[SPARK-4897] [PySpark] Python 3 support

This PR update PySpark to support Python 3 (tested with 3.4).

Known issue: unpickle array from Pyrolite is broken in Python 3, those tests are skipped.

TODO: ec2/spark-ec2.py is not fully tested with python3.

Author: Davies Liu <davies@databricks.com>
Author: twneale <twneale@gmail.com>
Author: Josh Rosen <joshrosen@databricks.com>

Closes #5173 from davies/python3 and squashes the following commits:

d7d6323 [Davies Liu] fix tests
6c52a98 [Davies Liu] fix mllib test
99e334f [Davies Liu] update timeout
b716610 [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3
cafd5ec [Davies Liu] adddress comments from @mengxr
bf225d7 [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3
179fc8d [Davies Liu] tuning flaky tests
8c8b957 [Davies Liu] fix ResourceWarning in Python 3
5c57c95 [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3
4006829 [Davies Liu] fix test
2fc0066 [Davies Liu] add python3 path
71535e9 [Davies Liu] fix xrange and divide
5a55ab4 [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3
125f12c [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3
ed498c8 [Davies Liu] fix compatibility with python 3
820e649 [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3
e8ce8c9 [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3
ad7c374 [Davies Liu] fix mllib test and warning
ef1fc2f [Davies Liu] fix tests
4eee14a [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3
20112ff [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3
59bb492 [Davies Liu] fix tests
1da268c [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3
ca0fdd3 [Davies Liu] fix code style
9563a15 [Davies Liu] add imap back for python 2
0b1ec04 [Davies Liu] make python examples work with Python 3
d2fd566 [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3
a716d34 [Davies Liu] test with python 3.4
f1700e8 [Davies Liu] fix test in python3
671b1db [Davies Liu] fix test in python3
692ff47 [Davies Liu] fix flaky test
7b9699f [Davies Liu] invalidate import cache for Python 3.3+
9c58497 [Davies Liu] fix kill worker
309bfbf [Davies Liu] keep compatibility
5707476 [Davies Liu] cleanup, fix hash of string in 3.3+
8662d5b [Davies Liu] Merge branch 'master' of github.com:apache/spark into python3
f53e1f0 [Davies Liu] fix tests
70b6b73 [Davies Liu] compile ec2/spark_ec2.py in python 3
a39167e [Davies Liu] support customize class in __main__
814c77b [Davies Liu] run unittests with python 3
7f4476e [Davies Liu] mllib tests passed
d737924 [Davies Liu] pass ml tests
375ea17 [Davies Liu] SQL tests pass
6cc42a9 [Davies Liu] rename
431a8de [Davies Liu] streaming tests pass
78901a7 [Davies Liu] fix hash of serializer in Python 3
24b2f2e [Davies Liu] pass all RDD tests
35f48fe [Davies Liu] run future again
1eebac2 [Davies Liu] fix conflict in ec2/spark_ec2.py
6e3c21d [Davies Liu] make cloudpickle work with Python3
2fb2db3 [Josh Rosen] Guard more changes behind sys.version; still doesn't run
1aa5e8f [twneale] Turned out `pickle.DictionaryType is dict` == True, so swapped it out
7354371 [twneale] buffer --> memoryview  I'm not super sure if this a valid change, but the 2.7 docs recommend using memoryview over buffer where possible, so hoping it'll work.
b69ccdf [twneale] Uses the pure python pickle._Pickler instead of c-extension _pickle.Pickler. It appears pyspark 2.7 uses the pure python pickler as well, so this shouldn't degrade pickling performance (?).
f40d925 [twneale] xrange --> range
e104215 [twneale] Replaces 2.7 types.InstsanceType with 3.4 `object`....could be horribly wrong depending on how types.InstanceType is used elsewhere in the package--see http://bugs.python.org/issue8206
79de9d0 [twneale] Replaces python2.7 `file` with 3.4 _io.TextIOWrapper
2adb42d [Josh Rosen] Fix up some import differences between Python 2 and 3
854be27 [Josh Rosen] Run `futurize` on Python code:
7c5b4ce [Josh Rosen] Remove Python 3 check in shell.py.

