def extract(dump_files, extractors=ALL_EXTRACTORS):
"""
Extracts cites from a set of `dump_files`.
:Parameters:
dump_files : str | `file`
A set of files MediaWiki XML dump files
(expects: pages-meta-history)
extractors : `list`(`extractor`)
A list of extractors to apply to the text
:Returns:
`iterable` -- a generator of extracted cites
"""
# Dump processor function
def process_dump(dump, path):
for page in dump:
if page.namespace != 0: continue
else:
for cite in extract_cite_history(page, extractors):
yield cite
# Map call
return mwxml.map(process_dump, dump_files)
def analyse_dumps(dumps, cohorts, pages, threads=None):
results = _init_cohort_contribs(cohorts)
_partial = partial(_analyse_single_dump, cohorts=cohorts, pages=pages)
for sub_res in mwxml.map(_partial, dumps, threads=threads):
for cohort in sub_res:
results[cohort].update(sub_res[cohort])
return results
def run(paths,
model,
sunset,
score_at,
rev_scores,
skip_scores_before,
processes,
verbose=False):
if score_at == "revision":
process_dump = revision_scores(model, sunset, skip_scores_before)
elif score_at == "latest":
process_dump = latest_scores(model, sunset, skip_scores_before)
else:
sunset_year = int(sunset.strftime("%Y"))
if score_at == "monthly":
dates = chain(*(zip([year] * 12, MONTHS)
for year in range(START_YEAR, sunset_year + 1)))
thresholds = [
mwtypes.Timestamp(str(year) + month + "01000000")
for year, month in dates
]
elif score_at == "biannually":
dates = chain(*(zip([year] * 2, ["01", "07"])
for year in range(START_YEAR, sunset_year + 1)))
thresholds = [
mwtypes.Timestamp(str(year) + month + "01000000")
for year, month in dates
]
elif score_at == "annually":
thresholds = [
mwtypes.Timestamp(str(year) + "0101000000")
for year in range(START_YEAR, sunset_year + 1)
]
else:
raise RuntimeError(
"{0} is not a valid 'score_at' value".format(score_at))
process_dump = threshold_scores(model, sunset, skip_scores_before,
thresholds)
results = mwxml.map(process_dump, paths, threads=processes)
for page_id, title, rev_id, timestamp, (e, score) in results:
if e is not None:
logger.error("Error while processing {0}({1}) @ {2}: {3}".format(
title, page_id, rev_id, e))
continue
weighted_sum = sum(CLASS_WEIGHTS[cls] * score['probability'][cls]
for cls in score['probability'])
rev_scores.write([
page_id, title, rev_id,
timestamp.short_format(), score['prediction'], weighted_sum
])
if verbose:
sys.stderr.write(score['prediction'] + " ")
sys.stderr.flush()
if verbose:
sys.stderr.write("\n")
def run(dump_files):
def process_dump(dump, path):
for page in dump:
if page.namespace != 3:
continue
last_templates = defaultdict(lambda: 0)
for revision in page:
if revision.text is None:
continue
current_templates = defaultdict(lambda: 0)
for template in subst_templates.extract(revision.text):
current_templates[template] += 1
for template, count in current_templates.items():
diff = current_templates[template] - \
last_templates[template]
if diff > 0:
yield (page.id, page.namespace, page.title,
revision.id, revision.timestamp,
revision.comment, template, diff)
current_templates = last_templates
writer = mysqltsv.Writer(sys.stdout, headers=HEADERS)
for values in mwxml.map(process_dump, dump_files):
sys.stderr.write(".")
