sporada/nlp
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Patrick Jentsch 2019-05-20 11:28:51 +02:00
parent ed26d24776
commit 5b7bc2a840
3 changed files with 128 additions and 104 deletions
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16
Dockerfile
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@ -1,7 +1,8 @@
FROM debian:stretch-slim FROM debian:stretch-slim
MAINTAINER Patrick Jentsch <p.jentsch@uni-bielefeld.de> LABEL maintainer="inf_sfb1288@lists.uni-bielefeld.de"
ENV DEBIAN_FRONTEND=noninteractive
ENV LANG=C.UTF-8 ENV LANG=C.UTF-8
RUN apt-get update && \ RUN apt-get update && \
@ -9,22 +10,20 @@ RUN apt-get update && \
build-essential \ build-essential \
ca-certificates \ ca-certificates \
python2.7 \ python2.7 \
python3 \ python3.5 \
python3-dev \ python3-dev \
python3-pip \ python3-pip \
python3-setuptools \ python3-setuptools \
wget wget
WORKDIR /root
# Install pyFlow # Install pyFlow
ENV PYFLOW_VERSION 1.1.20 ENV PYFLOW_VERSION 1.1.20
RUN wget -nv https://github.com/Illumina/pyflow/releases/download/v"$PYFLOW_VERSION"/pyflow-"$PYFLOW_VERSION".tar.gz && \ RUN wget -nv https://github.com/Illumina/pyflow/releases/download/v"$PYFLOW_VERSION"/pyflow-"$PYFLOW_VERSION".tar.gz && \
tar -xzf pyflow-"$PYFLOW_VERSION".tar.gz && \ tar -xzf pyflow-"$PYFLOW_VERSION".tar.gz && \
rm pyflow-"$PYFLOW_VERSION".tar.gz && \
cd pyflow-"$PYFLOW_VERSION" && \ cd pyflow-"$PYFLOW_VERSION" && \
python2.7 setup.py build install && \ python2.7 setup.py build install && \
cd .. cd .. && \
rm -r pyflow-"$PYFLOW_VERSION".tar.gz pyflow-"$PYFLOW_VERSION"
# Install spaCy # Install spaCy
RUN pip3 install wheel && pip3 install -U spacy && \ RUN pip3 install wheel && pip3 install -U spacy && \
@ -34,9 +33,8 @@ RUN pip3 install wheel && pip3 install -U spacy && \
python3 -m spacy download fr && \ python3 -m spacy download fr && \
python3 -m spacy download pt python3 -m spacy download pt
RUN mkdir files_for_nlp files_from_nlp
COPY nlp /usr/local/bin COPY nlp /usr/local/bin
COPY spacy_nlp /usr/local/bin COPY spacy_nlp /usr/local/bin
CMD ["/bin/bash"] ENTRYPOINT ["nlp"]
CMD ["--help"]

154
nlp
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@ -18,84 +18,105 @@ from pyflow import WorkflowRunner
def parse_arguments(): def parse_arguments():
parser = argparse.ArgumentParser( parser = argparse.ArgumentParser(
"Performs NLP of documents utilizing spaCy. \ description='Performs NLP of documents utilizing spaCy. The results are served as verticalized text files.'
Output is .vrt."
