Thanks Stephen, saw that, but this is already released version of spark-sklearn-0.3.0, tests should be working.
So just checking if I am doing anything wrong, version of other libraries etc.. Thanks Sudhir > On Apr 8, 2019, at 1:52 PM, Stephen Boesch <java...@gmail.com> wrote: > > There are several suggestions on this SOF > https://stackoverflow.com/questions/38984775/spark-errorexpected-zero-arguments-for-construction-of-classdict-for-numpy-cor > > 1 > > You need to convert the final value to a python list. You implement the > function as follows: > > def uniq_array(col_array): > x = np.unique(col_array) > return list(x) > This is because Spark doesn't understand the numpy array format. In order to > feed a python object that Spark DataFrames understand as an ArrayType, you > need to convert the output to a python list before returning it. > > > > > > > > The source of the problem is that object returned from the UDF doesn't > conform to the declared type. np.unique not only returns numpy.ndarray but > also converts numerics to the corresponding NumPy types which are not > compatible with DataFrame API. You can try something like this: > > udf(lambda x: list(set(x)), ArrayType(IntegerType())) > or this (to keep order) > > udf(lambda xs: list(OrderedDict((x, None) for x in xs)), > ArrayType(IntegerType())) > instead. > > If you really want np.unique you have to convert the output: > > udf(lambda x: np.unique(x).tolist(), ArrayType(IntegerType())) > > > > > > > > > > > > >> Am Mo., 8. Apr. 2019 um 11:43 Uhr schrieb Sudhir Babu Pothineni >> <sbpothin...@gmail.com>: >> >> >> >>> Trying to run tests in spark-sklearn, anybody check the below exception >>> >>> pip freeze: >>> >>> nose==1.3.7 >>> numpy==1.16.1 >>> pandas==0.19.2 >>> python-dateutil==2.7.5 >>> pytz==2018.9 >>> scikit-learn==0.19.2 >>> scipy==1.2.0 >>> six==1.12.0 >>> spark-sklearn==0.3.0 >>> >>> Spark version: >>> spark-2.2.3-bin-hadoop2.6/bin/pyspark >>> >>> >>> running into following exception: >>> >>> ====================================================================== >>> ERROR: test_scipy_sparse (spark_sklearn.converter_test.CSRVectorUDTTests) >>> ---------------------------------------------------------------------- >>> Traceback (most recent call last): >>> File >>> "/home/spothineni/Downloads/spark-sklearn-release-0.3.0/python/spark_sklearn/converter_test.py", >>> line 83, in test_scipy_sparse >>> self.assertEqual(df.count(), 1) >>> File >>> "/home/spothineni/Downloads/spark-2.4.1-bin-hadoop2.6/python/pyspark/sql/dataframe.py", >>> line 522, in count >>> return int(self._jdf.count()) >>> File >>> "/home/spothineni/Downloads/spark-2.4.1-bin-hadoop2.6/python/lib/py4j-0.10.7-src.zip/py4j/java_gateway.py", >>> line 1257, in __call__ >>> answer, self.gateway_client, self.target_id, self.name) >>> File >>> "/home/spothineni/Downloads/spark-2.4.1-bin-hadoop2.6/python/pyspark/sql/utils.py", >>> line 63, in deco >>> return f(*a, **kw) >>> File >>> "/home/spothineni/Downloads/spark-2.4.1-bin-hadoop2.6/python/lib/py4j-0.10.7-src.zip/py4j/protocol.py", >>> line 328, in get_return_value >>> format(target_id, ".", name), value) >>> Py4JJavaError: An error occurred while calling o652.count. >>> : org.apache.spark.SparkException: Job aborted due to stage failure: Task >>> 11 in stage 0.0 failed 1 times, most recent failure: Lost task 11.0 in >>> stage 0.0 (TID 11, 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.api.python.SerDeUtil$$anonfun$pythonToJava$1$$anonfun$apply$1.apply(SerDeUtil.scala:188) >>> at >>> org.apache.spark.api.python.SerDeUtil$$anonfun$pythonToJava$1$$anonfun$apply$1.apply(SerDeUtil.scala:187) >>> at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:435) >>> at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:441) >>> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409) >>> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409) >>> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409) >>> at >>> org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.agg_doAggregateWithoutKey_0$(Unknown >>> Source) >>> at >>> org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown >>> Source) >>> at >>> org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43) >>> at >>> org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$13$$anon$1.hasNext(WholeStageCodegenExec.scala:636) >>> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409) >>> at >>> org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:125) >>> at >>> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99) >>> at >>> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:55) >>> at org.apache.spark.scheduler.Task.run(Task.scala:121) >>> at >>> org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:403) >>> at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360) >>> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:409) >>> at >>> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) >>> at >>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) >>> at java.lang.Thread.run(Thread.java:745) >>> >>> Driver stacktrace: >>> at >>> org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1889) >>> at >>> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1877) >>> at >>> org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1876) >>> at >>> scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) >>> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48) >>> at >>> org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1876) >>> at >>> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926) >>> at >>> org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:926) >>> at scala.Option.foreach(Option.scala:257) >>> at >>> org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:926) >>> at >>> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:2110) >>> at >>> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2059) >>> at >>> org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:2048) >>> at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:49) >>> at >>> org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:737) >>> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2061) >>> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2082) >>> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2101) >>> at org.apache.spark.SparkContext.runJob(SparkContext.scala:2126) >>> at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:945) >>> at >>> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151) >>> at >>> org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112) >>> at org.apache.spark.rdd.RDD.withScope(RDD.scala:363) >>> at org.apache.spark.rdd.RDD.collect(RDD.scala:944) >>> at >>> org.apache.spark.sql.execution.SparkPlan.executeCollect(SparkPlan.scala:299) >>> at >>> org.apache.spark.sql.Dataset$$anonfun$count$1.apply(Dataset.scala:2830) >>> at >>> org.apache.spark.sql.Dataset$$anonfun$count$1.apply(Dataset.scala:2829) >>> at org.apache.spark.sql.Dataset$$anonfun$53.apply(Dataset.scala:3364) >>> at >>> org.apache.spark.sql.execution.SQLExecution$$anonfun$withNewExecutionId$1.apply(SQLExecution.scala:78) >>> at >>> org.apache.spark.sql.execution.SQLExecution$.withSQLConfPropagated(SQLExecution.scala:125) >>> at >>> org.apache.spark.sql.execution.SQLExecution$.withNewExecutionId(SQLExecution.scala:73) >>> at org.apache.spark.sql.Dataset.withAction(Dataset.scala:3363) >>> at org.apache.spark.sql.Dataset.count(Dataset.scala:2829) >>> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method) >>> at >>> sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62) >>> at >>> sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43) >>> at java.lang.reflect.Method.invoke(Method.java:498) >>> at py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244) >>> at py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357) >>> at py4j.Gateway.invoke(Gateway.java:282) >>> at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132) >>> at py4j.commands.CallCommand.execute(CallCommand.java:79) >>> at py4j.GatewayConnection.run(GatewayConnection.java:238) >>> at java.lang.Thread.run(Thread.java:745) >>> Caused by: 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.api.python.SerDeUtil$$anonfun$pythonToJava$1$$anonfun$apply$1.apply(SerDeUtil.scala:188) >>> at >>> org.apache.spark.api.python.SerDeUtil$$anonfun$pythonToJava$1$$anonfun$apply$1.apply(SerDeUtil.scala:187) >>> at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:435) >>> at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:441) >>> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409) >>> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409) >>> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409) >>> at >>> org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.agg_doAggregateWithoutKey_0$(Unknown >>> Source) >>> at >>> org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIteratorForCodegenStage1.processNext(Unknown >>> Source) >>> at >>> org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43) >>> at >>> org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$13$$anon$1.hasNext(WholeStageCodegenExec.scala:636) >>> at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:409) >>> at >>> org.apache.spark.shuffle.sort.BypassMergeSortShuffleWriter.write(BypassMergeSortShuffleWriter.java:125) >>> at >>> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:99) >>> at >>> org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:55) >>> at org.apache.spark.scheduler.Task.run(Task.scala:121) >>> at >>> org.apache.spark.executor.Executor$TaskRunner$$anonfun$10.apply(Executor.scala:403) >>> at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1360) >>> at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:409) >>> at >>> java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) >>> at >>> java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) >>> ... 1 more >>> >>>
