---
layout: global
title: Extracting, transforming and selecting features
displayTitle: Extracting, transforming and selecting features
---
This section covers algorithms for working with features, roughly divided into these groups:
* Extraction: Extracting features from "raw" data
* Transformation: Scaling, converting, or modifying features
* Selection: Selecting a subset from a larger set of features
* Locality Sensitive Hashing (LSH): This class of algorithms combines aspects of feature transformation with other algorithms.
**Table of Contents**
* This will become a table of contents (this text will be scraped).
{:toc}
# Feature Extractors
## 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.
`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. The hash function
used here is [MurmurHash 3](https://en.wikipedia.org/wiki/MurmurHash). 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. Since a simple modulo is used to transform the hash function to a column index,
it is advisable to use a power of two as the feature dimension, otherwise the features will
not be mapped evenly to the columns. The default feature dimension is `$2^{18} = 262,144$`.
An optional binary toggle parameter controls term frequency counts. When set to true all nonzero
frequency counts are set to 1. This is especially useful for discrete probabilistic models that
model binary, rather than integer, counts.
`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.
**Note:** `spark.ml` 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).
**Examples**
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.
Refer to the [HashingTF Scala docs](api/scala/index.html#org.apache.spark.ml.feature.HashingTF) and
the [IDF Scala docs](api/scala/index.html#org.apache.spark.ml.feature.IDF) for more details on the API.
{% include_example scala/org/apache/spark/examples/ml/TfIdfExample.scala %}
Refer to the [HashingTF Java docs](api/java/org/apache/spark/ml/feature/HashingTF.html) and the
[IDF Java docs](api/java/org/apache/spark/ml/feature/IDF.html) for more details on the API.
{% include_example java/org/apache/spark/examples/ml/JavaTfIdfExample.java %}
Refer to the [HashingTF Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.HashingTF) and
the [IDF Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.IDF) for more details on the API.
{% include_example python/ml/tf_idf_example.py %}
## Word2Vec
`Word2Vec` is an `Estimator` which takes sequences of words representing documents and trains a
`Word2VecModel`. The model maps each word to a unique fixed-size vector. The `Word2VecModel`
transforms each document into a vector using the average of all words in the document; this vector
can then be used as features for prediction, document similarity calculations, etc.
Please refer to the [MLlib user guide on Word2Vec](mllib-feature-extraction.html#word2vec) for more
details.
**Examples**
In the following code segment, we start with a set of documents, each of which is represented as a sequence of words. For each document, we transform it into a feature vector. This feature vector could then be passed to a learning algorithm.
Refer to the [Word2Vec Scala docs](api/scala/index.html#org.apache.spark.ml.feature.Word2Vec)
for more details on the API.
{% include_example scala/org/apache/spark/examples/ml/Word2VecExample.scala %}
Refer to the [Word2Vec Java docs](api/java/org/apache/spark/ml/feature/Word2Vec.html)
for more details on the API.
{% include_example java/org/apache/spark/examples/ml/JavaWord2VecExample.java %}
Refer to the [Word2Vec Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.Word2Vec)
for more details on the API.
{% include_example python/ml/word2vec_example.py %}
## CountVectorizer
`CountVectorizer` and `CountVectorizerModel` aim to help convert a collection of text documents
to vectors of token counts. When an a-priori dictionary is not available, `CountVectorizer` can
be used as an `Estimator` to extract the vocabulary, and generates a `CountVectorizerModel`. The
model produces sparse representations for the documents over the vocabulary, which can then be
passed to other algorithms like LDA.
During the fitting process, `CountVectorizer` will select the top `vocabSize` words ordered by
term frequency across the corpus. An optional parameter `minDF` also affects the fitting process
by specifying the minimum number (or fraction if < 1.0) of documents a term must appear in to be
included in the vocabulary. Another optional binary toggle parameter controls the output vector.
If set to true all nonzero counts are set to 1. This is especially useful for discrete probabilistic
models that model binary, rather than integer, counts.
**Examples**
Assume that we have the following DataFrame with columns `id` and `texts`:
~~~~
id | texts
----|----------
0 | Array("a", "b", "c")
1 | Array("a", "b", "b", "c", "a")
~~~~
each row in `texts` is a document of type Array[String].
Invoking fit of `CountVectorizer` produces a `CountVectorizerModel` with vocabulary (a, b, c).
Then the output column "vector" after transformation contains:
~~~~
id | texts | vector
----|---------------------------------|---------------
0 | Array("a", "b", "c") | (3,[0,1,2],[1.0,1.0,1.0])
1 | Array("a", "b", "b", "c", "a") | (3,[0,1,2],[2.0,2.0,1.0])
~~~~
Each vector represents the token counts of the document over the vocabulary.
Refer to the [CountVectorizer Scala docs](api/scala/index.html#org.apache.spark.ml.feature.CountVectorizer)
and the [CountVectorizerModel Scala docs](api/scala/index.html#org.apache.spark.ml.feature.CountVectorizerModel)
for more details on the API.
{% include_example scala/org/apache/spark/examples/ml/CountVectorizerExample.scala %}
Refer to the [CountVectorizer Java docs](api/java/org/apache/spark/ml/feature/CountVectorizer.html)
and the [CountVectorizerModel Java docs](api/java/org/apache/spark/ml/feature/CountVectorizerModel.html)
for more details on the API.
{% include_example java/org/apache/spark/examples/ml/JavaCountVectorizerExample.java %}
Refer to the [CountVectorizer Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.CountVectorizer)
and the [CountVectorizerModel Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.CountVectorizerModel)
for more details on the API.
{% include_example python/ml/count_vectorizer_example.py %}
# Feature Transformers
## Tokenizer
[Tokenization](http://en.wikipedia.org/wiki/Lexical_analysis#Tokenization) is the process of taking text (such as a sentence) and breaking it into individual terms (usually words). A simple [Tokenizer](api/scala/index.html#org.apache.spark.ml.feature.Tokenizer) class provides this functionality. The example below shows how to split sentences into sequences of words.
[RegexTokenizer](api/scala/index.html#org.apache.spark.ml.feature.RegexTokenizer) allows more
advanced tokenization based on regular expression (regex) matching.
By default, the parameter "pattern" (regex, default: `"\\s+"`) is used as delimiters to split the input text.
Alternatively, users can set parameter "gaps" to false indicating the regex "pattern" denotes
"tokens" rather than splitting gaps, and find all matching occurrences as the tokenization result.
**Examples**
Refer to the [Tokenizer Scala docs](api/scala/index.html#org.apache.spark.ml.feature.Tokenizer)
and the [RegexTokenizer Scala docs](api/scala/index.html#org.apache.spark.ml.feature.RegexTokenizer)
for more details on the API.
