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
- Feature Extractors
- Feature Transformers
- Tokenizer
- StopWordsRemover
- $n$-gram
- Binarizer
- PCA
- PolynomialExpansion
- Discrete Cosine Transform (DCT)
- StringIndexer
- IndexToString
- OneHotEncoder
- TargetEncoder
- VectorIndexer
- Interaction
- Normalizer
- StandardScaler
- RobustScaler
- MinMaxScaler
- MaxAbsScaler
- Bucketizer
- ElementwiseProduct
- SQLTransformer
- VectorAssembler
- VectorSizeHint
- QuantileDiscretizer
- Imputer
- Feature Selectors
- Locality Sensitive Hashing
Feature Extractors
TF-IDF
Term frequency-inverse document frequency (TF-IDF)
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.
A raw feature is mapped into an index (term) by applying a hash function. The hash function
used here is MurmurHash 3. 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 on the hashed value is used to determine the vector index,
it is advisable to use a power of two as the feature dimension, otherwise the features will not
be mapped evenly to the vector indices. 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
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 feature. Intuitively, it down-weights features which appear frequently in a corpus.
Note: spark.ml doesn’t provide tools for text segmentation.
We refer users to the Stanford NLP Group and
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 Python docs and the IDF Python docs for more details on the API.
from pyspark.ml.feature import HashingTF, IDF, Tokenizer
sentenceData = spark.createDataFrame([
(0.0, "Hi I heard about Spark"),
(0.0, "I wish Java could use case classes"),
(1.0, "Logistic regression models are neat")
], ["label", "sentence"])
tokenizer = Tokenizer(inputCol="sentence", outputCol="words")
wordsData = tokenizer.transform(sentenceData)
hashingTF = HashingTF(inputCol="words", outputCol="rawFeatures", numFeatures=20)
featurizedData = hashingTF.transform(wordsData)
# alternatively, CountVectorizer can also be used to get term frequency vectors
idf = IDF(inputCol="rawFeatures", outputCol="features")
idfModel = idf.fit(featurizedData)
rescaledData = idfModel.transform(featurizedData)
rescaledData.select("label", "features").show()Refer to the HashingTF Scala docs and the IDF Scala docs for more details on the API.
import org.apache.spark.ml.feature.{HashingTF, IDF, Tokenizer}
val sentenceData = spark.createDataFrame(Seq(
(0.0, "Hi I heard about Spark"),
(0.0, "I wish Java could use case classes"),
(1.0, "Logistic regression models are neat")
)).toDF("label", "sentence")
val tokenizer = new Tokenizer().setInputCol("sentence").setOutputCol("words")
val wordsData = tokenizer.transform(sentenceData)
val hashingTF = new HashingTF()
.setInputCol("words").setOutputCol("rawFeatures").setNumFeatures(20)
val featurizedData = hashingTF.transform(wordsData)
// alternatively, CountVectorizer can also be used to get term frequency vectors
val idf = new IDF().setInputCol("rawFeatures").setOutputCol("features")
val idfModel = idf.fit(featurizedData)
val rescaledData = idfModel.transform(featurizedData)
rescaledData.select("label", "features").show()Refer to the HashingTF Java docs and the IDF Java docs for more details on the API.
import java.util.Arrays;
import java.util.List;
import org.apache.spark.ml.feature.HashingTF;
import org.apache.spark.ml.feature.IDF;
import org.apache.spark.ml.feature.IDFModel;
import org.apache.spark.ml.feature.Tokenizer;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.SparkSession;
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;
List<Row> data = Arrays.asList(
RowFactory.create(0.0, "Hi I heard about Spark"),
RowFactory.create(0.0, "I wish Java could use case classes"),
RowFactory.create(1.0, "Logistic regression models are neat")
);
StructType schema = new StructType(new StructField[]{
new StructField("label", DataTypes.DoubleType, false, Metadata.empty()),
new StructField("sentence", DataTypes.StringType, false, Metadata.empty())
});
Dataset<Row> sentenceData = spark.createDataFrame(data, schema);
Tokenizer tokenizer = new Tokenizer().setInputCol("sentence").setOutputCol("words");
Dataset<Row> wordsData = tokenizer.transform(sentenceData);
int numFeatures = 20;
HashingTF hashingTF = new HashingTF()
.setInputCol("words")
.setOutputCol("rawFeatures")
.setNumFeatures(numFeatures);
Dataset<Row> featurizedData = hashingTF.transform(wordsData);
// alternatively, CountVectorizer can also be used to get term frequency vectors
IDF idf = new IDF().setInputCol("rawFeatures").setOutputCol("features");
IDFModel idfModel = idf.fit(featurizedData);
Dataset<Row> rescaledData = idfModel.transform(featurizedData);
rescaledData.select("label", "features").show();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 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 Python docs for more details on the API.
from pyspark.ml.feature import Word2Vec
# Input data: Each row is a bag of words from a sentence or document.
documentDF = spark.createDataFrame([
("Hi I heard about Spark".split(" "), ),
("I wish Java could use case classes".split(" "), ),
("Logistic regression models are neat".split(" "), )
], ["text"])
# Learn a mapping from words to Vectors.
word2Vec = Word2Vec(vectorSize=3, minCount=0, inputCol="text", outputCol="result")
model = word2Vec.fit(documentDF)
result = model.transform(documentDF)
for row in result.collect():
text, vector = row
print("Text: [%s] => \nVector: %s\n" % (", ".join(text), str(vector)))Refer to the Word2Vec Scala docs for more details on the API.
import org.apache.spark.ml.feature.Word2Vec
import org.apache.spark.ml.linalg.Vector
import org.apache.spark.sql.Row
// Input data: Each row is a bag of words from a sentence or document.
val documentDF = spark.createDataFrame(Seq(
"Hi I heard about Spark".split(" "),
"I wish Java could use case classes".split(" "),
"Logistic regression models are neat".split(" ")
).map(Tuple1.apply)).toDF("text")
// Learn a mapping from words to Vectors.
val word2Vec = new Word2Vec()
.setInputCol("text")
.setOutputCol("result")
.setVectorSize(3)
.setMinCount(0)
val model = word2Vec.fit(documentDF)
val result = model.transform(documentDF)
result.collect().foreach { case Row(text: Seq[_], features: Vector) =>
println(s"Text: [${text.mkString(", ")}] => \nVector: $features\n") }Refer to the Word2Vec Java docs for more details on the API.
import java.util.Arrays;
import java.util.List;
import org.apache.spark.ml.feature.Word2Vec;
import org.apache.spark.ml.feature.Word2VecModel;
import org.apache.spark.ml.linalg.Vector;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.types.*;
// Input data: Each row is a bag of words from a sentence or document.
List<Row> data = Arrays.asList(
RowFactory.create(Arrays.asList("Hi I heard about Spark".split(" "))),
RowFactory.create(Arrays.asList("I wish Java could use case classes".split(" "))),
RowFactory.create(Arrays.asList("Logistic regression models are neat".split(" ")))
);
StructType schema = new StructType(new StructField[]{
new StructField("text", new ArrayType(DataTypes.StringType, true), false, Metadata.empty())
});
Dataset<Row> documentDF = spark.createDataFrame(data, schema);
// Learn a mapping from words to Vectors.
Word2Vec word2Vec = new Word2Vec()
.setInputCol("text")
.setOutputCol("result")
.setVectorSize(3)
.setMinCount(0);
Word2VecModel model = word2Vec.fit(documentDF);
Dataset<Row> result = model.transform(documentDF);
for (Row row : result.collectAsList()) {
List<String> text = row.getList(0);
Vector vector = (Vector) row.get(1);
System.out.println("Text: " + text + " => \nVector: " + vector + "\n");
}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 Python docs and the CountVectorizerModel Python docs for more details on the API.
from pyspark.ml.feature import CountVectorizer
# Input data: Each row is a bag of words with a ID.
df = spark.createDataFrame([
(0, "a b c".split(" ")),
(1, "a b b c a".split(" "))
], ["id", "words"])
# fit a CountVectorizerModel from the corpus.
cv = CountVectorizer(inputCol="words", outputCol="features", vocabSize=3, minDF=2.0)
model = cv.fit(df)
result = model.transform(df)
result.show(truncate=False)Refer to the CountVectorizer Scala docs and the CountVectorizerModel Scala docs for more details on the API.
import org.apache.spark.ml.feature.{CountVectorizer, CountVectorizerModel}
val df = spark.createDataFrame(Seq(
(0, Array("a", "b", "c")),
(1, Array("a", "b", "b", "c", "a"))
)).toDF("id", "words")
// fit a CountVectorizerModel from the corpus
val cvModel: CountVectorizerModel = new CountVectorizer()
.setInputCol("words")
.setOutputCol("features")
.setVocabSize(3)
.setMinDF(2)
.fit(df)
// alternatively, define CountVectorizerModel with a-priori vocabulary
val cvm = new CountVectorizerModel(Array("a", "b", "c"))
.setInputCol("words")
.setOutputCol("features")
cvModel.transform(df).show(false)Refer to the CountVectorizer Java docs and the CountVectorizerModel Java docs for more details on the API.
import java.util.Arrays;
import java.util.List;
import org.apache.spark.ml.feature.CountVectorizer;
import org.apache.spark.ml.feature.CountVectorizerModel;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.types.*;
// Input data: Each row is a bag of words from a sentence or document.
List<Row> data = Arrays.asList(
RowFactory.create(Arrays.asList("a", "b", "c")),
RowFactory.create(Arrays.asList("a", "b", "b", "c", "a"))
);
StructType schema = new StructType(new StructField [] {
new StructField("text", new ArrayType(DataTypes.StringType, true), false, Metadata.empty())
});
Dataset<Row> df = spark.createDataFrame(data, schema);
// fit a CountVectorizerModel from the corpus
CountVectorizerModel cvModel = new CountVectorizer()
.setInputCol("text")
.setOutputCol("feature")
.setVocabSize(3)
.setMinDF(2)
.fit(df);
// alternatively, define CountVectorizerModel with a-priori vocabulary
CountVectorizerModel cvm = new CountVectorizerModel(new String[]{"a", "b", "c"})
.setInputCol("text")
.setOutputCol("feature");
cvModel.transform(df).show(false);FeatureHasher
Feature hashing projects a set of categorical or numerical features into a feature vector of specified dimension (typically substantially smaller than that of the original feature space). This is done using the hashing trick to map features to indices in the feature vector.
The FeatureHasher transformer operates on multiple columns. Each column may contain either
numeric or categorical features. Behavior and handling of column data types is as follows:
- Numeric columns: For numeric features, the hash value of the column name is used to map the
feature value to its index in the feature vector. By default, numeric features are not treated
as categorical (even when they are integers). To treat them as categorical, specify the relevant
columns using the
categoricalColsparameter. - String columns: For categorical features, the hash value of the string “column_name=value”
is used to map to the vector index, with an indicator value of
1.0. Thus, categorical features are “one-hot” encoded (similarly to using OneHotEncoder withdropLast=false). - Boolean columns: Boolean values are treated in the same way as string columns. That is,
boolean features are represented as “column_name=true” or “column_name=false”, with an indicator
value of
1.0.
Null (missing) values are ignored (implicitly zero in the resulting feature vector).
The hash function used here is also the MurmurHash 3 used in HashingTF. Since a simple modulo on the hashed value is used to determine the vector index, it is advisable to use a power of two as the numFeatures parameter; otherwise the features will not be mapped evenly to the vector indices.
Examples
Assume that we have a DataFrame with 4 input columns real, bool, stringNum, and string.
These different data types as input will illustrate the behavior of the transform to produce a
column of feature vectors.
real| bool|stringNum|string
----|-----|---------|------
2.2| true| 1| foo
3.3|false| 2| bar
4.4|false| 3| baz
5.5|false| 4| foo
Then the output of FeatureHasher.transform on this DataFrame is:
real|bool |stringNum|string|features
----|-----|---------|------|-------------------------------------------------------
2.2 |true |1 |foo |(262144,[51871, 63643,174475,253195],[1.0,1.0,2.2,1.0])
3.3 |false|2 |bar |(262144,[6031, 80619,140467,174475],[1.0,1.0,1.0,3.3])
4.4 |false|3 |baz |(262144,[24279,140467,174475,196810],[1.0,1.0,4.4,1.0])
5.5 |false|4 |foo |(262144,[63643,140467,168512,174475],[1.0,1.0,1.0,5.5])
The resulting feature vectors could then be passed to a learning algorithm.
Refer to the FeatureHasher Python docs for more details on the API.
from pyspark.ml.feature import FeatureHasher
dataset = spark.createDataFrame([
(2.2, True, "1", "foo"),
(3.3, False, "2", "bar"),
(4.4, False, "3", "baz"),
(5.5, False, "4", "foo")
], ["real", "bool", "stringNum", "string"])
hasher = FeatureHasher(inputCols=["real", "bool", "stringNum", "string"],
outputCol="features")
featurized = hasher.transform(dataset)
featurized.show(truncate=False)Refer to the FeatureHasher Scala docs for more details on the API.
import org.apache.spark.ml.feature.FeatureHasher
val dataset = spark.createDataFrame(Seq(
(2.2, true, "1", "foo"),
(3.3, false, "2", "bar"),
(4.4, false, "3", "baz"),
(5.5, false, "4", "foo")
)).toDF("real", "bool", "stringNum", "string")
val hasher = new FeatureHasher()
.setInputCols("real", "bool", "stringNum", "string")
.setOutputCol("features")
val featurized = hasher.transform(dataset)
featurized.show(false)Refer to the FeatureHasher Java docs for more details on the API.
import java.util.Arrays;
import java.util.List;
import org.apache.spark.ml.feature.FeatureHasher;
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;
List<Row> data = Arrays.asList(
RowFactory.create(2.2, true, "1", "foo"),
RowFactory.create(3.3, false, "2", "bar"),
RowFactory.create(4.4, false, "3", "baz"),
RowFactory.create(5.5, false, "4", "foo")
);
StructType schema = new StructType(new StructField[]{
new StructField("real", DataTypes.DoubleType, false, Metadata.empty()),
new StructField("bool", DataTypes.BooleanType, false, Metadata.empty()),
new StructField("stringNum", DataTypes.StringType, false, Metadata.empty()),
new StructField("string", DataTypes.StringType, false, Metadata.empty())
});
Dataset<Row> dataset = spark.createDataFrame(data, schema);
FeatureHasher hasher = new FeatureHasher()
.setInputCols(new String[]{"real", "bool", "stringNum", "string"})
.setOutputCol("features");
Dataset<Row> featurized = hasher.transform(dataset);
featurized.show(false);Feature Transformers
Tokenizer
Tokenization is the process of taking text (such as a sentence) and breaking it into individual terms (usually words). A simple Tokenizer class provides this functionality. The example below shows how to split sentences into sequences of words.
