebugString: prints full model (named after RDD.toDebugString)
Similar update to Python API:
* __repr__() now prints a model summary
* toDebugString() now prints the full model
CC: mengxr chouqin manishamde codedeft Small update (whomever can take a look). Thanks!
Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com>
Closes#2604 from jkbradley/dtrunner-update and squashes the following commits:
b2b3c60 [Joseph K. Bradley] re-added python sql doc test, temporarily removed before
07b1fae [Joseph K. Bradley] repr() now prints a model summary toDebugString() now prints the full model
1d0d93d [Joseph K. Bradley] Updated DT and RF to print less when toString is called. Added toDebugString for verbose printing.
22eac8c [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dtrunner-update
e007a95 [Joseph K. Bradley] Updated DecisionTreeRunner to accept a test dataset.
This PR adds RandomForest to MLlib. The implementation is basic, and future performance optimizations will be important. (Note: RFs = Random Forests.)
# Overview
## RandomForest
* trains multiple trees at once to reduce the number of passes over the data
* allows feature subsets at each node
* uses a queue of nodes instead of fixed groups for each level
This implementation is based an implementation by manishamde and the [Alpine Labs Sequoia Forest](https://github.com/AlpineNow/SparkML2) by codedeft (in particular, the TreePoint, BaggedPoint, and node queue implementations). Thank you for your inputs!
## Testing
Correctness: This has been tested for correctness with the test suites and with DecisionTreeRunner on example datasets.
Performance: This has been performance tested using [this branch of spark-perf](https://github.com/jkbradley/spark-perf/tree/rfs). Results below.
### Regression tests for DecisionTree
Summary: For training 1 tree, there are small regressions, especially from feature subsampling.
In the table below, each row is a single (random) dataset. The 2 different sets of result columns are for 2 different RF implementations:
* (numTrees): This is from an earlier commit, after implementing RandomForest to train multiple trees at once. It does not include any code for feature subsampling.
* (feature subsets): This is from this current PR's code, after implementing feature subsampling.
These tests were to identify regressions in DecisionTree, so they are training 1 tree with all of the features (i.e., no feature subsampling).
These were run on an EC2 cluster with 15 workers, training 1 tree with maxDepth = 5 (= 6 levels). Speedup values < 1 indicate slowdowns from the old DecisionTree implementation.
numInstances | numFeatures | runtime (sec) | speedup | runtime (sec) | speedup
---- | ---- | ---- | ---- | ---- | ----
| | (numTrees) | (numTrees) | (feature subsets) | (feature subsets)
20000 | 100 | 4.051 | 1.044433473 | 4.478 | 0.9448414471
20000 | 500 | 8.472 | 1.104461756 | 9.315 | 1.004508857
20000 | 1500 | 19.354 | 1.05854087 | 20.863 | 0.9819776638
20000 | 3500 | 43.674 | 1.072033704 | 45.887 | 1.020332556
200000 | 100 | 4.196 | 1.171830315 | 4.848 | 1.014232673
200000 | 500 | 8.926 | 1.082791844 | 9.771 | 0.989151571
200000 | 1500 | 20.58 | 1.068415938 | 22.134 | 0.9934038131
200000 | 3500 | 48.043 | 1.075203464 | 52.249 | 0.9886505005
2000000 | 100 | 4.944 | 1.01355178 | 5.796 | 0.8645617667
2000000 | 500 | 11.11 | 1.016831683 | 12.482 | 0.9050632911
2000000 | 1500 | 31.144 | 1.017852556 | 35.274 | 0.8986789136
2000000 | 3500 | 79.981 | 1.085382778 | 101.105 | 0.8586123337
20000000 | 100 | 8.304 | 0.9270231214 | 9.073 | 0.8484514494
20000000 | 500 | 28.174 | 1.083268262 | 34.236 | 0.8914592826
20000000 | 1500 | 143.97 | 0.9579634646 | 159.275 | 0.8659111599
### Tests for forests
I have run other tests with numTrees=10 and with sqrt(numFeatures), and those indicate that multi-model training and feature subsets can speed up training for forests, especially when training deeper trees.
# Details on specific classes
## Changes to DecisionTree
* Main train() method is now in RandomForest.
* findBestSplits() is no longer needed. (It split levels into groups, but we now use a queue of nodes.)
* Many small changes to support RFs. (Note: These methods should be moved to RandomForest.scala in a later PR, but are in DecisionTree.scala to make code comparison easier.)
## RandomForest
* Main train() method is from old DecisionTree.
* selectNodesToSplit: Note that it selects nodes and feature subsets jointly to track memory usage.
