Time stuff and switch to gensim for tokenisation
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@ -16,7 +16,8 @@ COPY requirements.txt /code/
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COPY discord-patched.tgz /code/
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RUN python -m venv /venv
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RUN . /venv/bin/activate && pip install -r requirements.txt && python -m spacy download en_core_web_sm
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RUN . /venv/bin/activate && pip install -r requirements.txt
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# && python -m spacy download en_core_web_sm
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RUN tar xf /code/discord-patched.tgz -C /venv/lib/python3.10/site-packages
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@ -15,7 +15,8 @@ pycld2
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morfessor
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six
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nltk
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spacy
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#spacy
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gensim
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python-Levenshtein
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orjson
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uvloop
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@ -5,6 +5,9 @@ import random
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# For key generation
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import string
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# For timing
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import time
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# Squash errors
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import warnings
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from concurrent.futures import ProcessPoolExecutor
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@ -16,11 +19,21 @@ from math import ceil
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import orjson
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import regex
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# Tokenisation
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import spacy
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# For 4chan message parsing
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from bs4 import BeautifulSoup
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# Tokenisation
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# import spacy
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from gensim.parsing.preprocessing import ( # stem_text,
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preprocess_string,
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remove_stopwords,
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strip_multiple_whitespaces,
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strip_non_alphanum,
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strip_numeric,
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strip_punctuation,
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strip_short,
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strip_tags,
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)
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from numpy import array_split
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from polyglot.detect.base import logger as polyglot_logger
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@ -38,30 +51,17 @@ import util
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# 4chan schema
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from schemas.ch4_s import ATTRMAP
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# For tokenisation
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# from gensim.parsing.preprocessing import (
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# strip_tags,
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# strip_punctuation,
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# strip_numeric,
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# stem_text,
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# strip_multiple_whitespaces,
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# strip_non_alphanum,
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# remove_stopwords,
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# strip_short,
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# preprocess_string,
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# )
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# CUSTOM_FILTERS = [
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# lambda x: x.lower(),
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# strip_tags, #
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# strip_punctuation, #
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# strip_multiple_whitespaces,
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# strip_numeric,
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# remove_stopwords,
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# strip_short,
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# #stem_text,
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# strip_non_alphanum, #
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# ]
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CUSTOM_FILTERS = [
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lambda x: x.lower(),
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strip_tags, #
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strip_punctuation, #
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strip_multiple_whitespaces,
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strip_numeric,
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remove_stopwords,
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strip_short,
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# stem_text,
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strip_non_alphanum, #
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]
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RE_BAD_CHARS = regex.compile(r"[\p{Cc}\p{Cs}]+")
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@ -70,8 +70,8 @@ polyglot_logger.setLevel("ERROR")
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warnings.filterwarnings("ignore", category=UserWarning, module="bs4")
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TAGS = ["NOUN", "ADJ", "VERB", "ADV"]
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nlp = spacy.load("en_core_web_sm", disable=["parser", "ner"])
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# TAGS = ["NOUN", "ADJ", "VERB", "ADV"]
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# nlp = spacy.load("en_core_web_sm", disable=["parser", "ner"])
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log = util.get_logger("process")
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@ -133,29 +133,48 @@ async def spawn_processing_threads(data):
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def process_data(data):
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to_store = []
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sentiment_time = 0.0
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regex_time = 0.0
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polyglot_time = 0.0
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date_time = 0.0
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nlp_time = 0.0
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normalise_time = 0.0
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hash_time = 0.0
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normal2_time = 0.0
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soup_time = 0.0
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total_time = 0.0
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# Initialise sentiment analyser
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analyzer = SentimentIntensityAnalyzer()
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for msg in data:
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total_start = time.process_time()
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# normalise fields
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start = time.process_time()
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for key, value in list(msg.items()):
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if value is None:
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del msg[key]
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time_took = (time.process_time() - start) * 1000
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normalise_time += time_took
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# Remove invalid UTF-8 characters
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# IRC and Discord
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start = time.process_time()
