[SPARK-13568] [ML] Create feature transformer to impute missing values

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

@@ -0,0 +1,259 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml.feature
+
+import org.apache.hadoop.fs.Path
+
+import org.apache.spark.SparkException
+import org.apache.spark.annotation.{Experimental, Since}
+import org.apache.spark.ml.{Estimator, Model}
+import org.apache.spark.ml.param._
+import org.apache.spark.ml.param.shared.HasInputCols
+import org.apache.spark.ml.util._
+import org.apache.spark.sql.{DataFrame, Dataset, Row}
+import org.apache.spark.sql.functions._
+import org.apache.spark.sql.types._
+
+/**
+ * Params for [[Imputer]] and [[ImputerModel]].
+ */
+private[feature] trait ImputerParams extends Params with HasInputCols {
+
+  /**
+   * The imputation strategy.
+   * 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.
+   * 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)))
+
+  /** @group getParam */
+  def getStrategy: String = $(strategy)
+
+  /**
+   * The placeholder for the missing values. All occurrences of missingValue will be imputed.
+   * Note that null values are always treated as missing.
+   * Default: Double.NaN
+   *
+   * @group param
+   */
+  final val missingValue: DoubleParam = new DoubleParam(this, "missingValue",
+    "The placeholder for the missing values. All occurrences of missingValue will be imputed")
+
+  /** @group getParam */
+  def getMissingValue: Double = $(missingValue)
+
+  /**
+   * Param for output column names.
+   * @group param
+   */
+  final val outputCols: StringArrayParam = new StringArrayParam(this, "outputCols",
+    "output column names")
+
+  /** @group getParam */
+  final def getOutputCols: Array[String] = $(outputCols)
+
+  /** Validates and transforms the input schema. */
+  protected def validateAndTransformSchema(schema: StructType): StructType = {
+    require($(inputCols).length == $(inputCols).distinct.length, s"inputCols contains" +
+      s" duplicates: (${$(inputCols).mkString(", ")})")
+    require($(outputCols).length == $(outputCols).distinct.length, s"outputCols contains" +
+      s" duplicates: (${$(outputCols).mkString(", ")})")
+    require($(inputCols).length == $(outputCols).length, s"inputCols(${$(inputCols).length})" +
+      s" and outputCols(${$(outputCols).length}) should have the same length")
+    val outputFields = $(inputCols).zip($(outputCols)).map { case (inputCol, outputCol) =>
+      val inputField = schema(inputCol)
+      SchemaUtils.checkColumnTypes(schema, inputCol, Seq(DoubleType, FloatType))
+      StructField(outputCol, inputField.dataType, inputField.nullable)
+    }
+    StructType(schema ++ outputFields)
+  }
+}
+
+/**
+ * :: Experimental ::
+ * Imputation estimator for completing missing values, either using the mean or the median
+ * of the column in which the missing values are located. The input column should be of
+ * DoubleType or FloatType. Currently Imputer does not support categorical features yet
+ * (SPARK-15041) and possibly creates incorrect values for a categorical feature.
+ *
+ * Note that the mean/median value is computed after filtering out missing values.
+ * All Null values in the input column are treated as missing, and so are also imputed. For
+ * computing median, DataFrameStatFunctions.approxQuantile is used with a relative error of 0.001.
+ */
+@Experimental
+class Imputer @Since("2.2.0")(override val uid: String)
+  extends Estimator[ImputerModel] with ImputerParams with DefaultParamsWritable {
+
+  @Since("2.2.0")
+  def this() = this(Identifiable.randomUID("imputer"))
+
+  /** @group setParam */
+  @Since("2.2.0")
+  def setInputCols(value: Array[String]): this.type = set(inputCols, value)
+
+  /** @group setParam */
+  @Since("2.2.0")
+  def setOutputCols(value: Array[String]): this.type = set(outputCols, value)
+
+  /**
+   * Imputation strategy. Available options are ["mean", "median"].
