def test_custom_plot():
Token.set_extension('plot', default={'color': 'aquamarine'})
for token in doc:
node_label = '{0} [{1}]\n({2} / {3})'.format(token.orth_, token.i,
token.pos_, token.tag_)
token._.plot['label'] = node_label
if token.dep_ in ['ROOT', 'acl']:
token._.plot['color'] = 'dodgerblue'
if token.dep_ in ['nsubj', 'dobj']:
token._.plot['color'] = 'deeppink1'
plot = visualise_spacy_tree.create_png(doc)
with open(os.path.join(example_plot_dir, 'custom_plot.png'), 'wb') as f:
f.write(plot)
def test_visualise_pattern():
for i, doc in enumerate(docs):
png = visualise_spacy_tree.create_png(doc)
filepath = 'examples/sentence_vis/sentence_{}.png'.format(i)
with open(filepath, 'wb') as f:
f.write(png)
feature_dict = {'DEP': 'dep_', 'TAG': 'tag_', 'LOWER': 'lower_'}
for test_i, case in enumerate(cases):
match_example = case['training_example']['match']
role_pattern_builder = RolePatternBuilder(feature_dict)
for features_i, features in enumerate(feature_combs):
role_pattern = role_pattern_builder.build(match_example,
features=features)
filepath = 'examples/spacy_dep_patterns/pattern_{}_{}.json'.format(
test_i, features_i)
with open(filepath, 'w') as f:
json.dump(role_pattern.spacy_dep_pattern, f, indent=2)
outpath = 'examples/pattern_vis/pattern_{0}_{1}.png'.format(
test_i, features_i)
role_pattern.write_vis(outpath, legend=True)
else:
schemes.append(span)
return schemes
# apply function
df2['Schemes1'] = df2.apply(
lambda x: all_schemes(x.Sent, x.Check_Schemes), axis=1)
# nlp_ie_12.py
# To understand the structure of the sentence.
doc = nlp(' Last year, I spoke about the Ujjwala programme, through which,
I am happy to report, 50 million free liquid-gas connections have
been provided so far')
png = visualise_spacy_tree.create_png(doc)
display(Image(png))
# nlp_ie_13.py
# rule to extract initiative name
def sent_subtree(text):
"""sent_subtree, In tree view to get the proper nouns in the subtree for
initiative name.
Args:
text:
"""
# pattern match for schemes or initiatives
def dep_analyzer(my_text):
#nlp = load_model('models1')
docx = nlp(my_text)
docx=darcolor(docx)
png = visualise_spacy_tree.create_png(docx)
return png
def test_partial_plot():
tokens = [doc[0], doc[1], doc[3]]
plot = visualise_spacy_tree.create_png(tokens)
with open(os.path.join(example_plot_dir, 'default_partial_plot.png'),
'wb') as f:
f.write(plot)
def test_default_plot():
plot = visualise_spacy_tree.create_png(doc)
with open(os.path.join(example_plot_dir, 'default_plot.png'), 'wb') as f:
f.write(plot)
def tree(doc: Union[Doc, Span]):
png = visualise_spacy_tree.create_png(doc)
return display(Image(png))