Java 类名:com.alibaba.alink.operator.stream.recommendation.SwingRecommStreamOp
Python 类名:SwingRecommStreamOp
Swing 是一种被广泛使用的item召回算法,算法详细介绍可以参考SwingTrainBatchOp组件。
该组件为Swing的流处理预测组件,输入为 SwingTrainBatchOp 输出的模型和要预测的item列。
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 | ✓ | ||
initRecommCol | 初始推荐列列名 | 初始推荐列列名 | String | 所选列类型为 [M_TABLE, STRING] | null | |
k | 推荐TOP数量 | 推荐TOP数量 | Integer | 10 | ||
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 |
from pyalink.alink import * import pandas as pd useLocalEnv(1) df_data = pd.DataFrame([ ["a1", "11L", 2.2], ["a1", "12L", 2.0], ["a2", "11L", 2.0], ["a2", "12L", 2.0], ["a3", "12L", 2.0], ["a3", "13L", 2.0], ["a4", "13L", 2.0], ["a4", "14L", 2.0], ["a5", "14L", 2.0], ["a5", "15L", 2.0], ["a6", "15L", 2.0], ["a6", "16L", 2.0], ]) data = BatchOperator.fromDataframe(df_data, schemaStr='user string, item string, rating double') model = SwingTrainBatchOp()\ .setUserCol("user")\ .setItemCol("item")\ .linkFrom(data) predictor = SwingRecommBatchOp()\ .setItemCol("item")\ .setRecommCol("prediction_result") predictor.linkFrom(model, data).print()
import org.apache.flink.types.Row; import com.alibaba.alink.operator.batch.BatchOperator; import com.alibaba.alink.operator.batch.recommendation.SwingRecommBatchOp; import com.alibaba.alink.operator.batch.recommendation.SwingTrainBatchOp; import com.alibaba.alink.operator.batch.source.MemSourceBatchOp; import org.junit.Test; import java.util.Arrays; import java.util.List; public class SwingRecommStreamOpTest { @Test public void testSwingRecommStreamOp() throws Exception { List <Row> df_data = Arrays.asList( Row.of("a1", "11L", 2.2), Row.of("a1", "12L", 2.0), Row.of("a2", "11L", 2.0), Row.of("a2", "12L", 2.0), Row.of("a3", "12L", 2.0), Row.of("a3", "13L", 2.0), Row.of("a4", "13L", 2.0), Row.of("a4", "14L", 2.0), Row.of("a5", "14L", 2.0), Row.of("a5", "15L", 2.0), Row.of("a6", "15L", 2.0), Row.of("a6", "16L", 2.0) ); BatchOperator <?> data = new MemSourceBatchOp(df_data, "user string, item string, rating double"); BatchOperator <?> model = new SwingTrainBatchOp() .setUserCol("user") .setItemCol("item") .linkFrom(data); BatchOperator <?> predictor = new SwingRecommBatchOp() .setItemCol("item") .setRecommCol("prediction_result"); predictor.linkFrom(model, data).print(); } }
user | item | rating | prediction_result |
---|---|---|---|
a6 | 15L | 2.0000 | null |
a4 | 13L | 2.0000 | null |
a6 | 16L | 2.0000 | null |
a5 | 14L | 2.0000 | null |
a3 | 13L | 2.0000 | null |
a1 | 12L | 2.0000 | {“item”:“["11L"]”,“score”:“[0.11662912368774414]”} |
a2 | 12L | 2.0000 | {“item”:“["11L"]”,“score”:“[0.11662912368774414]”} |
a1 | 11L | 2.2000 | {“item”:“["12L"]”,“score”:“[0.12805642187595367]”} |
a3 | 12L | 2.0000 | {“item”:“["11L"]”,“score”:“[0.11662912368774414]”} |
a4 | 14L | 2.0000 | null |
a5 | 15L | 2.0000 | null |
a2 | 11L | 2.0000 | {“item”:“["12L"]”,“score”:“[0.12805642187595367]”} |