| Property Name | Default | Meaning |
|---|---|---|
| edgePath | "" | the input path (hdfs) of edge table |
| featurePath | "" | the input path (hdfs) of feature table |
| labelPath | "" | the input path (hdfs) of label table |
| testLabelPath | "" | the input path (hdfs) of validate label table |
| torchModelPath | model.pt | the name of the model file, xx.pt in this example |
| predictOutputPath | "" | hdfs path to save the predict label for all nodes in the graph, set it if you need the label |
| embeddingPath | "" | hdfs path to save the embedding for all nodes in the graph, set it if you need the embedding vectors |
| outputModelPath | "" | hdfs path to save the training model file, which is also a torch model pt file, set it if you want to do predicting or incremental training in the next step |
| userFeaturePath | "" | the input path (hdfs) of user feature table |
| itemFeaturePath | "" | the input path (hdfs) of item feature table |
| userEmbeddingPath | "" | hdfs path to save the embedding for all user nodes in the graph, set it if you need the embedding vectors |
| itemEmbeddingPath | "" | hdfs path to save the embedding for all item nodes in the graph, set it if you need the embedding vectors |
| featureEmbedInputPath | "" | the embedding matrix for features(contains user, item), set it if you need to increment train only when data is high-sparse |
| Property Name | Default | Meaning |
|---|---|---|
| featureDim | -1 | the dimension for the feature for each node, which should be equal with the number when generate the model file |
| edgeFeatureDim | -1 | the dimension for the feature of edge, which should be equal with the number when generate the model file |
| itemTypes | 266 | types of item nodes, which is also the num of meta-paths, for HAN |
| userFeatureDim | -1 | the dimension for the feature for each user node, which should be equal with the number when generate the model file |
| itemFeatureDim | -1 | the dimension for the feature for each item node, which should be equal with the number when generate the model file |
| fieldNum | -1 | the field num of user, the default is -1, set it if you need only when data is high-sparse |
| featEmbedDim | -1 | the dim of user embedding, the default is -1, set it if you need only when data is high-sparse |
| userFieldNum | -1 | the field num of user, the default is -1, set it if you need only when data is high-sparse |
| itemFieldNum | -1 | the field num of item, the default is -1, set it if you need only when data is high-sparse |
| userFeatEmbedDim | -1 | the dim of user embedding, the default is -1, set it if you need only when data is high-sparse |
| itemFeatEmbedDim | -1 | the dim of item embedding, the default is -1, set it if you need only when data is high-sparse |
| fieldMultiHot | false | whether the field is multi-hot(only support the last field is multi-hot), the default is false, set it if you need only when data is high-sparse |
| format | sparse | should be sparse/dense |
| numLabels | 1 | the num of multi-label classification task if numLabels > 1, the default is 1 for single-label classification |
| Property Name | Default | Meaning |
|---|---|---|
| stepSize | 0.01 | the learning rate when training |
| decay | 0.001 | the decay of learning ratio, the default is 0 |
| optimizer | adma | adam/momentum/sgd/adagrad |
| numEpoch | 10 | number of epoches you want to run |
| testRatio | 0.5 | use how many nodes from the label file for testing(if testLabelPath is set, the testRatio is invalid) |
| trainRatio | 0.5 | randomly sampling part of samples to train; for unsupervised gnn algo |
| samples | 5 | the number of samples when sampling neighbors |
| userNumSamples | 5 | the number of samples of user neighbors training in the next step |
| itemNumSamples | 5 | the number of samples of item neighbors |
| second | true | whether use second hop sampling |
| batchSize | 100 | batch size for each optimizing step |
| actionType | train | hshould be train/predict |
| numBatchInit | 5 | we use a mini-batch way when initializing features and network structures on parameter servers. this parameter determines how many batches we uses in this step. |
| evals | acc | eval method, the default is acc, the optional value: acc,binary_acc,auc,precision,f1,rmse,recall;multi_auc for numLabels > 1 |
| periods | 1000 | save pt model every few epochs |
| saveCheckpoint | false | whether checkpoint ps model after init parameters to ps |
| checkpointInterval | 0 | save ps model every few epochs |
| validatePeriods | 5 | validate model every few epochs |
| useSharedSamples | false | whether reuse the samples to calculate the train acc to accelerate, the default is false |
| numPartitions | 10 | partition the data into how many partitions |
| psNumPartition | 10 | partition the data into how many partitions on ps |
| batchSizeMultiple | 10 | the multiple of batchsize used to accelerate when predict prediction or embedding |
| Property Name | Default | Meaning |
|---|---|---|
| input_dim | -1 | input dimention of node features |
| hidden_dim | -1 | hidden dimension of convolution layer |
| output_dim | -1 | the number of classes for supervised algo, or the dimension of output embedding for unsupervised algo |
| output_file | "model_name.pt" | output file name |
| input_user_dim | -1 | input dimention of user node features |
| input_item_dim | -1 | input dimention of item node features |
| input_user_field_num | -1 | the number of user field num, for high-sparse |
| input_item_field_num | -1 | the number of item field num, for high-sparse |
| input_user_embedding_dim | -1 | embedding dim of user node features, for high-sparse |
| input_item_embedding_dim | -1 | embedding dim of item node features, for high-sparse |
| field_num | -1 | field num of node features, for high-sparse |
| input_embedding_dim | -1 | embedding dim of node features, for high-sparse |
| encode | dense | the encode of feature, optional value:dense, one-hot, multi-hot |
| edge_input_dim | -1 | the dimension of edge feature |
| item_types | -1 | the num of item_types |
| negative_size | 1 | multiples of positive samples |
| heads | 1 | the number of attention heads, the default is 1 |
| dropout | 0 | the dropout ratio |
| edge_types | false | the dimension of edge feature |
| method | classification | the encode of feature, the default is classification, optional value:classification,regression for IGMC |
| task_type | classification | the type of task, the default value is classification, optional value:classification or multi-label-classification(multi labels for one node) |
| class_weights | "" | class weights for supervised GNN, in order to balance class, such as: 0.1,0.9 |
| Property Name | Result for predictOutputPath | Result for EmbeddingPath |
|---|---|---|
| Semi GraphSage | node label softmax | node embedding |
| DGI/Unsupervised | - | node embedding |
| RGCN | node label softmax | node embedding |
| EdgeProp | node label softmax embedding | - |
| GAT | node label softmax embedding | - |
| HAN | user-node label softmax embedding | - |
| Semi Bipartite GraphSage | user-node label softmax embedding | - |
| Unsupervised Bipartite GraphSage | - | node embedding |
| HGAT | - | node embedding |
| IGMC | src dst label | - |