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 Python docs and the RegexTokenizer Python docs for more details on the API.
from pyspark.ml.feature import Tokenizer, RegexTokenizer
from pyspark.sql.functions import col, udf
from pyspark.sql.types import IntegerType
sentenceDataFrame = spark.createDataFrame([
(0, "Hi I heard about Spark"),
(1, "I wish Java could use case classes"),
(2, "Logistic,regression,models,are,neat")
], ["id", "sentence"])
tokenizer = Tokenizer(inputCol="sentence", outputCol="words")
regexTokenizer = RegexTokenizer(inputCol="sentence", outputCol="words", pattern="\\W")
# alternatively, pattern="\\w+", gaps(False)
countTokens = udf(lambda words: len(words), IntegerType())
tokenized = tokenizer.transform(sentenceDataFrame)
tokenized.select("sentence", "words")\
.withColumn("tokens", countTokens(col("words"))).show(truncate=False)
regexTokenized = regexTokenizer.transform(sentenceDataFrame)
regexTokenized.select("sentence", "words") \
.withColumn("tokens", countTokens(col("words"))).show(truncate=False)Refer to the Tokenizer Scala docs and the RegexTokenizer Scala docs for more details on the API.
import org.apache.spark.ml.feature.{RegexTokenizer, Tokenizer}
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.functions._
val sentenceDataFrame = spark.createDataFrame(Seq(
(0, "Hi I heard about Spark"),
(1, "I wish Java could use case classes"),
(2, "Logistic,regression,models,are,neat")
)).toDF("id", "sentence")
val tokenizer = new Tokenizer().setInputCol("sentence").setOutputCol("words")
val regexTokenizer = new RegexTokenizer()
.setInputCol("sentence")
.setOutputCol("words")
.setPattern("\\W") // alternatively .setPattern("\\w+").setGaps(false)
val countTokens = udf { (words: Seq[String]) => words.length }
val tokenized = tokenizer.transform(sentenceDataFrame)
tokenized.select("sentence", "words")
.withColumn("tokens", countTokens(col("words"))).show(false)
val regexTokenized = regexTokenizer.transform(sentenceDataFrame)
regexTokenized.select("sentence", "words")
.withColumn("tokens", countTokens(col("words"))).show(false)Refer to the Tokenizer Java docs and the RegexTokenizer Java docs for more details on the API.
import java.util.Arrays;
import java.util.List;
import scala.collection.mutable.Seq;
import org.apache.spark.ml.feature.RegexTokenizer;
import org.apache.spark.ml.feature.Tokenizer;
import org.apache.spark.sql.Dataset;
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;
// col("...") is preferable to df.col("...")
import static org.apache.spark.sql.functions.call_udf;
import static org.apache.spark.sql.functions.col;
List<Row> data = Arrays.asList(
RowFactory.create(0, "Hi I heard about Spark"),
RowFactory.create(1, "I wish Java could use case classes"),
RowFactory.create(2, "Logistic,regression,models,are,neat")
);
StructType schema = new StructType(new StructField[]{
new StructField("id", DataTypes.IntegerType, false, Metadata.empty()),
new StructField("sentence", DataTypes.StringType, false, Metadata.empty())
});
Dataset<Row> sentenceDataFrame = spark.createDataFrame(data, schema);
Tokenizer tokenizer = new Tokenizer().setInputCol("sentence").setOutputCol("words");
RegexTokenizer regexTokenizer = new RegexTokenizer()
.setInputCol("sentence")
.setOutputCol("words")
.setPattern("\\W"); // alternatively .setPattern("\\w+").setGaps(false);
spark.udf().register(
"countTokens", (Seq<?> words) -> words.size(), DataTypes.IntegerType);
Dataset<Row> tokenized = tokenizer.transform(sentenceDataFrame);
tokenized.select("sentence", "words")
.withColumn("tokens", call_udf("countTokens", col("words")))
.show(false);
Dataset<Row> regexTokenized = regexTokenizer.transform(sentenceDataFrame);
regexTokenized.select("sentence", "words")
.withColumn("tokens", call_udf("countTokens", col("words")))
.show(false);StopWordsRemover
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) 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, balloon]
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, balloon] | [saw, red, balloon]
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 Python docs for more details on the API.
from pyspark.ml.feature import StopWordsRemover
sentenceData = spark.createDataFrame([
(0, ["I", "saw", "the", "red", "balloon"]),
(1, ["Mary", "had", "a", "little", "lamb"])
], ["id", "raw"])
remover = StopWordsRemover(inputCol="raw", outputCol="filtered")
remover.transform(sentenceData).show(truncate=False)Refer to the StopWordsRemover Scala docs for more details on the API.
import org.apache.spark.ml.feature.StopWordsRemover
val remover = new StopWordsRemover()
.setInputCol("raw")
.setOutputCol("filtered")
val dataSet = spark.createDataFrame(Seq(
(0, Seq("I", "saw", "the", "red", "balloon")),
(1, Seq("Mary", "had", "a", "little", "lamb"))
)).toDF("id", "raw")
remover.transform(dataSet).show(false)Refer to the StopWordsRemover Java docs for more details on the API.
import java.util.Arrays;
import java.util.List;
import org.apache.spark.ml.feature.StopWordsRemover;
import org.apache.spark.sql.Dataset;
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");
List<Row> data = Arrays.asList(
RowFactory.create(Arrays.asList("I", "saw", "the", "red", "balloon")),
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())
});
Dataset<Row> dataset = spark.createDataFrame(data, schema);
remover.transform(dataset).show(false);$n$-gram
An 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). 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 Python docs for more details on the API.
from pyspark.ml.feature import NGram
wordDataFrame = spark.createDataFrame([
(0, ["Hi", "I", "heard", "about", "Spark"]),
(1, ["I", "wish", "Java", "could", "use", "case", "classes"]),
(2, ["Logistic", "regression", "models", "are", "neat"])
], ["id", "words"])
ngram = NGram(n=2, inputCol="words", outputCol="ngrams")
ngramDataFrame = ngram.transform(wordDataFrame)
ngramDataFrame.select("ngrams").show(truncate=False)Refer to the NGram Scala docs for more details on the API.
import org.apache.spark.ml.feature.NGram
val wordDataFrame = spark.createDataFrame(Seq(
(0, Array("Hi", "I", "heard", "about", "Spark")),
(1, Array("I", "wish", "Java", "could", "use", "case", "classes")),
(2, Array("Logistic", "regression", "models", "are", "neat"))
)).toDF("id", "words")
val ngram = new NGram().setN(2).setInputCol("words").setOutputCol("ngrams")
val ngramDataFrame = ngram.transform(wordDataFrame)
ngramDataFrame.select("ngrams").show(false)Refer to the NGram Java docs for more details on the API.
import java.util.Arrays;
import java.util.List;
import org.apache.spark.ml.feature.NGram;
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;
List<Row> data = Arrays.asList(
RowFactory.create(0, Arrays.asList("Hi", "I", "heard", "about", "Spark")),
RowFactory.create(1, Arrays.asList("I", "wish", "Java", "could", "use", "case", "classes")),
RowFactory.create(2, Arrays.asList("Logistic", "regression", "models", "are", "neat"))
);
StructType schema = new StructType(new StructField[]{
new StructField("id", DataTypes.IntegerType, false, Metadata.empty()),
new StructField(
"words", DataTypes.createArrayType(DataTypes.StringType), false, Metadata.empty())
});
Dataset<Row> wordDataFrame = spark.createDataFrame(data, schema);
NGram ngramTransformer = new NGram().setN(2).setInputCol("words").setOutputCol("ngrams");
Dataset<Row> ngramDataFrame = ngramTransformer.transform(wordDataFrame);
ngramDataFrame.select("ngrams").show(false);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 Python docs for more details on the API.
from pyspark.ml.feature import Binarizer
continuousDataFrame = spark.createDataFrame([
(0, 0.1),
(1, 0.8),
(2, 0.2)
], ["id", "feature"])
binarizer = Binarizer(threshold=0.5, inputCol="feature", outputCol="binarized_feature")
binarizedDataFrame = binarizer.transform(continuousDataFrame)
print("Binarizer output with Threshold = %f" % binarizer.getThreshold())
binarizedDataFrame.show()Refer to the Binarizer Scala docs for more details on the API.
import org.apache.spark.ml.feature.Binarizer
val data = immutable.ArraySeq.unsafeWrapArray(Array((0, 0.1), (1, 0.8), (2, 0.2)))
val dataFrame = spark.createDataFrame(data).toDF("id", "feature")
val binarizer: Binarizer = new Binarizer()
.setInputCol("feature")
.setOutputCol("binarized_feature")
.setThreshold(0.5)
val binarizedDataFrame = binarizer.transform(dataFrame)
println(s"Binarizer output with Threshold = ${binarizer.getThreshold}")
binarizedDataFrame.show()Refer to the Binarizer Java docs for more details on the API.
import java.util.Arrays;
import java.util.List;
import org.apache.spark.ml.feature.Binarizer;
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;
List<Row> data = Arrays.asList(
RowFactory.create(0, 0.1),
RowFactory.create(1, 0.8),
RowFactory.create(2, 0.2)
);
StructType schema = new StructType(new StructField[]{
new StructField("id", DataTypes.IntegerType, false, Metadata.empty()),
new StructField("feature", DataTypes.DoubleType, false, Metadata.empty())
});
Dataset<Row> continuousDataFrame = spark.createDataFrame(data, schema);
Binarizer binarizer = new Binarizer()
.setInputCol("feature")
.setOutputCol("binarized_feature")
.setThreshold(0.5);
Dataset<Row> binarizedDataFrame = binarizer.transform(continuousDataFrame);
System.out.println("Binarizer output with Threshold = " + binarizer.getThreshold());
binarizedDataFrame.show();PCA
PCA 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 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 Python docs for more details on the API.
from pyspark.ml.feature import PCA
from pyspark.ml.linalg import Vectors
data = [(Vectors.sparse(5, [(1, 1.0), (3, 7.0)]),),
(Vectors.dense([2.0, 0.0, 3.0, 4.0, 5.0]),),
(Vectors.dense([4.0, 0.0, 0.0, 6.0, 7.0]),)]
df = spark.createDataFrame(data, ["features"])
pca = PCA(k=3, inputCol="features", outputCol="pcaFeatures")
model = pca.fit(df)
result = model.transform(df).select("pcaFeatures")
result.show(truncate=False)Refer to the PCA Scala docs for more details on the API.
import org.apache.spark.ml.feature.PCA
import org.apache.spark.ml.linalg.Vectors
val data = Array(
Vectors.sparse(5, Seq((1, 1.0), (3, 7.0))),
Vectors.dense(2.0, 0.0, 3.0, 4.0, 5.0),
Vectors.dense(4.0, 0.0, 0.0, 6.0, 7.0)
)
val df = spark.createDataFrame(immutable.ArraySeq.unsafeWrapArray(data.map(Tuple1.apply)))
.toDF("features")
val pca = new PCA()
.setInputCol("features")
.setOutputCol("pcaFeatures")
.setK(3)
.fit(df)
val result = pca.transform(df).select("pcaFeatures")
result.show(false)Refer to the PCA Java docs for more details on the API.
import java.util.Arrays;
import java.util.List;
import org.apache.spark.ml.feature.PCA;
import org.apache.spark.ml.feature.PCAModel;
import org.apache.spark.ml.linalg.VectorUDT;
import org.apache.spark.ml.linalg.Vectors;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.types.Metadata;
import org.apache.spark.sql.types.StructField;
import org.apache.spark.sql.types.StructType;
List<Row> data = Arrays.asList(
RowFactory.create(Vectors.sparse(5, new int[]{1, 3}, new double[]{1.0, 7.0})),
RowFactory.create(Vectors.dense(2.0, 0.0, 3.0, 4.0, 5.0)),
RowFactory.create(Vectors.dense(4.0, 0.0, 0.0, 6.0, 7.0))
);
StructType schema = new StructType(new StructField[]{
new StructField("features", new VectorUDT(), false, Metadata.empty()),
});
Dataset<Row> df = spark.createDataFrame(data, schema);
PCAModel pca = new PCA()
.setInputCol("features")
.setOutputCol("pcaFeatures")
.setK(3)
.fit(df);
Dataset<Row> result = pca.transform(df).select("pcaFeatures");
result.show(false);PolynomialExpansion
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 class provides this functionality. The example below shows how to expand your features into a 3-degree polynomial space.
Examples
Refer to the PolynomialExpansion Python docs for more details on the API.
from pyspark.ml.feature import PolynomialExpansion
from pyspark.ml.linalg import Vectors
df = spark.createDataFrame([
(Vectors.dense([2.0, 1.0]),),
(Vectors.dense([0.0, 0.0]),),
(Vectors.dense([3.0, -1.0]),)
], ["features"])
polyExpansion = PolynomialExpansion(degree=3, inputCol="features", outputCol="polyFeatures")
polyDF = polyExpansion.transform(df)
polyDF.show(truncate=False)Refer to the PolynomialExpansion Scala docs for more details on the API.
import org.apache.spark.ml.feature.PolynomialExpansion
import org.apache.spark.ml.linalg.Vectors
val data = Array(
Vectors.dense(2.0, 1.0),
Vectors.dense(0.0, 0.0),
Vectors.dense(3.0, -1.0)
)
val df = spark.createDataFrame(immutable.ArraySeq.unsafeWrapArray(data.map(Tuple1.apply)))
.toDF("features")
val polyExpansion = new PolynomialExpansion()
.setInputCol("features")
.setOutputCol("polyFeatures")
.setDegree(3)
val polyDF = polyExpansion.transform(df)
polyDF.show(false)Refer to the PolynomialExpansion Java docs for more details on the API.
import java.util.Arrays;
import java.util.List;
import org.apache.spark.ml.feature.PolynomialExpansion;
import org.apache.spark.ml.linalg.VectorUDT;
import org.apache.spark.ml.linalg.Vectors;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.types.Metadata;
import org.apache.spark.sql.types.StructField;
import org.apache.spark.sql.types.StructType;
PolynomialExpansion polyExpansion = new PolynomialExpansion()
.setInputCol("features")
.setOutputCol("polyFeatures")
.setDegree(3);
List<Row> data = Arrays.asList(
RowFactory.create(Vectors.dense(2.0, 1.0)),
RowFactory.create(Vectors.dense(0.0, 0.0)),
RowFactory.create(Vectors.dense(3.0, -1.0))
);
StructType schema = new StructType(new StructField[]{
new StructField("features", new VectorUDT(), false, Metadata.empty()),
});
Dataset<Row> df = spark.createDataFrame(data, schema);
Dataset<Row> polyDF = polyExpansion.transform(df);
polyDF.show(false);Discrete Cosine Transform (DCT)
The 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 class provides this functionality, implementing the 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 Python docs for more details on the API.
from pyspark.ml.feature import DCT
from pyspark.ml.linalg import Vectors
df = spark.createDataFrame([
(Vectors.dense([0.0, 1.0, -2.0, 3.0]),),
(Vectors.dense([-1.0, 2.0, 4.0, -7.0]),),
(Vectors.dense([14.0, -2.0, -5.0, 1.0]),)], ["features"])
dct = DCT(inverse=False, inputCol="features", outputCol="featuresDCT")
dctDf = dct.transform(df)
dctDf.select("featuresDCT").show(truncate=False)Refer to the DCT Scala docs for more details on the API.
import org.apache.spark.ml.feature.DCT
import org.apache.spark.ml.linalg.Vectors
val data = Seq(
Vectors.dense(0.0, 1.0, -2.0, 3.0),
Vectors.dense(-1.0, 2.0, 4.0, -7.0),
Vectors.dense(14.0, -2.0, -5.0, 1.0))
val df = spark.createDataFrame(data.map(Tuple1.apply)).toDF("features")
val dct = new DCT()
.setInputCol("features")
.setOutputCol("featuresDCT")
.setInverse(false)
val dctDf = dct.transform(df)
dctDf.select("featuresDCT").show(false)Refer to the DCT Java docs for more details on the API.
import java.util.Arrays;
import java.util.List;
import org.apache.spark.ml.feature.DCT;
import org.apache.spark.ml.linalg.VectorUDT;
import org.apache.spark.ml.linalg.Vectors;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.types.Metadata;
import org.apache.spark.sql.types.StructField;
import org.apache.spark.sql.types.StructType;
List<Row> data = Arrays.asList(
RowFactory.create(Vectors.dense(0.0, 1.0, -2.0, 3.0)),
RowFactory.create(Vectors.dense(-1.0, 2.0, 4.0, -7.0)),
RowFactory.create(Vectors.dense(14.0, -2.0, -5.0, 1.0))
);
StructType schema = new StructType(new StructField[]{
new StructField("features", new VectorUDT(), false, Metadata.empty()),
});
Dataset<Row> df = spark.createDataFrame(data, schema);
DCT dct = new DCT()
.setInputCol("features")
.setOutputCol("featuresDCT")
.setInverse(false);
Dataset<Row> dctDf = dct.transform(df);
dctDf.select("featuresDCT").show(false);StringIndexer
StringIndexer encodes a string column of labels to a column of label indices.
StringIndexer can encode multiple columns. The indices are in [0, numLabels), and four ordering options are supported:
“frequencyDesc”: descending order by label frequency (most frequent label assigned 0),
“frequencyAsc”: ascending order by label frequency (least frequent label assigned 0),
“alphabetDesc”: descending alphabetical order, and “alphabetAsc”: ascending alphabetical order
(default = “frequencyDesc”). Note that in case of equal frequency when under
“frequencyDesc”/”frequencyAsc”, the strings are further sorted by alphabet.