writer.write(values)
sys.stderr.write("\n")
def run(dump_paths, threads, output, verbose=False, extractor=None):
if len(dump_paths) == 0:
label_events = extract_labelings(mwxml.Dump.from_file(sys.stdin),
extractor, verbose=verbose)
else:
label_events = mwxml.map(lambda d, p:
extract_labelings(d, extractor, verbose),
dump_paths, threads=threads)
for labeling in label_events:
dump_observation(labeling, output)
def run(dump_paths, threads, output, verbose=False):
if len(dump_paths) == 0:
user_edits = extract_rev_data(mwxml.Dump.from_file(sys.stdin),
verbose=verbose)
else:
user_edits = mwxml.map(lambda d, p: extract_rev_data(d, verbose),
dump_paths,
threads=threads)
for edit in user_edits:
json.dump(edit, output)
output.write("\n")
def run(page_periods, scorer_model, dump_paths):
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s %(levelname)s:%(name)s -- %(message)s'
)
writer = mysqltsv.Writer(sys.stdout, headers=HEADERS)
def process_dump(dump, path):
for page in dump:
if page.namespace != 0 or page.id not in page_periods:
continue
else:
start_id, end_id = page_periods[page.id]
#sys.stderr.write(page.title + ": ");sys.stderr.flush()
pre_period_revision = None
for revision in page:
if revision.id < start_id:
#sys.stderr.write(".");sys.stderr.flush()
pre_period_revision = revision
if revision.id == end_id:
if pre_period_revision is not None:
start_text = pre_period_revision.text
start_id = pre_period_revision.id
else:
start_text = ""
start_id = None
start_score = generate_score(scorer_model, start_text)
#sys.stderr.write("s1");sys.stderr.flush()
end_score = generate_score(scorer_model, revision.text)
#sys.stderr.write("s2");sys.stderr.flush()
yield (page.id,
pre_period_revision.id,
start_score['prediction'], weighted_sum(start_score),
revision.id,
end_score['prediction'], weighted_sum(end_score))
break
#sys.stderr.write("\n")
for values in mwxml.map(process_dump, dump_paths):
writer.write(values)
def run(paths, model, sunset, score_at, rev_scores, skip_scores_before,
processes, verbose=False):
if score_at == "revision":
process_dump = revision_scores(model, sunset, skip_scores_before)
elif score_at == "latest":
process_dump = latest_scores(model, sunset, skip_scores_before)
else:
sunset_year = int(sunset.strftime("%Y"))
if score_at == "monthly":
dates = chain(*(zip([year] * 12, MONTHS)
for year in range(START_YEAR, sunset_year + 1)))
thresholds = [mwtypes.Timestamp(str(year) + month + "01000000")
for year, month in dates]
elif score_at == "biannually":
dates = chain(*(zip([year] * 2, ["01", "07"])
for year in range(START_YEAR, sunset_year + 1)))
thresholds = [mwtypes.Timestamp(str(year) + month + "01000000")
for year, month in dates]
elif score_at == "annually":
thresholds = [mwtypes.Timestamp(str(year) + "0101000000")
for year in range(START_YEAR, sunset_year + 1)]
else:
raise RuntimeError("{0} is not a valid 'score_at' value"
.format(score_at))
process_dump = threshold_scores(
model, sunset, skip_scores_before, thresholds)
results = mwxml.map(process_dump, paths, threads=processes)
for page_id, title, rev_id, timestamp, (e, score) in results:
if e is not None:
logger.error("Error while processing {0}({1}) @ {2}: {3}"
.format(title, page_id, rev_id, e))
continue
weighted_sum = sum(CLASS_WEIGHTS[cls] * score['probability'][cls]
for cls in score['probability'])
rev_scores.write(
[page_id, title, rev_id, timestamp.short_format(),
score['prediction'], weighted_sum])
if verbose:
sys.stderr.write(score['prediction'] + " ")
sys.stderr.flush()
if verbose:
sys.stderr.write("\n")
def run(dump_paths, page_labelings, output, threads, verbose=False):
logging.basicConfig(
level=logging.DEBUG if verbose else logging.WARNING,
format='%(asctime)s %(levelname)s:%(name)s -- %(message)s'
