init

init

py

nit

Author: zhengruifeng

mllib/src/main/scala/org/apache/spark/ml/feature/Imputer.scala

@@ -39,14 +39,16 @@ private[feature] trait ImputerParams extends Params with HasInputCol with HasInp
    * The imputation strategy. Currently only "mean" and "median" are supported.
    * If "mean", then replace missing values using the mean value of the feature.
    * If "median", then replace missing values using the approximate median value of the feature.
+   * If "mode", then replace missing using the most frequent value of the feature.
    * Default: mean
    *
    * @group param
    */
   final val strategy: Param[String] = new Param(this, "strategy", s"strategy for imputation. " +
     s"If ${Imputer.mean}, then replace missing values using the mean value of the feature. " +
-    s"If ${Imputer.median}, then replace missing values using the median value of the feature.",
-    ParamValidators.inArray[String](Array(Imputer.mean, Imputer.median)))
+    s"If ${Imputer.median}, then replace missing values using the median value of the feature. " +
+    s"If ${Imputer.mode}, then replace missing values using the most frequent value of " +
+    s"the feature.", ParamValidators.inArray[String](Imputer.supportedStrategies))
 
   /** @group getParam */
   def getStrategy: String = $(strategy)
@@ -104,7 +106,7 @@ private[feature] trait ImputerParams extends Params with HasInputCol with HasInp
  * For example, if the input column is IntegerType (1, 2, 4, null),
  * the output will be IntegerType (1, 2, 4, 2) after mean imputation.
  *
- * Note that the mean/median value is computed after filtering out missing values.
+ * Note that the mean/median/mode value is computed after filtering out missing values.
  * All Null values in the input columns are treated as missing, and so are also imputed. For
  * computing median, DataFrameStatFunctions.approxQuantile is used with a relative error of 0.001.
  */
@@ -132,7 +134,7 @@ class Imputer @Since("2.2.0") (@Since("2.2.0") override val uid: String)
   def setOutputCols(value: Array[String]): this.type = set(outputCols, value)
 
   /**
-   * Imputation strategy. Available options are ["mean", "median"].
+   * Imputation strategy. Available options are ["mean", "median", "mode"].
    * @group setParam
    */
   @Since("2.2.0")
@@ -151,39 +153,47 @@ class Imputer @Since("2.2.0") (@Since("2.2.0") override val uid: String)
     val spark = dataset.sparkSession
 
     val (inputColumns, _) = getInOutCols()
-
     val cols = inputColumns.map { inputCol =>
       when(col(inputCol).equalTo($(missingValue)), null)
         .when(col(inputCol).isNaN, null)
         .otherwise(col(inputCol))
-        .cast("double")
+        .cast(DoubleType)
         .as(inputCol)
     }
+    val numCols = cols.length
 
     val results = $(strategy) match {
       case Imputer.mean =>
         // Function avg will ignore null automatically.
         // For a column only containing null, avg will return null.
         val row = dataset.select(cols.map(avg): _*).head()
-        Array.range(0, inputColumns.length).map { i =>
-          if (row.isNullAt(i)) {
-            Double.NaN
-          } else {
-            row.getDouble(i)
-          }
-        }
+        Array.tabulate(numCols)(i => if (row.isNullAt(i)) Double.NaN else row.getDouble(i))
 
       case Imputer.median =>
         // Function approxQuantile will ignore null automatically.
         // For a column only containing null, approxQuantile will return an empty array.
         dataset.select(cols: _*).stat.approxQuantile(inputColumns, Array(0.5), $(relativeError))
-          .map { array =>
-            if (array.isEmpty) {
-              Double.NaN
-            } else {
-              array.head
-            }
+          .map(_.headOption.getOrElse(Double.NaN))
+
+      case Imputer.mode =>
+        val modes = dataset.select(cols: _*).rdd.flatMap { row =>
+          Iterator.range(0, numCols).flatMap { i =>
+            // Ignore null.
+            if (row.isNullAt(i)) Iterator.empty else Iterator.single((i, row.getDouble(i)), 1L)
           }
+        }.reduceByKey(_ + _).map { case ((i, v), c) => (i, (v, c))
+        }.reduceByKey { case ((v1, c1), (v2, c2)) =>
+          if (c1 > c2) {
+            (v1, c1)
+          } else if (c1 < c2) {
+            (v2, c2)
+          } else {
+            // Keep in line with sklearn.impute.SimpleImputer (using scipy.stats.mode).
+            // If there is more than one mode, choose the smallest one.
+            (math.min(v1, v2), c1)
+          }
+        }.mapValues(_._1).collectAsMap()
+        Array.tabulate(numCols)(i => modes.getOrElse(i, Double.NaN))
     }
 
     val emptyCols = inputColumns.zip(results).filter(_._2.isNaN).map(_._1)
@@ -212,6 +222,10 @@ object Imputer extends DefaultParamsReadable[Imputer] {
   /** strategy names that Imputer currently supports. */
   private[feature] val mean = "mean"
   private[feature] val median = "median"
+  private[feature] val mode = "mode"
+
+  /* Set of strategies that Imputer supports */
+  private[feature] val supportedStrategies = Array(mean, median, mode)
 
   @Since("2.2.0")
   override def load(path: String): Imputer = super.load(path)