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* [SPARK-4548] []SPARK-4517] improve performance of python broadcastDavies Liu2014-11-245-233/+80
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Re-implement the Python broadcast using file: 1) serialize the python object using cPickle, write into disks. 2) Create a wrapper in JVM (for the dumped file), it read data from during serialization 3) Using TorrentBroadcast or HttpBroadcast to transfer the data (compressed) into executors 4) During deserialization, writing the data into disk. 5) Passing the path into Python worker, read data from disk and unpickle it into python object, until the first access. It fixes the performance regression introduced in #2659, has similar performance as 1.1, but support object larger than 2G, also improve the memory efficiency (only one compressed copy in driver and executor). Testing with a 500M broadcast and 4 tasks (excluding the benefit from reused worker in 1.2): name | 1.1 | 1.2 with this patch | improvement ---------|--------|---------|-------- python-broadcast-w-bytes | 25.20 | 9.33 | 170.13% | python-broadcast-w-set | 4.13 | 4.50 | -8.35% | Testing with 100 tasks (16 CPUs): name | 1.1 | 1.2 with this patch | improvement ---------|--------|---------|-------- python-broadcast-w-bytes | 38.16 | 8.40 | 353.98% python-broadcast-w-set | 23.29 | 9.59 | 142.80% Author: Davies Liu <davies@databricks.com> Closes #3417 from davies/pybroadcast and squashes the following commits: 50a58e0 [Davies Liu] address comments b98de1d [Davies Liu] disable gc while unpickle e5ee6b9 [Davies Liu] support large string 09303b8 [Davies Liu] read all data into memory dde02dd [Davies Liu] improve performance of python broadcast (cherry picked from commit 6cf507685efd01df77d663145ae08e48c7f92948) Signed-off-by: Josh Rosen <joshrosen@databricks.com>
* [SPARK-4578] fix asDict() with nested Row()Davies Liu2014-11-242-4/+5
| | | | | | | | | | | | | The Row object is created on the fly once the field is accessed, so we should access them by getattr() in asDict(0 Author: Davies Liu <davies@databricks.com> Closes #3434 from davies/fix_asDict and squashes the following commits: b20f1e7 [Davies Liu] fix asDict() with nested Row() (cherry picked from commit 050616b408c60eae02256913ceb645912dbff62e) Signed-off-by: Patrick Wendell <pwendell@gmail.com>
* [SPARK-4562] [MLlib] speedup vectorDavies Liu2014-11-242-26/+53
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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 (cherry picked from commit b660de7a9cbdea3df4a37fbcf60c1c33c71782b8) Signed-off-by: Xiangrui Meng <meng@databricks.com>
* [SPARK-4531] [MLlib] cache serialized java objectDavies Liu2014-11-214-13/+8
| | | | | | | | | | | | | | | | | | | | | | 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 (cherry picked from commit ce95bd8e130b2c7688b94be40683bdd90d86012d) Signed-off-by: Xiangrui Meng <meng@databricks.com>
* [SPARK-4477] [PySpark] remove numpy from RDDSamplerDavies Liu2014-11-202-69/+40
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | In RDDSampler, it try use numpy to gain better performance for possion(), but the number of call of random() is only (1+faction) * N in the pure python implementation of possion(), so there is no much performance gain from numpy. numpy is not a dependent of pyspark, so it maybe introduce some problem, such as there is no numpy installed in slaves, but only installed master, as reported in SPARK-927. It also complicate the code a lot, so we may should remove numpy from RDDSampler. I also did some benchmark to verify that: ``` >>> from pyspark.mllib.random import RandomRDDs >>> rdd = RandomRDDs.uniformRDD(sc, 1 << 20, 1).cache() >>> rdd.count() # cache it >>> rdd.sample(True, 0.9).count() # measure this line ``` the results: |withReplacement | random | numpy.random | ------- | ------------ | ------- |True | 1.5 s| 1.4 s| |False| 0.6 s | 0.8 s| closes #2313 Note: this patch including some commits that not mirrored to github, it will be OK after it catches up. Author: Davies Liu <davies@databricks.com> Author: Xiangrui Meng <meng@databricks.com> Closes #3351 from davies/numpy and squashes the following commits: 5c438d7 [Davies Liu] fix comment c5b9252 [Davies Liu] Merge pull request #1 from mengxr/SPARK-4477 98eb31b [Xiangrui Meng] make poisson sampling slightly faster ee17d78 [Davies Liu] remove = for float 13f7b05 [Davies Liu] Merge branch 'master' of http://git-wip-us.apache.org/repos/asf/spark into numpy f583023 [Davies Liu] fix tests 51649f5 [Davies Liu] remove numpy in RDDSampler 78bf997 [Davies Liu] fix tests, do not use numpy in randomSplit, no performance gain f5fdf63 [Davies Liu] fix bug with int in weights 4dfa2cd [Davies Liu] refactor f866bcf [Davies Liu] remove unneeded change c7a2007 [Davies Liu] switch to python implementation 95a48ac [Davies Liu] Merge branch 'master' of github.com:apache/spark into randomSplit 0d9b256 [Davies Liu] refactor 1715ee3 [Davies Liu] address comments 41fce54 [Davies Liu] randomSplit() (cherry picked from commit d39f2e9c683a4ab78b29eb3c5668325bf8568e8c) Signed-off-by: Xiangrui Meng <meng@databricks.com>
* [SPARK-4439] [MLlib] add python api for random forestDavies Liu2014-11-202-23/+221