)
if len(dump_paths) == 0:
labelings = extract_text(mwxml.Dump.from_file(sys.stdin),
page_labelings, verbose=verbose)
else:
labelings = mwxml.map(lambda d, p:
extract_text(d, page_labelings, verbose),
dump_paths, threads=threads)
for labeling in labelings:
dump_observation(labeling, output)
def run(dump_paths, page_labelings, output, threads, verbose=False):
logging.basicConfig(
level=logging.DEBUG if verbose else logging.WARNING,
format='%(asctime)s %(levelname)s:%(name)s -- %(message)s')
if len(dump_paths) == 0:
labelings = extract_text(mwxml.Dump.from_file(sys.stdin),
page_labelings,
verbose=verbose)
else:
labelings = mwxml.map(lambda d, p: \
extract_text(d, page_labelings, verbose),
dump_paths, threads=threads)
for labeling in labelings:
dump_observation(labeling, output)
def run(dump_paths, threads, output, verbose=False, extractor=None):
logging.basicConfig(
level=logging.DEBUG if verbose else logging.WARNING,
format='%(asctime)s %(levelname)s:%(name)s -- %(message)s'
)
if len(dump_paths) == 0:
label_events = dump2labels(mwxml.Dump.from_file(sys.stdin),
extractor, verbose=verbose)
else:
label_events = mwxml.map(lambda d, p: \
dump2labels(d, extractor, verbose),
dump_paths,threads=threads)
for labeling in label_events:
json.dump(labeling, output)
output.write("\n")
def run(dump_files):
def process_dump(dump, path):
for page in dump:
if page.namespace != 3:
continue
last_links = defaultdict(lambda: 0)
for revision in page:
if revision.text is None:
continue
current_links = defaultdict(lambda: 0)
for link in teahouse_links.extract(revision.text):
current_links[link] += 1
for link, count in current_links.items():
diff = current_links[link] - last_links[link]
if diff > 0:
yield (
page.id,
page.namespace,
page.title,
revision.id,
revision.timestamp,
revision.comment,
link,
diff,
)
last_links = current_links
writer = mysqltsv.Writer(sys.stdout, headers=HEADERS)
for values in mwxml.map(process_dump, dump_files):
sys.stderr.write(".")
writer.write(values)
sys.stderr.write("\n")
def parse(src, dst):
files = glob.glob(src)
with open(dst, 'w', buffering=1000) as f:
for r in mwxml.map(map_, files):
f.write('\t'.join(map(str, r)) + '\n')
if page.redirect:
page_title_rd = normalise_title(page.redirect)
else:
page_title_rd = None
yield page_id, page_title, page_title_rd
print("Done processing path:", path)
# we get two dictionaries
# pageids={page_title:page_id} ## only for non-redirect-pages
# redirects={page_title:page_title_rd}, where page_title_rd is the redirected-to page-title ## onlöy for redirect mpages
print('1st pass: getting pageids and redirects tables')
pageids = {}
redirects = {}
pbar = tqdm()
for page_id, page_title, page_title_rd, in mwxml.map(
process_dump_pageids_redirects, paths, threads=threads):
if page_title_rd == None:
pageids[page_title] = page_id
elif len(page_title_rd) > 0:
redirects[page_title] = page_title_rd
else:
pass
pbar.update(1)
pbar.close()
print('extraction done.')
print("Number of pages", len(pageids))
print("Number of redirects", len(redirects))
################################################
# dump iteration on each page to get links and anchors
def run(paths, session, start, end, revert_radius, revert_window,
reverted_only, trusted_users, trusted_edits, rev_reverteds,
check_blocked, verbose=False):
def process_dump(dump, path):
for page in dump:
detector = mwreverts.Detector(radius=revert_radius)
window = deque(maxlen=revert_radius)
for revision in page:
revision.maybe_damaging = None
revision.reason = None
revert = detector.process(revision.sha1, revision)
if start and revision.timestamp < start:
continue
if end and revision.timestamp > end:
continue
window.append(revision)
if revert is not None:
# A revert!
for reverted in revert.reverteds:
if (revision.timestamp -
reverted.timestamp) <= revert_window and \
reverted.user is not None and \
revision.user is not None and \
reverted.user.text != revision.user.text and \
reverted.maybe_damaging is not False:
# Happened within the window
# wasn't a self revert and hasn't
# already been marked good.
reverted.maybe_damaging = True
reverted.reason = "Reverted by someone else"
if revert.reverted_to.maybe_damaging and \
revert.reverted_to.user.text != revision.user.text:
# Reverted back to my someone else. Mark it good
# again.
revert.reverted_to.maybe_damaging = False
revert.reverted_to.reason = "Reverted back by " + \
"someone else"
# Get user info
load_user_data = trusted_edits or check_blocked
if revision.user.id is not None and revision.user.id > 0 and \
load_user_data:
info = load_user_info(revision.user.text, session)
else:
info = User(revision.user.id, 0, set())
two_days_later = revision.timestamp + (60 * 60 * 24 * 2)
if trusted_users and info.id in trusted_users:
revision.maybe_damaging = False
revision.reason = "In trusted group"
elif check_blocked and user_recently_blocked(
revision.user.text, session, two_days_later):
# User was blocked. Edits may be damaging!
revision.maybe_damaging = True
revision.reason = "User was blocked from editing"
elif trusted_edits and info.editcount >= trusted_edits:
revision.maybe_damaging = False
revision.reason = "Enough edits to be trusted"
else:
revision.reason = "Unknown"
if len(window) == revert_radius:
old_revision = window.popleft()
yield (old_revision.id, old_revision.maybe_damaging,
old_revision.reason)
for old_revision in window:
yield (old_revision.id, old_revision.maybe_damaging,
old_revision.reason)
for rev_id, maybe_damaging, reason in mwxml.map(process_dump, paths):
rev_reverteds.write([rev_id, bool(maybe_damaging), reason])
if maybe_damaging:
if reason and "Reverted" in reason:
if verbose:
sys.stderr.write("r")
sys.stderr.flush()
elif reason and "blocked" in reason:
if verbose:
sys.stderr.write("b")
sys.stderr.flush()
else: # "Unknown" in reason:
if verbose:
sys.stderr.write(".")
sys.stderr.flush()
else:
if reason and "edits" in reason:
if verbose:
sys.stderr.write("e")
sys.stderr.flush()
elif reason and "Unknown" in reason:
if verbose:
sys.stderr.write(".")
sys.stderr.flush()
else: # "group" in reason:
if verbose:
sys.stderr.write("g")
sys.stderr.flush()
if verbose:
sys.stderr.write("\n")
##################
# Global variables
# TODO: if necessary, parametrize the script to take these as input
count = 0
# LIMIT_SENTS = 200000 ## see above
# Storage array of sentences
wiki_links = []
##################
# mwxml parallelism
# this might apply to english only
# maybe it's possible to split other dumps to part-files
pbar = tqdm(total=LIMIT_SENTS)
for title, sentence in mwxml.map(process_dump, paths, threads=10):
wiki_links.append((title, sentence))
count += 1
if count >= LIMIT_SENTS:
break
pbar.update(1)
pbar.close()
print('number of sentences extracted', len(wiki_links))
