def write_html(self):
ensure_path(self.html_dir)
self.write_index_file()
for term in self.terms:
term_file = os.path.join(self.html_dir, "%05d.html" % term.id)
term_fh = codecs.open(term_file, 'w', encoding='utf-8')
term.generate_html(fh=term_fh)
def _cleanup_cinfo(cinfo_file, cinfo_file_sorted):
run_command("gzip %s" % cinfo_file)
run_command("gzip %s" % cinfo_file_sorted)
info_dir = os.path.dirname(cinfo_file) + os.sep + 'info'
ensure_path(info_dir)
run_command("mv %s.gz %s" % (cinfo_file, info_dir))
run_command("mv %s.gz %s" % (cinfo_file_sorted, info_dir))
def _create_directories(self):
"""Create subdirectory structure in target_path."""
print "[--init] creating directory structure in %s" % (
self.target_path)
ensure_path(self.conf_path)
for subdir in config.PROCESSING_AREAS:
subdir_path = self.data_path + os.sep + subdir
ensure_path(subdir_path)
def prepare_io(filename, input_dataset, output_dataset):
"""Generate the file paths for the datasets and make sure the path to the file exists for
the output dataset. May need to add a version that deals with multiple output datasets."""
file_id = filename[1:] if filename.startswith(os.sep) else filename
file_in = os.path.join(input_dataset.path, 'files', file_id)
file_out = os.path.join(output_dataset.path, 'files', file_id)
ensure_path(os.path.dirname(file_out))
return file_in, file_out
def run_classifier(self, t1, corpus):
ensure_path(self.output)
self._create_mallet_file(corpus=corpus)
self._run_classifier()
self._calculate_scores()
self._create_info_files(t1)
for fname in (self.results_file, self.mallet_file, self.scores_s1):
print "[Classifier.run_classifier] Compressing", fname
compress(fname)
def __init__(self, corpus, index_name):
self.corpus = corpus
self.index_name = index_name
self.idx_dir = os.path.join(corpus, 'data', 'o1_index', index_name)
ensure_path(self.idx_dir)
self.db_info = InfoDatabase(self.idx_dir, 'db-info.sqlite')
self.db_years = YearsDatabase(self.idx_dir, 'db-years.sqlite')
self.db_summary = SummaryDatabase(self.idx_dir, 'db-terms.sqlite')
self.db_terms = {}
self.pp()
def run(self):
"""Run the trainer by finding the input data and building a model from it. Also
writes files with information on configuration settings, features, gold standard
term annotations and other things required to reproduce the model."""
if os.path.exists(self.train_dir):
exit("WARNING: Classifier model %s already exists" % self.train_dir)
ensure_path(self.train_dir)
ensure_path(self.info_dir)
self._find_datasets()
self._create_info_files()
self._create_mallet_file()
def run(self):
if os.path.exists(self.info_file_general):
sys.exit("WARNING: already have classifier results in %s" %
self.batch)
ensure_path(self.batch)
self._find_datasets()
self._create_mallet_file()
self._run_classifier()
self._calculate_scores()
self._run_eval()
self._create_info_files()
compress(self.results_file, self.mallet_file, self.scores_s1)
def merge_mallet_files(target_dir, mallet_files):
t1 = time.time()
target_file = os.path.join(target_dir, 'train.mallet')
info_file = os.path.join(target_dir, 'train.mallet.info')
print "\nMerging"
for f in mallet_files:
print ' ', f
print "Target mallet file\n ", target_file
merge_command = "cat %s > %s" % (' '.join(mallet_files), target_file)
print "\n$", merge_command, "\n"
ensure_path(target_dir)
os.system(merge_command)
write_info(info_file, mallet_files, t1)
def write_info(rconfig, dirname, filelist):
"""Generate a file with general information and copy the file list to the
annotation directory."""
print "Writing general info..."
ensure_path(dirname)
with open(os.path.join(dirname, 'annotate.info.general.txt'), 'w') as fh:
fh.write("$ python %s\n\n" % ' '.join(sys.argv))
fh.write("corpus = %s\n" % os.path.abspath(rconfig.corpus))
fh.write("file_list = %s\n" % os.path.abspath(filelist))
fh.write("config_file = %s\n" % \
os.path.basename(rconfig.pipeline_config_file))
fh.write("git_commit = %s" % get_git_commit())
print "Copying %s..." % (filelist)
shutil.copyfile(filelist,
os.path.join(dirname, 'annotate.info.filelist.txt'))
def _create_info_files(self):
if os.path.exists(self.info_file_general):
sys.exit("WARNING: already ran indexer for batch %s" % self.batch)
print "[Collector] initializing data/o1_index/%s directory" % self.batch
ensure_path(self.batch_dir)
with open(self.info_file_general, 'w') as fh:
fh.write("$ python %s\n\n" % ' '.join(sys.argv))
fh.write("batch = %s\n" % self.batch)
fh.write("file_list = %s\n" % self.file_list)
fh.write("config_file = %s\n" %
os.path.basename(rconfig.pipeline_config_file))
fh.write("git_commit = %s\n" % get_git_commit())
shutil.copyfile(self.rconfig.pipeline_config_file,
self.info_file_config)
shutil.copyfile(self.file_list, self.info_file_filelist)
def initialize_on_disk(self):
"""All that is guaranteed to exist is a directory like data/patents/en/d1_txt, but
sub structures is not there. Create the substructure and initial versions of all
needed files in configuration and state directories."""