{% include_example scala/org/apache/spark/examples/ml/TokenizerExample.scala %}
Refer to the [Tokenizer Java docs](api/java/org/apache/spark/ml/feature/Tokenizer.html)
and the [RegexTokenizer Java docs](api/java/org/apache/spark/ml/feature/RegexTokenizer.html)
for more details on the API.
{% include_example java/org/apache/spark/examples/ml/JavaTokenizerExample.java %}
Refer to the [Tokenizer Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.Tokenizer) and
the [RegexTokenizer Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.RegexTokenizer)
for more details on the API.
{% include_example python/ml/tokenizer_example.py %}
## 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. Default stop words for some languages are accessible
by calling `StopWordsRemover.loadDefaultStopWords(language)`, for which available
options are "danish", "dutch", "english", "finnish", "french", "german", "hungarian",
"italian", "norwegian", "portuguese", "russian", "spanish", "swedish" and "turkish".
A boolean parameter `caseSensitive` indicates if the matches should be case sensitive
(false by default).
**Examples**
Assume that we have the following DataFrame with columns `id` and `raw`:
~~~~
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.
Refer to the [StopWordsRemover Scala docs](api/scala/index.html#org.apache.spark.ml.feature.StopWordsRemover)
for more details on the API.
{% include_example scala/org/apache/spark/examples/ml/StopWordsRemoverExample.scala %}
Refer to the [StopWordsRemover Java docs](api/java/org/apache/spark/ml/feature/StopWordsRemover.html)
for more details on the API.
{% include_example java/org/apache/spark/examples/ml/JavaStopWordsRemoverExample.java %}
Refer to the [StopWordsRemover Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.StopWordsRemover)
for more details on the API.
{% include_example python/ml/stopwords_remover_example.py %}
## $n$-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.
**Examples**
Refer to the [NGram Scala docs](api/scala/index.html#org.apache.spark.ml.feature.NGram)
for more details on the API.
{% include_example scala/org/apache/spark/examples/ml/NGramExample.scala %}
Refer to the [NGram Java docs](api/java/org/apache/spark/ml/feature/NGram.html)
for more details on the API.
{% include_example java/org/apache/spark/examples/ml/JavaNGramExample.java %}
Refer to the [NGram Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.NGram)
for more details on the API.
{% include_example python/ml/n_gram_example.py %}
## Binarizer
Binarization is the process of thresholding numerical features to binary (0/1) features.
`Binarizer` takes the common parameters `inputCol` and `outputCol`, as well as the `threshold`
for binarization. Feature values greater than the threshold are binarized to 1.0; values equal
to or less than the threshold are binarized to 0.0. Both Vector and Double types are supported
for `inputCol`.
**Examples**
Refer to the [Binarizer Scala docs](api/scala/index.html#org.apache.spark.ml.feature.Binarizer)
for more details on the API.
{% include_example scala/org/apache/spark/examples/ml/BinarizerExample.scala %}
Refer to the [Binarizer Java docs](api/java/org/apache/spark/ml/feature/Binarizer.html)
for more details on the API.
{% include_example java/org/apache/spark/examples/ml/JavaBinarizerExample.java %}
Refer to the [Binarizer Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.Binarizer)
for more details on the API.
{% include_example python/ml/binarizer_example.py %}
## PCA
[PCA](http://en.wikipedia.org/wiki/Principal_component_analysis) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. A [PCA](api/scala/index.html#org.apache.spark.ml.feature.PCA) class trains a model to project vectors to a low-dimensional space using PCA. The example below shows how to project 5-dimensional feature vectors into 3-dimensional principal components.
**Examples**
Refer to the [PCA Scala docs](api/scala/index.html#org.apache.spark.ml.feature.PCA)
for more details on the API.
{% include_example scala/org/apache/spark/examples/ml/PCAExample.scala %}
Refer to the [PCA Java docs](api/java/org/apache/spark/ml/feature/PCA.html)
for more details on the API.
{% include_example java/org/apache/spark/examples/ml/JavaPCAExample.java %}
Refer to the [PCA Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.PCA)
for more details on the API.
{% include_example python/ml/pca_example.py %}
## PolynomialExpansion
[Polynomial expansion](http://en.wikipedia.org/wiki/Polynomial_expansion) is the process of expanding your features into a polynomial space, which is formulated by an n-degree combination of original dimensions. A [PolynomialExpansion](api/scala/index.html#org.apache.spark.ml.feature.PolynomialExpansion) class provides this functionality. The example below shows how to expand your features into a 3-degree polynomial space.
**Examples**
Refer to the [PolynomialExpansion Scala docs](api/scala/index.html#org.apache.spark.ml.feature.PolynomialExpansion)
for more details on the API.
{% include_example scala/org/apache/spark/examples/ml/PolynomialExpansionExample.scala %}
Refer to the [PolynomialExpansion Java docs](api/java/org/apache/spark/ml/feature/PolynomialExpansion.html)
for more details on the API.
{% include_example java/org/apache/spark/examples/ml/JavaPolynomialExpansionExample.java %}
Refer to the [PolynomialExpansion Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.PolynomialExpansion)
for more details on the API.
{% include_example python/ml/polynomial_expansion_example.py %}
## Discrete Cosine Transform (DCT)
The [Discrete Cosine
Transform](https://en.wikipedia.org/wiki/Discrete_cosine_transform)
transforms a length $N$ real-valued sequence in the time domain into
another length $N$ real-valued sequence in the frequency domain. A
[DCT](api/scala/index.html#org.apache.spark.ml.feature.DCT) class
provides this functionality, implementing the
[DCT-II](https://en.wikipedia.org/wiki/Discrete_cosine_transform#DCT-II)
and scaling the result by $1/\sqrt{2}$ such that the representing matrix
for the transform is unitary. No shift is applied to the transformed
sequence (e.g. the $0$th element of the transformed sequence is the
$0$th DCT coefficient and _not_ the $N/2$th).
**Examples**
Refer to the [DCT Scala docs](api/scala/index.html#org.apache.spark.ml.feature.DCT)
for more details on the API.
{% include_example scala/org/apache/spark/examples/ml/DCTExample.scala %}
Refer to the [DCT Java docs](api/java/org/apache/spark/ml/feature/DCT.html)
for more details on the API.
{% include_example java/org/apache/spark/examples/ml/JavaDCTExample.java %}
Refer to the [DCT Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.DCT)
for more details on the API.
{% include_example python/ml/dct_example.py %}
## 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`.
The unseen labels will be put at index numLabels if user chooses to keep them.
If the input column is numeric, we cast it to string and index the string
values. When downstream pipeline components such as `Estimator` or
`Transformer` make use of this string-indexed label, you must set the input
column of the component to this string-indexed column name. In many cases,
you can set the input column with `setInputCol`.