##################
# store two files
# training: to be used by the generate_training_data.py, because in the future we might want to use the whole sentence.
# test: this is effectively the data used by the backtesting protocol.
LIMIT_SENTS_SPLIT = len(wiki_links) // 2
print(LIMIT_SENTS_SPLIT)
def run(input_files, revisions_output_file, verbose):
def process_pages(stub_file_dump_object, file_url):
for stub_file_page in stub_file_dump_object:
for stub_file_page_revision in stub_file_page:
revision_comment = stub_file_page_revision.comment
revision_user_id = "NULL"
revision_user_text = "NULL"
if revision_comment is None:
revision_comment = "NULL"
if stub_file_page_revision.user is None:
logger.warning(
"No user. Fields will be NULL. Revision: {0}"
.format(stub_file_page_revision))
revision_user_id = "NULL"
revision_user_text = "NULL"
elif stub_file_page_revision.user.id is None:
revision_user_id = "NULL"
revision_user_text = stub_file_page_revision.user.text
else:
revision_user_id = stub_file_page_revision.user.id
revision_user_text = stub_file_page_revision.user.text
timestamp =\
datetime.datetime.\
utcfromtimestamp(stub_file_page_revision.timestamp)
cast_timestamp =\
str(timestamp.year).zfill(4) +\
str(timestamp.month).zfill(2) +\
str(timestamp.day).zfill(2) +\
str(timestamp.hour).zfill(2) +\
str(timestamp.minute).zfill(2) +\
str(timestamp.second).zfill(2)
yield stub_file_page_revision.page.title,\
stub_file_page_revision.id,\
revision_user_id,\
revision_user_text,\
revision_comment,\
stub_file_page.namespace,\
cast_timestamp
i = 0
for title, revision_id, user_id, user_text, comment, namespace, timestamp\
in mwxml.map(process_pages, input_files):
i += 1
revisions_output_file.write([title, revision_id, user_id, user_text,
comment, namespace, timestamp])
if verbose and i % 10000 == 0:
sys.stderr.write("Revisions processed: {0}\n".format(i))
sys.stderr.flush()
if verbose:
sys.stderr.write("Completed writing out result file\n")
sys.stderr.flush()
##################
# Global variables
# TODO: if necessary, parametrize the script to take these as input
count = 0
LIMIT_SENTS = 200000
LIMIT_SENTS_SPLIT = LIMIT_SENTS // 2
# Storage array of sentences
wiki_links = []
##################
# mwxml parallelism
# this might apply to english only
# maybe it's possible to split other dumps to part-files
pbar = tqdm(total=LIMIT_SENTS)
for title, sentence in mwxml.map(process_dump, paths):
wiki_links.append((title, sentence))
count += 1
if count >= LIMIT_SENTS:
break
pbar.update(1)
pbar.close()
print('number of sentences extracted', len(wiki_links))
##################
# store two files
# training: to be used by the generate_training_data.py, because in the future we might want to use the whole sentence.
# test: this is effectively the data used by the backtesting protocol.
# Store the sentences for training
with open("../../data/en/training/sentences_train.csv", "w") as f:
process_language(detect(
wikicode.strip_code().strip())))
except:
pass
languages = list(set(languages))
detected_languages = list(set(detected_languages))
yield page.id, wikitext_length, has_infobox, has_description_field, str(
languages), str(detected_languages)
print("total files: " + str(number_of_files))
output = mysqltsv.Writer(
open("data/sdoc/commonswiki_20171120_files_description.tsv", "w"),
headers=[
"page_id", "wikitext_length", "has_infobox", "has_description_field",
"languages", "detected_languages"
])
for page_id, wikitext_length, has_infobox, has_description_field, languages, detected_languages in mwxml.map(
process_dump, dump_files):
output.write([
page_id, wikitext_length, has_infobox, has_description_field,
languages, detected_languages
])
# compress the tsv file
# tar -czvf commonswiki_20171120_files_description.tar.gz commonswiki_20171120_files_description.tsv
for n,sec in enumerate(sections):
if n+1 < len(sections):
