推荐组件：精排 (RecommendationRankingBatchOp)

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

Python 类名：RecommendationRankingBatchOp

功能介绍

该组件功能是对召回的结果进行排序，并输出排序后的TopK个object，此处排序算法用户可以通过创建PipelineModel的方式定制，具体使用方式参见代码示例。

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
mTableCol	MTable 列名	召回列表列	String	✓	所选列类型为 [M_TABLE, STRING]
modelFilePath	模型的文件路径	模型的文件路径	String			null
outputCol	输出结果列	输出结果列列名，可选，默认null	String			null
rankingCol	用来排序的得分列	用来排序的得分列	String			null
reservedCols	算法保留列名	算法保留列	String[]			null
topN	前N的数据	挑选最近的N个数据	Integer		x >= 1	10

代码示例

Python 代码

from pyalink.alink import *

import pandas as pd

useLocalEnv(1)

import pandas as pd

data = pd.DataFrame([["u6", "0.0 1.0", 0.0, 1.0, 1, "{\"data\":{\"iid\":[18,19,88]},\"schema\":\"iid INT\"}"]])
predData = BatchOperator.fromDataframe(data, schemaStr='uid string, uf string, f0 double, f1 double, labels int, ilist string')
predData = predData.link(ToMTableBatchOp().setSelectedCol("ilist"))
data = pd.DataFrame([
            ["u0", "1.0 1.0", 1.0, 1.0, 1, 18],
			["u1", "1.0 1.0", 1.0, 1.0, 0, 19],
			["u2", "1.0 0.0", 1.0, 0.0, 1, 88],
			["u3", "1.0 0.0", 1.0, 0.0, 0, 18],
			["u4", "0.0 1.0", 0.0, 1.0, 1, 88],
			["u5", "0.0 1.0", 0.0, 1.0, 0, 19],
			["u6", "0.0 1.0", 0.0, 1.0, 1, 88]]);
trainData = BatchOperator.fromDataframe(data, schemaStr='uid string, uf string, f0 double, f1 double, labels int, iid string')
oneHotCols = ["uid", "f0", "f1", "iid"]
multiHotCols = ["uf"]
pipe = Pipeline() \
    .add( \
        OneHotEncoder() \
            .setSelectedCols(oneHotCols) \
			.setOutputCols(["ovec"])) \
    .add( \
		MultiHotEncoder().setDelimiter(" ") \
            .setSelectedCols(multiHotCols) \
            .setOutputCols(["mvec"])) \
	.add( \
		VectorAssembler() \
			.setSelectedCols(["ovec", "mvec"]) \
			.setOutputCol("vec")) \
	.add(
		LogisticRegression() \
            .setVectorCol("vec") \
			.setLabelCol("labels") \
			.setReservedCols(["uid", "iid"]) \
			.setPredictionDetailCol("detail") \
			.setPredictionCol("pred")) \
	.add( \
		JsonValue() \
			.setSelectedCol("detail") \
			.setJsonPath(["$.1"]) \
			.setOutputCols(["score"]))
lrModel = pipe.fit(trainData)
rank = RecommendationRankingBatchOp()\
			.setMTableCol("ilist")\
			.setOutputCol("il")\
			.setTopN(2)\
			.setRankingCol("score")\
			.setReservedCols(["uid", "labels"])
rank.linkFrom(lrModel.save(), predData).print()

Java 代码

import org.apache.flink.types.Row;

import com.alibaba.alink.operator.batch.BatchOperator;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import com.alibaba.alink.pipeline.Pipeline;
import com.alibaba.alink.pipeline.classification.LogisticRegression;
import com.alibaba.alink.pipeline.dataproc.JsonValue;
import com.alibaba.alink.pipeline.dataproc.vector.VectorAssembler;
import com.alibaba.alink.pipeline.feature.MultiHotEncoder;
import com.alibaba.alink.pipeline.feature.OneHotEncoder;
import org.junit.Test;

import java.util.Arrays;

public class RecommendationRankingTest {

	 @Test
	 public void test() throws Exception {

		Row[] predArray = new Row[] {
			Row.of("u6", "0.0 1.0", 0.0, 1.0, 1, "{\"data\":{\"iid\":[18,19,88]},"
				+ "\"schema\":\"iid INT\"}")
		};

		Row[] trainArray = new Row[] {
			Row.of("u0", "1.0 1.0", 1.0, 1.0, 1, 18),
			Row.of("u1", "1.0 1.0", 1.0, 1.0, 0, 19),
			Row.of("u2", "1.0 0.0", 1.0, 0.0, 1, 88),
			Row.of("u3", "1.0 0.0", 1.0, 0.0, 1, 18),
			Row.of("u4", "0.0 1.0", 0.0, 1.0, 1, 88),
			Row.of("u5", "0.0 1.0", 0.0, 1.0, 1, 19),
			Row.of("u6", "0.0 1.0", 0.0, 1.0, 1, 88)
		};
		BatchOperator <?> trainData = new MemSourceBatchOp(Arrays.asList(trainArray),
				new String[] {"uid", "uf", "f0", "f1", "labels", "iid"});
		BatchOperator <?> predData =  new MemSourceBatchOp(Arrays.asList(predArray),
				new String[] {"uid", "uf", "f0", "f1", "labels", "ilist"});

		String[] oneHotCols = new String[] {"uid", "f0", "f1", "iid"};
		String[] multiHotCols = new String[] {"uf"};

		Pipeline pipe = new Pipeline()
			.add(
				new OneHotEncoder()
					.setSelectedCols(oneHotCols)
					.setOutputCols("ovec"))
			.add(
				new MultiHotEncoder().setDelimiter(" ")
					.setSelectedCols(multiHotCols)
					.setOutputCols("mvec"))
			.add(
				new VectorAssembler()
					.setSelectedCols("ovec", "mvec")
					.setOutputCol("vec"))
			.add(
				new LogisticRegression()
					.setVectorCol("vec")
					.setLabelCol("labels")
					.setReservedCols("uid", "iid")
					.setPredictionDetailCol("detail")
					.setPredictionCol("pred"))
			.add(
				new JsonValue()
					.setSelectedCol("detail")
					.setJsonPath("$.1")
					.setOutputCols("score"));
		RecommendationRankingBatchOp rank = new RecommendationRankingBatchOp()
			.setMTableCol("ilist")
			.setOutputCol("ilist")
			.setTopN(2)
			.setRankingCol("score")
			.setReservedCols("uid", "labels");
		rank.linkFrom(pipe.fit(trainData).save(), predData).print();
	}
}

运行结果

uid	uf	f0	f1	labels	ilist
u6	0.0 1.0	0.0000	1.0000	1	{“data”:{“iid”:[18,88],“score”:[0.9999999999999553,0.9999999999999472]},“schema”:“iid INT,score DOUBLE”}