def run(session, observations, output, mid_level_wp, verbose):
for ob in fetch_page_wikiprojects(session,
observations,
mid_level_wp,
verbose=verbose):
dump_observation(ob, output)
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, 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(observations, model, verbose):
for ob in observations:
try:
features = list(solve(model.features, cache=ob['cache']))
ob['score'] = model.score(features)
del ob['cache']
dump_observation(ob, sys.stdout)
if verbose:
sys.stderr.write(".")
sys.stderr.flush()
except: # noqa: E722
# We're naughty to eat all exceptions.
sys.stderr.write(traceback.format_exc())
if verbose:
sys.stderr.write("\n")
def run(labelings, dependents, output, extractors, verbose=False):
extractor_pool = Pool(processes=extractors)
extractor = LabelingDependentExtractor(dependents)
for observation in extractor_pool.imap(extractor.extract_and_cache, labelings):
if observation is not None:
if verbose:
sys.stderr.write(".")
sys.stderr.flush()
dump_observation(observation, output)
else:
if verbose:
sys.stderr.write("-")
sys.stderr.flush()
if verbose:
sys.stderr.write("\n")
def run(labelings, dependents, output, extractors, verbose=False):
extractor_pool = Pool(processes=extractors)
extractor = LabelingDependentExtractor(dependents)
for observation in extractor_pool.imap(extractor.extract_and_cache,
labelings):
if observation is not None:
if verbose:
sys.stderr.write(".")
sys.stderr.flush()
dump_observation(observation, output)
else:
if verbose:
sys.stderr.write("-")
sys.stderr.flush()
if verbose:
sys.stderr.write("\n")
def run(host, obs, try_deleted_first, output, threads, verbose):
session = mwapi.Session(
host,
user_agent="Fetch text (wikigrammar) <*****@*****.**>",
formatversion=2)
mwapi.cli.do_login(session, host)
obs_batches = read_chunks(obs, 10)
text_fetcher = TextFetcher(session, try_deleted_first=try_deleted_first)
with ThreadPoolExecutor(max_workers=threads) as executor:
for obs_with_text in executor.map(text_fetcher.fetch_text,
obs_batches):
if verbose:
sys.stderr.write(str(len(obs_with_text)) + " ")
sys.stderr.flush()
for ob in obs_with_text:
dump_observation(ob, output)
if verbose:
sys.stderr.write("\n")
def run(labelings, output, session, verbose):
for labeling in fetch_text(session, labelings, verbose=verbose):
if labeling['text'] is not None:
dump_observation(labeling, output)
def run(labelings, output, session, verbose):
for labeling in fetch_text(session, labelings, verbose=verbose):
if labeling['text'] is not None:
dump_observation(labeling, output)
def run(session, observations, claims, output, verbose):
for ob in fetch_item_info(session, observations, claims, verbose=verbose):
dump_observation(ob, output)
def run(session, observations, claims, output, verbose):
for ob in fetch_item_info(session, observations, claims, verbose=verbose):
dump_observation(ob, output)
"rev_id": rev_id,
"cache": feature_values,
"approved": approved
}
except RuntimeError as e:
sys.stderr.write(str(e))
else:
print(observation)
training_features.append(observation)
print("Dump observations to file")
from revscoring.utilities.util import dump_observation, read_observations
with open("observations.json.bz2", "wt") as dumpfile:
for observation in training_features:
dump_observation(observation, dumpfile)
with open("observations.json.bz2", "r") as dumpfile:
training_features = list(read_observations(dumpfile))
from revscoring.scoring.models import GradientBoosting
is_approved = GradientBoosting(features,
labels=[True, False],
version="Demo",
learning_rate=0.01,
max_features="log2",
n_estimators=700,
max_depth=5,
population_rates={
False: 0.5,
def run(labelings, output, session, verbose):
for ob in fetch_text(session, labelings, verbose):
dump_observation(ob, output)
def run(observations, session, threads, output):
for obs in fetch_draft_texts(observations, session, threads):
dump_observation(obs, output)