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ``` 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 (cherry picked from commit 1c53a5db993193122bfa79574d2540149fe2cc08) Signed-off-by: Xiangrui Meng <meng@databricks.com>
* [SPARK-4228][SQL] SchemaRDD to JSONDan McClary2014-11-201-1/+16
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Here's a simple fix for SchemaRDD to JSON. Author: Dan McClary <dan.mcclary@gmail.com> Closes #3213 from dwmclary/SPARK-4228 and squashes the following commits: d714e1d [Dan McClary] fixed PEP 8 error cac2879 [Dan McClary] move pyspark comment and doctest to correct location f9471d3 [Dan McClary] added pyspark doc and doctest 6598cee [Dan McClary] adding complex type queries 1a5fd30 [Dan McClary] removing SPARK-4228 from SQLQuerySuite 4a651f0 [Dan McClary] cleaned PEP and Scala style failures. Moved tests to JsonSuite 47ceff6 [Dan McClary] cleaned up scala style issues 2ee1e70 [Dan McClary] moved rowToJSON to JsonRDD 4387dd5 [Dan McClary] Added UserDefinedType, cleaned up case formatting 8f7bfb6 [Dan McClary] Map type added to SchemaRDD.toJSON 1b11980 [Dan McClary] Map and UserDefinedTypes partially done 11d2016 [Dan McClary] formatting and unicode deserialization default fixed 6af72d1 [Dan McClary] deleted extaneous comment 4d11c0c [Dan McClary] JsonFactory rewrite of toJSON for SchemaRDD 149dafd [Dan McClary] wrapped scala toJSON in sql.py 5e5eb1b [Dan McClary] switched to Jackson for JSON processing 6c94a54 [Dan McClary] added toJSON to pyspark SchemaRDD aaeba58 [Dan McClary] added toJSON to pyspark SchemaRDD 1d171aa [Dan McClary] upated missing brace on if statement 319e3ba [Dan McClary] updated to upstream master with merged SPARK-4228 424f130 [Dan McClary] tests pass, ready for pull and PR 626a5b1 [Dan McClary] added toJSON to SchemaRDD f7d166a [Dan McClary] added toJSON method 5d34e37 [Dan McClary] merge resolved d6d19e9 [Dan McClary] pr example (cherry picked from commit b8e6886fb8ff8f667fb7e600cd727d8649cad1d1) Signed-off-by: Michael Armbrust <michael@databricks.com>
* [DOC][PySpark][Streaming] Fix docstring for sphinxKen Takagiwa2014-11-191-2/+2
| | | | | | | | | | | | | | This commit should be merged for 1.2 release. cc tdas Author: Ken Takagiwa <ugw.gi.world@gmail.com> Closes #3311 from giwa/patch-3 and squashes the following commits: ab474a8 [Ken Takagiwa] [DOC][PySpark][Streaming] Fix docstring for sphinx (cherry picked from commit 9b7bbcef8863ecd69e7511825ef9c93d8632dac2) Signed-off-by: Tathagata Das <tathagata.das1565@gmail.com>
* [SPARK-4384] [PySpark] improve sort spillingDavies Liu2014-11-191-1/+10
| | | | | | | | | | | | | | | If there some big broadcasts (or other object) in Python worker, the free memory could be used for sorting will be too small, then it will keep spilling small files into disks, finally failed with too many open files. This PR try to delay the spilling until the used memory goes over limit and start to increase since last spilling, it will increase the size of spilling files, improve the stability and performance in this cases. (We also do this in ExternalAggregator). Author: Davies Liu <davies@databricks.com> Closes #3252 from davies/sort and squashes the following commits: 711fb6c [Davies Liu] improve sort spilling (cherry picked from commit 73c8ea84a668f443eb18ce15ba97023da041d808) Signed-off-by: Josh Rosen <joshrosen@databricks.com>
* [SPARK-4327] [PySpark] Python API for RDD.randomSplit()Davies Liu2014-11-182-3/+41
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ``` pyspark.RDD.randomSplit(self, weights, seed=None) Randomly splits this RDD with the provided weights. :param weights: weights for splits, will be normalized if they don't sum to 1 :param seed: random seed :return: split RDDs in an list >>> rdd = sc.parallelize(range(10), 1) >>> rdd1, rdd2, rdd3 = rdd.randomSplit([0.4, 0.6, 1.0], 11) >>> rdd1.collect() [3, 6] >>> rdd2.collect() [0, 5, 7] >>> rdd3.collect() [1, 2, 4, 8, 9] ``` Author: Davies Liu <davies@databricks.com> Closes #3193 from davies/randomSplit and squashes the following commits: 78bf997 [Davies Liu] fix tests, do not use numpy in randomSplit, no performance gain f5fdf63 [Davies Liu] fix bug with int in weights 4dfa2cd [Davies Liu] refactor f866bcf [Davies Liu] remove unneeded change c7a2007 [Davies Liu] switch to python implementation 95a48ac [Davies Liu] Merge branch 'master' of github.com:apache/spark into randomSplit 0d9b256 [Davies Liu] refactor 1715ee3 [Davies Liu] address comments 41fce54 [Davies Liu] randomSplit() (cherry picked from commit 7f22fa81ebd5e501fcb0e1da5506d1d4fb9250cf) Signed-off-by: Xiangrui Meng <meng@databricks.com>
* [SPARK-3721] [PySpark] broadcast objects larger than 2GDavies Liu2014-11-186-17/+239
