如何在pyspark中标准化RDD？

Question

我创建了如下测试和培训数据：

data = sc.textFile(fileName)
training, testing = data.randomSplit([0.6, 0.4], seed=11L)

现在我想标准化每个功能。我找到了StandardScaler，我想使用以下代码来做到这一点：

from pyspark.ml.feature import StandardScaler

scaler = StandardScaler(inputCol="features", outputCol="scaledFeatures", withStd=True, withMean=True)  

# Compute summary statistics by fitting the StandardScaler
scalerModel = scaler.fit(training)

# Normalize each Train feature to have unit standard deviation.
scaledTrainData = scalerModel.transform(training)

# Normalize each Test feature to have unit standard deviation.
scaledTestData = scalerModel.transform(testing)

但是我收到以下错误：

AttributeError: 'PipelinedRDD' object has no attribute '_jdf'
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-15-32380b939084> in <module>()
      6 
      7 # Compute summary statistics by fitting the StandardScaler
----> 8 scalerModel = scaler.fit(training)
      9 
     10 # Normalize each Train feature to have unit standard deviation.

/databricks/spark/python/pyspark/ml/pipeline.py in fit(self, dataset, params)
     67                 return self.copy(params)._fit(dataset)
     68             else:
---> 69                 return self._fit(dataset)
     70         else:
     71             raise ValueError("Params must be either a param map or a list/tuple of param maps, "

/databricks/spark/python/pyspark/ml/wrapper.py in _fit(self, dataset)
    131 
    132     def _fit(self, dataset):
--> 133         java_model = self._fit_java(dataset)
    134         return self._create_model(java_model)
    135 

/databricks/spark/python/pyspark/ml/wrapper.py in _fit_java(self, dataset)
    128         """
    129         self._transfer_params_to_java()
--> 130         return self._java_obj.fit(dataset._jdf)
    131 
    132     def _fit(self, dataset):

AttributeError: 'PipelinedRDD' object has no attribute '_jdf'

有没有其他方法可以做到这一点？