Added user guide sections with code examples.
Also added small Java unit tests to test Java example in guide.
CC: mengxr
Author: Joseph K. Bradley <joseph@databricks.com>
Closes #6127 from jkbradley/feature-guide-2 and squashes the following commits:
cd47f4b [Joseph K. Bradley] Updated based on code review
f16bcec [Joseph K. Bradley] Fixed merge issues and update Python examples print calls for Python 3
0a862f9 [Joseph K. Bradley] Added Normalizer, StandardScaler to ml-features doc, plus small Java unit tests
a21c2d6 [Joseph K. Bradley] Updated ml-features.md with IDF
(cherry picked from commit 2728c3df66
)
Signed-off-by: Xiangrui Meng <meng@databricks.com>
31 KiB
layout | title | displayTitle |
---|---|---|
global | Feature Extraction, Transformation, and Selection - SparkML | <a href="ml-guide.html">ML</a> - 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
Table of Contents
- This will become a table of contents (this text will be scraped). {:toc}
Feature Extractors
TF-IDF (HashingTF and IDF)
Term Frequency-Inverse Document Frequency (TF-IDF) is a common text pre-processing step. In Spark ML, TF-IDF is separate into two parts: TF (+hashing) and IDF.
TF: HashingTF
is a Transformer
which takes sets of terms and converts those sets into fixed-length feature vectors. In text processing, a "set of terms" might be a bag of words.
The algorithm combines Term Frequency (TF) counts with the hashing trick for dimensionality reduction.
IDF: IDF
is an Estimator
which fits on a dataset and produces an IDFModel
. The IDFModel
takes feature vectors (generally created from HashingTF
) and scales each column. Intuitively, it down-weights columns which appear frequently in a corpus.
Please refer to the MLlib user guide on TF-IDF for more details on Term Frequency and Inverse Document Frequency. For API details, refer to the HashingTF API docs and the IDF API docs.
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.
val sentenceData = sqlContext.createDataFrame(Seq( (0, "Hi I heard about Spark"), (0, "I wish Java could use case classes"), (1, "Logistic regression models are neat") )).toDF("label", "sentence") val tokenizer = new Tokenizer().setInputCol("sentence").setOutputCol("words") val wordsData = tokenizer.transform(sentenceData) val hashingTF = new HashingTF().setInputCol("words").setOutputCol("rawFeatures").setNumFeatures(20) val featurizedData = hashingTF.transform(wordsData) val idf = new IDF().setInputCol("rawFeatures").setOutputCol("features") val idfModel = idf.fit(featurizedData) val rescaledData = idfModel.transform(featurizedData) rescaledData.select("features", "label").take(3).foreach(println) {% endhighlight %}
import org.apache.spark.api.java.JavaRDD; import org.apache.spark.ml.feature.HashingTF; import org.apache.spark.ml.feature.IDF; import org.apache.spark.ml.feature.Tokenizer; import org.apache.spark.mllib.linalg.Vector; import org.apache.spark.sql.DataFrame; 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;
JavaRDD jrdd = jsc.parallelize(Lists.newArrayList( RowFactory.create(0, "Hi I heard about Spark"), RowFactory.create(0, "I wish Java could use case classes"), RowFactory.create(1, "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()) }); DataFrame sentenceData = sqlContext.createDataFrame(jrdd, schema); Tokenizer tokenizer = new Tokenizer().setInputCol("sentence").setOutputCol("words"); DataFrame wordsData = tokenizer.transform(sentenceData); int numFeatures = 20; HashingTF hashingTF = new HashingTF() .setInputCol("words") .setOutputCol("rawFeatures") .setNumFeatures(numFeatures); DataFrame featurizedData = hashingTF.transform(wordsData); IDF idf = new IDF().setInputCol("rawFeatures").setOutputCol("features"); IDFModel idfModel = idf.fit(featurizedData); DataFrame rescaledData = idfModel.transform(featurizedData); for (Row r : rescaledData.select("features", "label").take(3)) { Vector features = r.getAs(0); Double label = r.getDouble(1); System.out.println(features); } {% endhighlight %}
sentenceData = sqlContext.createDataFrame([ (0, "Hi I heard about Spark"), (0, "I wish Java could use case classes"), (1, "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) idf = IDF(inputCol="rawFeatures", outputCol="features") idfModel = idf.fit(featurizedData) rescaledData = idfModel.transform(featurizedData) for features_label in rescaledData.select("features", "label").take(3): print(features_label) {% endhighlight %}
Word2Vec
Word2Vec
is an Estimator
which takes sequences of words that represents documents and trains a Word2VecModel
. The model is a Map(String, Vector)
essentially, which maps each word to an unique fix-sized vector. The Word2VecModel
transforms each documents into a vector using the average of all words in the document, which aims to other computations of documents such as similarity calculation consequencely. Please refer to the MLlib user guide on Word2Vec for more details on Word2Vec.
