Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/6127#discussion_r30863325
--- Diff: docs/ml-features.md ---
@@ -18,30 +18,38 @@ This section covers algorithms for working with
features, roughly divided into t
# Feature Extractors
-## Hashing Term-Frequency (HashingTF)
+## TF-IDF (HashingTF and IDF)
-`HashingTF` is a `Transformer` which takes sets of terms (e.g., `String`
terms can be sets of words) and converts those sets into fixed-length feature
vectors.
-The algorithm combines [Term Frequency
(TF)](http://en.wikipedia.org/wiki/Tf%E2%80%93idf) counts with the [hashing
trick](http://en.wikipedia.org/wiki/Feature_hashing) for dimensionality
reduction. Please refer to the [MLlib user guide on
TF-IDF](mllib-feature-extraction.html#tf-idf) for more details on
Term-Frequency.
+[Term Frequency-Inverse Document Frequency
(TF-IDF)](http://en.wikipedia.org/wiki/Tf%E2%80%93idf) is a common text
pre-processing step. In Spark ML, TF-IDF is separate into two parts: TF
(+hashing) and IDF.
-HashingTF is implemented in
-[HashingTF](api/scala/index.html#org.apache.spark.ml.feature.HashingTF).
-In the following code segment, we start with a set of sentences. We split
each sentence into words using `Tokenizer`. For each sentence (bag of words),
we hash it into a feature vector. This feature vector could then be passed to
a learning algorithm.
+**TF**: `HashingTF` is a `Transformer` which takes sets of terms and
converts those sets into fixed-length feature vectors. In text processing, a
"set of terms" might be a bag of words.
+The algorithm combines Term Frequency (TF) counts with the [hashing
trick](http://en.wikipedia.org/wiki/Feature_hashing) for dimensionality
reduction.
+
+**IDF**: `IDF` is an `Estimator` which fits on a dataset and produces an
`IDFModel`. The `IDFModel` takes feature vectors (generally created from
`HashingTF`) and scales each column. Intuitively, it down-weights columns
which appear frequently in a corpus.
+
+Please refer to the [MLlib user guide on
TF-IDF](mllib-feature-extraction.html#tf-idf) for more details on Term
Frequency and Inverse Document Frequency.
+For API details, refer to the [HashingTF API
docs](api/scala/index.html#org.apache.spark.ml.feature.HashingTF) and the [IDF
API docs](api/scala/index.html#org.apache.spark.ml.feature.IDF).
+
+In the following code segment, we start with a set of sentences. We split
each sentence into words using `Tokenizer`. For each sentence (bag of words),
we use `HashingTF` to hash the sentence into a feature vector. We use `IDF` to
rescale the feature vectors; this generally improves performance when using
text as features. Our feature vectors could then be passed to a learning
algorithm.
<div class="codetabs">
<div data-lang="scala" markdown="1">
{% highlight scala %}
-import org.apache.spark.ml.feature.{HashingTF, Tokenizer}
+import org.apache.spark.ml.feature.{HashingTF, IDF, Tokenizer}
-val sentenceDataFrame = sqlContext.createDataFrame(Seq(
+val sentenceData = sqlContext.createDataFrame(Seq(
(0, "Hi I heard about Spark"),
(0, "I wish Java could use case classes"),
(1, "Logistic regression models are neat")
)).toDF("label", "sentence")
val tokenizer = new
Tokenizer().setInputCol("sentence").setOutputCol("words")
-val wordsDataFrame = tokenizer.transform(sentenceDataFrame)
-val hashingTF = new
HashingTF().setInputCol("words").setOutputCol("features").setNumFeatures(20)
-val featurized = hashingTF.transform(wordsDataFrame)
-featurized.select("features", "label").take(3).foreach(println)
+val wordsData = tokenizer.transform(sentenceData)
+val hashingTF = new
HashingTF().setInputCol("words").setOutputCol("rawFeatures").setNumFeatures(20)
+val featurizedData = hashingTF.transform(wordsData)
+val idf = new IDF().setInputCol("rawFeatures").setOutputCol("features")
+val idfModel = idf.fit(featurizedData)
+val rescaledData = idfModel.transform(featurizedData)
+rescaledData.select("features", "label").take(3).foreach(println)
--- End diff --
I thought about that, but was worried users would copy this into their own
code and try to print out a huge dataset. What do you think?
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