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 Python docs for more details on the API.
from pyspark.ml.feature import StringIndexer
df = spark.createDataFrame(
[(0, "a"), (1, "b"), (2, "c"), (3, "a"), (4, "a"), (5, "c")],
["id", "category"])
indexer = StringIndexer(inputCol="category", outputCol="categoryIndex")
indexed = indexer.fit(df).transform(df)
indexed.show()Refer to the StringIndexer Scala docs for more details on the API.
import org.apache.spark.ml.feature.StringIndexer
val df = spark.createDataFrame(
Seq((0, "a"), (1, "b"), (2, "c"), (3, "a"), (4, "a"), (5, "c"))
).toDF("id", "category")
val indexer = new StringIndexer()
.setInputCol("category")
.setOutputCol("categoryIndex")
val indexed = indexer.fit(df).transform(df)
indexed.show()Refer to the StringIndexer Java docs for more details on the API.
import java.util.Arrays;
import java.util.List;
import org.apache.spark.ml.feature.StringIndexer;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.types.StructField;
import org.apache.spark.sql.types.StructType;
import static org.apache.spark.sql.types.DataTypes.*;
List<Row> data = Arrays.asList(
RowFactory.create(0, "a"),
RowFactory.create(1, "b"),
RowFactory.create(2, "c"),
RowFactory.create(3, "a"),
RowFactory.create(4, "a"),
RowFactory.create(5, "c")
);
StructType schema = new StructType(new StructField[]{
createStructField("id", IntegerType, false),
createStructField("category", StringType, false)
});
Dataset<Row> df = spark.createDataFrame(data, schema);
StringIndexer indexer = new StringIndexer()
.setInputCol("category")
.setOutputCol("categoryIndex");
Dataset<Row> indexed = indexer.fit(df).transform(df);
indexed.show();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 Python docs for more details on the API.
from pyspark.ml.feature import IndexToString, StringIndexer
df = spark.createDataFrame(
[(0, "a"), (1, "b"), (2, "c"), (3, "a"), (4, "a"), (5, "c")],
["id", "category"])
indexer = StringIndexer(inputCol="category", outputCol="categoryIndex")
model = indexer.fit(df)
indexed = model.transform(df)
print("Transformed string column '%s' to indexed column '%s'"
% (indexer.getInputCol(), indexer.getOutputCol()))
indexed.show()
print("StringIndexer will store labels in output column metadata\n")
converter = IndexToString(inputCol="categoryIndex", outputCol="originalCategory")
converted = converter.transform(indexed)
print("Transformed indexed column '%s' back to original string column '%s' using "
"labels in metadata" % (converter.getInputCol(), converter.getOutputCol()))
converted.select("id", "categoryIndex", "originalCategory").show()Refer to the IndexToString Scala docs for more details on the API.
import org.apache.spark.ml.attribute.Attribute
import org.apache.spark.ml.feature.{IndexToString, StringIndexer}
val df = spark.createDataFrame(Seq(
(0, "a"),
(1, "b"),
(2, "c"),
(3, "a"),
(4, "a"),
(5, "c")
)).toDF("id", "category")
val indexer = new StringIndexer()
.setInputCol("category")
.setOutputCol("categoryIndex")
.fit(df)
val indexed = indexer.transform(df)
println(s"Transformed string column '${indexer.getInputCol}' " +
s"to indexed column '${indexer.getOutputCol}'")
indexed.show()
val inputColSchema = indexed.schema(indexer.getOutputCol)
println(s"StringIndexer will store labels in output column metadata: " +
s"${Attribute.fromStructField(inputColSchema).toString}\n")
val converter = new IndexToString()
.setInputCol("categoryIndex")
.setOutputCol("originalCategory")
val converted = converter.transform(indexed)
println(s"Transformed indexed column '${converter.getInputCol}' back to original string " +
s"column '${converter.getOutputCol}' using labels in metadata")
converted.select("id", "categoryIndex", "originalCategory").show()Refer to the IndexToString Java docs for more details on the API.
import java.util.Arrays;
import java.util.List;
import org.apache.spark.ml.attribute.Attribute;
import org.apache.spark.ml.feature.IndexToString;
import org.apache.spark.ml.feature.StringIndexer;
import org.apache.spark.ml.feature.StringIndexerModel;
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;
List<Row> data = Arrays.asList(
RowFactory.create(0, "a"),
RowFactory.create(1, "b"),
RowFactory.create(2, "c"),
RowFactory.create(3, "a"),
RowFactory.create(4, "a"),
RowFactory.create(5, "c")
);
StructType schema = new StructType(new StructField[]{
new StructField("id", DataTypes.IntegerType, false, Metadata.empty()),
new StructField("category", DataTypes.StringType, false, Metadata.empty())
});
Dataset<Row> df = spark.createDataFrame(data, schema);
StringIndexerModel indexer = new StringIndexer()
.setInputCol("category")
.setOutputCol("categoryIndex")
.fit(df);
Dataset<Row> indexed = indexer.transform(df);
System.out.println("Transformed string column '" + indexer.getInputCol() + "' " +
"to indexed column '" + indexer.getOutputCol() + "'");
indexed.show();
StructField inputColSchema = indexed.schema().apply(indexer.getOutputCol());
System.out.println("StringIndexer will store labels in output column metadata: " +
Attribute.fromStructField(inputColSchema).toString() + "\n");
IndexToString converter = new IndexToString()
.setInputCol("categoryIndex")
.setOutputCol("originalCategory");
Dataset<Row> converted = converter.transform(indexed);
System.out.println("Transformed indexed column '" + converter.getInputCol() + "' back to " +
"original string column '" + converter.getOutputCol() + "' using labels in metadata");
converted.select("id", "categoryIndex", "originalCategory").show();OneHotEncoder
One-hot encoding maps a categorical feature, represented as a label index, to a binary vector with at most a single one-value indicating the presence of a specific feature value from among the set of all feature values. This encoding allows algorithms which expect continuous features, such as Logistic Regression, to use categorical features. For string type input data, it is common to encode categorical features using StringIndexer first.
OneHotEncoder can transform multiple columns, returning an one-hot-encoded output vector column for each input column. It is common to merge these vectors into a single feature vector using VectorAssembler.
OneHotEncoder supports the handleInvalid parameter to choose how to handle invalid input during transforming data. Available options include ‘keep’ (any invalid inputs are assigned to an extra categorical index) and ‘error’ (throw an error).
Examples
Refer to the OneHotEncoder Python docs for more details on the API.
from pyspark.ml.feature import OneHotEncoder
df = spark.createDataFrame([
(0.0, 1.0),
(1.0, 0.0),
(2.0, 1.0),
(0.0, 2.0),
(0.0, 1.0),
(2.0, 0.0)
], ["categoryIndex1", "categoryIndex2"])
encoder = OneHotEncoder(inputCols=["categoryIndex1", "categoryIndex2"],
outputCols=["categoryVec1", "categoryVec2"])
model = encoder.fit(df)
encoded = model.transform(df)
encoded.show()Refer to the OneHotEncoder Scala docs for more details on the API.
import org.apache.spark.ml.feature.OneHotEncoder
val df = spark.createDataFrame(Seq(
(0.0, 1.0),
(1.0, 0.0),
(2.0, 1.0),
(0.0, 2.0),
(0.0, 1.0),
(2.0, 0.0)
)).toDF("categoryIndex1", "categoryIndex2")
val encoder = new OneHotEncoder()
.setInputCols(Array("categoryIndex1", "categoryIndex2"))
.setOutputCols(Array("categoryVec1", "categoryVec2"))
val model = encoder.fit(df)
val encoded = model.transform(df)
encoded.show()Refer to the OneHotEncoder Java docs for more details on the API.
import java.util.Arrays;
import java.util.List;
import org.apache.spark.ml.feature.OneHotEncoder;
import org.apache.spark.ml.feature.OneHotEncoderModel;
import org.apache.spark.sql.Dataset;
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;
List<Row> data = Arrays.asList(
RowFactory.create(0.0, 1.0),
RowFactory.create(1.0, 0.0),
RowFactory.create(2.0, 1.0),
RowFactory.create(0.0, 2.0),
RowFactory.create(0.0, 1.0),
RowFactory.create(2.0, 0.0)
);
StructType schema = new StructType(new StructField[]{
new StructField("categoryIndex1", DataTypes.DoubleType, false, Metadata.empty()),
new StructField("categoryIndex2", DataTypes.DoubleType, false, Metadata.empty())
});
Dataset<Row> df = spark.createDataFrame(data, schema);
OneHotEncoder encoder = new OneHotEncoder()
.setInputCols(new String[] {"categoryIndex1", "categoryIndex2"})
.setOutputCols(new String[] {"categoryVec1", "categoryVec2"});
OneHotEncoderModel model = encoder.fit(df);
Dataset<Row> encoded = model.transform(df);
encoded.show();TargetEncoder
Target Encoding is a data-preprocessing technique that transforms high-cardinality categorical features into quasi-continuous scalar attributes suited for use in regression-type models. This paradigm maps individual values of an independent feature to a scalar, representing some estimate of the dependent attribute (meaning categorical values that exhibit similar statistics with respect to the target will have a similar representation).
By leveraging the relationship between categorical features and the target variable, Target Encoding usually performs better than One-Hot and does not require a final binary vector encoding, decreasing the overall dimensionality of the dataset.
User can specify input and output column names by setting inputCol and outputCol for single-column use cases, or inputCols and outputCols for multi-column use cases (both arrays required to have the same size). These columns are expected to contain categorical indices (positive integers), being missing values (null) treated as a separate category. Data type must be any subclass of ‘NumericType’. For string type input data, it is common to encode categorical features using StringIndexer first.
User can specify the target column name by setting label. This column is expected to contain the ground-truth labels from which encodings will be derived. Observations with missing label (null) are not considered when calculating estimates. Data type must be any subclass of ‘NumericType’.
TargetEncoder supports the handleInvalid parameter to choose how to handle invalid input, meaning categories not seen at training, when encoding new data. Available options include ‘keep’ (any invalid inputs are assigned to an extra categorical index) and ‘error’ (throw an exception).
TargetEncoder supports the targetType parameter to choose the label type when fitting data, affecting how estimates are calculated. Available options include ‘binary’ and ‘continuous’.
When set to ‘binary’, the target attribute $Y$ is expected to be binary, $Y\in{ 0,1 }$. The transformation maps individual values $X_{i}$ to the conditional probability of $Y$ given that $X=X_{i}\;$: $\;\; S_{i}=P(Y\mid X=X_{i})$. This approach is also known as bin-counting.
When set to ‘continuous’, the target attribute $Y$ is expected to be continuous, $Y\in\mathbb{Q}$. The transformation maps individual values $X_{i}$ to the average of $Y$ given that $X=X_{i}\;$: $\;\; S_{i}=E[Y\mid X=X_{i}]$. This approach is also known as mean-encoding.
TargetEncoder supports the smoothing parameter to tune how in-category stats and overall stats are blended. High-cardinality categorical features are usually unevenly distributed across all possible values of $X$.
Therefore, calculating encodings $S_{i}$ according only to in-class statistics makes this estimates very unreliable, and rarely seen categories will very likely cause overfitting in learning.
Smoothing prevents this behaviour by weighting in-class estimates with overall estimates according to the relative size of the particular class on the whole dataset.
$\;\;\; S_{i}=\lambda(n_{i})\, P(Y\mid X=X_{i})+(1-\lambda(n_{i}))\, P(Y)$ for the binary case
$\;\;\; S_{i}=\lambda(n_{i})\, E[Y\mid X=X_{i}]+(1-\lambda(n_{i}))\, E[Y]$ for the continuous case
being $\lambda(n_{i})$ a monotonically increasing function on $n_{i}$, bounded between 0 and 1.
Usually $\lambda(n_{i})$ is implemented as the parametric function $\lambda(n_{i})=\frac{n_{i}}{n_{i}+m}$, where $m$ is the smoothing factor, represented by smoothing parameter in TargetEncoder.
Examples
Building on the TargetEncoder example, let’s assume we have the following
DataFrame with columns feature and target (binary & continuous):
feature | target | target
| (bin) | (cont)
--------|--------|--------
1 | 0 | 1.3
1 | 1 | 2.5
1 | 0 | 1.6
2 | 1 | 1.8
2 | 0 | 2.4
3 | 1 | 3.2
Applying TargetEncoder with ‘binary’ target type,
feature as the input column,target (bin) as the label column
and encoded as the output column, we are able to fit a model
on the data to learn encodings and transform the data according
to these mappings:
feature | target | encoded
| (bin) |
--------|--------|--------
1 | 0 | 0.333
1 | 1 | 0.333
1 | 0 | 0.333
2 | 1 | 0.5
2 | 0 | 0.5
3 | 1 | 1.0
Applying TargetEncoder with ‘continuous’ target type,
feature as the input column,target (cont) as the label column
and encoded as the output column, we are able to fit a model
on the data to learn encodings and transform the data according
to these mappings:
feature | target | encoded
| (cont) |
--------|--------|--------
1 | 1.3 | 1.8
1 | 2.5 | 1.8
1 | 1.6 | 1.8
2 | 1.8 | 2.1
2 | 2.4 | 2.1
3 | 3.2 | 3.2
Refer to the TargetEncoder Python docs for more details on the API.
from pyspark.ml.feature import TargetEncoder
df = spark.createDataFrame(
[
(0.0, 1.0, 0, 10.0),
(1.0, 0.0, 1, 20.0),
(2.0, 1.0, 0, 30.0),
(0.0, 2.0, 1, 40.0),
(0.0, 1.0, 0, 50.0),
(2.0, 0.0, 1, 60.0),
],
["categoryIndex1", "categoryIndex2", "binaryLabel", "continuousLabel"],
)
# binary target
encoder = TargetEncoder(
inputCols=["categoryIndex1", "categoryIndex2"],
outputCols=["categoryIndex1Target", "categoryIndex2Target"],
labelCol="binaryLabel",
targetType="binary"
)
model = encoder.fit(df)
encoded = model.transform(df)
encoded.show()
# continuous target
encoder = TargetEncoder(
inputCols=["categoryIndex1", "categoryIndex2"],
outputCols=["categoryIndex1Target", "categoryIndex2Target"],
labelCol="continuousLabel",
targetType="continuous"
)
model = encoder.fit(df)
encoded = model.transform(df)
encoded.show()Refer to the TargetEncoder Scala docs for more details on the API.
import org.apache.spark.ml.feature.TargetEncoder
val df = spark.createDataFrame(Seq(
(0.0, 1.0, 0, 10.0),
(1.0, 0.0, 1, 20.0),
(2.0, 1.0, 0, 30.0),
(0.0, 2.0, 1, 40.0),
(0.0, 1.0, 0, 50.0),
(2.0, 0.0, 1, 60.0)
)).toDF("categoryIndex1", "categoryIndex2",
"binaryLabel", "continuousLabel")
// binary target
val bin_encoder = new TargetEncoder()
.setInputCols(Array("categoryIndex1", "categoryIndex2"))
.setOutputCols(Array("categoryIndex1Target", "categoryIndex2Target"))
.setLabelCol("binaryLabel")
.setTargetType("binary");
val bin_model = bin_encoder.fit(df)
val bin_encoded = bin_model.transform(df)
bin_encoded.show()
// continuous target
val cont_encoder = new TargetEncoder()
.setInputCols(Array("categoryIndex1", "categoryIndex2"))
.setOutputCols(Array("categoryIndex1Target", "categoryIndex2Target"))
.setLabelCol("continuousLabel")
.setTargetType("continuous");
val cont_model = cont_encoder.fit(df)
val cont_encoded = cont_model.transform(df)
cont_encoded.show()Refer to the TargetEncoder Java docs for more details on the API.
import org.apache.spark.ml.feature.TargetEncoder;
import org.apache.spark.ml.feature.TargetEncoderModel;
import org.apache.spark.sql.Dataset;
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;
import java.util.Arrays;
import java.util.List;
List<Row> data = Arrays.asList(
RowFactory.create(0.0, 1.0, 0, 10.0),
RowFactory.create(1.0, 0.0, 1, 20.0),
RowFactory.create(2.0, 1.0, 0, 30.0),
RowFactory.create(0.0, 2.0, 1, 40.0),
RowFactory.create(0.0, 1.0, 0, 50.0),
RowFactory.create(2.0, 0.0, 1, 60.0)
);
StructType schema = new StructType(new StructField[]{
new StructField("categoryIndex1", DataTypes.DoubleType, false, Metadata.empty()),
new StructField("categoryIndex2", DataTypes.DoubleType, false, Metadata.empty()),
new StructField("binaryLabel", DataTypes.DoubleType, false, Metadata.empty()),
new StructField("continuousLabel", DataTypes.DoubleType, false, Metadata.empty())
});
Dataset<Row> df = spark.createDataFrame(data, schema);
// binary target
TargetEncoder bin_encoder = new TargetEncoder()
.setInputCols(new String[] {"categoryIndex1", "categoryIndex2"})
.setOutputCols(new String[] {"categoryIndex1Target", "categoryIndex2Target"})
.setLabelCol("binaryLabel")
.setTargetType("binary");
TargetEncoderModel bin_model = bin_encoder.fit(df);
Dataset<Row> bin_encoded = bin_model.transform(df);
bin_encoded.show();
// continuous target
TargetEncoder cont_encoder = new TargetEncoder()
.setInputCols(new String[] {"categoryIndex1", "categoryIndex2"})
.setOutputCols(new String[] {"categoryIndex1Target", "categoryIndex2Target"})
.setLabelCol("continuousLabel")
.setTargetType("continuous");
TargetEncoderModel cont_model = cont_encoder.fit(df);
Dataset<Row> cont_encoded = cont_model.transform(df);
cont_encoded.show();VectorIndexer
VectorIndexer helps index categorical features in datasets of Vectors.
