[SPARK-28399][ML][PYTHON] implement RobustScaler

docs/ml-features.md

@@ -1009,6 +1009,51 @@ for more details on the API.
 </div>
 </div>
 
+
+## RobustScaler
+
+`RobustScaler` transforms a dataset of `Vector` rows, removing the median and scaling the data according to a specific quantile range (by default the IQR: Interquartile Range, quantile range between the 1st quartile and the 3rd quartile). Its behavior is quite similar to `StandardScaler`, however the median and the quantile range are used instead of mean and standard deviation, which make it robust to outliers. It takes parameters:
+
+* `lower`: 0.25 by default. Lower quantile to calculate quantile range, shared by all features.
+* `upper`: 0.75 by default. Upper quantile to calculate quantile range, shared by all features.
+* `withScaling`: True by default. Scales the data to quantile range.
+* `withCentering`: False by default. Centers the data with median before scaling. It will build a dense output, so take care when applying to sparse input.
+
+`RobustScaler` is an `Estimator` which can be `fit` on a dataset to produce a `RobustScalerModel`; this amounts to computing quantile statistics.  The model can then transform a `Vector` column in a dataset to have unit quantile range and/or zero median features.
+
+Note that if the quantile range of a feature is zero, it will return default `0.0` value in the `Vector` for that feature.
+
+**Examples**
+
+The following example demonstrates how to load a dataset in libsvm format and then normalize each feature to have unit quantile range.
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+Refer to the [RobustScaler Scala docs](api/scala/index.html#org.apache.spark.ml.feature.RobustScaler)
+for more details on the API.
+
+{% include_example scala/org/apache/spark/examples/ml/RobustScalerExample.scala %}
+</div>
+
+<div data-lang="java" markdown="1">
+
+Refer to the [RobustScaler Java docs](api/java/org/apache/spark/ml/feature/RobustScaler.html)
+for more details on the API.
+
+{% include_example java/org/apache/spark/examples/ml/JavaRobustScalerExample.java %}
+</div>
+
+<div data-lang="python" markdown="1">
+
+Refer to the [RobustScaler Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.RobustScaler)
+for more details on the API.
+
+{% include_example python/ml/robust_scaler_example.py %}
+</div>
+</div>
+
+
 ## MinMaxScaler
 
 `MinMaxScaler` transforms a dataset of `Vector` rows, rescaling each feature to a specific range (often [0, 1]).  It takes parameters:

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

@@ -0,0 +1,57 @@
+/*
+ * 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;
+
+import org.apache.spark.sql.SparkSession;
+
+// $example on$
+import org.apache.spark.ml.feature.RobustScaler;
+import org.apache.spark.ml.feature.RobustScalerModel;
+import org.apache.spark.sql.Dataset;
+import org.apache.spark.sql.Row;
+// $example off$
+
+public class JavaRobustScalerExample {
+  public static void main(String[] args) {
+    SparkSession spark = SparkSession
+      .builder()
+      .appName("JavaRobustScalerExample")
+      .getOrCreate();
+
+    // $example on$
+    Dataset<Row> dataFrame =
+      spark.read().format("libsvm").load("data/mllib/sample_libsvm_data.txt");
+
+    RobustScaler scaler = new RobustScaler()
+      .setInputCol("features")
+      .setOutputCol("scaledFeatures")
+      .setWithScaling(true)
+      .setWithCentering(false)
+      .setLower(0.25)
+      .setUpper(0.75);
+
+    // Compute summary statistics by fitting the RobustScaler
+    RobustScalerModel scalerModel = scaler.fit(dataFrame);
+
+    // Transform each feature to have unit quantile range.
+    Dataset<Row> scaledData = scalerModel.transform(dataFrame);
+    scaledData.show();
+    // $example off$
+    spark.stop();
+  }
+}

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

@@ -0,0 +1,45 @@
+#
+# 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.
+#
+
+from __future__ import print_function
+
+# $example on$
+from pyspark.ml.feature import RobustScaler
+# $example off$
+from pyspark.sql import SparkSession
+
+if __name__ == "__main__":
+    spark = SparkSession\
+        .builder\
+        .appName("RobustScalerExample")\
+        .getOrCreate()
+
+    # $example on$
+    dataFrame = spark.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
+    scaler = RobustScaler(inputCol="features", outputCol="scaledFeatures",
+                          withScaling=True, withCentering=False,
+                          lower=0.25, upper=0.75)
+
+    # Compute summary statistics by fitting the RobustScaler
+    scalerModel = scaler.fit(dataFrame)
+
+    # Transform each feature to have unit quantile range.
+    scaledData = scalerModel.transform(dataFrame)
+    scaledData.show()
+    # $example off$
+
+    spark.stop()

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

@@ -0,0 +1,55 @@
+/*
+ * 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.
+ */
+
+// scalastyle:off println
+package org.apache.spark.examples.ml
+
+// $example on$
+import org.apache.spark.ml.feature.RobustScaler
+// $example off$
+import org.apache.spark.sql.SparkSession
+
+object RobustScalerExample {
+  def main(args: Array[String]): Unit = {
+    val spark = SparkSession
+      .builder
+      .appName("RobustScalerExample")
+      .getOrCreate()
+
+    // $example on$
+    val dataFrame = spark.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
+
+    val scaler = new RobustScaler()
+      .setInputCol("features")
+      .setOutputCol("scaledFeatures")
+      .setWithScaling(true)
+      .setWithCentering(false)
+      .setLower(0.25)
+      .setUpper(0.75)
+
+    // Compute summary statistics by fitting the RobustScaler.
+    val scalerModel = scaler.fit(dataFrame)
+
+    // Transform each feature to have unit quantile range.
+    val scaledData = scalerModel.transform(dataFrame)
+    scaledData.show()
+    // $example off$
+
+    spark.stop()
+  }
+}
+// scalastyle:on println