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to_elastic.py
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import os
import mf_utils
import json
MAX_WORKERS = 8
MAX_MEMORY = 16000
from elasticsearch import Elasticsearch
from elasticsearch import helpers
import logging
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
DATEFORMAT = '%Y-%m-%d-%H-%M-%S'
IGNORE_PATHS = [
's2-corpus-000',
's2-corpus-001',
's2-corpus-002',
's2-corpus-004',
's2-corpus-006',
's2-corpus-007',
's2-corpus-008',
's2-corpus-009',
's2-corpus-010',
's2-corpus-011',
's2-corpus-012',
's2-corpus-013',
's2-corpus-014',
's2-corpus-015',
's2-corpus-016',
's2-corpus-017',
's2-corpus-018',
's2-corpus-020',
's2-corpus-021',
's2-corpus-022',
's2-corpus-023',
's2-corpus-024',
's2-corpus-026',
's2-corpus-027',
's2-corpus-028',
's2-corpus-029',
's2-corpus-031',
's2-corpus-034',
's2-corpus-035',
's2-corpus-036',
's2-corpus-039',
's2-corpus-040',
's2-corpus-041',
's2-corpus-042',
's2-corpus-043',
's2-corpus-044',
's2-corpus-045',
's2-corpus-046',
's2-corpus-047',
's2-corpus-048',
's2-corpus-050',
's2-corpus-051',
's2-corpus-052',
's2-corpus-053',
's2-corpus-054',
's2-corpus-055',
's2-corpus-056',
's2-corpus-057',
's2-corpus-058',
's2-corpus-059',
's2-corpus-060',
's2-corpus-061',
's2-corpus-062',
's2-corpus-063',
's2-corpus-064',
's2-corpus-065',
's2-corpus-066',
's2-corpus-068',
's2-corpus-069',
's2-corpus-070',
's2-corpus-071',
's2-corpus-073',
's2-corpus-075',
's2-corpus-076',
's2-corpus-077',
's2-corpus-078',
's2-corpus-079',
's2-corpus-080',
's2-corpus-081',
's2-corpus-082',
's2-corpus-083',
's2-corpus-084',
's2-corpus-086',
's2-corpus-087',
's2-corpus-088',
's2-corpus-089',
's2-corpus-090',
's2-corpus-091',
's2-corpus-092',
's2-corpus-093',
"s2-corpus-094",
"s2-corpus-095",
"s2-corpus-096",
"s2-corpus-097",
"s2-corpus-098",
"s2-corpus-099",
"s2-corpus-100",
"s2-corpus-101",
"s2-corpus-102",
"s2-corpus-103",
"s2-corpus-104",
"s2-corpus-105",
"s2-corpus-106",
"s2-corpus-107",
"s2-corpus-108",
"s2-corpus-109",
"s2-corpus-110",
"s2-corpus-112",
"s2-corpus-114",
"s2-corpus-115",
"s2-corpus-116",
"s2-corpus-117",
"s2-corpus-118",
"s2-corpus-074",
"s2-corpus-119",
"s2-corpus-120",
"s2-corpus-121",
"s2-corpus-122",
"s2-corpus-123",
"s2-corpus-125",
"s2-corpus-126",
"s2-corpus-127",
"s2-corpus-128",
"s2-corpus-129",
"s2-corpus-130",
"s2-corpus-131",
"s2-corpus-132",
"s2-corpus-133",
"s2-corpus-134",
"s2-corpus-136",
"s2-corpus-137",
"s2-corpus-138",
"s2-corpus-139",
"s2-corpus-140",
"s2-corpus-142",
"s2-corpus-143",
]
def create_logger(logger_name:str,level=logging.INFO):
custom_logger = logging.getLogger(logger_name)
ch1 = logging.StreamHandler()
ch1.setLevel(level)
ch1.setFormatter(formatter)
custom_logger.addHandler(ch1)
custom_logger.setLevel(level)
return custom_logger
INDEX="sem-scholar-index-3"
TYPE= "record"
SAVE_PROCESSED_DATA_PATH =os.path.join(mf_utils.data_path,'processed_data')
ONTOLOGY_CSV_PATH = os.path.join(SAVE_PROCESSED_DATA_PATH,'PageRankCollateFlow')