It can both automatically decide which features are categorical and convert original values to category indices. Specifically, it does the following:
- Take an input column of type Vector and a parameter
maxCategories. - Decide which features should be categorical based on the number of distinct values, where features with at most
maxCategoriesare declared categorical. - Compute 0-based category indices for each categorical feature.
- 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 Python docs for more details on the API.
from pyspark.ml.feature import VectorIndexer
data = spark.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
indexer = VectorIndexer(inputCol="features", outputCol="indexed", maxCategories=10)
indexerModel = indexer.fit(data)
categoricalFeatures = indexerModel.categoryMaps
print("Chose %d categorical features: %s" %
(len(categoricalFeatures), ", ".join(str(k) for k in categoricalFeatures.keys())))
# Create new column "indexed" with categorical values transformed to indices
indexedData = indexerModel.transform(data)
indexedData.show()Refer to the VectorIndexer Scala docs for more details on the API.
import org.apache.spark.ml.feature.VectorIndexer
val data = spark.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
val indexer = new VectorIndexer()
.setInputCol("features")
.setOutputCol("indexed")
.setMaxCategories(10)
val indexerModel = indexer.fit(data)
val categoricalFeatures: Set[Int] = indexerModel.categoryMaps.keys.toSet
println(s"Chose ${categoricalFeatures.size} " +
s"categorical features: ${categoricalFeatures.mkString(", ")}")
// Create new column "indexed" with categorical values transformed to indices
val indexedData = indexerModel.transform(data)
indexedData.show()Refer to the VectorIndexer Java docs for more details on the API.
import java.util.Map;
import org.apache.spark.ml.feature.VectorIndexer;
import org.apache.spark.ml.feature.VectorIndexerModel;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
Dataset<Row> data = spark.read().format("libsvm").load("data/mllib/sample_libsvm_data.txt");
VectorIndexer indexer = new VectorIndexer()
.setInputCol("features")
.setOutputCol("indexed")
.setMaxCategories(10);
VectorIndexerModel indexerModel = indexer.fit(data);
Map<Integer, Map<Double, Integer>> categoryMaps = indexerModel.javaCategoryMaps();
System.out.print("Chose " + categoryMaps.size() + " categorical features:");
for (Integer feature : categoryMaps.keySet()) {
System.out.print(" " + feature);
}
System.out.println();
// Create new column "indexed" with categorical values transformed to indices
Dataset<Row> indexedData = indexerModel.transform(data);
indexedData.show();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 Python docs for more details on the API.
from pyspark.ml.feature import Interaction, VectorAssembler
df = spark.createDataFrame(
[(1, 1, 2, 3, 8, 4, 5),
(2, 4, 3, 8, 7, 9, 8),
(3, 6, 1, 9, 2, 3, 6),
(4, 10, 8, 6, 9, 4, 5),
(5, 9, 2, 7, 10, 7, 3),
(6, 1, 1, 4, 2, 8, 4)],
["id1", "id2", "id3", "id4", "id5", "id6", "id7"])
assembler1 = VectorAssembler(inputCols=["id2", "id3", "id4"], outputCol="vec1")
assembled1 = assembler1.transform(df)
assembler2 = VectorAssembler(inputCols=["id5", "id6", "id7"], outputCol="vec2")
assembled2 = assembler2.transform(assembled1).select("id1", "vec1", "vec2")
interaction = Interaction(inputCols=["id1", "vec1", "vec2"], outputCol="interactedCol")
interacted = interaction.transform(assembled2)
interacted.show(truncate=False)Refer to the Interaction Scala docs for more details on the API.
import org.apache.spark.ml.feature.Interaction
import org.apache.spark.ml.feature.VectorAssembler
val df = spark.createDataFrame(Seq(
(1, 1, 2, 3, 8, 4, 5),
(2, 4, 3, 8, 7, 9, 8),
(3, 6, 1, 9, 2, 3, 6),
(4, 10, 8, 6, 9, 4, 5),
(5, 9, 2, 7, 10, 7, 3),
(6, 1, 1, 4, 2, 8, 4)
)).toDF("id1", "id2", "id3", "id4", "id5", "id6", "id7")
val assembler1 = new VectorAssembler().
setInputCols(Array("id2", "id3", "id4")).
setOutputCol("vec1")
val assembled1 = assembler1.transform(df)
val assembler2 = new VectorAssembler().
setInputCols(Array("id5", "id6", "id7")).
setOutputCol("vec2")
val assembled2 = assembler2.transform(assembled1).select("id1", "vec1", "vec2")
val interaction = new Interaction()
.setInputCols(Array("id1", "vec1", "vec2"))
.setOutputCol("interactedCol")
val interacted = interaction.transform(assembled2)
interacted.show(truncate = false)Refer to the Interaction Java docs for more details on the API.
List<Row> data = Arrays.asList(
RowFactory.create(1, 1, 2, 3, 8, 4, 5),
RowFactory.create(2, 4, 3, 8, 7, 9, 8),
RowFactory.create(3, 6, 1, 9, 2, 3, 6),
RowFactory.create(4, 10, 8, 6, 9, 4, 5),
RowFactory.create(5, 9, 2, 7, 10, 7, 3),
RowFactory.create(6, 1, 1, 4, 2, 8, 4)
);
StructType schema = new StructType(new StructField[]{
new StructField("id1", DataTypes.IntegerType, false, Metadata.empty()),
new StructField("id2", DataTypes.IntegerType, false, Metadata.empty()),
new StructField("id3", DataTypes.IntegerType, false, Metadata.empty()),
new StructField("id4", DataTypes.IntegerType, false, Metadata.empty()),
new StructField("id5", DataTypes.IntegerType, false, Metadata.empty()),
new StructField("id6", DataTypes.IntegerType, false, Metadata.empty()),
new StructField("id7", DataTypes.IntegerType, false, Metadata.empty())
});
Dataset<Row> df = spark.createDataFrame(data, schema);
VectorAssembler assembler1 = new VectorAssembler()
.setInputCols(new String[]{"id2", "id3", "id4"})
.setOutputCol("vec1");
Dataset<Row> assembled1 = assembler1.transform(df);
VectorAssembler assembler2 = new VectorAssembler()
.setInputCols(new String[]{"id5", "id6", "id7"})
.setOutputCol("vec2");
Dataset<Row> assembled2 = assembler2.transform(assembled1).select("id1", "vec1", "vec2");
Interaction interaction = new Interaction()
.setInputCols(new String[]{"id1","vec1","vec2"})
.setOutputCol("interactedCol");
Dataset<Row> interacted = interaction.transform(assembled2);
interacted.show(false);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 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 Python docs for more details on the API.
from pyspark.ml.feature import Normalizer
from pyspark.ml.linalg import Vectors
dataFrame = spark.createDataFrame([
(0, Vectors.dense([1.0, 0.5, -1.0]),),
(1, Vectors.dense([2.0, 1.0, 1.0]),),
(2, Vectors.dense([4.0, 10.0, 2.0]),)
], ["id", "features"])
# Normalize each Vector using $L^1$ norm.
normalizer = Normalizer(inputCol="features", outputCol="normFeatures", p=1.0)
l1NormData = normalizer.transform(dataFrame)
print("Normalized using L^1 norm")
l1NormData.show()
# Normalize each Vector using $L^\infty$ norm.
lInfNormData = normalizer.transform(dataFrame, {normalizer.p: float("inf")})
print("Normalized using L^inf norm")
lInfNormData.show()Refer to the Normalizer Scala docs for more details on the API.
import org.apache.spark.ml.feature.Normalizer
import org.apache.spark.ml.linalg.Vectors
val dataFrame = spark.createDataFrame(Seq(
(0, Vectors.dense(1.0, 0.5, -1.0)),
(1, Vectors.dense(2.0, 1.0, 1.0)),
(2, Vectors.dense(4.0, 10.0, 2.0))
)).toDF("id", "features")
// Normalize each Vector using $L^1$ norm.
val normalizer = new Normalizer()
.setInputCol("features")
.setOutputCol("normFeatures")
.setP(1.0)
val l1NormData = normalizer.transform(dataFrame)
println("Normalized using L^1 norm")
l1NormData.show()
// Normalize each Vector using $L^\infty$ norm.
val lInfNormData = normalizer.transform(dataFrame, normalizer.p -> Double.PositiveInfinity)
println("Normalized using L^inf norm")
lInfNormData.show()Refer to the Normalizer Java docs for more details on the API.
import java.util.Arrays;
import java.util.List;
import org.apache.spark.ml.feature.Normalizer;
import org.apache.spark.ml.linalg.Vectors;
import org.apache.spark.ml.linalg.VectorUDT;
import org.apache.spark.sql.Dataset;
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;
List<Row> data = Arrays.asList(
RowFactory.create(0, Vectors.dense(1.0, 0.1, -8.0)),
RowFactory.create(1, Vectors.dense(2.0, 1.0, -4.0)),
RowFactory.create(2, Vectors.dense(4.0, 10.0, 8.0))
);
StructType schema = new StructType(new StructField[]{
new StructField("id", DataTypes.IntegerType, false, Metadata.empty()),
new StructField("features", new VectorUDT(), false, Metadata.empty())
});
Dataset<Row> dataFrame = spark.createDataFrame(data, schema);
// Normalize each Vector using $L^1$ norm.
Normalizer normalizer = new Normalizer()
.setInputCol("features")
.setOutputCol("normFeatures")
.setP(1.0);
Dataset<Row> l1NormData = normalizer.transform(dataFrame);
l1NormData.show();
// Normalize each Vector using $L^\infty$ norm.
Dataset<Row> lInfNormData =
normalizer.transform(dataFrame, normalizer.p().w(Double.POSITIVE_INFINITY));
lInfNormData.show();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 Python docs for more details on the API.
from pyspark.ml.feature import StandardScaler
dataFrame = spark.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
scaler = StandardScaler(inputCol="features", outputCol="scaledFeatures",
withStd=True, withMean=False)
# Compute summary statistics by fitting the StandardScaler
scalerModel = scaler.fit(dataFrame)
# Normalize each feature to have unit standard deviation.
scaledData = scalerModel.transform(dataFrame)
scaledData.show()Refer to the StandardScaler Scala docs for more details on the API.
import org.apache.spark.ml.feature.StandardScaler
val dataFrame = spark.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
val scaler = new StandardScaler()
.setInputCol("features")
.setOutputCol("scaledFeatures")
.setWithStd(true)
.setWithMean(false)
// Compute summary statistics by fitting the StandardScaler.
val scalerModel = scaler.fit(dataFrame)
// Normalize each feature to have unit standard deviation.
val scaledData = scalerModel.transform(dataFrame)
scaledData.show()Refer to the StandardScaler Java docs for more details on the API.
import org.apache.spark.ml.feature.StandardScaler;
import org.apache.spark.ml.feature.StandardScalerModel;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
Dataset<Row> dataFrame =
spark.read().format("libsvm").load("data/mllib/sample_libsvm_data.txt");
StandardScaler scaler = new StandardScaler()
.setInputCol("features")
.setOutputCol("scaledFeatures")
.setWithStd(true)
.setWithMean(false);
// Compute summary statistics by fitting the StandardScaler
StandardScalerModel scalerModel = scaler.fit(dataFrame);
// Normalize each feature to have unit standard deviation.
Dataset<Row> scaledData = scalerModel.transform(dataFrame);
scaledData.show();RobustScaler
RobustScaler transforms a dataset of Vector rows, removing the median and scaling the data according to a specific quantile range (by default the IQR: Interquartile Range, quantile range between the 1st quartile and the 3rd quartile). Its behavior is quite similar to StandardScaler, however the median and the quantile range are used instead of mean and standard deviation, which make it robust to outliers. It takes parameters:
lower: 0.25 by default. Lower quantile to calculate quantile range, shared by all features.upper: 0.75 by default. Upper quantile to calculate quantile range, shared by all features.withScaling: True by default. Scales the data to quantile range.withCentering: False by default. Centers the data with median before scaling. It will build a dense output, so take care when applying to sparse input.
RobustScaler is an Estimator which can be fit on a dataset to produce a RobustScalerModel; this amounts to computing quantile statistics. The model can then transform a Vector column in a dataset to have unit quantile range and/or zero median features.
Note that if the quantile range 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 quantile range.
Refer to the RobustScaler Python docs for more details on the API.
from pyspark.ml.feature import RobustScaler
dataFrame = spark.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
scaler = RobustScaler(inputCol="features", outputCol="scaledFeatures",
withScaling=True, withCentering=False,
lower=0.25, upper=0.75)
# Compute summary statistics by fitting the RobustScaler
scalerModel = scaler.fit(dataFrame)
# Transform each feature to have unit quantile range.
scaledData = scalerModel.transform(dataFrame)
scaledData.show()Refer to the RobustScaler Scala docs for more details on the API.
import org.apache.spark.ml.feature.RobustScaler
val dataFrame = spark.read.format("libsvm").load("data/mllib/sample_libsvm_data.txt")
val scaler = new RobustScaler()
.setInputCol("features")
.setOutputCol("scaledFeatures")
.setWithScaling(true)
.setWithCentering(false)
.setLower(0.25)
.setUpper(0.75)
// Compute summary statistics by fitting the RobustScaler.
val scalerModel = scaler.fit(dataFrame)
// Transform each feature to have unit quantile range.
val scaledData = scalerModel.transform(dataFrame)
scaledData.show()Refer to the RobustScaler Java docs for more details on the API.
import org.apache.spark.ml.feature.RobustScaler;
import org.apache.spark.ml.feature.RobustScalerModel;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
Dataset<Row> dataFrame =
spark.read().format("libsvm").load("data/mllib/sample_libsvm_data.txt");
RobustScaler scaler = new RobustScaler()
.setInputCol("features")
.setOutputCol("scaledFeatures")
.setWithScaling(true)
.setWithCentering(false)
.setLower(0.25)
.setUpper(0.75);
// Compute summary statistics by fitting the RobustScaler
RobustScalerModel scalerModel = scaler.fit(dataFrame);
// Transform each feature to have unit quantile range.