2 files changed, 30 insertions, 18 deletions

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/MatrixFactorizationModelWrapper.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/MatrixFactorizationModelWrapper.scala
index ecd3b16598..534edac56b 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/MatrixFactorizationModelWrapper.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/MatrixFactorizationModelWrapper.scala
@@ -18,6 +18,7 @@
 package org.apache.spark.mllib.api.python
 
 import org.apache.spark.api.java.JavaRDD
+import org.apache.spark.mllib.linalg.Vectors
 import org.apache.spark.mllib.recommendation.{MatrixFactorizationModel, Rating}
 import org.apache.spark.rdd.RDD
 
@@ -31,10 +32,14 @@ private[python] class MatrixFactorizationModelWrapper(model: MatrixFactorization
     predict(SerDe.asTupleRDD(userAndProducts.rdd))
 
   def getUserFeatures: RDD[Array[Any]] = {
-    SerDe.fromTuple2RDD(userFeatures.asInstanceOf[RDD[(Any, Any)]])
+    SerDe.fromTuple2RDD(userFeatures.map {
+      case (user, feature) => (user, Vectors.dense(feature))
+    }.asInstanceOf[RDD[(Any, Any)]])
   }
 
   def getProductFeatures: RDD[Array[Any]] = {
-    SerDe.fromTuple2RDD(productFeatures.asInstanceOf[RDD[(Any, Any)]])
+    SerDe.fromTuple2RDD(productFeatures.map {
+      case (product, feature) => (product, Vectors.dense(feature))
+    }.asInstanceOf[RDD[(Any, Any)]])
   }
 }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
index ab15f0f36a..f976d2f97b 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala
@@ -28,7 +28,6 @@ import scala.reflect.ClassTag
 
 import net.razorvine.pickle._
 
-import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.api.java.{JavaRDD, JavaSparkContext}
 import org.apache.spark.api.python.SerDeUtil
 import org.apache.spark.mllib.classification._
@@ -40,15 +39,15 @@ import org.apache.spark.mllib.optimization._
 import org.apache.spark.mllib.random.{RandomRDDs => RG}
 import org.apache.spark.mllib.recommendation._
 import org.apache.spark.mllib.regression._
-import org.apache.spark.mllib.stat.{MultivariateStatisticalSummary, Statistics}
 import org.apache.spark.mllib.stat.correlation.CorrelationNames
 import org.apache.spark.mllib.stat.distribution.MultivariateGaussian
 import org.apache.spark.mllib.stat.test.ChiSqTestResult
-import org.apache.spark.mllib.tree.{GradientBoostedTrees, RandomForest, DecisionTree}
-import org.apache.spark.mllib.tree.configuration.{BoostingStrategy, Algo, Strategy}
+import org.apache.spark.mllib.stat.{MultivariateStatisticalSummary, Statistics}
+import org.apache.spark.mllib.tree.configuration.{Algo, BoostingStrategy, Strategy}
 import org.apache.spark.mllib.tree.impurity._
 import org.apache.spark.mllib.tree.loss.Losses
-import org.apache.spark.mllib.tree.model.{GradientBoostedTreesModel, RandomForestModel, DecisionTreeModel}
+import org.apache.spark.mllib.tree.model.{DecisionTreeModel, GradientBoostedTreesModel, RandomForestModel}
+import org.apache.spark.mllib.tree.{DecisionTree, GradientBoostedTrees, RandomForest}
 import org.apache.spark.mllib.util.MLUtils
 import org.apache.spark.rdd.RDD
 import org.apache.spark.storage.StorageLevel
@@ -279,7 +278,7 @@ private[python] class PythonMLLibAPI extends Serializable {
       data: JavaRDD[LabeledPoint],
       lambda: Double): JList[Object] = {
     val model = NaiveBayes.train(data.rdd, lambda)
-    List(Vectors.dense(model.labels), Vectors.dense(model.pi), model.theta).
+    List(Vectors.dense(model.labels), Vectors.dense(model.pi), model.theta.map(Vectors.dense)).
       map(_.asInstanceOf[Object]).asJava
   }
 
