From 871e85d9c14c6b19068cc732951a8ae8db61b411 Mon Sep 17 00:00:00 2001
From: Xusen Yin <yinxusen@gmail.com>
Date: Mon, 7 Dec 2015 13:16:47 -0800
Subject: [PATCH] [SPARK-11963][DOC] Add docs for QuantileDiscretizer
https://issues.apache.org/jira/browse/SPARK-11963
Author: Xusen Yin <yinxusen@gmail.com>
Closes #9962 from yinxusen/SPARK-11963.
---
docs/ml-features.md | 65 +++++++++++++++++
.../ml/JavaQuantileDiscretizerExample.java | 71 +++++++++++++++++++
.../ml/QuantileDiscretizerExample.scala | 49 +++++++++++++
3 files changed, 185 insertions(+)
create mode 100644 examples/src/main/java/org/apache/spark/examples/ml/JavaQuantileDiscretizerExample.java
create mode 100644 examples/src/main/scala/org/apache/spark/examples/ml/QuantileDiscretizerExample.scala
diff --git a/docs/ml-features.md b/docs/ml-features.md
index 05c2c96c5e..b499d6ec3b 100644
--- a/docs/ml-features.md
+++ b/docs/ml-features.md
@@ -1705,6 +1705,71 @@ print(output.select("features", "clicked").first())
</div>
</div>
+## QuantileDiscretizer
+
+`QuantileDiscretizer` takes a column with continuous features and outputs a column with binned
+categorical features.
+The bin ranges are chosen by taking a sample of the data and dividing it into roughly equal parts.
+The lower and upper bin bounds will be `-Infinity` and `+Infinity`, covering all real values.
+This attempts to find `numBuckets` partitions based on a sample of the given input data, but it may
+find fewer depending on the data sample values.
+
+Note that the result may be different every time you run it, since the sample strategy behind it is
+non-deterministic.
+
+**Examples**
+
+Assume that we have a DataFrame with the columns `id`, `hour`:
+
+~~~
+ id | hour
+----|------
+ 0 | 18.0
+----|------
+ 1 | 19.0
+----|------
+ 2 | 8.0
+----|------
+ 3 | 5.0
+----|------
+ 4 | 2.2
+~~~
+
+`hour` is a continuous feature with `Double` type. We want to turn the continuous feature into
+categorical one. Given `numBuckets = 3`, we should get the following DataFrame:
+
+~~~
+ id | hour | result
+----|------|------
+ 0 | 18.0 | 2.0
+----|------|------
+ 1 | 19.0 | 2.0
+----|------|------
+ 2 | 8.0 | 1.0
+----|------|------
+ 3 | 5.0 | 1.0
+----|------|------
+ 4 | 2.2 | 0.0
+~~~
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+Refer to the [QuantileDiscretizer Scala docs](api/scala/index.html#org.apache.spark.ml.feature.QuantileDiscretizer)
+for more details on the API.
+
+{% include_example scala/org/apache/spark/examples/ml/QuantileDiscretizerExample.scala %}
+</div>
+
+<div data-lang="java" markdown="1">
+
+Refer to the [QuantileDiscretizer Java docs](api/java/org/apache/spark/ml/feature/QuantileDiscretizer.html)
+for more details on the API.
+
+{% include_example java/org/apache/spark/examples/ml/JavaQuantileDiscretizerExample.java %}
+</div>
+</div>
+
# Feature Selectors
## VectorSlicer
diff --git a/examples/src/main/java/org/apache/spark/examples/ml/JavaQuantileDiscretizerExample.java b/examples/src/main/java/org/apache/spark/examples/ml/JavaQuantileDiscretizerExample.java
new file mode 100644
index 0000000000..251ae79d9a
--- /dev/null
+++ b/examples/src/main/java/org/apache/spark/examples/ml/JavaQuantileDiscretizerExample.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;
+
+import org.apache.spark.SparkConf;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.sql.SQLContext;
+// $example on$
+import java.util.Arrays;
+
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.ml.feature.QuantileDiscretizer;
+import org.apache.spark.sql.DataFrame;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.RowFactory;
+import org.apache.spark.sql.types.DataTypes;
+import org.apache.spark.sql.types.Metadata;
+import org.apache.spark.sql.types.StructField;
+import org.apache.spark.sql.types.StructType;
+// $example off$
+
+public class JavaQuantileDiscretizerExample {
+ public static void main(String[] args) {
+ SparkConf conf = new SparkConf().setAppName("JavaQuantileDiscretizerExample");
+ JavaSparkContext jsc = new JavaSparkContext(conf);
+ SQLContext sqlContext = new SQLContext(jsc);
+
+ // $example on$
+ JavaRDD<Row> jrdd = jsc.parallelize(
+ Arrays.asList(
+ RowFactory.create(0, 18.0),
+ RowFactory.create(1, 19.0),
+ RowFactory.create(2, 8.0),
+ RowFactory.create(3, 5.0),
+ RowFactory.create(4, 2.2)
+ )
+ );
+
+ StructType schema = new StructType(new StructField[]{
+ new StructField("id", DataTypes.IntegerType, false, Metadata.empty()),
+ new StructField("hour", DataTypes.DoubleType, false, Metadata.empty())
+ });
+
+ DataFrame df = sqlContext.createDataFrame(jrdd, schema);
+
+ QuantileDiscretizer discretizer = new QuantileDiscretizer()
+ .setInputCol("hour")
+ .setOutputCol("result")
+ .setNumBuckets(3);
+
+ DataFrame result = discretizer.fit(df).transform(df);
+ result.show();
+ // $example off$
+ jsc.stop();
+ }
+}
diff --git a/examples/src/main/scala/org/apache/spark/examples/ml/QuantileDiscretizerExample.scala b/examples/src/main/scala/org/apache/spark/examples/ml/QuantileDiscretizerExample.scala
new file mode 100644
index 0000000000..8f29b7eaa6
--- /dev/null
+++ b/examples/src/main/scala/org/apache/spark/examples/ml/QuantileDiscretizerExample.scala
@@ -0,0 +1,49 @@
+/*
+ * 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.QuantileDiscretizer
+// $example off$
+import org.apache.spark.sql.SQLContext
+import org.apache.spark.{SparkConf, SparkContext}
+
+object QuantileDiscretizerExample {
+ def main(args: Array[String]) {
+ val conf = new SparkConf().setAppName("QuantileDiscretizerExample")
+ val sc = new SparkContext(conf)
+ val sqlContext = new SQLContext(sc)
+ import sqlContext.implicits._
+
+ // $example on$
+ val data = Array((0, 18.0), (1, 19.0), (2, 8.0), (3, 5.0), (4, 2.2))
+ val df = sc.parallelize(data).toDF("id", "hour")
+
+ val discretizer = new QuantileDiscretizer()
+ .setInputCol("hour")
+ .setOutputCol("result")
+ .setNumBuckets(3)
+
+ val result = discretizer.fit(df).transform(df)
+ result.show()
+ // $example off$
+ sc.stop()
+ }
+}
+// scalastyle:on println
--
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