mirror of
https://github.com/adulau/napkin-text-analysis.git
synced 2024-05-20 14:22:11 +00:00
chg: [cli] black the napkin binary
This commit is contained in:
parent
a2a074436e
commit
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GPG key ID:
09E2CD4944E6CBCD
305
bin/napkin.py
305
bin/napkin.py
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@ -15,21 +15,78 @@ version = "0.9"
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parser = argparse.ArgumentParser(description="Extract statistical analysis of text")
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parser.add_argument('-v', help="verbose output")
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parser.add_argument('-f', help="file to analyse")
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parser.add_argument('-t', help="maximum value for the top list (default is 100) -1 is no limit", default=100)
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parser.add_argument('-s', help="display the overall statistics (default is False)", default=False, action='store_true')
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parser.add_argument('-o', help="output format (default is csv), json, readable", default="csv")
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parser.add_argument('-l', help="language used for the analysis (default is en)", default="en")
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parser.add_argument('-i', help="Use stdin instead of a filename", default=False, action='store_true')
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parser.add_argument('--verbatim', help="Don't use the lemmatized form, use verbatim. (default is the lematized form)", default=False, action='store_true')
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parser.add_argument('--no-flushdb', help="Don't flush the redisdb, useful when you want to process multiple files and aggregate the results. (by default the redis database is flushed at each run)", default=False, action='store_true')
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parser.add_argument('--binary', help="set output in binary instead of UTF-8 (default)", default=False, action='store_true')
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parser.add_argument('--analysis', help="Limit output to a specific analysis (verb, noun, hashtag, mention, digit, url, oov, labels, punct). (Default is all analysis are displayed)", default='all')
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parser.add_argument('--disable-parser', help="disable parser component in Spacy", default=False, action='store_true')
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parser.add_argument('--disable-tagger', help="disable tagger component in Spacy", default=False, action='store_true')
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parser.add_argument('--token-span', default= None, help='Find the sentences where a specific token is located')
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parser.add_argument('--table-format', help="set tabulate format (default is fancy_grid)", default="fancy_grid")
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parser.add_argument('--full-labels', help="store each label value in a ranked set (default is False)", action='store_true', default=False)
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#parser.add_argument('--geolocation', help="export geolocation (default is False)", action='store_true', default=False)
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parser.add_argument(
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'-t',
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help="maximum value for the top list (default is 100) -1 is no limit",
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default=100,
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)
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parser.add_argument(
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'-s',
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help="display the overall statistics (default is False)",
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default=False,
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action='store_true',
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)
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parser.add_argument(
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'-o', help="output format (default is csv), json, readable", default="csv"
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)
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parser.add_argument(
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'-l', help="language used for the analysis (default is en)", default="en"
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)
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parser.add_argument(
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'-i', help="Use stdin instead of a filename", default=False, action='store_true'
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)
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parser.add_argument(
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'--verbatim',
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help="Don't use the lemmatized form, use verbatim. (default is the lematized form)",
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default=False,
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action='store_true',
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)
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parser.add_argument(
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'--no-flushdb',
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help="Don't flush the redisdb, useful when you want to process multiple files and aggregate the results. (by default the redis database is flushed at each run)",
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default=False,
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action='store_true',
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)
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parser.add_argument(
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'--binary',
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help="set output in binary instead of UTF-8 (default)",
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default=False,
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action='store_true',
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)
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parser.add_argument(
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'--analysis',
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help="Limit output to a specific analysis (verb, noun, hashtag, mention, digit, url, oov, labels, punct). (Default is all analysis are displayed)",
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default='all',
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)
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parser.add_argument(
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'--disable-parser',
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help="disable parser component in Spacy",
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default=False,
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action='store_true',
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)
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parser.add_argument(
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'--disable-tagger',
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help="disable tagger component in Spacy",
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default=False,
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action='store_true',
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)
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parser.add_argument(
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'--token-span',
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default=None,
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help='Find the sentences where a specific token is located',
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)
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parser.add_argument(
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'--table-format',
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help="set tabulate format (default is fancy_grid)",
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default="fancy_grid",
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)
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parser.add_argument(
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'--full-labels',
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help="store each label value in a ranked set (default is False)",
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action='store_true',
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default=False,
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)
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# parser.add_argument('--geolocation', help="export geolocation (default is False)", action='store_true', default=False)
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args = parser.parse_args()
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@ -37,11 +94,13 @@ if args.f is None and not args.i:
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parser.print_help()
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sys.exit()
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#if args.geolocation:
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# if args.geolocation:
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# args.full_labels = True
