大规模MethPath2Vec (HugeMetaPath2VecTrainBatchOp)

Java 类名：com.alibaba.alink.operator.batch.huge.HugeMetaPath2VecTrainBatchOp

Python 类名：HugeMetaPath2VecTrainBatchOp

功能介绍

沿着之前random walk的思路往前走，metapath2vec的方法提出了控制随机游走的模式，这样就可以在生成的序列上根据节点类型的不同来控制序列游走，这样也就可以对异质网络（Heterogeneous Networks）进行表征学习。在游走之前需要设定一个metapath，也就是游走时节点类型的模式

metapath2vec: Scalable Representation Learning for Heterogeneous Networks

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
metaPath	游走的模式	一般为用字符串表示，例如 “ABDFA”	String	✓
sourceCol	起始点列名	用来指定起始点列	String	✓
targetCol	中止点点列名	用来指定中止点列	String	✓
typeCol	节点类型列名	用来指定节点类型列	String	✓
vertexCol	节点列名	用来指定节点列	String	✓	所选列类型为 [STRING]
walkLength	游走的长度	随机游走完向量的长度	Integer	✓
walkNum	路径数目	每一个起始点游走出多少条路径	Integer	✓
alpha	学习率	学习率	Double			0.025
batchSize	batch大小	batch大小, 按行计算	Integer		x >= 1
isToUndigraph	是否转无向图	选为true时，会将当前图转成无向图，然后再游走	Boolean			false
minCount	最小词频	最小词频	Integer			5
mode	metapath中word2vec的模式，分别为metapath2vec和metapath2vecpp	metapath的模式	String		“METAPATH2VEC”, “METAPATH2VECPP”	“METAPATH2VEC”
negative	负采样大小	负采样大小	Integer			5
numCheckpoint	checkPoint 数目	checkPoint 数目	Integer			1
numIter	迭代次数	迭代次数，默认为1。	Integer			1
randomWindow	是否使用随机窗口	是否使用随机窗口，默认使用	String			“true”
vectorSize	embedding的向量长度	embedding的向量长度	Integer		x >= 1	100
weightCol	权重列名	权重列对应的列名	String		所选列类型为 [BIGDECIMAL, BIGINTEGER, BYTE, DOUBLE, FLOAT, INTEGER, LONG, SHORT]	null
window	窗口大小	窗口大小	Integer			5

代码示例

Python 代码

from pyalink.alink import *

import pandas as pd

useLocalEnv(1)

df_data = pd.DataFrame([
    ["Bob", "Lucy", 1.],
    ["Lucy", "Bob", 1.],
    ["Lucy", "Bella", 1.],
    ["Bella", "Lucy", 1.],
    ["Alice", "Lisa", 1.],
    ["Lisa", "Alice", 1.],
    ["Lisa", "Karry", 1.],
    ["Karry", "Lisa", 1.],
    ["Karry", "Bella", 1.],
    ["Bella", "Karry", 1.]
])
source =  BatchOperator.fromDataframe(df_data, schemaStr='start string, end string, value double')
nodeType = pd.DataFrame([
    ["Bob", "A"],
    ["Bella", "A"],
    ["Karry", "A"],
    ["Lucy", "B"],
    ["Alice", "B"],
    ["Lisa", "B"],
    ["Karry", "B"]
])
type = BatchOperator.fromDataframe(nodeType, schemaStr='node string, type string')
metapathBatchOp = HugeMetaPath2VecTrainBatchOp() \
  .setSourceCol("start")            \
  .setTargetCol("end")              \
  .setWeightCol("value")            \
  .setVertexCol("node")             \
  .setTypeCol("type")               \
  .setMetaPath("ABA")               \
  .setWalkNum(2)                    \
  .setWalkLength(2)                 \
  .setMinCount(1)                   \
  .setVectorSize(4)
metapathBatchOp.linkFrom(source, type).print()

Java 代码

import org.apache.flink.types.Row;

import com.alibaba.alink.operator.batch.BatchOperator;
import com.alibaba.alink.operator.batch.huge.HugeMetaPath2VecTrainBatchOp;
import com.alibaba.alink.operator.batch.source.MemSourceBatchOp;
import org.junit.Test;

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

public class HugeMetaPath2VecTrainBatchOpTest {
	@Test
	public void testHugeMetaPath2VecTrainBatchOp() throws Exception {
		List <Row> df_data = Arrays.asList(
			Row.of("Bob", "Lucy", 1.),
			Row.of("Lucy", "Bob", 1.),
			Row.of("Lucy", "Bella", 1.),
			Row.of("Bella", "Lucy", 1.),
			Row.of("Alice", "Lisa", 1.),
			Row.of("Lisa", "Alice", 1.),
			Row.of("Lisa", "Karry", 1.),
			Row.of("Karry", "Lisa", 1.),
			Row.of("Karry", "Bella", 1.),
			Row.of("Bella", "Karry", 1.)
		);
		BatchOperator <?> source = new MemSourceBatchOp(df_data, "start string, end string, value double");
		List <Row> nodeType = Arrays.asList(
			Row.of("Bob", "A"),
			Row.of("Bella", "A"),
			Row.of("Karry", "A"),
			Row.of("Lucy", "B"),
			Row.of("Alice", "B"),
			Row.of("Lisa", "B"),
			Row.of("Karry", "B")
		);
		BatchOperator <?> type = new MemSourceBatchOp(nodeType, "node string, type string");
		BatchOperator <?> metapathBatchOp = new HugeMetaPath2VecTrainBatchOp()
			.setSourceCol("start")
			.setTargetCol("end")
			.setWeightCol("value")
			.setVertexCol("node")
			.setTypeCol("type")
			.setMetaPath("ABA")
			.setWalkNum(2)
			.setWalkLength(2)
			.setMinCount(1)
			.setVectorSize(4);
		metapathBatchOp.linkFrom(source, type).print();
	}
}

运行结果

node	vec
Karry	-0.028718041256070137,0.02825581468641758,0.12125638127326965,0.1207452341914177
Bella	0.03437831997871399,-0.0477546751499176,0.012570690363645554,-0.0958133116364479
Bob	0.024427175521850586,0.07044785469770432,-0.04175269603729248,-0.06182029843330383
Lucy	0.05776885524392128,0.08288335055112839,-0.06490718573331833,0.026563744992017746

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