[SPARK-9680] [MLLIB] [DOC] StopWordsRemovers user guide and Java compatibility test
* Adds user guide for ml.feature.StopWordsRemovers, ran code examples on my machine * Cleans up scaladocs for public methods * Adds test for Java compatibility * Follow up Python user guide code example is tracked by SPARK-10249 Author: Feynman Liang <fliang@databricks.com> Closes #8436 from feynmanliang/SPARK-10230.
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Feynman Liang
Xiangrui Meng
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@ -306,15 +306,111 @@ regexTokenizer = RegexTokenizer(inputCol="sentence", outputCol="words", pattern=
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</div>
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</div>
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## StopWordsRemover
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[Stop words](https://en.wikipedia.org/wiki/Stop_words) are words which
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should be excluded from the input, typically because the words appear
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frequently and don't carry as much meaning.
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`StopWordsRemover` takes as input a sequence of strings (e.g. the output
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of a [Tokenizer](ml-features.html#tokenizer)) and drops all the stop
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words from the input sequences. The list of stopwords is specified by
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the `stopWords` parameter. We provide [a list of stop
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words](http://ir.dcs.gla.ac.uk/resources/linguistic_utils/stop_words) by
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default, accessible by calling `getStopWords` on a newly instantiated
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`StopWordsRemover` instance.
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**Examples**
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Assume that we have the following DataFrame with columns `id` and `raw`:
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~~~~
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id | raw
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----|----------
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0 | [I, saw, the, red, baloon]
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1 | [Mary, had, a, little, lamb]
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~~~~
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Applying `StopWordsRemover` with `raw` as the input column and `filtered` as the output
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column, we should get the following:
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~~~~
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id | raw | filtered
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----|-----------------------------|--------------------
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0 | [I, saw, the, red, baloon] | [saw, red, baloon]
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1 | [Mary, had, a, little, lamb]|[Mary, little, lamb]
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~~~~
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In `filtered`, the stop words "I", "the", "had", and "a" have been
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filtered out.
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<div class="codetabs">
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<div data-lang="scala" markdown="1">
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[`StopWordsRemover`](api/scala/index.html#org.apache.spark.ml.feature.StopWordsRemover)
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takes an input column name, an output column name, a list of stop words,
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and a boolean indicating if the matches should be case sensitive (false
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by default).
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{% highlight scala %}
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import org.apache.spark.ml.feature.StopWordsRemover
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val remover = new StopWordsRemover()
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.setInputCol("raw")
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.setOutputCol("filtered")
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val dataSet = sqlContext.createDataFrame(Seq(
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(0, Seq("I", "saw", "the", "red", "baloon")),
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(1, Seq("Mary", "had", "a", "little", "lamb"))
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)).toDF("id", "raw")
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remover.transform(dataSet).show()
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{% endhighlight %}
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</div>
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<div data-lang="java" markdown="1">
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[`StopWordsRemover`](api/java/org/apache/spark/ml/feature/StopWordsRemover.html)
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takes an input column name, an output column name, a list of stop words,
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and a boolean indicating if the matches should be case sensitive (false
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by default).
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{% highlight java %}
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import java.util.Arrays;
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import org.apache.spark.api.java.JavaRDD;
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import org.apache.spark.ml.feature.StopWordsRemover;
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import org.apache.spark.sql.DataFrame;
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import org.apache.spark.sql.Row;
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import org.apache.spark.sql.RowFactory;
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import org.apache.spark.sql.types.DataTypes;
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import org.apache.spark.sql.types.Metadata;
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import org.apache.spark.sql.types.StructField;
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import org.apache.spark.sql.types.StructType;
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StopWordsRemover remover = new StopWordsRemover()
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.setInputCol("raw")
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.setOutputCol("filtered");
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JavaRDD<Row> rdd = jsc.parallelize(Arrays.asList(
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RowFactory.create(Arrays.asList("I", "saw", "the", "red", "baloon")),
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RowFactory.create(Arrays.asList("Mary", "had", "a", "little", "lamb"))
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));
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StructType schema = new StructType(new StructField[] {
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new StructField("raw", DataTypes.createArrayType(DataTypes.StringType), false, Metadata.empty())
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});
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DataFrame dataset = jsql.createDataFrame(rdd, schema);
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remover.transform(dataset).show();
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{% endhighlight %}
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</div>
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</div>
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## $n$-gram
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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.
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`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.
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`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.
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<div class="codetabs">
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<div data-lang="scala" markdown="1">
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<div class="codetabs">
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<div data-lang="scala" markdown="1">
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@ -0,0 +1,72 @@
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/*
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* Licensed to the Apache Software Foundation (ASF) under one or more
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* contributor license agreements. See the NOTICE file distributed with
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* this work for additional information regarding copyright ownership.
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* The ASF licenses this file to You under the Apache License, Version 2.0
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* (the "License"); you may not use this file except in compliance with
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* the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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package org.apache.spark.ml.feature;
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import java.util.Arrays;
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import org.junit.After;
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import org.junit.Before;
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import org.junit.Test;
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import org.apache.spark.api.java.JavaRDD;
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import org.apache.spark.api.java.JavaSparkContext;
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import org.apache.spark.sql.DataFrame;
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import org.apache.spark.sql.Row;
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import org.apache.spark.sql.RowFactory;
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import org.apache.spark.sql.SQLContext;
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import org.apache.spark.sql.types.DataTypes;
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import org.apache.spark.sql.types.Metadata;
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import org.apache.spark.sql.types.StructField;
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import org.apache.spark.sql.types.StructType;
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public class JavaStopWordsRemoverSuite {
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private transient JavaSparkContext jsc;
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private transient SQLContext jsql;
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@Before
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public void setUp() {
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jsc = new JavaSparkContext("local", "JavaStopWordsRemoverSuite");
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jsql = new SQLContext(jsc);
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}
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@After
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public void tearDown() {
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jsc.stop();
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jsc = null;
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}
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@Test
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public void javaCompatibilityTest() {
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StopWordsRemover remover = new StopWordsRemover()
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.setInputCol("raw")
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.setOutputCol("filtered");
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JavaRDD<Row> rdd = jsc.parallelize(Arrays.asList(
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RowFactory.create(Arrays.asList("I", "saw", "the", "red", "baloon")),
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RowFactory.create(Arrays.asList("Mary", "had", "a", "little", "lamb"))
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));
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StructType schema = new StructType(new StructField[] {
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new StructField("raw", DataTypes.createArrayType(DataTypes.StringType), false, Metadata.empty())
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});
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DataFrame dataset = jsql.createDataFrame(rdd, schema);
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remover.transform(dataset).collect();
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}
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}
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