在Pyspark中将UDF余弦相似度应用于分组的ML向量时出现问题

时间：2019-02-19 22:36:58

标签： python apache-spark pyspark apache-spark-sql apache-spark-ml

将UDF（dot_group）应用于分组数据时出现错误。此UDF的目的是计算由Vector列组成的每个组的ML features之间的成对余弦相似度。根据输入数据（prediction）的cdf列进行分组。结果应为ArrayType，其中每个项目都是结果相似度，并写入cosines列。这是我的尝试：

from pyspark.sql import SparkSession
from pyspark.sql.types import *
import pyspark.sql.functions as F
from pyspark.ml.linalg import Vectors
from itertools import combinations
from numpy import linalg as LA


def g_dot(M):
    combs = combinations(M, 2)
    return [i.dot(j) /(LA.norm(i) * LA.norm(j)) \
                                            for i, j in combs]
dot_group = F.udf(g_dot, ArrayType(DoubleType()))


cdf = spark.createDataFrame(
            [(1.0, Vectors.dense([0.0, 10.0, 0.5])), 
             (0.0, Vectors.dense([0.0, 1.0, 0.5])),
             (1.0, Vectors.dense([0.0, 10.0, 0.1])),
             (0.0, Vectors.dense([10.0, 10.0, 0.5])),
             (1.0, Vectors.dense([0.0, 0.0, 0.5])),],
            ["prediction", "features"])

dfs = cdf.groupBy(["prediction"]) \
         .agg(F.collect_list("features").alias("data")) \
         .withColumn("cosines", dot_group("data"))
dfs.show()

...出现以下错误。我不确定为什么会出现此错误，但是似乎在序列化numpy操作时遇到问题：

19/02/19 16:21:39 ERROR Executor: Exception in task 0.0 in stage 2093.0 (TID 1185)
net.razorvine.pickle.PickleException: expected zero arguments for construction of ClassDict (for numpy.dtype)
        at net.razorvine.pickle.objects.ClassDictConstructor.construct(ClassDictConstructor.java:23)
        at net.razorvine.pickle.Unpickler.load_reduce(Unpickler.java:707)
        at net.razorvine.pickle.Unpickler.dispatch(Unpickler.java:175)
        at net.razorvine.pickle.Unpickler.load(Unpickler.java:99)
        at net.razorvine.pickle.Unpickler.loads(Unpickler.java:112)
        at org.apache.spark.sql.execution.python.BatchEvalPythonExec$$anonfun$doExecute$1$$anonfun$apply$6.apply(BatchEvalPythonExec.scala:156)
        at org.apache.spark.sql.execution.python.BatchEvalPythonExec$$anonfun$doExecute$1$$anonfun$apply$6.apply(BatchEvalPythonExec.scala:155)
        at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:434)
        at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
        at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
        at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
        at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
        at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:395)
        at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:234)
        at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:228)
        at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827)
        at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827)
        at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
        at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
        at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
        at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
        at org.apache.spark.scheduler.Task.run(Task.scala:108)
        at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:338)
        at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
        at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
        at java.lang.Thread.run(Thread.java:748)
19/02/19 16:21:39 WARN TaskSetManager: Lost task 0.0 in stage 2093.0 (TID 1185, localhost, executor driver): net.razorvine.pickle.PickleException: expected zero arguments for construction of ClassDict (for numpy.dtype)

...
19/02/19 16:21:39 ERROR TaskSetManager: Task 0 in stage 2093.0 failed 1 times; aborting job
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Aplication/spark/spark-2.2.1-bin-hadoop2.7/python/pyspark/sql/dataframe.py", line 336, in show
    print(self._jdf.showString(n, 20))
  File "/Aplication/spark/spark-2.2.1-bin-hadoop2.7/python/lib/py4j-0.10.4-src.zip/py4j/java_gateway.py", line 1133, in __call__
  File "/Aplication/spark/spark-2.2.1-bin-hadoop2.7/python/pyspark/sql/utils.py", line 63, in deco
    return f(*a, **kw)
  File "/Aplication/spark/spark-2.2.1-bin-hadoop2.7/python/lib/py4j-0.10.4-src.zip/py4j/protocol.py", line 319, in get_return_value
py4j.protocol.Py4JJavaError: An error occurred while calling o2000.showString.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 2093.0 failed 1 times, most recent failure: Lost task 0.0 in stage 2093.0 (TID 1185, localhost, executor driver): net.razorvine.pickle.PickleException: expected zero arguments for construction of ClassDict (for numpy.dtype)
        at net.razorvine.pickle.objects.ClassDictConstructor.construct(ClassDictConstructor.java:23)
        at net.razorvine.pickle.Unpickler.load_reduce(Unpickler.java:707)
        at net.razorvine.pickle.Unpickler.dispatch(Unpickler.java:175)
        at net.razorvine.pickle.Unpickler.load(Unpickler.java:99)
        at net.razorvine.pickle.Unpickler.loads(Unpickler.java:112)
        at org.apache.spark.sql.execution.python.BatchEvalPythonExec$$anonfun$doExecute$1$$anonfun$apply$6.apply(BatchEvalPythonExec.scala:156)
        at org.apache.spark.sql.execution.python.BatchEvalPythonExec$$anonfun$doExecute$1$$anonfun$apply$6.apply(BatchEvalPythonExec.scala:155)
        at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:434)
        at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
        at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
        at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
        at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
        at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:395)
        at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:234)
        at org.apache.spark.sql.execution.SparkPlan$$anonfun$2.apply(SparkPlan.scala:228)
        at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827)
        at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$1$$anonfun$apply$25.apply(RDD.scala:827)
        at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
        at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
        at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
        at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
        at org.apache.spark.scheduler.Task.run(Task.scala:108)
        at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:338)
        at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1149)
        at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:624)
        at java.lang.Thread.run(Thread.java:748)

1 个答案:

答案 0 :(得分：1)

这是因为Spark SQL不支持NumPy类型。返回

之前，应将值转换为@F.udf(ArrayType(DoubleType())) def dot_group(M): combs = combinations(M, 2) return [ # or float(i.dot(j) / (LA.norm(i) * LA.norm(j))) (i.dot(j) / (LA.norm(i) * LA.norm(j))).tolist() for i, j in combs ] 
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