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)