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Running
on
Zero
| import json | |
| import os | |
| import pickle | |
| import random | |
| import numpy as np | |
| from Preprocessing.multilinguality.create_distance_lookups import CacheCreator | |
| from Utility.utils import load_json_from_path | |
| class SimilaritySolver: | |
| def __init__(self, | |
| tree_dist=None, | |
| map_dist=None, | |
| asp_dict=None, | |
| largest_value_map_dist=None, | |
| tree_dist_path=None, | |
| map_dist_path=None, | |
| asp_dict_path=None, | |
| iso_to_fullname=None, | |
| iso_to_fullname_path=None, | |
| learned_dist=None, | |
| learned_dist_path=None, | |
| oracle_dist=None, | |
| oracle_dist_path=None, | |
| force_reload=False): | |
| self.lang_1_to_lang_2_to_tree_dist = tree_dist | |
| self.lang_1_to_lang_2_to_map_dist = map_dist | |
| self.largest_value_map_dist = largest_value_map_dist | |
| self.asp_dict = asp_dict | |
| self.lang_1_to_lang_2_to_learned_dist = learned_dist | |
| self.lang_1_to_lang_2_to_oracle_dist = oracle_dist | |
| self.iso_to_fullname = iso_to_fullname | |
| iso_to_fullname_path = "iso_to_fullname.json" if not iso_to_fullname_path else iso_to_fullname_path | |
| if force_reload: | |
| tree_dist_path = 'lang_1_to_lang_2_to_tree_dist.json' if not tree_dist_path else tree_dist_path | |
| self.lang_1_to_lang_2_to_tree_dist = load_json_from_path(tree_dist_path) | |
| map_dist_path = 'lang_1_to_lang_2_to_map_dist.json' if not map_dist_path else map_dist_path | |
| self.lang_1_to_lang_2_to_map_dist = load_json_from_path(map_dist_path) | |
| self.largest_value_map_dist = 0.0 | |
| for _, values in self.lang_1_to_lang_2_to_map_dist.items(): | |
| for _, value in values.items(): | |
| self.largest_value_map_dist = max(self.largest_value_map_dist, value) | |
| learned_dist_path = 'lang_1_to_lang_2_to_learned_dist.json' if not learned_dist_path else tree_dist_path | |
| self.lang_1_to_lang_2_to_learned_dist = load_json_from_path(learned_dist_path) | |
| oracle_dist_path = 'lang_1_to_lang_2_to_oracle_dist.json' if not oracle_dist_path else oracle_dist_path | |
| self.lang_1_to_lang_2_to_oracle_dist = load_json_from_path(oracle_dist_path) | |
| asp_dict_path = "asp_dict.pkl" if not asp_dict_path else asp_dict_path | |
| with open(asp_dict_path, "rb") as f: | |
| self.asp_dict = pickle.load(f) | |
| self.iso_to_fullname = load_json_from_path(iso_to_fullname_path) | |
| pop_keys = list() | |
| for el in self.iso_to_fullname: | |
| if "Sign Language" in self.iso_to_fullname[el]: | |
| pop_keys.append(el) | |
| for pop_key in pop_keys: | |
| self.iso_to_fullname.pop(pop_key) | |
| with open(iso_to_fullname_path, 'w', encoding='utf-8') as f: | |
| json.dump(self.iso_to_fullname, f, ensure_ascii=False, indent=4) | |
| def find_closest_combined_distance(self, | |
| lang, | |
| supervised_langs, | |
| combined_distance="average", | |
| k=50, | |
| individual_distances=False, | |
| verbose=False, | |
| excluded_features=[], | |
| find_furthest=False): | |
| """Find the k closest languages according to a combination of map distance, tree distance, and ASP distance. | |