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | This patch will bring support for broadcasting objects larger than 2G. pickle, zlib, FrameSerializer and Array[Byte] all can not support objects larger than 2G, so this patch introduce LargeObjectSerializer to serialize broadcast objects, the object will be serialized and compressed into small chunks, it also change the type of Broadcast[Array[Byte]]] into Broadcast[Array[Array[Byte]]]]. Testing for support broadcast objects larger than 2G is slow and memory hungry, so this is tested manually, could be added into SparkPerf. Author: Davies Liu <davies@databricks.com> Author: Davies Liu <davies.liu@gmail.com> Closes #2659 from davies/huge and squashes the following commits: 7b57a14 [Davies Liu] add more tests for broadcast 28acff9 [Davies Liu] Merge branch 'master' of github.com:apache/spark into huge a2f6a02 [Davies Liu] bug fix 4820613 [Davies Liu] Merge branch 'master' of github.com:apache/spark into huge 5875c73 [Davies Liu] address comments 10a349b [Davies Liu] address comments 0c33016 [Davies Liu] Merge branch 'master' of github.com:apache/spark into huge 6182c8f [Davies Liu] Merge branch 'master' into huge d94b68f [Davies Liu] Merge branch 'master' of github.com:apache/spark into huge 2514848 [Davies Liu] address comments fda395b [Davies Liu] Merge branch 'master' of github.com:apache/spark into huge 1c2d928 [Davies Liu] fix scala style 091b107 [Davies Liu] broadcast objects larger than 2G (cherry picked from commit 4a377aff2d36b64a65b54192a987aba44b8f78e0) Signed-off-by: Josh Rosen <joshrosen@databricks.com>
* [SPARK-4306] [MLlib] Python API for LogisticRegressionWithLBFGSDavies Liu2014-11-181-4/+53
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ``` 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 (cherry picked from commit d2e29516f2064f93f3a9070c91fc7460706e0b0a) Signed-off-by: Xiangrui Meng <meng@databricks.com>
* [SPARK-4396] allow lookup by index in Python's RatingXiangrui Meng2014-11-181-11/+15
| | | | | | | | | | | | | | | | | | | | | In PySpark, ALS can take an RDD of (user, product, rating) tuples as input. However, model.predict outputs an RDD of Rating. So on the input side, users can use r[0], r[1], r[2], while on the output side, users have to use r.user, r.product, r.rating. We should allow lookup by index in Rating by making Rating a namedtuple. davies <!-- Reviewable:start --> [<img src="https://reviewable.io/review_button.png" height=40 alt="Review on Reviewable"/>](https://reviewable.io/reviews/apache/spark/3261) <!-- Reviewable:end --> Author: Xiangrui Meng <meng@databricks.com> Closes #3261 from mengxr/SPARK-4396 and squashes the following commits: 543aef0 [Xiangrui Meng] use named tuple to implement ALS 0b61bae [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into SPARK-4396 d3bd7d4 [Xiangrui Meng] allow lookup by index in Python's Rating (cherry picked from commit b54c6ab3c54e65238d6766832ea1f3fcd694f2fd) Signed-off-by: Xiangrui Meng <meng@databricks.com>
* [SPARK-4435] [MLlib] [PySpark] improve classificationDavies Liu2014-11-181-29/+106
| | | | | | | | | | | | | | | | | | 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 (cherry picked from commit 8fbf72b7903b5bbec8d949151aa4693b4af26ff5) Signed-off-by: Xiangrui Meng <meng@databricks.com>
* [SPARK-4415] [PySpark] JVM should exit after Python exitDavies Liu2014-11-141-1/+3
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | When JVM is started in a Python process, it should exit once the stdin is closed. test: add spark.driver.memory in conf/spark-defaults.conf ``` daviesdm:~/work/spark$ cat conf/spark-defaults.conf spark.driver.memory 8g daviesdm:~/work/spark$ bin/pyspark >>> quit daviesdm:~/work/spark$ jps 4931 Jps 286 daviesdm:~/work/spark$ python wc.py 943738 0.719928026199 daviesdm:~/work/spark$ jps 286 4990 Jps ``` Author: Davies Liu <davies@databricks.com> Closes #3274 from davies/exit and squashes the following commits: df0e524 [Davies Liu] address comments ce8599c [Davies Liu] address comments 050651f [Davies Liu] JVM should exit after Python exit (cherry picked from commit 7fe08b43c78bf9e8515f671e72aa03a83ea782f8) Signed-off-by: Andrew Or <andrew@databricks.com>
* [SPARK-4398][PySpark] specialize sc.parallelize(xrange)Xiangrui Meng2014-11-141-4/+21
| | | | | | | | | | | | | | | | `sc.parallelize(range(1 << 20), 1).count()` may take 15 seconds to finish and the rdd object stores the entire list, making task size very large. This PR adds a specialized version for xrange. JoshRosen davies Author: Xiangrui Meng <meng@databricks.com> Closes #3264 from mengxr/SPARK-4398 and squashes the following commits: 8953c41 [Xiangrui Meng] follow davies' suggestion cbd58e3 [Xiangrui Meng] specialize sc.parallelize(xrange) (cherry picked from commit abd581752f9314791a688690c07ad1bb68cc09fe) Signed-off-by: Xiangrui Meng <meng@databricks.com>
* [SPARK-4372][MLLIB] Make LR and SVM's default parameters consistent in Scala ↵Xiangrui Meng2014-11-132-35/+37
| | | | | | | | | | | | | | | | | | | | | 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 (cherry picked from commit 32218307edc6de2b08d5f7a0db6d566081d27197) Signed-off-by: Xiangrui Meng <meng@databricks.com>
* [SPARK-4348] [PySpark] [MLlib] rename random.py to rand.pyDavies Liu2014-11-136-20/+38
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | This PR rename random.py to rand.py to avoid the side affects of conflict with random module, but still keep the same interface as before. ``` >>> from pyspark.mllib.random import RandomRDDs ``` ``` $ pydoc pyspark.mllib.random Help on module random in pyspark.mllib: NAME random - Python package for random data generation. FILE /Users/davies/work/spark/python/pyspark/mllib/rand.py CLASSES __builtin__.object pyspark.mllib.random.RandomRDDs class RandomRDDs(__builtin__.object) | Generator methods for creating RDDs comprised of i.i.d samples from | some distribution. | | Static methods defined here: | | normalRDD(sc, size, numPartitions=None, seed=None) ``` cc mengxr reference link: http://xion.org.pl/2012/05/06/hacking-python-imports/ Author: Davies Liu <davies@databricks.com> Closes #3216 from davies/random and squashes the following commits: 7ac4e8b [Davies Liu] rename random.py to rand.py (cherry picked from commit ce0333f9a008348692bb9a200449d2d992e7825e) Signed-off-by: Xiangrui Meng <meng@databricks.com>
