二十种特征变换方法及Spark MLlib调用实例（Scala/Java/python）（一）
Tokenizer（分词器）
算法介绍：
Tokenization将文本划分为独立个体（通常为单词）。下面的例子展示了如何把句子划分为单词。
RegexTokenizer基于正则表达式提供更多的划分选项。默认情况下，参数“pattern”为划分文本的分隔符。或者，用户可以指定参数“gaps”来指明正则“patten”表示“tokens”而不是分隔符，这样来为分词结果找到所有可能匹配的情况。
示例调用：
Scala：
import org.apache.spark.ml.feature.{RegexTokenizer, Tokenizer}
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("label", "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 tokenized = tokenizer.transform(sentenceDataFrame)
tokenized.select("words", "label").take(3).foreach(println)
val regexTokenized = regexTokenizer.transform(sentenceDataFrame)
regexTokenized.select("words", "label").take(3).foreach(println)Java：
import java.util.Arrays;
import java.util.List;
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;
List 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("label", DataTypes.IntegerType, false, Metadata.empty()),
new StructField("sentence", DataTypes.StringType, false, Metadata.empty())
});
Dataset sentenceDataFrame = spark.createDataFrame(data, schema);
Tokenizer tokenizer = new Tokenizer().setInputCol("sentence").setOutputCol("words");
Dataset wordsDataFrame = tokenizer.transform(sentenceDataFrame);
for (Row r : wordsDataFrame.select("words", "label").takeAsList(3)) {
java.util.List words = r.getList(0);
for (String word : words) System.out.print(word + " ");
System.out.println();
}
RegexTokenizer regexTokenizer = new RegexTokenizer()
.setInputCol("sentence")
.setOutputCol("words")
.setPattern("\\W"); // alternatively .setPattern("\\w+").setGaps(false);
Python：
from pyspark.ml.feature import Tokenizer, RegexTokenizer
sentenceDataFrame = spark.createDataFrame([
(0, "Hi I heard about Spark"),
(1, "I wish Java could use case classes"),
(2, "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)
regexTokenizer = RegexTokenizer(inputCol="sentence", outputCol="words", pattern="\\W")
# alternatively, pattern="\\w+", gaps(False)StopWordsRemover
算法介绍：
停用词为在文档中频繁出现，但未承载太多意义的词语，他们不应该被包含在算法输入中。
StopWordsRemover的输入为一系列字符串（如分词器输出），输出中删除了所有停用词。停用词表由stopWords参数提供。一些语言的默认停用词表可以通过StopWordsRemover.loadDefaultStopWords(language)调用。布尔参数caseSensitive指明是否区分大小写（默认为否）。
示例：
假设我们有如下DataFrame，有id和raw两列：
id | raw
----|----------
0 | [I,saw, the, red, baloon]
1 |[Mary, had, a, little, lamb]
通过对raw列调用StopWordsRemover，我们可以得到筛选出的结果列如下：
id | raw | filtered
----|-----------------------------|--------------------
0 | [I,saw, the, red, baloon] | [saw, red, baloon]
1 |[Mary, had, a, little, lamb]|[Mary, little, lamb]
其中，“I”, “the”, “had”以及“a”被移除。
示例调用：
Scala：
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", "baloon")),
(1, Seq("Mary", "had", "a", "little", "lamb"))
)).toDF("id", "raw")
remover.transform(dataSet).show()Java：
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 data = Arrays.asList(
RowFactory.create(Arrays.asList("I", "saw", "the", "red", "baloon")),
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 dataset = spark.createDataFrame(data, schema);
remover.transform(dataset).show();
Python：
from pyspark.ml.feature import StopWordsRemover
sentenceData = spark.createDataFrame([
(0, ["I", "saw", "the", "red", "baloon"]),
(1, ["Mary", "had", "a", "little", "lamb"])
], ["label", "raw"])
remover = StopWordsRemover(inputCol="raw", outputCol="filtered")
remover.transform(sentenceData).show(truncate=False)n-gram
算法介绍：
一个n-gram是一个长度为整数n的字序列。NGram可以用来将输入转换为n-gram。
NGram的输入为一系列字符串（如分词器输出）。参数n决定每个n-gram包含的对象个数。结果包含一系列n-gram，其中每个n-gram代表一个空格分割的n个连续字符。如果输入少于n个字符串，将没有输出结果。
示例调用：
Scala：
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("label", "words")
val ngram = new NGram().setInputCol("words").setOutputCol("ngrams")
val ngramDataFrame = ngram.transform(wordDataFrame)
ngramDataFrame.take(3).map(_.getAs[Stream[String]]("ngrams").toList).foreach(println)Java：
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 data = Arrays.asList(
RowFactory.create(0.0, Arrays.asList("Hi", "I", "heard", "about", "Spark")),
RowFactory.create(1.0, Arrays.asList("I", "wish", "Java", "could", "use", "case", "classes")),
RowFactory.create(2.0, Arrays.asList("Logistic", "regression", "models", "are", "neat"))
