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* [FIX][DOC] Fix broken links in ml-guide.mdXiangrui Meng2014-12-041-1/+0
| | | | | | | | | | | | and some minor changes in ScalaDoc. Author: Xiangrui Meng <meng@databricks.com> Closes #3601 from mengxr/SPARK-4575-fix and squashes the following commits: c559768 [Xiangrui Meng] minor code update ce94da8 [Xiangrui Meng] Java Bean -> JavaBean 0b5c182 [Xiangrui Meng] fix links in ml-guide
* [SPARK-4575] [mllib] [docs] spark.ml pipelines doc + bug fixesJoseph K. Bradley2014-12-046-18/+34
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Documentation: * Added ml-guide.md, linked from mllib-guide.md * Updated mllib-guide.md with small section pointing to ml-guide.md Examples: * CrossValidatorExample * SimpleParamsExample * (I copied these + the SimpleTextClassificationPipeline example into the ml-guide.md) Bug fixes: * PipelineModel: did not use ParamMaps correctly * UnaryTransformer: issues with TypeTag serialization (Thanks to mengxr for that fix!) CC: mengxr shivaram etrain Documentation for Pipelines: I know the docs are not complete, but the goal is to have enough to let interested people get started using spark.ml and to add more docs once the package is more established/complete. Author: Joseph K. Bradley <joseph@databricks.com> Author: jkbradley <joseph.kurata.bradley@gmail.com> Author: Xiangrui Meng <meng@databricks.com> Closes #3588 from jkbradley/ml-package-docs and squashes the following commits: d393b5c [Joseph K. Bradley] fixed bug in Pipeline (typo from last commit). updated examples for CV and Params for spark.ml c38469c [Joseph K. Bradley] Updated ml-guide with CV examples 99f88c2 [Joseph K. Bradley] Fixed bug in PipelineModel.transform* with usage of params. Updated CrossValidatorExample to use more training examples so it is less likely to get a 0-size fold. ea34dc6 [jkbradley] Merge pull request #4 from mengxr/ml-package-docs 3b83ec0 [Xiangrui Meng] replace TypeTag with explicit datatype 41ad9b1 [Joseph K. Bradley] Added examples for spark.ml: SimpleParamsExample + Java version, CrossValidatorExample + Java version. CrossValidatorExample not working yet. Added programming guide for spark.ml, but need to add CrossValidatorExample to it once CrossValidatorExample works.
* [SPARK-4580] [SPARK-4610] [mllib] [docs] Documentation for tree ensembles + ↵Joseph K. Bradley2014-12-049-71/+71
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | DecisionTree API fix Major changes: * Added programming guide sections for tree ensembles * Added examples for tree ensembles * Updated DecisionTree programming guide with more info on parameters * **API change**: Standardized the tree parameter for the number of classes (for classification) Minor changes: * Updated decision tree documentation * Updated existing tree and tree ensemble examples * Use train/test split, and compute test error instead of training error. * Fixed decision_tree_runner.py to actually use the number of classes it computes from data. (small bug fix) Note: I know this is a lot of lines, but most is covered by: * Programming guide sections for gradient boosting and random forests. (The changes are probably best viewed by generating the docs locally.) * New examples (which were copied from the programming guide) * The "numClasses" renaming I have run all examples and relevant unit tests. CC: mengxr manishamde codedeft Author: Joseph K. Bradley <joseph@databricks.com> Author: Joseph K. Bradley <joseph.kurata.bradley@gmail.com> Closes #3461 from jkbradley/ensemble-docs and squashes the following commits: 70a75f3 [Joseph K. Bradley] updated forest vs boosting comparison d1de753 [Joseph K. Bradley] Added note about toString and toDebugString for DecisionTree to migration guide 8e87f8f [Joseph K. Bradley] Combined GBT and RandomForest guides into one ensembles guide 6fab846 [Joseph K. Bradley] small fixes based on review b9f8576 [Joseph K. Bradley] updated decision tree doc 375204c [Joseph K. Bradley] fixed python style 2b60b6e [Joseph K. Bradley] merged Java RandomForest examples into 1 file. added header. Fixed small bug in same example in the programming guide. 706d332 [Joseph K. Bradley] updated python DT runner to print full model if it is small c76c823 [Joseph K. Bradley] added migration guide for mllib abe5ed7 [Joseph K. Bradley] added examples for random forest in Java and Python to examples folder 07fc11d [Joseph K. Bradley] Renamed numClassesForClassification to numClasses everywhere in trees and ensembles. This is a breaking API change, but it was necessary to correct an API inconsistency in Spark 1.1 (where Python DecisionTree used numClasses but Scala used numClassesForClassification). cdfdfbc [Joseph K. Bradley] added examples for GBT 6372a2b [Joseph K. Bradley] updated decision tree examples to use random split. tested all of them. ad3e695 [Joseph K. Bradley] added gbt and random forest to programming guide. still need to update their examples
* [SPARK-4717][MLlib] Optimize BLAS library to avoid de-reference multiple ↵DB Tsai2014-12-031-39/+60
| | | | | | | | | | | | | | | times in loop Have a local reference to `values` and `indices` array in the `Vector` object so JVM can locate the value with one operation call. See `SPARK-4581` for similar optimization, and the bytecode analysis. Author: DB Tsai <dbtsai@alpinenow.com> Closes #3577 from dbtsai/blasopt and squashes the following commits: 62d38c4 [DB Tsai] formating 0316cef [DB Tsai] first commit
* [SPARK-4708][MLLib] Make k-mean runs two/three times faster with ↵DB Tsai2014-12-035-68/+70
| | | | | | | | | | | | | | | | | | | | | | | | | | | | dense/sparse sample Note that the usage of `breezeSquaredDistance` in `org.apache.spark.mllib.util.MLUtils.fastSquaredDistance` is in the critical path, and `breezeSquaredDistance` is slow. We should replace it with our own implementation. Here is the benchmark against mnist8m dataset. Before DenseVector: 70.04secs SparseVector: 59.05secs With this PR DenseVector: 30.58secs SparseVector: 21.14secs Author: DB Tsai <dbtsai@alpinenow.com> Closes #3565 from dbtsai/kmean and squashes the following commits: 08bc068 [DB Tsai] restyle de24662 [DB Tsai] address feedback b185a77 [DB Tsai] cleanup 4554ddd [DB Tsai] first commit
* [SPARK-4611][MLlib] Implement the efficient vector normDB Tsai2014-12-024-6/+79
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | The vector norm in breeze is implemented by `activeIterator` which is known to be very slow. In this PR, an efficient vector norm is implemented, and with this API, `Normalizer` and `k-means` have big performance improvement. Here is the benchmark against mnist8m dataset. a) `Normalizer` Before DenseVector: 68.25secs SparseVector: 17.01secs With this PR DenseVector: 12.71secs SparseVector: 2.73secs b) `k-means` Before DenseVector: 83.46secs SparseVector: 61.60secs With this PR DenseVector: 70.04secs SparseVector: 59.05secs Author: DB Tsai <dbtsai@alpinenow.com> Closes #3462 from dbtsai/norm and squashes the following commits: 63c7165 [DB Tsai] typo 0c3637f [DB Tsai] add import org.apache.spark.SparkContext._ back 6fa616c [DB Tsai] address feedback 9b7cb56 [DB Tsai] move norm to static method 0b632e6 [DB Tsai] kmeans dbed124 [DB Tsai] style c1a877c [DB Tsai] first commit