| Returns a dict of dicts (`individual_distances` optional) of the format {"supervised_lang_1": | |
| {"euclidean_distance": 5.39, "individual_distances": [<map_dist>, <tree_dist>, <asp_dist>]}, | |
| "supervised_lang_2": | |
| {...}, ...}""" | |
| if combined_distance not in ["average", "euclidean"]: | |
| raise ValueError("distance needs to be `average` or `euclidean`") | |
| combined_dict = {} | |
| supervised_langs = set(supervised_langs) if isinstance(supervised_langs, list) else supervised_langs | |
| # avoid error with `urk` | |
| if "urk" in supervised_langs: | |
| supervised_langs.remove("urk") | |
| if lang in supervised_langs: | |
| supervised_langs.remove(lang) | |
| for sup_lang in supervised_langs: | |
| map_dist = self.get_map_distance(lang, sup_lang) | |
| tree_dist = self.get_tree_distance(lang, sup_lang) | |
| asp_score = self.get_asp(lang, sup_lang, self.asp_dict) | |
| # if getting one of the scores failed, ignore this language | |
| if None in {map_dist, tree_dist, asp_score}: | |
| continue | |
| combined_dict[sup_lang] = {} | |
| asp_dist = 1 - asp_score # turn into dist since other 2 are also dists | |
| dist_list = [] | |
| if "map" not in excluded_features: | |
| dist_list.append(map_dist) | |
| if "asp" not in excluded_features: | |
| dist_list.append(asp_dist) | |
| if "tree" not in excluded_features: | |
| dist_list.append(tree_dist) | |
| dist_array = np.array(dist_list) | |
| if combined_distance == "euclidean": | |
| euclidean_dist = np.sqrt(np.sum(dist_array**2)) # no subtraction since lang has dist [0,0,0] | |
| combined_dict[sup_lang]["combined_distance"] = euclidean_dist | |
| elif combined_distance == "average": | |
| avg_dist = np.mean(dist_array) | |
| combined_dict[sup_lang]["combined_distance"] = avg_dist | |
| if individual_distances: | |
| combined_dict[sup_lang]["individual_distances"] = [map_dist, tree_dist, asp_dist] | |
| results = dict(sorted(combined_dict.items(), key=lambda x: x[1]["combined_distance"], reverse=find_furthest)[:k]) | |
| if verbose: | |
| sorted_by = "closest" if not find_furthest else "furthest" | |
| print(f"{k} {sorted_by} languages to {self.iso_to_fullname[lang]} w.r.t. the combined features are:") | |
| for result in results: | |
| try: | |
| print(self.iso_to_fullname[result]) | |
| print(results[result]) | |
| except KeyError: | |
| print("Full Name of Language Missing") | |
| return results | |
| def find_closest(self, distance_type, lang, supervised_langs, k=50, find_furthest=False, random_seed=42, verbose=False): | |
| """Find the k nearest languages in terms of a given feature. | |
| Returns a dict {language: distance} sorted by distance.""" | |
| distance_types = ["learned", "map", "tree", "asp", "random", "oracle"] | |
| if distance_type not in distance_types: | |
| raise ValueError(f"Invalid distance type '{distance_type}'. Expected one of {distance_types}") | |
| langs_to_dist = dict() | |
| supervised_langs = set(supervised_langs) if isinstance(supervised_langs, list) else supervised_langs | |