## RandomForestModel
* Stores an Array[DecisionTreeModel]
* Prediction:
* For classification, most common label. For regression, mean.
* We could support other methods later.
## examples/.../DecisionTreeRunner
* This now takes numTrees and featureSubsetStrategy, to support RFs.
## DTStatsAggregator
* 2 types of functionality (w/ and w/o subsampling features): These require different indexing methods. (We could treat both as subsampling, but this is less efficient
DTStatsAggregator is now abstract, and 2 child classes implement these 2 types of functionality.
## impurities
* These now take instance weights.
## Node
* Some vals changed to vars.
* This is unfortunately a public API change (DeveloperApi). This could be avoided by creating a LearningNode struct, but would be awkward.
## RandomForestSuite
Please let me know if there are missing tests!
## BaggedPoint
This wraps TreePoint and holds bootstrap weights/counts.
# Design decisions
* BaggedPoint: BaggedPoint is separate from TreePoint since it may be useful for other bagging algorithms later on.
* RandomForest public API: What options should be easily supported by the train* methods? Should ALL options be in the Java-friendly constructors? Should there be a constructor taking Strategy?
* Feature subsampling options: What options should be supported? scikit-learn supports the same options, except for "onethird." One option would be to allow users to specific fractions ("0.1"): the current options could be supported, and any unrecognized values would be parsed as Doubles in [0,1].
* Splits and bins are computed before bootstrapping, so all trees use the same discretization.
* One queue, instead of one queue per tree.
CC: mengxr manishamde codedeft chouqin Please let me know if you have suggestions---thanks!
Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com>
Author: qiping.lqp <qiping.lqp@alibaba-inc.com>
Author: chouqin <liqiping1991@gmail.com>
Closes#2435 from jkbradley/rfs-new and squashes the following commits:
c694174 [Joseph K. Bradley] Fixed typo
cc59d78 [Joseph K. Bradley] fixed imports
e25909f [Joseph K. Bradley] Simplified node group maps. Specifically, created NodeIndexInfo to store node index in agg and feature subsets, and no longer create extra maps in findBestSplits
fbe9a1e [Joseph K. Bradley] Changed default featureSubsetStrategy to be sqrt for classification, onethird for regression. Updated docs with references.
ef7c293 [Joseph K. Bradley] Updates based on code review. Most substantial changes: * Simplified DTStatsAggregator * Made RandomForestModel.trees public * Added test for regression to RandomForestSuite
593b13c [Joseph K. Bradley] Fixed bug in metadata for computing log2(num features). Now it checks >= 1.
a1a08df [Joseph K. Bradley] Removed old comments
866e766 [Joseph K. Bradley] Changed RandomForestSuite randomized tests to use multiple fixed random seeds.
ff8bb96 [Joseph K. Bradley] removed usage of null from RandomForest and replaced with Option
bf1a4c5 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into rfs-new
6b79c07 [Joseph K. Bradley] Added RandomForestSuite, and fixed small bugs, style issues.
d7753d4 [Joseph K. Bradley] Added numTrees and featureSubsetStrategy to DecisionTreeRunner (to support RandomForest). Fixed bugs so that RandomForest now runs.
746d43c [Joseph K. Bradley] Implemented feature subsampling. Tested DecisionTree but not RandomForest.
6309d1d [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into rfs-new. Added RandomForestModel.toString
b7ae594 [Joseph K. Bradley] Updated docs. Small fix for bug which does not cause errors: No longer allocate unused child nodes for leaf nodes.
121c74e [Joseph K. Bradley] Basic random forests are implemented. Random features per node not yet implemented. Test suite not implemented.
325d18a [Joseph K. Bradley] Merge branch 'chouqin-dt-preprune' into rfs-new
4ef9bf1 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into rfs-new
61b2e72 [Joseph K. Bradley] Added max of 10GB for maxMemoryInMB in Strategy.
a95e7c8 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into chouqin-dt-preprune
6da8571 [Joseph K. Bradley] RFs partly implemented, not done yet
eddd1eb [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into rfs-new
5c4ac33 [Joseph K. Bradley] Added check in Strategy to make sure minInstancesPerNode >= 1
0dd4d87 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-spark-3160
95c479d [Joseph K. Bradley] * Fixed typo in tree suite test "do not choose split that does not satisfy min instance per node requirements" * small style fixes
e2628b6 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into chouqin-dt-preprune
19b01af [Joseph K. Bradley] Merge remote-tracking branch 'chouqin/dt-preprune' into chouqin-dt-preprune
f1d11d1 [chouqin] fix typo
c7ebaf1 [chouqin] fix typo
39f9b60 [chouqin] change edge `minInstancesPerNode` to 2 and add one more test
c6e2dfc [Joseph K. Bradley] Added minInstancesPerNode and minInfoGain parameters to DecisionTreeRunner.scala and to Python API in tree.py
306120f [Joseph K. Bradley] Fixed typo in DecisionTreeModel.scala doc
eaa1dcf [Joseph K. Bradley] Added topNode doc in DecisionTree and scalastyle fix
d4d7864 [Joseph K. Bradley] Marked Node.build as deprecated
d4dbb99 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-spark-3160
1a8f0ad [Joseph K. Bradley] Eliminated pre-allocated nodes array in main train() method. * Nodes are constructed and added to the tree structure as needed during training.