**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`.
Additionally, there are three strategies regarding how `StringIndexer` will handle
unseen labels when you have fit a `StringIndexer` on one dataset and then use it
to transform another:
- throw an exception (which is the default)
- skip the row containing the unseen label entirely
- put unseen labels in a special additional bucket, at index numLabels
**Examples**
Let's go back to our previous example but this time reuse our previously defined
`StringIndexer` on the following dataset:
~~~~
id | category
----|----------
0 | a
1 | b
2 | c
3 | d
4 | e
~~~~
If you've not set how `StringIndexer` handles unseen labels or set it to
"error", an exception will be thrown.
However, if you had called `setHandleInvalid("skip")`, the following dataset
will be generated:
~~~~
id | category | categoryIndex
----|----------|---------------
0 | a | 0.0
1 | b | 2.0
2 | c | 1.0
~~~~
Notice that the rows containing "d" or "e" do not appear.
If you call `setHandleInvalid("keep")`, the following dataset
will be generated:
~~~~
id | category | categoryIndex
----|----------|---------------
0 | a | 0.0
1 | b | 2.0
2 | c | 1.0
3 | d | 3.0
4 | e | 3.0
~~~~
Notice that the rows containing "d" or "e" are mapped to index "3.0"
Refer to the [StringIndexer Scala docs](api/scala/index.html#org.apache.spark.ml.feature.StringIndexer)
for more details on the API.
{% include_example scala/org/apache/spark/examples/ml/StringIndexerExample.scala %}
Refer to the [StringIndexer Java docs](api/java/org/apache/spark/ml/feature/StringIndexer.html)
for more details on the API.
{% include_example java/org/apache/spark/examples/ml/JavaStringIndexerExample.java %}
Refer to the [StringIndexer Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.StringIndexer)
for more details on the API.
{% include_example python/ml/string_indexer_example.py %}
## IndexToString
Symmetrically to `StringIndexer`, `IndexToString` maps a column of label indices
back to a column containing the original labels as strings. A common use case
is to produce indices from labels with `StringIndexer`, train a model with those
indices and retrieve the original labels from the column of predicted indices
with `IndexToString`. However, you are free to supply your own labels.
**Examples**
Building on the `StringIndexer` example, let's assume we have the following
DataFrame with columns `id` and `categoryIndex`:
~~~~
id | categoryIndex
----|---------------
0 | 0.0
1 | 2.0
2 | 1.0
3 | 0.0
4 | 0.0
5 | 1.0
~~~~
Applying `IndexToString` with `categoryIndex` as the input column,
`originalCategory` as the output column, we are able to retrieve our original
labels (they will be inferred from the columns' metadata):
~~~~
id | categoryIndex | originalCategory
----|---------------|-----------------
0 | 0.0 | a
1 | 2.0 | b
2 | 1.0 | c
3 | 0.0 | a
4 | 0.0 | a
5 | 1.0 | c
~~~~
Refer to the [IndexToString Scala docs](api/scala/index.html#org.apache.spark.ml.feature.IndexToString)
for more details on the API.
{% include_example scala/org/apache/spark/examples/ml/IndexToStringExample.scala %}
Refer to the [IndexToString Java docs](api/java/org/apache/spark/ml/feature/IndexToString.html)
for more details on the API.
{% include_example java/org/apache/spark/examples/ml/JavaIndexToStringExample.java %}
Refer to the [IndexToString Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.IndexToString)
for more details on the API.
{% include_example python/ml/index_to_string_example.py %}
## 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.
**Examples**
Refer to the [OneHotEncoder Scala docs](api/scala/index.html#org.apache.spark.ml.feature.OneHotEncoder)
for more details on the API.
{% include_example scala/org/apache/spark/examples/ml/OneHotEncoderExample.scala %}
Refer to the [OneHotEncoder Java docs](api/java/org/apache/spark/ml/feature/OneHotEncoder.html)
for more details on the API.
{% include_example java/org/apache/spark/examples/ml/JavaOneHotEncoderExample.java %}
Refer to the [OneHotEncoder Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.OneHotEncoder)
for more details on the API.
{% include_example python/ml/onehot_encoder_example.py %}
## VectorIndexer
`VectorIndexer` helps index categorical features in datasets of `Vector`s.
It can both automatically decide which features are categorical and convert original values to category indices. Specifically, it does the following:
1. Take an input column of type [Vector](api/scala/index.html#org.apache.spark.ml.linalg.Vector) and a parameter `maxCategories`.
2. Decide which features should be categorical based on the number of distinct values, where features with at most `maxCategories` are declared categorical.
3. Compute 0-based category indices for each categorical feature.
4. Index categorical features and transform original feature values to indices.
Indexing categorical features allows algorithms such as Decision Trees and Tree Ensembles to treat categorical features appropriately, improving performance.
**Examples**
In the example below, we read in a dataset of labeled points and then use `VectorIndexer` to decide which features should be treated as categorical. We transform the categorical feature values to their indices. This transformed data could then be passed to algorithms such as `DecisionTreeRegressor` that handle categorical features.
Refer to the [VectorIndexer Scala docs](api/scala/index.html#org.apache.spark.ml.feature.VectorIndexer)
for more details on the API.
{% include_example scala/org/apache/spark/examples/ml/VectorIndexerExample.scala %}
Refer to the [VectorIndexer Java docs](api/java/org/apache/spark/ml/feature/VectorIndexer.html)
for more details on the API.
{% include_example java/org/apache/spark/examples/ml/JavaVectorIndexerExample.java %}
Refer to the [VectorIndexer Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.VectorIndexer)
for more details on the API.
{% include_example python/ml/vector_indexer_example.py %}
## Interaction
`Interaction` is a `Transformer` which takes vector or double-valued columns, and generates a single vector column that contains the product of all combinations of one value from each input column.
For example, if you have 2 vector type columns each of which has 3 dimensions as input columns, then you'll get a 9-dimensional vector as the output column.