* [SPARK-4369] [MLLib] fix TreeModel.predict() with RDDDavies Liu2014-11-121-12/+14
| | | | | | | | | | | | | | | 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 (cherry picked from commit bd86118c4e980f94916f892c76fb808fd4c8bd85) Signed-off-by: Xiangrui Meng <meng@databricks.com>
* [SPARK-4324] [PySpark] [MLlib] support numpy.array for all MLlib APIDavies Liu2014-11-107-32/+105
| | | | | | | | | | | | | | | | | | This PR check all of the existing Python MLlib API to make sure that numpy.array is supported as Vector (also RDD of numpy.array). It also improve some docstring and doctest. cc mateiz mengxr Author: Davies Liu <davies@databricks.com> Closes #3189 from davies/numpy and squashes the following commits: d5057c4 [Davies Liu] fix tests 6987611 [Davies Liu] support numpy.array for all MLlib API (cherry picked from commit 65083e93ddd552b7d3e4eb09f87c091ef2ae83a2) Signed-off-by: Xiangrui Meng <meng@databricks.com>
* [MLLIB] [PYTHON] SPARK-4221: Expose nonnegative ALS in the python APIMichelangelo D'Agostino2014-11-071-15/+25
| | | | | | | | | | | | | | | | | | 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. (cherry picked from commit 7e9d975676d56ace0e84c2200137e4cd4eba074a) Signed-off-by: Xiangrui Meng <meng@databricks.com>
* [SPARK-4304] [PySpark] Fix sort on empty RDDDavies Liu2014-11-072-0/+5
| | | | | | | | | | | | | | | | This PR fix sortBy()/sortByKey() on empty RDD. This should be back ported into 1.1/1.2 Author: Davies Liu <davies@databricks.com> Closes #3162 from davies/fix_sort and squashes the following commits: 84f64b7 [Davies Liu] add tests 52995b5 [Davies Liu] fix sortByKey() on empty RDD (cherry picked from commit 7779109796c90d789464ab0be35917f963bbe867) Signed-off-by: Josh Rosen <joshrosen@databricks.com>
* [SPARK-4186] add binaryFiles and binaryRecords in PythonDavies Liu2014-11-062-1/+50
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | add binaryFiles() and binaryRecords() in Python ``` binaryFiles(self, path, minPartitions=None): :: Developer API :: Read a directory of binary files from HDFS, a local file system (available on all nodes), or any Hadoop-supported file system URI as a byte array. Each file is read as a single record and returned in a key-value pair, where the key is the path of each file, the value is the content of each file. Note: Small files are preferred, large file is also allowable, but may cause bad performance. binaryRecords(self, path, recordLength): Load data from a flat binary file, assuming each record is a set of numbers with the specified numerical format (see ByteBuffer), and the number of bytes per record is constant. :param path: Directory to the input data files :param recordLength: The length at which to split the records ``` Author: Davies Liu <davies@databricks.com> Closes #3078 from davies/binary and squashes the following commits: cd0bdbd [Davies Liu] Merge branch 'master' of github.com:apache/spark into binary 3aa349b [Davies Liu] add experimental notes 24e84b6 [Davies Liu] Merge branch 'master' of github.com:apache/spark into binary 5ceaa8a [Davies Liu] Merge branch 'master' of github.com:apache/spark into binary 1900085 [Davies Liu] bugfix bb22442 [Davies Liu] add binaryFiles and binaryRecords in Python (cherry picked from commit b41a39e24038876359aeb7ce2bbbb4de2234e5f3) Signed-off-by: Matei Zaharia <matei@databricks.com>
* [SPARK-3964] [MLlib] [PySpark] add Hypothesis test Python APIDavies Liu2014-11-043-4/+153
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | ``` 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 (cherry picked from commit c8abddc5164d8cf11cdede6ab3d5d1ea08028708) Signed-off-by: Xiangrui Meng <meng@databricks.com>
* [SPARK-3886] [PySpark] simplify serializer, use AutoBatchedSerializer by ↵Davies Liu2014-11-038-186/+94
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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. (cherry picked from commit e4f42631a68b473ce706429915f3f08042af2119) Signed-off-by: Josh Rosen <joshrosen@databricks.com>
* [SPARK-3573][MLLIB] Make MLlib's Vector compatible with SQL's SchemaRDDXiangrui Meng2014-11-032-3/+86
| | | | | | | | | | | | | | | | | | 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 (cherry picked from commit 1a9c6cddadebdc53d083ac3e0da276ce979b5d1f) Signed-off-by: Xiangrui Meng <meng@databricks.com>
* [SPARK-4192][SQL] Internal API for Python UDTXiangrui Meng2014-11-032-3/+296
| | | | | | | | | | | | | | | | | | | | | | | | Following #2919, this PR adds Python UDT (for internal use only) with tests under "pyspark.tests". Before `SQLContext.applySchema`, we check whether we need to convert user-type instances into SQL recognizable data. In the current implementation, a Python UDT must be paired with a Scala UDT for serialization on the JVM side. A following PR will add VectorUDT in MLlib for both Scala and Python. marmbrus jkbradley davies Author: Xiangrui Meng <meng@databricks.com> Closes #3068 from mengxr/SPARK-4192-sql and squashes the following commits: acff637 [Xiangrui Meng] merge master dba5ea7 [Xiangrui Meng] only use pyClass for Python UDT output sqlType as well 2c9d7e4 [Xiangrui Meng] move import to global setup; update needsConversion 7c4a6a9 [Xiangrui Meng] address comments 75223db [Xiangrui Meng] minor update f740379 [Xiangrui Meng] remove UDT from default imports e98d9d0 [Xiangrui Meng] fix py style 4e84fce [Xiangrui Meng] remove local hive tests and add more tests 39f19e0 [Xiangrui Meng] add tests b7f666d [Xiangrui Meng] add Python UDT (cherry picked from commit 04450d11548cfb25d4fb77d4a33e3a7cd4254183) Signed-off-by: Xiangrui Meng <meng@databricks.com>