Dataset<Row> scaledData = scalerModel.transform(dataFrame);
scaledData.show();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 Python docs and the MinMaxScalerModel Python docs for more details on the API.
from pyspark.ml.feature import MinMaxScaler
from pyspark.ml.linalg import Vectors
dataFrame = spark.createDataFrame([
(0, Vectors.dense([1.0, 0.1, -1.0]),),
(1, Vectors.dense([2.0, 1.1, 1.0]),),
(2, Vectors.dense([3.0, 10.1, 3.0]),)
], ["id", "features"])
scaler = MinMaxScaler(inputCol="features", outputCol="scaledFeatures")
# Compute summary statistics and generate MinMaxScalerModel
scalerModel = scaler.fit(dataFrame)
# rescale each feature to range [min, max].
scaledData = scalerModel.transform(dataFrame)
print("Features scaled to range: [%f, %f]" % (scaler.getMin(), scaler.getMax()))
scaledData.select("features", "scaledFeatures").show()Refer to the MinMaxScaler Scala docs and the MinMaxScalerModel Scala docs for more details on the API.
import org.apache.spark.ml.feature.MinMaxScaler
import org.apache.spark.ml.linalg.Vectors
val dataFrame = spark.createDataFrame(Seq(
(0, Vectors.dense(1.0, 0.1, -1.0)),
(1, Vectors.dense(2.0, 1.1, 1.0)),
(2, Vectors.dense(3.0, 10.1, 3.0))
)).toDF("id", "features")
val scaler = new MinMaxScaler()
.setInputCol("features")
.setOutputCol("scaledFeatures")
// Compute summary statistics and generate MinMaxScalerModel
val scalerModel = scaler.fit(dataFrame)
// rescale each feature to range [min, max].
val scaledData = scalerModel.transform(dataFrame)
println(s"Features scaled to range: [${scaler.getMin}, ${scaler.getMax}]")
scaledData.select("features", "scaledFeatures").show()Refer to the MinMaxScaler Java docs and the MinMaxScalerModel Java docs for more details on the API.
import java.util.Arrays;
import java.util.List;
import org.apache.spark.ml.feature.MinMaxScaler;
import org.apache.spark.ml.feature.MinMaxScalerModel;
import org.apache.spark.ml.linalg.Vectors;
import org.apache.spark.ml.linalg.VectorUDT;
import org.apache.spark.sql.Dataset;
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;
List<Row> data = Arrays.asList(
RowFactory.create(0, Vectors.dense(1.0, 0.1, -1.0)),
RowFactory.create(1, Vectors.dense(2.0, 1.1, 1.0)),
RowFactory.create(2, Vectors.dense(3.0, 10.1, 3.0))
);
StructType schema = new StructType(new StructField[]{
new StructField("id", DataTypes.IntegerType, false, Metadata.empty()),
new StructField("features", new VectorUDT(), false, Metadata.empty())
});
Dataset<Row> dataFrame = spark.createDataFrame(data, schema);
MinMaxScaler scaler = new MinMaxScaler()
.setInputCol("features")
.setOutputCol("scaledFeatures");
// Compute summary statistics and generate MinMaxScalerModel
MinMaxScalerModel scalerModel = scaler.fit(dataFrame);
// rescale each feature to range [min, max].
Dataset<Row> scaledData = scalerModel.transform(dataFrame);
System.out.println("Features scaled to range: [" + scaler.getMin() + ", "
+ scaler.getMax() + "]");
scaledData.select("features", "scaledFeatures").show();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 Python docs and the MaxAbsScalerModel Python docs for more details on the API.
from pyspark.ml.feature import MaxAbsScaler
from pyspark.ml.linalg import Vectors
dataFrame = spark.createDataFrame([
(0, Vectors.dense([1.0, 0.1, -8.0]),),
(1, Vectors.dense([2.0, 1.0, -4.0]),),
(2, Vectors.dense([4.0, 10.0, 8.0]),)
], ["id", "features"])
scaler = MaxAbsScaler(inputCol="features", outputCol="scaledFeatures")
# Compute summary statistics and generate MaxAbsScalerModel
scalerModel = scaler.fit(dataFrame)
# rescale each feature to range [-1, 1].
scaledData = scalerModel.transform(dataFrame)
scaledData.select("features", "scaledFeatures").show()Refer to the MaxAbsScaler Scala docs and the MaxAbsScalerModel Scala docs for more details on the API.
import org.apache.spark.ml.feature.MaxAbsScaler
import org.apache.spark.ml.linalg.Vectors
val dataFrame = spark.createDataFrame(Seq(
(0, Vectors.dense(1.0, 0.1, -8.0)),
(1, Vectors.dense(2.0, 1.0, -4.0)),
(2, Vectors.dense(4.0, 10.0, 8.0))
)).toDF("id", "features")
val scaler = new MaxAbsScaler()
.setInputCol("features")
.setOutputCol("scaledFeatures")
// Compute summary statistics and generate MaxAbsScalerModel
val scalerModel = scaler.fit(dataFrame)
// rescale each feature to range [-1, 1]
val scaledData = scalerModel.transform(dataFrame)
scaledData.select("features", "scaledFeatures").show()Refer to the MaxAbsScaler Java docs and the MaxAbsScalerModel Java docs for more details on the API.
import java.util.Arrays;
import java.util.List;
import org.apache.spark.ml.feature.MaxAbsScaler;
import org.apache.spark.ml.feature.MaxAbsScalerModel;
import org.apache.spark.ml.linalg.Vectors;
import org.apache.spark.ml.linalg.VectorUDT;
import org.apache.spark.sql.Dataset;
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;
List<Row> data = Arrays.asList(
RowFactory.create(0, Vectors.dense(1.0, 0.1, -8.0)),
RowFactory.create(1, Vectors.dense(2.0, 1.0, -4.0)),
RowFactory.create(2, Vectors.dense(4.0, 10.0, 8.0))
);
StructType schema = new StructType(new StructField[]{
new StructField("id", DataTypes.IntegerType, false, Metadata.empty()),
new StructField("features", new VectorUDT(), false, Metadata.empty())
});
Dataset<Row> dataFrame = spark.createDataFrame(data, schema);
MaxAbsScaler scaler = new MaxAbsScaler()
.setInputCol("features")
.setOutputCol("scaledFeatures");
// Compute summary statistics and generate MaxAbsScalerModel
MaxAbsScalerModel scalerModel = scaler.fit(dataFrame);
// rescale each feature to range [-1, 1].
Dataset<Row> scaledData = scalerModel.transform(dataFrame);
scaledData.select("features", "scaledFeatures").show();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 ofsplitsareArray(Double.NegativeInfinity, 0.0, 1.0, Double.PositiveInfinity)andArray(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.
Examples
The following example demonstrates how to bucketize a column of Doubles into another index-wised column.
Refer to the Bucketizer Python docs for more details on the API.
from pyspark.ml.feature import Bucketizer
splits = [-float("inf"), -0.5, 0.0, 0.5, float("inf")]
data = [(-999.9,), (-0.5,), (-0.3,), (0.0,), (0.2,), (999.9,)]
dataFrame = spark.createDataFrame(data, ["features"])
bucketizer = Bucketizer(splits=splits, inputCol="features", outputCol="bucketedFeatures")
# Transform original data into its bucket index.
bucketedData = bucketizer.transform(dataFrame)
print("Bucketizer output with %d buckets" % (len(bucketizer.getSplits()) - 1))
bucketedData.show()Refer to the Bucketizer Scala docs for more details on the API.
import org.apache.spark.ml.feature.Bucketizer
val splits = Array(Double.NegativeInfinity, -0.5, 0.0, 0.5, Double.PositiveInfinity)
val data = Array(-999.9, -0.5, -0.3, 0.0, 0.2, 999.9)
val dataFrame = spark.createDataFrame(
immutable.ArraySeq.unsafeWrapArray(data.map(Tuple1.apply))).toDF("features")
val bucketizer = new Bucketizer()
.setInputCol("features")
.setOutputCol("bucketedFeatures")
.setSplits(splits)
// Transform original data into its bucket index.
val bucketedData = bucketizer.transform(dataFrame)
println(s"Bucketizer output with ${bucketizer.getSplits.length-1} buckets")
bucketedData.show()
val splitsArray = Array(
Array(Double.NegativeInfinity, -0.5, 0.0, 0.5, Double.PositiveInfinity),
Array(Double.NegativeInfinity, -0.3, 0.0, 0.3, Double.PositiveInfinity))
val data2 = Array(
(-999.9, -999.9),
(-0.5, -0.2),
(-0.3, -0.1),
(0.0, 0.0),
(0.2, 0.4),
(999.9, 999.9))
val dataFrame2 = spark.createDataFrame(immutable.ArraySeq.unsafeWrapArray(data2))
.toDF("features1", "features2")
val bucketizer2 = new Bucketizer()
.setInputCols(Array("features1", "features2"))
.setOutputCols(Array("bucketedFeatures1", "bucketedFeatures2"))
.setSplitsArray(splitsArray)
// Transform original data into its bucket index.
val bucketedData2 = bucketizer2.transform(dataFrame2)
println(s"Bucketizer output with [" +
s"${bucketizer2.getSplitsArray(0).length-1}, " +
s"${bucketizer2.getSplitsArray(1).length-1}] buckets for each input column")
bucketedData2.show()Refer to the Bucketizer Java docs for more details on the API.
import java.util.Arrays;
import java.util.List;
import org.apache.spark.ml.feature.Bucketizer;
import org.apache.spark.sql.Dataset;
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;
double[] splits = {Double.NEGATIVE_INFINITY, -0.5, 0.0, 0.5, Double.POSITIVE_INFINITY};
List<Row> data = Arrays.asList(
RowFactory.create(-999.9),
RowFactory.create(-0.5),
RowFactory.create(-0.3),
RowFactory.create(0.0),
RowFactory.create(0.2),
RowFactory.create(999.9)
);
StructType schema = new StructType(new StructField[]{
new StructField("features", DataTypes.DoubleType, false, Metadata.empty())
});
Dataset<Row> dataFrame = spark.createDataFrame(data, schema);
Bucketizer bucketizer = new Bucketizer()
.setInputCol("features")
.setOutputCol("bucketedFeatures")
.setSplits(splits);
// Transform original data into its bucket index.
Dataset<Row> bucketedData = bucketizer.transform(dataFrame);
System.out.println("Bucketizer output with " + (bucketizer.getSplits().length-1) + " buckets");
bucketedData.show();
// Bucketize multiple columns at one pass.
double[][] splitsArray = {
{Double.NEGATIVE_INFINITY, -0.5, 0.0, 0.5, Double.POSITIVE_INFINITY},
{Double.NEGATIVE_INFINITY, -0.3, 0.0, 0.3, Double.POSITIVE_INFINITY}
};
List<Row> data2 = Arrays.asList(
RowFactory.create(-999.9, -999.9),
RowFactory.create(-0.5, -0.2),
RowFactory.create(-0.3, -0.1),
RowFactory.create(0.0, 0.0),
RowFactory.create(0.2, 0.4),
RowFactory.create(999.9, 999.9)
);
StructType schema2 = new StructType(new StructField[]{
new StructField("features1", DataTypes.DoubleType, false, Metadata.empty()),
new StructField("features2", DataTypes.DoubleType, false, Metadata.empty())
});
Dataset<Row> dataFrame2 = spark.createDataFrame(data2, schema2);
Bucketizer bucketizer2 = new Bucketizer()
.setInputCols(new String[] {"features1", "features2"})
.setOutputCols(new String[] {"bucketedFeatures1", "bucketedFeatures2"})
.setSplitsArray(splitsArray);
// Transform original data into its bucket index.
Dataset<Row> bucketedData2 = bucketizer2.transform(dataFrame2);
System.out.println("Bucketizer output with [" +
(bucketizer2.getSplitsArray()[0].length-1) + ", " +
(bucketizer2.getSplitsArray()[1].length-1) + "] buckets for each input column");
bucketedData2.show();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 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 Python docs for more details on the API.
from pyspark.ml.feature import ElementwiseProduct
from pyspark.ml.linalg import Vectors
# Create some vector data; also works for sparse vectors
data = [(Vectors.dense([1.0, 2.0, 3.0]),), (Vectors.dense([4.0, 5.0, 6.0]),)]
df = spark.createDataFrame(data, ["vector"])
transformer = ElementwiseProduct(scalingVec=Vectors.dense([0.0, 1.0, 2.0]),
inputCol="vector", outputCol="transformedVector")
# Batch transform the vectors to create new column:
transformer.transform(df).show()Refer to the ElementwiseProduct Scala docs for more details on the API.
import org.apache.spark.ml.feature.ElementwiseProduct
import org.apache.spark.ml.linalg.Vectors
// Create some vector data; also works for sparse vectors
val dataFrame = spark.createDataFrame(Seq(
("a", Vectors.dense(1.0, 2.0, 3.0)),
("b", Vectors.dense(4.0, 5.0, 6.0)))).toDF("id", "vector")
val transformingVector = Vectors.dense(0.0, 1.0, 2.0)
val transformer = new ElementwiseProduct()
.setScalingVec(transformingVector)
.setInputCol("vector")
.setOutputCol("transformedVector")
// Batch transform the vectors to create new column:
transformer.transform(dataFrame).show()Refer to the ElementwiseProduct Java docs for more details on the API.
import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import org.apache.spark.ml.feature.ElementwiseProduct;
import org.apache.spark.ml.linalg.Vector;
import org.apache.spark.ml.linalg.VectorUDT;
import org.apache.spark.ml.linalg.Vectors;
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.StructField;
import org.apache.spark.sql.types.StructType;
// Create some vector data; also works for sparse vectors
List<Row> data = Arrays.asList(
RowFactory.create("a", Vectors.dense(1.0, 2.0, 3.0)),
RowFactory.create("b", Vectors.dense(4.0, 5.0, 6.0))
);
List<StructField> fields = new ArrayList<>(2);
fields.add(DataTypes.createStructField("id", DataTypes.StringType, false));
fields.add(DataTypes.createStructField("vector", new VectorUDT(), false));
StructType schema = DataTypes.createStructType(fields);
Dataset<Row> dataFrame = spark.createDataFrame(data, schema);
Vector transformingVector = Vectors.dense(0.0, 1.0, 2.0);
ElementwiseProduct transformer = new ElementwiseProduct()
.setScalingVec(transformingVector)
.setInputCol("vector")
.setOutputCol("transformedVector");
// Batch transform the vectors to create new column:
transformer.transform(dataFrame).show();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 > 5SELECT 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 Python docs for more details on the API.
from pyspark.ml.feature import SQLTransformer
df = spark.createDataFrame([
(0, 1.0, 3.0),
(2, 2.0, 5.0)
], ["id", "v1", "v2"])
sqlTrans = SQLTransformer(
statement="SELECT *, (v1 + v2) AS v3, (v1 * v2) AS v4 FROM __THIS__")
sqlTrans.transform(df).show()Refer to the SQLTransformer Scala docs for more details on the API.
import org.apache.spark.ml.feature.SQLTransformer
val df = spark.createDataFrame(
Seq((0, 1.0, 3.0), (2, 2.0, 5.0))).toDF("id", "v1", "v2")
val sqlTrans = new SQLTransformer().setStatement(
"SELECT *, (v1 + v2) AS v3, (v1 * v2) AS v4 FROM __THIS__")
sqlTrans.transform(df).show()Refer to the SQLTransformer Java docs for more details on the API.
import java.util.Arrays;
import java.util.List;
import org.apache.spark.ml.feature.SQLTransformer;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.types.*;
List<Row> data = Arrays.asList(
RowFactory.create(0, 1.0, 3.0),
RowFactory.create(2, 2.0, 5.0)
);
StructType schema = new StructType(new StructField [] {
new StructField("id", DataTypes.IntegerType, false, Metadata.empty()),
new StructField("v1", DataTypes.DoubleType, false, Metadata.empty()),
new StructField("v2", DataTypes.DoubleType, false, Metadata.empty())
});
Dataset<Row> df = spark.createDataFrame(data, schema);
SQLTransformer sqlTrans = new SQLTransformer().setStatement(
"SELECT *, (v1 + v2) AS v3, (v1 * v2) AS v4 FROM __THIS__");
sqlTrans.transform(df).show();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 Python docs for more details on the API.
from pyspark.ml.linalg import Vectors
from pyspark.ml.feature import VectorAssembler
dataset = spark.createDataFrame(
[(0, 18, 1.0, Vectors.dense([0.0, 10.0, 0.5]), 1.0)],
["id", "hour", "mobile", "userFeatures", "clicked"])
assembler = VectorAssembler(
inputCols=["hour", "mobile", "userFeatures"],
outputCol="features")
output = assembler.transform(dataset)
print("Assembled columns 'hour', 'mobile', 'userFeatures' to vector column 'features'")
output.select("features", "clicked").show(truncate=False)Refer to the VectorAssembler Scala docs for more details on the API.
import org.apache.spark.ml.feature.VectorAssembler
import org.apache.spark.ml.linalg.Vectors
val dataset = spark.createDataFrame(
Seq((0, 18, 1.0, Vectors.dense(0.0, 10.0, 0.5), 1.0))
).toDF("id", "hour", "mobile", "userFeatures", "clicked")
val assembler = new VectorAssembler()
.setInputCols(Array("hour", "mobile", "userFeatures"))
.setOutputCol("features")
val output = assembler.transform(dataset)
println("Assembled columns 'hour', 'mobile', 'userFeatures' to vector column 'features'")
output.select("features", "clicked").show(false)Refer to the VectorAssembler Java docs for more details on the API.
import java.util.Arrays;
import org.apache.spark.ml.feature.VectorAssembler;
import org.apache.spark.ml.linalg.VectorUDT;
import org.apache.spark.ml.linalg.Vectors;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.types.*;
import static org.apache.spark.sql.types.DataTypes.*;
StructType schema = createStructType(new StructField[]{
createStructField("id", IntegerType, false),
createStructField("hour", IntegerType, false),
createStructField("mobile", DoubleType, false),
createStructField("userFeatures", new VectorUDT(), false),
createStructField("clicked", DoubleType, false)
});
Row row = RowFactory.create(0, 18, 1.0, Vectors.dense(0.0, 10.0, 0.5), 1.0);
Dataset<Row> dataset = spark.createDataFrame(Arrays.asList(row), schema);
VectorAssembler assembler = new VectorAssembler()
.setInputCols(new String[]{"hour", "mobile", "userFeatures"})
.setOutputCol("features");
Dataset<Row> output = assembler.transform(dataset);
System.out.println("Assembled columns 'hour', 'mobile', 'userFeatures' to vector column " +
"'features'");
output.select("features", "clicked").show(false);VectorSizeHint
It can sometimes be useful to explicitly specify the size of the vectors for a column of
VectorType. For example, VectorAssembler uses size information from its input columns to
produce size information and metadata for its output column. While in some cases this information
can be obtained by inspecting the contents of the column, in a streaming dataframe the contents are
not available until the stream is started. VectorSizeHint allows a user to explicitly specify the
vector size for a column so that VectorAssembler, or other transformers that might
need to know vector size, can use that column as an input.