for subdir in ('config', 'state', 'files'):
ensure_path(os.path.join(self.path, subdir))
create_file(os.path.join(self.path, 'state', 'processed.txt'), "0\n")
create_file(os.path.join(self.path, 'state', 'processing-history.txt'))
trace, head = self.split_pipeline()
trace_str = pipeline_component_as_string(trace)
head_str = pipeline_component_as_string([head])
create_file(os.path.join(self.path, 'config', 'pipeline-head.txt'),
head_str)
create_file(os.path.join(self.path, 'config', 'pipeline-trace.txt'),
trace_str)
self.files_processed = 0
def run_itrainer(corpus,
filelist,
model,
features,
annotation_file,
phr_feats_file=None,
verbose=False):
mallet_file = os.path.join(model, 'itrain.mallet')
phr_feats_file = os.path.join(model, 'keyfeats.ta.dat')
ensure_path(model)
_itrainer_create_info_file(corpus, model, filelist, features,
annotation_file)
_itrainer_create_dat_file(phr_feats_file, corpus, filelist)
_itrainer_create_mallet_file(annotation_file, phr_feats_file, mallet_file)
patent_invention_train(mallet_file)
def _create_info_files(self):
if os.path.exists(self.info_file_general):
sys.exit("WARNING: already have matcher results in %s" %
self.output)
print "[Matcher] initializing data/o2_matcher/%s directory" % self.output
ensure_path(self.output_dir)
with open(self.info_file_general, 'w') as fh:
fh.write("$ python %s\n\n" % ' '.join(sys.argv))
fh.write("output = %s\n" % self.output)
fh.write("file_list = %s\n" % self.file_list)
#fh.write("config_file = %s\n" % os.path.basename(rconfig.pipeline_config_file))
fh.write("config_file = %s\n" % rconfig.pipeline_config_file)
fh.write("git_commit = %s\n" % get_git_commit())
if self.rconfig.pipeline_config_file is not None:
shutil.copyfile(self.rconfig.pipeline_config_file,
self.info_file_config)
shutil.copyfile(self.file_list, self.info_file_filelist)
def run_populate(rconfig, limit, verbose=False):
"""Populate xml directory in the target directory with limit files from the
source file list or the source directory."""
output_name = DOCUMENT_PROCESSING_IO[POPULATE]['out']
dataset = DataSet(POPULATE, output_name, rconfig)
# initialize data set if it does not exist, this is not contingent on
# anything because --populate is the first step
if not dataset.exists():
dataset.initialize_on_disk()
dataset.load_from_disk()
fspecs = get_lines(rconfig.filenames, dataset.files_processed, limit)
print "[--populate] adding %d files to %s" % (len(fspecs), dataset)
count = 0
for fspec in fspecs:
count += 1
src_file = fspec.source
dst_file = os.path.join(rconfig.target_path, 'data', output_name,
dataset.version_id, 'files', fspec.target)
# allow for compressed files, while being handed the name without
# extension
if not os.path.exists(src_file):
src_file += ".gz"
dst_file += ".gz"
if verbose:
print "[--populate] %04d %s" % (count, dst_file)
ensure_path(os.path.dirname(dst_file))
try:
shutil.copyfile(src_file, dst_file)
except IOError:
print " WARNING: source file does not exist, not copying"
print " %s" % src_file
# at some point there seemed to be an issue with compressing for Chinese,
# so added this to do language dependent compressing, there is now no
# difference for the population phase
if rconfig.language == 'en': compress(dst_file)
elif rconfig.language == 'cn': compress(dst_file)
# TODO: does this mean that you miss some if total_count % STEP != 0
if count % STEP == 0:
dataset.update_processed_count(STEP)
return (count % STEP, [dataset])
def run_iclassifier(corpus,
filelist,
model,
classification,
label_file='iclassify.MaxEnt.label',
verbose=False):
"""Run the invention classifier on the corpus using the model specified and
create a classification."""
print
print '[run_iclassifier] corpus =', corpus
print '[run_iclassifier] files =', filelist
print '[run_iclassifier] model =', model
print '[run_iclassifier] class =', classification
t1 = time.time()
ensure_path(classification)
create_info_files(corpus, model, filelist, classification)
# create classification/iclassify.mallet from given files in the corpus
invention.create_mallet_classify_file(corpus,
filelist,
classification,
"invention",
"1",
verbose=True)
t2 = time.time()
# create result files in the classification
invention.patent_invention_classify(None,
train_dir=model,
test_dir=classification)
t3 = time.time()
# creates the label file from the classifier output
print "[run_iclassifier] creating the .label file"
command = "cat %s/%s | egrep -v '^name' | egrep '\|.*\|' | python %s > %s/%s" \
% (classification, 'iclassify.MaxEnt.out', 'invention_top_scores.py',
classification, label_file)
print ' $', command
subprocess.call(command, shell=True)
t4 = time.time()
process_label_file(corpus, classification, label_file, verbose)
create_processing_time_file(classification, t1, t2, t3, t4)
print
def copy_and_compress(source, target_dir, target_file):
"""Copy source to target file, making sure there is a directory. Compress
the new file."""
ensure_path(target_dir)
shutil.copyfile(source, target_file)
compress(target_file)
print fname
fh_terms = open_input_file(fname)
count = 0
for line in fh_terms:
count += 1
if count > 100000: break
if count % 500000 == 0: print ' ', count
fields = line.split("\t")
term = fields[0]
term_count = int(fields[2])
terms[term] = terms.get(term, 0) + term_count
return terms
if __name__ == '__main__':
target_dir = sys.argv[1]
result_files = []
for exp in sys.argv[2:]:
files = glob.glob(exp)
result_files.extend(files)
ensure_path(target_dir)
infofile = target_dir + '/merged_term_frequencies.info.txt'
fh_info = codecs.open(infofile, 'w', encoding='utf-8')
fh_info.write("git commit = %s\n\n" % get_git_commit())
for fname in result_files:
fh_info.write(fname + u"\n")
merge_result_files(target_dir, result_files)