* [SPARK-3594] [PySpark] [SQL] take more rows to infer schema or samplingDavies Liu2014-11-032-68/+147
| | | | | | | | | | | | | | | | | | | | | | | | | | | This patch will try to infer schema for RDD which has empty value (None, [], {}) in the first row. It will try first 100 rows and merge the types into schema, also merge fields of StructType together. If there is still NullType in schema, then it will show an warning, tell user to try with sampling. If sampling is presented, it will infer schema from all the rows after sampling. Also, add samplingRatio for jsonFile() and jsonRDD() Author: Davies Liu <davies.liu@gmail.com> Author: Davies Liu <davies@databricks.com> Closes #2716 from davies/infer and squashes the following commits: e678f6d [Davies Liu] Merge branch 'master' of github.com:apache/spark into infer 34b5c63 [Davies Liu] Merge branch 'master' of github.com:apache/spark into infer 567dc60 [Davies Liu] update docs 9767b27 [Davies Liu] Merge branch 'master' into infer e48d7fb [Davies Liu] fix tests 29e94d5 [Davies Liu] let NullType inherit from PrimitiveType ee5d524 [Davies Liu] Merge branch 'master' of github.com:apache/spark into infer 540d1d5 [Davies Liu] merge fields for StructType f93fd84 [Davies Liu] add more tests 3603e00 [Davies Liu] take more rows to infer schema, or infer the schema by sampling the RDD (cherry picked from commit 24544fbce05665ab4999a1fe5aac434d29cd912c) Signed-off-by: Michael Armbrust <michael@databricks.com>
* [SPARK-4148][PySpark] fix seed distribution and add some tests for rdd.sampleXiangrui Meng2014-11-033-9/+20
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | The current way of seed distribution makes the random sequences from partition i and i+1 offset by 1. ~~~ In [14]: import random In [15]: r1 = random.Random(10) In [16]: r1.randint(0, 1) Out[16]: 1 In [17]: r1.random() Out[17]: 0.4288890546751146 In [18]: r1.random() Out[18]: 0.5780913011344704 In [19]: r2 = random.Random(10) In [20]: r2.randint(0, 1) Out[20]: 1 In [21]: r2.randint(0, 1) Out[21]: 0 In [22]: r2.random() Out[22]: 0.5780913011344704 ~~~ Note: The new tests are not for this bug fix. Author: Xiangrui Meng <meng@databricks.com> Closes #3010 from mengxr/SPARK-4148 and squashes the following commits: 869ae4b [Xiangrui Meng] move tests tests.py c1bacd9 [Xiangrui Meng] fix seed distribution and add some tests for rdd.sample (cherry picked from commit 3cca1962207745814b9d83e791713c91b659c36c) Signed-off-by: Xiangrui Meng <meng@databricks.com>
* [SPARK-3930] [SPARK-3933] Support fixed-precision decimal in SQL, and some ↵Matei Zaharia2014-11-011-3/+32
| | | | | | | | | | | | | | | | | | | | | | | | | optimizations - Adds optional precision and scale to Spark SQL's decimal type, which behave similarly to those in Hive 13 (https://cwiki.apache.org/confluence/download/attachments/27362075/Hive_Decimal_Precision_Scale_Support.pdf) - Replaces our internal representation of decimals with a Decimal class that can store small values in a mutable Long, saving memory in this situation and letting some operations happen directly on Longs This is still marked WIP because there are a few TODOs, but I'll remove that tag when done. Author: Matei Zaharia <matei@databricks.com> Closes #2983 from mateiz/decimal-1 and squashes the following commits: 35e6b02 [Matei Zaharia] Fix issues after merge 227f24a [Matei Zaharia] Review comments 31f915e [Matei Zaharia] Implement Davies's suggestions in Python eb84820 [Matei Zaharia] Support reading/writing decimals as fixed-length binary in Parquet 4dc6bae [Matei Zaharia] Fix decimal support in PySpark d1d9d68 [Matei Zaharia] Fix compile error and test issues after rebase b28933d [Matei Zaharia] Support decimal precision/scale in Hive metastore 2118c0d [Matei Zaharia] Some test and bug fixes 81db9cb [Matei Zaharia] Added mutable Decimal that will be more efficient for small precisions 7af0c3b [Matei Zaharia] Add optional precision and scale to DecimalType, but use Unlimited for now ec0a947 [Matei Zaharia] Make the result of AVG on Decimals be Decimal, not Double
* [SPARK-3569][SQL] Add metadata field to StructFieldXiangrui Meng2014-11-011-4/+11