+   * @group setParam
+   */
+  @Since("2.2.0")
+  def setStrategy(value: String): this.type = set(strategy, value)
+
+  /** @group setParam */
+  @Since("2.2.0")
+  def setMissingValue(value: Double): this.type = set(missingValue, value)
+
+  setDefault(strategy -> Imputer.mean, missingValue -> Double.NaN)
+
+  override def fit(dataset: Dataset[_]): ImputerModel = {
+    transformSchema(dataset.schema, logging = true)
+    val spark = dataset.sparkSession
+    import spark.implicits._
+    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 = $(strategy) match {
+        case Imputer.mean => filtered.select(avg(inputCol)).as[Double].first()
+        case Imputer.median => filtered.stat.approxQuantile(inputCol, Array(0.5), 0.001).head
+      }
+      surrogate
+    }
+
+    val rows = spark.sparkContext.parallelize(Seq(Row.fromSeq(surrogates)))
+    val schema = StructType($(inputCols).map(col => StructField(col, DoubleType, nullable = false)))
+    val surrogateDF = spark.createDataFrame(rows, schema)
+    copyValues(new ImputerModel(uid, surrogateDF).setParent(this))
+  }
+
+  override def transformSchema(schema: StructType): StructType = {
+    validateAndTransformSchema(schema)
+  }
+
+  override def copy(extra: ParamMap): Imputer = defaultCopy(extra)
+}
+
+@Since("2.2.0")
+object Imputer extends DefaultParamsReadable[Imputer] {
+
+  /** strategy names that Imputer currently supports. */
+  private[ml] val mean = "mean"
+  private[ml] val median = "median"
+
+  @Since("2.2.0")
+  override def load(path: String): Imputer = super.load(path)
+}
+
+/**
+ * :: Experimental ::
+ * Model fitted by [[Imputer]].
+ *
+ * @param surrogateDF a DataFrame contains inputCols and their corresponding surrogates, which are
+ *                    used to replace the missing values in the input DataFrame.
+ */
+@Experimental
+class ImputerModel private[ml](
+    override val uid: String,
+    val surrogateDF: DataFrame)
+  extends Model[ImputerModel] with ImputerParams with MLWritable {
+
+  import ImputerModel._
+
+  /** @group setParam */
+  def setInputCols(value: Array[String]): this.type = set(inputCols, value)
+
+  /** @group setParam */
+  def setOutputCols(value: Array[String]): this.type = set(outputCols, value)
+
+  override def transform(dataset: Dataset[_]): DataFrame = {
+    transformSchema(dataset.schema, logging = true)
+    var outputDF = dataset
+    val surrogates = surrogateDF.select($(inputCols).map(col): _*).head().toSeq
+
+    $(inputCols).zip($(outputCols)).zip(surrogates).foreach {
+      case ((inputCol, outputCol), surrogate) =>
+        val inputType = dataset.schema(inputCol).dataType
+        val ic = col(inputCol)
+        outputDF = outputDF.withColumn(outputCol,
+          when(ic.isNull, surrogate)
+          .when(ic === $(missingValue), surrogate)
+          .otherwise(ic)
+          .cast(inputType))
+    }
+    outputDF.toDF()
+  }
+
+  override def transformSchema(schema: StructType): StructType = {
+    validateAndTransformSchema(schema)
+  }
+
+  override def copy(extra: ParamMap): ImputerModel = {
+    val copied = new ImputerModel(uid, surrogateDF)
+    copyValues(copied, extra).setParent(parent)
+  }
+
+  @Since("2.2.0")
+  override def write: MLWriter = new ImputerModelWriter(this)
+}
+
+
+@Since("2.2.0")
+object ImputerModel extends MLReadable[ImputerModel] {
+
+  private[ImputerModel] class ImputerModelWriter(instance: ImputerModel) extends MLWriter {
+
+    override protected def saveImpl(path: String): Unit = {
+      DefaultParamsWriter.saveMetadata(instance, path, sc)
+      val dataPath = new Path(path, "data").toString
+      instance.surrogateDF.repartition(1).write.parquet(dataPath)
+    }
+  }
+
+  private class ImputerReader extends MLReader[ImputerModel] {
+
+    private val className = classOf[ImputerModel].getName
+
+    override def load(path: String): ImputerModel = {
+      val metadata = DefaultParamsReader.loadMetadata(path, sc, className)
+      val dataPath = new Path(path, "data").toString
+      val surrogateDF = sqlContext.read.parquet(dataPath)
+      val model = new ImputerModel(metadata.uid, surrogateDF)
+      DefaultParamsReader.getAndSetParams(model, metadata)
+      model
+    }
+  }
+
+  @Since("2.2.0")
+  override def read: MLReader[ImputerModel] = new ImputerReader
+
+  @Since("2.2.0")
+  override def load(path: String): ImputerModel = super.load(path)
+}