* [SPARK-4614][MLLIB] Slight API changes in Matrix and MatricesXiangrui Meng2014-11-262-11/+59
| | | | | | | | | | | | | Before we have a full picture of the operators we want to add, it might be safer to hide `Matrix.transposeMultiply` in 1.2.0. Another update we want to change is `Matrix.randn` and `Matrix.rand`, both of which should take a `Random` implementation. Otherwise, it is very likely to produce inconsistent RDDs. I also added some unit tests for matrix factory methods. All APIs are new in 1.2, so there is no incompatible changes. brkyvz Author: Xiangrui Meng <meng@databricks.com> Closes #3468 from mengxr/SPARK-4614 and squashes the following commits: 3b0e4e2 [Xiangrui Meng] add mima excludes 6bfd8a4 [Xiangrui Meng] hide transposeMultiply; add rng to rand and randn; add unit tests
* [SPARK-4604][MLLIB] make MatrixFactorizationModel publicXiangrui Meng2014-11-252-3/+81
| | | | | | | | | | | | User could construct an MF model directly. I added a note about the performance. Author: Xiangrui Meng <meng@databricks.com> Closes #3459 from mengxr/SPARK-4604 and squashes the following commits: f64bcd3 [Xiangrui Meng] organize imports ed08214 [Xiangrui Meng] check preconditions and unit tests a624c12 [Xiangrui Meng] make MatrixFactorizationModel public
* [SPARK-4583] [mllib] LogLoss for GradientBoostedTrees fix + doc updatesJoseph K. Bradley2014-11-256-72/+146
| | | | | | | | | | | | | | | | | | | | | | | Currently, the LogLoss used by GradientBoostedTrees has 2 issues: * the gradient (and therefore loss) does not match that used by Friedman (1999) * the error computation uses 0/1 accuracy, not log loss This PR updates LogLoss. It also adds some doc for boosting and forests. I tested it on sample data and made sure the log loss is monotonically decreasing with each boosting iteration. CC: mengxr manishamde codedeft Author: Joseph K. Bradley <joseph@databricks.com> Closes #3439 from jkbradley/gbt-loss-fix and squashes the following commits: cfec17e [Joseph K. Bradley] removed forgotten temp comments a27eb6d [Joseph K. Bradley] corrections to last log loss commit ed5da2c [Joseph K. Bradley] updated LogLoss (boosting) for numerical stability 5e52bff [Joseph K. Bradley] * Removed the 1/2 from SquaredError. This also required updating the test suite since it effectively doubles the gradient and loss. * Added doc for developers within RandomForest. * Small cleanup in test suite (generating data only once) e57897a [Joseph K. Bradley] Fixed LogLoss for GradientBoostedTrees, and updated doc for losses, forests, and boosting
* [SPARK-4581][MLlib] Refactorize StandardScaler to improve the transformation ↵DB Tsai2014-11-251-20/+50
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | performance The following optimizations are done to improve the StandardScaler model transformation performance. 1) Covert Breeze dense vector to primitive vector to reduce the overhead. 2) Since mean can be potentially a sparse vector, we explicitly convert it to dense primitive vector. 3) Have a local reference to `shift` and `factor` array so JVM can locate the value with one operation call. 4) In pattern matching part, we use the mllib SparseVector/DenseVector instead of breeze's vector to make the codebase cleaner. Benchmark with mnist8m dataset: Before, DenseVector withMean and withStd: 50.97secs DenseVector withMean and withoutStd: 42.11secs DenseVector withoutMean and withStd: 8.75secs SparseVector withoutMean and withStd: 5.437secs With this PR, DenseVector withMean and withStd: 5.76secs DenseVector withMean and withoutStd: 5.28secs DenseVector withoutMean and withStd: 5.30secs SparseVector withoutMean and withStd: 1.27secs Note that without the local reference copy of `factor` and `shift` arrays, the runtime is almost three time slower. DenseVector withMean and withStd: 18.15secs DenseVector withMean and withoutStd: 18.05secs DenseVector withoutMean and withStd: 18.54secs SparseVector withoutMean and withStd: 2.01secs The following code, ```scala while (i < size) { values(i) = (values(i) - shift(i)) * factor(i) i += 1 } ``` will generate the bytecode ``` L13 LINENUMBER 106 L13 FRAME FULL [org/apache/spark/mllib/feature/StandardScalerModel org/apache/spark/mllib/linalg/Vector org/apache/spark/mllib/linalg/Vector org/apache/spark/mllib/linalg/DenseVector T [D I I] [] ILOAD 7 ILOAD 6 IF_ICMPGE L14 L15 LINENUMBER 107 L15 ALOAD 5 ILOAD 7 ALOAD 5 ILOAD 7 DALOAD ALOAD 0 INVOKESPECIAL org/apache/spark/mllib/feature/StandardScalerModel.shift ()[D ILOAD 7 DALOAD DSUB ALOAD 0 INVOKESPECIAL org/apache/spark/mllib/feature/StandardScalerModel.factor ()[D ILOAD 7 DALOAD DMUL DASTORE L16 LINENUMBER 108 L16 ILOAD 7 ICONST_1 IADD ISTORE 7 GOTO L13 ``` , while with the local reference of the `shift` and `factor` arrays, the bytecode will be ``` L14 LINENUMBER 107 L14 ALOAD 0 INVOKESPECIAL org/apache/spark/mllib/feature/StandardScalerModel.factor ()[D ASTORE 9 L15 LINENUMBER 108 L15 FRAME FULL [org/apache/spark/mllib/feature/StandardScalerModel org/apache/spark/mllib/linalg/Vector [D org/apache/spark/mllib/linalg/Vector org/apache/spark/mllib/linalg/DenseVector T [D I I [D] [] ILOAD 8 ILOAD 7 IF_ICMPGE L16 L17 LINENUMBER 109 L17 ALOAD 6 ILOAD 8 ALOAD 6 ILOAD 8 DALOAD ALOAD 2 ILOAD 8 DALOAD DSUB ALOAD 9 ILOAD 8 DALOAD DMUL DASTORE L18 LINENUMBER 110 L18 ILOAD 8 ICONST_1 IADD ISTORE 8 GOTO L15 ``` You can see that with local reference, the both of the arrays will be in the stack, so JVM can access the value without calling `INVOKESPECIAL`. Author: DB Tsai <dbtsai@alpinenow.com> Closes #3435 from dbtsai/standardscaler and squashes the following commits: 85885a9 [DB Tsai] revert to have lazy in shift array. daf2b06 [DB Tsai] Address the feedback cdb5cef [DB Tsai] small change 9c51eef [DB Tsai] style fc795e4 [DB Tsai] update 5bffd3d [DB Tsai] first commit
* [SPARK-4526][MLLIB]GradientDescent get a wrong gradient value according to ↵GuoQiang Li2014-11-251-19/+26
| | | | | | | | | | | | | | | | | the gradient formula. This is caused by the miniBatchSize parameter.The number of `RDD.sample` returns is not fixed. cc mengxr Author: GuoQiang Li <witgo@qq.com> Closes #3399 from witgo/GradientDescent and squashes the following commits: 13cb228 [GuoQiang Li] review commit 668ab66 [GuoQiang Li] Double to Long b6aa11a [GuoQiang Li] Check miniBatchSize is greater than 0 0b5c3e3 [GuoQiang Li] Minor fix 12e7424 [GuoQiang Li] GradientDescent get a wrong gradient value according to the gradient formula, which is caused by the miniBatchSize parameter.