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | Add `metadata: Metadata` to `StructField` to store extra information of columns. `Metadata` is a simple wrapper over `Map[String, Any]` with value types restricted to Boolean, Long, Double, String, Metadata, and arrays of those types. SerDe is via JSON. Metadata is preserved through simple operations like `SELECT`. marmbrus liancheng Author: Xiangrui Meng <meng@databricks.com> Author: Michael Armbrust <michael@databricks.com> Closes #2701 from mengxr/structfield-metadata and squashes the following commits: dedda56 [Xiangrui Meng] merge remote 5ef930a [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into structfield-metadata c35203f [Xiangrui Meng] Merge pull request #1 from marmbrus/pr/2701 886b85c [Michael Armbrust] Expose Metadata and MetadataBuilder through the public scala and java packages. 589f314 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into structfield-metadata 1e2abcf [Xiangrui Meng] change default value of metadata to None in python 611d3c2 [Xiangrui Meng] move metadata from Expr to NamedExpr ddfcfad [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into structfield-metadata a438440 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into structfield-metadata 4266f4d [Xiangrui Meng] add StructField.toString back for backward compatibility 3f49aab [Xiangrui Meng] remove StructField.toString 24a9f80 [Xiangrui Meng] Merge remote-tracking branch 'apache/master' into structfield-metadata 473a7c5 [Xiangrui Meng] merge master c9d7301 [Xiangrui Meng] organize imports 1fcbf13 [Xiangrui Meng] change metadata type in StructField for Scala/Java 60cc131 [Xiangrui Meng] add doc and header 60614c7 [Xiangrui Meng] add metadata e42c452 [Xiangrui Meng] merge master 93518fb [Xiangrui Meng] support metadata in python 905bb89 [Xiangrui Meng] java conversions 618e349 [Xiangrui Meng] make tests work in scala 61b8e0f [Xiangrui Meng] merge master 7e5a322 [Xiangrui Meng] do not output metadata in StructField.toString c41a664 [Xiangrui Meng] merge master d8af0ed [Xiangrui Meng] move tests to SQLQuerySuite 67fdebb [Xiangrui Meng] add test on join d65072e [Xiangrui Meng] remove Map.empty 367d237 [Xiangrui Meng] add test c194d5e [Xiangrui Meng] add metadata field to StructField and Attribute
* [SPARK-3870] EOL character enforcementKousuke Saruta2014-10-312-249/+249
| | | | | | | | | | | | | | | | | | | We have shell scripts and Windows batch files, so we should enforce proper EOL character. Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp> Closes #2726 from sarutak/eol-enforcement and squashes the following commits: 9748c3f [Kousuke Saruta] Fixed make.bat 252de89 [Kousuke Saruta] Removed extra characters from make.bat 5b81c00 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into eol-enforcement 8633ed2 [Kousuke Saruta] merge branch 'master' of git://git.apache.org/spark into eol-enforcement 5d630d8 [Kousuke Saruta] Merged ba10797 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into eol-enforcement 7407515 [Kousuke Saruta] Merge branch 'master' of git://git.apache.org/spark into eol-enforcement 772fd4e [Kousuke Saruta] Normized EOL character in make.bat and compute-classpath.cmd ac7f873 [Kousuke Saruta] Added an entry for .gitattributes to .rat-excludes 1570e77 [Kousuke Saruta] Added .gitattributes
* [SPARK-4150][PySpark] return self in rdd.setNameXiangrui Meng2014-10-311-2/+2
| | | | | | | | | | | Then we can do `rdd.setName('abc').cache().count()`. Author: Xiangrui Meng <meng@databricks.com> Closes #3011 from mengxr/rdd-setname and squashes the following commits: 10d0d60 [Xiangrui Meng] update test 4ac3bbd [Xiangrui Meng] return self in rdd.setName
* [SPARK-3826][SQL]enable hive-thriftserver to support hive-0.13.1wangfei2014-10-311-27/+0
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | In #2241 hive-thriftserver is not enabled. This patch enable hive-thriftserver to support hive-0.13.1 by using a shim layer refer to #2241. 1 A light shim layer(code in sql/hive-thriftserver/hive-version) for each different hive version to handle api compatibility 2 New pom profiles "hive-default" and "hive-versions"(copy from #2241) to activate different hive version 3 SBT cmd for different version as follows: hive-0.12.0 --- sbt/sbt -Phive,hadoop-2.3 -Phive-0.12.0 assembly hive-0.13.1 --- sbt/sbt -Phive,hadoop-2.3 -Phive-0.13.1 assembly 4 Since hive-thriftserver depend on hive subproject, this patch should be merged with #2241 to enable hive-0.13.1 for hive-thriftserver Author: wangfei <wangfei1@huawei.com> Author: scwf <wangfei1@huawei.com> Closes #2685 from scwf/shim-thriftserver1 and squashes the following commits: f26f3be [wangfei] remove clean to save time f5cac74 [wangfei] remove local hivecontext test 578234d [wangfei] use new shaded hive 18fb1ff [wangfei] exclude kryo in hive pom fa21d09 [wangfei] clean package assembly/assembly 8a4daf2 [wangfei] minor fix 0d7f6cf [wangfei] address comments f7c93ae [wangfei] adding build with hive 0.13 before running tests bcf943f [wangfei] Merge branch 'master' of https://github.com/apache/spark into shim-thriftserver1 c359822 [wangfei] reuse getCommandProcessor in hiveshim 52674a4 [scwf] sql/hive included since examples depend on it 3529e98 [scwf] move hive module to hive profile f51ff4e [wangfei] update and fix conflicts f48d3a5 [scwf] Merge branch 'master' of https://github.com/apache/spark into shim-thriftserver1 41f727b [scwf] revert pom changes 13afde0 [scwf] fix small bug 4b681f4 [scwf] enable thriftserver in profile hive-0.13.1 0bc53aa [scwf] fixed when result filed is null dfd1c63 [scwf] update run-tests to run hive-0.12.0 default now c6da3ce [scwf] Merge branch 'master' of https://github.com/apache/spark into shim-thriftserver 7c66b8e [scwf] update pom according spark-2706 ae47489 [scwf] update and fix conflicts
* [SPARK-4124] [MLlib] [PySpark] simplify serialization in MLlib Python APIDavies Liu2014-10-3011-347/+242
