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Posted to issues@spark.apache.org by "zhengruifeng (JIRA)" <ji...@apache.org> on 2017/08/10 06:07:00 UTC

[jira] [Created] (SPARK-21690) one-pass imputer

zhengruifeng created SPARK-21690:
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             Summary: one-pass imputer
                 Key: SPARK-21690
                 URL: https://issues.apache.org/jira/browse/SPARK-21690
             Project: Spark
          Issue Type: Improvement
          Components: ML
    Affects Versions: 2.2.1
            Reporter: zhengruifeng


{code}
    val surrogates = $(inputCols).map { inputCol =>
      val ic = col(inputCol)
      val filtered = dataset.select(ic.cast(DoubleType))
        .filter(ic.isNotNull && ic =!= $(missingValue) && !ic.isNaN)
      if(filtered.take(1).length == 0) {
        throw new SparkException(s"surrogate cannot be computed. " +
          s"All the values in $inputCol are Null, Nan or missingValue(${$(missingValue)})")
      }
      val surrogate = $(strategy) match {
        case Imputer.mean => filtered.select(avg(inputCol)).as[Double].first()
        case Imputer.median => filtered.stat.approxQuantile(inputCol, Array(0.5), 0.001).head
      }
      surrogate
    }
{code}

Current impl of {{Imputer}} process one column after after another. In this place, we should parallelize the processing in a more efficient way.




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