* [SPARK-4596][MLLib] Refactorize Normalizer to make code cleanerDB Tsai2014-11-251-10/+19
| | | | | | | | | | | | | | | In this refactoring, the performance will be slightly increased due to removing the overhead from breeze vector. The bottleneck is still in breeze norm which is implemented by activeIterator. This inefficiency of breeze norm will be addressed in next PR. At least, this PR makes the code more consistent in the codebase. Author: DB Tsai <dbtsai@alpinenow.com> Closes #3446 from dbtsai/normalizer and squashes the following commits: e20a2b9 [DB Tsai] first commit
* [SPARK-4582][MLLIB] get raw vectors for further processing in Word2Vectkaessmann2014-11-241-0/+7
| | | | | | | | | | | | | | | | | | | | | This is #3309 for the master branch. e.g. clustering Author: tkaessmann <tobias.kaessmanns24.com> Closes #3309 from tkaessmann/branch-1.2 and squashes the following commits: e3a3142 [tkaessmann] changes the comment for getVectors 58d3d83 [tkaessmann] removes sign from comment a5be213 [tkaessmann] fixes getVectors to fit code guidelines 3782fa9 [tkaessmann] get raw vectors for further processing Author: tkaessmann <tobias.kaessmann@s24.com> Closes #3437 from mengxr/SPARK-4582 and squashes the following commits: 6c666b4 [tkaessmann] get raw vectors for further processing in Word2Vec
* [SPARK-4562] [MLlib] speedup vectorDavies Liu2014-11-241-8/+65
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | This PR change the underline array of DenseVector to numpy.ndarray to avoid the conversion, because most of the users will using numpy.array. It also improve the serialization of DenseVector. Before this change: trial | trainingTime | testTime -------|--------|-------- 0 | 5.126 | 1.786 1 |2.698 |1.693 After the change: trial | trainingTime | testTime -------|--------|-------- 0 |4.692 |0.554 1 |2.307 |0.525 This could partially fix the performance regression during test. Author: Davies Liu <davies@databricks.com> Closes #3420 from davies/ser2 and squashes the following commits: 0e1e6f3 [Davies Liu] fix tests 426f5db [Davies Liu] impove toArray() 44707ec [Davies Liu] add name for ISO-8859-1 fa7d791 [Davies Liu] address comments 1cfb137 [Davies Liu] handle zero sparse vector 2548ee2 [Davies Liu] fix tests 9e6389d [Davies Liu] bugfix 470f702 [Davies Liu] speed up DenseMatrix f0d3c40 [Davies Liu] speedup SparseVector ef6ce70 [Davies Liu] speed up dense vector
* [SPARK-4431][MLlib] Implement efficient foreachActive for dense and sparse ↵DB Tsai2014-11-213-72/+105
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | vector Previously, we were using Breeze's activeIterator to access the non-zero elements in dense/sparse vector. Due to the overhead, we switched back to native `while loop` in #SPARK-4129. However, #SPARK-4129 requires de-reference the dv.values/sv.values in each access to the value, which is very expensive. Also, in MultivariateOnlineSummarizer, we're using Breeze's dense vector to store the partial stats, and this is very expensive compared with using primitive scala array. In this PR, efficient foreachActive is implemented to unify the code path for dense and sparse vector operation which makes codebase easier to maintain. Breeze dense vector is replaced by primitive array to reduce the overhead further. Benchmarking with mnist8m dataset on single JVM with first 200 samples loaded in memory, and repeating 5000 times. Before change: Sparse Vector - 30.02 Dense Vector - 38.27 With this PR: Sparse Vector - 6.29 Dense Vector - 11.72 Author: DB Tsai <dbtsai@alpinenow.com> Closes #3288 from dbtsai/activeIterator and squashes the following commits: 844b0e6 [DB Tsai] formating 03dd693 [DB Tsai] futher performance tunning. 1907ae1 [DB Tsai] address feedback 98448bb [DB Tsai] Made the override final, and had a local copy of variables which made the accessing a single step operation. c0cbd5a [DB Tsai] fix a bug 6441f92 [DB Tsai] Finished SPARK-4431
* [SPARK-4531] [MLlib] cache serialized java objectDavies Liu2014-11-213-80/+56
| | | | | | | | | | | | | | | | | | | The Pyrolite is pretty slow (comparing to the adhoc serializer in 1.1), it cause much performance regression in 1.2, because we cache the serialized Python object in JVM, deserialize them into Java object in each step. This PR change to cache the deserialized JavaRDD instead of PythonRDD to avoid the deserialization of Pyrolite. It should have similar memory usage as before, but much faster. Author: Davies Liu <davies@databricks.com> Closes #3397 from davies/cache and squashes the following commits: 7f6e6ce [Davies Liu] Update -> Updater 4b52edd [Davies Liu] using named argument 63b984e [Davies Liu] fix 7da0332 [Davies Liu] add unpersist() dff33e1 [Davies Liu] address comments c2bdfc2 [Davies Liu] refactor d572f00 [Davies Liu] Merge branch 'master' into cache f1063e1 [Davies Liu] cache serialized java object
* [SPARK-4439] [MLlib] add python api for random forestDavies Liu2014-11-202-10/+40
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ``` class RandomForestModel | A model trained by RandomForest | | numTrees(self) | Get number of trees in forest. | | predict(self, x) | Predict values for a single data point or an RDD of points using the model trained. | | toDebugString(self) | Full model | | totalNumNodes(self) | Get total number of nodes, summed over all trees in the forest. | class RandomForest | trainClassifier(cls, data, numClassesForClassification, categoricalFeaturesInfo, numTrees, featureSubsetStrategy='auto', impurity='gini', maxDepth=4, maxBins=32, seed=None): | Method to train a decision tree model for binary or multiclass classification. | | :param data: Training dataset: RDD of LabeledPoint. | Labels should take values {0, 1, ..., numClasses-1}. | :param numClassesForClassification: number of classes for classification. | :param categoricalFeaturesInfo: Map storing arity of categorical features. | E.g., an entry (n -> k) indicates that feature n is categorical | with k categories indexed from 0: {0, 1, ..., k-1}. | :param numTrees: Number of trees in the random forest. | :param featureSubsetStrategy: Number of features to consider for splits at each node. | Supported: "auto" (default), "all", "sqrt", "log2", "onethird". | If "auto" is set, this parameter is set based on numTrees: | if numTrees == 1, set to "all"; | if numTrees > 1 (forest) set to "sqrt". | :param impurity: Criterion used for information gain calculation. | Supported values: "gini" (recommended) or "entropy". | :param maxDepth: Maximum depth of the tree. E.g., depth 0 means 1 leaf node; depth 1 means | 1 internal node + 2 leaf nodes. (default: 4) | :param maxBins: maximum number of bins used for splitting features (default: 100) | :param seed: Random seed for bootstrapping and choosing feature subsets. | :return: RandomForestModel that can be used for prediction | | trainRegressor(cls, data, categoricalFeaturesInfo, numTrees, featureSubsetStrategy='auto', impurity='variance', maxDepth=4, maxBins=32, seed=None): | Method to train a decision tree model for regression. | | :param data: Training dataset: RDD of LabeledPoint. | Labels are real numbers. | :param categoricalFeaturesInfo: Map storing arity of categorical features. | E.g., an entry (n -> k) indicates that feature n is categorical | with k categories indexed from 0: {0, 1, ..., k-1}. | :param numTrees: Number of trees in the random forest. | :param featureSubsetStrategy: Number of features to consider for splits at each node. | Supported: "auto" (default), "all", "sqrt", "log2", "onethird". | If "auto" is set, this parameter is set based on numTrees: | if numTrees == 1, set to "all"; | if numTrees > 1 (forest) set to "onethird". | :param impurity: Criterion used for information gain calculation. | Supported values: "variance". | :param maxDepth: Maximum depth of the tree. E.g., depth 0 means 1 leaf node; depth 1 means | 1 internal node + 2 leaf nodes.