ALS:打分推荐 (AlsRateRecommStreamOp)

Java 类名:com.alibaba.alink.operator.stream.recommendation.AlsRateRecommStreamOp

Python 类名:AlsRateRecommStreamOp

功能介绍

使用ALS (Alternating Lease Square)训练的模型对(user,item)输入流对进行实时评分预测。这里的ALS模型可以是隐式模型,也可以是显式模型。

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
itemCol Item列列名 Item列列名 String
recommCol 推荐结果列名 推荐结果列名 String
userCol User列列名 User列列名 String
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
modelStreamFilePath 模型流的文件路径 模型流的文件路径 String null
modelStreamScanInterval 扫描模型路径的时间间隔 描模型路径的时间间隔,单位秒 Integer 10
modelStreamStartTime 模型流的起始时间 模型流的起始时间。默认从当前时刻开始读。使用yyyy-mm-dd hh:mm:ss.fffffffff格式,详见Timestamp.valueOf(String s) String null

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, 1, 0.6],
    [4, 2, 0.3],
    [4, 3, 0.4],
])

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

als = AlsTrainBatchOp().setUserCol("user").setItemCol("item").setRateCol("rating") \
    .setNumIter(10).setRank(10).setLambda(0.01)
model = als.linkFrom(data)

predictor = AlsRateRecommStreamOp(model) \
    .setUserCol("user").setItemCol("item").setRecommCol("predicted_rating")

predictor.linkFrom(sdata).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.recommendation.AlsTrainBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import com.alibaba.alink.operator.stream.StreamOperator;
import com.alibaba.alink.operator.stream.recommendation.AlsRateRecommStreamOp;
import com.alibaba.alink.operator.stream.source.MemSourceStreamOp;
import org.junit.Test;

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

public class AlsRateRecommStreamOpTest {
	@Test
	public void testAlsRateRecommStreamOp() 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, 1, 0.6),
			Row.of(4, 2, 0.3),
			Row.of(4, 3, 0.4)
		);
		BatchOperator <?> data = new MemSourceBatchOp(df_data, "user int, item int, rating double");
		StreamOperator <?> sdata = new MemSourceStreamOp(df_data, "user int, item int, rating double");
		BatchOperator <?> als = new AlsTrainBatchOp().setUserCol("user").setItemCol("item").setRateCol("rating")
			.setNumIter(10).setRank(10).setLambda(0.01);
		BatchOperator model = als.linkFrom(data);
		StreamOperator <?> predictor = new AlsRateRecommStreamOp(model)
			.setUserCol("user").setItemCol("item").setRecommCol("predicted_rating");
		predictor.linkFrom(sdata).print();
		StreamOperator.execute();
	}
}

运行结果

user item rating predicted_rating
2 2 0.8000 0.7669
4 2 0.3000 0.2989
1 1 0.6000 0.5810
2 3 0.6000 0.5809
4 1 0.6000 0.5753
4 3 0.4000 0.3833