This PR includes some code simplifications and re-organization which will be helpful for implementing random forests. The main changes are that the nodes and parentImpurities arrays are no longer pre-allocated in the main train() method.
Also added 2 bug fixes:
* maxMemoryUsage calculation
* over-allocation of space for bins in DTStatsAggregator for unordered features.
Relation to RFs:
* Since RFs will be deeper and will therefore be more likely sparse (not full trees), it could be a cost savings to avoid pre-allocating a full tree.
* The associated re-organization also reduces bookkeeping, which will make RFs easier to implement.
* The return code doneTraining may be generalized to include cases such as nodes ready for local training.
Details:
No longer pre-allocate parentImpurities array in main train() method.
* parentImpurities values are now stored in individual nodes (in Node.stats.impurity).
* These were not really needed. They were used in calculateGainForSplit(), but they can be calculated anyways using parentNodeAgg.
No longer using Node.build since tree structure is constructed on-the-fly.
* Did not eliminate since it is public (Developer) API. Marked as deprecated.
Eliminated pre-allocated nodes array in main train() method.
* Nodes are constructed and added to the tree structure as needed during training.
* Moved tree construction from main train() method into findBestSplitsPerGroup() since there is no need to keep the (split, gain) array for an entire level of nodes. Only one element of that array is needed at a time, so we do not the array.
findBestSplits() now returns 2 items:
* rootNode (newly created root node on first iteration, same root node on later iterations)
* doneTraining (indicating if all nodes at that level were leafs)
Updated DecisionTreeSuite. Notes:
* Improved test "Second level node building with vs. without groups"
** generateOrderedLabeledPoints() modified so that it really does require 2 levels of internal nodes.
* Related update: Added Node.deepCopy (private[tree]), used for test suite
CC: mengxr
Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com>
Closes#2341 from jkbradley/dt-spark-3160 and squashes the following commits:
07dd1ee [Joseph K. Bradley] Fixed overflow bug with computing maxMemoryUsage in DecisionTree. Also fixed bug with over-allocating space in DTStatsAggregator for unordered features.
debe072 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-spark-3160
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
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.
2ab763b [Joseph K. Bradley] Simplifications to DecisionTree code:
These two parameters can act as early stop rules to do pre-pruning. When a split cause cause left or right child to have less than `minInstancesPerNode` or has less information gain than `minInfoGain`, current node will not be split by this split.
When there is no possible splits that satisfy requirements, there is no useful information gain stats, but we still need to calculate the predict value for current node. So I separated calculation of predict from calculation of information gain, which can also save computation when the number of possible splits is large. Please see [SPARK-3272](https://issues.apache.org/jira/browse/SPARK-3272) for more details.
CC: mengxr manishamde jkbradley, please help me review this, thanks.
Author: qiping.lqp <qiping.lqp@alibaba-inc.com>
Author: chouqin <liqiping1991@gmail.com>
Closes#2332 from chouqin/dt-preprune and squashes the following commits:
f1d11d1 [chouqin] fix typo
c7ebaf1 [chouqin] fix typo
39f9b60 [chouqin] change edge `minInstancesPerNode` to 2 and add one more test
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
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:
Summary:
1. Variable numBins for each feature [SPARK-3043]
2. Reduced data reshaping in aggregation [SPARK-3043]
3. Choose ordering for ordered categorical features adaptively [SPARK-3156]
4. Changed nodes to use 1-indexing [SPARK-3086]
5. Small clean-ups
Note: This PR looks bigger than it is since I moved several functions from inside findBestSplitsPerGroup to outside of it (to make it clear what was being serialized in the aggregation).
Speedups: This update helps most when many features use few bins but a few features use many bins. Some example results on speedups with 2M examples, 3.5K features (15-worker EC2 cluster):
* Example where old code was reasonably efficient (1/2 continuous, 1/4 binary, 1/4 20-category): 164.813 --> 116.491 sec
* Example where old code wasted many bins (1/10 continuous, 81/100 binary, 9/100 20-category): 128.701 --> 39.334 sec
Details:
(1) Variable numBins for each feature [SPARK-3043]
DecisionTreeMetadata now computes a variable numBins for each feature. It also tracks numSplits.
(2) Reduced data reshaping in aggregation [SPARK-3043]
Added DTStatsAggregator, a wrapper around the aggregate statistics array for easy but efficient indexing.
* Added ImpurityAggregator and ImpurityCalculator classes, to make DecisionTree code more oblivious to the type of impurity.
* Design note: I originally tried creating Impurity classes which stored data and storing the aggregates in an Array[Array[Array[Impurity]]]. However, this led to significant slowdowns, perhaps because of overhead in creating so many objects.
The aggregate statistics are never reshaped, and cumulative sums are computed in-place.