(default: 4) | :param maxBins: maximum number of bins used for splitting features (default: 100) | :param seed: Random seed for bootstrapping and choosing feature subsets. | :return: RandomForestModel that can be used for prediction | ``` Author: Davies Liu <davies@databricks.com> Closes #3320 from davies/forest and squashes the following commits: 8003dfc [Davies Liu] reorder 53cf510 [Davies Liu] fix docs 4ca593d [Davies Liu] fix docs e0df852 [Davies Liu] fix docs 0431746 [Davies Liu] rebased 2b6f239 [Davies Liu] Merge branch 'master' of github.com:apache/spark into forest 885abee [Davies Liu] address comments dae7fc0 [Davies Liu] address comments 89a000f [Davies Liu] fix docs 565d476 [Davies Liu] add python api for random forest
* [SPARK-4486][MLLIB] Improve GradientBoosting APIs and docXiangrui Meng2014-11-2017-405/+360
| | | | | | | | | | | | | | | | | | | | | | | | | | | There are some inconsistencies in the gradient boosting APIs. The target is a general boosting meta-algorithm, but the implementation is attached to trees. This was partially due to the delay of SPARK-1856. But for the 1.2 release, we should make the APIs consistent. 1. WeightedEnsembleModel -> private[tree] TreeEnsembleModel and renamed members accordingly. 1. GradientBoosting -> GradientBoostedTrees 1. Add RandomForestModel and GradientBoostedTreesModel and hide CombiningStrategy 1. Slightly refactored TreeEnsembleModel (Vote takes weights into consideration.) 1. Remove `trainClassifier` and `trainRegressor` from `GradientBoostedTrees` because they are the same as `train` 1. Rename class `train` method to `run` because it hides the static methods with the same name in Java. Deprecated `DecisionTree.train` class method. 1. Simplify BoostingStrategy and make sure the input strategy is not modified. Users should put algo and numClasses in treeStrategy. We create ensembleStrategy inside boosting. 1. Fix a bug in GradientBoostedTreesSuite with AbsoluteError 1. doc updates manishamde jkbradley Author: Xiangrui Meng <meng@databricks.com> Closes #3374 from mengxr/SPARK-4486 and squashes the following commits: 7097251 [Xiangrui Meng] address joseph's comments 98dea09 [Xiangrui Meng] address manish's comments 4aae3b7 [Xiangrui Meng] add RandomForestModel and GradientBoostedTreesModel, hide CombiningStrategy ea4c467 [Xiangrui Meng] fix unit tests 751da4e [Xiangrui Meng] rename class method train -> run 19030a5 [Xiangrui Meng] update boosting public APIs
* Bumping version to 1.3.0-SNAPSHOT.Marcelo Vanzin2014-11-181-1/+1
| | | | | | | | | | | | Author: Marcelo Vanzin <vanzin@cloudera.com> Closes #3277 from vanzin/version-1.3 and squashes the following commits: 7c3c396 [Marcelo Vanzin] Added temp repo to sbt build. 5f404ff [Marcelo Vanzin] Add another exclusion. 19457e7 [Marcelo Vanzin] Update old version to 1.2, add temporary 1.2 repo. 3c8d705 [Marcelo Vanzin] Workaround for MIMA checks. e940810 [Marcelo Vanzin] Bumping version to 1.3.0-SNAPSHOT.
* [SPARK-4306] [MLlib] Python API for LogisticRegressionWithLBFGSDavies Liu2014-11-181-0/+35
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ``` class LogisticRegressionWithLBFGS | train(cls, data, iterations=100, initialWeights=None, corrections=10, tolerance=0.0001, regParam=0.01, intercept=False) | Train a logistic regression model on the given data. | | :param data: The training data, an RDD of LabeledPoint. | :param iterations: The number of iterations (default: 100). | :param initialWeights: The initial weights (default: None). | :param regParam: The regularizer parameter (default: 0.01). | :param regType: The type of regularizer used for training | our model. | :Allowed values: | - "l1" for using L1 regularization | - "l2" for using L2 regularization | - None for no regularization | (default: "l2") | :param intercept: Boolean parameter which indicates the use | or not of the augmented representation for | training data (i.e. whether bias features | are activated or not). | :param corrections: The number of corrections used in the LBFGS update (default: 10). | :param tolerance: The convergence tolerance of iterations for L-BFGS (default: 1e-4). | | >>> data = [ | ... LabeledPoint(0.0, [0.0, 1.0]), | ... LabeledPoint(1.0, [1.0, 0.0]), | ... ] | >>> lrm = LogisticRegressionWithLBFGS.train(sc.parallelize(data)) | >>> lrm.predict([1.0, 0.0]) | 1 | >>> lrm.predict([0.0, 1.0]) | 0 | >>> lrm.predict(sc.parallelize([[1.0, 0.0], [0.0, 1.0]])).collect() | [1, 0] ``` Author: Davies Liu <davies@databricks.com> Closes #3307 from davies/lbfgs and squashes the following commits: 34bd986 [Davies Liu] Merge branch 'master' of http://git-wip-us.apache.org/repos/asf/spark into lbfgs 5a945a6 [Davies Liu] address comments 941061b [Davies Liu] Merge branch 'master' of github.com:apache/spark into lbfgs 03e5543 [Davies Liu] add it to docs ed2f9a8 [Davies Liu] add regType 76cd1b6 [Davies Liu] reorder arguments 4429a74 [Davies Liu] Update classification.py 9252783 [Davies Liu] python api for LogisticRegressionWithLBFGS
* [SPARK-4435] [MLlib] [PySpark] improve classificationDavies Liu2014-11-182-2/+2
| | | | | | | | | | | | | | | This PR add setThrehold() and clearThreshold() for LogisticRegressionModel and SVMModel, also support RDD of vector in LogisticRegressionModel.predict(), SVNModel.predict() and NaiveBayes.predict() Author: Davies Liu <davies@databricks.com> Closes #3305 from davies/setThreshold and squashes the following commits: d0b835f [Davies Liu] Merge branch 'master' of github.com:apache/spark into setThreshold e4acd76 [Davies Liu] address comments 2231a5f [Davies Liu] bugfix 7bd9009 [Davies Liu] address comments 0b0a8a7 [Davies Liu] address comments c1e5573 [Davies Liu] improve classification
* ALS implicit: added missing parameter alpha in doc stringFelix Maximilian Möller2014-11-181-2/+3
| | | | | | | | | Author: Felix Maximilian Möller <felixmaximilian.moeller@immobilienscout24.de> Closes #3343 from felixmaximilian/fix-documentation and squashes the following commits: 43dcdfb [Felix Maximilian Möller] Removed the information about the switch implicitPrefs. The parameter implicitPrefs cannot be set in this context because it is inherent true when calling the trainImplicit method. 7d172ba [Felix Maximilian Möller] added missing parameter alpha in doc string.