**Examples**
Assume that we have the following DataFrame with the columns "id1", "vec1", and "vec2":
~~~~
id1|vec1 |vec2
---|--------------|--------------
1 |[1.0,2.0,3.0] |[8.0,4.0,5.0]
2 |[4.0,3.0,8.0] |[7.0,9.0,8.0]
3 |[6.0,1.0,9.0] |[2.0,3.0,6.0]
4 |[10.0,8.0,6.0]|[9.0,4.0,5.0]
5 |[9.0,2.0,7.0] |[10.0,7.0,3.0]
6 |[1.0,1.0,4.0] |[2.0,8.0,4.0]
~~~~
Applying `Interaction` with those input columns,
then `interactedCol` as the output column contains:
~~~~
id1|vec1 |vec2 |interactedCol
---|--------------|--------------|------------------------------------------------------
1 |[1.0,2.0,3.0] |[8.0,4.0,5.0] |[8.0,4.0,5.0,16.0,8.0,10.0,24.0,12.0,15.0]
2 |[4.0,3.0,8.0] |[7.0,9.0,8.0] |[56.0,72.0,64.0,42.0,54.0,48.0,112.0,144.0,128.0]
3 |[6.0,1.0,9.0] |[2.0,3.0,6.0] |[36.0,54.0,108.0,6.0,9.0,18.0,54.0,81.0,162.0]
4 |[10.0,8.0,6.0]|[9.0,4.0,5.0] |[360.0,160.0,200.0,288.0,128.0,160.0,216.0,96.0,120.0]
5 |[9.0,2.0,7.0] |[10.0,7.0,3.0]|[450.0,315.0,135.0,100.0,70.0,30.0,350.0,245.0,105.0]
6 |[1.0,1.0,4.0] |[2.0,8.0,4.0] |[12.0,48.0,24.0,12.0,48.0,24.0,48.0,192.0,96.0]
~~~~
Refer to the [Interaction Scala docs](api/scala/index.html#org.apache.spark.ml.feature.Interaction)
for more details on the API.
{% include_example scala/org/apache/spark/examples/ml/InteractionExample.scala %}
Refer to the [Interaction Java docs](api/java/org/apache/spark/ml/feature/Interaction.html)
for more details on the API.
{% include_example java/org/apache/spark/examples/ml/JavaInteractionExample.java %}
## Normalizer
`Normalizer` is a `Transformer` which transforms a dataset of `Vector` rows, normalizing each `Vector` to have unit norm. It takes parameter `p`, which specifies the [p-norm](http://en.wikipedia.org/wiki/Norm_%28mathematics%29#p-norm) used for normalization. ($p = 2$ by default.) This normalization can help standardize your input data and improve the behavior of learning algorithms.
**Examples**
The following example demonstrates how to load a dataset in libsvm format and then normalize each row to have unit $L^1$ norm and unit $L^\infty$ norm.
Refer to the [Normalizer Scala docs](api/scala/index.html#org.apache.spark.ml.feature.Normalizer)
for more details on the API.
{% include_example scala/org/apache/spark/examples/ml/NormalizerExample.scala %}
Refer to the [Normalizer Java docs](api/java/org/apache/spark/ml/feature/Normalizer.html)
for more details on the API.
{% include_example java/org/apache/spark/examples/ml/JavaNormalizerExample.java %}
Refer to the [Normalizer Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.Normalizer)
for more details on the API.
{% include_example python/ml/normalizer_example.py %}
## StandardScaler
`StandardScaler` transforms a dataset of `Vector` rows, normalizing each feature to have unit standard deviation and/or zero mean. It takes parameters:
* `withStd`: True by default. Scales the data to unit standard deviation.
* `withMean`: False by default. Centers the data with mean before scaling. It will build a dense output, so take care when applying to sparse input.
`StandardScaler` is an `Estimator` which can be `fit` on a dataset to produce a `StandardScalerModel`; this amounts to computing summary statistics. The model can then transform a `Vector` column in a dataset to have unit standard deviation and/or zero mean features.
Note that if the standard deviation of a feature is zero, it will return default `0.0` value in the `Vector` for that feature.
**Examples**
The following example demonstrates how to load a dataset in libsvm format and then normalize each feature to have unit standard deviation.
Refer to the [StandardScaler Scala docs](api/scala/index.html#org.apache.spark.ml.feature.StandardScaler)
for more details on the API.
{% include_example scala/org/apache/spark/examples/ml/StandardScalerExample.scala %}
Refer to the [StandardScaler Java docs](api/java/org/apache/spark/ml/feature/StandardScaler.html)
for more details on the API.
{% include_example java/org/apache/spark/examples/ml/JavaStandardScalerExample.java %}
Refer to the [StandardScaler Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.StandardScaler)
for more details on the API.
{% include_example python/ml/standard_scaler_example.py %}
## MinMaxScaler
`MinMaxScaler` transforms a dataset of `Vector` rows, rescaling each feature to a specific range (often [0, 1]). It takes parameters:
* `min`: 0.0 by default. Lower bound after transformation, shared by all features.
* `max`: 1.0 by default. Upper bound after transformation, shared by all features.
`MinMaxScaler` computes summary statistics on a data set and produces a `MinMaxScalerModel`. The model can then transform each feature individually such that it is in the given range.
The rescaled value for a feature E is calculated as,
`\begin{equation}
Rescaled(e_i) = \frac{e_i - E_{min}}{E_{max} - E_{min}} * (max - min) + min
\end{equation}`
For the case `$E_{max} == E_{min}$`, `$Rescaled(e_i) = 0.5 * (max + min)$`
Note that since zero values will probably be transformed to non-zero values, output of the transformer will be `DenseVector` even for sparse input.
**Examples**
The following example demonstrates how to load a dataset in libsvm format and then rescale each feature to [0, 1].
Refer to the [MinMaxScaler Scala docs](api/scala/index.html#org.apache.spark.ml.feature.MinMaxScaler)
and the [MinMaxScalerModel Scala docs](api/scala/index.html#org.apache.spark.ml.feature.MinMaxScalerModel)
for more details on the API.
{% include_example scala/org/apache/spark/examples/ml/MinMaxScalerExample.scala %}
Refer to the [MinMaxScaler Java docs](api/java/org/apache/spark/ml/feature/MinMaxScaler.html)
and the [MinMaxScalerModel Java docs](api/java/org/apache/spark/ml/feature/MinMaxScalerModel.html)
for more details on the API.
{% include_example java/org/apache/spark/examples/ml/JavaMinMaxScalerExample.java %}
Refer to the [MinMaxScaler Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.MinMaxScaler)
and the [MinMaxScalerModel Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.MinMaxScalerModel)
for more details on the API.
{% include_example python/ml/min_max_scaler_example.py %}
## MaxAbsScaler
`MaxAbsScaler` transforms a dataset of `Vector` rows, rescaling each feature to range [-1, 1]
by dividing through the maximum absolute value in each feature. It does not shift/center the
data, and thus does not destroy any sparsity.
`MaxAbsScaler` computes summary statistics on a data set and produces a `MaxAbsScalerModel`. The
model can then transform each feature individually to range [-1, 1].
**Examples**
The following example demonstrates how to load a dataset in libsvm format and then rescale each feature to [-1, 1].
Refer to the [MaxAbsScaler Scala docs](api/scala/index.html#org.apache.spark.ml.feature.MaxAbsScaler)
and the [MaxAbsScalerModel Scala docs](api/scala/index.html#org.apache.spark.ml.feature.MaxAbsScalerModel)
for more details on the API.
{% include_example scala/org/apache/spark/examples/ml/MaxAbsScalerExample.scala %}
Refer to the [MaxAbsScaler Java docs](api/java/org/apache/spark/ml/feature/MaxAbsScaler.html)
and the [MaxAbsScalerModel Java docs](api/java/org/apache/spark/ml/feature/MaxAbsScalerModel.html)
for more details on the API.