Word2Vec is implemented in Word2Vec. In the following code segment, we start with a set of documents, each of them 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.
// Input data: Each row is a bag of words from a sentence or document. val documentDF = sqlContext.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.select("result").take(3).foreach(println) {% endhighlight %}
import org.apache.spark.api.java.JavaRDD; import org.apache.spark.api.java.JavaSparkContext; import org.apache.spark.sql.DataFrame; import org.apache.spark.sql.Row; import org.apache.spark.sql.RowFactory; import org.apache.spark.sql.SQLContext; import org.apache.spark.sql.types.*;
JavaSparkContext jsc = ... SQLContext sqlContext = ...
// Input data: Each row is a bag of words from a sentence or document. JavaRDD jrdd = jsc.parallelize(Lists.newArrayList( RowFactory.create(Lists.newArrayList("Hi I heard about Spark".split(" "))), RowFactory.create(Lists.newArrayList("I wish Java could use case classes".split(" "))), RowFactory.create(Lists.newArrayList("Logistic regression models are neat".split(" "))) )); StructType schema = new StructType(new StructField[]{ new StructField("text", new ArrayType(DataTypes.StringType, true), false, Metadata.empty()) }); DataFrame documentDF = sqlContext.createDataFrame(jrdd, 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); DataFrame result = model.transform(documentDF); for (Row r: result.select("result").take(3)) { System.out.println(r); } {% endhighlight %}
Input data: Each row is a bag of words from a sentence or document.
documentDF = sqlContext.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 feature in result.select("result").take(3): print(feature) {% endhighlight %}
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.
Note: A more advanced tokenizer is provided via RegexTokenizer.
val sentenceDataFrame = sqlContext.createDataFrame(Seq( (0, "Hi I heard about Spark"), (0, "I wish Java could use case classes"), (1, "Logistic regression models are neat") )).toDF("label", "sentence") val tokenizer = new Tokenizer().setInputCol("sentence").setOutputCol("words") val wordsDataFrame = tokenizer.transform(sentenceDataFrame) wordsDataFrame.select("words", "label").take(3).foreach(println) {% endhighlight %}
import org.apache.spark.api.java.JavaRDD; import org.apache.spark.ml.feature.Tokenizer; import org.apache.spark.mllib.linalg.Vector; import org.apache.spark.sql.DataFrame; 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;
JavaRDD jrdd = jsc.parallelize(Lists.newArrayList( RowFactory.create(0, "Hi I heard about Spark"), RowFactory.create(0, "I wish Java could use case classes"), RowFactory.create(1, "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()) }); DataFrame sentenceDataFrame = sqlContext.createDataFrame(jrdd, schema); Tokenizer tokenizer = new Tokenizer().setInputCol("sentence").setOutputCol("words"); DataFrame wordsDataFrame = tokenizer.transform(sentenceDataFrame); for (Row r : wordsDataFrame.select("words", "label").take(3)) { java.util.List words = r.getList(0); for (String word : words) System.out.print(word + " "); System.out.println(); } {% endhighlight %}
sentenceDataFrame = sqlContext.createDataFrame([ (0, "Hi I heard about Spark"), (0, "I wish Java could use case classes"), (1, "Logistic regression models are neat") ], ["label", "sentence"]) tokenizer = Tokenizer(inputCol="sentence", outputCol="words") wordsDataFrame = tokenizer.transform(sentenceDataFrame) for words_label in wordsDataFrame.select("words", "label").take(3): print(words_label) {% endhighlight %}
Binarizer
Binarization is the process of thresholding numerical features to binary features. As some probabilistic estimators make assumption that the input data is distributed according to Bernoulli distribution, a binarizer is useful for pre-processing the input data with continuous numerical features.
A simple Binarizer class provides this functionality. Besides the common parameters of inputCol
and outputCol
, Binarizer
has the parameter threshold
used for binarizing continuous numerical features. The features greater than the threshold, will be binarized to 1.0. The features equal to or less than the threshold, will be binarized to 0.0. The example below shows how to binarize numerical features.