Updated the layout of aggregation functions. The update simplifies things by (1) dividing features into ordered/unordered (instead of ordered/unordered/continuous) and (2) making use of the DTStatsAggregator for indexing.
For this update, the following functions were refactored:
* updateBinForOrderedFeature
* updateBinForUnorderedFeature
* binaryOrNotCategoricalBinSeqOp
* multiclassWithCategoricalBinSeqOp
* regressionBinSeqOp
The above 5 functions were replaced with:
* orderedBinSeqOp
* someUnorderedBinSeqOp
Other changes:
* calculateGainForSplit now treats all feature types the same way.
* Eliminated extractLeftRightNodeAggregates.
(3) Choose ordering for ordered categorical features adaptively [SPARK-3156]
Updated binsToBestSplit():
* This now computes cumulative sums of stats for ordered features.
* For ordered categorical features, it chooses an ordering for categories. (This uses to be done by findSplitsBins.)
* Uses iterators to shorten code and avoid building an Array[Array[InformationGainStats]].
Side effects:
* In findSplitsBins: A sample of the data is only taken for data with continuous features. It is not needed for data with only categorical features.
* In findSplitsBins: splits and bins are no longer pre-computed for ordered categorical features since they are not needed.
* TreePoint binning is simpler for categorical features.
(4) Changed nodes to use 1-indexing [SPARK-3086]
Nodes used to be indexed from 0. Now they are indexed from 1.
Node indexing functions are now collected in object Node (Node.scala).
(5) Small clean-ups
Eliminated functions extractNodeInfo() and extractInfoForLowerLevels() to reduce duplicate code.
Eliminated InvalidBinIndex since it is no longer used.
CC: mengxr manishamde Please let me know if you have thoughts on this—thanks!
Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com>
Closes#2125 from jkbradley/dt-opt3alt and squashes the following commits:
42c192a [Joseph K. Bradley] Merge branch 'rfs' into dt-opt3alt
d3cc46b [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt3alt
00e4404 [Joseph K. Bradley] optimization for TreePoint construction (pre-computing featureArity and isUnordered as arrays)
425716c [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into rfs
a2acea5 [Joseph K. Bradley] Small optimizations based on profiling
aa4e4df [Joseph K. Bradley] Updated DTStatsAggregator with bug fix (nodeString should not be multiplied by statsSize)
4651154 [Joseph K. Bradley] Changed numBins semantics for unordered features. * Before: numBins = numSplits = (1 << k - 1) - 1 * Now: numBins = 2 * numSplits = 2 * [(1 << k - 1) - 1] * This also involved changing the semantics of: ** DecisionTreeMetadata.numUnorderedBins()
1e3b1c7 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt3alt
1485fcc [Joseph K. Bradley] Made some DecisionTree methods private.
92f934f [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt3alt
e676da1 [Joseph K. Bradley] Updated documentation for DecisionTree
37ca845 [Joseph K. Bradley] Fixed problem with how DecisionTree handles ordered categorical features.
105f8ab [Joseph K. Bradley] Removed commented-out getEmptyBinAggregates from DecisionTree
062c31d [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt3alt
6d32ccd [Joseph K. Bradley] In DecisionTree.binsToBestSplit, changed loops to iterators to shorten code.
807cd00 [Joseph K. Bradley] Finished DTStatsAggregator, a wrapper around the aggregate statistics for easy but hopefully efficient indexing. Modified old ImpurityAggregator classes and renamed them ImpurityCalculator; added ImpurityAggregator classes which work with DTStatsAggregator but do not store data. Unit tests all succeed.
f2166fd [Joseph K. Bradley] still working on DTStatsAggregator
92f7118 [Joseph K. Bradley] Added partly written DTStatsAggregator
fd8df30 [Joseph K. Bradley] Moved some aggregation helpers outside of findBestSplitsPerGroup
d7c53ee [Joseph K. Bradley] Added more doc for ImpurityAggregator
a40f8f1 [Joseph K. Bradley] Changed nodes to be indexed from 1. Tests work.
95cad7c [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt3
5f94342 [Joseph K. Bradley] Added treeAggregate since not yet merged from master. Moved node indexing functions to Node.
61c4509 [Joseph K. Bradley] Fixed bugs from merge: missing DT timer call, and numBins setting. Cleaned up DT Suite some.
3ba7166 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt3
b314659 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-opt3
9c83363 [Joseph K. Bradley] partial merge but not done yet
45f7ea7 [Joseph K. Bradley] partial merge, not yet done
5fce635 [Joseph K. Bradley] Merge branch 'dt-opt2' into dt-opt3
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
dd4d3aa [Joseph K. Bradley] Mid-process in bug fix: bug for binary classification with categorical features * Bug: Categorical features were all treated as ordered for binary classification. This is possible but would require the bin ordering to be determined on-the-fly after the aggregation. Currently, the ordering is determined a priori and fixed for all splits. * (Temp) Fix: Treat low-arity categorical features as unordered for binary classification. * Related change: I removed most tests for isMulticlass in the code. I instead test metadata for whether there are unordered features. * Status: The bug may be fixed, but more testing needs to be done.
