我正在尝试使用MLLib在Pyspark中运行逻辑回归。该模型运行但我无法获得任何指标。
我的数据采用csv格式，我将其转换如下：
def load(prefix):
lines = spark.read.text(prefix).rdd
parts = lines.map(lambda row: row.value.split(","))
ratingsRDD = parts.map(lambda p: Row(pct = str(p[0]), date = str(p[1]), res_burg_label=int(p[2]), com_burg=int(p[3]), res_burg=int(p[4]), mvl=int(p[5]), street_rob=int(p[6])))
return spark.createDataFrame(ratingsRDD).cache()
training = load("csv")
df = training.select('A', 'B', 'C', 'D')
temp = df.rdd.map(lambda line:LabeledPoint(line[0],[line[1:]]))
(trainingData, testData) = temp.randomSplit([0.7, 0.3])
model = LogisticRegressionWithSGD.train(trainingData)
from pyspark.mllib.evaluation import MulticlassMetrics
predictions = model.predict(testData.map(lambda x: x.features))
labelsAndPredictions = testData.map(lambda lp: lp.label).zip(predictions)
一切正常，直到这里。我也使用这部分作为随机森林的输入，它工作得很好。但是，当将此用于Logistic回归或Naive Bayes时，我遇到了指标问题。我想知道这是否与格式有关，因为错误是关于维度问题...
一旦我尝试访问以下指标，我就会收到错误：
from pyspark.mllib.evaluation import BinaryClassificationMetrics
metrics = BinaryClassificationMetrics(labelsAndPredictions)
print("Area under PR = %s" % metrics.areaUnderPR)
错误：
Traceback (most recent call last):
Traceback (most recent call last):
File "/tmp/zeppelin_pyspark-2645257958953635503.py", line 367, in <module>
raise Exception(traceback.format_exc())
Exception: Traceback (most recent call last):
File "/tmp/zeppelin_pyspark-2645257958953635503.py", line 360, in <module>
exec(code, _zcUserQueryNameSpace)
File "<stdin>", line 1, in <module>
File "/usr/lib/spark/python/pyspark/mllib/evaluation.py", line 72, in areaUnderPR
return self.call("areaUnderPR")
File "/usr/lib/spark/python/pyspark/mllib/common.py", line 146, in call
return callJavaFunc(self._sc, getattr(self._java_model, name), *a)
File "/usr/lib/spark/python/pyspark/mllib/common.py", line 123, in callJavaFunc
return _java2py(sc, func(*args))
File "/usr/lib/spark/python/lib/py4j-0.10.4-src.zip/py4j/java_gateway.py", line 1133, in __call__
answer, self.gateway_client, self.target_id, self.name)
File "/usr/lib/spark/python/pyspark/sql/utils.py", line 63, in deco
return f(*a, **kw)
File "/usr/lib/spark/python/lib/py4j-0.10.4-src.zip/py4j/protocol.py", line 319, in get_return_value
format(target_id, ".", name), value)
Py4JJavaError: An error occurred while calling o2656.areaUnderPR.
: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 770.0 failed 4 times, most recent failure: Lost task 0.3 in stage 770.0 (TID 831, ip-172-31-82-213.ec2.internal, executor 1): org.apache.spark.api.python.PythonException: Traceback (most recent call last):
File "/mnt1/yarn/usercache/zeppelin/appcache/application_1521221169368_0001/container_1521221169368_0001_01_000002/pyspark.zip/pyspark/worker.py", line 177, in main
process()
File "/mnt1/yarn/usercache/zeppelin/appcache/application_1521221169368_0001/container_1521221169368_0001_01_000002/pyspark.zip/pyspark/worker.py", line 172, in process
serializer.dump_stream(func(split_index, iterator), outfile)
File "/mnt1/yarn/usercache/zeppelin/appcache/application_1521221169368_0001/container_1521221169368_0001_01_000002/pyspark.zip/pyspark/serializers.py", line 220, in dump_stream
self.serializer.dump_stream(self._batched(iterator), stream)
File "/mnt1/yarn/usercache/zeppelin/appcache/application_1521221169368_0001/container_1521221169368_0001_01_000002/pyspark.zip/pyspark/serializers.py", line 138, in dump_stream
for obj in iterator:
File "/mnt1/yarn/usercache/zeppelin/appcache/application_1521221169368_0001/container_1521221169368_0001_01_000002/pyspark.zip/pyspark/serializers.py", line 209, in _batched
for item in iterator:
File "/usr/lib/spark/python/pyspark/mllib/classification.py", line 202, in <lambda>
File "/mnt1/yarn/usercache/zeppelin/appcache/application_1521221169368_0001/container_1521221169368_0001_01_000002/pyspark.zip/pyspark/mllib/classification.py", line 206, in predict
margin = self.weights.dot(x) + self._intercept
File "/mnt1/yarn/usercache/zeppelin/appcache/application_1521221169368_0001/container_1521221169368_0001_01_000002/pyspark.zip/pyspark/mllib/linalg/__init__.py", line 372, in dot
assert len(self) == _vector_size(other), "dimension mismatch"
AssertionError: dimension mismatch
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.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.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.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.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.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.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.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.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.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.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
