[MLlib] SPARK-1536: multiclass classification support for decision tree #886
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Parameterizing max memory.
Fixing scalastyle issue.
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@manishamde I think the issue is that there are missing ++ operators for concatenating excludes in the MimaExcludes.scala file. Does adding them fix it? |
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@jkbradley Added the operator. The merge with master changed the format and On Thu, Jul 17, 2014 at 3:29 PM, jkbradley notifications@github.com wrote:
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QA tests have started for PR 886. This patch merges cleanly. |
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QA results for PR 886: |
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QA tests have started for PR 886. This patch merges cleanly. |
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QA results for PR 886: |
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QA tests have started for PR 886. This patch merges cleanly. |
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QA results for PR 886: |
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@mengxr LGTM. @manishamde Thanks for the fixes! |
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@jkbradley Thanks! I just made another very minor commit to format MimaExcludes.scala. |
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QA tests have started for PR 886. This patch merges cleanly. |
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QA results for PR 886: |
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@manishamde Thanks for adding multiclass support to decision tree! And thank everyone for the code review! Merged into master. |
The ability to perform multiclass classification is a big advantage for using decision trees and was a highly requested feature for mllib. This pull request adds multiclass classification support to the MLlib decision tree. It also adds sample weights support using WeightedLabeledPoint class for handling unbalanced datasets during classification. It will also support algorithms such as AdaBoost which requires instances to be weighted. It handles the special case where the categorical variables cannot be ordered for multiclass classification and thus the optimizations used for speeding up binary classification cannot be directly used for multiclass classification with categorical variables. More specifically, for m categories in a categorical feature, it analyses all the ```2^(m-1) - 1``` categorical splits provided that #splits are less than the maxBins provided in the input. This condition will not be met for features with large number of categories -- using decision trees is not recommended for such datasets in general since the categorical features are favored over continuous features. Moreover, the user can use a combination of tricks (increasing bin size of the tree algorithms, use binary encoding for categorical features or use one-vs-all classification strategy) to avoid these constraints. The new code is accompanied by unit tests and has also been tested on the iris and covtype datasets. cc: mengxr, etrain, hirakendu, atalwalkar, srowen Author: Manish Amde <manish9ue@gmail.com> Author: manishamde <manish9ue@gmail.com> Author: Evan Sparks <sparks@cs.berkeley.edu> Closes apache#886 from manishamde/multiclass and squashes the following commits: 26f8acc [Manish Amde] another attempt at fixing mima c5b2d04 [Manish Amde] more MIMA fixes 1ce7212 [Manish Amde] change problem filter for mima 10fdd82 [Manish Amde] fixing MIMA excludes e1c970d [Manish Amde] merged master abf2901 [Manish Amde] adding classes to MimaExcludes.scala 45e767a [Manish Amde] adding developer api annotation for overriden methods c8428c4 [Manish Amde] fixing weird multiline bug afced16 [Manish Amde] removed label weights support 2d85a48 [Manish Amde] minor: fixed scalastyle issues reprise 4e85f2c [Manish Amde] minor: fixed scalastyle issues b2ae41f [Manish Amde] minor: scalastyle e4c1321 [Manish Amde] using while loop for regression histograms d75ac32 [Manish Amde] removed WeightedLabeledPoint from this PR 0fecd38 [Manish Amde] minor: add newline to EOF 2061cf5 [Manish Amde] merged from master 06b1690 [Manish Amde] fixed off-by-one error in bin to split conversion 9cc3e31 [Manish Amde] added implicit conversion import 5c1b2ca [Manish Amde] doc for PointConverter class 485eaae [Manish Amde] implicit conversion from LabeledPoint to WeightedLabeledPoint 3d7f911 [Manish Amde] updated doc 8e44ab8 [Manish Amde] updated doc adc7315 [Manish Amde] support ordered categorical splits for multiclass classification e3e8843 [Manish Amde] minor code formatting 23d4268 [Manish Amde] minor: another minor code style 34ee7b9 [Manish Amde] minor: code style 237762d [Manish Amde] renaming functions 12e6d0a [Manish Amde] minor: removing line in doc 9a90c93 [Manish Amde] Merge branch 'master' into multiclass 1892a2c [Manish Amde] tests and use multiclass binaggregate length when atleast one categorical feature is present f5f6b83 [Manish Amde] multiclass for continous variables 8cfd3b6 [Manish Amde] working for categorical multiclass classification 828ff16 [Manish Amde] added categorical variable test bce835f [Manish Amde] code cleanup 7e5f08c [Manish Amde] minor doc 1dd2735 [Manish Amde] bin search logic for multiclass f16a9bb [Manish Amde] fixing while loop d811425 [Manish Amde] multiclass bin aggregate logic ab5cb21 [Manish Amde] multiclass logic d8e4a11 [Manish Amde] sample weights ed5a2df [Manish Amde] fixed classification requirements d012be7 [Manish Amde] fixed while loop 18d2835 [Manish Amde] changing default values for num classes 6b912dc [Manish Amde] added numclasses to tree runner, predict logic for multiclass, add multiclass option to train 75f2bfc [Manish Amde] minor code style fix e547151 [Manish Amde] minor modifications 34549d0 [Manish Amde] fixing error during merge 098e8c5 [Manish Amde] merged master e006f9d [Manish Amde] changing variable names 5c78e1a [Manish Amde] added multiclass support 6c7af22 [Manish Amde] prepared for multiclass without breaking binary classification 46e06ee [Manish Amde] minor mods 3f85a17 [Manish Amde] tests for multiclass classification 4d5f70c [Manish Amde] added multiclass support for find splits bins 46f909c [Manish Amde] todo for multiclass support 455bea9 [Manish Amde] fixed tests 14aea48 [Manish Amde] changing instance format to weighted labeled point a1a6e09 [Manish Amde] added weighted point class 968ca9d [Manish Amde] merged master 7fc9545 [Manish Amde] added docs ce004a1 [Manish Amde] minor formatting b27ad2c [Manish Amde] formatting 426bb28 [Manish Amde] programming guide blurb 8053fed [Manish Amde] more formatting 5eca9e4 [Manish Amde] grammar 4731cda [Manish Amde] formatting 5e82202 [Manish Amde] added documentation, fixed off by 1 error in max level calculation cbd9f14 [Manish Amde] modified scala.math to math dad9652 [Manish Amde] removed unused imports e0426ee [Manish Amde] renamed parameter 718506b [Manish Amde] added unit test 1517155 [Manish Amde] updated documentation 9dbdabe [Manish Amde] merge from master 719d009 [Manish Amde] updating user documentation fecf89a [manishamde] Merge pull request apache#6 from etrain/deep_tree 0287772 [Evan Sparks] Fixing scalastyle issue. 2f1e093 [Manish Amde] minor: added doc for maxMemory parameter 2f6072c [manishamde] Merge pull request apache#5 from etrain/deep_tree abc5a23 [Evan Sparks] Parameterizing max memory. 50b143a [Manish Amde] adding support for very deep trees
…fication The PR changes outdated scaladocs for Gini and Entropy classes. Since PR #886 Spark supports multiclass classification, but the docs tell only about binary classification. Author: Wojciech Jurczyk <wojciech.jurczyk@codilime.com> Closes #11252 from wjur/wjur/docs_multiclass. (cherry picked from commit 6e0b3d7) Signed-off-by: Reynold Xin <rxin@databricks.com>
The ability to perform multiclass classification is a big advantage for using decision trees and was a highly requested feature for mllib. This pull request adds multiclass classification support to the MLlib decision tree. It also adds sample weights support using WeightedLabeledPoint class for handling unbalanced datasets during classification. It will also support algorithms such as AdaBoost which requires instances to be weighted.
It handles the special case where the categorical variables cannot be ordered for multiclass classification and thus the optimizations used for speeding up binary classification cannot be directly used for multiclass classification with categorical variables. More specifically, for m categories in a categorical feature, it analyses all the
2^(m-1) - 1categorical splits provided that #splits are less than the maxBins provided in the input. This condition will not be met for features with large number of categories -- using decision trees is not recommended for such datasets in general since the categorical features are favored over continuous features. Moreover, the user can use a combination of tricks (increasing bin size of the tree algorithms, use binary encoding for categorical features or use one-vs-all classification strategy) to avoid these constraints.The new code is accompanied by unit tests and has also been tested on the iris and covtype datasets.
cc: @mengxr, @etrain, @hirakendu, @atalwalkar, @srowen