| # avoid error with `urk` | |
| if "urk" in supervised_langs: | |
| supervised_langs.remove("urk") | |
| if lang in supervised_langs: | |
| supervised_langs.remove(lang) | |
| if distance_type == "learned": | |
| for sup_lang in supervised_langs: | |
| dist = self.get_learned_distance(lang, sup_lang) | |
| if dist is not None: | |
| langs_to_dist[sup_lang] = dist | |
| elif distance_type == "map": | |
| for sup_lang in supervised_langs: | |
| dist = self.get_map_distance(lang, sup_lang) | |
| if dist is not None: | |
| langs_to_dist[sup_lang] = dist | |
| elif distance_type == "tree": | |
| for sup_lang in supervised_langs: | |
| dist = self.get_tree_distance(lang, sup_lang) | |
| if dist is not None: | |
| langs_to_dist[sup_lang] = dist | |
| elif distance_type == "asp": | |
| for sup_lang in supervised_langs: | |
| asp_score = self.get_asp(lang, sup_lang, self.asp_dict) | |
| if asp_score is not None: | |
| asp_dist = 1 - asp_score | |
| langs_to_dist[sup_lang] = asp_dist | |
| elif distance_type == "oracle": | |
| for sup_lang in supervised_langs: | |
| dist = self.get_oracle_distance(lang, sup_lang) | |
| if dist is not None: | |
| langs_to_dist[sup_lang] = dist | |
| elif distance_type == "random": | |
| random.seed(random_seed) | |
| random_langs = random.sample(supervised_langs, k) | |
| # create dict with all 0.5 values | |
| random_dict = {rand_lang: 0.5 for rand_lang in random_langs} | |
| return random_dict | |
| # sort results by distance and only keep the first k entries | |
| results = dict(sorted(langs_to_dist.items(), key=lambda x: x[1], reverse=find_furthest)[:k]) | |
| if verbose: | |
| sorted_by = "closest" if not find_furthest else "furthest" | |
| print(f"{k} {sorted_by} languages to {self.iso_to_fullname[lang]} w.r.t. {distance_type} are:") | |
| for result in results: | |
| try: | |
| print(self.iso_to_fullname[result]) | |
| print(results[result]) | |
| except KeyError: | |
| print("Full Name of Language Missing") | |
| return results | |
| def get_map_distance(self, lang_1, lang_2): | |
| """Returns normalized map distance between two languages. | |
| If no value can be retrieved, returns None.""" | |
| try: | |
| dist = self.lang_1_to_lang_2_to_map_dist[lang_1][lang_2] | |
| except KeyError: | |
| try: | |
| dist = self.lang_1_to_lang_2_to_map_dist[lang_2][lang_1] | |
| except KeyError: | |
| return None | |
| dist = dist / self.largest_value_map_dist # normalize | |
| return dist | |
| def get_tree_distance(self, lang_1, lang_2): | |
| """Returns normalized tree distance between two languages. | |
| If no value can be retrieved, returns None.""" | |
| try: | |
| dist = self.lang_1_to_lang_2_to_tree_dist[lang_1][lang_2] | |
| except KeyError: | |
| try: | |
| dist = self.lang_1_to_lang_2_to_tree_dist[lang_2][lang_1] | |
| except KeyError: | |
| return None | |
| return dist | |
| def get_learned_distance(self, lang_1, lang_2): | |
| """Returns normalized learned distance between two languages. | |
| If no value can be retrieved, returns None.""" | |
| try: | |
