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diff --git a/docs/ml-features.md b/docs/ml-features.md index 62de483898..89a9bad570 100644 --- a/docs/ml-features.md +++ b/docs/ml-features.md @@ -306,15 +306,111 @@ regexTokenizer = RegexTokenizer(inputCol="sentence", outputCol="words", pattern= </div> </div> +## StopWordsRemover +[Stop words](https://en.wikipedia.org/wiki/Stop_words) are words which +should be excluded from the input, typically because the words appear +frequently and don't carry as much meaning. + +`StopWordsRemover` takes as input a sequence of strings (e.g. the output +of a [Tokenizer](ml-features.html#tokenizer)) and drops all the stop +words from the input sequences. The list of stopwords is specified by +the `stopWords` parameter. We provide [a list of stop +words](http://ir.dcs.gla.ac.uk/resources/linguistic_utils/stop_words) by +default, accessible by calling `getStopWords` on a newly instantiated +`StopWordsRemover` instance. -## $n$-gram +**Examples** -An [n-gram](https://en.wikipedia.org/wiki/N-gram) is a sequence of $n$ tokens (typically words) for some integer $n$. The `NGram` class can be used to transform input features into $n$-grams. +Assume that we have the following DataFrame with columns `id` and `raw`: -`NGram` takes as input a sequence of strings (e.g. the output of a [Tokenizer](ml-features.html#tokenizer). The parameter `n` is used to determine the number of terms in each $n$-gram. The output will consist of a sequence of $n$-grams where each $n$-gram is represented by a space-delimited string of $n$ consecutive words. If the input sequence contains fewer than `n` strings, no output is produced. +~~~~ + id | raw +----|---------- + 0 | [I, saw, the, red, baloon] + 1 | [Mary, had, a, little, lamb] +~~~~ + +Applying `StopWordsRemover` with `raw` as the input column and `filtered` as the output +column, we should get the following: + +~~~~ + id | raw | filtered +----|-----------------------------|-------------------- + 0 | [I, saw, the, red, baloon] | [saw, red, baloon] + 1 | [Mary, had, a, little, lamb]|[Mary, little, lamb] +~~~~ + +In `filtered`, the stop words "I", "the", "had", and "a" have been +filtered out. <div class="codetabs"> + <div data-lang="scala" markdown="1"> + +[`StopWordsRemover`](api/scala/index.html#org.apache.spark.ml.feature.StopWordsRemover) +takes an input column name, an output column name, a list of stop words, +and a boolean indicating if the matches should be case sensitive (false +by default). + +{% highlight scala %} +import org.apache.spark.ml.feature.StopWordsRemover + +val remover = new StopWordsRemover() + .setInputCol("raw") + .setOutputCol("filtered") +val dataSet = sqlContext.createDataFrame(Seq( + (0, Seq("I", "saw", "the", "red", "baloon")), + (1, Seq("Mary", "had", "a", "little", "lamb")) +)).toDF("id", "raw") + +remover.transform(dataSet).show() +{% endhighlight %} +</div> + +<div data-lang="java" markdown="1"> + +[`StopWordsRemover`](api/java/org/apache/spark/ml/feature/StopWordsRemover.html) +takes an input column name, an output column name, a list of stop words, +and a boolean indicating if the matches should be case sensitive (false +by default). + +{% highlight java %} +import java.util.Arrays; + +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.ml.feature.StopWordsRemover; +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; + +StopWordsRemover remover = new StopWordsRemover() + .setInputCol("raw") + .setOutputCol("filtered"); + +JavaRDD<Row> rdd = jsc.parallelize(Arrays.asList( + RowFactory.create(Arrays.asList("I", "saw", "the", "red", "baloon")), + RowFactory.create(Arrays.asList("Mary", "had", "a", "little", "lamb")) +)); +StructType schema = new StructType(new StructField[] { + new StructField("raw", DataTypes.createArrayType(DataTypes.StringType), false, Metadata.empty()) +}); +DataFrame dataset = jsql.createDataFrame(rdd, schema); + +remover.transform(dataset).show(); +{% endhighlight %} +</div> +</div> + +## $n$-gram + +An [n-gram](https://en.wikipedia.org/wiki/N-gram) is a sequence of $n$ tokens (typically words) for some integer $n$. The `NGram` class can be used to transform input features into $n$-grams. + +`NGram` takes as input a sequence of strings (e.g. the output of a [Tokenizer](ml-features.html#tokenizer)). The parameter `n` is used to determine the number of terms in each $n$-gram. The output will consist of a sequence of $n$-grams where each $n$-gram is represented by a space-delimited string of $n$ consecutive words. If the input sequence contains fewer than `n` strings, no output is produced. + <div class="codetabs"> <div data-lang="scala" markdown="1"> |