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From MLnick <...@git.apache.org>
Subject [GitHub] spark pull request: [SPARK-15182] [ML] Copy MLlib doc to ML: ml.fe...
Date Wed, 11 May 2016 08:19:51 GMT
Github user MLnick commented on a diff in the pull request:

https://github.com/apache/spark/pull/12957#discussion_r62807971

--- Diff: docs/ml-features.md ---
@@ -18,27 +18,58 @@ This section covers algorithms for working with features, roughly
divided into t

# Feature Extractors

-## TF-IDF (HashingTF and IDF)
-
-[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.
+## TF-IDF
+
+[Term frequency-inverse document frequency (TF-IDF)](http://en.wikipedia.org/wiki/Tf%E2%80%93idf)

+is a feature vectorization method widely used in text mining to reflect the importance
of a term
+to a document in the corpus. Denote a term by $t$, a document by $d$, and the corpus
by $D$.
+Term frequency $TF(t, d)$ is the number of times that term $t$ appears in document
$d$, while
+document frequency $DF(t, D)$ is the number of documents that contains term $t$.
If we only use
+term frequency to measure the importance, it is very easy to over-emphasize terms that
appear very
+often but carry little information about the document, e.g., "a", "the", and "of". If
a term appears
+very often across the corpus, it means it doesn't carry special information about a particular
document.
+Inverse document frequency is a numerical measure of how much information a term provides:
+$+IDF(t, D) = \log \frac{|D| + 1}{DF(t, D) + 1}, +$
+where $|D|$ is the total number of documents in the corpus. Since logarithm is used,
if a term
+appears in all documents, its IDF value becomes 0. Note that a smoothing term is applied
to avoid
+dividing by zero for terms outside the corpus. The TF-IDF measure is simply the product
of TF and IDF:
+$+TFIDF(t, d, D) = TF(t, d) \cdot IDF(t, D). +$
+There are several variants on the definition of term frequency and document frequency.
+In MLlib, we separate TF and IDF to make them flexible.

**TF**: Both HashingTF and CountVectorizer can be used to generate the term frequency
vectors.

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.
+HashingTF utilizes the [hashing trick](http://en.wikipedia.org/wiki/Feature_hashing).
+A raw feature is mapped into an index (term) by applying a hash function. Then term frequencies

+are calculated based on the mapped indices. This approach avoids the need to compute
a global
+term-to-index map, which can be expensive for a large corpus, but it suffers from potential
hash
+collisions, where different raw features may become the same term after hashing. To reduce
the
+chance of collision, we can increase the target feature dimension, i.e., the number of
buckets
+of the hash table. The default feature dimension is $2^{18} = 262,144$.

CountVectorizer converts text documents to vectors of term counts. Refer to [CountVectorizer
](ml-features.html#countvectorizer) for more details.

**IDF**: IDF is an Estimator which is fit on a dataset and produces an IDFModel.
The
-IDFModel takes feature vectors (generally created from HashingTF or CountVectorizer)
and scales each column.
-Intuitively, it down-weights columns which appear frequently in a corpus.
+IDFModel takes feature vectors (generally created from HashingTF or CountVectorizer)
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 RDD-based API.

-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.
+**Note:** spark.mllib doesn't provide tools for text segmentation.
+We refer users to the [Stanford NLP Group](http://nlp.stanford.edu/) and
+[scalanlp/chalk](https://github.com/scalanlp/chalk).

-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.
+In the following code segment, we start with a set of sentences.  We split each sentence
into words
--- End diff --

Let's add an ### Example header above this line.

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