def __init__(self, root, **kwargs):
"""
Initialize a PLoS reader with a specific corpus. Corpus
information is contained in 'root/corpus_info.json' file. The
@type root: string
@param root: The directory path to the corpus directory.
"""
self._root = root
fp = open( '%s/corpus_info.json' % (root), 'r' )
self._corpus_info = info = json.load(fp)
fp.close()
# doc_part is specific to PLoS and research article in general.
# 'abstract' and 'body' are currently supported.
# The corpus contains seperate text for each, but the
# reader is initialized to readi only one.
if 'doc_part' in kwargs:
self._doc_part = doc_part = kwargs['doc_part']
del kwargs['doc_part']
else:
self._doc_part = doc_part = 'body'
if 'fileids' not in kwargs:
fileids = [ doi2fn(d, doc_part) for d in info['d2c'].keys() ]
else:
fileids = kwargs['fileids']
# cat_map f -> [ c1, c2, ...]
# The fileids depend on what the doc_part is ('body', 'abstract')
cat_map = {}
for d,cat in info['d2c'].iteritems():
cat_map[doi2fn(d, doc_part)] = cat
kwargs['cat_map'] = cat_map
# Subclass of Categorized Plaintext Corpus Reader
CategorizedPlaintextCorpusReader.__init__(self, root, fileids, **kwargs)
def _write_doc(self, base_dir, doc, doi):
"""
Write the abstract and body files.
"""
fn_body = '{d}/{f}'.format(d=base_dir, f=doi2fn(doi, 'body'))
fn_abstract = '{d}/{f}'.format(d=base_dir, f=doi2fn(doi, 'abstract'))
with codecs.open(fn_body, 'w', encoding='utf-8') as fd_body:
fd_body.write(doc['body'])
with codecs.open(fn_abstract, 'w', encoding='utf-8') as fd_abstract:
fd_abstract.write(doc['abstract'][0])
return
def _article_info(self, doc, doi):
fields = ['title', 'author',
'editor', 'publication_date',
'article_type', 'journal',
'id' ]
article_info = field_list_to_dict(doc, fields)
article_info['page_url'] = article_page_url(doi, pretty=True)
article_info['xml_url'] = article_xml_url(doi, pretty=True)
article_info['body_fid'] = doi2fn(doi, 'body')
article_info['abstract_fid'] = doi2fn(doi, 'abstract')
return article_info
def _write_doc(self, base_dir, doc, doi):
"""
Write the abstract and body files.
"""
fn_body = '{d}/{f}'.format(d=base_dir, f=doi2fn(doi, 'body'))
fn_abstract = '{d}/{f}'.format(d=base_dir, f=doi2fn(doi, 'abstract'))
with codecs.open(fn_body, 'w', encoding='utf-8') as fd_body:
fd_body.write(doc['body'])
with codecs.open(fn_abstract, 'w', encoding='utf-8') as fd_abstract:
fd_abstract.write(doc['abstract'][0])
return
def _article_info(self, doc, doi):
fields = [
'title', 'author', 'editor', 'publication_date', 'article_type',
'journal', 'id'
]
article_info = field_list_to_dict(doc, fields)
article_info['page_url'] = article_page_url(doi, pretty=True)
article_info['xml_url'] = article_xml_url(doi, pretty=True)
article_info['body_fid'] = doi2fn(doi, 'body')
article_info['abstract_fid'] = doi2fn(doi, 'abstract')
return article_info
def __init__(self, root, **kwargs):
"""
Initialize a PLoS reader with a specific corpus. Corpus
information is contained in 'root/corpus_info.json' file. The
@type root: string
@param root: The directory path to the corpus.
"""
self._root = root
# corpus type is specific to Plos_builder
# full - all documents that were built.
# partial - documents excluding training
# training - documents intended for training
if 'corpus_type' in kwargs:
self._corpus_type = kwargs['corpus_type']
del kwargs['corpus_type']
else:
self._corpus_type = 'full'
fn = '{d}/{t}_corpus_info.json'.format(d=root, t=self._corpus_type)
with open( fn, 'r' ) as fp:
self._corpus_info = info = json.load(fp)
# doc_part is specific to PLoS and research article.
# 'abstract' and 'body' are currently supported.
# The corpus contains seperate text for each, but the
# reader is initialized to read only one.
if 'doc_part' in kwargs:
self._doc_part = doc_part = kwargs['doc_part']
del kwargs['doc_part']
else:
self._doc_part = doc_part = 'body'
if 'fileids' not in kwargs:
fileids = [ doi2fn(d, doc_part) for d in self.dois() ]
else:
fileids = kwargs['fileids']
# cat_map f -> [ c1, c2, ...]
# The fileids depend on what the doc_part is ('body', 'abstract')
kwargs['cat_map'] = { doi2fn(d, doc_part) : cat for d,cat in info['dois_to_categories'].iteritems() }
# Subclass of Categorized Plaintext Corpus Reader
CategorizedPlaintextCorpusReader.__init__(self, root, fileids, **kwargs)
def doi2fid(self, doi_lst=None):
"""
"""
dois = listafy(doi_lst, self._corpus_info['d2c'])
return zip(dois, doi2fn(dois, self._doc_part))
def add(self, docs):
"""
Create a json file for each doc in the document list.
@type docs: list
@param docs: A list containing the results of a PLoS search query.
Each item is a dictionary with QUERY_RTN_FLDS as keys.
@return: Nothing
"""
root = self._root
info = self._corpus_info
d2cmap = {}
c2dmap = {}
d2infomap = {}
amap = info['article_link']
xmap = info['xml_link']
# Build all the lists and mappings
for doc in docs:
doi = doc['id']
# If the doc has not subject, add []
if 'subject' not in doc:
doc['subject'] = []
# File id -> [ c1, c2, .... ]
d2cmap[doi] = subjs = doc['subject']
# Category -> [ f1, f2, .... ]
for s in subjs:
if s in c2dmap:
c2dmap[s].append(doi)
else:
c2dmap[s] = [doi]
# doi -> article link
amap[doi] = articleUrl(doi)
# doi -> artilce xml link
xmap[doi] = articleXML(doi)
# Depending on the article type some of these might not exist.
jrnl = doc['journal'] if 'journal' in doc else ''
if 'publication_date' in doc:
pub_date = doc['publication_date']
else:
pub_date = ''
atype = doc['article_type'] if 'article_type' in doc else ''
title = doc['title'] if 'title' in doc else ''
author = doc['author'] if 'author' in doc else []
d2infomap[doi] = (jrnl, pub_date, atype, title, author)
fnames = [doi2fn(doi, 'body') for doi in d2cmap.keys()]
fnames_docs = zip(fnames, docs)
# Dump doc_part 'body' into individual files.
for fn, doc in fnames_docs:
fd = codecs.open('%s/%s' % (root, fn), 'w', encoding='utf-8')
fd.write(doc['body'])
fd.close()
fnames = [doi2fn(doi, 'abstract') for doi in d2cmap.keys()]
fnames_docs = zip(fnames, docs)
# Dump doc_part 'abstract' into individual files.
for fn, doc in fnames_docs:
fd = codecs.open('%s/%s' % (root, fn), 'w', encoding='utf-8')
fd.write(doc['abstract'][0])
fd.close()
# Update the corpus info
info['d2c'].update(d2cmap)
c2d = info['c2d']
for k, v in c2dmap.iteritems():
if k not in c2d:
c2d[k] = []
c2d[k].extend(v)
info['d2info'].update(d2infomap)
return
