.limit(10)
.offset(0)
)
sv = SentenceNgramViewer(candidates, session)
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sv
# In[ ]:
c = sv.get_selected()
c
# ## Bicluster Dataframe formation
# In[ ]:
url = "https://zenodo.org/record/1035500/files/"
dep_path = "part-ii-dependency-paths-chemical-disease-sorted-with-themes.txt"
file_dist = "part-i-chemical-disease-path-theme-distributions.txt"
output_file = "data/compound_disease/biclustering/compound_disease_bicluster_results.tsv"
# In[ ]:
# NOTE: This if-then statement is only to avoid opening the viewer during automated testing of this notebook
# You should ignore this!
import os
if 'CI' not in os.environ:
sv = SentenceNgramViewer(indep_results['CG_ALL'][1], session)
else:
sv = None
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sv
# In[ ]:
c = sv.get_selected() if sv else list(fp.union(fn))[0]
c
# In[ ]:
c.labels
# In[ ]:
train_hand_marginals_indep_df.iloc[L_train_labeled.get_row_index(c)]
# ## Write Marginals of best model to File for Next Notebook
# Lastly we write out the generative model's output into a file. Reason for this will be used in the [next notebook](4.sentence-level-prediction.ipynb), where we aim to use a noise aware discriminator model to correct for the generative models' errors.
# In[ ]: