как конвертировать датафрейм в BlockMatrix в pyspark - PullRequest
0 голосов
/ 26 июня 2018

Я пытаюсь вычислить матрицу сходства пользователей по их метаданным.После этого вопроса я нашел вектор данных TF-IDF.После этого я его нормализовал.Результат приведен ниже

data.select('norm').take(10)

это результат

Однако, когда я попытался получить blockMatrix из кадра данных по этому коду

mat = IndexedRowMatrix(
    data.select("memberid", "norm").rdd.map(lambda row: IndexedRow(row.memberid, row.norm.toArray()))).toBlockMatrix()

Я получил следующую ошибку

---------------------------------------------------------------------------
Py4JJavaError                             Traceback (most recent call last)
<ipython-input-123-32e41e450739> in <module>()
      1 from pyspark.mllib.linalg.distributed import IndexedRow, IndexedRowMatrix
      2 mat = IndexedRowMatrix(
----> 3     data.select("memberid", "norm").rdd.map(lambda row: IndexedRow(row.memberid, row.norm.toArray()))).toBlockMatrix()

~/spark/python/pyspark/mllib/linalg/distributed.py in __init__(self, rows, numRows, numCols)
    374             # both be easily serialized.  We will convert back to
    375             # IndexedRows on the Scala side.
--> 376             java_matrix = callMLlibFunc("createIndexedRowMatrix", rows.toDF(),
    377                                         long(numRows), int(numCols))
    378         elif (isinstance(rows, JavaObject)

~/spark/python/pyspark/sql/session.py in toDF(self, schema, sampleRatio)
     55         [Row(name=u'Alice', age=1)]
     56         """
---> 57         return sparkSession.createDataFrame(self, schema, sampleRatio)
     58 
     59     RDD.toDF = toDF

~/spark/python/pyspark/sql/session.py in createDataFrame(self, data, schema, samplingRatio, verifySchema)
    520 
    521         if isinstance(data, RDD):
--> 522             rdd, schema = self._createFromRDD(data.map(prepare), schema, samplingRatio)
    523         else:
    524             rdd, schema = self._createFromLocal(map(prepare, data), schema)

~/spark/python/pyspark/sql/session.py in _createFromRDD(self, rdd, schema, samplingRatio)
    358         """
    359         if schema is None or isinstance(schema, (list, tuple)):
--> 360             struct = self._inferSchema(rdd, samplingRatio)
    361             converter = _create_converter(struct)
    362             rdd = rdd.map(converter)

~/spark/python/pyspark/sql/session.py in _inferSchema(self, rdd, samplingRatio)
    329         :return: :class:`pyspark.sql.types.StructType`
    330         """
--> 331         first = rdd.first()
    332         if not first:
    333             raise ValueError("The first row in RDD is empty, "

~/spark/python/pyspark/rdd.py in first(self)
   1326         ValueError: RDD is empty
   1327         """
-> 1328         rs = self.take(1)
   1329         if rs:
   1330             return rs[0]

~/spark/python/pyspark/rdd.py in take(self, num)
   1308 
   1309             p = range(partsScanned, min(partsScanned + numPartsToTry, totalParts))
-> 1310             res = self.context.runJob(self, takeUpToNumLeft, p)
   1311 
   1312             items += res

~/spark/python/pyspark/context.py in runJob(self, rdd, partitionFunc, partitions, allowLocal)
    931         # SparkContext#runJob.
    932         mappedRDD = rdd.mapPartitions(partitionFunc)
--> 933         port = self._jvm.PythonRDD.runJob(self._jsc.sc(), mappedRDD._jrdd, partitions)
    934         return list(_load_from_socket(port, mappedRDD._jrdd_deserializer))
    935 

~/spark/python/lib/py4j-0.10.3-src.zip/py4j/java_gateway.py in __call__(self, *args)
   1131         answer = self.gateway_client.send_command(command)
   1132         return_value = get_return_value(
-> 1133             answer, self.gateway_client, self.target_id, self.name)
   1134 
   1135         for temp_arg in temp_args:

~/spark/python/pyspark/sql/utils.py in deco(*a, **kw)
     61     def deco(*a, **kw):
     62         try:
---> 63             return f(*a, **kw)
     64         except py4j.protocol.Py4JJavaError as e:
     65             s = e.java_exception.toString()

~/spark/python/lib/py4j-0.10.3-src.zip/py4j/protocol.py in get_return_value(answer, gateway_client, target_id, name)
    317                 raise Py4JJavaError(
    318                     "An error occurred while calling {0}{1}{2}.\n".
--> 319                     format(target_id, ".", name), value)
    320             else:
    321                 raise Py4JError(

Py4JJavaError: An error occurred while calling z:org.apache.spark.api.python.PythonRDD.runJob.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 105.0 failed 1 times, most recent failure: Lost task 0.0 in stage 105.0 (TID 1063, localhost): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
  File "/home/aziz/spark/python/lib/pyspark.zip/pyspark/worker.py", line 172, in main
    process()
  File "/home/aziz/spark/python/lib/pyspark.zip/pyspark/worker.py", line 167, in process
    serializer.dump_stream(func(split_index, iterator), outfile)
  File "/home/aziz/spark/python/lib/pyspark.zip/pyspark/serializers.py", line 263, in dump_stream
    vs = list(itertools.islice(iterator, batch))
  File "/home/aziz/spark/python/pyspark/rdd.py", line 1306, in takeUpToNumLeft
    yield next(iterator)
  File "<ipython-input-123-32e41e450739>", line 3, in <lambda>
  File "/home/aziz/spark/python/lib/pyspark.zip/pyspark/mllib/linalg/distributed.py", line 313, in __init__
    self.index = long(index)
ValueError: invalid literal for int() with base 10: '006CCBB6-2304-4A52-8DAD-A88729FCC79F'

    at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:193)
    at org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:234)
    at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:152)
    at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:63)
    at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:319)
    at org.apache.spark.rdd.RDD.iterator(RDD.scala:283)
    at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:70)
    at org.apache.spark.scheduler.Task.run(Task.scala:86)
    at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:274)
    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) 
Добро пожаловать на сайт PullRequest, где вы можете задавать вопросы и получать ответы от других членов сообщества.
...