);
StructType schema = new StructType(new StructField[]{
new StructField("label", DataTypes.DoubleType, false, Metadata.empty()),
new StructField(
"words", DataTypes.createArrayType(DataTypes.StringType), false, Metadata.empty())
});
Dataset wordDataFrame = spark.createDataFrame(data, schema);
NGram ngramTransformer = new NGram().setInputCol("words").setOutputCol("ngrams");
Dataset ngramDataFrame = ngramTransformer.transform(wordDataFrame);
for (Row r : ngramDataFrame.select("ngrams", "label").takeAsList(3)) {
java.util.List ngrams = r.getList(0);
for (String ngram : ngrams) System.out.print(ngram + " --- ");
System.out.println();
}
Python：
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"])
], ["label", "words"])
ngram = NGram(inputCol="words", outputCol="ngrams")
ngramDataFrame = ngram.transform(wordDataFrame)
for ngrams_label in ngramDataFrame.select("ngrams", "label").take(3):
print(ngrams_label)Binarizer
算法介绍：
二值化是根据阀值将连续数值特征转换为0-1特征的过程。
Binarizer参数有输入、输出以及阀值。特征值大于阀值将映射为1.0，特征值小于等于阀值将映射为0.0。
示例调用：
Scala：
import org.apache.spark.ml.feature.Binarizer
val data = Array((0, 0.1), (1, 0.8), (2, 0.2))
val dataFrame = spark.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)Java：
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 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 continuousDataFrame = spark.createDataFrame(data, schema);
Binarizer binarizer = new Binarizer()
.setInputCol("feature")
.setOutputCol("binarized_feature")
.setThreshold(0.5);
Dataset binarizedDataFrame = binarizer.transform(continuousDataFrame);
Dataset binarizedFeatures = binarizedDataFrame.select("binarized_feature");
for (Row r : binarizedFeatures.collectAsList()) {
Double binarized_value = r.getDouble(0);
System.out.println(binarized_value);
}
Python：
from pyspark.ml.feature import Binarizer
continuousDataFrame = spark.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)PCA
算法介绍：
主成分分析是一种统计学方法，它使用正交转换从一系列可能相关的变量中提取线性无关变量集，提取出的变量集中的元素称为主成分。使用PCA方法可以对变量集合进行降维。下面的示例将会展示如何将5维特征向量转换为3维主成分向量。
示例调用：
Scala：
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(data.map(Tuple1.apply)).toDF("features")
val pca = new PCA()
.setInputCol("features")
.setOutputCol("pcaFeatures")
.setK(3)
.fit(df)
val pcaDF = pca.transform(df)
val result = pcaDF.select("pcaFeatures")
result.show()Java：
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 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 df = spark.createDataFrame(data, schema);
PCAModel pca = new PCA()
.setInputCol("features")
.setOutputCol("pcaFeatures")
.setK(3)
.fit(df);
Dataset result = pca.transform(df).select("pcaFeatures");
result.show();
Python：
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)PolynomialExpansion
算法介绍：
多项式扩展通过产生n维组合将原始特征将特征扩展到多项式空间。下面的示例会介绍如何将你的特征集拓展到3维多项式空间。
示例调用：
Scala：
import org.apache.spark.ml.feature.PolynomialExpansion
import org.apache.spark.ml.linalg.Vectors
val data = Array(
Vectors.dense(-2.0, 2.3),
Vectors.dense(0.0, 0.0),
Vectors.dense(0.6, -1.1)
)
val df = spark.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)Java：
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 data = Arrays.asList(
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()),
});
Dataset df = spark.createDataFrame(data, schema);
Dataset polyDF = polyExpansion.transform(df);
List rows = polyDF.select("polyFeatures").takeAsList(3);
for (Row r : rows) {
System.out.println(r.get(0));
}
Python：
from pyspark.ml.feature import PolynomialExpansion
from pyspark.ml.linalg import Vectors
df = spark\
.createDataFrame([(Vectors.dense([-2.0, 2.3]),),
(Vectors.dense([0.0, 0.0]),),
(Vectors.dense([0.6, -1.1]),)],
["features"])
px = PolynomialExpansion(degree=3, inputCol="features", outputCol="polyFeatures")
polyDF = px.transform(df)
for expanded in polyDF.select("polyFeatures").take(3):
print(expanded)Discrete Cosine Transform(DCT)
算法介绍：
离散余弦变换是与傅里叶变换相关的一种变换，它类似于离散傅立叶变换但是只使用实数。离散余弦变换相当于一个长度大概是它两倍的离散傅里叶变换，这个离散傅里叶变换是对一个实偶函数进行的（因为一个实偶函数的傅里叶变换仍然是一个实偶函数）。离散余弦变换，经常被信号处理和图像处理使用，用于对信号和图像（包括静止图像和运动图像）进行有损数据压缩。
示例调用：
Scala：
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(3)Java：