438a660 [Joseph K. Bradley] removed subsampling for mnist8m from DT
86e217f [Joseph K. Bradley] added cache to DT input
e3c84cc [Joseph K. Bradley] Added stuff fro mnist8m to D T Runner
51ef781 [Joseph K. Bradley] Fixed bug introduced by last commit: Variance impurity calculation was incorrect since counts were swapped accidentally
fd65372 [Joseph K. Bradley] Major changes: * Created ImpurityAggregator classes, rather than old aggregates. * Feature split/bin semantics are based on ordered vs. unordered ** E.g.: numSplits = numBins for all unordered features, and numSplits = numBins - 1 for all ordered features. * numBins can differ for each feature
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
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
Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp>
Closes#2248 from sarutak/SPARK-3372 and squashes the following commits:
73a28b8 [Kousuke Saruta] Replaced UTF-8 hyphen with ascii hyphen
Kill this bug fast before it does damage.
Author: Reza Zadeh <rizlar@gmail.com>
Closes#2224 from rezazadeh/indexrmbug and squashes the following commits:
53386d6 [Reza Zadeh] Squash bug in IndexedRowMatrix
to re-construct k-means models freeman-lab
Author: Xiangrui Meng <meng@databricks.com>
Closes#2112 from mengxr/public-constructors and squashes the following commits:
18d53a9 [Xiangrui Meng] make KMeans constructor public
Sorry I didn't realize this in #2043. Ishiihara
Author: Xiangrui Meng <meng@databricks.com>
Closes#2049 from mengxr/more-w2v and squashes the following commits:
050b1c5 [Xiangrui Meng] output shuffle data directly
or Jenkins will complain about no Apache header in checkpoint files. tdas rxin
Author: Xiangrui Meng <meng@databricks.com>
Closes#2046 from mengxr/tmp-checkpoint and squashes the following commits:
0d3ec73 [Xiangrui Meng] remove ssc.stop
9797843 [Xiangrui Meng] change checkpointDir to lazy val
89964ab [Xiangrui Meng] use temp folder for checkpoint
because NB treats feature values as term frequencies. jkbradley
Author: Xiangrui Meng <meng@databricks.com>
Closes#2038 from mengxr/nb-neg and squashes the following commits:
52c37c3 [Xiangrui Meng] address comments
65f892d [Xiangrui Meng] detect negative values in nb
very minor update Ishiihara
Author: Xiangrui Meng <meng@databricks.com>
Closes#2043 from mengxr/minor-w2v and squashes the following commits:
be649fd [Xiangrui Meng] remove map because we only need append
eccefcc [Xiangrui Meng] minor updates to word2vec
Though we don't use default argument for methods in RandomRDDs, it is still not easy for Java users to use because the output type is either `RDD[Double]` or `RDD[Vector]`. Java users should expect `JavaDoubleRDD` and `JavaRDD[Vector]`, respectively. We should create dedicated methods for Java users, and allow default arguments in Scala methods in RandomRDDs, to make life easier for both Java and Scala users. This PR also contains documentation for random data generation. brkyvz
Author: Xiangrui Meng <meng@databricks.com>
Closes#2041 from mengxr/stat-doc and squashes the following commits:
fc5eedf [Xiangrui Meng] add missing comma
ffde810 [Xiangrui Meng] address comments
aef6d07 [Xiangrui Meng] add doc for random data generation
b99d94b [Xiangrui Meng] add java-friendly methods to RandomRDDs
Refactored tests for streaming linear regression to use existing streaming test utilities. Summary of changes:
- Made ``mllib`` depend on tests from ``streaming``
- Rewrote accuracy and convergence tests to use ``setupStreams`` and ``runStreams``
- Added new test for the accuracy of predictions generated by ``predictOnValue``
These tests should run faster, be easier to extend/maintain, and provide a reference for new tests.
mengxr tdas
Author: freeman <the.freeman.lab@gmail.com>
Closes#2037 from freeman-lab/streamingLR-predict-tests and squashes the following commits:
e851ca7 [freeman] Fixed long lines
50eb0bf [freeman] Refactored tests to use streaming test tools
32c43c2 [freeman] Added test for prediction
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
mengxr
Remove transform(dataset: RDD[String]) from public API.
Author: Liquan Pei <liquanpei@gmail.com>
Closes#2010 from Ishiihara/Word2Vec-api and squashes the following commits:
17b1031 [Liquan Pei] remove transform(dataset: RDD[String]) from public API
mengxr Please review the code. Adding weights in reduceByKey soon.
Only output model entry for words appeared in the partition before merging and use reduceByKey to combine model. In general, this implementation is 30s or so faster than implementation using big array.
