def prepare_computation(id):
"""Prepare computation parameters according to discussion preferences"""
from assembl.models import Content
post = Content.get(id)
active = any([
post.discussion.preferences['watson_' + x]
for x in watson_languages.keys()
])
if active and post.body:
api_version = config.get("watson_api_version", "2018-03-16")
features = {}
post_loc = post.body.first_original().locale.root_locale
for feature_name, langs in watson_languages.items():
if not post.discussion.preferences['watson_' + feature_name]:
continue
if post_loc not in langs:
continue
features[feature_name] = watson_feature_classes[feature_name]()
if not features:
return
features = Features(**features)
get_or_create_computation_on_post(post, "watson_" + api_version,
features._to_dict())
return True
def get_similar_posts(discussion, post_id=None, text=None, cutoff=0.15):
post_ids = discussion.db.query(Content.id).filter_by(
discussion_id=discussion.id).all()
post_ids = [x for (x,) in post_ids]
(subcorpus, tfidf_model, gensim_model, similarity
) = get_similarity_matrix(discussion)
lang = discussion.discussion_locales[0].split('_')[0]
bowizer = BOWizer(lang)
assert post_id or text, "Please give a text or a post_id"
if post_id:
words = bowizer.post_to_bow(Content.get(post_id))
else:
words = bowizer.text_to_bow(text)
query_vec = gensim_model[tfidf_model[words]]
results = [(v, post_ids[n]) for (n, v) in enumerate(similarity[query_vec])]
results.sort(reverse=True)
# forget self and duplicates
results = [x for x in results if x[0] < 0.999]
cutoff *= results[0][0]
results = [(post_id, score) for (score, post_id) in results
if score > cutoff]
return results
def prepare_computation(id):
"""Prepare computation parameters according to discussion preferences"""
from assembl.models import Content
post = Content.get(id)
active = any([post.discussion.preferences['watson_' + x]
for x in watson_languages.keys()])
if active and post.body:
api_version = config.get("watson_api_version", "2018-03-16")
features = {}
post_loc = post.body.first_original().locale.root_locale
for feature_name, langs in watson_languages.items():
if not post.discussion.preferences['watson_' + feature_name]:
continue
if post_loc not in langs:
continue
features[feature_name] = watson_feature_classes[feature_name]()
if not features:
return
features = Features(**features)
get_or_create_computation_on_post(
post, "watson_" + api_version, features._to_dict())
return True
