322 lines
13 KiB
Python
322 lines
13 KiB
Python
from __future__ import print_function
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import os, sys
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from os.path import join
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import tarfile
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import xml.etree.ElementTree as ET
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from sklearn.datasets import get_data_home
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import pickle
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from util.file import download_file, list_dirs, list_files
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import rdflib
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from rdflib.namespace import RDF, SKOS
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from rdflib import URIRef
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import zipfile
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from data.languages import JRC_LANGS
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from collections import Counter
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from random import shuffle
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from data.languages import lang_set
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"""
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JRC Acquis' Nomenclature:
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bg = Bulgarian
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cs = Czech
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da = Danish
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de = German
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el = Greek
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en = English
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es = Spanish
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et = Estonian
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fi = Finnish
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fr = French
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hu = Hungarian
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it = Italian
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lt = Lithuanian
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lv = Latvian
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nl = Dutch
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mt = Maltese
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pl = Polish
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pt = Portuguese
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ro = Romanian
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sk = Slovak
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sl = Slovene
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sv = Swedish
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"""
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class JRCAcquis_Document:
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def __init__(self, id, name, lang, year, head, body, categories):
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self.id = id
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self.parallel_id = name
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self.lang = lang
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self.year = year
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self.text = body if not head else head + "\n" + body
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self.categories = categories
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# this is a workaround... for some reason, acutes are codified in a non-standard manner in titles
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# however, it seems that the title is often appearing as the first paragraph in the text/body (with
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# standard codification), so it might be preferable not to read the header after all (as here by default)
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def _proc_acute(text):
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for ch in ['a','e','i','o','u']:
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text = text.replace('%'+ch+'acute%',ch)
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return text
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def parse_document(file, year, head=False):
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root = ET.parse(file).getroot()
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doc_name = root.attrib['n'] # e.g., '22006A0211(01)'
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doc_lang = root.attrib['lang'] # e.g., 'es'
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doc_id = root.attrib['id'] # e.g., 'jrc22006A0211_01-es'
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doc_categories = [cat.text for cat in root.findall('.//teiHeader/profileDesc/textClass/classCode[@scheme="eurovoc"]')]
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doc_head = _proc_acute(root.find('.//text/body/head').text) if head else ''
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doc_body = '\n'.join([p.text for p in root.findall('.//text/body/div[@type="body"]/p')])
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def raise_if_empty(field, from_file):
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if isinstance(field, str):
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if not field.strip():
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raise ValueError("Empty field in file %s" % from_file)
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raise_if_empty(doc_name, file)
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raise_if_empty(doc_lang, file)
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raise_if_empty(doc_id, file)
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if head: raise_if_empty(doc_head, file)
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raise_if_empty(doc_body, file)
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return JRCAcquis_Document(id=doc_id, name=doc_name, lang=doc_lang, year=year, head=doc_head, body=doc_body, categories=doc_categories)
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# removes documents without a counterpart in all other languages
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def _force_parallel(doclist, langs):
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n_langs = len(langs)
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par_id_count = Counter([d.parallel_id for d in doclist])
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parallel_doc_ids = set([id for id,count in par_id_count.items() if count==n_langs])
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return [doc for doc in doclist if doc.parallel_id in parallel_doc_ids]
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def random_sampling_avoiding_parallel(doclist):
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random_order = list(range(len(doclist)))
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shuffle(random_order)
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sampled_request = []
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parallel_ids = set()
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for ind in random_order:
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pid = doclist[ind].parallel_id
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if pid not in parallel_ids:
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sampled_request.append(doclist[ind])
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parallel_ids.add(pid)
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print('random_sampling_no_parallel:: from {} documents to {} documents'.format(len(doclist), len(sampled_request)))
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return sampled_request
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#filters out documents which do not contain any category in the cat_filter list, and filter all labels not in cat_filter
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def _filter_by_category(doclist, cat_filter):
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if not isinstance(cat_filter, frozenset):
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cat_filter = frozenset(cat_filter)
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filtered = []
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for doc in doclist:
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doc.categories = list(cat_filter & set(doc.categories))
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if doc.categories:
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doc.categories.sort()
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filtered.append(doc)
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print("filtered %d documents out without categories in the filter list" % (len(doclist) - len(filtered)))
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return filtered
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#filters out categories with less than cat_threshold documents (and filters documents containing those categories)
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def _filter_by_frequency(doclist, cat_threshold):
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cat_count = Counter()
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for d in doclist:
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cat_count.update(d.categories)
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freq_categories = [cat for cat,count in cat_count.items() if count>cat_threshold]
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freq_categories.sort()
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return _filter_by_category(doclist, freq_categories), freq_categories