* [SPARK-4422][MLLIB]In some cases, Vectors.fromBreeze get wrong results.GuoQiang Li2014-11-162-1/+8
| | | | | | | | | | | | cc mengxr Author: GuoQiang Li <witgo@qq.com> Closes #3281 from witgo/SPARK-4422 and squashes the following commits: 5f1fa5e [GuoQiang Li] import order 50783bd [GuoQiang Li] review commits 7a10123 [GuoQiang Li] In some cases, Vectors.fromBreeze get wrong results.
* [SPARK-4372][MLLIB] Make LR and SVM's default parameters consistent in Scala ↵Xiangrui Meng2014-11-136-42/+56
| | | | | | | | | | | | | | | | | | and Python The current default regParam is 1.0 and regType is claimed to be none in Python (but actually it is l2), while regParam = 0.0 and regType is L2 in Scala. We should make the default values consistent. This PR sets the default regType to L2 and regParam to 0.01. Note that the default regParam value in LIBLINEAR (and hence scikit-learn) is 1.0. However, we use average loss instead of total loss in our formulation. Hence regParam=1.0 is definitely too heavy. In LinearRegression, we set regParam=0.0 and regType=None, because we have separate classes for Lasso and Ridge, both of which use regParam=0.01 as the default. davies atalwalkar Author: Xiangrui Meng <meng@databricks.com> Closes #3232 from mengxr/SPARK-4372 and squashes the following commits: 9979837 [Xiangrui Meng] update Ridge/Lasso to use default regParam 0.01 cast input arguments d3ba096 [Xiangrui Meng] change 'none' back to None 1909a6e [Xiangrui Meng] change default regParam to 0.01 and regType to L2 in LR and SVM
* [SPARK-4378][MLLIB] make ALS more Java-friendlyXiangrui Meng2014-11-133-53/+53
| | | | | | | | | | | Add Java-friendly version of `run` and `predict`, and use bulk prediction in Java unit tests. The user guide update will come later (though we may not save many lines of code there). srowen Author: Xiangrui Meng <meng@databricks.com> Closes #3240 from mengxr/SPARK-4378 and squashes the following commits: 6581503 [Xiangrui Meng] check number of predictions 6c8bbd1 [Xiangrui Meng] make ALS more Java-friendly
* [SPARK-4256] Make Binary Evaluation Metrics functions defined in cases where ↵Andrew Bullen2014-11-122-27/+113
| | | | | | | | | | | | | | | | there ar... ...e 0 positive or 0 negative examples. Author: Andrew Bullen <andrew.bullen@workday.com> Closes #3118 from abull/master and squashes the following commits: c2bf2b1 [Andrew Bullen] [SPARK-4256] Update Code formatting for BinaryClassificationMetricsSpec 36b0533 [Andrew Bullen] [SYMAN-4256] Extract BinaryClassificationMetricsSuite assertions into private method 4d2f79a [Andrew Bullen] [SPARK-4256] Refactor classification metrics tests - extract comparison functions in test f411e70 [Andrew Bullen] [SPARK-4256] Define precision as 1.0 when there are no positive examples; update code formatting per pull request comments d9a09ef [Andrew Bullen] Make Binary Evaluation Metrics functions defined in cases where there are 0 positive or 0 negative examples.
* [SPARK-4373][MLLIB] fix MLlib maven testsXiangrui Meng2014-11-1236-82/+108
| | | | | | | | | | | We want to make sure there is at most one spark context inside the same jvm. JoshRosen Author: Xiangrui Meng <meng@databricks.com> Closes #3235 from mengxr/SPARK-4373 and squashes the following commits: 6574b69 [Xiangrui Meng] rename LocalSparkContext to MLlibTestSparkContext 913d48d [Xiangrui Meng] make sure there is at most one spark context inside the same jvm
* [SPARK-4369] [MLLib] fix TreeModel.predict() with RDDDavies Liu2014-11-121-0/+12
| | | | | | | | | | | | Fix TreeModel.predict() with RDD, added tests for it. (Also checked that other models don't have this issue) Author: Davies Liu <davies@databricks.com> Closes #3230 from davies/predict and squashes the following commits: 81172aa [Davies Liu] fix predict
* [SPARK-3530][MLLIB] pipeline and parameters with examplesXiangrui Meng2014-11-1231-16/+2246
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | This PR adds package "org.apache.spark.ml" with pipeline and parameters, as discussed on the JIRA. This is a joint work of jkbradley etrain shivaram and many others who helped on the design, also with help from marmbrus and liancheng on the Spark SQL side. The design doc can be found at: https://docs.google.com/document/d/1rVwXRjWKfIb-7PI6b86ipytwbUH7irSNLF1_6dLmh8o/edit?usp=sharing **org.apache.spark.ml** This is a new package with new set of ML APIs that address practical machine learning pipelines. (Sorry for taking so long!) It will be an alpha component, so this is definitely not something set in stone. The new set of APIs, inspired by the MLI project from AMPLab and scikit-learn, takes leverage on Spark SQL's schema support and execution plan optimization. It introduces the following components that help build a practical pipeline: 1. Transformer, which transforms a dataset into another 2. Estimator, which fits models to data, where models are transformers 3. Evaluator, which evaluates model output and returns a scalar metric 4. Pipeline, a simple pipeline that consists of transformers and estimators Parameters could be supplied at fit/transform or embedded with components. 1. Param: a strong-typed parameter key with self-contained doc 2. ParamMap: a param -> value map 3. Params: trait for components with parameters For any component that implements `Params`, user can easily check the doc by calling `explainParams`: ~~~ > val lr = new LogisticRegression > lr.explainParams maxIter: max number of iterations (default: 100) regParam: regularization constant (default: 0.1) labelCol: label column name (default: label) featuresCol: features column name (default: features) ~~~ or user can check individual param: ~~~ > lr.maxIter maxIter: max number of iterations (default: 100) ~~~ **Please start with the example code in test suites and under `org.apache.spark.examples.ml`, where I put several examples:** 1. run a simple logistic regression job ~~~ val lr = new LogisticRegression() .setMaxIter(10) .setRegParam(1.0) val model = lr.fit(dataset) model.transform(dataset, model.threshold -> 0.8) // overwrite threshold .select('label, 'score, 'prediction).collect() .foreach(println) ~~~ 2. run logistic regression with cross-validation and grid search using areaUnderROC (default) as the metric ~~~ val lr = new LogisticRegression val lrParamMaps = new ParamGridBuilder() .addGrid(lr.regParam, Array(0.1, 100.0)) .addGrid(lr.maxIter, Array(0, 5)) .build() val eval = new BinaryClassificationEvaluator val cv = new CrossValidator() .setEstimator(lr) .setEstimatorParamMaps(lrParamMaps) .setEvaluator(eval) .setNumFolds(3) val bestModel = cv.fit(dataset) ~~~ 3. run a pipeline that consists of a standard scaler and a logistic regression component ~~~ val scaler = new StandardScaler() .setInputCol("features") .setOutputCol("scaledFeatures") val lr = new LogisticRegression() .setFeaturesCol(scaler.getOutputCol) val pipeline = new Pipeline() .setStages(Array(scaler, lr)) val model = pipeline.fit(dataset) val predictions = model.transform(dataset) .select('label, 'score, 'prediction) .collect() .foreach(println) ~~~ 4. a simple text classification pipeline, which recognizes "spark": ~~~ val