To use VectorSizeHint a user must set the inputCol and size parameters. Applying this
transformer to a dataframe produces a new dataframe with updated metadata for inputCol specifying
the vector size. Downstream operations on the resulting dataframe can get this size using the
metadata.
VectorSizeHint can also take an optional handleInvalid parameter which controls its
behaviour when the vector column contains nulls or vectors of the wrong size. By default
handleInvalid is set to “error”, indicating an exception should be thrown. This parameter can
also be set to “skip”, indicating that rows containing invalid values should be filtered out from
the resulting dataframe, or “optimistic”, indicating that the column should not be checked for
invalid values and all rows should be kept. Note that the use of “optimistic” can cause the
resulting dataframe to be in an inconsistent state, meaning the metadata for the column
VectorSizeHint was applied to does not match the contents of that column. Users should take care
to avoid this kind of inconsistent state.
Refer to the VectorSizeHint Python docs for more details on the API.
from pyspark.ml.linalg import Vectors
from pyspark.ml.feature import (VectorSizeHint, VectorAssembler)
dataset = spark.createDataFrame(
[(0, 18, 1.0, Vectors.dense([0.0, 10.0, 0.5]), 1.0),
(0, 18, 1.0, Vectors.dense([0.0, 10.0]), 0.0)],
["id", "hour", "mobile", "userFeatures", "clicked"])
sizeHint = VectorSizeHint(
inputCol="userFeatures",
handleInvalid="skip",
size=3)
datasetWithSize = sizeHint.transform(dataset)
print("Rows where 'userFeatures' is not the right size are filtered out")
datasetWithSize.show(truncate=False)
assembler = VectorAssembler(
inputCols=["hour", "mobile", "userFeatures"],
outputCol="features")
# This dataframe can be used by downstream transformers as before
output = assembler.transform(datasetWithSize)
print("Assembled columns 'hour', 'mobile', 'userFeatures' to vector column 'features'")
output.select("features", "clicked").show(truncate=False)Refer to the VectorSizeHint Scala docs for more details on the API.
import org.apache.spark.ml.feature.{VectorAssembler, VectorSizeHint}
import org.apache.spark.ml.linalg.Vectors
val dataset = spark.createDataFrame(
Seq(
(0, 18, 1.0, Vectors.dense(0.0, 10.0, 0.5), 1.0),
(0, 18, 1.0, Vectors.dense(0.0, 10.0), 0.0))
).toDF("id", "hour", "mobile", "userFeatures", "clicked")
val sizeHint = new VectorSizeHint()
.setInputCol("userFeatures")
.setHandleInvalid("skip")
.setSize(3)
val datasetWithSize = sizeHint.transform(dataset)
println("Rows where 'userFeatures' is not the right size are filtered out")
datasetWithSize.show(false)
val assembler = new VectorAssembler()
.setInputCols(Array("hour", "mobile", "userFeatures"))
.setOutputCol("features")
// This dataframe can be used by downstream transformers as before
val output = assembler.transform(datasetWithSize)
println("Assembled columns 'hour', 'mobile', 'userFeatures' to vector column 'features'")
output.select("features", "clicked").show(false)Refer to the VectorSizeHint Java docs for more details on the API.
import java.util.Arrays;
import org.apache.spark.ml.feature.VectorAssembler;
import org.apache.spark.ml.feature.VectorSizeHint;
import org.apache.spark.ml.linalg.VectorUDT;
import org.apache.spark.ml.linalg.Vectors;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.types.StructField;
import org.apache.spark.sql.types.StructType;
import static org.apache.spark.sql.types.DataTypes.*;
StructType schema = createStructType(new StructField[]{
createStructField("id", IntegerType, false),
createStructField("hour", IntegerType, false),
createStructField("mobile", DoubleType, false),
createStructField("userFeatures", new VectorUDT(), false),
createStructField("clicked", DoubleType, false)
});
Row row0 = RowFactory.create(0, 18, 1.0, Vectors.dense(0.0, 10.0, 0.5), 1.0);
Row row1 = RowFactory.create(0, 18, 1.0, Vectors.dense(0.0, 10.0), 0.0);
Dataset<Row> dataset = spark.createDataFrame(Arrays.asList(row0, row1), schema);
VectorSizeHint sizeHint = new VectorSizeHint()
.setInputCol("userFeatures")
.setHandleInvalid("skip")
.setSize(3);
Dataset<Row> datasetWithSize = sizeHint.transform(dataset);
System.out.println("Rows where 'userFeatures' is not the right size are filtered out");
datasetWithSize.show(false);
VectorAssembler assembler = new VectorAssembler()
.setInputCols(new String[]{"hour", "mobile", "userFeatures"})
.setOutputCol("features");
// This dataframe can be used by downstream transformers as before
Dataset<Row> output = assembler.transform(datasetWithSize);
System.out.println("Assembled columns 'hour', 'mobile', 'userFeatures' to vector column " +
"'features'");
output.select("features", "clicked").show(false);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 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 Python docs for more details on the API.
from pyspark.ml.feature import QuantileDiscretizer
data = [(0, 18.0), (1, 19.0), (2, 8.0), (3, 5.0), (4, 2.2)]
df = spark.createDataFrame(data, ["id", "hour"])
discretizer = QuantileDiscretizer(numBuckets=3, inputCol="hour", outputCol="result")
result = discretizer.fit(df).transform(df)
result.show()Refer to the QuantileDiscretizer Scala docs for more details on the API.
import org.apache.spark.ml.feature.QuantileDiscretizer
val data = Array((0, 18.0), (1, 19.0), (2, 8.0), (3, 5.0), (4, 2.2))
val df = spark.createDataFrame(immutable.ArraySeq.unsafeWrapArray(data)).toDF("id", "hour")
val discretizer = new QuantileDiscretizer()
.setInputCol("hour")
.setOutputCol("result")
.setNumBuckets(3)
val result = discretizer.fit(df).transform(df)
result.show(false)Refer to the QuantileDiscretizer Java docs for more details on the API.
import java.util.Arrays;
import java.util.List;
import org.apache.spark.ml.feature.QuantileDiscretizer;
import org.apache.spark.sql.Dataset;
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;
List<Row> data = Arrays.asList(
RowFactory.create(0, 18.0),
RowFactory.create(1, 19.0),
RowFactory.create(2, 8.0),
RowFactory.create(3, 5.0),
RowFactory.create(4, 2.2)
);
StructType schema = new StructType(new StructField[]{
new StructField("id", DataTypes.IntegerType, false, Metadata.empty()),
new StructField("hour", DataTypes.DoubleType, false, Metadata.empty())
});
Dataset<Row> df = spark.createDataFrame(data, schema);
QuantileDiscretizer discretizer = new QuantileDiscretizer()
.setInputCol("hour")
.setOutputCol("result")
.setNumBuckets(3);
Dataset<Row> result = discretizer.fit(df).transform(df);
result.show(false);Imputer
The Imputer estimator completes missing values in a dataset, using the mean, median or mode
of the columns in which the missing values are located. The input columns should be of
numeric type. Currently Imputer does not support categorical features and possibly
creates incorrect values for columns containing categorical features. Imputer can impute custom values
other than ‘NaN’ by .setMissingValue(custom_value). For example, .setMissingValue(0) will impute
all occurrences of (0).
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 Python docs for more details on the API.
from pyspark.ml.feature import Imputer
df = spark.createDataFrame([
(1.0, float("nan")),
(2.0, float("nan")),
(float("nan"), 3.0),
(4.0, 4.0),
(5.0, 5.0)
], ["a", "b"])
imputer = Imputer(inputCols=["a", "b"], outputCols=["out_a", "out_b"])
model = imputer.fit(df)
model.transform(df).show()Refer to the Imputer Scala docs for more details on the API.
import org.apache.spark.ml.feature.Imputer
val df = spark.createDataFrame(Seq(
(1.0, Double.NaN),
(2.0, Double.NaN),
(Double.NaN, 3.0),
(4.0, 4.0),
(5.0, 5.0)
)).toDF("a", "b")
val imputer = new Imputer()
.setInputCols(Array("a", "b"))
.setOutputCols(Array("out_a", "out_b"))
val model = imputer.fit(df)
model.transform(df).show()Refer to the Imputer Java docs for more details on the API.
import java.util.Arrays;
import java.util.List;
import org.apache.spark.ml.feature.Imputer;
import org.apache.spark.ml.feature.ImputerModel;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.types.*;
List<Row> data = Arrays.asList(
RowFactory.create(1.0, Double.NaN),
RowFactory.create(2.0, Double.NaN),
RowFactory.create(Double.NaN, 3.0),
RowFactory.create(4.0, 4.0),
RowFactory.create(5.0, 5.0)
);
StructType schema = new StructType(new StructField[]{
createStructField("a", DoubleType, false),
createStructField("b", DoubleType, false)
});
Dataset<Row> df = spark.createDataFrame(data, schema);
Imputer imputer = new Imputer()
.setInputCols(new String[]{"a", "b"})
.setOutputCols(new String[]{"out_a", "out_b"});
ImputerModel model = imputer.fit(df);
model.transform(df).show();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,
-
Integer indices that represent the indices into the vector,
setIndices(). -
String indices that represent the names of features into the vector,
setNames(). This requires the vector column to have anAttributeGroupsince the implementation matches on the name field of anAttribute.
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 Python docs for more details on the API.
from pyspark.ml.feature import VectorSlicer
from pyspark.ml.linalg import Vectors
from pyspark.sql.types import Row
df = spark.createDataFrame([
Row(userFeatures=Vectors.sparse(3, {0: -2.0, 1: 2.3})),
Row(userFeatures=Vectors.dense([-2.0, 2.3, 0.0]))])
slicer = VectorSlicer(inputCol="userFeatures", outputCol="features", indices=[1])
output = slicer.transform(df)
output.select("userFeatures", "features").show()Refer to the VectorSlicer Scala docs for more details on the API.
import java.util.Arrays
import org.apache.spark.ml.attribute.{Attribute, AttributeGroup, NumericAttribute}
import org.apache.spark.ml.feature.VectorSlicer
import org.apache.spark.ml.linalg.Vectors
import org.apache.spark.sql.{Row, SparkSession}
import org.apache.spark.sql.types.StructType
val data = Arrays.asList(
Row(Vectors.sparse(3, Seq((0, -2.0), (1, 2.3)))),
Row(Vectors.dense(-2.0, 2.3, 0.0))
)
val defaultAttr = NumericAttribute.defaultAttr
val attrs = Array("f1", "f2", "f3").map(defaultAttr.withName)
val attrGroup = new AttributeGroup("userFeatures", attrs.asInstanceOf[Array[Attribute]])
val dataset = spark.createDataFrame(data, StructType(Array(attrGroup.toStructField())))
val slicer = new VectorSlicer().setInputCol("userFeatures").setOutputCol("features")
slicer.setIndices(Array(1)).setNames(Array("f3"))
// or slicer.setIndices(Array(1, 2)), or slicer.setNames(Array("f2", "f3"))
val output = slicer.transform(dataset)
output.show(false)Refer to the VectorSlicer Java docs for more details on the API.
import java.util.Arrays;
import java.util.List;
import org.apache.spark.ml.attribute.Attribute;
import org.apache.spark.ml.attribute.AttributeGroup;
import org.apache.spark.ml.attribute.NumericAttribute;
import org.apache.spark.ml.feature.VectorSlicer;
import org.apache.spark.ml.linalg.Vectors;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.types.*;
Attribute[] attrs = {
NumericAttribute.defaultAttr().withName("f1"),
NumericAttribute.defaultAttr().withName("f2"),
NumericAttribute.defaultAttr().withName("f3")
};
AttributeGroup group = new AttributeGroup("userFeatures", attrs);
List<Row> data = Arrays.asList(
RowFactory.create(Vectors.sparse(3, new int[]{0, 1}, new double[]{-2.0, 2.3})),
RowFactory.create(Vectors.dense(-2.0, 2.3, 0.0))
);
Dataset<Row> dataset =
spark.createDataFrame(data, (new StructType()).add(group.toStructField()));
VectorSlicer vectorSlicer = new VectorSlicer()
.setInputCol("userFeatures").setOutputCol("features");
vectorSlicer.setIndices(new int[]{1}).setNames(new String[]{"f3"});
// or slicer.setIndices(new int[]{1, 2}), or slicer.setNames(new String[]{"f2", "f3"})
Dataset<Row> output = vectorSlicer.transform(dataset);
output.show(false);RFormula
RFormula selects columns specified by an R model formula.
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 + bmeans modely ~ w0 + w1 * a + w2 * bwherew0is the intercept andw1, w2are coefficients.y ~ a + b + a:b - 1means modely ~ w1 * a + w2 * b + w3 * a * bwherew1, w2, w3are 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, numeric columns will be cast to doubles.
As to string input columns, they will first be transformed with StringIndexer using ordering determined by stringOrderType,
and the last category after ordering is dropped, then the doubles will be one-hot encoded.
Suppose a string feature column containing values {'b', 'a', 'b', 'a', 'c', 'b'}, we set stringOrderType to control the encoding:
stringOrderType | Category mapped to 0 by StringIndexer | Category dropped by RFormula
----------------|---------------------------------------|---------------------------------
'frequencyDesc' | most frequent category ('b') | least frequent category ('c')
'frequencyAsc' | least frequent category ('c') | most frequent category ('b')
'alphabetDesc' | last alphabetical category ('c') | first alphabetical category ('a')
'alphabetAsc' | first alphabetical category ('a') | last alphabetical category ('c')
If the label column is of type string, it will be first transformed to double with StringIndexer using frequencyDesc ordering.
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.
Note: The ordering option stringOrderType is NOT used for the label column. When the label column is indexed, it uses the default descending frequency ordering in StringIndexer.
