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Neptune ML 中的 Gremlin 节点分类查询
对于 Neptune ML 中的 Gremlin 节点分类：
模型是在顶点的一个属性上训练的。此属性的唯一值集合称为一组节点类，或者简称为类。
可以从节点分类模型中推理顶点属性的节点类或分类属性值。当此属性尚未附加到顶点时，这很有用。
要从节点分类模型中获取一个或多个类，您需要使用带有谓词
Neptune#ml.classification的with()步骤来配置properties()步骤。如果这些是顶点属性，则输出格式与您期望的格式类似。
注意
节点分类仅适用于字符串属性值。这意味着不支持诸如 0 或 1 之类的数值属性值，但支持等同的字符串 "0" 和 "1"。同样，布尔属性值 true 和 false 不起作用，但 "true" 和 "false" 起作用。
以下是一个示例节点分类查询：
g.with( "Neptune#ml.endpoint","node-classification-movie-lens-endpoint" ) .with( "Neptune#ml.iamRoleArn","arn:aws:iam::0123456789:role/sagemaker-role" ) .with( "Neptune#ml.limit", 2 ) .with( "Neptune#ml.threshold", 0.5D ) .V( "movie_1", "movie_2", "movie_3" ) .properties("genre").with("Neptune#ml.classification")
此查询的输出如下所示：
==>vp[genre->Action] ==>vp[genre->Crime] ==>vp[genre->Comedy]
在上面的查询中，V() 和 properties() 步骤的使用方式如下：
V() 步骤包含要从节点分类模型中获取类的一组顶点：
.V( "movie_1", "movie_2", "movie_3" )
properties() 步骤包含训练模型所依据的键，并具有 .with("Neptune#ml.classification") 以表明这是节点分类机器学习推理查询。
目前不支持在 properties().with("Neptune#ml.classification") 步骤中使用多个属性键。例如，以下查询会导致异常：
g.with("Neptune#ml.endpoint", "node-classification-movie-lens-endpoint") .with("Neptune#ml.iamRoleArn","arn:aws:iam::0123456789:role/sagemaker-role") .V( "movie_1", "movie_2", "movie_3" ) .properties("genre", "other_label").with("Neptune#ml.classification")
有关具体的错误消息，请参阅 Neptune ML 异常列表。
properties().with("Neptune#ml.classification") 步骤可以与以下任何步骤结合使用：
value()value().is()hasValue()has(value,"")key()key().is()hasKey()has(key,"")path()
其它节点分类查询
如果推理端点和相应的 IAM 角色均已保存在数据库集群参数组中，则节点分类查询可以像这样简单：
g.V("movie_1", "movie_2", "movie_3").properties("genre").with("Neptune#ml.classification")
您可以使用 union() 步骤在查询中混用顶点属性和类：
g.with("Neptune#ml.endpoint","node-classification-movie-lens-endpoint") .with("Neptune#ml.iamRoleArn","arn:aws:iam::0123456789:role/sagemaker-role") .V( "movie_1", "movie_2", "movie_3" ) .union( properties("genre").with("Neptune#ml.classification"), properties("genre") )
您也可以进行无界查询，如下所示：
g.with("Neptune#ml.endpoint","node-classification-movie-lens-endpoint") .with("Neptune#ml.iamRoleArn","arn:aws:iam::0123456789:role/sagemaker-role") .V() .properties("genre").with("Neptune#ml.classification")
您可以使用 select() 步骤以及 as() 步骤一起检索节点类和顶点：
g.with("Neptune#ml.endpoint","node-classification-movie-lens-endpoint") .with("Neptune#ml.iamRoleArn","arn:aws:iam::0123456789:role/sagemaker-role") .V( "movie_1", "movie_2", "movie_3" ).as("vertex") .properties("genre").with("Neptune#ml.classification").as("properties") .select("vertex","properties")
还可以按节点类进行筛选，如以下示例所示：
g.with("Neptune#ml.endpoint", "node-classification-movie-lens-endpoint") .with("Neptune#ml.iamRoleArn","arn:aws:iam::0123456789:role/sagemaker-role") .V( "movie_1", "movie_2", "movie_3" ) .properties("genre").with("Neptune#ml.classification") .has(value, "Horror") g.with("Neptune#ml.endpoint","node-classification-movie-lens-endpoint") .with("Neptune#ml.iamRoleArn","arn:aws:iam::0123456789:role/sagemaker-role") .V( "movie_1", "movie_2", "movie_3" ) .properties("genre").with("Neptune#ml.classification") .has(value, P.eq("Action")) g.with("Neptune#ml.endpoint","node-classification-movie-lens-endpoint") .with("Neptune#ml.iamRoleArn","arn:aws:iam::0123456789:role/sagemaker-role") .V( "movie_1", "movie_2", "movie_3" ) .properties("genre").with("Neptune#ml.classification") .has(value, P.within("Action", "Horror"))
您可以使用 Neptune#ml.score 谓词获得节点分类置信度分数：
g.with("Neptune#ml.endpoint","node-classification-movie-lens-endpoint") .with("Neptune#ml.iamRoleArn","arn:aws:iam::0123456789:role/sagemaker-role") .V( "movie_1", "movie_2", "movie_3" ) .properties("genre", "Neptune#ml.score").with("Neptune#ml.classification")
响应将如下所示：
==>vp[genre->Action] ==>vp[Neptune#ml.score->0.01234567] ==>vp[genre->Crime] ==>vp[Neptune#ml.score->0.543210] ==>vp[genre->Comedy] ==>vp[Neptune#ml.score->0.10101]
在节点分类查询中使用归纳推理
假设您要在 Jupyter 笔记本的现有图形中添加一个新节点，如下所示：
%%gremlin g.addV('label1').property(id,'101').as('newV') .V('1').as('oldV1') .V('2').as('oldV2') .addE('eLabel1').from('newV').to('oldV1') .addE('eLabel2').from('oldV2').to('newV')
然后，您可以使用归纳推理查询来获得反映了新节点的流派和置信度分数：
%%gremlin g.with("Neptune#ml.endpoint", "nc-ep") .with("Neptune#ml.iamRoleArn", "arn:aws:iam::123456789012:role/NeptuneMLRole") .V('101').properties("genre", "Neptune#ml.score") .with("Neptune#ml.classification") .with("Neptune#ml.inductiveInference")
但是，如果您多次运行此查询，您得到的结果可能会有所不同：
# First time ==>vp[genre->Action] ==>vp[Neptune#ml.score->0.12345678] # Second time ==>vp[genre->Action] ==>vp[Neptune#ml.score->0.21365921]
您可以将同样的查询设定为确定性：
%%gremlin g.with("Neptune#ml.endpoint", "nc-ep") .with("Neptune#ml.iamRoleArn", "arn:aws:iam::123456789012:role/NeptuneMLRole") .V('101').properties("genre", "Neptune#ml.score") .with("Neptune#ml.classification") .with("Neptune#ml.inductiveInference") .with("Neptune#ml.deterministic")
在这种情况下，每次的结果都大致相同：
# First time ==>vp[genre->Action] ==>vp[Neptune#ml.score->0.12345678] # Second time ==>vp[genre->Action] ==>vp[Neptune#ml.score->0.12345678]