From 8e0a072f78b4902d5f7ccc6b15232ed202a117f9 Mon Sep 17 00:00:00 2001
From: Eric Liang <ekl@databricks.com>
Date: Wed, 19 Aug 2015 15:43:08 -0700
Subject: [SPARK-9895] User Guide for RFormula Feature Transformer

mengxr

Author: Eric Liang <ekl@databricks.com>

Closes #8293 from ericl/docs-2.
---
 docs/ml-features.md | 108 ++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 108 insertions(+)

(limited to 'docs/ml-features.md')

diff --git a/docs/ml-features.md b/docs/ml-features.md
index d0e8eeb7a7..6309db97be 100644
--- a/docs/ml-features.md
+++ b/docs/ml-features.md
@@ -1477,3 +1477,111 @@ print(output.select("features", "clicked").first())
 </div>
 </div>
 
+## RFormula
+
+`RFormula` selects columns specified by an [R model formula](https://stat.ethz.ch/R-manual/R-devel/library/stats/html/formula.html). It produces a vector column of features and a double column of labels. Like when formulas are used in R for linear regression, string input columns will be one-hot encoded, and numeric columns will be cast to doubles. If not already present in the DataFrame, the output label column will be created from the specified response variable in the formula.
+
+**Examples**
+
+Assume that we have a DataFrame with the columns `id`, `country`, `hour`, and `clicked`:
+
+~~~
+id | country | hour | clicked
+---|---------|------|---------
+ 7 | "US"    | 18   | 1.0
+ 8 | "CA"    | 12   | 0.0
+ 9 | "NZ"    | 15   | 0.0
+~~~
+
+If we use `RFormula` with a formula string of `clicked ~ country + hour`, which indicates that we want to
+predict `clicked` based on `country` and `hour`, after transformation we should get the following DataFrame:
+
+~~~
+id | country | hour | clicked | features         | label
+---|---------|------|---------|------------------|-------
+ 7 | "US"    | 18   | 1.0     | [0.0, 0.0, 18.0] | 1.0
+ 8 | "CA"    | 12   | 0.0     | [0.0, 1.0, 12.0] | 0.0
+ 9 | "NZ"    | 15   | 0.0     | [1.0, 0.0, 15.0] | 0.0
+~~~
+
+<div class="codetabs">
+<div data-lang="scala" markdown="1">
+
+[`RFormula`](api/scala/index.html#org.apache.spark.ml.feature.RFormula) takes an R formula string, and optional parameters for the names of its output columns.
+
+{% highlight scala %}
+import org.apache.spark.ml.feature.RFormula
+
+val dataset = sqlContext.createDataFrame(Seq(
+  (7, "US", 18, 1.0),
+  (8, "CA", 12, 0.0),
+  (9, "NZ", 15, 0.0)
+)).toDF("id", "country", "hour", "clicked")
+val formula = new RFormula()
+  .setFormula("clicked ~ country + hour")
+  .setFeaturesCol("features")
+  .setLabelCol("label")
+val output = formula.fit(dataset).transform(dataset)
+output.select("features", "label").show()
+{% endhighlight %}
+</div>
+
+<div data-lang="java" markdown="1">
+
+[`RFormula`](api/java/org/apache/spark/ml/feature/RFormula.html) takes an R formula string, and optional parameters for the names of its output columns.
+
+{% highlight java %}
+import java.util.Arrays;
+
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.ml.feature.RFormula;
+import org.apache.spark.sql.DataFrame;
+import org.apache.spark.sql.Row;
+import org.apache.spark.sql.RowFactory;
+import org.apache.spark.sql.types.*;
+import static org.apache.spark.sql.types.DataTypes.*;
+
+StructType schema = createStructType(new StructField[] {
+  createStructField("id", IntegerType, false),
+  createStructField("country", StringType, false),
+  createStructField("hour", IntegerType, false),
+  createStructField("clicked", DoubleType, false)
+});
+JavaRDD<Row> rdd = jsc.parallelize(Arrays.asList(
+  RowFactory.create(7, "US", 18, 1.0),
+  RowFactory.create(8, "CA", 12, 0.0),
+  RowFactory.create(9, "NZ", 15, 0.0)
+));
+DataFrame dataset = sqlContext.createDataFrame(rdd, schema);
+
+RFormula formula = new RFormula()
+  .setFormula("clicked ~ country + hour")
+  .setFeaturesCol("features")
+  .setLabelCol("label");
+
+DataFrame output = formula.fit(dataset).transform(dataset);
+output.select("features", "label").show();
+{% endhighlight %}
+</div>
+
+<div data-lang="python" markdown="1">
+
+[`RFormula`](api/python/pyspark.ml.html#pyspark.ml.feature.RFormula) takes an R formula string, and optional parameters for the names of its output columns.
+
+{% highlight python %}
+from pyspark.ml.feature import RFormula
+
+dataset = sqlContext.createDataFrame(
+    [(7, "US", 18, 1.0),
+     (8, "CA", 12, 0.0),
+     (9, "NZ", 15, 0.0)],
+    ["id", "country", "hour", "clicked"])
+formula = RFormula(
+    formula="clicked ~ country + hour",
+    featuresCol="features",
+    labelCol="label")
+output = formula.fit(dataset).transform(dataset)
+output.select("features", "label").show()
+{% endhighlight %}
+</div>
+</div>
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