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if not args.binary:
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redisdb = redis.Redis(host="localhost", port=6379, db=5, encoding='utf-8', decode_responses=True)
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redisdb = redis.Redis(
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host="localhost", port=6379, db=5, encoding='utf-8', decode_responses=True
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)
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else:
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redisdb = redis.Redis(host="localhost", port=6379, db=5)
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@ -61,7 +120,7 @@ if args.disable_tagger:
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disable.append("tagger")
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if args.l == "fr":
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try :
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try:
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nlp = spacy.load("fr_core_news_md", disable=disable)
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except:
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print("Downloading missing model")
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@ -90,116 +149,148 @@ if args.i:
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detect_lang = cld3.get_language(text)
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if detect_lang[0] != args.l:
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sys.exit("Language detected ({}) is different than the NLP used ({})".format(detect_lang[0], args.l))
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sys.exit(
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"Language detected ({}) is different than the NLP used ({})".format(
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detect_lang[0], args.l
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)
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)
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doc = nlp(text)
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analysis = ["verb", "noun", "hashtag", "mention",
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"digit", "url", "oov", "labels",
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"punct", "email"]
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analysis = [
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"verb",
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"noun",
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"hashtag",
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"mention",
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"digit",
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"url",
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"oov",
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"labels",
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"punct",
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"email",
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]
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if args.token_span and not disable:
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analysis.append("span")
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redisdb.hset("stats", "token", doc.__len__())
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labels = [ "EVENT", "PERCENT", "MONEY", "FAC", "TIME", "QUANTITY", "WORK_OF_ART", "LANGUAGE", "PRODUCT", "LOC", "LAW", "DATE", "ORDINAL", "NORP", "ORG", "CARDINAL", "GPE", "PERSON"]
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labels = [
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"EVENT",
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"PERCENT",
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"MONEY",
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"FAC",
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"TIME",
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"QUANTITY",
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"WORK_OF_ART",
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"LANGUAGE",
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"PRODUCT",
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"LOC",
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"LAW",
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"DATE",
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"ORDINAL",
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"NORP",
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"ORG",
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"CARDINAL",
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"GPE",
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"PERSON",
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]
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for entity in doc.ents:
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redisdb.zincrby("labels", 1, entity.label_)
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if not args.full_labels:
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continue
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if entity.label_ in labels:
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redisdb.zincrby("label:{}".format(entity.label_), 1, entity.text)
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redisdb.zincrby("labels", 1, entity.label_)
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if not args.full_labels:
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continue
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if entity.label_ in labels:
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redisdb.zincrby("label:{}".format(entity.label_), 1, entity.text)
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for token in doc:
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if args.token_span is not None and not disable:
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if token.text == args.token_span:
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redisdb.zincrby("span", 1, token.sent.as_doc().text)
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if token.pos_ == "VERB" and not token.is_oov and len(token) > 1:
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if not args.verbatim:
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redisdb.zincrby("verb", 1, token.lemma_)
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else:
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redisdb.zincrby("verb", 1, token.text)
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redisdb.hincrby("stats", "verb", 1)
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continue
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if token.pos_ == "NOUN" and not token.is_oov and len(token) > 1:
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if not args.verbatim:
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redisdb.zincrby("noun", 1, token.lemma_)
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else:
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redisdb.zincrby("noun", 1, token.text)
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redisdb.hincrby("stats", "noun", 1)
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continue
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if token.pos_ == "PUNCT" and not token.is_oov:
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redisdb.zincrby("punct", 1, "{}".format(token))
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redisdb.hincrby("stats", "punct", 1)
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continue
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if args.token_span is not None and not disable:
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if token.text == args.token_span:
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redisdb.zincrby("span", 1, token.sent.as_doc().text)
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if token.pos_ == "VERB" and not token.is_oov and len(token) > 1:
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if not args.verbatim:
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redisdb.zincrby("verb", 1, token.lemma_)
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else:
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redisdb.zincrby("verb", 1, token.text)
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redisdb.hincrby("stats", "verb", 1)
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continue
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if token.pos_ == "NOUN" and not token.is_oov and len(token) > 1:
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if not args.verbatim:
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redisdb.zincrby("noun", 1, token.lemma_)
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else:
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redisdb.zincrby("noun", 1, token.text)
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redisdb.hincrby("stats", "noun", 1)
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continue
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if token.pos_ == "PUNCT" and not token.is_oov:
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redisdb.zincrby("punct", 1, "{}".format(token))
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redisdb.hincrby("stats", "punct", 1)
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continue
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if token.is_oov:
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value = "{}".format(token)
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if value.startswith('#'):