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#select top most_frequent categories (and filters documents containing those categories)
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def _most_common(doclist, most_frequent):
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cat_count = Counter()
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for d in doclist:
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cat_count.update(d.categories)
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freq_categories = [cat for cat,count in cat_count.most_common(most_frequent)]
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freq_categories.sort()
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return _filter_by_category(doclist, freq_categories), freq_categories
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def _get_categories(request):
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final_cats = set()
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for d in request:
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final_cats.update(d.categories)
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return list(final_cats)
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def fetch_jrcacquis(langs=None, data_path=None, years=None, ignore_unclassified=True, cat_filter=None, cat_threshold=0,
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parallel=None, most_frequent=-1, DOWNLOAD_URL_BASE ='http://optima.jrc.it/Acquis/JRC-Acquis.3.0/corpus/'):
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assert parallel in [None, 'force', 'avoid'], 'parallel mode not supported'
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if not langs:
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langs = JRC_LANGS
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else:
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if isinstance(langs, str): langs = [langs]
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for l in langs:
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if l not in JRC_LANGS:
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raise ValueError('Language %s is not among the valid languages in JRC-Acquis v3' % l)
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if not data_path:
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data_path = get_data_home()
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if not os.path.exists(data_path):
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os.mkdir(data_path)
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request = []
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total_read = 0
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for l in langs:
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file_name = 'jrc-'+l+'.tgz'
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archive_path = join(data_path, file_name)
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if not os.path.exists(archive_path):
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print("downloading language-specific dataset (once and for all) into %s" % data_path)
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DOWNLOAD_URL = join(DOWNLOAD_URL_BASE, file_name)
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download_file(DOWNLOAD_URL, archive_path)
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print("untarring dataset...")
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tarfile.open(archive_path, 'r:gz').extractall(data_path)
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documents_dir = join(data_path, l)
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print("Reading documents...")
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read = 0
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for dir in list_dirs(documents_dir):
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year = int(dir)
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if years==None or year in years:
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year_dir = join(documents_dir,dir)
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pickle_name = join(data_path, 'jrc_' + l + '_' + dir + '.pickle')
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if os.path.exists(pickle_name):
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print("loading from file %s" % pickle_name)
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l_y_documents = pickle.load(open(pickle_name, "rb"))
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read += len(l_y_documents)
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else:
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l_y_documents = []
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all_documents = list_files(year_dir)
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empty = 0
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for i,doc_file in enumerate(all_documents):
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try:
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jrc_doc = parse_document(join(year_dir, doc_file), year)
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except ValueError:
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jrc_doc = None
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if jrc_doc and (not ignore_unclassified or jrc_doc.categories):
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l_y_documents.append(jrc_doc)
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else: empty += 1
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if len(all_documents)>50 and ((i+1) % (len(all_documents)/50) == 0):
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print('\r\tfrom %s: completed %d%%' % (year_dir, int((i+1)*100.0/len(all_documents))), end='')
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read+=1
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print('\r\tfrom %s: completed 100%% read %d documents (discarded %d without categories or empty fields)\n' % (year_dir, i+1, empty), end='')
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print("\t\t(Pickling object for future runs in %s)" % pickle_name)
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pickle.dump(l_y_documents, open(pickle_name, 'wb'), pickle.HIGHEST_PROTOCOL)
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request += l_y_documents
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print("Read %d documents for language %s\n" % (read, l))
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total_read += read
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print("Read %d documents in total" % (total_read))
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if parallel=='force':
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request = _force_parallel(request, langs)
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elif parallel == 'avoid':
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request = random_sampling_avoiding_parallel(request)
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final_cats = _get_categories(request)
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if cat_filter:
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request = _filter_by_category(request, cat_filter)
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final_cats = _get_categories(request)
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if cat_threshold > 0:
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request, final_cats = _filter_by_frequency(request, cat_threshold)
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if most_frequent != -1 and len(final_cats) > most_frequent:
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request, final_cats = _most_common(request, most_frequent)
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return request, final_cats
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def print_cat_analysis(request):
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cat_count = Counter()
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for d in request:
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cat_count.update(d.categories)
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print("Number of active categories: {}".format(len(cat_count)))
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print(cat_count.most_common())
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# inspects the Eurovoc thesaurus in order to select a subset of categories
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# currently, only 'broadest' policy (i.e., take all categories with no parent category), and 'all' is implemented
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def inspect_eurovoc(data_path, eurovoc_skos_core_concepts_filename='eurovoc_in_skos_core_concepts.rdf',
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eurovoc_url="http://publications.europa.eu/mdr/resource/thesaurus/eurovoc-20160630-0/skos/eurovoc_in_skos_core_concepts.zip",
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select="broadest"):
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fullpath_pickle = join(data_path, select+'_concepts.pickle')
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if os.path.exists(fullpath_pickle):
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print("Pickled object found in %s. Loading it." % fullpath_pickle)
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return pickle.load(open(fullpath_pickle,'rb'))
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fullpath = join(data_path, eurovoc_skos_core_concepts_filename)
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if not os.path.exists(fullpath):
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print("Path %s does not exist. Trying to download the skos EuroVoc file from %s" % (data_path, eurovoc_url))
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download_file(eurovoc_url, fullpath)
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print("Unzipping file...")
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zipped = zipfile.ZipFile(data_path + '.zip', 'r')
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zipped.extract("eurovoc_in_skos_core_concepts.rdf", data_path)
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zipped.close()
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print("Parsing %s" %fullpath)
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g = rdflib.Graph()
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g.parse(location=fullpath, format="application/rdf+xml")
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if select == "all":
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print("Selecting all concepts")
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all_concepts = list(g.subjects(RDF.type, SKOS.Concept))
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all_concepts = [c.toPython().split('/')[-1] for c in all_concepts]
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all_concepts.sort()
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selected_concepts = all_concepts
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elif select=="broadest":
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print("Selecting broadest concepts (those without any other broader concept linked to it)")
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all_concepts = set(g.subjects(RDF.type, SKOS.Concept))
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narrower_concepts = set(g.subjects(SKOS.broader, None))
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broadest_concepts = [c.toPython().split('/')[-1] for c in (all_concepts - narrower_concepts)]
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broadest_concepts.sort()
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selected_concepts = broadest_concepts
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elif select=="leaves":
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print("Selecting leaves concepts (those not linked as broader of any other concept)")
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all_concepts = set(g.subjects(RDF.type, SKOS.Concept))
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broad_concepts = set(g.objects(None, SKOS.broader))
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leave_concepts = [c.toPython().split('/')[-1] for c in (all_concepts - broad_concepts)]
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leave_concepts.sort()
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selected_concepts = leave_concepts
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else:
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raise ValueError("Selection policy %s is not currently supported" % select)
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print("%d %s concepts found" % (len(selected_concepts), leave_concepts))
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print("Pickling concept list for faster further requests in %s" % fullpath_pickle)
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pickle.dump(selected_concepts, open(fullpath_pickle, 'wb'), pickle.HIGHEST_PROTOCOL)
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return selected_concepts
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if __name__ == '__main__':
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def single_label_fragment(doclist):
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single = [d for d in doclist if len(d.categories) < 2]
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final_categories = set([d.categories[0] if d.categories else [] for d in single])
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print('{} single-label documents ({} categories) from the original {} documents'.format(len(single),
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len(final_categories),
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len(doclist)))
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return single, list(final_categories)
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train_years = list(range(1986, 2006))
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test_years = [2006]
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cat_policy = 'leaves'
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most_common_cat = 300
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# JRC_DATAPATH = "/media/moreo/1TB Volume/Datasets/JRC_Acquis_v3"
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JRC_DATAPATH = "/storage/andrea/FUNNELING/data/JRC_Acquis_v3"
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langs = lang_set['JRC_NLTK']
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cat_list = inspect_eurovoc(JRC_DATAPATH, select=cat_policy)
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sys.exit()
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training_docs, label_names = fetch_jrcacquis(langs=langs, data_path=JRC_DATAPATH, years=train_years,cat_filter=cat_list, cat_threshold=1, parallel=None,most_frequent=most_common_cat)
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test_docs, label_namestest = fetch_jrcacquis(langs=langs, data_path=JRC_DATAPATH, years=test_years, cat_filter=label_names,parallel='force')
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print('JRC-train: {} documents, {} labels'.format(len(training_docs), len(label_names)))
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print('JRC-test: {} documents, {} labels'.format(len(test_docs), len(label_namestest)))
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training_docs, label_names = single_label_fragment(training_docs)
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test_docs, label_namestest = single_label_fragment(test_docs)
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print('JRC-train: {} documents, {} labels'.format(len(training_docs), len(label_names)))
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print('JRC-test: {} documents, {} labels'.format(len(test_docs), len(label_namestest)))
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