Java 类名：com.alibaba.alink.operator.batch.feature.BucketizerBatchOp
Python 类名：BucketizerBatchOp

功能介绍

给定切分点，将连续变量分桶，需要选择需要进行切分的单列或多列，同时给出选中每列的切分点，每列切分点都是一个double数组，需要严格递增。

参数说明

名称 中文名称 描述 类型 是否必须？ 取值范围 默认值
selectedCols 选择的列名 计算列对应的列名列表 String[] ✓
cutsArray 多列的切分点 多列的切分点 double[][]
dropLast 是否删除最后一个元素 删除最后一个元素是为了保证线性无关性。默认true Boolean true
encode 编码方法 编码方法 String “VECTOR”, “ASSEMBLED_VECTOR”, “INDEX” “INDEX”
handleInvalid 未知token处理策略 未知token处理策略。”keep”表示用最大id加1代替, “skip”表示补null， “error”表示抛异常 String “KEEP”, “ERROR”, “SKIP” “KEEP”
leftOpen 是否左开右闭 左开右闭为true，左闭右开为false Boolean true
outputCols 输出结果列列名数组 输出结果列列名数组，可选，默认null String[] null
reservedCols 算法保留列名 算法保留列 String[] null
numThreads 组件多线程线程个数 组件多线程线程个数 Integer 1

代码示例

Python 代码

  1. from pyalink.alink import *
  3. import pandas as pd
  5. useLocalEnv(1)
  7. df = pd.DataFrame([
  8.     [1.1, True, "2", "A"],
  9.     [1.1, False, "2", "B"],
  10.     [1.1, True, "1", "B"],
  11.     [2.2, True, "1", "A"]
  12. ])
  14. inOp1 = BatchOperator.fromDataframe(df, schemaStr='double double, bool boolean, number int, str string')
  15. inOp2 = StreamOperator.fromDataframe(df, schemaStr='double double, bool boolean, number int, str string')
  17. bucketizer = BucketizerBatchOp().setSelectedCols(["double","number"]).setCutsArray([[1.0,2.0,2.2,4.0],[0.0,1.1]])
  18. bucketizer.linkFrom(inOp1).print()
  20. bucketizer = BucketizerStreamOp().setSelectedCols(["double"]).setCutsArray([[2.0]])
  21. bucketizer.linkFrom(inOp2).print()
  23. StreamOperator.execute()

Java 代码

  1. import org.apache.flink.types.Row;
  3. import com.alibaba.alink.operator.batch.BatchOperator;
  4. import com.alibaba.alink.operator.batch.feature.BucketizerBatchOp;
  5. import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
  6. import com.alibaba.alink.operator.stream.StreamOperator;
  7. import com.alibaba.alink.operator.stream.feature.BucketizerStreamOp;
  8. import com.alibaba.alink.operator.stream.source.MemSourceStreamOp;
  9. import org.junit.Test;
  11. import java.util.Arrays;
  12. import java.util.List;
  14. public class BucketizerBatchOpTest {
  15.     @Test
  16.     public void testBucketizerBatchOp() throws Exception {
  17.         List <Row> df = Arrays.asList(
  18.             Row.of(1.1, true, 2, "A"),
  19.             Row.of(1.1, false, 2, "B"),
  20.             Row.of(1.1, true, 1, "B"),
  21.             Row.of(2.2, true, 1, "A")
  22.         );
  23.         BatchOperator <?> inOp1 = new MemSourceBatchOp(df, "double double, bool boolean, number int, str string");
  24.         StreamOperator <?> inOp2 = new MemSourceStreamOp(df, "double double, bool boolean, number int, str string");
  25.         BatchOperator <?> bucketizer = new BucketizerBatchOp().setSelectedCols("double","number").setCutsArray(
  26.             new double[] {1.0,2.0,2.2,4.0},new double[]{0.0,1.1});
  27.         bucketizer.linkFrom(inOp1).print();
  28.         StreamOperator <?> bucketizer2 = new BucketizerStreamOp().setSelectedCols("double").setCutsArray(
  29.             new double[] {2.0});
  30.         bucketizer2.linkFrom(inOp2).print();
  31.         StreamOperator.execute();
  32.     }
  33. }

运行结果

输出数据

批预测结果

| double | bool | number | str | | —- | —- | —- | —- |

| 0 | true | 2 | A |

| 1 | false | 2 | B |

| 2 | true | 1 | B |

| 3 | true | 1 | A |

流预测结果

| double | bool | number | str | | —- | —- | —- | —- |

| 0 | false | 2 | B |

| 0 | true | 1 | B |

| 0 | true | 2 | A |

| 1 | true | 1 | A |