我正在训练一个机器学习模型Pypark.ml公司JupyterLab笔记本中AWS EMR上s3 bucket的on.json数据。这个bucket不是我的,但是我认为access工作得很好,因为数据预处理、特征工程等工作得很好。但是,当我调用cv.fit(training_data)
函数时,训练过程会一直运行到几乎完成(由状态栏指示),但随后会抛出一个错误:
Exception in thread cell_monitor-64:
Traceback (most recent call last):
File "/opt/conda/lib/python3.7/threading.py", line 917, in _bootstrap_inner
self.run()
File "/opt/conda/lib/python3.7/threading.py", line 865, in run
self._target(*self._args, **self._kwargs)
File "/opt/conda/lib/python3.7/site-packages/awseditorssparkmonitoringwidget-1.0-py3.7.egg/awseditorssparkmonitoringwidget/cellmonitor.py", line 178, in cell_monitor
job_binned_stages[job_id][stage_id] = all_stages[stage_id]
KeyError: 6571
我还没有找到关于这个错误的任何信息。怎么回事?你知道吗
这是我的管道:
train, test = clean_df.randomSplit([0.8, 0.2], seed=42)
va1 = VectorAssembler(inputCols="vars", outputCol="vars")
scaler = StandardScaler(inputCol="to_scale", outputCol="scaled_features")
va2 = VectorAssembler(inputCols=["more_vars","scaled_features"], outputCol="features")
gbt = GBTClassifier()
pipeline = Pipeline(stages=[va1, scaler,va2,gbt])
paramGrid = ParamGridBuilder()\
.addGrid(gbt.maxDepth, [2, 5])\
.addGrid(gbt.maxIter, [10, 100])\
.build()
crossval = CrossValidator(estimator=pipeline,
estimatorParamMaps=paramGrid,
evaluator=MulticlassClassificationEvaluator(metricName='f1'),
numFolds=3)
cvModel = crossval.fit(train)
其次,我有一个预感,我可能在python3.8中解决了这个问题;我可以在EMR上安装python3.8吗?你知道吗
目前没有回答
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