) )
parser.add_argument("-i", parser.add_argument(
dest="inputDir", '-i',
help="Input directory.", dest='input_dir',
required=True) required=True
parser.add_argument("-l", )
dest='lang', parser.add_argument(
help="Language for NLP", '-l',
required=True) choices=['de', 'en', 'es', 'fr', 'pt'],
parser.add_argument("-o", dest='lang',
dest="outputDir", required=True
help="Output directory.", )
required=True) parser.add_argument(
parser.add_argument("--nCores", '-o',
default=min(4, multiprocessing.cpu_count()), dest='output_dir',
dest="nCores", required=True
help="Total number of cores available.", )
required=False, parser.add_argument(
type=int) '--nCores',
default=min(4, multiprocessing.cpu_count()),
dest='n_cores',
help='total number of cores available',
required=False,
type=int
)
return parser.parse_args() return parser.parse_args()
class NLPWorkflow(WorkflowRunner): class NLPWorkflow(WorkflowRunner):
def __init__(self, jobs, lang, nCores): def __init__(self, args):
self.jobs = jobs self.jobs = analyze_jobs(args.input_dir, args.output_dir)
self.lang = lang self.lang = args.lang
self.nCores = nCores self.n_cores = args.n_cores
def workflow(self): def workflow(self):
### if len(self.jobs) == 0:
# Task "mkdir_job": create output directories return
# Dependencies: None
###
mkdir_jobs = []
mkdir_job_number = 0
for job in self.jobs:
mkdir_job_number += 1
cmd = 'mkdir -p "%s"' % (
job["output_dir"]
)
mkdir_jobs.append(self.addTask(label="mkdir_job_-_%i" % (mkdir_job_number), command=cmd))
### '''
# Task "spacy_nlp_job": perform NLP ' ##################################################
# Dependencies: mkdir_jobs ' # Create output directories #
### ' ##################################################
self.waitForTasks() '''
create_output_directories_jobs = []
for index, job in enumerate(self.jobs):
cmd = 'mkdir -p "%s"' % (job['output_dir'])
create_output_directories_jobs.append(
self.addTask(
command=cmd,
label='create_output_directories_job_-_%i' % (index)
)
)
'''
' ##################################################
' # Natural language processing #
' ##################################################
'''
nlp_jobs = [] nlp_jobs = []
nlp_job_number = 0 nlp_job_n_cores = min(
for job in self.jobs: self.n_cores,
nlp_job_number += 1 max(1, int(self.n_cores / len(self.jobs)))
cmd = 'spacy_nlp -i "%s" -o "%s" -l "%s"' % ( )
job["path"], for index, job in enumerate(self.jobs):
os.path.join(job["output_dir"], os.path.basename(job["path"]).rsplit(".", 1)[0] + ".vrt"), cmd = 'spacy_nlp -l "%s" "%s" "%s"' % (
self.lang self.lang,
job['path'],
os.path.join(job['output_dir'], job['name'] + '.vrt')
)
nlp_jobs.append(
self.addTask(
command=cmd,
dependencies='create_output_directories_job_-_%i' % (index),
label='nlp_job_-_%i' % (index),
nCores=nlp_job_n_cores
)
) )
nlp_jobs.append(self.addTask(label="nlp_job_-_%i" % (nlp_job_number), command=cmd, dependencies=mkdir_jobs, nCores=min(4, self.nCores)))
def analyze_jobs(inputDir, outputDir, level=1): def analyze_jobs(input_dir, output_dir):
jobs = [] jobs = []
if level > 2: for file in os.listdir(input_dir):
return jobs if os.path.isdir(os.path.join(input_dir, file)):
for file in os.listdir(inputDir):
if os.path.isdir(os.path.join(inputDir, file)):
jobs += analyze_jobs( jobs += analyze_jobs(
os.path.join(inputDir, file), os.path.join(input_dir, file),
os.path.join(outputDir, file), os.path.join(output_dir, file),
level + 1 )
elif file.endswith('.txt'):
jobs.append(
{
'filename': file,
'name': file.rsplit('.', 1)[0],
'output_dir': os.path.join(output_dir, file),
'path': os.path.join(input_dir, file)
}
) )
elif file.endswith(".txt"):
jobs.append({"path": os.path.join(inputDir, file), "output_dir": os.path.join(outputDir, file.rsplit(".", 1)[0])})
return jobs return jobs
@ -103,15 +124,12 @@ def analyze_jobs(inputDir, outputDir, level=1):
def main(): def main():
args = parse_arguments() args = parse_arguments()
wflow = NLPWorkflow( wflow = NLPWorkflow(args)
analyze_jobs(args.inputDir, args.outputDir),
args.lang, retval = wflow.run(dataDirRoot=args.output_dir, nCores=args.n_cores)
args.nCores
)
retval = wflow.run(nCores=args.nCores)
sys.exit(retval) sys.exit(retval)
if __name__ == "__main__": if __name__ == '__main__':
main() main()

62
spacy_nlp
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@ -1,48 +1,53 @@
#!/usr/bin/env python3 #!/usr/bin/env python3
# coding=utf-8 # coding=utf-8
import argparse import argparse
import os import os
import spacy import spacy
import textwrap import textwrap
parser = argparse.ArgumentParser(
parser = argparse.ArgumentParser(description="Tag a .txt file with spaCy and \ description='Tag a text file with spaCy and save it as a verticalized text file.'
