[SPARK-19969] [ML] Imputer doc and example

docs/ml-features.md

@@ -1284,6 +1284,72 @@ for more details on the API.
 
 </div>
 
+
+## Imputer
+
+The `Imputer` transformer completes missing values in a dataset, either using the mean or the 
+median of the columns in which the missing values are located. The input columns should be of
+`DoubleType` or `FloatType`. Currently `Imputer` does not support categorical features and possibly
+creates incorrect values for columns containing categorical features.
+
+**Note** all `null` values in the input columns are treated as missing, and so are also imputed.
+
+**Examples**
+
+Suppose that we have a DataFrame with the columns `a` and `b`:
+
+~~~
+      a     |      b      
+------------|-----------
+     1.0    | Double.NaN
+     2.0    | Double.NaN
+ Double.NaN |     3.0   
+     4.0    |     4.0   
+     5.0    |     5.0   
+~~~
+
+In this example, Imputer will replace all occurrences of `Double.NaN` (the default for the missing value)
+with the mean (the default imputation strategy) computed from the other values in the corresponding columns.
+In this example, the surrogate values for columns `a` and `b` are 3.0 and 4.0 respectively. After
+transformation, the missing values in the output columns will be replaced by the surrogate value for
+the relevant column.
+
+~~~
+      a     |      b     | out_a | out_b   
+------------|------------|-------|-------
+     1.0    | Double.NaN |  1.0  |  4.0 
+     2.0    | Double.NaN |  2.0  |  4.0 
+ Double.NaN |     3.0    |  3.0  |  3.0 
+     4.0    |     4.0    |  4.0  |  4.0
+     5.0    |     5.0    |  5.0  |  5.0 
+~~~
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+Refer to the [Imputer Scala docs](api/scala/index.html#org.apache.spark.ml.feature.Imputer)
+for more details on the API.
+
+{% include_example scala/org/apache/spark/examples/ml/ImputerExample.scala %}
+</div>
+
+<div data-lang="java" markdown="1">
+
+Refer to the [Imputer Java docs](api/java/org/apache/spark/ml/feature/Imputer.html)
+for more details on the API.
+
+{% include_example java/org/apache/spark/examples/ml/JavaImputerExample.java %}
+</div>
+
+<div data-lang="python" markdown="1">
+
+Refer to the [Imputer Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.Imputer)
+for more details on the API.
+
+{% include_example python/ml/imputer_example.py %}
+</div>
+</div>
+
 # Feature Selectors
 
 ## VectorSlicer

examples/src/main/java/org/apache/spark/examples/ml/JavaImputerExample.java

@@ -0,0 +1,71 @@
+/*
+ * 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.examples.ml;
+
+// $example on$
+import java.util.Arrays;
+import java.util.List;
+
+import org.apache.spark.ml.feature.Imputer;
+import org.apache.spark.ml.feature.ImputerModel;
+import org.apache.spark.sql.Dataset;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.RowFactory;
+import org.apache.spark.sql.SparkSession;
+import org.apache.spark.sql.types.*;
+// $example off$
+
+import static org.apache.spark.sql.types.DataTypes.*;
+
+/**
+ * An example demonstrating Imputer.
+ * Run with:
+ *   bin/run-example ml.JavaImputerExample
+ */
+public class JavaImputerExample {
+  public static void main(String[] args) {
+    SparkSession spark = SparkSession
+      .builder()
+      .appName("JavaImputerExample")
+      .getOrCreate();
+
+    // $example on$
+    List<Row> data = Arrays.asList(
+      RowFactory.create(1.0, Double.NaN),
+      RowFactory.create(2.0, Double.NaN),
+      RowFactory.create(Double.NaN, 3.0),
+      RowFactory.create(4.0, 4.0),
+      RowFactory.create(5.0, 5.0)
+    );
+    StructType schema = new StructType(new StructField[]{
+      createStructField("a", DoubleType, false),
+      createStructField("b", DoubleType, false)
+    });
+    Dataset<Row> df = spark.createDataFrame(data, schema);
+
+    Imputer imputer = new Imputer()
+      .setInputCols(new String[]{"a", "b"})
+      .setOutputCols(new String[]{"out_a", "out_b"});
+
+    ImputerModel model = imputer.fit(df);
+    model.transform(df).show();
+    // $example off$
+
+    spark.stop();
+  }
+}

examples/src/main/python/ml/imputer_example.py

@@ -0,0 +1,50 @@
+#
+# 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.
+#
+
+# $example on$
+from pyspark.ml.feature import Imputer
+# $example off$
+from pyspark.sql import SparkSession
+
+"""
+An example demonstrating Imputer.
+Run with:
+  bin/spark-submit examples/src/main/python/ml/imputer_example.py
+"""
+
+if __name__ == "__main__":
+    spark = SparkSession\
+        .builder\
+        .appName("ImputerExample")\
+        .getOrCreate()
+
+    # $example on$
+    df = spark.createDataFrame([
+        (1.0, float("nan")),
+        (2.0, float("nan")),
+        (float("nan"), 3.0),
+        (4.0, 4.0),
+        (5.0, 5.0)
+    ], ["a", "b"])
+
+    imputer = Imputer(inputCols=["a", "b"], outputCols=["out_a", "out_b"])
+    model = imputer.fit(df)
+
+    model.transform(df).select("a", "b", "out_a", "out_b").show()
+    # $example off$
+
+    spark.stop()

examples/src/main/scala/org/apache/spark/examples/ml/ImputerExample.scala

@@ -0,0 +1,56 @@
+/*
+ * 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.examples.ml
+
+// $example on$
+import org.apache.spark.ml.feature.Imputer
+// $example off$
+import org.apache.spark.sql.SparkSession
+
+/**
+ * An example demonstrating Imputer.
+ * Run with:
+ *   bin/run-example ml.ImputerExample
+ */
+object ImputerExample {
+
+  def main(args: Array[String]): Unit = {
+    val spark = SparkSession.builder
+      .appName("ImputerExample")
+      .getOrCreate()
+
+    // $example on$
+    val df = spark.createDataFrame(Seq(
+      (1.0, Double.NaN),
+      (2.0, Double.NaN),
+      (Double.NaN, 3.0),
+      (4.0, 4.0),
+      (5.0, 5.0)
+    )).toDF("a", "b")
+
+    val imputer = new Imputer()
+      .setInputCols(Array("a", "b"))
+      .setOutputCols(Array("out_a", "out_b"))
+
+    val model = imputer.fit(df)
+    model.transform(df).show()
+    // $example off$
+
+    spark.stop()
+  }
+}

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

@@ -35,7 +35,7 @@ import org.apache.spark.sql.types._
 private[feature] trait ImputerParams extends Params with HasInputCols {
 
   /**
-   * The imputation strategy.
+   * 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.
    * Default: mean