secText = text[sections[n][1]:sections[n+1][0]]
else:
secText = text[sections[n][1]:] #for the last sextion
#print(sec)
sectionContent[sections[n][2]] = Counter(secText.lower().split())
idf = []
[idf.extend(words.keys()) for sec,words in sectionContent.items()]
idf = Counter(idf)
weigthed = {}
for sec,content in sectionContent.items():
tfIdfSec = tfIdf(content,idf,sum(content.values()),N)
weigthed[sec] = fasttextDistanceTfIdf(tfIdfSec,lang_dictionary)
yield wikidatadict[page.title], weigthed
#except:
#pass
f = open('multiLanguageFromDumpsFastextarticlesInSixLang/sections-articles_%s.json' % lang,'w')
# writer = csv.writer(f,delimiter='\t')
for result in mwxml.map(process_dump, paths, threads = 168):
try:
q = result[0]
secs = result[1]
for sec,vector in secs.items():
if not isinstance(vector,int):
f.write(json.dumps([q,sec,vector.tolist()])+'\n')
except Exception as e:
print('error: ',e)
def run(paths, session, start, end, revert_radius, revert_window,
reverted_only, trusted_users, trusted_edits, rev_reverteds,
check_blocked, verbose=False):
def process_dump(dump, path):
for page in dump:
detector = mwreverts.Detector(radius=revert_radius)
window = deque(maxlen=revert_radius)
for revision in page:
revision.maybe_damaging = None
revision.reason = None
revert = detector.process(revision.sha1, revision)
if start and revision.timestamp < start:
continue
if end and revision.timestamp > end:
continue
window.append(revision)
if revert is not None:
# A revert!
for reverted in revert.reverteds:
if (revision.timestamp - \
reverted.timestamp) <= revert_window and \
reverted.user is not None and revision.user is not None and \
reverted.user.text != revision.user.text and \
reverted.maybe_damaging is not False:
# Happened within the window
# wasn't a self revert and hasn't
# already been marked good.
reverted.maybe_damaging = True
reverted.reason = "Reverted by someone else"
if revert.reverted_to.maybe_damaging and \
revert.reverted_to.user.text != revision.user.text:
# Reverted back to my someone else. Mark it good
# again.
revert.reverted_to.maybe_damaging = False
revert.reverted_to.reason = "Reverted back by " + \
"someone else"
# Get user info
load_user_data = trusted_edits or check_blocked
if revision.user.id is not None and revision.user.id > 0 and \
load_user_data:
info = load_user_info(revision.user.text, session)
else:
info = User(revision.user.id, 0, set())
two_days_later = revision.timestamp + (60 * 60 * 24 * 2)
if trusted_users and info.id in trusted_users:
revision.maybe_damaging = False
revision.reason = "In trusted group"
elif check_blocked and user_recently_blocked(
revision.user.text, session, two_days_later):
# User was blocked. Edits may be damaging!
revision.maybe_damaging = True
revision.reason = "User was blocked from editing"
elif trusted_edits and info.editcount >= trusted_edits:
revision.maybe_damaging = False
revision.reason = "Enough edits to be trusted"
else:
revision.reason = "Unknown"
if len(window) == revert_radius:
old_revision = window.popleft()
yield (old_revision.id, old_revision.maybe_damaging,
old_revision.reason)
for old_revision in window:
yield (old_revision.id, old_revision.maybe_damaging,
old_revision.reason)
for rev_id, maybe_damaging, reason in mwxml.map(process_dump, paths):
rev_reverteds.write([rev_id, bool(maybe_damaging), reason])
if maybe_damaging:
if reason and "Reverted" in reason:
if verbose:
sys.stderr.write("r")
sys.stderr.flush()
elif reason and "blocked" in reason:
if verbose:
sys.stderr.write("b")
sys.stderr.flush()
else: # "Unknown" in reason:
if verbose:
sys.stderr.write(".")
sys.stderr.flush()
else:
if reason and "edits" in reason:
if verbose:
sys.stderr.write("e")
sys.stderr.flush()
elif reason and "Unknown" in reason:
if verbose:
sys.stderr.write(".")
sys.stderr.flush()
else: # "group" in reason:
if verbose:
sys.stderr.write("g")
sys.stderr.flush()
if verbose:
sys.stderr.write("\n")
