使用标准标准时，跨度向量与密集体

Question

我正在使用以下代码将Pyspark DataFrame归一化

from pyspark.ml.feature import StandardScaler, VectorAssembler
from pyspark.ml import Pipeline
cols = ["a", "b", "c"]
df = spark.createDataFrame([(1, 0, 3), (2, 3, 2), (1, 3, 1), (3, 0, 3)], cols)
Pipeline(stages=[
    VectorAssembler(inputCols=cols, outputCol='features'), 
    StandardScaler(withMean=True, inputCol='features', outputCol='scaledFeatures')
]).fit(df).transform(df).select(cols + ['scaledFeatures']).head()

这给出了预期的结果：

Row(a=1, b=0, c=3, scaledFeatures=DenseVector([-0.7833, -0.866, 0.7833]))

但是，当我在较大的数据集上运行管道时，从镶木quet文件加载时，我会收到以下例外：

16/12/21 09:47:50 WARN TaskSetManager: Lost task 0.0 in stage 60.0 (TID 6370, 10.231.153.67): org.apache.spark.SparkException: Failed to execute user defined function($anonfu
n$2: (vector) => vector)
        at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply2_2$(Unknown Source)
        at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(Unknown Source)
        at org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificUnsafeProjection.apply(Unknown Source)
        at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
        at scala.collection.Iterator$$anon$11.next(Iterator.scala:409)
        at org.apache.spark.api.python.SerDeUtil$AutoBatchedPickler.next(SerDeUtil.scala:121)
        at org.apache.spark.api.python.SerDeUtil$AutoBatchedPickler.next(SerDeUtil.scala:112)
        at scala.collection.Iterator$class.foreach(Iterator.scala:893)
        at org.apache.spark.api.python.SerDeUtil$AutoBatchedPickler.foreach(SerDeUtil.scala:112)
        at org.apache.spark.api.python.PythonRDD$.writeIteratorToStream(PythonRDD.scala:504)
        at org.apache.spark.api.python.PythonRunner$WriterThread$$anonfun$run$3.apply(PythonRDD.scala:328)
        at org.apache.spark.util.Utils$.logUncaughtExceptions(Utils.scala:1877)
        at org.apache.spark.api.python.PythonRunner$WriterThread.run(PythonRDD.scala:269)
Caused by: java.lang.IllegalArgumentException: Do not support vector type class org.apache.spark.mllib.linalg.SparseVector
        at org.apache.spark.mllib.feature.StandardScalerModel.transform(StandardScaler.scala:160)
        at org.apache.spark.ml.feature.StandardScalerModel$$anonfun$2.apply(StandardScaler.scala:167)
        at org.apache.spark.ml.feature.StandardScalerModel$$anonfun$2.apply(StandardScaler.scala:167)
        ... 13 more

我注意到，这里的vectorAssembler已将我的列转换为mllib.linalg.sparsevector，而不是第一种情况下使用的密度向量。

有什么想法我如何解决这个问题？

Answer 1

我注意到您想将其创建到自定义转换中，以将其直接包含在管道中。

这应该为您做。

from pyspark import keyword_only  
from pyspark.ml.pipeline import Transformer  
from pyspark.ml.param.shared import HasInputCol, HasOutputCol
from pyspark.ml.linalg import SparseVector, DenseVector, VectorUDT
from pyspark.sql.functions import udf

class AsDenseTransformer(Transformer, HasInputCol, HasOutputCol):  
    @keyword_only
    def __init__(self, inputCol=None, outputCol=None):
        super(AsDenseTransformer, self).__init__()
        kwargs = self.__init__._input_kwargs
        self.setParams(**kwargs)
    @keyword_only
    def setParams(self, inputCol=None, outputCol=None):
        kwargs = self.setParams._input_kwargs
        return self._set(**kwargs)
    def _transform(self, dataset):
        out_col = self.getOutputCol()
        in_col = dataset[self.getInputCol()]
        asDense = udf(lambda s: DenseVector(s.toArray()), VectorUDT()) 
        return dataset.withColumn(out_col,  asDense(in_col))

定义了它后，您可以将其初始化为vectorAssembler之后的管道中包含的转换。

Pipeline(stages=[
    VectorAssembler(inputCols=cols, outputCol='features'),
    AsDenseTransformer(inputCol='features', outputCol='features'),
    StandardScaler(withMean=True, inputCol='features', outputCol='scaledFeatures')
]).fit(df).transform(df).select(cols + ['scaledFeatures']).head()