关联规则预测 (ApplyAssociationRuleBatchOp)

Java 类名：com.alibaba.alink.operator.batch.associationrule.ApplyAssociationRuleBatchOp

Python 类名：ApplyAssociationRuleBatchOp

功能介绍

• 应用关联规则，计算命中的关联规则的输出。
• 模型由FpGrowthBatchOp训练得出，通过getSideOutputAssociationRules()函数获取模型

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
selectedCol	选中的列名	计算列对应的列名	String	✓	所选列类型为 [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
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([
    ["A,B,C,D"],
    ["B,C,E"],
    ["A,B,C,E"],
    ["B,D,E"],
    ["A,B,C,D"],
])

data = BatchOperator.fromDataframe(df, schemaStr='items string')

fpGrowth = FpGrowthBatchOp() \
    .setItemsCol("items") \
    .setMinSupportPercent(0.4) \
    .setMinConfidence(0.6)

fpGrowth.linkFrom(data)

fpGrowth.print()
fpGrowth.getSideOutput(0).print()

ApplyAssociationRuleBatchOp()\
    .setSelectedCol("items") \
    .setOutputCol("result") \
    .linkFrom(fpGrowth.getSideOutput(0), data).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.testutil.AlinkTestBase;
import org.junit.Test;

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

public class ApplyAssociationRuleBatchOpTest {
	@Test
	public void testFpGrowth() throws Exception {
		List <Row> rows = Arrays.asList(
			Row.of("A,B,C,D"),
			Row.of("B,C,E"),
			Row.of("A,B,C,E"),
			Row.of("B,D,E"),
			Row.of("A,B,C,D")
			);

		BatchOperator data = new MemSourceBatchOp(rows, "items string");

		FpGrowthBatchOp fpGrowth = new FpGrowthBatchOp()
			.setItemsCol("items")
			.setMinSupportPercent(0.4)
			.setMinConfidence(0.6);

		fpGrowth.linkFrom(data);
		fpGrowth.print();
		fpGrowth.getSideOutputAssociationRules().print();

		ApplyAssociationRuleBatchOp op = new ApplyAssociationRuleBatchOp()
			.setSelectedCol("items")
			.setOutputCol("result")
			.linkFrom(fpGrowth.getSideOutputAssociationRules(), data);
		op.print();
	}
}

运行结果

频繁项集输出：

itemset	supportcount	itemcount
E	3	1
B,E	3	2
C,E	2	2
B,C,E	2	3
D	3	1
B,D	3	2
C,D	2	2
B,C,D	2	3
A,D	2	2
B,A,D	2	3
C,A,D	2	3
B,C,A,D	2	4
A	3	1
B,A	3	2
C,A	3	2
B,C,A	3	3
C	4	1
B,C	4	2
B	5	1

关联规则输出：

rule	itemcount	lift	support_percent	confidence_percent	transaction_count
D=>B	2	1.0000	0.6000	1.0000	3
D=>A	2	1.1111	0.4000	0.6667	2
C,D=>B	3	1.0000	0.4000	1.0000	2
A,D=>B	3	1.0000	0.4000	1.0000	2
B,D=>A	3	1.1111	0.4000	0.6667	2
A,D=>C	3	1.2500	0.4000	1.0000	2
C,D=>A	3	1.6667	0.4000	1.0000	2
C,A,D=>B	4	1.0000	0.4000	1.0000	2
B,A,D=>C	4	1.2500	0.4000	1.0000	2
B,C,D=>A	4	1.6667	0.4000	1.0000	2
C=>A	2	1.2500	0.6000	0.7500	3
C=>B	2	1.0000	0.8000	1.0000	4
B,C=>A	3	1.2500	0.6000	0.7500	3
E=>B	2	1.0000	0.6000	1.0000	3
C,E=>B	3	1.0000	0.4000	1.0000	2
B=>E	2	1.0000	0.6000	0.6000	3
B=>D	2	1.0000	0.6000	0.6000	3
B=>A	2	1.0000	0.6000	0.6000	3
B=>C	2	1.0000	0.8000	0.8000	4
A=>D	2	1.1111	0.4000	0.6667	2
A=>B	2	1.0000	0.6000	1.0000	3
A=>C	2	1.2500	0.6000	1.0000	3
B,A=>D	3	1.1111	0.4000	0.6667	2
C,A=>D	3	1.1111	0.4000	0.6667	2
C,A=>B	3	1.0000	0.6000	1.0000	3
B,A=>C	3	1.2500	0.6000	1.0000	3
B,C,A=>D	4	1.1111	0.4000	0.6667	2

关联规则预测输出

items	result
A,B,C,D	E
B,C,E	A,D
A,B,C,E	D
B,D,E	A,C
A,B,C,D	E