def query_by_vec_online(table, vec, top_k=5):
init_word2vec()
db = EmailDB(table)
scores = []
for email in db.select():
if email.message_id in CACHE:
candidate_vec = CACHE[email.message_id]
else:
doc = body2doc(email.body)
print "[cache doc]", doc
candidate_vec = avg_word_vec(doc)
CACHE[email.message_id] = candidate_vec
score = np.dot(vec, candidate_vec) / npla.norm(vec) / npla.norm(candidate_vec) # cosine similarity.
score = float(score)
scores.append((email, score))
scores = [pair for pair in scores if pair[1] == pair[1]]
scores = sorted(scores, key=lambda pair: pair[1], reverse=True)
print "[matched]", scores[:top_k]
return scores[:top_k]
# implement autoreply to emails.
from surrobot.core.mail2vec import avg_word_vec, init_word2vec
from surrobot.core.preprocess import body2doc
from surrobot.db.embedding import query_by_vec_online
from surrobot.db.db import EmailDB
from pprint import pprint
init_word2vec()
def query_by_body(body, top_k=5):
''' query top_k most probable replies based on raw email body '''
doc = body2doc(body)
print '[doc]', doc
vec = avg_word_vec(doc)
# retrieve top-k past inquery emails.
candidates = query_by_vec_online('inbox', vec, top_k)
# get past replies.
db = EmailDB('outbox')
replies = []
for (email, score) in candidates:
reply = db.select(
where='message_id=:message_id',
data=dict(message_id=email.message_id)
)
reply = list(reply)[0].body
replies.append((reply, score))
return replies
if __name__ == '__main__':