0278a11 [chouqin] remove `noSplit` and set `Predict` private to tree
d593ec7 [chouqin] fix docs and change minInstancesPerNode to 1
2ab763b [Joseph K. Bradley] Simplifications to DecisionTree code:
efcc736 [qiping.lqp] fix bug
10b8012 [qiping.lqp] fix style
6728fad [qiping.lqp] minor fix: remove empty lines
bb465ca [qiping.lqp] Merge branch 'master' of https://github.com/apache/spark into dt-preprune
cadd569 [qiping.lqp] add api docs
46b891f [qiping.lqp] fix bug
e72c7e4 [qiping.lqp] add comments
845c6fa [qiping.lqp] fix style
f195e83 [qiping.lqp] fix style
987cbf4 [qiping.lqp] fix bug
ff34845 [qiping.lqp] separate calculation of predict of node from calculation of info gain
ac42378 [qiping.lqp] add min info gain and min instances per node parameters in decision tree
https://issues.apache.org/jira/browse/SPARK-3389
Author: Uri Laserson <laserson@cloudera.com>
Closes#2256 from laserson/SPARK-3389 and squashes the following commits:
0ed363e [Uri Laserson] PEP8'd the python file
0b4b380 [Uri Laserson] Moved converter to examples and added python example
eecf4dc [Uri Laserson] [SPARK-3389] Add Converter for ease of Parquet reading in PySpark
Author: Matthew Farrellee <matt@redhat.com>
Closes#2304 from mattf/SPARK-1701-partition-over-slice-for-python-examples and squashes the following commits:
928a581 [Matthew Farrellee] [SPARK-1701] [PySpark] remove slice terminology from python examples
Added minInstancesPerNode, minInfoGain params to:
* DecisionTreeRunner.scala example
* Python API (tree.py)
Also:
* Fixed typo in tree suite test "do not choose split that does not satisfy min instance per node requirements"
* small style fixes
CC: mengxr
Author: qiping.lqp <qiping.lqp@alibaba-inc.com>
Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com>
Author: chouqin <liqiping1991@gmail.com>
Closes#2349 from jkbradley/chouqin-dt-preprune and squashes the following commits:
61b2e72 [Joseph K. Bradley] Added max of 10GB for maxMemoryInMB in Strategy.
a95e7c8 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into chouqin-dt-preprune
95c479d [Joseph K. Bradley] * Fixed typo in tree suite test "do not choose split that does not satisfy min instance per node requirements" * small style fixes
e2628b6 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into chouqin-dt-preprune
19b01af [Joseph K. Bradley] Merge remote-tracking branch 'chouqin/dt-preprune' into chouqin-dt-preprune
f1d11d1 [chouqin] fix typo
c7ebaf1 [chouqin] fix typo
39f9b60 [chouqin] change edge `minInstancesPerNode` to 2 and add one more test
c6e2dfc [Joseph K. Bradley] Added minInstancesPerNode and minInfoGain parameters to DecisionTreeRunner.scala and to Python API in tree.py
0278a11 [chouqin] remove `noSplit` and set `Predict` private to tree
d593ec7 [chouqin] fix docs and change minInstancesPerNode to 1
efcc736 [qiping.lqp] fix bug
10b8012 [qiping.lqp] fix style
6728fad [qiping.lqp] minor fix: remove empty lines
bb465ca [qiping.lqp] Merge branch 'master' of https://github.com/apache/spark into dt-preprune
cadd569 [qiping.lqp] add api docs
46b891f [qiping.lqp] fix bug
e72c7e4 [qiping.lqp] add comments
845c6fa [qiping.lqp] fix style
f195e83 [qiping.lqp] fix style
987cbf4 [qiping.lqp] fix bug
ff34845 [qiping.lqp] separate calculation of predict of node from calculation of info gain
ac42378 [qiping.lqp] add min info gain and min instances per node parameters in decision tree
...'t depend on
Publish local in maven term is `install`
and publish otherwise is `deploy`
So disabled both for following projects.