training = sparkContext.parallelize(Seq( LabeledDocument(0L, "a b c d e spark", 1.0), LabeledDocument(1L, "b d", 0.0), LabeledDocument(2L, "spark f g h", 1.0), LabeledDocument(3L, "hadoop mapreduce", 0.0))) val tokenizer = new Tokenizer() .setInputCol("text") .setOutputCol("words") val hashingTF = new HashingTF() .setInputCol(tokenizer.getOutputCol) .setOutputCol("features") val lr = new LogisticRegression() .setMaxIter(10) val pipeline = new Pipeline() .setStages(Array(tokenizer, hashingTF, lr)) val model = pipeline.fit(training) val test = sparkContext.parallelize(Seq( Document(4L, "spark i j k"), Document(5L, "l m"), Document(6L, "mapreduce spark"), Document(7L, "apache hadoop"))) model.transform(test) .select('id, 'text, 'prediction, 'score) .collect() .foreach(println) ~~~ Java examples are very similar. I put example code that creates a simple text classification pipeline in Scala and Java, where a simple tokenizer is defined as a transformer outside `org.apache.spark.ml`. **What are missing now and will be added soon:** 1. ~~Runtime check of schemas. So before we touch the data, we will go through the schema and make sure column names and types match the input parameters.~~ 2. ~~Java examples.~~ 3. ~~Store training parameters in trained models.~~ 4. (later) Serialization and Python API. Author: Xiangrui Meng <meng@databricks.com> Closes #3099 from mengxr/SPARK-3530 and squashes the following commits: 2cc93fd [Xiangrui Meng] hide APIs as much as I can 34319ba [Xiangrui Meng] use local instead local[2] for unit tests 2524251 [Xiangrui Meng] rename PipelineStage.transform to transformSchema c9daab4 [Xiangrui Meng] remove mockito version 1397ab5 [Xiangrui Meng] use sqlContext from LocalSparkContext instead of TestSQLContext 6ffc389 [Xiangrui Meng] try to fix unit test a59d8b7 [Xiangrui Meng] doc updates 977fd9d [Xiangrui Meng] add scala ml package object 6d97fe6 [Xiangrui Meng] add AlphaComponent annotation 731f0e4 [Xiangrui Meng] update package doc 0435076 [Xiangrui Meng] remove ;this from setters fa21d9b [Xiangrui Meng] update extends indentation f1091b3 [Xiangrui Meng] typo 228a9f4 [Xiangrui Meng] do not persist before calling binary classification metrics f51cd27 [Xiangrui Meng] rename default to defaultValue b3be094 [Xiangrui Meng] refactor schema transform in lr 8791e8e [Xiangrui Meng] rename copyValues to inheritValues and make it do the right thing 51f1c06 [Xiangrui Meng] remove leftover code in Transformer 494b632 [Xiangrui Meng] compure score once ad678e9 [Xiangrui Meng] more doc for Transformer 4306ed4 [Xiangrui Meng] org imports in text pipeline 6e7c1c7 [Xiangrui Meng] update pipeline 4f9e34f [Xiangrui Meng] more doc for pipeline aa5dbd4 [Xiangrui Meng] fix typo 11be383 [Xiangrui Meng] fix unit tests 3df7952 [Xiangrui Meng] clean up 986593e [Xiangrui Meng] re-org java test suites 2b11211 [Xiangrui Meng] remove external data deps 9fd4933 [Xiangrui Meng] add unit test for pipeline 2a0df46 [Xiangrui Meng] update tests 2d52e4d [Xiangrui Meng] add @AlphaComponent to package-info 27582a4 [Xiangrui Meng] doc changes 73a000b [Xiangrui Meng] add schema transformation layer 6736e87 [Xiangrui Meng] more doc / remove HasMetricName trait 80a8b5e [Xiangrui Meng] rename SimpleTransformer to UnaryTransformer 62ca2bb [Xiangrui Meng] check param parent in set/get 1622349 [Xiangrui Meng] add getModel to PipelineModel a0e0054 [Xiangrui Meng] update StandardScaler to use SimpleTransformer d0faa04 [Xiangrui Meng] remove implicit mapping from ParamMap c7f6921 [Xiangrui Meng] move ParamGridBuilder test to ParamGridBuilderSuite e246f29 [Xiangrui Meng] re-org: 7772430 [Xiangrui Meng] remove modelParams add a simple text classification pipeline b95c408 [Xiangrui Meng] remove implicits add unit tests to params bab3e5b [Xiangrui Meng] update params fe0ee92 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-3530 6e86d98 [Xiangrui Meng] some code clean-up 2d040b3 [Xiangrui Meng] implement setters inside each class, add Params.copyValues [ci skip] fd751fc [Xiangrui Meng] add java-friendly versions of fit and tranform 3f810cd [Xiangrui Meng] use multi-model training api in cv 5b8f413 [Xiangrui Meng] rename model to modelParams 9d2d35d [Xiangrui Meng] test varargs and chain model params f46e927 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-3530 1ef26e0 [Xiangrui Meng] specialize methods/types for Java df293ed [Xiangrui Meng] switch to setter/getter 376db0a [Xiangrui Meng] pipeline and parameters
* [SPARK-4355][MLLIB] fix OnlineSummarizer.merge when other.mean is zeroXiangrui Meng2014-11-122-45/+51
| | | | | | | | | | See inline comment about the bug. I also did some code clean-up. dbtsai I moved `update` to a private method of `MultivariateOnlineSummarizer`. I don't think it will cause performance regression, but it would be great if you have some time to test. Author: Xiangrui Meng <meng@databricks.com> Closes #3220 from mengxr/SPARK-4355 and squashes the following commits: 5ef601f [Xiangrui Meng] fix OnlineSummarizer.merge when other.mean is zero and some code clean-up
* [MLLIB] SPARK-4347: Reducing GradientBoostingSuite run time.Manish Amde2014-11-111-5/+5
| | | | | | | | | | | | | | | | | | | | | Before: [info] GradientBoostingSuite: [info] - Regression with continuous features: SquaredError (22 seconds, 115 milliseconds) [info] - Regression with continuous features: Absolute Error (19 seconds, 330 milliseconds) [info] - Binary classification with continuous features: Log Loss (19 seconds, 17 milliseconds) After: [info] - Regression with continuous features: SquaredError (7 seconds, 69 milliseconds) [info] - Regression with continuous features: Absolute Error (4 seconds, 617 milliseconds) [info] - Binary classification with continuous features: Log Loss (4 seconds, 658 milliseconds) cc: mengxr, jkbradley Author: Manish Amde <manish9ue@gmail.com> Closes #3214 from manishamde/gbt_test_speedup and squashes the following commits: 8994552 [Manish Amde] reducing gbt test run times
* [MLLIB] [PYTHON] SPARK-4221: Expose nonnegative ALS in the python APIMichelangelo D'Agostino2014-11-071-6/+33
| | | | | | | | | | | | | | | SPARK-1553 added alternating nonnegative least squares to MLLib, however it's not possible to access it via the python API. This pull request resolves that. Author: Michelangelo D'Agostino <mdagostino@civisanalytics.com> Closes #3095 from mdagost/python_nmf and squashes the following commits: a6743ad [Michelangelo D'Agostino] Use setters instead of static methods in PythonMLLibAPI. Remove the new static methods I added. Set seed in tests. Change ratings to ratingsRDD in both train and trainImplicit for consistency. 7cffd39 [Michelangelo D'Agostino] Swapped nonnegative and seed in a few more places. 3fdc851 [Michelangelo D'Agostino] Moved seed to the end of the python parameter list. bdcc154 [Michelangelo D'Agostino] Change seed type to java.lang.Long so that it can handle null. cedf043 [Michelangelo D'Agostino] Added in ability to set the seed from python and made that play nice with the nonnegative changes. Also made the python ALS tests more exact. a72fdc9 [Michelangelo D'Agostino] Expose nonnegative ALS in the python API.