Author: Liquan Pei <liquanpei@gmail.com>
Closes#1932 from Ishiihara/Word2Vec-improve2 and squashes the following commits:
d5377a9 [Liquan Pei] use syn0Global and syn1Global to represent model
cad2011 [Liquan Pei] bug fix for synModify array out of bound
083aa66 [Liquan Pei] update synGlobal in place and reduce synOut size
9075e1c [Liquan Pei] combine syn0Global and syn1Global to synGlobal
aa2ab36 [Liquan Pei] use reduceByKey to combine models
There is a bug determining the column index. dorx
Author: Xiangrui Meng <meng@databricks.com>
Closes#1997 from mengxr/chisq-index and squashes the following commits:
8fc2ab2 [Xiangrui Meng] fix col indexing bug and add a check for number of distinct values
DecisionTree needs to match each example to a node at each iteration. It currently does this with a set of filters very inefficiently: For each example, it examines each node at the current level and traces up to the root to see if that example should be handled by that node.
Fix: Filter top-down using the partly built tree itself.
Major changes:
* Eliminated Filter class, findBinsForLevel() method.
* Set up node parent links in main loop over levels in train().
* Added predictNodeIndex() for filtering top-down.
* Added DTMetadata class
Other changes:
* Pre-compute set of unorderedFeatures.
Notes for following expected PR based on [https://issues.apache.org/jira/browse/SPARK-3043]:
* The unorderedFeatures set will next be stored in a metadata structure to simplify function calls (to store other items such as the data in strategy).
I've done initial tests indicating that this speeds things up, but am only now running large-scale ones.
CC: mengxr manishamde chouqin Any comments are welcome---thanks!
Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com>
Closes#1975 from jkbradley/dt-opt2 and squashes the following commits:
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
- promote nullHypothesis field in ChiSqTestResult to TestResult. Every test should have a null hypothesis
- correct null hypothesis statement for independence test
- p-value: 0.01 -> 0.1
Author: Xiangrui Meng <meng@databricks.com>
Closes#1982 from mengxr/fix-chisq and squashes the following commits:
5f0de02 [Xiangrui Meng] make ChiSqTestResult constructor package private
bc74ea1 [Xiangrui Meng] update chisq-test
`RandomRDDGenerators` means factory for `RandomRDDGenerator`. However, its methods return RDDs but not RDDGenerators. So a more proper (and shorter) name would be `RandomRDDs`.
dorx brkyvz
Author: Xiangrui Meng <meng@databricks.com>
Closes#1979 from mengxr/randomrdds and squashes the following commits:
b161a2d [Xiangrui Meng] rename RandomRDDGenerators to RandomRDDs
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
The current implementation requires sorting individual columns, which could be done with a global sort.
result on a 32-node cluster:
m | n | prev | this
---|---|-------|-----
1000000 | 50 | 55s | 9s
10000000 | 50 | 97s | 76s
1000000 | 100 | 119s | 15s
Author: Xiangrui Meng <meng@databricks.com>
Closes#1917 from mengxr/spearman and squashes the following commits:
4d5d262 [Xiangrui Meng] remove unused import
85c48de [Xiangrui Meng] minor updates
a048d0c [Xiangrui Meng] remove cache and set a limit to cachedIds
b98bb18 [Xiangrui Meng] add comments
0846e07 [Xiangrui Meng] first version
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
DecisionTree improvements:
(1) TreePoint representation to avoid binning multiple times
(2) Bug fix: isSampleValid indexed bins incorrectly for unordered categorical features
(3) Timing for DecisionTree internals
Details:
(1) TreePoint representation to avoid binning multiple times
[https://issues.apache.org/jira/browse/SPARK-3022]
Added private[tree] TreePoint class for representing binned feature values.
The input RDD of LabeledPoint is converted to the TreePoint representation initially and then cached. This avoids the previous problem of re-computing bins multiple times.
(2) Bug fix: isSampleValid indexed bins incorrectly for unordered categorical features
[https://issues.apache.org/jira/browse/SPARK-3041]
isSampleValid used to treat unordered categorical features incorrectly: It treated the bins as if indexed by featured values, rather than by subsets of values/categories.
* exhibited for unordered features (multi-class classification with categorical features of low arity)
* Fix: Index bins correctly for unordered categorical features.
(3) Timing for DecisionTree internals
Added tree/impl/TimeTracker.scala class which is private[tree] for now, for timing key parts of DT code.
Prints timing info via logDebug.
CC: mengxr manishamde chouqin Very similar update, with one bug fix. Many apologies for the conflicting update, but I hope that a few more optimizations I have on the way (which depend on this update) will prove valuable to you: SPARK-3042 and SPARK-3043
Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com>
Closes#1950 from jkbradley/dt-opt1 and squashes the following commits:
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
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
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
In theory, the scale of your inputs are irrelevant to logistic regression.