{% include_example java/org/apache/spark/examples/ml/JavaMaxAbsScalerExample.java %}
Refer to the [MaxAbsScaler Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.MaxAbsScaler)
and the [MaxAbsScalerModel Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.MaxAbsScalerModel)
for more details on the API.
{% include_example python/ml/max_abs_scaler_example.py %}
## Bucketizer
`Bucketizer` transforms a column of continuous features to a column of feature buckets, where the buckets are specified by users. It takes a parameter:
* `splits`: Parameter for mapping continuous features into buckets. With n+1 splits, there are n buckets. A bucket defined by splits x,y holds values in the range [x,y) except the last bucket, which also includes y. Splits should be strictly increasing. Values at -inf, inf must be explicitly provided to cover all Double values; Otherwise, values outside the splits specified will be treated as errors. Two examples of `splits` are `Array(Double.NegativeInfinity, 0.0, 1.0, Double.PositiveInfinity)` and `Array(0.0, 1.0, 2.0)`.
Note that if you have no idea of the upper and lower bounds of the targeted column, you should add `Double.NegativeInfinity` and `Double.PositiveInfinity` as the bounds of your splits to prevent a potential out of Bucketizer bounds exception.
Note also that the splits that you provided have to be in strictly increasing order, i.e. `s0 < s1 < s2 < ... < sn`.
More details can be found in the API docs for [Bucketizer](api/scala/index.html#org.apache.spark.ml.feature.Bucketizer).
**Examples**
The following example demonstrates how to bucketize a column of `Double`s into another index-wised column.
Refer to the [Bucketizer Scala docs](api/scala/index.html#org.apache.spark.ml.feature.Bucketizer)
for more details on the API.
{% include_example scala/org/apache/spark/examples/ml/BucketizerExample.scala %}
Refer to the [Bucketizer Java docs](api/java/org/apache/spark/ml/feature/Bucketizer.html)
for more details on the API.
{% include_example java/org/apache/spark/examples/ml/JavaBucketizerExample.java %}
Refer to the [Bucketizer Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.Bucketizer)
for more details on the API.
{% include_example python/ml/bucketizer_example.py %}
## ElementwiseProduct
ElementwiseProduct multiplies each input vector by a provided "weight" vector, using element-wise multiplication. In other words, it scales each column of the dataset by a scalar multiplier. This represents the [Hadamard product](https://en.wikipedia.org/wiki/Hadamard_product_%28matrices%29) between the input vector, `v` and transforming vector, `w`, to yield a result vector.
`\[ \begin{pmatrix}
v_1 \\
\vdots \\
v_N
\end{pmatrix} \circ \begin{pmatrix}
w_1 \\
\vdots \\
w_N
\end{pmatrix}
= \begin{pmatrix}
v_1 w_1 \\
\vdots \\
v_N w_N
\end{pmatrix}
\]`
**Examples**
This example below demonstrates how to transform vectors using a transforming vector value.
Refer to the [ElementwiseProduct Scala docs](api/scala/index.html#org.apache.spark.ml.feature.ElementwiseProduct)
for more details on the API.
{% include_example scala/org/apache/spark/examples/ml/ElementwiseProductExample.scala %}
Refer to the [ElementwiseProduct Java docs](api/java/org/apache/spark/ml/feature/ElementwiseProduct.html)
for more details on the API.
{% include_example java/org/apache/spark/examples/ml/JavaElementwiseProductExample.java %}
Refer to the [ElementwiseProduct Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.ElementwiseProduct)
for more details on the API.
{% include_example python/ml/elementwise_product_example.py %}
## SQLTransformer
`SQLTransformer` implements the transformations which are defined by SQL statement.
Currently we only support SQL syntax like `"SELECT ... FROM __THIS__ ..."`
where `"__THIS__"` represents the underlying table of the input dataset.
The select clause specifies the fields, constants, and expressions to display in
the output, and can be any select clause that Spark SQL supports. Users can also
use Spark SQL built-in function and UDFs to operate on these selected columns.
For example, `SQLTransformer` supports statements like:
* `SELECT a, a + b AS a_b FROM __THIS__`
* `SELECT a, SQRT(b) AS b_sqrt FROM __THIS__ where a > 5`
* `SELECT a, b, SUM(c) AS c_sum FROM __THIS__ GROUP BY a, b`
**Examples**
Assume that we have the following DataFrame with columns `id`, `v1` and `v2`:
~~~~
id | v1 | v2
----|-----|-----
0 | 1.0 | 3.0
2 | 2.0 | 5.0
~~~~
This is the output of the `SQLTransformer` with statement `"SELECT *, (v1 + v2) AS v3, (v1 * v2) AS v4 FROM __THIS__"`:
~~~~
id | v1 | v2 | v3 | v4
----|-----|-----|-----|-----
0 | 1.0 | 3.0 | 4.0 | 3.0
2 | 2.0 | 5.0 | 7.0 |10.0
~~~~
Refer to the [SQLTransformer Scala docs](api/scala/index.html#org.apache.spark.ml.feature.SQLTransformer)
for more details on the API.
{% include_example scala/org/apache/spark/examples/ml/SQLTransformerExample.scala %}
Refer to the [SQLTransformer Java docs](api/java/org/apache/spark/ml/feature/SQLTransformer.html)
for more details on the API.
{% include_example java/org/apache/spark/examples/ml/JavaSQLTransformerExample.java %}
Refer to the [SQLTransformer Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.SQLTransformer) for more details on the API.
{% include_example python/ml/sql_transformer.py %}
## VectorAssembler
`VectorAssembler` is a transformer that combines a given list of columns into a single vector
column.
It is useful for combining raw features and features generated by different feature transformers
into a single feature vector, in order to train ML models like logistic regression and decision
trees.
`VectorAssembler` accepts the following input column types: all numeric types, boolean type,
and vector type.
In each row, the values of the input columns will be concatenated into a vector in the specified
order.
**Examples**
Assume that we have a DataFrame with the columns `id`, `hour`, `mobile`, `userFeatures`,
and `clicked`:
~~~
id | hour | mobile | userFeatures | clicked
----|------|--------|------------------|---------
0 | 18 | 1.0 | [0.0, 10.0, 0.5] | 1.0
~~~
`userFeatures` is a vector column that contains three user features.
We want to combine `hour`, `mobile`, and `userFeatures` into a single feature vector
called `features` and use it to predict `clicked` or not.
