Thank you for providing this wonderful tool for study. I changed the second option into all for LINE (line 48 of GraphEmbedding/examples/line_wiki.py), and encountered the following error:
....
97/97 - 1s - loss: nan - first_order_loss: nan - second_order_loss: 0.0503
Epoch 48/50
97/97 - 1s - loss: nan - first_order_loss: nan - second_order_loss: 0.0480
Epoch 49/50
97/97 - 1s - loss: nan - first_order_loss: nan - second_order_loss: 0.0485
Epoch 50/50
97/97 - 1s - loss: nan - first_order_loss: nan - second_order_loss: 0.0472
Training classifier using 80.00% nodes...
Traceback (most recent call last):
File "line_wiki.py", line 52, in <module>
evaluate_embeddings(embeddings)
File "line_wiki.py", line 19, in evaluate_embeddings
clf.split_train_evaluate(X, Y, tr_frac)
File "/export/d1/shuaiw/GraphEmbedding/env/lib/python3.6/site-packages/ge-0.0.0-py3.6.egg/ge/classify.py", line 66, in split_train_evaluate
File "/export/d1/shuaiw/GraphEmbedding/env/lib/python3.6/site-packages/ge-0.0.0-py3.6.egg/ge/classify.py", line 34, in train
File "/export/d1/shuaiw/GraphEmbedding/env/lib/python3.6/site-packages/scikit_learn-0.22.2.post1-py3.6-linux-x86_64.egg/sklearn/multiclass.py", line 239, in fit
for i, column in enumerate(columns))
File "/export/d1/shuaiw/GraphEmbedding/env/lib/python3.6/site-packages/joblib-0.13.0-py3.6.egg/joblib/parallel.py", line 917, in __call__
if self.dispatch_one_batch(iterator):
File "/export/d1/shuaiw/GraphEmbedding/env/lib/python3.6/site-packages/joblib-0.13.0-py3.6.egg/joblib/parallel.py", line 759, in dispatch_one_batch
self._dispatch(tasks)
File "/export/d1/shuaiw/GraphEmbedding/env/lib/python3.6/site-packages/joblib-0.13.0-py3.6.egg/joblib/parallel.py", line 716, in _dispatch
job = self._backend.apply_async(batch, callback=cb)
File "/export/d1/shuaiw/GraphEmbedding/env/lib/python3.6/site-packages/joblib-0.13.0-py3.6.egg/joblib/_parallel_backends.py", line 182, in apply_async
result = ImmediateResult(func)
File "/export/d1/shuaiw/GraphEmbedding/env/lib/python3.6/site-packages/joblib-0.13.0-py3.6.egg/joblib/_parallel_backends.py", line 549, in __init__
self.results = batch()
File "/export/d1/shuaiw/GraphEmbedding/env/lib/python3.6/site-packages/joblib-0.13.0-py3.6.egg/joblib/parallel.py", line 225, in __call__
for func, args, kwargs in self.items]
File "/export/d1/shuaiw/GraphEmbedding/env/lib/python3.6/site-packages/joblib-0.13.0-py3.6.egg/joblib/parallel.py", line 225, in <listcomp>
for func, args, kwargs in self.items]
File "/export/d1/shuaiw/GraphEmbedding/env/lib/python3.6/site-packages/scikit_learn-0.22.2.post1-py3.6-linux-x86_64.egg/sklearn/multiclass.py", line 79, in _fit_binary
estimator.fit(X, y)
File "/export/d1/shuaiw/GraphEmbedding/env/lib/python3.6/site-packages/scikit_learn-0.22.2.post1-py3.6-linux-x86_64.egg/sklearn/linear_model/_logistic.py", line 1527, in fit
accept_large_sparse=solver != 'liblinear')
File "/export/d1/shuaiw/GraphEmbedding/env/lib/python3.6/site-packages/scikit_learn-0.22.2.post1-py3.6-linux-x86_64.egg/sklearn/utils/validation.py", line 755, in check_X_y
estimator=estimator)
File "/export/d1/shuaiw/GraphEmbedding/env/lib/python3.6/site-packages/scikit_learn-0.22.2.post1-py3.6-linux-x86_64.egg/sklearn/utils/validation.py", line 578, in check_array
allow_nan=force_all_finite == 'allow-nan')
File "/export/d1/shuaiw/GraphEmbedding/env/lib/python3.6/site-packages/scikit_learn-0.22.2.post1-py3.6-linux-x86_64.egg/sklearn/utils/validation.py", line 60, in _assert_all_finite
msg_dtype if msg_dtype is not None else X.dtype)
ValueError: Input contains NaN, infinity or a value too large for dtype('float64').
Could anyone take a look and see if that can be fixed? Thank you very much!
Hello!
Thank you for providing this wonderful tool for study. I changed the
secondoption intoallforLINE(line 48 ofGraphEmbedding/examples/line_wiki.py), and encountered the following error:Could anyone take a look and see if that can be fixed? Thank you very much!