| dist = self.lang_1_to_lang_2_to_learned_dist[lang_1][lang_2] | |
| except KeyError: | |
| try: | |
| dist = self.lang_1_to_lang_2_to_learned_dist[lang_2][lang_1] | |
| except KeyError: | |
| return None | |
| return dist | |
| def get_oracle_distance(self, lang_1, lang_2): | |
| """Returns oracle language embedding distance (MSE) between two languages. | |
| If no value can be retrieved, returns None.""" | |
| try: | |
| dist = self.lang_1_to_lang_2_to_oracle_dist[lang_1][lang_2] | |
| except KeyError: | |
| try: | |
| dist = self.lang_1_to_lang_2_to_oracle_dist[lang_2][lang_1] | |
| except KeyError: | |
| return None | |
| return dist | |
| def get_asp(self, lang_a, lang_b, path_to_dict): | |
| """Look up and return the ASP between lang_a and lang_b from (pre-calculated) dictionary at path_to_dict. | |
| Note: This is a SIMILARITY measure, NOT a distance!""" | |
| asp_dict = load_asp_dict(path_to_dict) | |
| lang_list = list(asp_dict) # list of all languages, to get lang_b's index | |
| lang_b_idx = lang_list.index(lang_b) # lang_b's index | |
| try: | |
| asp = asp_dict[lang_a][lang_b_idx] # asp_dict's structure: {lang: numpy array of all corresponding ASPs} | |
| except KeyError: | |
| return None | |
| return asp | |
| def load_asp_dict(path_to_dict): | |
| """If the input is already a dict, return it, else load dict from input path and return the dict.""" | |
| if isinstance(path_to_dict, dict): | |
| return path_to_dict | |
| else: | |
| with open(path_to_dict, 'rb') as dictfile: | |
| asp_dict = pickle.load(dictfile) | |
| return asp_dict | |
| if __name__ == '__main__': | |
| if not (os.path.exists("lang_1_to_lang_2_to_map_dist.json") and \ | |
| os.path.exists("lang_1_to_lang_2_to_tree_dist.json") and \ | |
| os.path.exists("lang_1_to_lang_2_to_oracle_dist.json") and \ | |
| os.path.exists("lang_1_to_lang_2_to_learned_dist.json") and \ | |
| os.path.exists("asp_dict.pkl")): | |
| CacheCreator() | |
| ss = SimilaritySolver() | |
| ss.find_closest("asp", | |
| "aym", | |
| ['eng', 'deu', 'fra', 'spa', 'cmn', 'por', 'pol', 'ita', 'nld', 'ell', 'fin', 'vie', 'rus', 'hun', 'bem', 'swh', 'amh', 'wol', 'mal', 'chv', 'iba', 'jav', 'fon', 'hau', 'lbb', 'kik', 'lin', 'lug', 'luo', 'sxb', 'yor', 'nya', 'loz', 'toi', 'afr', 'arb', 'asm', 'ast', 'azj', 'bel', 'bul', 'ben', 'bos', 'cat', | |
| 'ceb', 'sdh', 'ces', 'cym', 'dan', 'ekk', 'pes', 'fil', 'gle', 'glg', 'guj', 'heb', 'hin', 'hrv', 'hye', 'ind', 'ibo', 'isl', 'kat', 'kam', 'kea', 'kaz', 'khm', 'kan', 'kor', 'ltz', 'lao', 'lit', 'lvs', 'mri', 'mkd', 'xng', 'mar', 'zsm', 'mlt', 'oci', 'ory', 'pan', 'pst', 'ron', 'snd', 'slk', 'slv', 'sna', | |
| 'som', 'srp', 'swe', 'tam', 'tel', 'tgk', 'tur', 'ukr', 'umb', 'urd', 'uzn', 'bhd', 'kfs', 'dgo', 'gbk', 'bgc', 'xnr', 'kfx', 'mjl', 'bfz', 'acf', 'bss', 'inb', 'nca', 'quh', 'wap', 'acr', 'bus', 'dgr', 'maz', 'nch', 'qul', 'tav', 'wmw', 'acu', 'byr', 'dik', 'iou', 'mbb', 'ncj', 'qvc', 'tbc', 'xed', 'agd', | |