If we set `VectorAssembler`'s input columns to `hour`, `mobile`, and `userFeatures` and
output column to `features`, after transformation we should get the following DataFrame:
~~~
id | hour | mobile | userFeatures | clicked | features
----|------|--------|------------------|---------|-----------------------------
0 | 18 | 1.0 | [0.0, 10.0, 0.5] | 1.0 | [18.0, 1.0, 0.0, 10.0, 0.5]
~~~
Refer to the [VectorAssembler Scala docs](api/scala/index.html#org.apache.spark.ml.feature.VectorAssembler)
for more details on the API.
{% include_example scala/org/apache/spark/examples/ml/VectorAssemblerExample.scala %}
Refer to the [VectorAssembler Java docs](api/java/org/apache/spark/ml/feature/VectorAssembler.html)
for more details on the API.
{% include_example java/org/apache/spark/examples/ml/JavaVectorAssemblerExample.java %}
Refer to the [VectorAssembler Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.VectorAssembler)
for more details on the API.
{% include_example python/ml/vector_assembler_example.py %}
## QuantileDiscretizer
`QuantileDiscretizer` takes a column with continuous features and outputs a column with binned
categorical features. The number of bins is set by the `numBuckets` parameter. It is possible
that the number of buckets used will be smaller than this value, for example, if there are too few
distinct values of the input to create enough distinct quantiles.
NaN values:
NaN values will be removed from the column during `QuantileDiscretizer` fitting. This will produce
a `Bucketizer` model for making predictions. During the transformation, `Bucketizer`
will raise an error when it finds NaN values in the dataset, but the user can also choose to either
keep or remove NaN values within the dataset by setting `handleInvalid`. If the user chooses to keep
NaN values, they will be handled specially and placed into their own bucket, for example, if 4 buckets
are used, then non-NaN data will be put into buckets[0-3], but NaNs will be counted in a special bucket[4].
Algorithm: The bin ranges are chosen using an approximate algorithm (see the documentation for
[approxQuantile](api/scala/index.html#org.apache.spark.sql.DataFrameStatFunctions) for a
detailed description). The precision of the approximation can be controlled with the
`relativeError` parameter. When set to zero, exact quantiles are calculated
(**Note:** Computing exact quantiles is an expensive operation). The lower and upper bin bounds
will be `-Infinity` and `+Infinity` covering all real values.
**Examples**
Assume that we have a DataFrame with the columns `id`, `hour`:
~~~
id | hour
----|------
0 | 18.0
----|------
1 | 19.0
----|------
2 | 8.0
----|------
3 | 5.0
----|------
4 | 2.2
~~~
`hour` is a continuous feature with `Double` type. We want to turn the continuous feature into
a categorical one. Given `numBuckets = 3`, we should get the following DataFrame:
~~~
id | hour | result
----|------|------
0 | 18.0 | 2.0
----|------|------
1 | 19.0 | 2.0
----|------|------
2 | 8.0 | 1.0
----|------|------
3 | 5.0 | 1.0
----|------|------
4 | 2.2 | 0.0
~~~
Refer to the [QuantileDiscretizer Scala docs](api/scala/index.html#org.apache.spark.ml.feature.QuantileDiscretizer)
for more details on the API.
{% include_example scala/org/apache/spark/examples/ml/QuantileDiscretizerExample.scala %}
Refer to the [QuantileDiscretizer Java docs](api/java/org/apache/spark/ml/feature/QuantileDiscretizer.html)
for more details on the API.
{% include_example java/org/apache/spark/examples/ml/JavaQuantileDiscretizerExample.java %}
Refer to the [QuantileDiscretizer Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.QuantileDiscretizer)
for more details on the API.
{% include_example python/ml/quantile_discretizer_example.py %}
## Imputer
The `Imputer` transformer completes missing values in a dataset, either using the mean or the
median of the columns in which the missing values are located. The input columns should be of
`DoubleType` or `FloatType`. Currently `Imputer` does not support categorical features and possibly
creates incorrect values for columns containing categorical features.
**Note** all `null` values in the input columns are treated as missing, and so are also imputed.
**Examples**
Suppose that we have a DataFrame with the columns `a` and `b`:
~~~
a | b
------------|-----------
1.0 | Double.NaN
2.0 | Double.NaN
Double.NaN | 3.0
4.0 | 4.0
5.0 | 5.0
~~~
In this example, Imputer will replace all occurrences of `Double.NaN` (the default for the missing value)
with the mean (the default imputation strategy) computed from the other values in the corresponding columns.
In this example, the surrogate values for columns `a` and `b` are 3.0 and 4.0 respectively. After
transformation, the missing values in the output columns will be replaced by the surrogate value for
the relevant column.
~~~
a | b | out_a | out_b
------------|------------|-------|-------
1.0 | Double.NaN | 1.0 | 4.0
2.0 | Double.NaN | 2.0 | 4.0
Double.NaN | 3.0 | 3.0 | 3.0
4.0 | 4.0 | 4.0 | 4.0
5.0 | 5.0 | 5.0 | 5.0
~~~
Refer to the [Imputer Scala docs](api/scala/index.html#org.apache.spark.ml.feature.Imputer)
for more details on the API.
{% include_example scala/org/apache/spark/examples/ml/ImputerExample.scala %}
Refer to the [Imputer Java docs](api/java/org/apache/spark/ml/feature/Imputer.html)
for more details on the API.
{% include_example java/org/apache/spark/examples/ml/JavaImputerExample.java %}
Refer to the [Imputer Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.Imputer)
for more details on the API.
{% include_example python/ml/imputer_example.py %}
# Feature Selectors
## VectorSlicer
`VectorSlicer` is a transformer that takes a feature vector and outputs a new feature vector with a
sub-array of the original features. It is useful for extracting features from a vector column.
`VectorSlicer` accepts a vector column with specified indices, then outputs a new vector column
whose values are selected via those indices. There are two types of indices,
1. Integer indices that represent the indices into the vector, `setIndices()`.
2. String indices that represent the names of features into the vector, `setNames()`.
*This requires the vector column to have an `AttributeGroup` since the implementation matches on
the name field of an `Attribute`.*
Specification by integer and string are both acceptable. Moreover, you can use integer index and
string name simultaneously. At least one feature must be selected. Duplicate features are not
allowed, so there can be no overlap between selected indices and names. Note that if names of
features are selected, an exception will be thrown if empty input attributes are encountered.
The output vector will order features with the selected indices first (in the order given),
followed by the selected names (in the order given).
**Examples**
Suppose that we have a DataFrame with the column `userFeatures`:
~~~
userFeatures
------------------
[0.0, 10.0, 0.5]
~~~
`userFeatures` is a vector column that contains three user features. Assume that the first column
of `userFeatures` are all zeros, so we want to remove it and select only the last two columns.
The `VectorSlicer` selects the last two elements with `setIndices(1, 2)` then produces a new vector
column named `features`:
~~~
userFeatures | features
------------------|-----------------------------
[0.0, 10.0, 0.5] | [10.0, 0.5]
~~~
Suppose also that we have potential input attributes for the `userFeatures`, i.e.