You can "theoretically" multiply X1 by 1E6 and the estimate for β1 will
adjust accordingly. It will be 1E-6 times smaller than the original β1, due
to the invariance property of MLEs.
However, during the optimization process, the convergence (rate)
depends on the condition number of the training dataset. Scaling
the variables often reduces this condition number, thus improving
the convergence rate.
Without reducing the condition number, some training datasets
mixing the columns with different scales may not be able to converge.
GLMNET and LIBSVM packages perform the scaling to reduce
the condition number, and return the weights in the original scale.
See page 9 in http://cran.r-project.org/web/packages/glmnet/glmnet.pdf
Here, if useFeatureScaling is enabled, we will standardize the training
features by dividing the variance of each column (without subtracting
the mean to densify the sparse vector), and train the model in the
scaled space. Then we transform the coefficients from the scaled space
to the original scale as GLMNET and LIBSVM do.
Currently, it's only enabled in LogisticRegressionWithLBFGS.
Author: DB Tsai <dbtsai@alpinenow.com>
Closes#1897 from dbtsai/dbtsai-feature-scaling and squashes the following commits:
f19fc02 [DB Tsai] Added more comments
1d85289 [DB Tsai] Improve the convergence rate by minimize the condition number in LOR with LBFGS
As mentioned in SPARK-2465, using `MEMORY_AND_DISK_SER` for user/product in/out links together with `spark.rdd.compress=true` can help reduce the space requirement by a lot, at the cost of speed. It might be useful to add this option so people can run ALS on much bigger datasets.
Another option for the method name is `setIntermediateRDDStorageLevel`.
Author: Xiangrui Meng <meng@databricks.com>
Closes#1913 from mengxr/als-storagelevel and squashes the following commits:
d942017 [Xiangrui Meng] rename to setIntermediateRDDStorageLevel
7550029 [Xiangrui Meng] add ALS.setIntermediateDataStorageLevel
Iterator.fill uses less memory
Author: Xiangrui Meng <meng@databricks.com>
Closes#1930 from mengxr/rand-gen-iter and squashes the following commits:
24178ca [Xiangrui Meng] use Iterator.fill instead of Array.fill
For both Scala and Python.
The ser/de util functions were moved out of `PythonMLLibAPI` and into their own object to avoid creating the `PythonMLLibAPI` object inside of `MultivariateStatisticalSummarySerialized`, which is then referenced inside of a method in `PythonMLLibAPI`.
`MultivariateStatisticalSummarySerialized` was created to serialize the `Vector` fields in `MultivariateStatisticalSummary`.
Author: Doris Xin <doris.s.xin@gmail.com>
Closes#1911 from dorx/colStats and squashes the following commits:
77b9924 [Doris Xin] developerAPI tag
de9cbbe [Doris Xin] reviewer comments and moved more ser/de
459faba [Doris Xin] colStats in Statistics for both Scala and Python
mengxr
Correctly set vectorSize and alpha in Word2Vec training.
Author: Liquan Pei <liquanpei@gmail.com>
Closes#1900 from Ishiihara/Word2Vec-bugfix and squashes the following commits:
85f64f2 [Liquan Pei] correctly set vectorSize and alpha
for training with LBFGS Optimizer which will converge faster than SGD.
Author: DB Tsai <dbtsai@alpinenow.com>
Closes#1862 from dbtsai/dbtsai-lbfgs-lor and squashes the following commits:
aa84b81 [DB Tsai] small change
f852bcd [DB Tsai] Remove duplicate method
f119fdc [DB Tsai] Formatting
97776aa [DB Tsai] address more feedback
85b4a91 [DB Tsai] address feedback
3cf50c2 [DB Tsai] LogisticRegressionWithLBFGS interface
This is part of SPARK-2828:
1. separate IDF model from IDF algorithm (which generates a model)
2. separate StandardScaler model from StandardScaler
CC: dbtsai
Author: Xiangrui Meng <meng@databricks.com>
Closes#1814 from mengxr/feature-api-update and squashes the following commits:
40d863b [Xiangrui Meng] move mean and variance to model
48a0fff [Xiangrui Meng] separate Model from StandardScaler algorithm
89f3486 [Xiangrui Meng] update IDF to separate Model from Algorithm
Added some checks to Strategy to print out meaningful error messages when given invalid DecisionTree parameters.
CC mengxr
Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com>
Closes#1821 from jkbradley/dt-robustness and squashes the following commits:
4dc449a [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-robustness
7a61f7b [Joseph K. Bradley] Added some checks to Strategy to print out meaningful error messages when given invalid DecisionTree parameters
Added 6 static train methods to match Python API, but without default arguments (but with Python default args noted in docs).
Added factory classes for Algo and Impurity, but made private[mllib].
