def closest_to(klass, term):
try:
# words to exclude when attempting to match country names
exclude = set(["vaccine"])
big_term = term.lower()
vax_tuples = []
for obj in klass.objects.all():
big_en = ""
big_fr = ""
if obj.abbr_en is not None:
big_en = obj.abbr_en.lower()
# calculate edit distance between word from term and word from english name
vax_tuples.append((dm(big_term, big_en), obj, obj.abbr_en))
if obj.abbr_fr is not None:
big_fr = obj.abbr_fr.lower()
if big_fr != big_en:
# calculate edit distance between word from term and word from french name
vax_tuples.append((dm(big_term, big_fr), obj, obj.abbr_fr))
# sort tuples by ascending edit distance, pluck the 5 closest matches
closest = sorted(vax_tuples, key=itemgetter(0))[:10]
# return only the objects
return set(map(itemgetter(1), closest))
except Exception, e:
print 'BANG'
print e
def country_aware_closest_to(klass, term, country_pk):
try:
big_term = term.lower()
# grrr edge cases
big_term = big_term.replace('meningitis', 'mening')
big_term = big_term.replace('measles', 'mea')
countrystocks = CountryStock.objects.filter(country=country_pk)
# fetch all vaccines that this country has stocks of
cs_vaccines = map(attrgetter('vaccine'), countrystocks)
# get list of vaccines who's name and/or any abbreviations
# contain any words in the given term
vax_partials = [obj for obj in klass.objects.all()\
if not set(_split_term(big_term)).isdisjoint(set(obj._field_list()))]
if len(vax_partials) > 0:
vax_partials_groups = set(map(attrgetter('group'), vax_partials))
vax_relatives = list(itertools.chain(*[g.vaccine_set.all() for g in vax_partials_groups]))
only_objs = set(vax_partials).union(set(vax_relatives))
# if there are no overlapping terms, try to find
# similar vaccines by edit distance
else:
vax_tuples = []
for obj in klass.objects.all():
big_en = ""
big_fr = ""
if obj.abbr_en is not None:
big_en = obj.abbr_en.lower()
# calculate edit distance between word from term and word from english name
vax_tuples.append((dm(big_term, big_en), obj, obj.abbr_en))
if obj.abbr_fr is not None:
big_fr = obj.abbr_fr.lower()
if big_fr != big_en:
# calculate edit distance between word from term and word from french name
vax_tuples.append((dm(big_term, big_fr), obj, obj.abbr_fr))
# sort tuples by ascending edit distance, pluck closest matches
closest = sorted(vax_tuples, key=itemgetter(0))[:10]
# return only the objects
only_objs = set(map(itemgetter(1), closest)).union(vax_partials)
# see if any closest matches are vaccines this country stocks
if only_objs.isdisjoint(set(cs_vaccines)):
# if not, return top matches and None
return list(only_objs)[:20], None
else:
# if so, return the ones that country doesnt stock and the ones that the country does stock
return only_objs.difference(set(cs_vaccines)), only_objs.intersection(set(cs_vaccines))
except Exception, e:
print 'BANG country aware closest to'
print e
def closest_to(klass, term):
try:
# grr edge cases...
if 'OPT' in term.upper():
term = 'palestinian'
if 'PALESTINE' in term.upper():
term = 'palestinian'
if 'GAZA' in term.upper():
term = 'palestinian'
if "SERBIA" in term.upper():
term = 'serbia and montenegro'
if "LIBYA" in term.upper():
term = 'libyan arab JAMAHIRIYA'
if "DRC" in term.upper():
term = 'congo'
if "IVORY" in term.upper():
term = "COTE D'IVOIRE"
if "VERT" in term.upper():
term = "cape verde"
if "PACIFIC" in term.upper():
term = "fiji"
# words to exclude when attempting to match country names
exclude = set(["democratic", "peoples", "republic", "the", "of", "american", "french", "brazzaville", "islamic", "people's", "territory", "kingdom", "démocratique", "république", "territories", "françaises", "française", "islands", "british", "britannique", "américaines", "britanniques", "western", "occidental", "république-unie", "république", "l'ex-république", "démocratique", "equatorial", "équatoriale", "territoire", "plurinational", "américaines", "conakry", "states", "états", "outlying", "éloignées", "federation", "fédération", "pays", "sultanate"])
# replace hyphens, exclude any words from exclude set, and pluck the longest remaining word
big_term = max(set(term.lower().replace('-', ' ').split()).difference(exclude), key=len)
country_tuples = []
for obj in klass.objects.all():
big_en = ""
big_fr = ""
if obj.name is not None:
# pluck longest word in english name that does not appear in exclude
big_en = max(set(word.lower().strip(",") for word in obj.name.replace('-', ' ').split()).difference(exclude), key=len)
# calculate edit distance between word from term and word from english name
country_tuples.append((dm(big_term, big_en), obj, obj.name))
if obj.name_fr is not None:
# pluck longest word in french name that does not appear in exclude
big_fr = max(set(word.lower().strip(",") for word in obj.name_fr.replace('-', ' ').split()).difference(exclude), key=len)
if big_fr != big_en:
# calculate edit distance between word from term and word from french name
country_tuples.append((dm(big_term, big_fr), obj, obj.name_fr))
# sort tuples by ascending edit distance, pluck the 5 closest matches
closest = sorted(country_tuples, key=itemgetter(0))[:5]
# return only the objects
return set(map(itemgetter(1), closest))
except Exception, e:
print 'BANG'
print e