Author: Prashant Sharma <prashant.s@imaginea.com>
Closes#2329 from ScrapCodes/SPARK-3452/maven-skip-install and squashes the following commits:
257b79a [Prashant Sharma] [SPARK-3452] Maven build should skip publishing artifacts people shouldn't depend on
Author: Prashant Sharma <prashant.s@imaginea.com>
Closes#2331 from ScrapCodes/compilation-warn and squashes the following commits:
44c1e76 [Prashant Sharma] Minor - Fix trivial compilation warnings.
Adjust the default values of decision tree, based on the memory requirement discussed in https://github.com/apache/spark/pull/2125 :
1. maxMemoryInMB: 128 -> 256
2. maxBins: 100 -> 32
3. maxDepth: 4 -> 5 (in some example code)
jkbradley
Author: Xiangrui Meng <meng@databricks.com>
Closes#2322 from mengxr/tree-defaults and squashes the following commits:
cda453a [Xiangrui Meng] fix tests
5900445 [Xiangrui Meng] update comments
8c81831 [Xiangrui Meng] update default values of tree:
Author: GuoQiang Li <witgo@qq.com>
Closes#2268 from witgo/SPARK-3397 and squashes the following commits:
eaf913f [GuoQiang Li] Bump pom.xml version number of master branch to 1.2.0-SNAPSHOT
This PR resolves [SPARK-3361](https://issues.apache.org/jira/browse/SPARK-3361) by expanding the PEP 8 checks to cover the remaining Python code base:
* The EC2 script
* All Python / PySpark examples
Author: Nicholas Chammas <nicholas.chammas@gmail.com>
Closes#2297 from nchammas/pep8-rulez and squashes the following commits:
1e5ac9a [Nicholas Chammas] PEP 8 fixes to Python examples
c3dbeff [Nicholas Chammas] PEP 8 fixes to EC2 script
65ef6e8 [Nicholas Chammas] expand PEP 8 checks
PR #720 made multiple changes to GraphGenerator.logNormalGraph including:
* Replacing the call to functions for generating random vertices and edges with in-line implementations with different equations. Based on reading the Pregel paper, I believe the in-line functions are incorrect.
* Hard-coding of RNG seeds so that method now generates the same graph for a given number of vertices, edges, mu, and sigma -- user is not able to override seed or specify that seed should be randomly generated.
* Backwards-incompatible change to logNormalGraph signature with introduction of new required parameter.
* Failed to update scala docs and programming guide for API changes
* Added a Synthetic Benchmark in the examples.
This PR:
* Removes the in-line calls and calls original vertex / edge generation functions again
* Adds an optional seed parameter for deterministic behavior (when desired)
* Keeps the number of partitions parameter that was added.
* Keeps compatibility with the synthetic benchmark example
* Maintains backwards-compatible API
Author: RJ Nowling <rnowling@gmail.com>
Author: Ankur Dave <ankurdave@gmail.com>
Closes#2168 from rnowling/graphgenrand and squashes the following commits:
f1cd79f [Ankur Dave] Style fixes
e11918e [RJ Nowling] Fix bad comparisons in unit tests
785ac70 [RJ Nowling] Fix style error
c70868d [RJ Nowling] Fix logNormalGraph scala doc for seed
41fd1f8 [RJ Nowling] Fix logNormalGraph scala doc for seed
799f002 [RJ Nowling] Added test for different seeds for sampleLogNormal
43949ad [RJ Nowling] Added test for different seeds for generateRandomEdges
2faf75f [RJ Nowling] Added unit test for logNormalGraph
82f22397 [RJ Nowling] Add unit test for sampleLogNormal
b99cba9 [RJ Nowling] Make sampleLogNormal private to Spark (vs private) for unit testing
6803da1 [RJ Nowling] Add GraphGeneratorsSuite with test for generateRandomEdges
1c8fc44 [RJ Nowling] Connected components part of SynthBenchmark was failing to call count on RDD before printing
dfbb6dd [RJ Nowling] Fix parameter name in SynthBenchmark docs
b5eeb80 [RJ Nowling] Add optional seed parameter to SynthBenchmark and set default to randomly generate a seed
1ff8d30 [RJ Nowling] Fix bug in generateRandomEdges where numVertices instead of numEdges was used to control number of edges to generate
98bb73c [RJ Nowling] Add documentation for logNormalGraph parameters
d40141a [RJ Nowling] Fix style error
684804d [RJ Nowling] revert PR #720 which introduce errors in logNormalGraph and messed up seeding of RNGs. Add user-defined optional seed for deterministic behavior
c183136 [RJ Nowling] Fix to deterministic GraphGenerators.logNormalGraph that allows generating graphs randomly using optional seed.
