Created
June 20, 2019 19:50
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print("Now let's do some simple things within torch:") | |
from torchbiggraph.model import DotComparator | |
src_entity_offset = dictionary["entities"]["user_id"].index("0") # France | |
dest_1_entity_offset = dictionary["entities"]["user_id"].index("7") # Paris | |
dest_2_entity_offset = dictionary["entities"]["user_id"].index("1") # Paris | |
rel_type_index = dictionary["relations"].index("follow") # note we only have one... | |
with h5py.File("model/example_2/embeddings_user_id_0.v10.h5", "r") as hf: | |
src_embedding = hf["embeddings"][src_entity_offset, :] | |
dest_1_embedding = hf["embeddings"][dest_1_entity_offset, :] | |
dest_2_embedding = hf["embeddings"][dest_2_entity_offset, :] | |
dest_embeddings = hf["embeddings"][...] | |
import torch | |
comparator = DotComparator() | |
scores_1, _, _ = comparator( | |
comparator.prepare(torch.tensor(src_embedding.reshape([1,1,10]))), | |
comparator.prepare(torch.tensor(dest_1_embedding.reshape([1,1,10]))), | |
torch.empty(1, 0, 10), # Left-hand side negatives, not needed | |
torch.empty(1, 0, 10), # Right-hand side negatives, not needed | |
) | |
scores_2, _, _ = comparator( | |
comparator.prepare(torch.tensor(src_embedding.reshape([1,1,10]))), | |
comparator.prepare(torch.tensor(dest_2_embedding.reshape([1,1,10]))), | |
torch.empty(1, 0, 10), # Left-hand side negatives, not needed | |
torch.empty(1, 0, 10), # Right-hand side negatives, not needed | |
) | |
print(scores_1) | |
print(scores_2) |
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