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Codestyle
This commit is contained in:
Patrick Jentsch
parent
43717de313
commit
af293d6141
48
nlp
48
nlp
@ -18,33 +18,23 @@ from pyflow import WorkflowRunner
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def parse_arguments():
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parser = argparse.ArgumentParser(
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description='Performs NLP of documents utilizing spaCy. The results are served as verticalized text files.'
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)
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parser.add_argument(
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'-i',
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dest='input_dir',
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required=True
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description=('Performs NLP of documents utilizing spaCy. The results '
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'are served as verticalized text files.')
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)
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parser.add_argument('-i', dest='input_dir', required=True)
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parser.add_argument(
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'-l',
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choices=['de', 'el', 'en', 'es', 'fr', 'it', 'nl', 'pt'],
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dest='lang',
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required=True
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)
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parser.add_argument(
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'-o',
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dest='output_dir',
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required=True
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)
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parser.add_argument(
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'--nCores',
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parser.add_argument('-o', dest='output_dir', required=True)
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parser.add_argument('--nCores',
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default=min(4, multiprocessing.cpu_count()),
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dest='n_cores',
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help='total number of cores available',
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required=False,
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type=int
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)
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type=int)
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return parser.parse_args()
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@ -65,11 +55,11 @@ class NLPWorkflow(WorkflowRunner):
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'''
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create_output_directories_jobs = []
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for index, job in enumerate(self.jobs):
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cmd = 'mkdir -p "%s"' % (job['output_dir'])
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cmd = 'mkdir -p "{}"'.format(job['output_dir'])
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create_output_directories_jobs.append(
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self.addTask(
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command=cmd,
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label='create_output_directories_job_-_%i' % (index)
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label='create_output_directories_job_-_{}'.format(index)
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)
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)
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@ -84,7 +74,7 @@ class NLPWorkflow(WorkflowRunner):
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max(1, int(self.n_cores / len(self.jobs)))
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)
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for index, job in enumerate(self.jobs):
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cmd = 'spacy_nlp -l "%s" "%s" "%s"' % (
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cmd = 'spacy_nlp -l "{}" "{}" "{}"'.format(
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self.lang,
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job['path'],
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os.path.join(job['output_dir'], job['name'] + '.vrt')
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@ -92,8 +82,10 @@ class NLPWorkflow(WorkflowRunner):
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nlp_jobs.append(
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self.addTask(
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command=cmd,
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dependencies='create_output_directories_job_-_%i' % (index),
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label='nlp_job_-_%i' % (index),
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dependencies='create_output_directories_job_-_{}'.format(
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index
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),
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label='nlp_job_-_{}'.format(index),
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nCores=nlp_job_n_cores
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)
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)
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@ -104,19 +96,13 @@ def analyze_jobs(input_dir, output_dir):
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for file in os.listdir(input_dir):
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if os.path.isdir(os.path.join(input_dir, file)):
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jobs += analyze_jobs(
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os.path.join(input_dir, file),
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os.path.join(output_dir, file),
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)
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jobs += analyze_jobs(os.path.join(input_dir, file),
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os.path.join(output_dir, file))
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elif file.endswith('.txt'):
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jobs.append(
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{
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'filename': file,
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jobs.append({'filename': file,
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'name': file.rsplit('.', 1)[0],
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'output_dir': os.path.join(output_dir, file),
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'path': os.path.join(input_dir, file)
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}
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)
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'path': os.path.join(input_dir, file)})
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return jobs
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53
spacy_nlp
53
spacy_nlp
@ -7,29 +7,25 @@ import spacy
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import textwrap
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parser = argparse.ArgumentParser(
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description='Tag a text file with spaCy and save it as a verticalized text file.'
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description=('Tag a text file with spaCy and save it as a verticalized '
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'text file.')
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)
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parser.add_argument(
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'i',
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metavar='txt-sourcefile',
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)
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parser.add_argument(
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'-l',
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parser.add_argument('i', metavar='txt-sourcefile')
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parser.add_argument('-l',
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choices=['de', 'el', 'en', 'es', 'fr', 'it', 'nl', 'pt'],
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dest='lang',
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required=True
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)
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parser.add_argument(
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'o',
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metavar='vrt-destfile',
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)
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required=True)
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parser.add_argument('o', metavar='vrt-destfile')
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args = parser.parse_args()
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SPACY_MODELS = {
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'de': 'de_core_news_sm', 'el': 'el_core_news_sm', 'en': 'en_core_web_sm',
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'es': 'es_core_news_sm', 'fr': 'fr_core_news_sm', 'it': 'it_core_news_sm',
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'nl': 'nl_core_news_sm', 'pt': 'pt_core_news_sm'
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}
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SPACY_MODELS = {'de': 'de_core_news_sm',
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'el': 'el_core_news_sm',
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'en': 'en_core_web_sm',
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'es': 'es_core_news_sm',
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'fr': 'fr_core_news_sm',
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'it': 'it_core_news_sm',
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'nl': 'nl_core_news_sm',
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'pt': 'pt_core_news_sm'}
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# Set the language model for spacy
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nlp = spacy.load(SPACY_MODELS[args.lang])
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@ -45,9 +41,9 @@ with open(args.i) as input_file:
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output_file = open(args.o, 'w+')
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output_file.write(
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'<?xml version="1.0" encoding="UTF-8"?>\n<corpus>\n<text id="%s">\n' % (
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os.path.basename(args.i).rsplit(".", 1)[0]
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)
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'<?xml version="1.0" encoding="UTF-8"?>\n'
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'<corpus>\n'
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'<text id="{}">\n'.format(os.path.basename(args.i).rsplit(".", 1)[0])
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)
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for text in texts:
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# Run spacy nlp over the text (partial string if above 1 million chars)
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@ -61,11 +57,18 @@ for text in texts:
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# Write all information in .vrt style to the output file
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# text, lemma, simple_pos, pos, ner
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output_file.write(
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token.text + '\t' + token.lemma_ + '\t'
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+ token.pos_ + '\t' + token.tag_ + '\t'
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+ (token.ent_type_ if token.ent_type_ != '' else 'NULL') + '\n'
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'{}\t{}\t{}\t{}\t{}\n'.format(
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token.text,
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token.lemma_,
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token.pos_,
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token.tag_,
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token.ent_type_ if token.ent_type_ != '' else 'NULL'
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)
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)
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output_file.write('</s>\n')
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output_file.write('</text>\n</corpus>')
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output_file.write(
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'</text>\n'
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'</corpus>'
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)
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output_file.close()
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