val data = Array( (0, 0.1), (1, 0.8), (2, 0.2) ) val dataFrame: DataFrame = sqlContext.createDataFrame(data).toDF("label", "feature")
val binarizer: Binarizer = new Binarizer() .setInputCol("feature") .setOutputCol("binarized_feature") .setThreshold(0.5)
val binarizedDataFrame = binarizer.transform(dataFrame) val binarizedFeatures = binarizedDataFrame.select("binarized_feature") binarizedFeatures.collect().foreach(println) {% endhighlight %}
import org.apache.spark.api.java.JavaRDD; import org.apache.spark.ml.feature.Binarizer; import org.apache.spark.sql.DataFrame; 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;
JavaRDD jrdd = jsc.parallelize(Lists.newArrayList( RowFactory.create(0, 0.1), RowFactory.create(1, 0.8), RowFactory.create(2, 0.2) )); StructType schema = new StructType(new StructField[]{ new StructField("label", DataTypes.DoubleType, false, Metadata.empty()), new StructField("feature", DataTypes.DoubleType, false, Metadata.empty()) }); DataFrame continuousDataFrame = jsql.createDataFrame(jrdd, schema); Binarizer binarizer = new Binarizer() .setInputCol("feature") .setOutputCol("binarized_feature") .setThreshold(0.5); DataFrame binarizedDataFrame = binarizer.transform(continuousDataFrame); DataFrame binarizedFeatures = binarizedDataFrame.select("binarized_feature"); for (Row r : binarizedFeatures.collect()) { Double binarized_value = r.getDouble(0); System.out.println(binarized_value); } {% endhighlight %}
continuousDataFrame = sqlContext.createDataFrame([ (0, 0.1), (1, 0.8), (2, 0.2) ], ["label", "feature"]) binarizer = Binarizer(threshold=0.5, inputCol="feature", outputCol="binarized_feature") binarizedDataFrame = binarizer.transform(continuousDataFrame) binarizedFeatures = binarizedDataFrame.select("binarized_feature") for binarized_feature, in binarizedFeatures.collect(): print(binarized_feature) {% endhighlight %}
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.
val data = Array( Vectors.dense(-2.0, 2.3), Vectors.dense(0.0, 0.0), Vectors.dense(0.6, -1.1) ) val df = sqlContext.createDataFrame(data.map(Tuple1.apply)).toDF("features") val polynomialExpansion = new PolynomialExpansion() .setInputCol("features") .setOutputCol("polyFeatures") .setDegree(3) val polyDF = polynomialExpansion.transform(df) polyDF.select("polyFeatures").take(3).foreach(println) {% endhighlight %}
import org.apache.spark.api.java.JavaRDD; import org.apache.spark.api.java.JavaSparkContext; import org.apache.spark.mllib.linalg.Vector; import org.apache.spark.mllib.linalg.VectorUDT; import org.apache.spark.mllib.linalg.Vectors; import org.apache.spark.sql.DataFrame; import org.apache.spark.sql.Row; import org.apache.spark.sql.RowFactory; import org.apache.spark.sql.SQLContext; import org.apache.spark.sql.types.Metadata; import org.apache.spark.sql.types.StructField; import org.apache.spark.sql.types.StructType;
JavaSparkContext jsc = ... SQLContext jsql = ... PolynomialExpansion polyExpansion = new PolynomialExpansion() .setInputCol("features") .setOutputCol("polyFeatures") .setDegree(3); JavaRDD data = jsc.parallelize(Lists.newArrayList( RowFactory.create(Vectors.dense(-2.0, 2.3)), RowFactory.create(Vectors.dense(0.0, 0.0)), RowFactory.create(Vectors.dense(0.6, -1.1)) )); StructType schema = new StructType(new StructField[] { new StructField("features", new VectorUDT(), false, Metadata.empty()), }); DataFrame df = jsql.createDataFrame(data, schema); DataFrame polyDF = polyExpansion.transform(df); Row[] row = polyDF.select("polyFeatures").take(3); for (Row r : row) { System.out.println(r.get(0)); } {% endhighlight %}
df = sqlContext.createDataFrame( [(Vectors.dense([-2.0, 2.3]), ), (Vectors.dense([0.0, 0.0]), ), (Vectors.dense([0.6, -1.1]), )], ["features"]) px = PolynomialExpansion(degree=2, inputCol="features", outputCol="polyFeatures") polyDF = px.transform(df) for expanded in polyDF.select("polyFeatures").take(3): print(expanded) {% endhighlight %}
OneHotEncoder
One-hot encoding maps a column of label indices to a column of binary vectors, with at most a single one-value. This encoding allows algorithms which expect continuous features, such as Logistic Regression, to use categorical features
val df = sqlContext.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)
val encoder = new OneHotEncoder().setInputCol("categoryIndex"). setOutputCol("categoryVec") val encoded = encoder.transform(indexed) encoded.select("id", "categoryVec").foreach(println) {% endhighlight %}
import org.apache.spark.api.java.JavaRDD; import org.apache.spark.ml.feature.OneHotEncoder; import org.apache.spark.ml.feature.StringIndexer; import org.apache.spark.ml.feature.StringIndexerModel; import org.apache.spark.sql.DataFrame; 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;
JavaRDD jrdd = jsc.parallelize(Lists.newArrayList( 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.DoubleType, false, Metadata.empty()), new StructField("category", DataTypes.StringType, false, Metadata.empty()) }); DataFrame df = sqlContext.createDataFrame(jrdd, schema); StringIndexerModel indexer = new StringIndexer() .setInputCol("category") .setOutputCol("categoryIndex") .fit(df); DataFrame indexed = indexer.transform(df);
OneHotEncoder encoder = new OneHotEncoder() .setInputCol("categoryIndex") .setOutputCol("categoryVec"); DataFrame encoded = encoder.transform(indexed); {% endhighlight %}
df = sqlContext.createDataFrame([ (0, "a"), (1, "b"), (2, "c"), (3, "a"), (4, "a"), (5, "c") ], ["id", "category"])
stringIndexer = StringIndexer(inputCol="category", outputCol="categoryIndex") model = stringIndexer.fit(df) indexed = model.transform(df) encoder = OneHotEncoder(includeFirst=False, inputCol="categoryIndex", outputCol="categoryVec") encoded = encoder.transform(indexed) {% endhighlight %}
VectorIndexer
VectorIndexer
helps index categorical features in datasets of Vector
s.