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 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 df = spark.createDataFrame(data, schema);
DCT dct = new DCT()
.setInputCol("features")
.setOutputCol("featuresDCT")
.setInverse(false);
Dataset dctDf = dct.transform(df);
dctDf.select("featuresDCT").show(3);
Python：
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)
for dcts in dctDf.select("featuresDCT").take(3):
print(dcts)STringindexer
算法介绍：
StringIndexer将字符串标签编码为标签指标。指标取值范围为[0,numLabels]，按照标签出现频率排序，所以出现最频繁的标签其指标为0。如果输入列为数值型，我们先将之映射到字符串然后再对字符串的值进行指标。如果下游的管道节点需要使用字符串－指标标签，则必须将输入和钻还为字符串－指标列名。
示例：
假设我们有DataFrame数据含有id和category两列：
id | category
----|----------
0 | a
1 | b
2 | c
3 | a
4 | a
5 | c
category是有3种取值的字符串列，使用StringIndexer进行转换后我们可以得到如下输出：
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
另外，如果在转换新数据时出现了在训练中未出现的标签，StringIndexer将会报错（默认值）或者跳过未出现的标签实例。
示例调用：
Scala：
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()Java：
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 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 df = spark.createDataFrame(data, schema);
StringIndexer indexer = new StringIndexer()
.setInputCol("category")
.setOutputCol("categoryIndex");
Dataset indexed = indexer.fit(df).transform(df);
indexed.show();
Python：
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()IndexToString
算法介绍：
与StringIndexer对应，IndexToString将指标标签映射回原始字符串标签。一个常用的场景是先通过StringIndexer产生指标标签，然后使用指标标签进行训练，最后再对预测结果使用IndexToString来获取其原始的标签字符串。
示例：
假设我们有如下的DataFrame包含id和categoryIndex两列：
id | categoryIndex
----|---------------
0 | 0.0
1 | 2.0
2 | 1.0
3 | 0.0
4 | 0.0
5 | 1.0
使用originalCategory我们可以获取其原始的标签字符串如下：
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
示例调用：
Scala：
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)
val converter = new IndexToString()
.setInputCol("categoryIndex")
.setOutputCol("originalCategory")
val converted = converter.transform(indexed)
converted.select("id", "originalCategory").show()Java：
import java.util.Arrays;
import java.util.List;
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 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 df = spark.createDataFrame(data, schema);
StringIndexerModel indexer = new StringIndexer()
.setInputCol("category")
.setOutputCol("categoryIndex")
.fit(df);
Dataset indexed = indexer.transform(df);
IndexToString converter = new IndexToString()
.setInputCol("categoryIndex")
.setOutputCol("originalCategory");
Dataset converted = converter.transform(indexed);
converted.select("id", "originalCategory").show();
Python：
from pyspark.ml.feature import IndexToString, StringIndexer
df = spark.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)
converter = IndexToString(inputCol="categoryIndex", outputCol="originalCategory")
converted = converter.transform(indexed)
converted.select("id", "originalCategory").show()OneHotEncoder
算法介绍：
独热编码将标签指标映射为二值向量，其中最多一个单值。这种编码被用于将种类特征使用到需要连续特征的算法，如逻辑回归等。
示例调用：
Scala：
import org.apache.spark.ml.feature.{OneHotEncoder, 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)
val encoder = new OneHotEncoder()
.setInputCol("categoryIndex")
.setOutputCol("categoryVec")
val encoded = encoder.transform(indexed)
encoded.select("id", "categoryVec").show()Java：
import java.util.Arrays;
import java.util.List;
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.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 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 df = spark.createDataFrame(data, schema);
StringIndexerModel indexer = new StringIndexer()
.setInputCol("category")
.setOutputCol("categoryIndex")
.fit(df);
Dataset indexed = indexer.transform(df);
OneHotEncoder encoder = new OneHotEncoder()
.setInputCol("categoryIndex")
.setOutputCol("categoryVec");
Dataset encoded = encoder.transform(indexed);
encoded.select("id", "categoryVec").show();
Python：
from pyspark.ml.feature import OneHotEncoder, StringIndexer
df = spark.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(dropLast=False, inputCol="categoryIndex", outputCol="categoryVec")
encoded = encoder.transform(indexed)
encoded.select("id", "categoryVec").show()