192 lines
6.4 KiB
Python
192 lines
6.4 KiB
Python
from pathlib import Path
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from corpusparser import Parser
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import argparse
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import logging
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import json
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from pymongo import MongoClient
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import pymongo
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import sys
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from multiprocessing import Pool
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import time
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CORPORA = ["kres", "ssj"]
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# logging.basicConfig(filename=Path("/var/tmp/corpusparser.log"), filemode='a', level=logging.INFO)
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logger = logging.getLogger(__name__)
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# lfh = logging.FileHandler("/project/logs/fill-database.log")
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lfh = logging.StreamHandler(sys.stdout)
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formatter = logging.Formatter("%(asctime)s %(levelname)s %(message)s")
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lfh.setFormatter(formatter)
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logger.addHandler(lfh)
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logger.setLevel(logging.INFO)
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n_chunks = -1
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def enriched_lemma(token):
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return (token["lemma"] if token["msd"][0] == "G" else token["lemma"] + "_")
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def _helper_tid_to_token(tid, tokens):
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for t in tokens:
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if t["tid"] == tid:
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return t
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return None
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def _db_preprocess(e):
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if e["srl_links"] is None:
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e["headwords"] = []
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e["functors"] = []
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else:
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hw_tids = list(set([x["from"] for x in e["srl_links"]]))
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hw_tokens = [_helper_tid_to_token(tid, e["tokens"]) for tid in hw_tids]
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headwords = [enriched_lemma(t) for t in hw_tokens]
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e["headwords"] = headwords
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functors = list(set([x["afun"] for x in e["srl_links"]]))
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e["functors"] = functors
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return e
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# handler for concurrency
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def _handle_kres_file_chunk(kres_file_chunk):
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tstart = time.time()
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kres_chunk_idx = kres_file_chunk[0]
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kres_chunk = kres_file_chunk[1]
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dbclient = None
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db_payload = []
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if args.output == "db":
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# mongoclient needs to be created after forking
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dbclient = MongoClient(
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"mongodb://{}".format(args.dbaddr),
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username=args.dbuser,
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password=args.dbpass,
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authSource="valdb",
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authMechanism='SCRAM-SHA-1'
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)
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# dbclient.valdb["kres"]
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for kres_file in kres_chunk:
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try:
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kres_data = kres_parser.parse_xml_file(kres_file)
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except:
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logger.error("Failed to parse file: {}".format(kres_file))
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continue
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if args.output == "file":
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kres_outdir = outdir / "kres_json"
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kres_outdir.mkdir(parents=True, exist_ok=True)
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kres_outfile = kres_outdir / Path(kres_file.name.split(".")[0]).with_suffix(".json")
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with kres_outfile.open("w") as fp:
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json.dump(kres_data, fp)
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elif args.output == "db":
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"""
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if dbclient.valdb["kres"].find({"sid": kres_data[0]["sid"]}).count() > 0:
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logger.info("File {} already in DB, closing chunk ({}/{})".format(
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kres_file, kres_chunk_idx, n_chunks))
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dbclient.close()
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return
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"""
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kres_data_1 = [_db_preprocess(x) for x in kres_data]
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db_payload += kres_data_1
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try:
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dbclient.valdb["kres"].insert_many(db_payload, ordered=False) # much much better (just make sure sid has a unique index)
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except:
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logger.error("Failed inserting kres files chunk ({}/{}) in {:.2f} s".format(
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kres_chunk_idx, n_chunks, time.time() - tstart))
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logger.info("Db insert: chunks ({}/{}) in {:.2f} s".format(
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kres_chunk_idx, n_chunks, time.time() - tstart))
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dbclient.close()
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def _get_dbclient(args):
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dbclient = MongoClient(
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"mongodb://{}".format(args.dbaddr),
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username=args.dbuser,
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password=args.dbpass,
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authSource="valdb",
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authMechanism='SCRAM-SHA-1'
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)
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return dbclient
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Parsing corpora kres and ssj500k.")
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parser.add_argument('--corpus', required=True)
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parser.add_argument('--kres-folder', required=False)
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parser.add_argument('--kres-srl-folder', required=False)
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parser.add_argument('--ssj-file', required=False)
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parser.add_argument('--output', required=False, default=None)
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parser.add_argument('--outdir', required=False, default=None)
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parser.add_argument('--dbaddr', required=False, default=None)
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parser.add_argument('--dbuser', required=False, default=None)
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parser.add_argument('--dbpass', required=False, default=None)
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parser.add_argument('--cores', required=False, default=1)
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parser.add_argument('--chunk-size', required=False, default=3)
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args = parser.parse_args()
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corpus = args.corpus
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assert (corpus in CORPORA), "Wrong corpus name."
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outdir = None
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if args.output == "file":
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outdir = Path(args.outdir)
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outdir.mkdir(parents=True, exist_ok=True)
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elif args.output == "db":
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dbclient = _get_dbclient(args)
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dbclient.valdb[corpus].ensure_index([("sid", pymongo.ASCENDING)])
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dbclient.valdb[corpus].ensure_index([("headwords", pymongo.ASCENDING)])
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dbclient.valdb[corpus].ensure_index([("functors", pymongo.ASCENDING)])
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dbclient.close()
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if corpus == "ssj":
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logger.info("Parsing Ssj: {}".format(args.ssj_file))
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ssj_parser = Parser(logger=logger, corpus="ssj")
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ssj_data = ssj_parser.parse_xml_file(Path(args.ssj_file))
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if args.output == "file":
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ssj_outfile = outdir / "ssj500k.json"
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with ssj_outfile.open("w") as fp:
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json.dump(ssj_data, fp)
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elif args.output == "db":
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dbclient = _get_dbclient(args)
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valdb = dbclient.valdb
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ssj_col = valdb["ssj"]
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for sentence in ssj_data:
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sentence = _db_preprocess(sentence)
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ssj_col.update({"sid": sentence["sid"]}, sentence, upsert=True)
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dbclient.close()
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if corpus == "kres":
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# Kres
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logger.info("Parsing Kres: {}".format(args.kres_folder))
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kres_parser = Parser(
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logger=logger,
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corpus="kres",
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kres_srl_folder=args.kres_srl_folder
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)
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kres_files = [x for x in Path(args.kres_folder).iterdir()]
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kres_files = sorted(kres_files, key=lambda x: x.name)
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kres_files_chunks = []
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i = 0
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while i < len(kres_files):
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# kres_files_chunks += kres_files[i:(i+args.chunk_size)]
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new_i = i + int(args.chunk_size)
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kres_files_chunks += [kres_files[i:new_i]]
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i = new_i
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kres_files_chunks = [x for x in enumerate(kres_files_chunks)]
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n_chunks = len(kres_files_chunks)
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p = Pool(int(args.cores))
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p.map(_handle_kres_file_chunk, kres_files_chunks)
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logger.info("Finished parsing.")
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