train.csv数据：
id,name,age,sex
1,lyy,20,F
2,rdd,20,M
3,nyc,18,M
4,mzy,10,M
数据读取：
1 SparkSession spark = SparkSession.builder().enableHiveSupport()
2 .getOrCreate();
3 Dataset<Row> dataset = spark
4 .read()
5 .format("org.apache.spark.sql.execution.datasources.csv.CSVFileFormat")
6 .option("header", true)
7 .option("inferSchema", true)
8 .option("delimiter", ",")
9 //.load("file:///E:/git/bigdata_sparkIDE/spark-ide/workspace/test/SparkMLTest/SanFranciscoCrime/document/kaggle-旧金山犯罪分类/train-new.csv") //PreProcess1
10 .load("file:///E:/git/bigdata_sparkIDE/spark-ide/workspace/test/SparkMLTest/DataPreprocessing/document/train.csv") //PreProcess2
11 .persist();
1 public static void PreProcess2(Dataset<Row> data) {
2
3 data.printSchema();
4 // 重新索引标签值
5 StringIndexerModel labelIndexer = new StringIndexer()
6 .setInputCol("sex")
7 .setOutputCol("label")
8 .fit(data);
9
10 StringIndexerModel nameIndexer = new StringIndexer()
11 .setInputCol("name")
12 .setOutputCol("namenum")
13 .fit(data);
14
15
16 /* 会报错：Exception in thread "main" java.lang.IllegalArgumentException: Field "namenum" does not exist.
17 * 原因是：Model类型调用fit时，要求数据集中必须包含InputCol所指定的列名
18 * 不会将Pipeline某个stage的输出作为InputCol，即使那个stage的OutputCol指定的列名与其相同也不行
19 * StringIndexerModel name1Indexer = new StringIndexer()
20 .setInputCol("namenum")
21 .setOutputCol("namenum1")
22 .fit(data);*/
23
24
25 /* 错误原因StringIndexerModel错误一样，features并不是data的列
26 * VectorIndexerModel featureIndexer = new VectorIndexer()
27 .setInputCol("features")
28 .setOutputCol("indexfeatures")
29 .setMaxCategories(4)
30 .fit(data);*/
31
32 //成功
33 //原因说明：非model时，转换器不会调用fit,而会使用Pipeline某个stage的输出作为InputCol
34 //由于stage[2]即 assembler已经生成features，故而该处直接使用；
35 //但是该类型时不能单独使用，必须依赖Pipeline
36 VectorIndexer featureIndexer = new VectorIndexer()
37 .setInputCol("features")
38 .setOutputCol("indexfeatures")
39 .setMaxCategories(4);
40
41 //由上述分析可知，该处输入的列可以是多个stage的输出组成，因为VectorAssembler非model
42 //因此可以使用中间生成结果，且可以使用多个
43 VectorAssembler assembler = new VectorAssembler()
44 .setInputCols("id,namenum,age".split(","))
45 .setOutputCol("features");
46
47 //这里的stage的顺序很重要，一定按照依赖关系顺序放入，如下顺序就会报错：
48 //Exception in thread "main" java.lang.IllegalArgumentException: Field "features" does not exist.
49 //Pipeline pipeline = new Pipeline().setStages(new PipelineStage[] {labelIndexer,nameIndexer,featureIndexer,assembler});
50
51 //将featureIndexer放到assembler即可
52 Pipeline pipeline = new Pipeline().setStages(new PipelineStage[] {labelIndexer,nameIndexer,assembler,featureIndexer});
53
54 // Train model. This also runs the indexers.
55 PipelineModel model = pipeline.fit(data);
56
57 // Make predictions.
58 Dataset<Row> result = model.transform(data);
59
60 result.show(10, false);
61
62 }
root
|-- id: integer (nullable = true)
|-- name: string (nullable = true)
|-- age: integer (nullable = true)
|-- sex: string (nullable = true)
+---+----+---+---+-----+-------+--------------+-------------+
|id |name|age|sex|label|namenum|features |indexfeatures|
+---+----+---+---+-----+-------+--------------+-------------+
|1 |lyy |20 |F |1.0 |1.0 |[1.0,1.0,20.0]|[0.0,1.0,2.0]|
|2 |rdd |20 |M |0.0 |2.0 |[2.0,2.0,20.0]|[1.0,2.0,2.0]|
|3 |nyc |18 |M |0.0 |0.0 |[3.0,0.0,18.0]|[2.0,0.0,1.0]|
|4 |mzy |10 |M |0.0 |3.0 |[4.0,3.0,10.0]|[3.0,3.0,0.0]|
+---+----+---+---+-----+-------+--------------+-------------+
综上分析，可以将原有代码做一简化：
1 public static void PreProcess2(Dataset<Row> data) {
2
3 data.printSchema();
4 // 重新索引标签值
5 StringIndexer labelIndexer = new StringIndexer()
6 .setInputCol("sex")
7 .setOutputCol("label");
8
9 StringIndexer nameIndexer = new StringIndexer()
10 .setInputCol("name")
11 .setOutputCol("namenum");
12
13 VectorIndexer featureIndexer = new VectorIndexer()
14 .setInputCol("features")
15 .setOutputCol("indexfeatures")
16 .setMaxCategories(4);
17
18
19 VectorAssembler assembler = new VectorAssembler()
20 .setInputCols("id,namenum,age".split(","))
21 .setOutputCol("features");
22
23 Pipeline pipeline = new Pipeline().setStages(new PipelineStage[] {labelIndexer,nameIndexer,assembler,featureIndexer});
24
25 // Train model. This also runs the indexers.
26 PipelineModel model = pipeline.fit(data); //以这里的data为基准数据
27
28 // Make predictions.
29 Dataset<Row> result = model.transform(data);
30
31 result.show(10, false);
32
33 }
运行结果：
root |-- id: integer (nullable = true) |-- name: string (nullable = true) |-- age: integer (nullable = true) |-- sex: string (nullable = true) +---+----+---+---+-----+-------+--------------+-------------+ |id |name|age|sex|label|namenum|features |indexfeatures| +---+----+---+---+-----+-------+--------------+-------------+ |1 |lyy |20 |F |1.0 |1.0 |[1.0,1.0,20.0]|[0.0,1.0,2.0]| |2 |rdd |20 |M |0.0 |2.0 |[2.0,2.0,20.0]|[1.0,2.0,2.0]| |3 |nyc |18 |M |0.0 |0.0 |[3.0,0.0,18.0]|[2.0,0.0,1.0]| |4 |mzy |10 |M |0.0 |3.0 |[4.0,3.0,10.0]|[3.0,3.0,0.0]| +---+----+---+---+-----+-------+--------------+-------------+
原文地址：https://www.cnblogs.com/lyy-blog/p/9523026.html
时间： 2024-10-18 20:08:57