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$Worker.run(ThreadPoolExecutor.java:624)
at java.lang.Thread.run(Thread.java:748)
Driver stacktrace:
at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1708)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1696)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1695)
at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)
at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1695)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:855)
at org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:855)
at scala.Option.foreach(Option.scala:257)
at org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:855)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1923)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1878)
at org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1867)
at org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)
at org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:671)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2029)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2050)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2069)
at org.apache.spark.SparkContext.runJob(SparkContext.scala:2094)
at org.apache.spark.rdd.RDD$$anonfun$collect$1.apply(RDD.scala:936)
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.collect(RDD.scala:935)
at org.apache.spark.mllib.evaluation.BinaryClassificationMetrics.x$4$lzycompute(BinaryClassificationMetrics.scala:192)
at org.apache.spark.mllib.evaluation.BinaryClassificationMetrics.x$4(BinaryClassificationMetrics.scala:146)
at org.apache.spark.mllib.evaluation.BinaryClassificationMetrics.confusions$lzycompute(BinaryClassificationMetrics.scala:148)
at org.apache.spark.mllib.evaluation.BinaryClassificationMetrics.confusions(BinaryClassificationMetrics.scala:148)
at org.apache.spark.mllib.evaluation.BinaryClassificationMetrics.createCurve(BinaryClassificationMetrics.scala:223)
at org.apache.spark.mllib.evaluation.BinaryClassificationMetrics.pr(BinaryClassificationMetrics.scala:107)
at org.apache.spark.mllib.evaluation.BinaryClassificationMetrics.areaUnderPR(BinaryClassificationMetrics.scala:117)
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 "/mnt1/yarn/usercache/zeppelin/appcache/application_1521221169368_0001/container_1521221169368_0001_01_000002/pyspark.zip/pyspark/worker.py", line 177, in main
process()
File "/mnt1/yarn/usercache/zeppelin/appcache/application_1521221169368_0001/container_1521221169368_0001_01_000002/pyspark.zip/pyspark/worker.py", line 172, in process
serializer.dump_stream(func(split_index, iterator), outfile)
File "/mnt1/yarn/usercache/zeppelin/appcache/application_1521221169368_0001/container_1521221169368_0001_01_000002/pyspark.zip/pyspark/serializers.py", line 220, in dump_stream
self.serializer.dump_stream(self._batched(iterator), stream)
File "/mnt1/yarn/usercache/zeppelin/appcache/application_1521221169368_0001/container_1521221169368_0001_01_000002/pyspark.zip/pyspark/serializers.py", line 138, in dump_stream
for obj in iterator:
File "/mnt1/yarn/usercache/zeppelin/appcache/application_1521221169368_0001/container_1521221169368_0001_01_000002/pyspark.zip/pyspark/serializers.py", line 209, in _batched
for item in iterator:
File "/usr/lib/spark/python/pyspark/mllib/classification.py", line 202, in <lambda>
File "/mnt1/yarn/usercache/zeppelin/appcache/application_1521221169368_0001/container_1521221169368_0001_01_000002/pyspark.zip/pyspark/mllib/classification.py", line 206, in predict
margin = self.weights.dot(x) + self._intercept
File "/mnt1/yarn/usercache/zeppelin/appcache/application_1521221169368_0001/container_1521221169368_0001_01_000002/pyspark.zip/pyspark/mllib/linalg/__init__.py", line 372, in dot
assert len(self) == _vector_size(other), "dimension mismatch"
AssertionError: dimension mismatch
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.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.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.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.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.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.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.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.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.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.ShuffleMapTask.runTask(ShuffleMapTask.scala:96)
at org.apache.spark.scheduler.ShuffleMapTask.runTask(ShuffleMapTask.scala:53)
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)
... 1 more
答案 0 :(得分：0)
实际上，错误在于使用模型预测训练数据：model.predict(testData.map(lambda x: x.features))由于testData.map(lambda x: x.features)和trainingData的维度不匹配，这应该是相同的。
由于RDD具有延迟操作，因此在调用MulticlassMetrics时遇到它。