11.3 Quick Start: Using Graph Machine Learning on PGQL Property Graphs

This tutorial helps you get started on applying the DeepWalk machine learning algorithm on a PGQL property graph.

The instructions assume that the PGQL property graph is already existing in your current database.
Run the following steps to build and work with a Deep Walk model.
  1. Load the PGQL property graph into the graph server (PGX).
    opg4j> var instance = GraphServer.getInstance("https://localhost:7007", "<username>", "<password>".toCharArray())
instance ==> ServerInstance[embedded=false,baseUrl=https://localhost:7007]
opg4j> var session=instance.createSession("mySession")
session ==> PgxSession[ID=5af9c362-10a3-4a7c-953c-602553d4606b,source=mySession]
opg4j> var graph = session.readGraphByName("BANK_GRAPH",GraphSource.PG_PGQL)
graph ==> PgxGraph[name=BANK_GRAPH,N=1000,E=4997,created=1684315831352]
    ServerInstance instance = GraphServer.getInstance("https://localhost:7007", "<username>", "<password>".toCharArray());
PgxSession session = instance.createSession("my-session");
PgxGraph graph = session.readGraphByName("BANK_GRAPH",GraphSource.PG_PGQL);
    >>> instance = graph_server.get_instance("https://localhost:7007","<username>","<password>")
>>> session = instance.create_session("my_session")
>>> graph = session.read_graph_by_name("BANK_GRAPH", "pg_pgql")
>>> graph
PgxGraph(name: BANK_GRAPH, v: 1000, e: 4997, directed: True, memory(Mb): 0)
  2. Build a Deep Walk model using customized hyper-parameters as shown:
    opg4j> var model = session.createAnalyst().deepWalkModelBuilder().
...>                 setMinWordFrequency(1).
...>                 setBatchSize(512).
...>                 setNumEpochs(1).
...>                 setLayerSize(100).
...>                 setLearningRate(0.05).
...>                 setMinLearningRate(0.0001).
...>                 setWindowSize(3).
...>                 setWalksPerVertex(6).
...>                 setWalkLength(4).
...>                 setNegativeSample(2).
...>                 build()
model ==> oracle.pgx.api.mllib.DeepWalkModel@6e0f259e
    import oracle.pgx.api.mllib.DeepWalkModel;
DeepWalkModel model= session.createAnalyst().deepWalkModelBuilder()
        .setMinWordFrequency(1)
        .setBatchSize(512)
        .setNumEpochs(1)
        .setLayerSize(100)
        .setLearningRate(0.05)
        .setMinLearningRate(0.0001)
        .setWindowSize(3)
        .setWalksPerVertex(6)
        .setWalkLength(4)
        .setNegativeSample(2)
        .build();
    >>> model = session.create_analyst().deepwalk_builder(min_word_frequency= 1,
...                                 batch_size= 512,
...                                 num_epochs= 1,
...                                 layer_size= 100,
...                                 learning_rate= 0.05,
...                                 min_learning_rate= 0.0001,
...                                 window_size= 3,
...                                 walks_per_vertex= 6,
...                                 walk_length= 4,
...                                 negative_sample= 2)
  3. Train the Deep Walk model as shown:
    opg4j> model.fit(graph)
    model.fit(graph);
    >>> model.fit(graph)
  4. Get the loss value as shown:
    opg4j> var loss = model.getLoss()
loss ==> -2.097562355629634E-5
    double loss = model.getLoss();
    >>> loss = model.loss
>>> loss
-2.0706271243398078e-05
  5. Compute similar vertices as shown:
    opg4j> var similars = model.computeSimilars("ACCOUNTS(280)",10)
batchSimilars ==> oracle.pgx.api.frames.internal.PgxFrameImpl@308e465b
opg4j> batchSimilars.print()
    import oracle.pgx.api.frames.*;

PgxFrame similars = model.computeSimilars("ACCOUNTS(280)", 10);
similars.print();
    >>> similars = model.compute_similars("ACCOUNTS(280)",10)
>>> similars.print()

    The example produces a similar output:

    +-------------------------------------+
| dstVertex     | similarity          |
+-------------------------------------+
| ACCOUNTS(280) | 1.0                 |
| ACCOUNTS(486) | 0.3253505229949951  |
| ACCOUNTS(615) | 0.2806776463985443  |
| ACCOUNTS(660) | 0.27348122000694275 |
| ACCOUNTS(737) | 0.2734076678752899  |
| ACCOUNTS(368) | 0.2707795202732086  |
| ACCOUNTS(479) | 0.27019545435905457 |
| ACCOUNTS(845) | 0.2618815004825592  |
| ACCOUNTS(834) | 0.2543807625770569  |
| ACCOUNTS(249) | 0.24260951578617096 |
+-------------------------------------+
  6. Get all trained vectors and store them in a database table as shown:
    opg4j> var vertexVectors = model.getTrainedVertexVectors().flattenAll()
vertexVectors ==> oracle.pgx.api.frames.internal.PgxFrameImpl@46cb9794
opg4j> vertexVectors.write().db().name("deepwalkframe").tablename("vertexVectors").overwrite(true).store()
    PgxFrame vertexVectors = model.getTrainedVertexVectors().flattenAll();
vertexVectors.write()
  .db()
  .name("vertex vectors")
  .tablename("vertexVectors")
  .overwrite(true)
  .store();
    >>> vertex_vectors = model.trained_vectors.flatten_all()
>>> vertex_vectors.write().db(). \
...         table_name("vertex_vectors"). \
...         overwrite(True). \
...         store()
  7. Store the trained model in the database as shown:
    opg4j> model.export().db().
...>   modelstore("bank_model").
...>   modelname("model").
...>   description("DeepWalk Model for Bank data").
...>   store()
    model.export().db()
  .modelstore("bank_model")  
  .modelname("model2")                 
  .description("DeepWalk Model for Bank data") 
  .store();
    >>> model.export().db(model_store="bank_model",
...                   model_name="model",
...                   model_description="DeepWalk Model for Bank data")
  8. Load a pre-trained model from the database as shown:
    opg4j> session.createAnalyst().loadDeepWalkModel().db().
...>   modelstore("bank_model").
...>   modelname("model").
...>   load()
    model = session.createAnalyst().loadDeepWalkModel().db()
  .modelstore("bank_model")
  .modelname("model")   
  .load();
    >>> model = session.create_analyst().get_deepwalk_model_loader().db(model_store="bank_model",
...                                        model_name="model")
  9. Destroy the model as shown:
    opg4j> model.destroy()
    model.destroy();
    >>> model.destroy()
    See Using the Machine Learning Library (PgxML) for Graphs for more information on the supported machine learning algorithms.

Parent topic: Quick Starts for Using PGQL Property Graphs