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 Python docs for more details on the API.
from pyspark.ml.feature import RFormula
dataset = spark.createDataFrame(
[(7, "US", 18, 1.0),
(8, "CA", 12, 0.0),
(9, "NZ", 15, 0.0)],
["id", "country", "hour", "clicked"])
formula = RFormula(
formula="clicked ~ country + hour",
featuresCol="features",
labelCol="label")
output = formula.fit(dataset).transform(dataset)
output.select("features", "label").show()Refer to the RFormula Scala docs for more details on the API.
import org.apache.spark.ml.feature.RFormula
val dataset = spark.createDataFrame(Seq(
(7, "US", 18, 1.0),
(8, "CA", 12, 0.0),
(9, "NZ", 15, 0.0)
)).toDF("id", "country", "hour", "clicked")
val formula = new RFormula()
.setFormula("clicked ~ country + hour")
.setFeaturesCol("features")
.setLabelCol("label")
val output = formula.fit(dataset).transform(dataset)
output.select("features", "label").show()Refer to the RFormula Java docs for more details on the API.
import java.util.Arrays;
import java.util.List;
import org.apache.spark.ml.feature.RFormula;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.types.StructField;
import org.apache.spark.sql.types.StructType;
import static org.apache.spark.sql.types.DataTypes.*;
StructType schema = createStructType(new StructField[]{
createStructField("id", IntegerType, false),
createStructField("country", StringType, false),
createStructField("hour", IntegerType, false),
createStructField("clicked", DoubleType, false)
});
List<Row> data = Arrays.asList(
RowFactory.create(7, "US", 18, 1.0),
RowFactory.create(8, "CA", 12, 0.0),
RowFactory.create(9, "NZ", 15, 0.0)
);
Dataset<Row> dataset = spark.createDataFrame(data, schema);
RFormula formula = new RFormula()
.setFormula("clicked ~ country + hour")
.setFeaturesCol("features")
.setLabelCol("label");
Dataset<Row> output = formula.fit(dataset).transform(dataset);
output.select("features", "label").show();ChiSqSelector
ChiSqSelector stands for Chi-Squared feature selection. It operates on labeled data with
categorical features. ChiSqSelector uses the
Chi-Squared test of independence to decide which
features to choose. It supports five selection methods: numTopFeatures, percentile, fpr, fdr, fwe:
numTopFeatureschooses a fixed number of top features according to a chi-squared test. This is akin to yielding the features with the most predictive power.percentileis similar tonumTopFeaturesbut chooses a fraction of all features instead of a fixed number.fprchooses all features whose p-values are below a threshold, thus controlling the false positive rate of selection.fdruses the Benjamini-Hochberg procedure to choose all features whose false discovery rate is below a threshold.fwechooses 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 isnumTopFeatures, with the default number of top features set to 50. The user can choose a selection method usingsetSelectorType.
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 Python docs for more details on the API.
from pyspark.ml.feature import ChiSqSelector
from pyspark.ml.linalg import Vectors
df = spark.createDataFrame([
(7, Vectors.dense([0.0, 0.0, 18.0, 1.0]), 1.0,),
(8, Vectors.dense([0.0, 1.0, 12.0, 0.0]), 0.0,),
(9, Vectors.dense([1.0, 0.0, 15.0, 0.1]), 0.0,)], ["id", "features", "clicked"])
selector = ChiSqSelector(numTopFeatures=1, featuresCol="features",
outputCol="selectedFeatures", labelCol="clicked")
result = selector.fit(df).transform(df)
print("ChiSqSelector output with top %d features selected" % selector.getNumTopFeatures())
result.show()Refer to the ChiSqSelector Scala docs for more details on the API.
import org.apache.spark.ml.feature.ChiSqSelector
import org.apache.spark.ml.linalg.Vectors
val data = Seq(
(7, Vectors.dense(0.0, 0.0, 18.0, 1.0), 1.0),
(8, Vectors.dense(0.0, 1.0, 12.0, 0.0), 0.0),
(9, Vectors.dense(1.0, 0.0, 15.0, 0.1), 0.0)
)
val df = spark.createDataset(data).toDF("id", "features", "clicked")
val selector = new ChiSqSelector()
.setNumTopFeatures(1)
.setFeaturesCol("features")
.setLabelCol("clicked")
.setOutputCol("selectedFeatures")
val result = selector.fit(df).transform(df)
println(s"ChiSqSelector output with top ${selector.getNumTopFeatures} features selected")
result.show()Refer to the ChiSqSelector Java docs for more details on the API.
import java.util.Arrays;
import java.util.List;
import org.apache.spark.ml.feature.ChiSqSelector;
import org.apache.spark.ml.linalg.VectorUDT;
import org.apache.spark.ml.linalg.Vectors;
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;
List<Row> data = Arrays.asList(
RowFactory.create(7, Vectors.dense(0.0, 0.0, 18.0, 1.0), 1.0),
RowFactory.create(8, Vectors.dense(0.0, 1.0, 12.0, 0.0), 0.0),
RowFactory.create(9, Vectors.dense(1.0, 0.0, 15.0, 0.1), 0.0)
);
StructType schema = new StructType(new StructField[]{
new StructField("id", DataTypes.IntegerType, false, Metadata.empty()),
new StructField("features", new VectorUDT(), false, Metadata.empty()),
new StructField("clicked", DataTypes.DoubleType, false, Metadata.empty())
});
Dataset<Row> df = spark.createDataFrame(data, schema);
ChiSqSelector selector = new ChiSqSelector()
.setNumTopFeatures(1)
.setFeaturesCol("features")
.setLabelCol("clicked")
.setOutputCol("selectedFeatures");
Dataset<Row> result = selector.fit(df).transform(df);
System.out.println("ChiSqSelector output with top " + selector.getNumTopFeatures()
+ " features selected");
result.show();UnivariateFeatureSelector
UnivariateFeatureSelector operates on categorical/continuous labels with categorical/continuous features.
User can set featureType and labelType, and Spark will pick the score function to use based on the specified
featureType and labelType.
featureType | labelType |score function
------------|------------|--------------
categorical |categorical | chi-squared (chi2)
continuous |categorical | ANOVATest (f_classif)
continuous |continuous | F-value (f_regression)
It supports five selection modes: numTopFeatures, percentile, fpr, fdr, fwe:
numTopFeatureschooses a fixed number of top features.percentileis similar tonumTopFeaturesbut chooses a fraction of all features instead of a fixed number.fprchooses all features whose p-values are below a threshold, thus controlling the false positive rate of selection.fdruses the Benjamini-Hochberg procedure to choose all features whose false discovery rate is below a threshold.fwechooses 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 mode is numTopFeatures, with the default selectionThreshold sets to 50.
Examples
Assume that we have a DataFrame with the columns id, features, and label, which is used as
our target to be predicted:
id | features | label
---|--------------------------------|---------
1 | [1.7, 4.4, 7.6, 5.8, 9.6, 2.3] | 3.0
2 | [8.8, 7.3, 5.7, 7.3, 2.2, 4.1] | 2.0
3 | [1.2, 9.5, 2.5, 3.1, 8.7, 2.5] | 3.0
4 | [3.7, 9.2, 6.1, 4.1, 7.5, 3.8] | 2.0
5 | [8.9, 5.2, 7.8, 8.3, 5.2, 3.0] | 4.0
6 | [7.9, 8.5, 9.2, 4.0, 9.4, 2.1] | 4.0
If we set featureType to continuous and labelType to categorical with numTopFeatures = 1, the
last column in our features is chosen as the most useful feature:
id | features | label | selectedFeatures
---|--------------------------------|---------|------------------
1 | [1.7, 4.4, 7.6, 5.8, 9.6, 2.3] | 3.0 | [2.3]
2 | [8.8, 7.3, 5.7, 7.3, 2.2, 4.1] | 2.0 | [4.1]
3 | [1.2, 9.5, 2.5, 3.1, 8.7, 2.5] | 3.0 | [2.5]
4 | [3.7, 9.2, 6.1, 4.1, 7.5, 3.8] | 2.0 | [3.8]
5 | [8.9, 5.2, 7.8, 8.3, 5.2, 3.0] | 4.0 | [3.0]
6 | [7.9, 8.5, 9.2, 4.0, 9.4, 2.1] | 4.0 | [2.1]
Refer to the UnivariateFeatureSelector Python docs for more details on the API.
from pyspark.ml.feature import UnivariateFeatureSelector
from pyspark.ml.linalg import Vectors
df = spark.createDataFrame([
(1, Vectors.dense([1.7, 4.4, 7.6, 5.8, 9.6, 2.3]), 3.0,),
(2, Vectors.dense([8.8, 7.3, 5.7, 7.3, 2.2, 4.1]), 2.0,),
(3, Vectors.dense([1.2, 9.5, 2.5, 3.1, 8.7, 2.5]), 3.0,),
(4, Vectors.dense([3.7, 9.2, 6.1, 4.1, 7.5, 3.8]), 2.0,),
(5, Vectors.dense([8.9, 5.2, 7.8, 8.3, 5.2, 3.0]), 4.0,),
(6, Vectors.dense([7.9, 8.5, 9.2, 4.0, 9.4, 2.1]), 4.0,)], ["id", "features", "label"])
selector = UnivariateFeatureSelector(featuresCol="features", outputCol="selectedFeatures",
labelCol="label", selectionMode="numTopFeatures")
selector.setFeatureType("continuous").setLabelType("categorical").setSelectionThreshold(1)
result = selector.fit(df).transform(df)
print("UnivariateFeatureSelector output with top %d features selected using f_classif"
% selector.getSelectionThreshold())
result.show()Refer to the UnivariateFeatureSelector Scala docs for more details on the API.
import org.apache.spark.ml.feature.UnivariateFeatureSelector
import org.apache.spark.ml.linalg.Vectors
val data = Seq(
(1, Vectors.dense(1.7, 4.4, 7.6, 5.8, 9.6, 2.3), 3.0),
(2, Vectors.dense(8.8, 7.3, 5.7, 7.3, 2.2, 4.1), 2.0),
(3, Vectors.dense(1.2, 9.5, 2.5, 3.1, 8.7, 2.5), 3.0),
(4, Vectors.dense(3.7, 9.2, 6.1, 4.1, 7.5, 3.8), 2.0),
(5, Vectors.dense(8.9, 5.2, 7.8, 8.3, 5.2, 3.0), 4.0),
(6, Vectors.dense(7.9, 8.5, 9.2, 4.0, 9.4, 2.1), 4.0)
)
val df = spark.createDataset(data).toDF("id", "features", "label")
val selector = new UnivariateFeatureSelector()
.setFeatureType("continuous")
.setLabelType("categorical")
.setSelectionMode("numTopFeatures")
.setSelectionThreshold(1)
.setFeaturesCol("features")
.setLabelCol("label")
.setOutputCol("selectedFeatures")
val result = selector.fit(df).transform(df)
println(s"UnivariateFeatureSelector output with top ${selector.getSelectionThreshold}" +
s" features selected using f_classif")
result.show()Refer to the UnivariateFeatureSelector Java docs for more details on the API.
import java.util.Arrays;
import java.util.List;
import org.apache.spark.ml.feature.UnivariateFeatureSelector;
import org.apache.spark.ml.linalg.VectorUDT;
import org.apache.spark.ml.linalg.Vectors;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.types.*;
List<Row> data = Arrays.asList(
RowFactory.create(1, Vectors.dense(1.7, 4.4, 7.6, 5.8, 9.6, 2.3), 3.0),
RowFactory.create(2, Vectors.dense(8.8, 7.3, 5.7, 7.3, 2.2, 4.1), 2.0),
RowFactory.create(3, Vectors.dense(1.2, 9.5, 2.5, 3.1, 8.7, 2.5), 3.0),
RowFactory.create(4, Vectors.dense(3.7, 9.2, 6.1, 4.1, 7.5, 3.8), 2.0),
RowFactory.create(5, Vectors.dense(8.9, 5.2, 7.8, 8.3, 5.2, 3.0), 4.0),
RowFactory.create(6, Vectors.dense(7.9, 8.5, 9.2, 4.0, 9.4, 2.1), 4.0)
);
StructType schema = new StructType(new StructField[]{
new StructField("id", DataTypes.IntegerType, false, Metadata.empty()),
new StructField("features", new VectorUDT(), false, Metadata.empty()),
new StructField("label", DataTypes.DoubleType, false, Metadata.empty())
});
Dataset<Row> df = spark.createDataFrame(data, schema);
UnivariateFeatureSelector selector = new UnivariateFeatureSelector()
.setFeatureType("continuous")
.setLabelType("categorical")
.setSelectionMode("numTopFeatures")
.setSelectionThreshold(1)
.setFeaturesCol("features")
.setLabelCol("label")
.setOutputCol("selectedFeatures");
Dataset<Row> result = selector.fit(df).transform(df);
System.out.println("UnivariateFeatureSelector output with top "
+ selector.getSelectionThreshold() + " features selected using f_classif");
result.show();VarianceThresholdSelector
VarianceThresholdSelector is a selector that removes low-variance features. Features with a
(sample) variance not greater than the varianceThreshold will be removed. If not set, varianceThreshold
defaults to 0, which means only features with variance 0 (i.e. features that have the same value in all samples)
will be removed.
Examples
Assume that we have a DataFrame with the columns id and features, which is used as
our target to be predicted:
id | features
---|--------------------------------
1 | [6.0, 7.0, 0.0, 7.0, 6.0, 0.0]
2 | [0.0, 9.0, 6.0, 0.0, 5.0, 9.0]
3 | [0.0, 9.0, 3.0, 0.0, 5.0, 5.0]
4 | [0.0, 9.0, 8.0, 5.0, 6.0, 4.0]
5 | [8.0, 9.0, 6.0, 5.0, 4.0, 4.0]
6 | [8.0, 9.0, 6.0, 0.0, 0.0, 0.0]
The sample variances for the 6 features are 16.67, 0.67, 8.17, 10.17,
5.07, and 11.47 respectively. If we use VarianceThresholdSelector with
varianceThreshold = 8.0, then the features with variance <= 8.0 are removed:
id | features | selectedFeatures
---|--------------------------------|-------------------
1 | [6.0, 7.0, 0.0, 7.0, 6.0, 0.0] | [6.0,0.0,7.0,0.0]
2 | [0.0, 9.0, 6.0, 0.0, 5.0, 9.0] | [0.0,6.0,0.0,9.0]
3 | [0.0, 9.0, 3.0, 0.0, 5.0, 5.0] | [0.0,3.0,0.0,5.0]
4 | [0.0, 9.0, 8.0, 5.0, 6.0, 4.0] | [0.0,8.0,5.0,4.0]
5 | [8.0, 9.0, 6.0, 5.0, 4.0, 4.0] | [8.0,6.0,5.0,4.0]
6 | [8.0, 9.0, 6.0, 0.0, 0.0, 0.0] | [8.0,6.0,0.0,0.0]
Refer to the VarianceThresholdSelector Python docs for more details on the API.
from pyspark.ml.feature import VarianceThresholdSelector
from pyspark.ml.linalg import Vectors
df = spark.createDataFrame([
(1, Vectors.dense([6.0, 7.0, 0.0, 7.0, 6.0, 0.0])),
(2, Vectors.dense([0.0, 9.0, 6.0, 0.0, 5.0, 9.0])),
(3, Vectors.dense([0.0, 9.0, 3.0, 0.0, 5.0, 5.0])),
(4, Vectors.dense([0.0, 9.0, 8.0, 5.0, 6.0, 4.0])),
(5, Vectors.dense([8.0, 9.0, 6.0, 5.0, 4.0, 4.0])),
(6, Vectors.dense([8.0, 9.0, 6.0, 0.0, 0.0, 0.0]))], ["id", "features"])
selector = VarianceThresholdSelector(varianceThreshold=8.0, outputCol="selectedFeatures")
result = selector.fit(df).transform(df)
print("Output: Features with variance lower than %f are removed." %
selector.getVarianceThreshold())
result.show()Refer to the VarianceThresholdSelector Scala docs for more details on the API.
import org.apache.spark.ml.feature.VarianceThresholdSelector
import org.apache.spark.ml.linalg.Vectors
val data = Seq(
(1, Vectors.dense(6.0, 7.0, 0.0, 7.0, 6.0, 0.0)),
(2, Vectors.dense(0.0, 9.0, 6.0, 0.0, 5.0, 9.0)),
(3, Vectors.dense(0.0, 9.0, 3.0, 0.0, 5.0, 5.0)),
(4, Vectors.dense(0.0, 9.0, 8.0, 5.0, 6.0, 4.0)),
(5, Vectors.dense(8.0, 9.0, 6.0, 5.0, 4.0, 4.0)),
(6, Vectors.dense(8.0, 9.0, 6.0, 0.0, 0.0, 0.0))
)
val df = spark.createDataset(data).toDF("id", "features")
val selector = new VarianceThresholdSelector()
.setVarianceThreshold(8.0)
.setFeaturesCol("features")
.setOutputCol("selectedFeatures")
val result = selector.fit(df).transform(df)
println(s"Output: Features with variance lower than" +
s" ${selector.getVarianceThreshold} are removed.")
result.show()Refer to the VarianceThresholdSelector Java docs for more details on the API.
