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Update
This commit is contained in:
Patrick Jentsch
parent
ed26d24776
commit
5b7bc2a840
16
Dockerfile
16
Dockerfile
@ -1,7 +1,8 @@
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FROM debian:stretch-slim
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MAINTAINER Patrick Jentsch <p.jentsch@uni-bielefeld.de>
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LABEL maintainer="inf_sfb1288@lists.uni-bielefeld.de"
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ENV DEBIAN_FRONTEND=noninteractive
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ENV LANG=C.UTF-8
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RUN apt-get update && \
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@ -9,22 +10,20 @@ RUN apt-get update && \
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build-essential \
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ca-certificates \
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python2.7 \
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python3 \
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python3.5 \
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python3-dev \
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python3-pip \
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python3-setuptools \
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wget
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WORKDIR /root
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# Install pyFlow
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ENV PYFLOW_VERSION 1.1.20
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RUN wget -nv https://github.com/Illumina/pyflow/releases/download/v"$PYFLOW_VERSION"/pyflow-"$PYFLOW_VERSION".tar.gz && \
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tar -xzf pyflow-"$PYFLOW_VERSION".tar.gz && \
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rm pyflow-"$PYFLOW_VERSION".tar.gz && \
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cd pyflow-"$PYFLOW_VERSION" && \
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python2.7 setup.py build install && \
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cd ..
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cd .. && \
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rm -r pyflow-"$PYFLOW_VERSION".tar.gz pyflow-"$PYFLOW_VERSION"
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# Install spaCy
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RUN pip3 install wheel && pip3 install -U spacy && \
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@ -34,9 +33,8 @@ RUN pip3 install wheel && pip3 install -U spacy && \
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python3 -m spacy download fr && \
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python3 -m spacy download pt
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RUN mkdir files_for_nlp files_from_nlp
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COPY nlp /usr/local/bin
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COPY spacy_nlp /usr/local/bin
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CMD ["/bin/bash"]
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ENTRYPOINT ["nlp"]
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CMD ["--help"]
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154
nlp
154
nlp
@ -18,84 +18,105 @@ from pyflow import WorkflowRunner
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def parse_arguments():
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parser = argparse.ArgumentParser(
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"Performs NLP of documents utilizing spaCy. \
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Output is .vrt."
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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("-i",
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dest="inputDir",
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help="Input directory.",
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required=True)
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parser.add_argument("-l",
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dest='lang',
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help="Language for NLP",
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required=True)
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parser.add_argument("-o",
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dest="outputDir",
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help="Output directory.",
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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="nCores",
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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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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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)
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parser.add_argument(
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'-l',
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choices=['de', 'en', 'es', 'fr', '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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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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return parser.parse_args()
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class NLPWorkflow(WorkflowRunner):
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def __init__(self, jobs, lang, nCores):
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self.jobs = jobs
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self.lang = lang
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self.nCores = nCores
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def __init__(self, args):
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self.jobs = analyze_jobs(args.input_dir, args.output_dir)
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self.lang = args.lang
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self.n_cores = args.n_cores
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def workflow(self):
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###
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# Task "mkdir_job": create output directories
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# Dependencies: None
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###
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mkdir_jobs = []
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mkdir_job_number = 0
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for job in self.jobs:
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mkdir_job_number += 1
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cmd = 'mkdir -p "%s"' % (
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job["output_dir"]
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)
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mkdir_jobs.append(self.addTask(label="mkdir_job_-_%i" % (mkdir_job_number), command=cmd))
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if len(self.jobs) == 0:
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return
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###
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# Task "spacy_nlp_job": perform NLP
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# Dependencies: mkdir_jobs
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###
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self.waitForTasks()
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'''
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' ##################################################
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' # Create output directories #
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' ##################################################
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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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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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)
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)
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'''
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' ##################################################
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' # Natural language processing #
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' ##################################################
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'''
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nlp_jobs = []
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nlp_job_number = 0
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for job in self.jobs:
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nlp_job_number += 1
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cmd = 'spacy_nlp -i "%s" -o "%s" -l "%s"' % (
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job["path"],
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os.path.join(job["output_dir"], os.path.basename(job["path"]).rsplit(".", 1)[0] + ".vrt"),
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self.lang
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nlp_job_n_cores = min(
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self.n_cores,
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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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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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)
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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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nCores=nlp_job_n_cores
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)
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)
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nlp_jobs.append(self.addTask(label="nlp_job_-_%i" % (nlp_job_number), command=cmd, dependencies=mkdir_jobs, nCores=min(4, self.nCores)))
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def analyze_jobs(inputDir, outputDir, level=1):
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def analyze_jobs(input_dir, output_dir):
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jobs = []
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if level > 2:
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return jobs
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for file in os.listdir(inputDir):
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if os.path.isdir(os.path.join(inputDir, file)):
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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(inputDir, file),
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os.path.join(outputDir, file),
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level + 1
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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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elif file.endswith('.txt'):
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jobs.append(
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{
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'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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elif file.endswith(".txt"):
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jobs.append({"path": os.path.join(inputDir, file), "output_dir": os.path.join(outputDir, file.rsplit(".", 1)[0])})
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return jobs
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@ -103,15 +124,12 @@ def analyze_jobs(inputDir, outputDir, level=1):
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def main():
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args = parse_arguments()
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wflow = NLPWorkflow(
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analyze_jobs(args.inputDir, args.outputDir),
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args.lang,
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args.nCores
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)
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wflow = NLPWorkflow(args)
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retval = wflow.run(dataDirRoot=args.output_dir, nCores=args.n_cores)
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retval = wflow.run(nCores=args.nCores)
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sys.exit(retval)
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if __name__ == "__main__":
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if __name__ == '__main__':
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main()
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62
spacy_nlp
62
spacy_nlp
@ -1,48 +1,53 @@
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#!/usr/bin/env python3
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# coding=utf-8
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import argparse
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import os
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import spacy
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import textwrap
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parser = argparse.ArgumentParser(description="Tag a .txt file with spaCy and \
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save it in .vrt format")
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parser.add_argument("-i",
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dest="input",
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help="Input file.",
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required=True)
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parser.add_argument("-l",
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choices=["de", "en", "es", "fr", "pt"],
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dest="lang",
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help="Language for tagging",
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required=True)
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parser.add_argument("-o",
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dest="output",
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help="Output file.",
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required=True)
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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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)
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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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choices=['de', 'en', 'es', 'fr', '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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args = parser.parse_args()
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SPACY_MODELS = {"de": "de_core_news_sm", "en": "en_core_web_sm",
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"es": "es_core_news_sm", "fr": "fr_core_news_sm",
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"pt": "pt_core_news_sm"}
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SPACY_MODELS = {
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'de': 'de_core_news_sm', 'en': 'en_core_web_sm', 'es': 'es_core_news_sm',
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'fr': 'fr_core_news_sm', 'pt': 'pt_core_news_sm'
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}
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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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# Read text from the input file and if neccessary split it into parts with a
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# length of less than 1 million characters.
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with open(args.input) as input_file:
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with open(args.i) as input_file:
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text = input_file.read()
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texts = textwrap.wrap(text, 1000000, break_long_words=False)
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text = None
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# Create and open the output file
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output_file = open(args.output, "w+")
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output_file.write('<?xml version="1.0" encoding="UTF-8"?>\n<corpus>\n<text id="' + os.path.basename(args.input).rsplit(".", 1)[0] + '">\n')
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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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)
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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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doc = nlp(text)
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@ -54,9 +59,12 @@ for text in texts:
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continue
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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(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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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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)
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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.close()
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