在pyspark中预处理数据 [英] Preprocessing data in pyspark
本文介绍了在pyspark中预处理数据的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
已经查看了spark/example目录中的kmeans示例，我试图对一组纬度和经度数据进行K-means聚类.我已将.csv数据导入到spark数据框(约100万行)中，并尝试读取该数据框作为我的k均值模型的输入，但是我仍然遇到错误.
having looked at the kmeans example in the spark/example directory, I am trying to do K-means clustering on a set of latitude and longitude data. I have imported .csv data into a spark dataframe (~1M rows) and attempted to read the dataframe as an input to my k-means model however I keep getting an error.
我的spark数据框如下所示:
My spark dataframe looks like:
ID col1 col2 Latitude Longitude
ford ... ... 22.2 13.5
landrover ... ... 21.4 13.8
mercedes ... ... 21.8 14.1
bmw ... ... 28.9 18.0
... ... ... .... ....
这是我的代码:
from pyspark.ml.clustering import KMeans
from pyspark.ml.linalg import Vectors
df = spark.read.csv('file.csv')
spark_rdd = df.rdd.sortByKey()
parsedData = spark_rdd.map(lambda x: Vectors.dense(x[3],x[4])).sortByKey()
kmeans = KMeans().setK(2).setSeed(1)
model = kmeans.fit(parsedData)
sum_of_squared_errors = model.computeCost(parsedData)
print str(sum_of_squared_errors)
centers = model.clusterCenters()
for center in centers:
print(center)
我得到的错误如下:
Py4JJavaError Traceback (most recent call last)
<ipython-input-32-76d5a466dc4c> in <module>()
3
4 spark_rdd = df.rdd.sortByKey()
----> 5 parsedData = spark_rdd.map(lambda x: Vectors.dense(x[3],x[4])).sortByKey()
6
~/Downloads/spark-2.1.0-bin-hadoop2.7/python/pyspark/rdd.pyc in sortByKey(self, ascending, numPartitions, keyfunc)
660 # the key-space into bins such that the bins have roughly the same
661 # number of (key, value) pairs falling into them
--> 662 rddSize = self.count()
663 if not rddSize:
664 return self # empty RDD
~/Downloads/spark-2.1.0-bin-hadoop2.7/python/pyspark/rdd.pyc in count(self)
1039 3
1040 """
-> 1041 return self.mapPartitions(lambda i: [sum(1 for _ in i)]).sum()
1042
1043 def stats(self):
~/Downloads/spark-2.1.0-bin-hadoop2.7/python/pyspark/rdd.pyc in sum(self)
1030 6.0
1031 """
-> 1032 return self.mapPartitions(lambda x: [sum(x)]).fold(0, operator.add)
1033
1034 def count(self):
~/Downloads/spark-2.1.0-bin-hadoop2.7/python/pyspark/rdd.pyc in fold(self, zeroValue, op)
904 # zeroValue provided to each partition is unique from the one provided
905 # to the final reduce call
--> 906 vals = self.mapPartitions(func).collect()
907 return reduce(op, vals, zeroValue)
908
~/Downloads/spark-2.1.0-bin-hadoop2.7/python/pyspark/rdd.pyc in collect(self)
807 """
808 with SCCallSiteSync(self.context) as css:
--> 809 port = self.ctx._jvm.PythonRDD.collectAndServe(self._jrdd.rdd())
810 return list(_load_from_socket(port, self._jrdd_deserializer))
811
/usr/local/lib/python2.7/dist-packages/py4j/java_gateway.pyc 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:
~/Downloads/spark-2.1.0-bin-hadoop2.7/python/pyspark/sql/utils.pyc 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()
/usr/local/lib/python2.7/dist-packages/py4j/protocol.pyc 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.collectAndServe.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 26.0 failed 4 times, most recent failure: Lost task 0.3 in stage 26.0 (TID 139, 10.3.1.31, executor 1): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "~/Downloads/spark-2.1.0-bin-hadoop2.7/python/pyspark/worker.py", line 174, in main
process()
File "~/Downloads/spark-2.1.0-bin-hadoop2.7/python/pyspark/worker.py", line 169, in process
serializer.dump_stream(func(split_index, iterator), outfile)
File "~/Downloads/spark-2.1.0-bin-hadoop2.7/python/pyspark/serializers.py", line 138, in dump_stream
for obj in iterator:
File "~/Downloads/spark-2.1.0-bin-hadoop2.7/python/pyspark/rdd.py", line 1752, in add_shuffle_key
for k, v in iterator:
ValueError: too many values to unpack
...