| | | | | | | | | | | | | | | | | 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-4133] [SQL] [PySpark] type conversionfor python udfDavies Liu2014-10-281-3/+13
| | | | | | | | | | | | | | | | Call Python UDF on ArrayType/MapType/PrimitiveType, the returnType can also be ArrayType/MapType/PrimitiveType. For StructType, it will act as tuple (without attributes). If returnType is StructType, it also should be tuple. Author: Davies Liu <davies@databricks.com> Closes #2973 from davies/udf_array and squashes the following commits: 306956e [Davies Liu] Merge branch 'master' of github.com:apache/spark into udf_array 2c00e43 [Davies Liu] fix merge 11395fa [Davies Liu] Merge branch 'master' of github.com:apache/spark into udf_array 9df50a2 [Davies Liu] address comments 79afb4e [Davies Liu] type conversionfor python udf
* [SPARK-3988][SQL] add public API for date typeDaoyuan Wang2014-10-281-18/+39
| | | | | | | | | | | | | | | | Add json and python api for date type. By using Pickle, `java.sql.Date` was serialized as calendar, and recognized in python as `datetime.datetime`. Author: Daoyuan Wang <daoyuan.wang@intel.com> Closes #2901 from adrian-wang/spark3988 and squashes the following commits: c51a24d [Daoyuan Wang] convert datetime to date 5670626 [Daoyuan Wang] minor line combine f760d8e [Daoyuan Wang] fix indent 444f100 [Daoyuan Wang] fix a typo 1d74448 [Daoyuan Wang] fix scala style 8d7dd22 [Daoyuan Wang] add json and python api for date type
* [SPARK-4058] [PySpark] Log file name is hard coded even though there is a ↵Kousuke Saruta2014-10-281-2/+2
| | | | | | | | | | | | variable '$LOG_FILE ' In a script 'python/run-tests', log file name is represented by a variable 'LOG_FILE' and it is used in run-tests. But, there are some hard-coded log file name in the script. Author: Kousuke Saruta <sarutak@oss.nttdata.co.jp> Closes #2905 from sarutak/SPARK-4058 and squashes the following commits: 7710490 [Kousuke Saruta] Fixed python/run-tests not to use hard-coded log file name
* [SPARK-3961] [MLlib] [PySpark] Python API for mllib.featureDavies Liu2014-10-282-57/+354
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 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
* SPARK-4022 [CORE] [MLLIB] Replace colt dependency (LGPL) with commons-mathSean Owen2014-10-271-1/+1
| | | | | | | | | | | | | | | This change replaces usages of colt with commons-math3 equivalents, and makes some minor necessary adjustments to related code and tests to match. Author: Sean Owen <sowen@cloudera.com> Closes #2928 from srowen/SPARK-4022 and squashes the following commits: 61a232f [Sean Owen] Fix failure due to different sampling in JavaAPISuite.sample() 16d66b8 [Sean Owen] Simplify seeding with call to reseedRandomGenerator a1a78e0 [Sean Owen] Use Well19937c 31c7641 [Sean Owen] Fix Python Poisson test by choosing a different seed; about 88% of seeds should work but 1 didn't, it seems 5c9c67f [Sean Owen] Additional test fixes from review d8f88e0 [Sean Owen] Replace colt with commons-math3. Some tests do not pass yet.
* [SPARK-4088] [PySpark] Python worker should exit after socket is closed by JVMDavies Liu2014-10-251-5/+7
| | | | | | | | | | | | | | In case of take() or exception in Python, python worker may exit before JVM read() all the response, then the write thread may raise "Connection reset" exception. Python should always wait JVM to close the socket first. cc JoshRosen This is a warm fix, or the tests will be flaky, sorry for that. Author: Davies Liu <davies@databricks.com> Closes #2941 from davies/fix_exit and squashes the following commits: 9d4d21e [Davies Liu] fix race
* [SPARK-4051] [SQL] [PySpark] Convert Row into dictionaryDavies Liu2014-10-242-0/+21
| | | | | | | | | | | | | | | | Added a method to Row to turn row into dict: ``` >>> row = Row(a=1) >>> row.asDict() {'a': 1} ``` Author: Davies Liu <davies@databricks.com> Closes #2896 from davies/dict and squashes the following commits: 8d97366 [Davies Liu] convert Row into dict
* [SPARK-2652] [PySpark] donot use KyroSerializer as default serializerDavies Liu2014-10-231-1/+0
| | | | | | | | | | | | KyroSerializer can not serialize customized class without registered explicitly, use it as default serializer in PySpark will introduce some regression in MLlib. cc mengxr Author: Davies Liu <davies@databricks.com> Closes #2916 from davies/revert and squashes the following commits: 43eb6d3 [Davies Liu] donot use KyroSerializer as default serializer
* [SPARK-3993] [PySpark] fix bug while reuse worker after take()Davies Liu2014-10-234-4/+32
| | | | | | | | | | | | | | After take(), maybe there are some garbage left in the socket, then next task assigned to this worker will hang because of corrupted data. We should make sure the socket is clean before reuse it, write END_OF_STREAM at the end, and check it after read out all result from python. Author: Davies Liu <davies.liu@gmail.com> Author: Davies Liu <davies@databricks.com> Closes #2838 from davies/fix_reuse and squashes the following commits: 8872914 [Davies Liu] fix tests 660875b [Davies Liu] fix bug while reuse worker after take()
* Fix for sampling error in NumPy v1.9 [SPARK-3995][PYSPARK]freeman2014-10-222-2/+8
