def build_all_datasets():
# Build up the cache of Wikipedia editor usernames
if prompt_and_print.prompt_for_build_wikipedia_username_cache():
crosssite_username_dataset_mgr.build_wikipedia_editor_username_cache()
# Prompt to ask from which site we want to build a dataset
try:
site = prompt_and_print.prompt_for_site()
except KeyError:
print "Sorry, that is not a recognized site. Exiting."
return
# Build up the spreadsheet of usernames that
# on exist both Wikipedia and the passed site
if prompt_and_print.prompt_for_build_username_csv():
crosssite_username_dataset_mgr.build_crosssite_username_dataset(site)
# Get the confirmed usernames from the spreadsheet since these will
# be the usernames from which Wikipedia edits and short texts are fetched
crosssite_usernames = crosssite_username_dataset_mgr.get_confirmed_usernames(site)
# Build the spreadsheet of articles that
# these usernames have edited on Wikipedia
if prompt_and_print.prompt_for_build_edits_csv():
wikipedia_edits_dataset_mgr.build_wikipedia_edits_dataset(crosssite_usernames, site)
# Build the spreadsheet of short texts that
# these usernames have posted on the input site
if prompt_and_print.prompt_for_build_shorttexts_csv(site):
short_text_dataset_mgr.build_shorttexts_dataset(crosssite_usernames, site)
# Get the shorttexts fetched from the given site
shorttext_rows = short_text_dataset_mgr.get_shorttext_rows(site)
# Cache the nouns and Named Entities detected by nltk, which are used when validating entities later
if prompt_and_print.prompt_for_cache_nltk_entities(site):
nltk_extraction_dataset_mgr.extract_entities(shorttext_rows, site)
# Build the spreadsheet of named entities that are
# contained within these short texts on the given site
if prompt_and_print.prompt_for_build_entity_csv(site):
entity_dataset_mgr.build_entities_dataset(shorttext_rows, site)
def __filter_invalid_entities__(site, ne_objs):
crosssite_usernames = crosssite_username_dataset_mgr.get_confirmed_usernames(site)
en_lang_users = site.get_en_lang_users(crosssite_usernames)
valid_entity_cache = nltk_extraction_dataset_mgr.get_nltk_entity_cache(site)
valid_ne_objs = [ne_obj for ne_obj in ne_objs if ne_obj.is_valid_entity(en_lang_users, valid_entity_cache)]
return valid_ne_objs