推荐结果TopK采样处理 (LeaveTopKObjectOutBatchOp)

Java 类名：com.alibaba.alink.operator.batch.recommendation.LeaveTopKObjectOutBatchOp

Python 类名：LeaveTopKObjectOutBatchOp

功能介绍

将推荐结果按取topK部分作为一个输出表。

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
groupCol	分组列	分组单列名，必选	String	✓
objectCol	Object列列名	Object列列名	String	✓
outputCol	输出结果列列名	输出结果列列名，必选	String	✓
rateCol	打分列列名	打分列列名	String	✓	所选列类型为 [BIGDECIMAL, BIGINTEGER, BYTE, DOUBLE, FLOAT, INTEGER, LONG, SHORT]
fraction	拆分到测试集最大数据比例	拆分到测试集最大数据比例	Double		0.0 <= x <= 1.0	1.0
k	推荐TOP数量	推荐TOP数量	Integer			10
rateThreshold	打分阈值	打分阈值	Double			-Infinity

代码示例

Python 代码

from pyalink.alink import *

import pandas as pd

useLocalEnv(1)

df_data = pd.DataFrame([
    [1, 1, 0.6],
    [2, 2, 0.8],
    [2, 3, 0.6],
    [4, 0, 0.6],
    [6, 4, 0.3],
    [4, 7, 0.4],
    [2, 6, 0.6],
    [4, 5, 0.6],
    [4, 6, 0.3],
    [4, 3, 0.4]
])

data = BatchOperator.fromDataframe(df_data, schemaStr='user bigint, item bigint, rating double')

spliter = LeaveTopKObjectOutBatchOp()\
			.setK(2)\
			.setGroupCol("user")\
			.setObjectCol("item")\
			.setOutputCol("label")\
            .setRateCol("rating")
spliter.linkFrom(data).print()
spliter.getSideOutput(0).print()

Java 代码

import org.apache.flink.types.Row;

import com.alibaba.alink.operator.batch.BatchOperator;
import com.alibaba.alink.operator.batch.recommendation.LeaveTopKObjectOutBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import org.junit.Test;

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

public class LeaveTopKObjectOutBatchOpTest {
	@Test
	public void testLeaveTopKObjectOutBatchOp() throws Exception {
		List <Row> df_data = Arrays.asList(
			Row.of(1, 1, 0.6),
			Row.of(2, 2, 0.8),
			Row.of(2, 3, 0.6),
			Row.of(4, 0, 0.6),
			Row.of(6, 4, 0.3),
			Row.of(4, 7, 0.4),
			Row.of(2, 6, 0.6),
			Row.of(4, 5, 0.6),
			Row.of(4, 6, 0.3),
			Row.of(4, 3, 0.4)
		);
		BatchOperator <?> data = new MemSourceBatchOp(df_data, "user int, item int, rating double");
		BatchOperator <?> spliter = new LeaveTopKObjectOutBatchOp()
			.setK(2)
			.setGroupCol("user")
			.setObjectCol("item")
			.setOutputCol("label")
			.setRateCol("rating");
		spliter.linkFrom(data).print();
		spliter.getSideOutput(0).print();
	}
}

运行结果

user	label
1	{“item”:“[1]”,“rating”:“[0.6]”}
6	{“item”:“[4]”,“rating”:“[0.3]”}
4	{“item”:“[0,5]”,“rating”:“[0.6,0.6]”}
2	{“item”:“[2,3]”,“rating”:“[0.8,0.6]”}

user	item	rating
4	7	0.4000
4	3	0.4000
4	6	0.3000
2	6	0.6000