異機種間グラフを使用したUnsupervised GraphWiseモデルの作成

15.3.4 異機種間グラフを使用したUnsupervised GraphWiseモデルの作成

プロバイダおよび機能が異なるパーティション化されたグラフを使用して、Unsupervised GraphWiseモデルを作成できます。

opg4j> analyst.unsupervisedGraphWiseModelBuilder().
        setVertexInputPropertyNames("vertex_provider1_features", "vertex_provider2_features").
        setEdgeInputPropertyNames("edge_provider_features").
        setVertexTargetPropertyName("target_property").
        build()

UnsupervisedGraphWiseModel model = analyst.unsupervisedGraphWiseModelBuilder()
    .setVertexInputPropertyNames("vertex_provider1_features", "vertex_provider2_features")
    .setEdgeInputPropertyNames("edge_provider_features")
    .setVertexTargetPropertyName("target_property")
    .build();

params = dict(vertex_input_property_names=["vertex_provider1_features", "vertex_provider2_features"],
              edge_input_property_names=["edge_provider_features"])
model = analyst.unsupervised_graphwise_builder(**params)

また、次のように特定のプロバイダを選択できます。

opg4j> var model = analyst.unsupervisedGraphWiseModelBuilder().
         setVertexInputPropertyNames("vertex_provider1_features", "vertex_provider2_features").
         setEdgeInputPropertyNames("edge_provider_features").
         setTargetVertexLabels("provider1").
         build()

UnsupervisedGraphWiseModel model = analyst.unsupervisedGraphWiseModelBuilder()
    .setVertexInputPropertyNames("vertex_provider1_features", "vertex_provider2_features")
    .setEdgeInputPropertyNames("edge_provider_features")
    .setTargetVertexLabels("provider1")
    .build();

params = dict(vertex_input_property_names=["vertex_provider1_features", "vertex_provider2_features"],
              edge_input_property_names=["edge_provider_features"],
              target_vertex_labels=["provider1"])
model = analyst.unsupervised_graphwise_builder(**params)

各レイヤーで埋込みのフローを制御する場合は、必要な接続を有効または無効にできます。デフォルトでは、すべての接続が有効です。

opg4j> var convLayerConfig = analyst.graphWiseConvLayerConfigBuilder().
         setNumSampledNeighbors(25).
         useVertexToVertexConnection(true).
         useEdgeToVertexConnection(true).
         useEdgeToEdgeConnection(false).
         useVertexToEdgeConnection(false).
         build()
opg4j> var model = analyst.unsupervisedGraphWiseModelBuilder().
         setVertexInputPropertyNames("vertex_provider1_features", "vertex_provider2_features").
         setEdgeInputPropertyNames("edge_provider_features").
         setTargetVertexLabels("provider1").
         build()

GraphWiseConvLayerConfig convLayerConfig = analyst.graphWiseConvLayerConfigBuilder()
    .setNumSampledNeighbors(10)
    .useVertexToVertexConnection(true)
    .useEdgeToVertexConnection(true)
    .useEdgeToEdgeConnection(false)
    .useVertexToEdgeConnection(false)
    .build();

UnsupervisedGraphWiseModel model = analyst.unsupervisedGraphWiseModelBuilder()
    .setVertexInputPropertyNames("vertex_provider1_features", "vertex_provider2_features")
    .setEdgeInputPropertyNames("edge_provider_features")
    .setTargetVertexLabels("provider1")
    .setConvLayerConfigs(convLayerConfig)
    .build();

conv_layer_config = dict(num_sampled_neighbors=25,
                         activation_fn='tanh',
                         weight_init_scheme='xavier',
                         neighbor_weight_property_name=weightProperty,
                         vertex_to_vertex_connection=True,
                         edge_to_vertex_connection=True,
                         vertex_to_edge_connection=False,
                         edge_to_edge_connection=False)

conv_layer = analyst.graphwise_conv_layer_config(**conv_layer_config)

params = dict(vertex_input_property_names=["vertex_provider1_features", "vertex_provider2_features"],
              edge_input_property_names=["edge_provider_features"],
              target_vertex_labels=["provider1"],
              conv_layer_config=[conv_layer])

model = analyst.unsupervised_graphwise_builder(**params)

親トピック: Unsupervised GraphWiseアルゴリズムの使用