`["f1", "f2", "f3"]`, then we can use `setNames("f2", "f3")` to select them.
~~~
userFeatures | features
------------------|-----------------------------
[0.0, 10.0, 0.5] | [10.0, 0.5]
["f1", "f2", "f3"] | ["f2", "f3"]
~~~
Refer to the [VectorSlicer Scala docs](api/scala/index.html#org.apache.spark.ml.feature.VectorSlicer)
for more details on the API.
{% include_example scala/org/apache/spark/examples/ml/VectorSlicerExample.scala %}
Refer to the [VectorSlicer Java docs](api/java/org/apache/spark/ml/feature/VectorSlicer.html)
for more details on the API.
{% include_example java/org/apache/spark/examples/ml/JavaVectorSlicerExample.java %}
Refer to the [VectorSlicer Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.VectorSlicer)
for more details on the API.
{% include_example python/ml/vector_slicer_example.py %}
## RFormula
`RFormula` selects columns specified by an [R model formula](https://stat.ethz.ch/R-manual/R-devel/library/stats/html/formula.html).
Currently we support a limited subset of the R operators, including '~', '.', ':', '+', and '-'.
The basic operators are:
* `~` separate target and terms
* `+` concat terms, "+ 0" means removing intercept
* `-` remove a term, "- 1" means removing intercept
* `:` interaction (multiplication for numeric values, or binarized categorical values)
* `.` all columns except target
Suppose `a` and `b` are double columns, we use the following simple examples to illustrate the effect of `RFormula`:
* `y ~ a + b` means model `y ~ w0 + w1 * a + w2 * b` where `w0` is the intercept and `w1, w2` are coefficients.
* `y ~ a + b + a:b - 1` means model `y ~ w1 * a + w2 * b + w3 * a * b` where `w1, w2, w3` are coefficients.
`RFormula` produces a vector column of features and a double or string column of label.
Like when formulas are used in R for linear regression, string input columns will be one-hot encoded, and numeric columns will be cast to doubles.
If the label column is of type string, it will be first transformed to double with `StringIndexer`.
If the label column does not exist in the DataFrame, the output label column will be created from the specified response variable in the formula.
**Examples**
Assume that we have a DataFrame with the columns `id`, `country`, `hour`, and `clicked`:
~~~
id | country | hour | clicked
---|---------|------|---------
7 | "US" | 18 | 1.0
8 | "CA" | 12 | 0.0
9 | "NZ" | 15 | 0.0
~~~
If we use `RFormula` with a formula string of `clicked ~ country + hour`, which indicates that we want to
predict `clicked` based on `country` and `hour`, after transformation we should get the following DataFrame:
~~~
id | country | hour | clicked | features | label
---|---------|------|---------|------------------|-------
7 | "US" | 18 | 1.0 | [0.0, 0.0, 18.0] | 1.0
8 | "CA" | 12 | 0.0 | [0.0, 1.0, 12.0] | 0.0
9 | "NZ" | 15 | 0.0 | [1.0, 0.0, 15.0] | 0.0
~~~
Refer to the [RFormula Scala docs](api/scala/index.html#org.apache.spark.ml.feature.RFormula)
for more details on the API.
{% include_example scala/org/apache/spark/examples/ml/RFormulaExample.scala %}
Refer to the [RFormula Java docs](api/java/org/apache/spark/ml/feature/RFormula.html)
for more details on the API.
{% include_example java/org/apache/spark/examples/ml/JavaRFormulaExample.java %}
Refer to the [RFormula Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.RFormula)
for more details on the API.
{% include_example python/ml/rformula_example.py %}
## ChiSqSelector
`ChiSqSelector` stands for Chi-Squared feature selection. It operates on labeled data with
categorical features. ChiSqSelector uses the
[Chi-Squared test of independence](https://en.wikipedia.org/wiki/Chi-squared_test) to decide which
features to choose. It supports five selection methods: `numTopFeatures`, `percentile`, `fpr`, `fdr`, `fwe`:
* `numTopFeatures` chooses a fixed number of top features according to a chi-squared test. This is akin to yielding the features with the most predictive power.
* `percentile` is similar to `numTopFeatures` but chooses a fraction of all features instead of a fixed number.
* `fpr` chooses all features whose p-values are below a threshold, thus controlling the false positive rate of selection.
* `fdr` uses the [Benjamini-Hochberg procedure](https://en.wikipedia.org/wiki/False_discovery_rate#Benjamini.E2.80.93Hochberg_procedure) to choose all features whose false discovery rate is below a threshold.
* `fwe` chooses all features whose p-values are below a threshold. The threshold is scaled by 1/numFeatures, thus controlling the family-wise error rate of selection.
By default, the selection method is `numTopFeatures`, with the default number of top features set to 50.
The user can choose a selection method using `setSelectorType`.
**Examples**
Assume that we have a DataFrame with the columns `id`, `features`, and `clicked`, which is used as
our target to be predicted:
~~~
id | features | clicked
---|-----------------------|---------
7 | [0.0, 0.0, 18.0, 1.0] | 1.0
8 | [0.0, 1.0, 12.0, 0.0] | 0.0
9 | [1.0, 0.0, 15.0, 0.1] | 0.0
~~~
If we use `ChiSqSelector` with `numTopFeatures = 1`, then according to our label `clicked` the
last column in our `features` is chosen as the most useful feature:
~~~
id | features | clicked | selectedFeatures
---|-----------------------|---------|------------------
7 | [0.0, 0.0, 18.0, 1.0] | 1.0 | [1.0]
8 | [0.0, 1.0, 12.0, 0.0] | 0.0 | [0.0]
9 | [1.0, 0.0, 15.0, 0.1] | 0.0 | [0.1]
~~~
Refer to the [ChiSqSelector Scala docs](api/scala/index.html#org.apache.spark.ml.feature.ChiSqSelector)
for more details on the API.
{% include_example scala/org/apache/spark/examples/ml/ChiSqSelectorExample.scala %}
Refer to the [ChiSqSelector Java docs](api/java/org/apache/spark/ml/feature/ChiSqSelector.html)
for more details on the API.
{% include_example java/org/apache/spark/examples/ml/JavaChiSqSelectorExample.java %}
Refer to the [ChiSqSelector Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.ChiSqSelector)
for more details on the API.
{% include_example python/ml/chisq_selector_example.py %}
# Locality Sensitive Hashing
[Locality Sensitive Hashing (LSH)](https://en.wikipedia.org/wiki/Locality-sensitive_hashing) is an important class of hashing techniques, which is commonly used in clustering, approximate nearest neighbor search and outlier detection with large datasets.