* [SPARK-4197] [mllib] GradientBoosting API cleanup and examples in Scala, JavaJoseph K. Bradley2014-11-054-187/+145
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ### Summary * Made it easier to construct default Strategy and BoostingStrategy and to set parameters using simple types. * Added Scala and Java examples for GradientBoostedTrees * small cleanups and fixes ### Details GradientBoosting bug fixes (“bug” = bad default options) * Force boostingStrategy.weakLearnerParams.algo = Regression * Force boostingStrategy.weakLearnerParams.impurity = impurity.Variance * Only persist data if not yet persisted (since it causes an error if persisted twice) BoostingStrategy * numEstimators: renamed to numIterations * removed subsamplingRate (duplicated by Strategy) * removed categoricalFeaturesInfo since it belongs with the weak learner params (since boosting can be oblivious to feature type) * Changed algo to var (not val) and added BeanProperty, with overload taking String argument * Added assertValid() method * Updated defaultParams() method and eliminated defaultWeakLearnerParams() since that belongs in Strategy Strategy (for DecisionTree) * Changed algo to var (not val) and added BeanProperty, with overload taking String argument * Added setCategoricalFeaturesInfo method taking Java Map. * Cleaned up assertValid * Changed val’s to def’s since parameters can now be changed. CC: manishamde mengxr codedeft Author: Joseph K. Bradley <joseph@databricks.com> Closes #3094 from jkbradley/gbt-api and squashes the following commits: 7a27e22 [Joseph K. Bradley] scalastyle fix 52013d5 [Joseph K. Bradley] Merge remote-tracking branch 'upstream/master' into gbt-api e9b8410 [Joseph K. Bradley] Summary of changes
* [SPARK-3964] [MLlib] [PySpark] add Hypothesis test Python APIDavies Liu2014-11-041-0/+26
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ``` pyspark.mllib.stat.StatisticschiSqTest(observed, expected=None) :: Experimental :: If `observed` is Vector, conduct Pearson's chi-squared goodness of fit test of the observed data against the expected distribution, or againt the uniform distribution (by default), with each category having an expected frequency of `1 / len(observed)`. (Note: `observed` cannot contain negative values) If `observed` is matrix, conduct Pearson's independence test on the input contingency matrix, which cannot contain negative entries or columns or rows that sum up to 0. If `observed` is an RDD of LabeledPoint, conduct Pearson's independence test for every feature against the label across the input RDD. For each feature, the (feature, label) pairs are converted into a contingency matrix for which the chi-squared statistic is computed. All label and feature values must be categorical. :param observed: it could be a vector containing the observed categorical counts/relative frequencies, or the contingency matrix (containing either counts or relative frequencies), or an RDD of LabeledPoint containing the labeled dataset with categorical features. Real-valued features will be treated as categorical for each distinct value. :param expected: Vector containing the expected categorical counts/relative frequencies. `expected` is rescaled if the `expected` sum differs from the `observed` sum. :return: ChiSquaredTest object containing the test statistic, degrees of freedom, p-value, the method used, and the null hypothesis. ``` Author: Davies Liu <davies@databricks.com> Closes #3091 from davies/his and squashes the following commits: 145d16c [Davies Liu] address comments 0ab0764 [Davies Liu] fix float 5097d54 [Davies Liu] add Hypothesis test Python API
* [Spark-4060] [MLlib] exposing special rdd functions to the publicNiklas Wilcke2014-11-044-11/+13
| | | | | | | | Author: Niklas Wilcke <1wilcke@informatik.uni-hamburg.de> Closes #2907 from numbnut/master and squashes the following commits: 7f7c767 [Niklas Wilcke] [Spark-4060] [MLlib] exposing special rdd functions to the public, #2907
* [SPARK-3886] [PySpark] simplify serializer, use AutoBatchedSerializer by ↵Davies Liu2014-11-031-1/+1
| | | | | | | | | | | | | | | | | | | | | | | | | | | default. This PR simplify serializer, always use batched serializer (AutoBatchedSerializer as default), even batch size is 1. Author: Davies Liu <davies@databricks.com> This patch had conflicts when merged, resolved by Committer: Josh Rosen <joshrosen@databricks.com> Closes #2920 from davies/fix_autobatch and squashes the following commits: e544ef9 [Davies Liu] revert unrelated change 6880b14 [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch 1d557fc [Davies Liu] fix tests 8180907 [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch 76abdce [Davies Liu] clean up 53fa60b [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch d7ac751 [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch 2cc2497 [Davies Liu] Merge branch 'master' of github.com:apache/spark into fix_autobatch b4292ce [Davies Liu] fix bug in master d79744c [Davies Liu] recover hive tests be37ece [Davies Liu] refactor eb3938d [Davies Liu] refactor serializer in scala 8d77ef2 [Davies Liu] simplify serializer, use AutoBatchedSerializer by default.
* [SPARK-3573][MLLIB] Make MLlib's Vector compatible with SQL's SchemaRDDXiangrui Meng2014-11-033-2/+83
| | | | | | | | | | | | | | | Register MLlib's Vector as a SQL user-defined type (UDT) in both Scala and Python. With this PR, we can easily map a RDD[LabeledPoint] to a SchemaRDD, and then select columns or save to a Parquet file. Examples in Scala/Python are attached. The Scala code was copied from jkbradley. ~~This PR contains the changes from #3068 . I will rebase after #3068 is merged.~~ marmbrus jkbradley Author: Xiangrui Meng <meng@databricks.com> Closes #3070 from mengxr/SPARK-3573 and squashes the following commits: 3a0b6e5 [Xiangrui Meng] organize imports 236f0a0 [Xiangrui Meng] register vector as UDT and provide dataset examples
* [FIX][MLLIB] fix seed in BaggedPointSuiteXiangrui Meng2014-11-031-5/+5
| | | | | | | | | | | | Saw Jenkins test failures due to random seeds. jkbradley manishamde Author: Xiangrui Meng <meng@databricks.com> Closes #3084 from mengxr/fix-baggedpoint-suite and squashes the following commits: f735a43 [Xiangrui Meng] fix seed in BaggedPointSuite
* [SPARK-3161][MLLIB] Adding a node Id caching mechanism for training deci...Sung Chung2014-11-015-39/+382
| | | | | | | | | | ...sion trees. jkbradley mengxr chouqin Please review this. Author: Sung Chung <schung@alpinenow.com> Closes #2868 from codedeft/SPARK-3161 and squashes the following commits: 5f5a156 [Sung Chung] [SPARK-3161][MLLIB] Adding a node Id caching mechanism for training decision trees.