CC: mengxr dorx Please let me know if there are other changes which would help with API consistency---thanks!
Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com>
Closes#1798 from jkbradley/dt-python-consistency and squashes the following commits:
6f7edf8 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-python-consistency
a0d7dbe [Joseph K. Bradley] DecisionTree: In Java-friendly train* methods, changed to use JavaRDD instead of RDD.
ee1d236 [Joseph K. Bradley] DecisionTree API updates: * Removed train() function in Python API (tree.py) ** Removed corresponding function in Scala/Java API (the ones taking basic types)
00f820e [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-python-consistency
fe6dbfa [Joseph K. Bradley] removed unnecessary imports
e358661 [Joseph K. Bradley] DecisionTree API change: * Added 6 static train methods to match Python API, but without default arguments (but with Python default args noted in docs).
c699850 [Joseph K. Bradley] a few doc comments
eaf84c0 [Joseph K. Bradley] Added DecisionTree static train() methods API to match Python, but without default parameters
This is part of SPARK-2828:
1. added a Java-friendly fit method to Word2Vec with tests
2. change DeveloperApi to Experimental for Normalizer & StandardScaler
3. change default feature dimension to 2^20 in HashingTF
Author: Xiangrui Meng <meng@databricks.com>
Closes#1807 from mengxr/feature-api-check and squashes the following commits:
773c1a9 [Xiangrui Meng] change default numFeatures to 2^20 in HashingTF change annotation from DeveloperApi to Experimental in Normalizer and StandardScaler
883e122 [Xiangrui Meng] add @Experimental to Word2VecModel add a Java-friendly method to Word2Vec.fit with tests
to ensure that the return object is itself.
Author: DB Tsai <dbtsai@alpinenow.com>
Closes#1796 from dbtsai/dbtsai-kmeans and squashes the following commits:
658989e [DB Tsai] Alpine Data Labs
It also moves the model to local in order to map `RDD[String]` to `RDD[Vector]`.
Ishiihara
Author: Xiangrui Meng <meng@databricks.com>
Closes#1790 from mengxr/word2vec-fix and squashes the following commits:
a87146c [Xiangrui Meng] add setters and make a default constructor
e5c923b [Xiangrui Meng] fix random seed in word2vec; move model to local
This is a pull request regarding SPARK-2510 at https://issues.apache.org/jira/browse/SPARK-2510. Word2Vec creates vector representation of words in a text corpus. The algorithm first constructs a vocabulary from the corpus and then learns vector representation of words in the vocabulary. The vector representation can be used as features in natural language processing and machine learning algorithms.
To make our implementation more scalable, we train each partition separately and merge the model of each partition after each iteration. To make the model more accurate, multiple iterations may be needed.
To investigate the vector representations is to find the closest words for a query word. For example, the top 20 closest words to "china" are for 1 partition and 1 iteration :
taiwan 0.8077646146334014
korea 0.740913304563621
japan 0.7240667798885471
republic 0.7107151279078352
thailand 0.6953217332072862
tibet 0.6916782118129544
mongolia 0.6800858715972612
macau 0.6794925677480378
singapore 0.6594048695593799
manchuria 0.658989931844148
laos 0.6512978726001666
nepal 0.6380792327845325
mainland 0.6365469459587788
myanmar 0.6358614338840394
macedonia 0.6322366180313249
xinjiang 0.6285291551708028
russia 0.6279951236068411
india 0.6272874944023487
shanghai 0.6234544135576999
macao 0.6220588462925876
The result with 10 partitions and 5 iterations is:
taiwan 0.8310495079388313
india 0.7737171315919039
japan 0.756777901233668
korea 0.7429767187102452
indonesia 0.7407557427278356
pakistan 0.712883426985585
mainland 0.7053379963140822
thailand 0.696298191073948
mongolia 0.693690656871415
laos 0.6913069680735292
macau 0.6903427690029617
republic 0.6766381604813666
malaysia 0.676460699141784
singapore 0.6728790997360923
malaya 0.672345232966194
manchuria 0.6703732292753156
macedonia 0.6637955686322028
myanmar 0.6589462882439646
kazakhstan 0.657017801081494
cambodia 0.6542383836451932
Author: Liquan Pei <lpei@gopivotal.com>
Author: Xiangrui Meng <meng@databricks.com>
Author: Liquan Pei <liquanpei@gmail.com>
Closes#1719 from Ishiihara/master and squashes the following commits:
2ba9483 [Liquan Pei] minor fix for Word2Vec test
e248441 [Liquan Pei] minor style change
26a948d [Liquan Pei] Merge pull request #1 from mengxr/Ishiihara-master
c14da41 [Xiangrui Meng] fix styles
384c771 [Xiangrui Meng] remove minCount and window from constructor change model to use float instead of double
e93e726 [Liquan Pei] use treeAggregate instead of aggregate
1a8fb41 [Liquan Pei] use weighted sum in combOp
7efbb6f [Liquan Pei] use broadcast version of vocab in aggregate
6bcc8be [Liquan Pei] add multiple iteration support
720b5a3 [Liquan Pei] Add test for Word2Vec algorithm, minor fixes
2e92b59 [Liquan Pei] modify according to feedback
57dc50d [Liquan Pei] code formatting
e4a04d3 [Liquan Pei] minor fix
0aafb1b [Liquan Pei] Add comments, minor fixes
8d6befe [Liquan Pei] initial commit
Feature scaling is a method used to standardize the range of independent variables or features of data. In data processing, it is generally performed during the data preprocessing step.