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redisdb.zincrby("hashtag", 1, value[1:])
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redisdb.hincrby("stats", "hashtag", 1)
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continue
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if value.startswith('@'):
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redisdb.zincrby("mention", 1, value[1:])
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redisdb.hincrby("stats", "mention", 1)
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continue
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if token.is_digit:
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redisdb.zincrby("digit", 1, value)
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redisdb.hincrby("stats", "digit", 1)
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continue
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if token.is_space:
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redisdb.hincrby("stats", "space", 1)
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continue
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if token.like_url:
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redisdb.zincrby("url", 1, value)
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redisdb.hincrby("stats", "url", 1)
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continue
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if token.like_email:
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redisdb.zincrby("email", 1, value)
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redisdb.hincrby("stats", "email", 1)
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continue
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redisdb.zincrby("oov", 1, value)
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redisdb.hincrby("stats", "oov", 1)
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if token.is_oov:
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value = "{}".format(token)
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if value.startswith('#'):
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redisdb.zincrby("hashtag", 1, value[1:])
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redisdb.hincrby("stats", "hashtag", 1)
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continue
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if value.startswith('@'):
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redisdb.zincrby("mention", 1, value[1:])
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redisdb.hincrby("stats", "mention", 1)
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continue
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if token.is_digit:
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redisdb.zincrby("digit", 1, value)
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redisdb.hincrby("stats", "digit", 1)
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continue
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if token.is_space:
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redisdb.hincrby("stats", "space", 1)
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continue
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if token.like_url:
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redisdb.zincrby("url", 1, value)
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redisdb.hincrby("stats", "url", 1)
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continue
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if token.like_email:
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redisdb.zincrby("email", 1, value)
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redisdb.hincrby("stats", "email", 1)
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continue
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redisdb.zincrby("oov", 1, value)
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redisdb.hincrby("stats", "oov", 1)
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if args.o == "json":
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output_json = {"format":"napkin", "version": version}
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output_json = {"format": "napkin", "version": version}
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for anal in analysis:
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more_info = ""
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if args.analysis == "all" or args.analysis == anal:
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pass
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else:
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continue
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if anal == "span":
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more_info = "for {}".format(args.token_span)
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if args.o == "readable":
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previous_value = None
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x = redisdb.zrevrange(anal, 0, args.t, withscores=True, score_cast_func=int)
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more_info = ""
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if args.analysis == "all" or args.analysis == anal:
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pass
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else:
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continue
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if anal == "span":
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more_info = "for {}".format(args.token_span)
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if args.o == "readable":
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previous_value = None
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x = redisdb.zrevrange(anal, 0, args.t, withscores=True, score_cast_func=int)
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if args.o == "csv":
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print()
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elif args.o == "readable":
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header = ["\033[1mTop {} of {} {}\033[0m".format(args.t, anal, more_info)]
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readable_table = []
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elif args.o == "json":
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output_json.update({anal: []})
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for a in x:
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if args.o == "csv":
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print()
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print("{},{},{}".format(anal, a[0], a[1]))
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elif args.o == "readable":
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header = ["\033[1mTop {} of {} {}\033[0m".format(args.t, anal, more_info)]
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readable_table = []
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if previous_value == a[1]:
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readable_table.append(["{}".format(a[0])])
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elif previous_value is None or a[1] < previous_value:
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previous_value = a[1]
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readable_table.append(["{} occurences".format(a[1])])
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readable_table.append(["{}".format(a[0])])
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elif args.o == "json":
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output_json.update({anal:[]})
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for a in x:
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if args.o == "csv":
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print("{},{},{}".format(anal,a[0],a[1]))
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elif args.o == "readable":
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if previous_value == a[1]:
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readable_table.append(["{}".format(a[0])])
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elif previous_value is None or a[1] < previous_value:
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previous_value = a[1]
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readable_table.append(["{} occurences".format(a[1])])
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readable_table.append(["{}".format(a[0])])
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elif args.o == "json":
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output_json[anal].append(a)
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if args.o == "readable":
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print(tabulate(readable_table, header, tablefmt=args.table_format))
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if args.o == "csv":
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print("#")
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output_json[anal].append(a)
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if args.o == "readable":
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print(tabulate(readable_table, header, tablefmt=args.table_format))
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if args.o == "csv":
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print("#")
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if args.s:
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print(redisdb.hgetall('stats'))
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