# def doc_generator(df):
# df_iter = df.iterrows()
# for index, document in df_iter:
# yield {
# "_index": 'sem-scholar-index',
# "_type": "_doc",
# "_id" : f"{document['id']}",
# "_source": document.to_dict(),
# }
# raise StopIteration
def rec_to_actions(df):
import json
for record in df.to_dict(orient="records"):
# yield({'index': {'_id': record['id']}})
yield (json.dumps(record))
def sync_data():
logger = create_logger('ES_Import_Logger')
from elasticsearch import Elasticsearch
from elasticsearch import helpers
es = Elasticsearch(http_compress=True)
present_folders = mf_utils.list_folders(f'processed_data/PageRankCollateFlow')
s3_paths = [os.path.join(f,'ontology_processed.csv') for f in present_folders if f not in IGNORE_PATHS]
for csv_df,pth in load_main_csvs(s3_paths):
csv_df = clean_df(csv_df)
actions = [
{
'_op_type': 'index',
'_index': INDEX,
# '_type': 'doc',
'_id': j['id'],
'_source':j.to_dict()
}
for _,j in csv_df.iterrows()
]
success,_ = helpers.bulk(es,actions,chunk_size=500,index=INDEX, doc_type='doc',stats_only=True,refresh=True )
logger.info(f"Finished Flushing Data For {pth}")
# for _,j in csv_df.iterrows():
# _id = j['id']
# # print(j.to_dict())
# es.index(index=INDEX,doc_type='_doc',id=_id,body=j.to_dict())
# print(f"Finished Flushing Data For {pth}")
# break
def clean_df(df):
import numpy as np
def parse_rows(x):
x['ontology_enhanced'] = json.loads(x['ontology_enhanced'].replace("'",'"'))
x['ontology_semantic'] = json.loads(x['ontology_semantic'].replace("'",'"'))
x['ontology_syntactic'] = json.loads(x['ontology_syntactic'].replace("'",'"'))
x['ontology_union'] = json.loads(x['ontology_union'].replace("'",'"'))
try:
x['authors'] = json.loads(x['authors'].replace("'",'"'))
except:
x['authors'] = []
try:
x['sources'] = json.loads(x['sources'].replace("'",'"'))
except:
x['sources'] = []
try:
x['fieldsOfStudy'] = json.loads(x['authors'].replace("'",'"'))
except:
x['fieldsOfStudy'] = []
x['inCitations'] = json.loads(x['inCitations'].replace("'",'"'))
x['outCitations'] = json.loads(x['outCitations'].replace("'",'"'))
try:
x['pdfUrls'] = json.loads(x['pdfUrls'].replace("'",'"'))
except:
x['pdfUrls'] = []
x['page_rank'] = x['page_rank'] * 10**8
return x
df1 = df.replace(np.nan, '', regex=True)
df1 = df1[['id','entities', 'magId',
'journalVolume', 'journalPages', 'pmid', 'fieldsOfStudy', 'year',
'outCitations', 's2Url', 's2PdfUrl', 'authors', 'journalName',
'paperAbstract', 'inCitations', 'pdfUrls', 'title', 'doi', 'sources',
'doiUrl', 'venue', 'num_out_ctn', 'num_in_ctn', 'num_fields',
'ontology_enhanced', 'ontology_semantic', 'ontology_syntactic',
'ontology_union', 'page_rank']]
df1 = df1.apply(lambda x:parse_rows(x),axis=1)
return df1
def load_main_csvs(s3_paths):
from metaflow import S3
import pandas
def form_df(pth):
try:
df = pandas.read_csv(pth.path)
print(f"Retrieved Df for Key {pth.key}")
return df
except:
print(f"Couldn't Extract Dataframe For {pth.key}")
return None
n = 0
for pth in s3_paths:
with S3(s3root=ONTOLOGY_CSV_PATH) as s3:
s3_obj = s3.get(pth)
df = form_df(s3_obj)
if df is None:
continue
yield (df,pth)
n+=1
if __name__ == '__main__':
sync_data()