| 'bzh', 'djk', 'ipi', 'mbc', 'ncl', 'qve', 'tbg', 'xon', 'agg', 'bzj', 'dop', 'jac', 'mbh', 'ncu', 'qvh', 'tbl', 'xtd', 'agn', 'caa', 'jic', 'mbj', 'ndj', 'qvm', 'tbz', 'xtm', 'agr', 'cab', 'emp', 'jiv', 'mbt', 'nfa', 'qvn', 'tca', 'yaa', 'agu', 'cap', 'jvn', 'mca', 'ngp', 'qvs', 'tcs', 'yad', 'aia', 'car', | |
| 'ese', 'mcb', 'ngu', 'qvw', 'yal', 'cax', 'kaq', 'mcd', 'nhe', 'qvz', 'tee', 'ycn', 'ake', 'cbc', 'far', 'mco', 'qwh', 'yka', 'alp', 'cbi', 'kdc', 'mcp', 'nhu', 'qxh', 'ame', 'cbr', 'gai', 'kde', 'mcq', 'nhw', 'qxn', 'tew', 'yre', 'amf', 'cbs', 'gam', 'kdl', 'mdy', 'nhy', 'qxo', 'tfr', 'yva', 'amk', 'cbt', | |
| 'geb', 'kek', 'med', 'nin', 'rai', 'zaa', 'apb', 'cbu', 'glk', 'ken', 'mee', 'nko', 'rgu', 'zab', 'apr', 'cbv', 'meq', 'tgo', 'zac', 'arl', 'cco', 'gng', 'kje', 'met', 'nlg', 'rop', 'tgp', 'zad', 'grc', 'klv', 'mgh', 'nnq', 'rro', 'zai', 'ata', 'cek', 'gub', 'kmu', 'mib', 'noa', 'ruf', 'tna', 'zam', 'atb', | |
| 'cgc', 'guh', 'kne', 'mie', 'not', 'rug', 'tnk', 'zao', 'atg', 'chf', 'knf', 'mih', 'npl', 'tnn', 'zar', 'awb', 'chz', 'gum', 'knj', 'mil', 'sab', 'tnp', 'zas', 'cjo', 'guo', 'ksr', 'mio', 'obo', 'seh', 'toc', 'zav', 'azg', 'cle', 'gux', 'kue', 'mit', 'omw', 'sey', 'tos', 'zaw', 'azz', 'cme', 'gvc', 'kvn', | |
| 'miz', 'ood', 'sgb', 'tpi', 'zca', 'bao', 'cni', 'gwi', 'kwd', 'mkl', 'shp', 'tpt', 'zga', 'bba', 'cnl', 'gym', 'kwf', 'mkn', 'ote', 'sja', 'trc', 'ziw', 'bbb', 'cnt', 'gyr', 'kwi', 'mop', 'otq', 'snn', 'ttc', 'zlm', 'cof', 'hat', 'kyc', 'mox', 'pab', 'snp', 'tte', 'zos', 'bgt', 'con', 'kyf', 'mpm', 'pad', | |
| 'tue', 'zpc', 'bjr', 'cot', 'kyg', 'mpp', 'soy', 'tuf', 'zpl', 'bjv', 'cpa', 'kyq', 'mpx', 'pao', 'tuo', 'zpm', 'bjz', 'cpb', 'hlt', 'kyz', 'mqb', 'pib', 'spp', 'zpo', 'bkd', 'cpu', 'hns', 'lac', 'mqj', 'pir', 'spy', 'txq', 'zpu', 'blz', 'crn', 'hto', 'lat', 'msy', 'pjt', 'sri', 'txu', 'zpz', 'bmr', 'cso', | |
| 'hub', 'lex', 'mto', 'pls', 'srm', 'udu', 'ztq', 'bmu', 'ctu', 'lgl', 'muy', 'poi', 'srn', 'zty', 'bnp', 'cuc', 'lid', 'mxb', 'stp', 'upv', 'zyp', 'boa', 'cui', 'huu', 'mxq', 'sus', 'ura', 'boj', 'cuk', 'huv', 'llg', 'mxt', 'poy', 'suz', 'urb', 'box', 'cwe', 'hvn', 'prf', 'urt', 'bpr', 'cya', 'ign', 'lww', | |
| 'myk', 'ptu', 'usp', 'bps', 'daa', 'ikk', 'maj', 'myy', 'vid', 'bqc', 'dah', 'nab', 'qub', 'tac', 'bqp', 'ded', 'imo', 'maq', 'nas', 'quf', 'taj', 'vmy'], | |
| k=5, verbose=True) | |
| ss.find_closest_combined_distance("aym", | |
| ['eng', 'deu', 'fra', 'spa', 'cmn', 'por', 'pol', 'ita', 'nld', 'ell', 'fin', 'vie', 'rus', 'hun', 'bem', 'swh', 'amh', 'wol', 'mal', 'chv', 'iba', 'jav', 'fon', 'hau', 'lbb', 'kik', 'lin', 'lug', 'luo', 'sxb', 'yor', 'nya', 'loz', 'toi', 'afr', 'arb', 'asm', 'ast', 'azj', 'bel', 'bul', 'ben', 'bos', 'cat', | |
| 'ceb', 'sdh', 'ces', 'cym', 'dan', 'ekk', 'pes', 'fil', 'gle', 'glg', 'guj', 'heb', 'hin', 'hrv', 'hye', 'ind', 'ibo', 'isl', 'kat', 'kam', 'kea', 'kaz', 'khm', 'kan', 'kor', 'ltz', 'lao', 'lit', 'lvs', 'mri', 'mkd', 'xng', 'mar', 'zsm', 'mlt', 'oci', 'ory', 'pan', 'pst', 'ron', 'snd', 'slk', 'slv', 'sna', | |
