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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。此属性的唯一值集被称为一组节点类，或简单地称为类。
可以从节点分类模型推断顶点属性的节点类或类别属性值。在此属性尚未附加到顶点的情况下，这很有用。
为了从节点分类模型中获取一个或多个类，你需要使用
with()跟谓词一步Neptune#ml.classification要配置properties()Step。如果这些是顶点属性，输出格式与您期望的格式相似。
节点分类仅适用于字符串属性值。这意味着数字属性值，例如0要么1不支持，尽管字符串等效"0"和"1"是。同样，布尔值属性值true和false不起作用，但"true"和"false"执行该操作
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")
此查询的输出将类似于下面这样：
==>vp[genre->Action] ==>vp[genre->Crime] ==>vp[genre->Comedy]
在上面的查询中，V()和properties()步骤的使用方法如下：
这些区域有：V()步骤包含要从节点分类模型中获取类的顶点集：
.V( "movie_1", "movie_2", "movie_3" )
这些区域有：properties()step 包含模型训练的关键，并且具有.with("Neptune#ml.classification")表明这是节点分类 ML 推理查询。
目前不支持多个属性键properties().with("Neptune#ml.classification")Step。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")
有关具体错误消息，请参阅Neptune ML 例外列表.
一个properties().with("Neptune#ml.classification")step 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()
其他节点分类查询
如果推理终端节点和相应的 IAM 角色已保存在数据库集群参数组中，则节点分类查询可以像以下那样简单：
g.V("movie_1", "movie_2", "movie_3").properties("genre").with("Neptune#ml.classification")
您可以使用在查询中混合顶点属性和类union()步骤：
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") )
你也可以做一个无界查询，例如这样：
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()单步进入as()步骤：
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")
您还可以对节点类进行筛选，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：
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")
该回应将如下所示：
==>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]