I keep getting the following error when I try and run the code on my laptop with no changes.

Train model
Traceback (most recent call last):
  File "C:\Anaconda\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py", line 1334, in _do_call
    return fn(*args)
  File "C:\Anaconda\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py", line 1319, in _run_fn
    options, feed_dict, fetch_list, target_list, run_metadata)
  File "C:\Anaconda\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py", line 1407, in _call_tf_sessionrun
    run_metadata)
tensorflow.python.framework.errors_impl.InternalError: Unsupported feed type

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "main.py", line 279, in <module>
    outs = sess.run([opt.opt_op, opt.cost, opt.batch_edge_type_idx], feed_dict=feed_dict)
  File "C:\Anaconda\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py", line 929, in run
    run_metadata_ptr)
  File "C:\Anaconda\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py", line 1152, in _run
    feed_dict_tensor, options, run_metadata)
  File "C:\Anaconda\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py", line 1328, in _do_run
    run_metadata)
  File "C:\Anaconda\envs\tensorflow\lib\site-packages\tensorflow\python\client\session.py", line 1348, in _do_call
    raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InternalError: Unsupported feed type

I don't understand where the problem is. Please help.

Typically, DDI prediction is a pairwise classification problem.
In the given toy example, you artificially generate a small graph.
My concern is that Decagon seems to treat a DDI and its corresponding reverse one as two different edges. But, I am confused by how to calculate the metrics with the predicted results for potential DDI triplets and the reverse ones.
For instance, "Drug A's metabolism is increased when combined with Drug B" (symbolized as triplet (A, metabolism increased, B)) is semantically equal to the reverse one "Drug B can increase the metabolism of Drug A" (symbolized as triplet (B, increase metabolism, A)).

ModuleNotFoundError Traceback (most recent call last)
in
12 from sklearn import metrics
13
---> 14 from decagon.optimizer import DecagonOptimizer
15 from decagon.model import DecagonModel
16 from decagon.minibatch import EdgeMinibatchIterator

ModuleNotFoundError: No module named 'decagon'

It seems that the model takes into consideration a single edge type on each batch iteration, so it only uses that corresponding W_r, is only that matrix updated on that iteration?

Hello! Could you tell me the meaning of every variable in main.py?
Thanks

@marinkaz Thank you so much for your work! Would you please explain to me why the adj_mats_orig contains both gene_adj and gene_adj.transpose (the same to drug_drug_adj_list)? I think that a 'gene_adj' is enough, as the code below.
Looking forward to your reply. Thank you!
` #data representation

adj_mats_orig = {
(0, 0): [gene_adj, gene_adj.transpose(copy=True)],
(0, 1): [gene_drug_adj],
(1, 0): [drug_gene_adj],
(1, 1): drug_drug_adj_list + [x.transpose(copy=True) for x in drug_drug_adj_list],
}`

`#In my view, the data representation should be as follows.

adj_mats_orig = {
(0, 0): [gene_adj],
(0, 1): [gene_drug_adj],
(1, 0): [drug_gene_adj],
(1, 1): drug_drug_adj_list,
}`

The design of your adjacency matrix adj_mats_orig and the way you split the train/test set will cause a huge data leakage problem in your training, because your validation and test set is created independently for gene_adj and gene_adj.transpose(copy=True), and therefore the edges from the validation / test set in gene_adj is actually included in the training set of gene_adj.transpose(copy=True).

Same problem goes for the train / validate set between gene_drug_adj and drug_gene_adj. The validation edges from gene_drug_adj are actually used for training in drug_gene_adj, and vise versa.

Could you please clarify?
Thanks!

Originally posted by @hurleyLi in #7 (comment)

Thank you for providing this repo but wondering if this repo is maintained or not? there are lots of dependency issue in the requirement file and time consuming to solve all of those issue.

AttributeError: module 'tensorflow' has no attribute 'app'

(base) C:\Users\chert\decagon>python main.py
Traceback (most recent call last):
File "main.py", line 14, in
from decagon.deep.optimizer import DecagonOptimizer
File "C:\Users\chert\decagon\decagon\deep\optimizer.py", line 4, in
flags = tf.app.flags
AttributeError: module 'tensorflow' has no attribute 'app'

(base) C:\Users\chert\decagon>

how to solve this error ??

How to solve this error ?

Using cached https://files.pythonhosted.org/packages/69/cb/f5be453359271714c01b9bd06126eaf2e368f1fddfff30818754b5ac2328/funcsigs-1.0.2-py2.py3-none-any.whl
Collecting futures==3.2.0 (from -r requirements.txt (line 8))
ERROR: Could not find a version that satisfies the requirement futures==3.2.0 (from -r requirements.txt (line 8)) (from versions: 0.2.python3, 0.1, 0.2, 1.0, 2.0, 2.1, 2.1.1, 2.1.2, 2.1.3, 2.1.4, 2.1.5, 2.1.6, 2.2.0, 3.0.0, 3.0.1, 3.0.2, 3.0.3, 3.0.4, 3.0.5, 3.1.0, 3.1.1)
ERROR: No matching distribution found for futures==3.2.0 (from -r requirements.txt (line 8))

Could you tell the meaning of variable n_drugdrug_rel_types in main.py?

The authors did not provide the source code for using the real dataset.

Any chance you will upload the trained, final model? Thanks!

Please provide instructions on how to use the data set with the code.

I viewed the whole code and found that the code only use toy dummy data to train model. So I don't really understand how you use those data to train GCN model. Can you supply the code or instructions about how to use real-world data to train model?