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How to train the encoder for our own data? (A Knowledge graph and sample query) #16
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I have the same question. |
+1 |
Thanks for the question and sorry for the late reply. There is not currently a user-facing mechanism to incorporate custom datasets due to the need to define things like train/test split and subgraph sampling -- in general one can create a new DataSource (see common/data.py) to handle new datasets. Note that a pretrained model (such as the one provided in the repo) may be able to handle testing on new datasets, in which case subgraph_matching/alignment.py can load in new graphs to evaluate on. If the goal is to train on new datasets, as a bit of a hack, one could append an "elif" after this line:
with a spec for a new dataset: and train using the command line option |
Thanks @qema, I was able to train the network using my custom datasets, however, I get only around 70 % validation accuracy. |
Hi @rd27995, please see the new |
Hi,
I have a target graph in the form of a directed networkx graphs with 14M nodes and 54M edges.
I wanted to know how can I make use of this target graph along with another query graph (of size 30 Nodes 33 Edges) to train the encoder?
I can only see options to make use of inbuilt datasets in PyTorch gemetric. Is there any simpler way I can use my own datasets?
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