save it in .vrt format") )
parser.add_argument("-i", parser.add_argument(
dest="input", 'i',
help="Input file.", metavar='txt-sourcefile',
required=True) )
parser.add_argument("-l", parser.add_argument(
choices=["de", "en", "es", "fr", "pt"], '-l',
dest="lang", choices=['de', 'en', 'es', 'fr', 'pt'],
help="Language for tagging", dest='lang',
required=True) required=True
parser.add_argument("-o", )
dest="output", parser.add_argument(
help="Output file.", 'o',
required=True) metavar='vrt-destfile',
)
args = parser.parse_args() args = parser.parse_args()
SPACY_MODELS = {
SPACY_MODELS = {"de": "de_core_news_sm", "en": "en_core_web_sm", 'de': 'de_core_news_sm', 'en': 'en_core_web_sm', 'es': 'es_core_news_sm',
"es": "es_core_news_sm", "fr": "fr_core_news_sm", 'fr': 'fr_core_news_sm', 'pt': 'pt_core_news_sm'
"pt": "pt_core_news_sm"} }
# Set the language model for spacy # Set the language model for spacy
nlp = spacy.load(SPACY_MODELS[args.lang]) nlp = spacy.load(SPACY_MODELS[args.lang])
# Read text from the input file and if neccessary split it into parts with a # Read text from the input file and if neccessary split it into parts with a
# length of less than 1 million characters. # length of less than 1 million characters.
with open(args.input) as input_file: with open(args.i) as input_file:
text = input_file.read() text = input_file.read()
texts = textwrap.wrap(text, 1000000, break_long_words=False) texts = textwrap.wrap(text, 1000000, break_long_words=False)
text = None text = None
# Create and open the output file # Create and open the output file
output_file = open(args.output, "w+") output_file = open(args.o, 'w+')
output_file.write('<?xml version="1.0" encoding="UTF-8"?>\n<corpus>\n<text id="' + os.path.basename(args.input).rsplit(".", 1)[0] + '">\n')
output_file.write(
'<?xml version="1.0" encoding="UTF-8"?>\n<corpus>\n<text id="%s">\n' % (
os.path.basename(args.i).rsplit(".", 1)[0]
)
)
for text in texts: for text in texts:
# Run spacy nlp over the text (partial string if above 1 million chars) # Run spacy nlp over the text (partial string if above 1 million chars)
doc = nlp(text) doc = nlp(text)
@ -54,9 +59,12 @@ for text in texts:
continue continue
# Write all information in .vrt style to the output file # Write all information in .vrt style to the output file
# text, lemma, simple_pos, pos, ner # text, lemma, simple_pos, pos, ner
output_file.write(token.text + "\t" + token.lemma_ + "\t" output_file.write(
+ token.pos_ + "\t" + token.tag_ + "\t" token.text + '\t' + token.lemma_ + '\t'
+ (token.ent_type_ if token.ent_type_ != "" else "NULL") + "\n") + token.pos_ + '\t' + token.tag_ + '\t'
+ (token.ent_type_ if token.ent_type_ != '' else 'NULL') + '\n'
)
output_file.write('</s>\n') output_file.write('</s>\n')
output_file.write('</text>\n</corpus>') output_file.write('</text>\n</corpus>')
output_file.close() output_file.close()