| | | | | | | | | | | | | | Change maximum value for default seed during RDD sampling so that it is strictly less than 2 ** 32. This prevents a bug in the most recent version of NumPy, which cannot accept random seeds above this bound. Adds an extra test that uses the default seed (instead of setting it manually, as in the docstrings). mengxr Author: freeman <the.freeman.lab@gmail.com> Closes #2889 from freeman-lab/pyspark-sampling and squashes the following commits: dc385ef [freeman] Change maximum value for default seed
* SPARK-3770: Make userFeatures accessible from pythonMichelangelo D'Agostino2014-10-211-0/+31
| | | | | | | | | | | | | | | | | | | https://issues.apache.org/jira/browse/SPARK-3770 We need access to the underlying latent user features from python. However, the userFeatures RDD from the MatrixFactorizationModel isn't accessible from the python bindings. I've added a method to the underlying scala class to turn the RDD[(Int, Array[Double])] to an RDD[String]. This is then accessed from the python recommendation.py Author: Michelangelo D'Agostino <mdagostino@civisanalytics.com> Closes #2636 from mdagost/mf_user_features and squashes the following commits: c98f9e2 [Michelangelo D'Agostino] Added unit tests for userFeatures and productFeatures and merged master. d5eadf8 [Michelangelo D'Agostino] Merge branch 'master' into mf_user_features 2481a2a [Michelangelo D'Agostino] Merged master and resolved conflict. a6ffb96 [Michelangelo D'Agostino] Eliminated a function from our first approach to this problem that is no longer needed now that we added the fromTuple2RDD function. 2aa1bf8 [Michelangelo D'Agostino] Implemented a function called fromTuple2RDD in PythonMLLibAPI and used it to expose the MF userFeatures and productFeatures in python. 34cb2a2 [Michelangelo D'Agostino] A couple of lint cleanups and a comment. cdd98e3 [Michelangelo D'Agostino] It's working now. e1fbe5e [Michelangelo D'Agostino] Added scala function to stringify userFeatures for access in python.
* replace awaitTransformation with awaitTermination in scaladoc/javadocHolden Karau2014-10-211-1/+1
| | | | | | | | | Author: Holden Karau <holden@pigscanfly.ca> Closes #2861 from holdenk/SPARK-4015-Documentation-in-the-streaming-context-references-non-existent-function and squashes the following commits: 081db8a [Holden Karau] fix pyspark streaming doc too 0e03863 [Holden Karau] replace awaitTransformation with awaitTermination
* [SPARK-4023] [MLlib] [PySpark] convert rdd into RDD of VectorDavies Liu2014-10-212-4/+24
| | | | | | | | | | | | | Convert the input rdd to RDD of Vector. cc mengxr Author: Davies Liu <davies@databricks.com> Closes #2870 from davies/fix4023 and squashes the following commits: 1eac767 [Davies Liu] address comments 0871576 [Davies Liu] convert rdd into RDD of Vector
* [SPARK-3207][MLLIB]Choose splits for continuous features in DecisionTree ↵Qiping Li2014-10-201-2/+2
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | more adaptively DecisionTree splits on continuous features by choosing an array of values from a subsample of the data. Currently, it does not check for identical values in the subsample, so it could end up having multiple copies of the same split. In this PR, we choose splits for a continuous feature in 3 steps: 1. Sort sample values for this feature 2. Get number of occurrence of each distinct value 3. Iterate the value count array computed in step 2 to choose splits. After find splits, `numSplits` and `numBins` in metadata will be updated. CC: mengxr manishamde jkbradley, please help me review this, thanks. Author: Qiping Li <liqiping1991@gmail.com> Author: chouqin <liqiping1991@gmail.com> Author: liqi <liqiping1991@gmail.com> Author: qiping.lqp <qiping.lqp@alibaba-inc.com> Closes #2780 from chouqin/dt-findsplits and squashes the following commits: 18d0301 [Qiping Li] check explicitly findsplits return distinct splits 8dc28ab [chouqin] remove blank lines ffc920f [chouqin] adjust code based on comments and add more test cases 9857039 [chouqin] Merge branch 'master' of https://github.com/apache/spark into dt-findsplits d353596 [qiping.lqp] fix pyspark doc test 9e64699 [Qiping Li] fix random forest unit test 3c72913 [Qiping Li] fix random forest unit test 092efcb [Qiping Li] fix bug f69f47f [Qiping Li] fix bug ab303a4 [Qiping Li] fix bug af6dc97 [Qiping Li] fix bug 2a8267a [Qiping Li] fix bug c339a61 [Qiping Li] fix bug 369f812 [Qiping Li] fix style 8f46af6 [Qiping Li] add comments and unit test 9e7138e [Qiping Li] Merge branch 'dt-findsplits' of https://github.com/chouqin/spark into dt-findsplits 1b25a35 [Qiping Li] Merge branch 'master' of https://github.com/apache/spark into dt-findsplits 0cd744a [liqi] fix bug 3652823 [Qiping Li] fix bug af7cb79 [Qiping Li] Choose splits for continuous features in DecisionTree more adaptively
* [SPARK-3952] [Streaming] [PySpark] add Python examples in Streaming ↵Davies Liu2014-10-182-3/+15
| | | | | | | | | | | | | | | | | | Programming Guide Having Python examples in Streaming Programming Guide. Also add RecoverableNetworkWordCount example. Author: Davies Liu <davies.liu@gmail.com> Author: Davies Liu <davies@databricks.com> Closes #2808 from davies/pyguide and squashes the following commits: 8d4bec4 [Davies Liu] update readme 26a7e37 [Davies Liu] fix format 3821c4d [Davies Liu] address comments, add missing file 7e4bb8a [Davies Liu] add Python examples in Streaming Programming Guide
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