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