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diff --git a/docs/ml-features.md b/docs/ml-features.md index 9ee5696122..f88c0248c1 100644 --- a/docs/ml-features.md +++ b/docs/ml-features.md @@ -456,6 +456,122 @@ for expanded in polyDF.select("polyFeatures").take(3): </div> </div> +## StringIndexer + +`StringIndexer` encodes a string column of labels to a column of label indices. +The indices are in `[0, numLabels)`, ordered by label frequencies. +So the most frequent label gets index `0`. +If the input column is numeric, we cast it to string and index the string values. + +**Examples** + +Assume that we have the following DataFrame with columns `id` and `category`: + +~~~~ + id | category +----|---------- + 0 | a + 1 | b + 2 | c + 3 | a + 4 | a + 5 | c +~~~~ + +`category` is a string column with three labels: "a", "b", and "c". +Applying `StringIndexer` with `category` as the input column and `categoryIndex` as the output +column, we should get the following: + +~~~~ + id | category | categoryIndex +----|----------|--------------- + 0 | a | 0.0 + 1 | b | 2.0 + 2 | c | 1.0 + 3 | a | 0.0 + 4 | a | 0.0 + 5 | c | 1.0 +~~~~ + +"a" gets index `0` because it is the most frequent, followed by "c" with index `1` and "b" with +index `2`. + +<div class="codetabs"> + +<div data-lang="scala" markdown="1"> + +[`StringIndexer`](api/scala/index.html#org.apache.spark.ml.feature.StringIndexer) takes an input +column name and an output column name. + +{% highlight scala %} +import org.apache.spark.ml.feature.StringIndexer + +val df = sqlContext.createDataFrame( + Seq((0, "a"), (1, "b"), (2, "c"), (3, "a"), (4, "a"), (5, "c")) +).toDF("id", "category") +val indexer = new StringIndexer() + .setInputCol("category") + .setOutputCol("categoryIndex") +val indexed = indexer.fit(df).transform(df) +indexed.show() +{% endhighlight %} +</div> + +<div data-lang="java" markdown="1"> +[`StringIndexer`](api/java/org/apache/spark/ml/feature/StringIndexer.html) takes an input column +name and an output column name. + +{% highlight java %} +import java.util.Arrays; + +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.ml.feature.StringIndexer; +import org.apache.spark.sql.DataFrame; +import org.apache.spark.sql.Row; +import org.apache.spark.sql.RowFactory; +import org.apache.spark.sql.types.StructField; +import org.apache.spark.sql.types.StructType; +import static org.apache.spark.sql.types.DataTypes.*; + +JavaRDD<Row> jrdd = jsc.parallelize(Arrays.asList( + RowFactory.create(0, "a"), + RowFactory.create(1, "b"), + RowFactory.create(2, "c"), + RowFactory.create(3, "a"), + RowFactory.create(4, "a"), + RowFactory.create(5, "c") +)); +StructType schema = new StructType(new StructField[] { + createStructField("id", DoubleType, false), + createStructField("category", StringType, false) +}); +DataFrame df = sqlContext.createDataFrame(jrdd, schema); +StringIndexer indexer = new StringIndexer() + .setInputCol("category") + .setOutputCol("categoryIndex"); +DataFrame indexed = indexer.fit(df).transform(df); +indexed.show(); +{% endhighlight %} +</div> + +<div data-lang="python" markdown="1"> + +[`StringIndexer`](api/python/pyspark.ml.html#pyspark.ml.feature.StringIndexer) takes an input +column name and an output column name. + +{% highlight python %} +from pyspark.ml.feature import StringIndexer + +df = sqlContext.createDataFrame( + [(0, "a"), (1, "b"), (2, "c"), (3, "a"), (4, "a"), (5, "c")], + ["id", "category"]) +indexer = StringIndexer(inputCol="category", outputCol="categoryIndex") +indexed = indexer.fit(df).transform(df) +indexed.show() +{% endhighlight %} +</div> +</div> + ## OneHotEncoder [One-hot encoding](http://en.wikipedia.org/wiki/One-hot) maps a column of label indices to a column of binary vectors, with at most a single one-value. This encoding allows algorithms which expect continuous features, such as Logistic Regression, to use categorical features |