015010c [RJ Nowling] Fixed GraphGenerator logNormalGraph API to make backward-incompatible change in commit 894ecde04
to support ~/spark/bin/run-example GraphXAnalytics triangles
/soc-LiveJournal1.txt --numEPart=256
Author: Larry Xiao <xiaodi@sjtu.edu.cn>
Closes#1766 from larryxiao/1986 and squashes the following commits:
bb77cd9 [Larry Xiao] [SPARK-1986][GraphX]move lib.Analytics to org.apache.spark.examples
Different places in the code were instantiating Configuration / YarnConfiguration objects in different ways. This could lead to confusion for people who actually expected "spark.hadoop.*" options to end up in the configs used by Spark code, since that would only happen for the SparkContext's config.
This change modifies most places to use SparkHadoopUtil to initialize configs, and make that method do the translation that previously was only done inside SparkContext.
The places that were not changed fall in one of the following categories:
- Test code where this doesn't really matter
- Places deep in the code where plumbing SparkConf would be too difficult for very little gain
- Default values for arguments - since the caller can provide their own config in that case
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#1843 from vanzin/SPARK-2889 and squashes the following commits:
52daf35 [Marcelo Vanzin] Merge branch 'master' into SPARK-2889
f179013 [Marcelo Vanzin] Merge branch 'master' into SPARK-2889
51e71cf [Marcelo Vanzin] Add test to ensure that overriding Yarn configs works.
53f9506 [Marcelo Vanzin] Add DeveloperApi annotation.
3d345cb [Marcelo Vanzin] Restore old method for backwards compat.
fc45067 [Marcelo Vanzin] Merge branch 'master' into SPARK-2889
0ac3fdf [Marcelo Vanzin] Merge branch 'master' into SPARK-2889
3f26760 [Marcelo Vanzin] Compilation fix.
f16cadd [Marcelo Vanzin] Initialize config in SparkHadoopUtil.
b8ab173 [Marcelo Vanzin] Update Utils API to take a Configuration argument.
1e7003f [Marcelo Vanzin] Replace explicit Configuration instantiation with SparkHadoopUtil.
`./bin/spark-example` should be `./bin/run-example` in DenseKMeans and SparseNaiveBayes
Author: wangfei <wangfei_hello@126.com>
Closes#2193 from scwf/run-example and squashes the following commits:
207eb3a [wangfei] spark-example should be run-example
27a8999 [wangfei] ./bin/spark-example should be ./bin/run-example
def sum[B >: A](implicit num: Numeric[B]): B = foldLeft(num.zero)(num.plus)
Using values.sum is easier to understand than using values.foldLeft(0)(_ + _), so we'd better use values.sum instead of values.foldLeft(0)(_ + _)
Author: Yadong Qi <qiyadong2010@gmail.com>
Closes#2182 from watermen/bug-fix3 and squashes the following commits:
17be9fb [Yadong Qi] Update CheckpointSuite.scala
714bda5 [Yadong Qi] Update BasicOperationsSuite.scala
57e704c [Yadong Qi] Update StatefulNetworkWordCount.scala
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes#1895 from sarutak/SPARK-2976 and squashes the following commits:
1cf7e69 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into SPARK-2976
d1e0666 [Kousuke Saruta] Modified styles
c5e80a4 [Kousuke Saruta] Remove tab from JavaPageRank.java and JavaKinesisWordCountASL.java
c003b36 [Kousuke Saruta] Removed tab from sorttable.js
Updated DecisionTree documentation, with examples for Java, Python.
Added same Java example to code as well.
CC: @mengxr @manishamde @atalwalkar
Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com>
Closes#2063 from jkbradley/dt-docs and squashes the following commits:
2dd2c19 [Joseph K. Bradley] Last updates based on github review.
9dd1b6b [Joseph K. Bradley] Updated decision tree doc.
d802369 [Joseph K. Bradley] Updates based on comments: cache data, corrected doc text.
b9bee04 [Joseph K. Bradley] Updated DT examples
57eee9f [Joseph K. Bradley] Created JavaDecisionTree example from example in docs, and corrected doc example as needed.
d939a92 [Joseph K. Bradley] Updated DecisionTree documentation. Added Java, Python examples.
For further discussion, please check the JIRA entry.
This change moves Guava classes to a different package so that they don't conflict with the user-provided Guava (or the Hadoop-provided one). Since one class (Optional) was exposed through Spark's public API, that class was forked from Guava at the current dependency version (14.0.1) so that it can be kept going forward (until the API is cleaned).