@@ -335,7 +334,7 @@ private[python] class PythonMLLibAPI extends Serializable {
           mu += model.gaussians(i).mu
           sigma += model.gaussians(i).sigma
       }    
-      List(wt.toArray, mu.toArray, sigma.toArray).map(_.asInstanceOf[Object]).asJava
+      List(Vectors.dense(wt.toArray), mu.toArray, sigma.toArray).map(_.asInstanceOf[Object]).asJava
     } finally {
       data.rdd.unpersist(blocking = false)
     }
@@ -346,20 +345,20 @@ private[python] class PythonMLLibAPI extends Serializable {
    */
   def predictSoftGMM(
       data: JavaRDD[Vector],
-      wt: Object,
+      wt: Vector,
       mu: Array[Object],
-      si: Array[Object]):  RDD[Array[Double]]  = {
+      si: Array[Object]):  RDD[Vector]  = {
 
-      val weight = wt.asInstanceOf[Array[Double]]
+      val weight = wt.toArray
       val mean = mu.map(_.asInstanceOf[DenseVector])
       val sigma = si.map(_.asInstanceOf[DenseMatrix])
       val gaussians = Array.tabulate(weight.length){
         i => new MultivariateGaussian(mean(i), sigma(i))
       }      
       val model = new GaussianMixtureModel(weight, gaussians)
-      model.predictSoft(data)
+      model.predictSoft(data).map(Vectors.dense)
   }
-  
+
   /**
    * Java stub for Python mllib ALS.train().  This stub returns a handle
    * to the Java object instead of the content of the Java object.  Extra care
@@ -936,6 +935,14 @@ private[spark] object SerDe extends Serializable {
       out.write(code)
     }
 
+    protected def getBytes(obj: Object): Array[Byte] = {
+      if (obj.getClass.isArray) {
+        obj.asInstanceOf[Array[Byte]]
+      } else {
+        obj.asInstanceOf[String].getBytes(LATIN1)
+      }
+    }
+
     private[python] def saveState(obj: Object, out: OutputStream, pickler: Pickler)
   }
 
@@ -961,7 +968,7 @@ private[spark] object SerDe extends Serializable {
       if (args.length != 1) {
         throw new PickleException("should be 1")
       }
-      val bytes = args(0).asInstanceOf[String].getBytes(LATIN1)
+      val bytes = getBytes(args(0))
       val bb = ByteBuffer.wrap(bytes, 0, bytes.length)
       bb.order(ByteOrder.nativeOrder())
       val db = bb.asDoubleBuffer()
@@ -994,7 +1001,7 @@ private[spark] object SerDe extends Serializable {
       if (args.length != 3) {
         throw new PickleException("should be 3")
       }
-      val bytes = args(2).asInstanceOf[String].getBytes(LATIN1)
+      val bytes = getBytes(args(2))
       val n = bytes.length / 8
       val values = new Array[Double](n)
       val order = ByteOrder.nativeOrder()
@@ -1031,8 +1038,8 @@ private[spark] object SerDe extends Serializable {
         throw new PickleException("should be 3")
       }
       val size = args(0).asInstanceOf[Int]
-      val indiceBytes = args(1).asInstanceOf[String].getBytes(LATIN1)
-      val valueBytes = args(2).asInstanceOf[String].getBytes(LATIN1)
+      val indiceBytes = getBytes(args(1))
+      val valueBytes = getBytes(args(2))
       val n = indiceBytes.length / 4
       val indices = new Array[Int](n)
       val values = new Array[Double](n)