In this work, a trait called `VectorTransformer` is defined for generic transformation on a vector. It contains one method to be implemented, `transform` which applies transformation on a vector.
There are two implementations of `VectorTransformer` now, and they all can be easily extended with PMML transformation support.
1) `StandardScaler` - Standardizes features by removing the mean and scaling to unit variance using column summary statistics on the samples in the training set.
2) `Normalizer` - Normalizes samples individually to unit L^n norm
Author: DB Tsai <dbtsai@alpinenow.com>
Closes#1207 from dbtsai/dbtsai-feature-scaling and squashes the following commits:
78c15d3 [DB Tsai] Alpine Data Labs
Bug fix: Before, when an RDD was created in Java and passed to DecisionTree.train(), the fake class tag caused problems.
* Fix: DecisionTree: Used new RDD.retag() method to allow passing RDDs from Java.
Other improvements to Decision Trees for easy-of-use with Java:
* impurity classes: Added instance() methods to help with Java interface.
* Strategy: Added Java-friendly constructor
--> Note: I removed quantileCalculationStrategy from the Java-friendly constructor since (a) it is a special class and (b) there is only 1 option currently. I suspect we will redo the API before the other options are included.
CC: mengxr
Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com>
Closes#1740 from jkbradley/dt-java-new and squashes the following commits:
0805dc6 [Joseph K. Bradley] Changed Strategy to use JavaConverters instead of JavaConversions
519b1b7 [Joseph K. Bradley] * Organized imports in JavaDecisionTreeSuite.java * Using JavaConverters instead of JavaConversions in DecisionTreeSuite.scala
f7b5ca1 [Joseph K. Bradley] Improvements to make it easier to run DecisionTree from Java. * DecisionTree: Used new RDD.retag() method to allow passing RDDs from Java. * impurity classes: Added instance() methods to help with Java interface. * Strategy: Added Java-friendly constructor ** Note: I removed quantileCalculationStrategy from the Java-friendly constructor since (a) it is a special class and (b) there is only 1 option currently. I suspect we will redo the API before the other options are included.
d78ada6 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-java
320853f [Joseph K. Bradley] Added JavaDecisionTreeSuite, partly written
13a585e [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into dt-java
f1a8283 [Joseph K. Bradley] Added old JavaDecisionTreeSuite, to be updated later
225822f [Joseph K. Bradley] Bug: In DecisionTree, the method sequentialBinSearchForOrderedCategoricalFeatureInClassification() indexed bins from 0 to (math.pow(2, featureCategories.toInt - 1) - 1). This upper bound is the bound for unordered categorical features, not ordered ones. The upper bound should be the arity (i.e., max value) of the feature.
Added experimental Python API for Decision Trees.
API:
* class DecisionTreeModel
** predict() for single examples and RDDs, taking both feature vectors and LabeledPoints
** numNodes()
** depth()
** __str__()
* class DecisionTree
** trainClassifier()
** trainRegressor()
** train()
Examples and testing:
* Added example testing classification and regression with batch prediction: examples/src/main/python/mllib/tree.py
* Have also tested example usage in doc of python/pyspark/mllib/tree.py which tests single-example prediction with dense and sparse vectors
Also: Small bug fix in python/pyspark/mllib/_common.py: In _linear_predictor_typecheck, changed check for RDD to use isinstance() instead of type() in order to catch RDD subclasses.
CC mengxr manishamde
Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com>
Closes#1727 from jkbradley/decisiontree-python-new and squashes the following commits:
3744488 [Joseph K. Bradley] Renamed test tree.py to decision_tree_runner.py Small updates based on github review.
6b86a9d [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-python-new
affceb9 [Joseph K. Bradley] * Fixed bug in doc tests in pyspark/mllib/util.py caused by change in loadLibSVMFile behavior. (It used to threshold labels at 0 to make them 0/1, but it now leaves them as they are.) * Fixed small bug in loadLibSVMFile: If a data file had no features, then loadLibSVMFile would create a single all-zero feature.