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if "msg" in msg:
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msg["msg"] = RE_BAD_CHARS.sub("", msg["msg"])
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# 4chan - since we change the attributes below
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if "com" in msg:
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msg["com"] = RE_BAD_CHARS.sub("", msg["com"])
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time_took = (time.process_time() - start) * 1000
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regex_time += time_took
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if msg["src"] == "4ch":
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board = msg["net"]
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thread = msg["channel"]
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# Calculate hash for post
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start = time.process_time()
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post_normalised = orjson.dumps(msg, option=orjson.OPT_SORT_KEYS)
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hash = siphash(hash_key, post_normalised)
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hash = str(hash)
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@ -169,11 +188,18 @@ def process_data(data):
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else:
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msg["type"] = "update"
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db.r.set(redis_key, hash)
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time_took = (time.process_time() - start) * 1000
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hash_time += time_took
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start = time.process_time()
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for key2, value in list(msg.items()):
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if key2 in ATTRMAP:
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msg[ATTRMAP[key2]] = msg[key2]
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del msg[key2]
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time_took = (time.process_time() - start) * 1000
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normal2_time += time_took
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start = time.process_time()
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if "ts" in msg:
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old_time = msg["ts"]
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# '08/30/22(Tue)02:25:37'
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@ -187,15 +213,22 @@ def process_data(data):
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msg["ts"] = new_ts
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else:
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raise Exception("No TS in msg")
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time_took = (time.process_time() - start) * 1000
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date_time += time_took
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start = time.process_time()
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if "msg" in msg:
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soup = BeautifulSoup(msg["msg"], "html.parser")
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msg_str = soup.get_text(separator="\n")
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msg["msg"] = msg_str
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time_took = (time.process_time() - start) * 1000
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soup_time += time_took
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# Annotate sentiment/NLP
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if "msg" in msg:
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RE_BAD_CHARS.sub("", msg["msg"])
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# RE_BAD_CHARS.sub("", msg["msg"])
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# Language
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start = time.process_time()
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text = Text(msg["msg"])
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try:
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lang_code = text.language.code
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@ -206,22 +239,45 @@ def process_data(data):
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log.error(f"Error detecting language: {e}")
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# So below block doesn't fail
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lang_code = None
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time_took = (time.process_time() - start) * 1000
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polyglot_time += time_took
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# Blatant discrimination
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if lang_code == "en":
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# Sentiment
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start = time.process_time()
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vs = analyzer.polarity_scores(str(msg["msg"]))
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addendum = vs["compound"]
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msg["sentiment"] = addendum
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time_took = (time.process_time() - start) * 1000
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sentiment_time += time_took
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# Tokens
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n = nlp(msg["msg"])
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for tag in TAGS:
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tag_name = tag.lower()
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tags_flag = [token.lemma_ for token in n if token.pos_ == tag]
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msg[f"words_{tag_name}"] = tags_flag
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# Tokens
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start = time.process_time()
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tokens = preprocess_string(msg["msg"], CUSTOM_FILTERS)
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msg["tokens"] = tokens
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# n = nlp(msg["msg"])
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# for tag in TAGS:
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# tag_name = tag.lower()
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# tags_flag = [token.lemma_ for token in n if token.pos_ == tag]
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# msg[f"words_{tag_name}"] = tags_flag
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time_took = (time.process_time() - start) * 1000
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nlp_time += time_took
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# Add the mutated message to the return buffer
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to_store.append(msg)
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total_time += (time.process_time() - total_start) * 1000
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log.debug("=====================================")
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log.debug(f"Sentiment: {sentiment_time}")
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log.debug(f"Regex: {regex_time}")
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log.debug(f"Polyglot: {polyglot_time}")
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log.debug(f"Date: {date_time}")
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log.debug(f"NLP: {nlp_time}")
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log.debug(f"Normalise: {normalise_time}")
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log.debug(f"Hash: {hash_time}")
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log.debug(f"Normal2: {normal2_time}")
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log.debug(f"Soup: {soup_time}")
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log.debug(f"Total: {total_time}")
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log.debug("=====================================")
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return to_store
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@ -16,7 +16,8 @@ pycld2
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morfessor
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six
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nltk
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spacy
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#spacy
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gensim
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python-Levenshtein
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orjson
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uvloop
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