问题
I am trying to use data from a spark dataframe as the input for my k-means model. However I keep getting errors. (Check section after code)
My spark dataframe and looks like this (and has around 1M rows):
ID col1 col2 Latitude Longitude
13 ... ... 22.2 13.5
62 ... ... 21.4 13.8
24 ... ... 21.8 14.1
71 ... ... 28.9 18.0
... ... ... .... ....
Here is my code:
from pyspark.ml.clustering import KMeans
from pyspark.ml.linalg import Vectors
df = spark.read.csv("file.csv")
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)
sum_of_square_error = model.computeCost(feature_df)
print str(sum_of_square_error)
centers = model.clusterCenters()
for center in centers:
print(center)
However, I get the error:
Py4JJavaError Traceback (most recent call last)
<ipython-input-145-f50a6cbe7243> in <module>()
7
8 kmeans = KMeans().setK(2).setSeed(1)
----> 9 model = kmeans.fit(feature_df)
10
11
~/Downloads/spark-2.1.0-bin-hadoop2.7/python/pyspark/ml/base.py in fit(self, dataset, params)
62 return self.copy(params)._fit(dataset)
63 else:
---> 64 return self._fit(dataset)
65 else:
66 raise ValueError("Params must be either a param map or a list/tuple of param maps, "
~/Downloads/spark-2.1.0-bin-hadoop2.7/python/pyspark/ml/wrapper.py in _fit(self, dataset)
234
235 def _fit(self, dataset):
--> 236 java_model = self._fit_java(dataset)
237 return self._create_model(java_model)
238
~/Downloads/spark-2.1.0-bin-hadoop2.7/python/pyspark/ml/wrapper.py in _fit_java(self, dataset)
231 """
232 self._transfer_params_to_java()
--> 233 return self._java_obj.fit(dataset._jdf)
234
235 def _fit(self, dataset):
/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 o3552.fit.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 5 in stage 457.0 failed 4 times, most recent failure: Lost task 5.3 in stage 457.0 (TID 2308, 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 "<ipython-input-145-f50a6cbe7243>", line 4, 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: GOLF
at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:193)
at org.apache.spark.api.python.PythonRunner$$anon$1.next(PythonRDD.scala:156)
at org.apache.spark.api.python.PythonRunner$$anon$1.next(PythonRDD.scala:152)
at org.apache.spark.InterruptibleIterator.next(InterruptibleIterator.scala:43)
at scala.collection.Iterator$$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 scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at scala.collection.Iterator$$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:377)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at org.apache.spark.storage.memory.MemoryStore.putIteratorAsValues(MemoryStore.scala:215)
at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:957)
at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:948)
at org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:888)
at org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:948)
at org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:694)
at org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:334)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:285)
at org.apache.spark.rdd.ZippedPartitionsRDD2.compute(ZippedPartitionsRDD.scala:89)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:99)
at org.apache.spark.executor.Executor$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.SparkContext.runJob(SparkContext.scala:1958)
at org.apache.spark.rdd.RDD.count(RDD.scala:1157)
at org.apache.spark.rdd.RDD$$anonfun$takeSample$1.apply(RDD.scala:567)
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:362)
at org.apache.spark.rdd.RDD.takeSample(RDD.scala:556)
at org.apache.spark.mllib.clustering.KMeans.initKMeansParallel(KMeans.scala:353)
at org.apache.spark.mllib.clustering.KMeans.runAlgorithm(KMeans.scala:256)
at org.apache.spark.mllib.clustering.KMeans.run(KMeans.scala:227)
at org.apache.spark.ml.clustering.KMeans.fit(KMeans.scala:319)
at sun.reflect.GeneratedMethodAccessor89.invoke(Unknown Source)
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 "<ipython-input-145-f50a6cbe7243>", line 4, 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: GOLFE
at org.apache.spark.api.python.PythonRunner$$anon$1.read(PythonRDD.scala:193)
at org.apache.spark.api.python.PythonRunner$$anon$1.next(PythonRDD.scala:156)
at org.apache.spark.api.python.PythonRunner$$anon$1.next(PythonRDD.scala:152)
at org.apache.spark.InterruptibleIterator.next(InterruptibleIterator.scala:43)
at scala.collection.Iterator$$anon$12.nextCur(Iterator.scala:434)
at scala.collection.Iterator$$anon$12.hasNext(Iterator.scala:440)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
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$$anon$1.hasNext(WholeStageCodegenExec.scala:377)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)
at org.apache.spark.storage.memory.MemoryStore.putIteratorAsValues(MemoryStore.scala:215)
at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:957)
at org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:948)
at org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:888)
at org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:948)
at org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:694)
at org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:334)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:285)
at org.apache.spark.rdd.ZippedPartitionsRDD2.compute(ZippedPartitionsRDD.scala:89)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)
at org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)
at org.apache.spark.rdd.RDD.iterator(RDD.scala:287)
at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)
at org.apache.spark.scheduler.Task.run(Task.scala:99)
at org.apache.spark.executor.Executor$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
The strange thing is that the error is different every time I run it. The 3 types of errors I get are:
UnicodeEncodeError: 'decimal' codec can't encode characters in position 3-5: invalid decimal Unicode string
invalid literal for float(): 2017-04
ValueError: could not convert string to float: GOLF
Correct me if I am wrong, but I think some value of data in the columns may be incorrect (eg. occasional strings inside latitude and longitude column)
Is there a way to check if the value in each row of 'Latitude' is in fact a float? Is there a way to check if the value in each row of 'ID' is an integer?
I would like to discard the rows which contain values of the incorrect data type. Perhaps there a way of doing this using df.filter()?
I would greatly appreciate any help. Thanks.
UPDATE: I even tried df.describe('ID', 'Latitude', 'Longitude').show() and it returns numeric values for count, mean, stddev, min, max values for each column, indicating to me that they must all be numbers..?
回答1:
you should maybe have continued on the same thread since it's the same problem. For reference : Preprocessing data in pyspark
Here you need to convert Latitude / Longitude to float and remove null values with dropna before injecting the data in Kmean, because it seems these columns contain some strings that cannot be cast to a numeric value, so preprocess df with something like :
df2 = (df
.withColumn("Latitude", col("Latitude").cast("float"))
.withColumn("Longitude", col("Longitude").cast("float"))
.dropna()
)
spark_rdd = df2.rdd ...
来源：https://stackoverflow.com/questions/44950532/pyspark-valueerror-could-not-convert-string-to-float-invalid-literal-for-fl