任何帮助将不胜感激.谢谢
Any help would be greatly appreciated. Thanks
编辑:谢谢您回复@ Duf59.请注意，数据框的每个ID都有多个数据点(例如，陆虎"有50个数据点，宝马"有70个数据点，奔驰"有80个数据点，等等.
EDIT: Thank you for you reply @Duf59 . Please note the dataframe has multiple data points for each ID (eg. 50 data points for 'landrover', 70 datapoints for 'bmw', 80 data points for 'mercedes' etc.
当我使用您的方法时，出现以下错误:---------------------------------------------------------------------------
When I use your method, I get the following error: ---------------------------------------------------------------------------
Py4JJavaError Traceback (most recent call last)
<ipython-input-53-37fce322868d> in <module>()
5
6 spark_rdd = df.rdd.map(lambda row: (row["ID"], Vectors.dense(row["Latitude"],row["Longitude"])))
----> 7 feature_df = spark_rdd.toDF(["ID", "features"])
8 feature_df.show()
9
~/Downloads/spark-2.1.0-bin-hadoop2.7/python/pyspark/sql/session.pyc 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
~/Downloads/spark-2.1.0-bin-hadoop2.7/python/pyspark/sql/session.pyc in createDataFrame(self, data, schema, samplingRatio, verifySchema)
518
519 if isinstance(data, RDD):
--> 520 rdd, schema = self._createFromRDD(data.map(prepare), schema, samplingRatio)
521 else:
522 rdd, schema = self._createFromLocal(map(prepare, data), schema)
~/Downloads/spark-2.1.0-bin-hadoop2.7/python/pyspark/sql/session.pyc 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)
~/Downloads/spark-2.1.0-bin-hadoop2.7/python/pyspark/sql/session.pyc 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, "
~/Downloads/spark-2.1.0-bin-hadoop2.7/python/pyspark/rdd.pyc in first(self)
1359 ValueError: RDD is empty
1360 """
-> 1361 rs = self.take(1)
1362 if rs:
1363 return rs[0]
~/Downloads/spark-2.1.0-bin-hadoop2.7/python/pyspark/rdd.pyc in take(self, num)
1341
1342 p = range(partsScanned, min(partsScanned + numPartsToTry, totalParts))
-> 1343 res = self.context.runJob(self, takeUpToNumLeft, p)
1344
1345 items += res
~/Downloads/spark-2.1.0-bin-hadoop2.7/python/pyspark/context.pyc in runJob(self, rdd, partitionFunc, partitions, allowLocal)
963 # SparkContext#runJob.
964 mappedRDD = rdd.mapPartitions(partitionFunc)
--> 965 port = self._jvm.PythonRDD.runJob(self._jsc.sc(), mappedRDD._jrdd, partitions)
966 return list(_load_from_socket(port, mappedRDD._jrdd_deserializer))
967
/usr/local/lib/python2.7/dist-packages/py4j/java_gateway.pyc 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:
~/Downloads/spark-2.1.0-bin-hadoop2.7/python/pyspark/sql/utils.pyc 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()
/usr/local/lib/python2.7/dist-packages/py4j/protocol.pyc 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 134.0 failed 4 times, most recent failure: Lost task 0.3 in stage 134.0 (TID 557, 10.3.1.31, executor 1): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "~/Downloads/spark-2.1.0-bin-hadoop2.7/python/pyspark/worker.py", line 174, in main
process()
File "~/Downloads/spark-2.1.0-bin-hadoop2.7/python/pyspark/worker.py", line 169, in process
serializer.dump_stream(func(split_index, iterator), outfile)
File "~/Downloads/spark-2.1.0-bin-hadoop2.7/python/pyspark/serializers.py", line 268, in dump_stream
vs = list(itertools.islice(iterator, batch))
File "~/Downloads/spark-2.1.0-bin-hadoop2.7/python/pyspark/rdd.py", line 1339, in takeUpToNumLeft
yield next(iterator)
File "<ipython-input-53-37fce322868d>", line 6, in <lambda>
File "~/Downloads/spark-2.1.0-bin-hadoop2.7/python/pyspark/ml/linalg/__init__.py", line 790, in dense
return DenseVector(elements)
File "~/Downloads/spark-2.1.0-bin-hadoop2.7/python/pyspark/ml/linalg/__init__.py", line 275, in __init__
ar = np.array(ar, dtype=np.float64)
ValueError: could not convert string to float: Latitude
at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:193)
at org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:234)
at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:152)