It can both automatically decide which features are categorical and convert original values to category indices. Specifically, it does the following:
- 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
maxCategories
are 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.
Please refer to the VectorIndexer API docs for more details.
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.
val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF() 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} categorical features: " + categoricalFeatures.mkString(", "))
// Create new column "indexed" with categorical values transformed to indices val indexedData = indexerModel.transform(data) {% endhighlight %}
import org.apache.spark.api.java.JavaRDD; import org.apache.spark.ml.feature.VectorIndexer; import org.apache.spark.ml.feature.VectorIndexerModel; import org.apache.spark.mllib.regression.LabeledPoint; import org.apache.spark.mllib.util.MLUtils; import org.apache.spark.sql.DataFrame;
JavaRDD rdd = MLUtils.loadLibSVMFile(sc.sc(), "data/mllib/sample_libsvm_data.txt").toJavaRDD(); DataFrame data = sqlContext.createDataFrame(rdd, LabeledPoint.class); 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 DataFrame indexedData = indexerModel.transform(data); {% endhighlight %}
data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt").toDF() indexer = VectorIndexer(inputCol="features", outputCol="indexed", maxCategories=10) indexerModel = indexer.fit(data)
Create new column "indexed" with categorical values transformed to indices
indexedData = indexerModel.transform(data) {% endhighlight %}
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.
The following example demonstrates how to load a dataset in libsvm format and then normalize each row to have unit L^2
norm and unit L^\infty
norm.
val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt") val dataFrame = sqlContext.createDataFrame(data)
// Normalize each Vector using L^1
norm.
val normalizer = new Normalizer()
.setInputCol("features")
.setOutputCol("normFeatures")
.setP(1.0)
val l1NormData = normalizer.transform(dataFrame)
// Normalize each Vector using L^\infty
norm.
val lInfNormData = normalizer.transform(dataFrame, normalizer.p -> Double.PositiveInfinity)
{% endhighlight %}
JavaRDD data = MLUtils.loadLibSVMFile(jsc.sc(), "data/mllib/sample_libsvm_data.txt").toJavaRDD(); DataFrame dataFrame = jsql.createDataFrame(data, LabeledPoint.class);
// Normalize each Vector using L^1
norm.
Normalizer normalizer = new Normalizer()
.setInputCol("features")
.setOutputCol("normFeatures")
.setP(1.0);
DataFrame l1NormData = normalizer.transform(dataFrame);
// Normalize each Vector using L^\infty
norm.
DataFrame lInfNormData =
normalizer.transform(dataFrame, normalizer.p().w(Double.POSITIVE_INFINITY));
{% endhighlight %}
data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt") dataFrame = sqlContext.createDataFrame(data)
Normalize each Vector using L^1
norm.
normalizer = Normalizer(inputCol="features", outputCol="normFeatures", p=1.0) l1NormData = normalizer.transform(dataFrame)
Normalize each Vector using L^\infty
norm.
lInfNormData = normalizer.transform(dataFrame, {normalizer.p: float("inf")}) {% endhighlight %}
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 this does not work on sparse input and will raise an exception.
StandardScaler
is a Model
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.
More details can be found in the API docs for StandardScaler and StandardScalerModel.
The following example demonstrates how to load a dataset in libsvm format and then normalize each feature to have unit standard deviation.
val data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt") val dataFrame = sqlContext.createDataFrame(data) 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) {% endhighlight %}
JavaRDD data = MLUtils.loadLibSVMFile(jsc.sc(), "data/mllib/sample_libsvm_data.txt").toJavaRDD(); DataFrame dataFrame = jsql.createDataFrame(data, LabeledPoint.class); 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. DataFrame scaledData = scalerModel.transform(dataFrame); {% endhighlight %}
data = MLUtils.loadLibSVMFile(sc, "data/mllib/sample_libsvm_data.txt") dataFrame = sqlContext.createDataFrame(data) 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) {% endhighlight %}