The general idea of LSH is to use a family of functions ("LSH families") to hash data points into buckets, so that the data points which are close to each other are in the same buckets with high probability, while data points that are far away from each other are very likely in different buckets. An LSH family is formally defined as follows.
In a metric space `(M, d)`, where `M` is a set and `d` is a distance function on `M`, an LSH family is a family of functions `h` that satisfy the following properties:
`\[
\forall p, q \in M,\\
d(p,q) \leq r1 \Rightarrow Pr(h(p)=h(q)) \geq p1\\
d(p,q) \geq r2 \Rightarrow Pr(h(p)=h(q)) \leq p2
\]`
This LSH family is called `(r1, r2, p1, p2)`-sensitive.
In Spark, different LSH families are implemented in separate classes (e.g., `MinHash`), and APIs for feature transformation, approximate similarity join and approximate nearest neighbor are provided in each class.
In LSH, we define a false positive as a pair of distant input features (with `$d(p,q) \geq r2$`) which are hashed into the same bucket, and we define a false negative as a pair of nearby features (with `$d(p,q) \leq r1$`) which are hashed into different buckets.
## LSH Operations
We describe the major types of operations which LSH can be used for. A fitted LSH model has methods for each of these operations.
### Feature Transformation
Feature transformation is the basic functionality to add hashed values as a new column. This can be useful for dimensionality reduction. Users can specify input and output column names by setting `inputCol` and `outputCol`.
LSH also supports multiple LSH hash tables. Users can specify the number of hash tables by setting `numHashTables`. This is also used for [OR-amplification](https://en.wikipedia.org/wiki/Locality-sensitive_hashing#Amplification) in approximate similarity join and approximate nearest neighbor. Increasing the number of hash tables will increase the accuracy but will also increase communication cost and running time.
The type of `outputCol` is `Seq[Vector]` where the dimension of the array equals `numHashTables`, and the dimensions of the vectors are currently set to 1. In future releases, we will implement AND-amplification so that users can specify the dimensions of these vectors.
### Approximate Similarity Join
Approximate similarity join takes two datasets and approximately returns pairs of rows in the datasets whose distance is smaller than a user-defined threshold. Approximate similarity join supports both joining two different datasets and self-joining. Self-joining will produce some duplicate pairs.
Approximate similarity join accepts both transformed and untransformed datasets as input. If an untransformed dataset is used, it will be transformed automatically. In this case, the hash signature will be created as `outputCol`.
In the joined dataset, the origin datasets can be queried in `datasetA` and `datasetB`. A distance column will be added to the output dataset to show the true distance between each pair of rows returned.
### Approximate Nearest Neighbor Search
Approximate nearest neighbor search takes a dataset (of feature vectors) and a key (a single feature vector), and it approximately returns a specified number of rows in the dataset that are closest to the vector.
Approximate nearest neighbor search accepts both transformed and untransformed datasets as input. If an untransformed dataset is used, it will be transformed automatically. In this case, the hash signature will be created as `outputCol`.
A distance column will be added to the output dataset to show the true distance between each output row and the searched key.
**Note:** Approximate nearest neighbor search will return fewer than `k` rows when there are not enough candidates in the hash bucket.
## LSH Algorithms
### Bucketed Random Projection for Euclidean Distance
[Bucketed Random Projection](https://en.wikipedia.org/wiki/Locality-sensitive_hashing#Stable_distributions) is an LSH family for Euclidean distance. The Euclidean distance is defined as follows:
`\[
d(\mathbf{x}, \mathbf{y}) = \sqrt{\sum_i (x_i - y_i)^2}
\]`
Its LSH family projects feature vectors `$\mathbf{x}$` onto a random unit vector `$\mathbf{v}$` and portions the projected results into hash buckets:
`\[
h(\mathbf{x}) = \Big\lfloor \frac{\mathbf{x} \cdot \mathbf{v}}{r} \Big\rfloor
\]`
where `r` is a user-defined bucket length. The bucket length can be used to control the average size of hash buckets (and thus the number of buckets). A larger bucket length (i.e., fewer buckets) increases the probability of features being hashed to the same bucket (increasing the numbers of true and false positives).
Bucketed Random Projection accepts arbitrary vectors as input features, and supports both sparse and dense vectors.
Refer to the [BucketedRandomProjectionLSH Scala docs](api/scala/index.html#org.apache.spark.ml.feature.BucketedRandomProjectionLSH)
for more details on the API.
{% include_example scala/org/apache/spark/examples/ml/BucketedRandomProjectionLSHExample.scala %}
Refer to the [BucketedRandomProjectionLSH Java docs](api/java/org/apache/spark/ml/feature/BucketedRandomProjectionLSH.html)
for more details on the API.
{% include_example java/org/apache/spark/examples/ml/JavaBucketedRandomProjectionLSHExample.java %}
Refer to the [BucketedRandomProjectionLSH Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.BucketedRandomProjectionLSH)
for more details on the API.
{% include_example python/ml/bucketed_random_projection_lsh_example.py %}
### MinHash for Jaccard Distance
[MinHash](https://en.wikipedia.org/wiki/MinHash) is an LSH family for Jaccard distance where input features are sets of natural numbers. Jaccard distance of two sets is defined by the cardinality of their intersection and union:
`\[
d(\mathbf{A}, \mathbf{B}) = 1 - \frac{|\mathbf{A} \cap \mathbf{B}|}{|\mathbf{A} \cup \mathbf{B}|}
\]`
MinHash applies a random hash function `g` to each element in the set and take the minimum of all hashed values:
`\[
h(\mathbf{A}) = \min_{a \in \mathbf{A}}(g(a))
\]`
The input sets for MinHash are represented as binary vectors, where the vector indices represent the elements themselves and the non-zero values in the vector represent the presence of that element in the set. While both dense and sparse vectors are supported, typically sparse vectors are recommended for efficiency. For example, `Vectors.sparse(10, Array[(2, 1.0), (3, 1.0), (5, 1.0)])` means there are 10 elements in the space. This set contains elem 2, elem 3 and elem 5. All non-zero values are treated as binary "1" values.
**Note:** Empty sets cannot be transformed by MinHash, which means any input vector must have at least 1 non-zero entry.
Refer to the [MinHashLSH Scala docs](api/scala/index.html#org.apache.spark.ml.feature.MinHashLSH)
for more details on the API.
{% include_example scala/org/apache/spark/examples/ml/MinHashLSHExample.scala %}
Refer to the [MinHashLSH Java docs](api/java/org/apache/spark/ml/feature/MinHashLSH.html)
for more details on the API.
{% include_example java/org/apache/spark/examples/ml/JavaMinHashLSHExample.java %}
Refer to the [MinHashLSH Python docs](api/python/pyspark.ml.html#pyspark.ml.feature.MinHashLSH)
for more details on the API.
{% include_example python/ml/min_hash_lsh_example.py %}