at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:63)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:99)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:282)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
at java.lang.Thread.run(Thread.java:748)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1435)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1423)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1422)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1422)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:802)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:802)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1650)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1605)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1594)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:628)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1918)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1931)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:1944)
at org.apache.spark.api.python.PythonRDD$.runJob(PythonRDD.scala:441)
at org.apache.spark.api.python.PythonRDD.runJob(PythonRDD.scala)
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:280)
at py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)
at py4j.commands.CallCommand.execute(CallCommand.java:79)
at py4j.GatewayConnection.run(GatewayConnection.java:214)
at java.lang.Thread.run(Thread.java:748)
Caused by: org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "~/Downloads/spark-2.1.0-bin-hadoop2.7/python/pyspark/worker.py", line 174, in main
process()
File "~/Downloads/spark-2.1.0-bin-hadoop2.7/python/pyspark/worker.py", line 169, in process
serializer.dump_stream(func(split_index, iterator), outfile)
File "~/Downloads/spark-2.1.0-bin-hadoop2.7/python/pyspark/serializers.py", line 268, in dump_stream
vs = list(itertools.islice(iterator, batch))
File "~/Downloads/spark-2.1.0-bin-hadoop2.7/python/pyspark/rdd.py", line 1339, in takeUpToNumLeft
yield next(iterator)
File "<ipython-input-53-37fce322868d>", line 6, in <lambda>
File "~/Downloads/spark-2.1.0-bin-hadoop2.7/python/pyspark/ml/linalg/__init__.py", line 790, in dense
return DenseVector(elements)
File "~/Downloads/spark-2.1.0-bin-hadoop2.7/python/pyspark/ml/linalg/__init__.py", line 275, in __init__
ar = np.array(ar, dtype=np.float64)
ValueError: could not convert string to float: Latitude
at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:193)
at org.apache.spark.api.python.PythonRunner$$anon$1.<init>(PythonRDD.scala:234)
at org.apache.spark.api.python.PythonRunner.compute(PythonRDD.scala:152)
at org.apache.spark.api.python.PythonRDD.compute(PythonRDD.scala:63)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:99)
at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:282)
at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
... 1 more
推荐答案
您的错误是因为您将 sortByKey()应用于不是 PairwiseRDD 的RDD( df.rdd 为您提供行的RDD，在您的情况下，每个行都有5个值). * byKey 方法在 PairwiseRDDs 上运行，即RDD包含长度为2的元组或其他可以解包的结构，例如 k，v = pair
Your error is because you apply sortByKey() to a RDD which is not a PairwiseRDD (df.rdd gives you a RDD of Rows, and in your case each Row has 5 values). *byKey methods operate on PairwiseRDDs, that is RDD which contains tuples of length 2 or other structure which can be unpack like k, v = pair.
除此之外，您正在尝试将ml算法与RDD一起使用.您应该在此处为kmean模型提供一个数据框(默认情况下， kmeans.fit 需要一个名为 features 的列的数据框).您可以在此处查找文档.
Apart from that, you are trying to use ml algorithm with RDD. You should feed the kmean model with a dataframe here (by default, kmeans.fit expects a dataframe with a column named features). You can lookup the doc here.
您可以做的是:
spark_rdd = df.rdd.map(lambda row: (row["ID"], Vectors.dense(row["Latitude"],row["Longitude"])))
feature_df = spark_rdd.toDF(["ID", "features"])
kmeans = KMeans().setK(2).setSeed(1)
model = kmeans.fit(feature_df)
这篇关于在pyspark中预处理数据的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
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