Dear Phi, In the following piece of code you generate the validation set but never return it, instead you use the test set instead of validation set and there is no actual test set to be used for testing purposes.
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val_edges_false = [] |
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while len(val_edges_false) < len(val_edges): |
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idx_i = np.random.randint(0, adj.shape[0]) |
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idx_j = np.random.randint(0, adj.shape[0]) |
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if idx_i == idx_j: |
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continue |
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if ismember([idx_i, idx_j], train_edges): |
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continue |
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if ismember([idx_j, idx_i], train_edges): |
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continue |
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if ismember([idx_i, idx_j], val_edges): |
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continue |
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if ismember([idx_j, idx_i], val_edges): |
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continue |
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if val_edges_false: |
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if ismember([idx_j, idx_i], np.array(val_edges_false)): |
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continue |
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if ismember([idx_i, idx_j], np.array(val_edges_false)): |
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continue |
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val_edges_false.append([idx_i, idx_j]) |
Am I missing something?
Dear Phi, In the following piece of code you generate the validation set but never return it, instead you use the test set instead of validation set and there is no actual test set to be used for testing purposes.
graph-representation-learning/longae/utils_gcn.py
Lines 153 to 172 in 48d5978
Am I missing something?