Java 类名:com.alibaba.alink.operator.batch.graph.MetaPath2VecBatchOp
Python 类名:MetaPath2VecBatchOp
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
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 | ||
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 |
from pyalink.alink import * import pandas as pd useLocalEnv(1) df = 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, schemaStr="start string, end string, value double") df2 = pd.DataFrame([ ["Bob", "A"], ["Bella", "A"], ["Karry", "A"], ["Lucy", "B"], ["Alice", "B"], ["Lisa", "B"], ["Karry", "B"] ]) type = BatchOperator.fromDataframe(df2, schemaStr="node string, type string") metapathBatchOp = MetaPath2VecBatchOp() \ .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()
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 |