* [SPARK-4121] Set commons-math3 version based on hadoop profiles, instead of ↵Xiangrui Meng2014-11-011-0/+4
| | | | | | | | | | | | | | | | | | | shading In #2928 , we shade commons-math3 to prevent future conflicts with hadoop. It caused problems with our Jenkins master build with maven. Some tests used local-cluster mode, where the assembly jar contains relocated math3 classes, while mllib test code still compiles with core and the untouched math3 classes. This PR sets commons-math3 version based on hadoop profiles. pwendell JoshRosen srowen Author: Xiangrui Meng <meng@databricks.com> Closes #3023 from mengxr/SPARK-4121-alt and squashes the following commits: 580f6d9 [Xiangrui Meng] replace tab by spaces 7f71f08 [Xiangrui Meng] revert changes to PoissonSampler to avoid conflicts d3353d9 [Xiangrui Meng] do not shade commons-math3 b4180dc [Xiangrui Meng] temp work
* Streaming KMeans [MLLIB][SPARK-3254]freeman2014-10-312-0/+425
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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
* [MLLIB] SPARK-1547: Add Gradient Boosting to MLlibManish Amde2014-10-3119-265/+1329
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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
* [MLLIB] SPARK-2329 Add multi-label evaluation metricsAlexander Ulanov2014-10-312-0/+260
| | | | | | | | | | | | | | | | | | | | | | Implementation of various multi-label classification measures, including: Hamming-loss, strict and default Accuracy, macro-averaged Precision, Recall and F1-measure based on documents and labels, micro-averaged measures: https://issues.apache.org/jira/browse/SPARK-2329 Multi-class measures are currently in the following pull request: https://github.com/apache/spark/pull/1155 Author: Alexander Ulanov <nashb@yandex.ru> Author: avulanov <nashb@yandex.ru> Closes #1270 from avulanov/multilabelmetrics and squashes the following commits: fc8175e [Alexander Ulanov] Merge with previous updates 43a613e [Alexander Ulanov] Addressing reviewers comments: change Set to Array 517a594 [avulanov] Addressing reviewers comments: Scala style cf4222bc [avulanov] Addressing reviewers comments: renaming. Added label method that returns the list of labels 1843f73 [Alexander Ulanov] Scala style fix 79e8476 [Alexander Ulanov] Replacing fold(_ + _) with sum as suggested by srowen ca46765 [Alexander Ulanov] Cosmetic changes: Apache header and parameter explanation 40593f5 [Alexander Ulanov] Multi-label metrics: Hamming-loss, strict and normal accuracy, fix to macro measures, bunch of tests ad62df0 [Alexander Ulanov] Comments and scala style check 154164b [Alexander Ulanov] Multilabel evaluation metics and tests: macro precision and recall averaged by docs, micro and per-class precision and recall averaged by class
* [SPARK-3250] Implement Gap Sampling optimization for random samplingErik Erlandson2014-10-301-2/+2
| | | | | | | | | | | More efficient sampling, based on Gap Sampling optimization: http://erikerlandson.github.io/blog/2014/09/11/faster-random-samples-with-gap-sampling/ Author: Erik Erlandson <eerlands@redhat.com> Closes #2455 from erikerlandson/spark-3250-pr and squashes the following commits: 72496bc [Erik Erlandson] [SPARK-3250] Implement Gap Sampling optimization for random sampling
* [SPARK-4124] [MLlib] [PySpark] simplify serialization in MLlib Python APIDavies Liu2014-10-301-39/+45
| | | | | | | | | | | | | | | | | Create several helper functions to call MLlib Java API, convert the arguments to Java type and convert return value to Python object automatically, this simplify serialization in MLlib Python API very much. After this, the MLlib Python API does not need to deal with serialization details anymore, it's easier to add new API. cc mengxr Author: Davies Liu <davies@databricks.com> Closes #2995 from davies/cleanup and squashes the following commits: 8fa6ec6 [Davies Liu] address comments 16b85a0 [Davies Liu] Merge branch 'master' of github.com:apache/spark into cleanup 43743e5 [Davies Liu] bugfix 731331f [Davies Liu] simplify serialization in MLlib Python API
* SPARK-4111 [MLlib] add regression metricsYanbo Liang2014-10-302-0/+141
| | | | | | | | | | | | | | | | Add RegressionMetrics.scala as regression metrics used for evaluation and corresponding test case RegressionMetricsSuite.scala. Author: Yanbo Liang <yanbohappy@gmail.com> Author: liangyanbo <liangyanbo@meituan.com> Closes #2978 from yanbohappy/regression_metrics and squashes the following commits: 730d0a9 [Yanbo Liang] more clearly annotation 3d0bec1 [Yanbo Liang] rename and keep code style a8ad3e3 [Yanbo Liang] simplify code for keeping style d454909 [Yanbo Liang] rename parameter and function names, delete unused columns, add reference 2e56282 [liangyanbo] rename r2_score() and remove unused column 43bb12b [liangyanbo] add regression metrics
* [SPARK-4130][MLlib] Fixing libSVM parser bug with extra whitespaceJoseph E. Gonzalez2014-10-301-1/+1
| | | | | | | | | | | | This simple patch filters out extra whitespace entries. Author: Joseph E. Gonzalez <joseph.e.gonzalez@gmail.com> Author: Joey <joseph.e.gonzalez@gmail.com> Closes #2996 from jegonzal/loadLibSVM and squashes the following commits: e0227ab [Joey] improving readability e028e84 [Joseph E. Gonzalez] fixing whitespace bug in loadLibSVMFile when parsing libSVM files
* [SPARK-4129][MLlib] Performance tuning in MultivariateOnlineSummarizerDB Tsai2014-10-291-4/+21
| | | | | | | | | | | | | | | | | | | | | | | | | | | In MultivariateOnlineSummarizer, breeze's activeIterator is used to loop through the nonZero elements in the vector. However, activeIterator doesn't perform well due to lots of overhead. In this PR, native while loop is used for both DenseVector and SparseVector. The benchmark result with 20 executors using mnist8m dataset: Before: DenseVector: 48.2 seconds SparseVector: 16.3 seconds After: DenseVector: 17.8 seconds SparseVector: 11.2 seconds Since MultivariateOnlineSummarizer is used in several places, the overall performance gain in mllib library will be significant with this PR. Author: DB Tsai <dbtsai@alpinenow.com> Closes #2992 from dbtsai/SPARK-4129 and squashes the following commits: b99db6c [DB Tsai] fixed java.lang.ArrayIndexOutOfBoundsException 2b5e882 [DB Tsai] small refactoring ebe3e74 [DB Tsai] First commit
* [SPARK-3961] [MLlib] [PySpark] Python API for mllib.featureDavies Liu2014-10-283-4/+60
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Added completed Python API for MLlib.feature Normalizer StandardScalerModel StandardScaler HashTF IDFModel IDF cc mengxr Author: Davies Liu <davies@databricks.com> Author: Davies Liu <davies.liu@gmail.com> Closes #2819 from davies/feature and squashes the following commits: 4f48f48 [Davies Liu] add a note for HashingTF 67f6d21 [Davies Liu] address comments b628693 [Davies Liu] rollback changes in Word2Vec efb4f4f [Davies Liu] Merge branch 'master' into feature 806c7c2 [Davies Liu] address comments 3abb8c2 [Davies Liu] address comments 59781b9 [Davies Liu] Merge branch 'master' of github.com:apache/spark into feature a405ae7 [Davies Liu] fix tests 7a1891a [Davies Liu] fix tests 486795f [Davies Liu] update programming guide, HashTF -> HashingTF 8a50584 [Davies Liu] Python API for mllib.feature
* [MLlib] SPARK-3987: add test case on objective value for NNLScoderxiang2014-10-272-1/+31
| | | | | | | | | | Also update step parameter to pass the proposed test Author: coderxiang <shuoxiangpub@gmail.com> Closes #2965 from coderxiang/nnls-test and squashes the following commits: 24b06f9 [coderxiang] add test case on objective value for NNLS; update step parameter to pass the test
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