分桶 (BucketizerStreamOp)

Java 类名:com.alibaba.alink.operator.stream.feature.BucketizerStreamOp

Python 类名:BucketizerStreamOp

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。

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

from pyalink.alink import *

import pandas as pd

useLocalEnv(1)

df = pd.DataFrame([
    [1.1, True, "2", "A"],
    [1.1, False, "2", "B"],
    [1.1, True, "1", "B"],
    [2.2, True, "1", "A"]
])

inOp1 = BatchOperator.fromDataframe(df, schemaStr='double double, bool boolean, number int, str string')
inOp2 = StreamOperator.fromDataframe(df, schemaStr='double double, bool boolean, number int, str string')

bucketizer = BucketizerBatchOp().setSelectedCols(["double"]).setCutsArray([[2.0]])
bucketizer.linkFrom(inOp1).print()

bucketizer = BucketizerStreamOp().setSelectedCols(["double"]).setCutsArray([[2.0]])
bucketizer.linkFrom(inOp2).print()

StreamOperator.execute()

Java 代码

import org.apache.flink.types.Row;

import com.alibaba.alink.operator.batch.BatchOperator;
import com.alibaba.alink.operator.batch.feature.BucketizerBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import com.alibaba.alink.operator.stream.StreamOperator;
import com.alibaba.alink.operator.stream.feature.BucketizerStreamOp;
import com.alibaba.alink.operator.stream.source.MemSourceStreamOp;
import org.junit.Test;

import java.util.Arrays;
import java.util.List;

public class BucketizerStreamOpTest {
	@Test
	public void testBucketizerStreamOp() throws Exception {
		List <Row> df = Arrays.asList(
			Row.of(1.1, true, 2, "A"),
			Row.of(1.1, false, 2, "B"),
			Row.of(1.1, true, 1, "B"),
			Row.of(2.2, true, 1, "A")
		);
		BatchOperator <?> inOp1 = new MemSourceBatchOp(df, "double double, bool boolean, number int, str string");
		StreamOperator <?> inOp2 = new MemSourceStreamOp(df, "double double, bool boolean, number int, str string");
		double[] cutsArr = {2.0};
		BatchOperator <?> bucketizer = new BucketizerBatchOp().setSelectedCols("double").setCutsArray(cutsArr);
		bucketizer.linkFrom(inOp1).print();
		StreamOperator <?> bucketizer2 = new BucketizerStreamOp().setSelectedCols("double").setCutsArray(cutsArr);
		bucketizer2.linkFrom(inOp2).print();
		StreamOperator.execute();
	}
}

运行结果

批预测结果

double bool number str
0 true 2 A
0 false 2 B
0 true 1 B
1 true 1 A

流预测结果

double bool number str
0 true 2 A
0 true 1 B
0 false 2 B
1 true 1 A