将 UDF 余弦相似度应用于 Pyspark 中的分组 ML 向量的问题
Problem applying UDF cosine similarity to grouped ML vectors in Pyspark
我在将 UDF (dot_group) 应用于分组数据时遇到错误。该 UDF 旨在计算由 features 列组成的每个组的 ML Vector 之间的成对余弦相似度。分组是根据输入数据的 prediction 列 (cdf) 进行的。结果应该是 ArrayType，其中每个项目都是结果相似性，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()
... 这给出了以下错误。我不确定为什么会出现此错误，但似乎序列化 numpy 操作存在问题：
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$$anonfun$doExecute$$anonfun$apply.apply(BatchEvalPythonExec.scala:156)
at org.apache.spark.sql.execution.python.BatchEvalPythonExec$$anonfun$doExecute$$anonfun$apply.apply(BatchEvalPythonExec.scala:155)
at scala.collection.Iterator$$anon.nextCur(Iterator.scala:434)
at scala.collection.Iterator$$anon.hasNext(Iterator.scala:440)
at scala.collection.Iterator$$anon.hasNext(Iterator.scala:408)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$$anon.hasNext(WholeStageCodegenExec.scala:395)
at org.apache.spark.sql.execution.SparkPlan$$anonfun.apply(SparkPlan.scala:234)
at org.apache.spark.sql.execution.SparkPlan$$anonfun.apply(SparkPlan.scala:228)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$$anonfun$apply.apply(RDD.scala:827)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$$anonfun$apply.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$$anonfun$apply.apply(BatchEvalPythonExec.scala:156)
at org.apache.spark.sql.execution.python.BatchEvalPythonExec$$anonfun$doExecute$$anonfun$apply.apply(BatchEvalPythonExec.scala:155)
at scala.collection.Iterator$$anon.nextCur(Iterator.scala:434)
at scala.collection.Iterator$$anon.hasNext(Iterator.scala:440)
at scala.collection.Iterator$$anon.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$$anonfun$$anon.hasNext(WholeStageCodegenExec.scala:395)
at org.apache.spark.sql.execution.SparkPlan$$anonfun.apply(SparkPlan.scala:234)
at org.apache.spark.sql.execution.SparkPlan$$anonfun.apply(SparkPlan.scala:228)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$$anonfun$apply.apply(RDD.scala:827)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$$anonfun$apply.apply(RDD.scala:827)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:108)
at org.apache.spark.executor.Executor$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)
那是因为 Spark SQL 不支持 NumPy 类型。在返回
之前，您应该将值转换为 float
@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
]
我在将 UDF (dot_group) 应用于分组数据时遇到错误。该 UDF 旨在计算由 features 列组成的每个组的 ML Vector 之间的成对余弦相似度。分组是根据输入数据的 prediction 列 (cdf) 进行的。结果应该是 ArrayType，其中每个项目都是结果相似性，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()
... 这给出了以下错误。我不确定为什么会出现此错误，但似乎序列化 numpy 操作存在问题：
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$$anonfun$doExecute$$anonfun$apply.apply(BatchEvalPythonExec.scala:156)
at org.apache.spark.sql.execution.python.BatchEvalPythonExec$$anonfun$doExecute$$anonfun$apply.apply(BatchEvalPythonExec.scala:155)
at scala.collection.Iterator$$anon.nextCur(Iterator.scala:434)
at scala.collection.Iterator$$anon.hasNext(Iterator.scala:440)
at scala.collection.Iterator$$anon.hasNext(Iterator.scala:408)
at org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)
at org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)
at org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$$anon.hasNext(WholeStageCodegenExec.scala:395)
at org.apache.spark.sql.execution.SparkPlan$$anonfun.apply(SparkPlan.scala:234)
at org.apache.spark.sql.execution.SparkPlan$$anonfun.apply(SparkPlan.scala:228)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$$anonfun$apply.apply(RDD.scala:827)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$$anonfun$apply.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$$anonfun$apply.apply(BatchEvalPythonExec.scala:156)
at org.apache.spark.sql.execution.python.BatchEvalPythonExec$$anonfun$doExecute$$anonfun$apply.apply(BatchEvalPythonExec.scala:155)
at scala.collection.Iterator$$anon.nextCur(Iterator.scala:434)
at scala.collection.Iterator$$anon.hasNext(Iterator.scala:440)
at scala.collection.Iterator$$anon.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$$anonfun$$anon.hasNext(WholeStageCodegenExec.scala:395)
at org.apache.spark.sql.execution.SparkPlan$$anonfun.apply(SparkPlan.scala:234)
at org.apache.spark.sql.execution.SparkPlan$$anonfun.apply(SparkPlan.scala:228)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$$anonfun$apply.apply(RDD.scala:827)
at org.apache.spark.rdd.RDD$$anonfun$mapPartitionsInternal$$anonfun$apply.apply(RDD.scala:827)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:108)
at org.apache.spark.executor.Executor$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)
那是因为 Spark SQL 不支持 NumPy 类型。在返回
之前，您应该将值转换为float
@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
]