Note this change has a few implications:
- *all* classes in the final jars will reference the relocated classes. If Hadoop classes are included (i.e. "-Phadoop-provided" is not activated), those will also reference the Guava 14 classes (instead of the Guava 11 classes from the Hadoop classpath).
- if the Guava version in Spark is ever changed, the new Guava will still reference the forked Optional class; this may or may not be a problem, but in the long term it's better to think about removing Optional from the public API.
For the end user, there are two visible implications:
- Guava is not provided as a transitive dependency anymore (since it's "provided" in Spark)
- At runtime, unless they provide their own, they'll either have no Guava or Hadoop's version of Guava (11), depending on how they set up their classpath.
Note that this patch does not change the sbt deliverables; those will still contain guava in its original package, and provide guava as a compile-time dependency. This assumes that maven is the canonical build, and sbt-built artifacts are not (officially) published.
Author: Marcelo Vanzin <vanzin@cloudera.com>
Closes#1813 from vanzin/SPARK-2848 and squashes the following commits:
9bdffb0 [Marcelo Vanzin] Undo sbt build changes.
819b445 [Marcelo Vanzin] Review feedback.
05e0a3d [Marcelo Vanzin] Merge branch 'master' into SPARK-2848
fef4370 [Marcelo Vanzin] Unfork Optional.java.
d3ea8e1 [Marcelo Vanzin] Exclude asm classes from final jar.
637189b [Marcelo Vanzin] Add hacky filter to prefer Spark's copy of Optional.
2fec990 [Marcelo Vanzin] Shade Guava in the sbt build.
616998e [Marcelo Vanzin] Shade Guava in the maven build, fork Guava's Optional.java.
It is useful in streaming to allow users to carry extra data with the prediction, for monitoring the prediction error for example. freeman-lab
Author: Xiangrui Meng <meng@databricks.com>
Closes#2023 from mengxr/predict-on-values and squashes the following commits:
cac47b8 [Xiangrui Meng] add classtag
2821b3b [Xiangrui Meng] use mapValues
0925efa [Xiangrui Meng] add predictOnValues to StreamingLR and fix predictOn
Added examples for statistical summarization:
* Scala: StatisticalSummary.scala
** Tests: correlation, MultivariateOnlineSummarizer
* python: statistical_summary.py
** Tests: correlation (since MultivariateOnlineSummarizer has no Python API)
Added examples for random and sampled RDDs:
* Scala: RandomAndSampledRDDs.scala
* python: random_and_sampled_rdds.py
* Both test:
** RandomRDDGenerators.normalRDD, normalVectorRDD
** RDD.sample, takeSample, sampleByKey
Added sc.stop() to all examples.
CorrelationSuite.scala
* Added 1 test for RDDs with only 1 value
RowMatrix.scala
* numCols(): Added check for numRows = 0, with error message.
* computeCovariance(): Added check for numRows <= 1, with error message.
Python SparseVector (pyspark/mllib/linalg.py)
* Added toDense() function
python/run-tests script
* Added stat.py (doc test)
CC: mengxr dorx Main changes were examples to show usage across APIs.
Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com>
Closes#1878 from jkbradley/mllib-stats-api-check and squashes the following commits:
ea5c047 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into mllib-stats-api-check
dafebe2 [Joseph K. Bradley] Bug fixes for examples SampledRDDs.scala and sampled_rdds.py: Check for division by 0 and for missing key in maps.
8d1e555 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into mllib-stats-api-check
60c72d9 [Joseph K. Bradley] Fixed stat.py doc test to work for Python versions printing nan or NaN.
b20d90a [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into mllib-stats-api-check
4e5d15e [Joseph K. Bradley] Changed pyspark/mllib/stat.py doc tests to use NaN instead of nan.
32173b7 [Joseph K. Bradley] Stats examples update.
c8c20dc [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into mllib-stats-api-check
cf70b07 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into mllib-stats-api-check
0b7cec3 [Joseph K. Bradley] Small updates based on code review. Renamed statistical_summary.py to correlations.py
ab48f6e [Joseph K. Bradley] RowMatrix.scala * numCols(): Added check for numRows = 0, with error message. * computeCovariance(): Added check for numRows <= 1, with error message.
65e4ebc [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into mllib-stats-api-check
8195c78 [Joseph K. Bradley] Added examples for random and sampled RDDs: * Scala: RandomAndSampledRDDs.scala * python: random_and_sampled_rdds.py * Both test: ** RandomRDDGenerators.normalRDD, normalVectorRDD ** RDD.sample, takeSample, sampleByKey
064985b [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into mllib-stats-api-check
ee918e9 [Joseph K. Bradley] Added examples for statistical summarization: * Scala: StatisticalSummary.scala ** Tests: correlation, MultivariateOnlineSummarizer * python: statistical_summary.py ** Tests: correlation (since MultivariateOnlineSummarizer has no Python API)
Small DecisionTree updates:
* Changed main DecisionTree aggregate to treeAggregate.
