def encode():
title_repo = CassandraDatabase(project_name='papers',
repo_name='title',
id_sql_type='BIGINT',
content_sql_type="TEXT")
summary_repo = CassandraDatabase(project_name='papers',
repo_name='summary',
id_sql_type='BIGINT',
content_sql_type="TEXT")
encoded_title_repo = CassandraDatabase(project_name='papers',
repo_name='title_reviews',
id_sql_type='BIGINT',
content_sql_type="TEXT")
encoded_summary_repo = CassandraDatabase(project_name='papers',
repo_name='summary_reviews',
id_sql_type='BIGINT',
content_sql_type="TEXT")
# encoder = USERpcClient()
encoder = InferRpcClient()
# path = 'C:\zorba\storage\\vectorizer.joblib'
# vectorizer = load(path)
i = 0
for id, row in title_repo.list():
print(i)
i += 1
title_vec = str(encoder.encode(row)['encoded'])
summary_vec = str(encoder.encode(summary_repo.read(id)[0])['encoded'])
# title_vec = str(vectorizer.transform([row]).toarray()[0].tolist())
# summary_vec = str(vectorizer.transform([summary_repo.read(id)[0]]).toarray()[0].tolist())
encoded_title_repo.write(id, title_vec)
encoded_summary_repo.write(id, summary_vec)
def title_sents():
repo = CassandraDatabase(project_name='papers',
repo_name='title',
id_sql_type='BIGINT',
content_sql_type="TEXT")
sent_sum_map_repo = CassandraDatabase(project_name='papers',
repo_name='sent_sum_map',
id_sql_type='BIGINT',
content_sql_type="TEXT")
loc = 0
top3 = 0
top10 = 0
k = 0
for id, row in repo.list():
k += 1
print(k)
result = ast.literal_eval(
q.query(json.dumps({
"text": row,
"count": 203
})))
sims = result['result']
inter = result['keywords']
papers_ids = []
for sent_id in list(sims.keys()):
paper_id = int(sent_sum_map_repo.read(sent_id)[0])
if paper_id not in papers_ids:
papers_ids.append(paper_id)
if paper_id == id:
index = len(papers_ids)
break
# papers_ids = np.array(papers_ids)
# index = np.where(np.array(papers_ids) == id)[0][0]
if index < 3:
top3 += 1
if index < 10:
top10 += 1
loc += index
print('{:.2f} top3 {} top 10 {}'.format(loc / repo.count(), top3, top10))
'''tootal count 203. USE: [11.40, 3: 11, 10: 143], upvotes: [17.77, 3: 107, 10: 141],
from services.cassandra_ import CassandraDatabase
import ast
import random
meta_repo = CassandraDatabase(project_name='papers',
repo_name='meta',
id_sql_type='BIGINT',
content_sql_type="TEXT")
sent_sum_map_repo = CassandraDatabase(project_name='papers',
repo_name='sent_sum_map',
id_sql_type='BIGINT',
content_sql_type="TEXT")
encoded_sents_repo = CassandraDatabase(project_name='papers',
repo_name='sents_count_vec',
id_sql_type='BIGINT',
content_sql_type="TEXT")
for id, row in meta_repo.list():
meta = ast.literal_eval(row.replace('nan', '\'\''))
ids = meta['children']
print(id, ids, random.choice(ids))
print(encoded_sents_repo.read(random.choice(ids))[0])
break
for child_id in ids:
sent_sum_map_repo.write(child_id, str(id))
from services.cassandra_ import CassandraDatabase
from services.cassandra_ import CassandraDatabase
import ast
import spacy
nlp = spacy.load('en_core_web_sm')
meta_repo = CassandraDatabase(project_name='papers',
repo_name='meta',
id_sql_type='BIGINT',
content_sql_type="TEXT")
summary_repo = CassandraDatabase(project_name='papers',
repo_name='summary',
id_sql_type='BIGINT',
content_sql_type="TEXT")
extractor = KeywordExtractorRpcClient()
i = 0
for id, row in meta_repo.list():
meta = ast.literal_eval(row.replace('nan', '\'\''))
keywords = meta['keywords']
text = summary_repo.read(id)[0]
if len(keywords) != 1:
keywords += list(extractor.extract(text)['keywords'])
else:
keywords = list(extractor.extract(text)['keywords'])
keywords_lemmas = [token.lemma_ for token in nlp(' '.join(keywords))]
meta['keywords_lemmas'] = list(set(keywords_lemmas))
meta['keywords'] = list(set(keywords))
meta_repo.write(id, str(meta))
from joblib import dump, load
from services.cassandra_ import CassandraDatabase
import time
from sklearn.feature_extraction import stop_words
title_repo = CassandraDatabase(project_name='papers',
repo_name='title',
id_sql_type='BIGINT',
content_sql_type="TEXT")
summary_repo = CassandraDatabase(project_name='papers',
repo_name='summary',
id_sql_type='BIGINT',
content_sql_type="TEXT")
stop_words = stop_words.ENGLISH_STOP_WORDS
corpus = []
for id, row in summary_repo.list():
corpus.append(row)
corpus.append(title_repo.read(id)[0])
path = 'C:\zorba\storage\\vectorizer.joblib'
vectorizer = CountVectorizer(stop_words=stop_words)
t1 = time.time()
x = vectorizer.fit(corpus)
print(time.time() - t1)
print(len(vectorizer.get_feature_names()))
dump(vectorizer, path)
# vectorizer = load(path)