67a29bc [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-python-new
cf46ad7 [Joseph K. Bradley] Python DecisionTreeModel * predict(empty RDD) returns an empty RDD instead of an error. * Removed support for calling predict() on LabeledPoint and RDD[LabeledPoint] * predict() does not cache serialized RDD any more.
aa29873 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-python-new
bf21be4 [Joseph K. Bradley] removed old run() func from DecisionTree
fa10ea7 [Joseph K. Bradley] Small style update
7968692 [Joseph K. Bradley] small braces typo fix
e34c263 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-python-new
4801b40 [Joseph K. Bradley] Small style update to DecisionTreeSuite
db0eab2 [Joseph K. Bradley] Merge branch 'decisiontree-bugfix2' into decisiontree-python-new
6873fa9 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-python-new
225822f [Joseph K. Bradley] Bug: In DecisionTree, the method sequentialBinSearchForOrderedCategoricalFeatureInClassification() indexed bins from 0 to (math.pow(2, featureCategories.toInt - 1) - 1). This upper bound is the bound for unordered categorical features, not ordered ones. The upper bound should be the arity (i.e., max value) of the feature.
93953f1 [Joseph K. Bradley] Likely done with Python API.
6df89a9 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-python-new
4562c08 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-python-new
665ba78 [Joseph K. Bradley] Small updates towards Python DecisionTree API
188cb0d [Joseph K. Bradley] Merge branch 'decisiontree-bugfix' into decisiontree-python-new
6622247 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-python-new
b8fac57 [Joseph K. Bradley] Finished Python DecisionTree API and example but need to test a bit more.
2b20c61 [Joseph K. Bradley] Small doc and style updates
1b29c13 [Joseph K. Bradley] Merge branch 'decisiontree-bugfix' into decisiontree-python-new
584449a [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-python-new
dab0b67 [Joseph K. Bradley] Added documentation for DecisionTree internals
8bb8aa0 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-bugfix
978cfcf [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-bugfix
6eed482 [Joseph K. Bradley] In DecisionTree: Changed from using procedural syntax for functions returning Unit to explicitly writing Unit return type.
376dca2 [Joseph K. Bradley] Updated meaning of maxDepth by 1 to fit scikit-learn and rpart. * In code, replaced usages of maxDepth <-- maxDepth + 1 * In params, replace settings of maxDepth <-- maxDepth - 1
e06e423 [Joseph K. Bradley] Merge branch 'decisiontree-bugfix' into decisiontree-python-new
bab3f19 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-python-new
59750f8 [Joseph K. Bradley] * Updated Strategy to check numClassesForClassification only if algo=Classification. * Updates based on comments: ** DecisionTreeRunner *** Made dataFormat arg default to libsvm ** Small cleanups ** tree.Node: Made recursive helper methods private, and renamed them.
52e17c5 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-bugfix
f5a036c [Joseph K. Bradley] Merge branch 'decisiontree-bugfix' into decisiontree-python-new
da50db7 [Joseph K. Bradley] Added one more test to DecisionTreeSuite: stump with 2 continuous variables for binary classification. Caused problems in past, but fixed now.
8e227ea [Joseph K. Bradley] Changed Strategy so it only requires numClassesForClassification >= 2 for classification
cd1d933 [Joseph K. Bradley] Merge branch 'decisiontree-bugfix' into decisiontree-python-new
8ea8750 [Joseph K. Bradley] Bug fix: Off-by-1 when finding thresholds for splits for continuous features.
8a758db [Joseph K. Bradley] Merge branch 'decisiontree-bugfix' into decisiontree-python-new
5fe44ed [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-python-new
2283df8 [Joseph K. Bradley] 2 bug fixes.
73fbea2 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into decisiontree-bugfix
5f920a1 [Joseph K. Bradley] Demonstration of bug before submitting fix: Updated DecisionTreeSuite so that 3 tests fail. Will describe bug in next commit.
f825352 [Joseph K. Bradley] Wrote Python API and example for DecisionTree. Also added toString, depth, and numNodes methods to DecisionTreeModel.
Author: GuoQiang Li <witgo@qq.com>
Closes#1369 from witgo/SPARK-1470_new and squashes the following commits:
66a1641 [GuoQiang Li] IncompatibleResultTypeProblem
73a89ba [GuoQiang Li] Use the scala-logging wrapper instead of the directly sfl4j api.
The RandomRDDGenerators used to only output RDD[Double].
Now RandomRDDGenerators.randomRDD can be used to generate a random RDD[T] via a class that extends RandomDataGenerator, by supplying a type T and overriding the nextValue() function as they wish.
Author: Burak <brkyvz@gmail.com>
Closes#1732 from brkyvz/SPARK-2801 and squashes the following commits:
c94a694 [Burak] [SPARK-2801][MLlib] Missing ClassTags added
22d96fe [Burak] [SPARK-2801][MLlib]: DistributionGenerator renamed to RandomDataGenerator, generic types added for RandomRDD instead of Double