* Fixed bug in python example decision_tree_runner.py with missing argument (since categoricalFeaturesInfo is no longer an optional argument for trainClassifier).
* Fixed same bug in python doc tests, and added tree.py to doc tests.
CC: mengxr
Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com>
Closes#2015 from jkbradley/dt-opt2 and squashes the following commits:
b5114fa [Joseph K. Bradley] Fixed python tree.py doc test (extra newline)
8e4665d [Joseph K. Bradley] Added tree.py to python doc tests. Fixed bug from missing categoricalFeaturesInfo argument.
b7b2922 [Joseph K. Bradley] Fixed bug in python example decision_tree_runner.py with missing argument. Changed main DecisionTree aggregate to treeAggregate.
85bbc1f [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt2
66d076f [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt2
a0ed0da [Joseph K. Bradley] Renamed DTMetadata to DecisionTreeMetadata. Small doc updates.
3726d20 [Joseph K. Bradley] Small code improvements based on code review.
ac0b9f8 [Joseph K. Bradley] Small updates based on code review. Main change: Now using << instead of math.pow.
db0d773 [Joseph K. Bradley] scala style fix
6a38f48 [Joseph K. Bradley] Added DTMetadata class for cleaner code
931a3a7 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt2
797f68a [Joseph K. Bradley] Fixed DecisionTreeSuite bug for training second level. Needed to update treePointToNodeIndex with groupShift.
f40381c [Joseph K. Bradley] Merge branch 'dt-opt1' into dt-opt2
5f2dec2 [Joseph K. Bradley] Fixed scalastyle issue in TreePoint
6b5651e [Joseph K. Bradley] Updates based on code review. 1 major change: persisting to memory + disk, not just memory.
2d2aaaf [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt1
26d10dd [Joseph K. Bradley] Removed tree/model/Filter.scala since no longer used. Removed debugging println calls in DecisionTree.scala.
356daba [Joseph K. Bradley] Merge branch 'dt-opt1' into dt-opt2
430d782 [Joseph K. Bradley] Added more debug info on binning error. Added some docs.
d036089 [Joseph K. Bradley] Print timing info to logDebug.
e66f1b1 [Joseph K. Bradley] TreePoint * Updated doc * Made some methods private
8464a6e [Joseph K. Bradley] Moved TimeTracker to tree/impl/ in its own file, and cleaned it up. Removed debugging println calls from DecisionTree. Made TreePoint extend Serialiable
a87e08f [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt1
c1565a5 [Joseph K. Bradley] Small DecisionTree updates: * Simplification: Updated calculateGainForSplit to take aggregates for a single (feature, split) pair. * Internal doc: findAggForOrderedFeatureClassification
b914f3b [Joseph K. Bradley] DecisionTree optimization: eliminated filters + small changes
b2ed1f3 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt
0f676e2 [Joseph K. Bradley] Optimizations + Bug fix for DecisionTree
3211f02 [Joseph K. Bradley] Optimizing DecisionTree * Added TreePoint representation to avoid calling findBin multiple times. * (not working yet, but debugging)
f61e9d2 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-timing
bcf874a [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-timing
511ec85 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-timing
a95bc22 [Joseph K. Bradley] timing for DecisionTree internals
Move `parse()` from `LabeledPointParser` to `LabeledPoint` and make it public. This breaks binary compatibility only when a user uses synthesized methods like `tupled` and `curried`, which is rare.
`LabeledPoint.parse` is more consistent with `Vectors.parse`, which is why `LabeledPointParser` is not preferred.
freeman-lab tdas
Author: Xiangrui Meng <meng@databricks.com>
Closes#1952 from mengxr/labelparser and squashes the following commits:
c818fb2 [Xiangrui Meng] merge master
ce20e6f [Xiangrui Meng] update mima excludes
b386b8d [Xiangrui Meng] fix tests
2436b3d [Xiangrui Meng] add parse() to LabeledPoint
Should ask users to set parameters through the optimizer. dbtsai
Author: Xiangrui Meng <meng@databricks.com>
Closes#1973 from mengxr/lr-lbfgs and squashes the following commits:
e3efbb1 [Xiangrui Meng] fix tests
21b3579 [Xiangrui Meng] fix method name
641eea4 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into lr-lbfgs
456ab7c [Xiangrui Meng] update LRWithLBFGS
JIRA: https://issues.apache.org/jira/browse/SPARK-2736
This patch includes:
1. An Avro converter that converts Avro data types to Python. It handles all 3 Avro data mappings (Generic, Specific and Reflect).