import java.util.Arrays;
import java.util.List;
import org.apache.spark.ml.feature.VarianceThresholdSelector;
import org.apache.spark.ml.linalg.VectorUDT;
import org.apache.spark.ml.linalg.Vectors;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.types.*;
List<Row> data = Arrays.asList(
RowFactory.create(1, Vectors.dense(6.0, 7.0, 0.0, 7.0, 6.0, 0.0)),
RowFactory.create(2, Vectors.dense(0.0, 9.0, 6.0, 0.0, 5.0, 9.0)),
RowFactory.create(3, Vectors.dense(0.0, 9.0, 3.0, 0.0, 5.0, 5.0)),
RowFactory.create(4, Vectors.dense(0.0, 9.0, 8.0, 5.0, 6.0, 4.0)),
RowFactory.create(5, Vectors.dense(8.0, 9.0, 6.0, 5.0, 4.0, 4.0)),
RowFactory.create(6, Vectors.dense(8.0, 9.0, 6.0, 0.0, 0.0, 0.0))
);
StructType schema = new StructType(new StructField[]{
new StructField("id", DataTypes.IntegerType, false, Metadata.empty()),
new StructField("features", new VectorUDT(), false, Metadata.empty())
});
Dataset<Row> df = spark.createDataFrame(data, schema);
VarianceThresholdSelector selector = new VarianceThresholdSelector()
.setVarianceThreshold(8.0)
.setFeaturesCol("features")
.setOutputCol("selectedFeatures");
Dataset<Row> result = selector.fit(df).transform(df);
System.out.println("Output: Features with variance lower than "
+ selector.getVarianceThreshold() + " are removed.");
result.show();Locality Sensitive Hashing
Locality Sensitive Hashing (LSH) 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 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 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 Python docs for more details on the API.
from pyspark.ml.feature import BucketedRandomProjectionLSH
from pyspark.ml.linalg import Vectors
from pyspark.sql.functions import col
dataA = [(0, Vectors.dense([1.0, 1.0]),),
(1, Vectors.dense([1.0, -1.0]),),
(2, Vectors.dense([-1.0, -1.0]),),
(3, Vectors.dense([-1.0, 1.0]),)]
dfA = spark.createDataFrame(dataA, ["id", "features"])
dataB = [(4, Vectors.dense([1.0, 0.0]),),
(5, Vectors.dense([-1.0, 0.0]),),
(6, Vectors.dense([0.0, 1.0]),),
(7, Vectors.dense([0.0, -1.0]),)]
dfB = spark.createDataFrame(dataB, ["id", "features"])
key = Vectors.dense([1.0, 0.0])
brp = BucketedRandomProjectionLSH(inputCol="features", outputCol="hashes", bucketLength=2.0,
numHashTables=3)
model = brp.fit(dfA)
# Feature Transformation
print("The hashed dataset where hashed values are stored in the column 'hashes':")
model.transform(dfA).show()
# Compute the locality sensitive hashes for the input rows, then perform approximate
# similarity join.
# We could avoid computing hashes by passing in the already-transformed dataset, e.g.
# `model.approxSimilarityJoin(transformedA, transformedB, 1.5)`
print("Approximately joining dfA and dfB on Euclidean distance smaller than 1.5:")
model.approxSimilarityJoin(dfA, dfB, 1.5, distCol="EuclideanDistance")\
.select(col("datasetA.id").alias("idA"),
col("datasetB.id").alias("idB"),
col("EuclideanDistance")).show()
# Compute the locality sensitive hashes for the input rows, then perform approximate nearest
# neighbor search.
# We could avoid computing hashes by passing in the already-transformed dataset, e.g.
# `model.approxNearestNeighbors(transformedA, key, 2)`
print("Approximately searching dfA for 2 nearest neighbors of the key:")
model.approxNearestNeighbors(dfA, key, 2).show()Refer to the BucketedRandomProjectionLSH Scala docs for more details on the API.
import org.apache.spark.ml.feature.BucketedRandomProjectionLSH
import org.apache.spark.ml.linalg.Vectors
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.functions.col
val dfA = spark.createDataFrame(Seq(
(0, Vectors.dense(1.0, 1.0)),
(1, Vectors.dense(1.0, -1.0)),
(2, Vectors.dense(-1.0, -1.0)),
(3, Vectors.dense(-1.0, 1.0))
)).toDF("id", "features")
val dfB = spark.createDataFrame(Seq(
(4, Vectors.dense(1.0, 0.0)),
(5, Vectors.dense(-1.0, 0.0)),
(6, Vectors.dense(0.0, 1.0)),
(7, Vectors.dense(0.0, -1.0))
)).toDF("id", "features")
val key = Vectors.dense(1.0, 0.0)
val brp = new BucketedRandomProjectionLSH()
.setBucketLength(2.0)
.setNumHashTables(3)
.setInputCol("features")
.setOutputCol("hashes")
val model = brp.fit(dfA)
// Feature Transformation
println("The hashed dataset where hashed values are stored in the column 'hashes':")
model.transform(dfA).show()
// Compute the locality sensitive hashes for the input rows, then perform approximate
// similarity join.
// We could avoid computing hashes by passing in the already-transformed dataset, e.g.
// `model.approxSimilarityJoin(transformedA, transformedB, 1.5)`
println("Approximately joining dfA and dfB on Euclidean distance smaller than 1.5:")
model.approxSimilarityJoin(dfA, dfB, 1.5, "EuclideanDistance")
.select(col("datasetA.id").alias("idA"),
col("datasetB.id").alias("idB"),
col("EuclideanDistance")).show()
// Compute the locality sensitive hashes for the input rows, then perform approximate nearest
// neighbor search.
// We could avoid computing hashes by passing in the already-transformed dataset, e.g.
// `model.approxNearestNeighbors(transformedA, key, 2)`
println("Approximately searching dfA for 2 nearest neighbors of the key:")
model.approxNearestNeighbors(dfA, key, 2).show()Refer to the BucketedRandomProjectionLSH Java docs for more details on the API.
import java.util.Arrays;
import java.util.List;
import org.apache.spark.ml.feature.BucketedRandomProjectionLSH;
import org.apache.spark.ml.feature.BucketedRandomProjectionLSHModel;
import org.apache.spark.ml.linalg.Vector;
import org.apache.spark.ml.linalg.Vectors;
import org.apache.spark.ml.linalg.VectorUDT;
import org.apache.spark.sql.Dataset;
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;
import static org.apache.spark.sql.functions.col;
List<Row> dataA = Arrays.asList(
RowFactory.create(0, Vectors.dense(1.0, 1.0)),
RowFactory.create(1, Vectors.dense(1.0, -1.0)),
RowFactory.create(2, Vectors.dense(-1.0, -1.0)),
RowFactory.create(3, Vectors.dense(-1.0, 1.0))
);
List<Row> dataB = Arrays.asList(
RowFactory.create(4, Vectors.dense(1.0, 0.0)),
RowFactory.create(5, Vectors.dense(-1.0, 0.0)),
RowFactory.create(6, Vectors.dense(0.0, 1.0)),
RowFactory.create(7, Vectors.dense(0.0, -1.0))
);
StructType schema = new StructType(new StructField[]{
new StructField("id", DataTypes.IntegerType, false, Metadata.empty()),
new StructField("features", new VectorUDT(), false, Metadata.empty())
});
Dataset<Row> dfA = spark.createDataFrame(dataA, schema);
Dataset<Row> dfB = spark.createDataFrame(dataB, schema);
Vector key = Vectors.dense(1.0, 0.0);
BucketedRandomProjectionLSH mh = new BucketedRandomProjectionLSH()
.setBucketLength(2.0)
.setNumHashTables(3)
.setInputCol("features")
.setOutputCol("hashes");
BucketedRandomProjectionLSHModel model = mh.fit(dfA);
// Feature Transformation
System.out.println("The hashed dataset where hashed values are stored in the column 'hashes':");
model.transform(dfA).show();
// Compute the locality sensitive hashes for the input rows, then perform approximate
// similarity join.
// We could avoid computing hashes by passing in the already-transformed dataset, e.g.
// `model.approxSimilarityJoin(transformedA, transformedB, 1.5)`
System.out.println("Approximately joining dfA and dfB on distance smaller than 1.5:");
model.approxSimilarityJoin(dfA, dfB, 1.5, "EuclideanDistance")
.select(col("datasetA.id").alias("idA"),
col("datasetB.id").alias("idB"),
col("EuclideanDistance")).show();
// Compute the locality sensitive hashes for the input rows, then perform approximate nearest
// neighbor search.
// We could avoid computing hashes by passing in the already-transformed dataset, e.g.
// `model.approxNearestNeighbors(transformedA, key, 2)`
System.out.println("Approximately searching dfA for 2 nearest neighbors of the key:");
model.approxNearestNeighbors(dfA, key, 2).show();MinHash for Jaccard Distance
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 Python docs for more details on the API.
from pyspark.ml.feature import MinHashLSH
from pyspark.ml.linalg import Vectors
from pyspark.sql.functions import col
dataA = [(0, Vectors.sparse(6, [0, 1, 2], [1.0, 1.0, 1.0]),),
(1, Vectors.sparse(6, [2, 3, 4], [1.0, 1.0, 1.0]),),
(2, Vectors.sparse(6, [0, 2, 4], [1.0, 1.0, 1.0]),)]
dfA = spark.createDataFrame(dataA, ["id", "features"])
dataB = [(3, Vectors.sparse(6, [1, 3, 5], [1.0, 1.0, 1.0]),),
(4, Vectors.sparse(6, [2, 3, 5], [1.0, 1.0, 1.0]),),
(5, Vectors.sparse(6, [1, 2, 4], [1.0, 1.0, 1.0]),)]
dfB = spark.createDataFrame(dataB, ["id", "features"])
key = Vectors.sparse(6, [1, 3], [1.0, 1.0])
mh = MinHashLSH(inputCol="features", outputCol="hashes", numHashTables=5)
model = mh.fit(dfA)
# Feature Transformation
print("The hashed dataset where hashed values are stored in the column 'hashes':")
model.transform(dfA).show()
# Compute the locality sensitive hashes for the input rows, then perform approximate
# similarity join.
# We could avoid computing hashes by passing in the already-transformed dataset, e.g.
# `model.approxSimilarityJoin(transformedA, transformedB, 0.6)`
print("Approximately joining dfA and dfB on distance smaller than 0.6:")
model.approxSimilarityJoin(dfA, dfB, 0.6, distCol="JaccardDistance")\
.select(col("datasetA.id").alias("idA"),
col("datasetB.id").alias("idB"),
col("JaccardDistance")).show()
# Compute the locality sensitive hashes for the input rows, then perform approximate nearest
# neighbor search.
# We could avoid computing hashes by passing in the already-transformed dataset, e.g.
# `model.approxNearestNeighbors(transformedA, key, 2)`
# It may return less than 2 rows when not enough approximate near-neighbor candidates are
# found.
print("Approximately searching dfA for 2 nearest neighbors of the key:")
model.approxNearestNeighbors(dfA, key, 2).show()Refer to the MinHashLSH Scala docs for more details on the API.
import org.apache.spark.ml.feature.MinHashLSH
import org.apache.spark.ml.linalg.Vectors
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.functions.col
val dfA = spark.createDataFrame(Seq(
(0, Vectors.sparse(6, Seq((0, 1.0), (1, 1.0), (2, 1.0)))),
(1, Vectors.sparse(6, Seq((2, 1.0), (3, 1.0), (4, 1.0)))),
(2, Vectors.sparse(6, Seq((0, 1.0), (2, 1.0), (4, 1.0))))
)).toDF("id", "features")
val dfB = spark.createDataFrame(Seq(
(3, Vectors.sparse(6, Seq((1, 1.0), (3, 1.0), (5, 1.0)))),
(4, Vectors.sparse(6, Seq((2, 1.0), (3, 1.0), (5, 1.0)))),
(5, Vectors.sparse(6, Seq((1, 1.0), (2, 1.0), (4, 1.0))))
)).toDF("id", "features")
val key = Vectors.sparse(6, Seq((1, 1.0), (3, 1.0)))
val mh = new MinHashLSH()
.setNumHashTables(5)
.setInputCol("features")
.setOutputCol("hashes")
val model = mh.fit(dfA)
// Feature Transformation
println("The hashed dataset where hashed values are stored in the column 'hashes':")
model.transform(dfA).show()
// Compute the locality sensitive hashes for the input rows, then perform approximate
// similarity join.
// We could avoid computing hashes by passing in the already-transformed dataset, e.g.
// `model.approxSimilarityJoin(transformedA, transformedB, 0.6)`
println("Approximately joining dfA and dfB on Jaccard distance smaller than 0.6:")
model.approxSimilarityJoin(dfA, dfB, 0.6, "JaccardDistance")
.select(col("datasetA.id").alias("idA"),
col("datasetB.id").alias("idB"),
col("JaccardDistance")).show()
// Compute the locality sensitive hashes for the input rows, then perform approximate nearest
// neighbor search.
// We could avoid computing hashes by passing in the already-transformed dataset, e.g.
// `model.approxNearestNeighbors(transformedA, key, 2)`
// It may return less than 2 rows when not enough approximate near-neighbor candidates are
// found.
println("Approximately searching dfA for 2 nearest neighbors of the key:")
model.approxNearestNeighbors(dfA, key, 2).show()Refer to the MinHashLSH Java docs for more details on the API.
import java.util.Arrays;
import java.util.List;
import org.apache.spark.ml.feature.MinHashLSH;
import org.apache.spark.ml.feature.MinHashLSHModel;
import org.apache.spark.ml.linalg.Vector;
import org.apache.spark.ml.linalg.VectorUDT;
import org.apache.spark.ml.linalg.Vectors;
import org.apache.spark.sql.Dataset;
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;
import static org.apache.spark.sql.functions.col;
List<Row> dataA = Arrays.asList(
RowFactory.create(0, Vectors.sparse(6, new int[]{0, 1, 2}, new double[]{1.0, 1.0, 1.0})),
RowFactory.create(1, Vectors.sparse(6, new int[]{2, 3, 4}, new double[]{1.0, 1.0, 1.0})),
RowFactory.create(2, Vectors.sparse(6, new int[]{0, 2, 4}, new double[]{1.0, 1.0, 1.0}))
);
List<Row> dataB = Arrays.asList(
RowFactory.create(0, Vectors.sparse(6, new int[]{1, 3, 5}, new double[]{1.0, 1.0, 1.0})),
RowFactory.create(1, Vectors.sparse(6, new int[]{2, 3, 5}, new double[]{1.0, 1.0, 1.0})),
RowFactory.create(2, Vectors.sparse(6, new int[]{1, 2, 4}, new double[]{1.0, 1.0, 1.0}))
);
StructType schema = new StructType(new StructField[]{
new StructField("id", DataTypes.IntegerType, false, Metadata.empty()),
new StructField("features", new VectorUDT(), false, Metadata.empty())
});
Dataset<Row> dfA = spark.createDataFrame(dataA, schema);
Dataset<Row> dfB = spark.createDataFrame(dataB, schema);
int[] indices = {1, 3};
double[] values = {1.0, 1.0};
Vector key = Vectors.sparse(6, indices, values);
MinHashLSH mh = new MinHashLSH()
.setNumHashTables(5)
.setInputCol("features")
.setOutputCol("hashes");
MinHashLSHModel model = mh.fit(dfA);
// Feature Transformation
System.out.println("The hashed dataset where hashed values are stored in the column 'hashes':");
model.transform(dfA).show();
// Compute the locality sensitive hashes for the input rows, then perform approximate
// similarity join.
// We could avoid computing hashes by passing in the already-transformed dataset, e.g.
// `model.approxSimilarityJoin(transformedA, transformedB, 0.6)`
System.out.println("Approximately joining dfA and dfB on Jaccard distance smaller than 0.6:");
model.approxSimilarityJoin(dfA, dfB, 0.6, "JaccardDistance")
.select(col("datasetA.id").alias("idA"),
col("datasetB.id").alias("idB"),
col("JaccardDistance")).show();
// Compute the locality sensitive hashes for the input rows, then perform approximate nearest
// neighbor search.
// We could avoid computing hashes by passing in the already-transformed dataset, e.g.
// `model.approxNearestNeighbors(transformedA, key, 2)`
// It may return less than 2 rows when not enough approximate near-neighbor candidates are
// found.
System.out.println("Approximately searching dfA for 2 nearest neighbors of the key:");
model.approxNearestNeighbors(dfA, key, 2).show();