2. An example Python script for reading Avro files using AvroKeyInputFormat and the converter.
3. Fixing a classloading issue.
cc @MLnick @JoshRosen @mateiz
Author: Kan Zhang <kzhang@apache.org>
Closes#1916 from kanzhang/SPARK-2736 and squashes the following commits:
02443f8 [Kan Zhang] [SPARK-2736] Adding .avsc files to .rat-excludes
f74e9a9 [Kan Zhang] [SPARK-2736] nit: clazz -> className
82cc505 [Kan Zhang] [SPARK-2736] Update data sample
0be7761 [Kan Zhang] [SPARK-2736] Example pyspark script and data files
c8e5881 [Kan Zhang] [SPARK-2736] Trying to work with all 3 Avro data models
2271a5b [Kan Zhang] [SPARK-2736] Using the right class loader to find Avro classes
536876b [Kan Zhang] [SPARK-2736] Adding Avro to Java converter
Many users have reported being confused by the distinction between the `sql` and `hql` methods. Specifically, many users think that `sql(...)` cannot be used to read hive tables. In this PR I introduce a new configuration option `spark.sql.dialect` that picks which dialect with be used for parsing. For SQLContext this must be set to `sql`. In `HiveContext` it defaults to `hiveql` but can also be set to `sql`.
The `hql` and `hiveql` methods continue to act the same but are now marked as deprecated.
**This is a possibly breaking change for some users unless they set the dialect manually, though this is unlikely.**
For example: `hiveContex.sql("SELECT 1")` will now throw a parsing exception by default.
Author: Michael Armbrust <michael@databricks.com>
Closes#1746 from marmbrus/sqlLanguageConf and squashes the following commits:
ad375cc [Michael Armbrust] Merge remote-tracking branch 'apache/master' into sqlLanguageConf
20c43f8 [Michael Armbrust] override function instead of just setting the value
7e4ae93 [Michael Armbrust] Deprecate hql() method in favor of a config option, 'spark.sql.dialect'
There have been user complaints that the difference between `registerAsTable` and `saveAsTable` is too subtle. This PR addresses this by renaming `registerAsTable` to `registerTempTable`, which more clearly reflects what is happening. `registerAsTable` remains, but will cause a deprecation warning.
Author: Michael Armbrust <michael@databricks.com>
Closes#1743 from marmbrus/registerTempTable and squashes the following commits:
d031348 [Michael Armbrust] Merge remote-tracking branch 'apache/master' into registerTempTable
4dff086 [Michael Armbrust] Fix .java files too
89a2f12 [Michael Armbrust] Merge remote-tracking branch 'apache/master' into registerTempTable
0b7b71e [Michael Armbrust] Rename registerAsTable to registerTempTable
Author: Chris Fregly <chris@fregly.com>
Closes#1434 from cfregly/master and squashes the following commits:
4774581 [Chris Fregly] updated docs, renamed retry to retryRandom to be more clear, removed retries around store() method
0393795 [Chris Fregly] moved Kinesis examples out of examples/ and back into extras/kinesis-asl
691a6be [Chris Fregly] fixed tests and formatting, fixed a bug with JavaKinesisWordCount during union of streams
0e1c67b [Chris Fregly] Merge remote-tracking branch 'upstream/master'
74e5c7c [Chris Fregly] updated per TD's feedback. simplified examples, updated docs
e33cbeb [Chris Fregly] Merge remote-tracking branch 'upstream/master'
bf614e9 [Chris Fregly] per matei's feedback: moved the kinesis examples into the examples/ dir
d17ca6d [Chris Fregly] per TD's feedback: updated docs, simplified the KinesisUtils api
912640c [Chris Fregly] changed the foundKinesis class to be a publically-avail class
db3eefd [Chris Fregly] Merge remote-tracking branch 'upstream/master'
21de67f [Chris Fregly] Merge remote-tracking branch 'upstream/master'
6c39561 [Chris Fregly] parameterized the versions of the aws java sdk and kinesis client
338997e [Chris Fregly] improve build docs for kinesis
828f8ae [Chris Fregly] more cleanup
e7c8978 [Chris Fregly] Merge remote-tracking branch 'upstream/master'
cd68c0d [Chris Fregly] fixed typos and backward compatibility
d18e680 [Chris Fregly] Merge remote-tracking branch 'upstream/master'
b3b0ff1 [Chris Fregly] [SPARK-1981] Add AWS Kinesis streaming support