| 'som', 'srp', 'swe', 'tam', 'tel', 'tgk', 'tur', 'ukr', 'umb', 'urd', 'uzn', 'bhd', 'kfs', 'dgo', 'gbk', 'bgc', 'xnr', 'kfx', 'mjl', 'bfz', 'acf', 'bss', 'inb', 'nca', 'quh', 'wap', 'acr', 'bus', 'dgr', 'maz', 'nch', 'qul', 'tav', 'wmw', 'acu', 'byr', 'dik', 'iou', 'mbb', 'ncj', 'qvc', 'tbc', 'xed', 'agd', | |
| 'bzh', 'djk', 'ipi', 'mbc', 'ncl', 'qve', 'tbg', 'xon', 'agg', 'bzj', 'dop', 'jac', 'mbh', 'ncu', 'qvh', 'tbl', 'xtd', 'agn', 'caa', 'jic', 'mbj', 'ndj', 'qvm', 'tbz', 'xtm', 'agr', 'cab', 'emp', 'jiv', 'mbt', 'nfa', 'qvn', 'tca', 'yaa', 'agu', 'cap', 'jvn', 'mca', 'ngp', 'qvs', 'tcs', 'yad', 'aia', 'car', | |
| 'ese', 'mcb', 'ngu', 'qvw', 'yal', 'cax', 'kaq', 'mcd', 'nhe', 'qvz', 'tee', 'ycn', 'ake', 'cbc', 'far', 'mco', 'qwh', 'yka', 'alp', 'cbi', 'kdc', 'mcp', 'nhu', 'qxh', 'ame', 'cbr', 'gai', 'kde', 'mcq', 'nhw', 'qxn', 'tew', 'yre', 'amf', 'cbs', 'gam', 'kdl', 'mdy', 'nhy', 'qxo', 'tfr', 'yva', 'amk', 'cbt', | |
| 'geb', 'kek', 'med', 'nin', 'rai', 'zaa', 'apb', 'cbu', 'glk', 'ken', 'mee', 'nko', 'rgu', 'zab', 'apr', 'cbv', 'meq', 'tgo', 'zac', 'arl', 'cco', 'gng', 'kje', 'met', 'nlg', 'rop', 'tgp', 'zad', 'grc', 'klv', 'mgh', 'nnq', 'rro', 'zai', 'ata', 'cek', 'gub', 'kmu', 'mib', 'noa', 'ruf', 'tna', 'zam', 'atb', | |
| 'cgc', 'guh', 'kne', 'mie', 'not', 'rug', 'tnk', 'zao', 'atg', 'chf', 'knf', 'mih', 'npl', 'tnn', 'zar', 'awb', 'chz', 'gum', 'knj', 'mil', 'sab', 'tnp', 'zas', 'cjo', 'guo', 'ksr', 'mio', 'obo', 'seh', 'toc', 'zav', 'azg', 'cle', 'gux', 'kue', 'mit', 'omw', 'sey', 'tos', 'zaw', 'azz', 'cme', 'gvc', 'kvn', | |
| 'miz', 'ood', 'sgb', 'tpi', 'zca', 'bao', 'cni', 'gwi', 'kwd', 'mkl', 'shp', 'tpt', 'zga', 'bba', 'cnl', 'gym', 'kwf', 'mkn', 'ote', 'sja', 'trc', 'ziw', 'bbb', 'cnt', 'gyr', 'kwi', 'mop', 'otq', 'snn', 'ttc', 'zlm', 'cof', 'hat', 'kyc', 'mox', 'pab', 'snp', 'tte', 'zos', 'bgt', 'con', 'kyf', 'mpm', 'pad', | |
| 'tue', 'zpc', 'bjr', 'cot', 'kyg', 'mpp', 'soy', 'tuf', 'zpl', 'bjv', 'cpa', 'kyq', 'mpx', 'pao', 'tuo', 'zpm', 'bjz', 'cpb', 'hlt', 'kyz', 'mqb', 'pib', 'spp', 'zpo', 'bkd', 'cpu', 'hns', 'lac', 'mqj', 'pir', 'spy', 'txq', 'zpu', 'blz', 'crn', 'hto', 'lat', 'msy', 'pjt', 'sri', 'txu', 'zpz', 'bmr', 'cso', | |
| 'hub', 'lex', 'mto', 'pls', 'srm', 'udu', 'ztq', 'bmu', 'ctu', 'lgl', 'muy', 'poi', 'srn', 'zty', 'bnp', 'cuc', 'lid', 'mxb', 'stp', 'upv', 'zyp', 'boa', 'cui', 'huu', 'mxq', 'sus', 'ura', 'boj', 'cuk', 'huv', 'llg', 'mxt', 'poy', 'suz', 'urb', 'box', 'cwe', 'hvn', 'prf', 'urt', 'bpr', 'cya', 'ign', 'lww', | |
| 'myk', 'ptu', 'usp', 'bps', 'daa', 'ikk', 'maj', 'myy', 'vid', 'bqc', 'dah', 'nab', 'qub', 'tac', 'bqp', 'ded', 'imo', 'maq', 'nas', 'quf', 'taj', 'vmy'], | |
| distance="average", | |
| k=5, | |
| verbose=True) | |
| ss.find_closest("map", | |
| "aym", | |
| ['eng', 'deu', 'fra', 'spa', 'cmn', 'por', 'pol', 'ita', 'nld', 'ell', 'fin', 'vie', 'rus', 'hun', 'bem', 'swh', 'amh', 'wol', 'mal', 'chv', 'iba', 'jav', 'fon', 'hau', 'lbb', 'kik', 'lin', 'lug', 'luo', 'sxb', 'yor', 'nya', 'loz', 'toi', 'afr', 'arb', 'asm', 'ast', 'azj', 'bel', 'bul', 'ben', 'bos', 'cat', | |
| 'ceb', 'sdh', 'ces', 'cym', 'dan', 'ekk', 'pes', 'fil', 'gle', 'glg', 'guj', 'heb', 'hin', 'hrv', 'hye', 'ind', 'ibo', 'isl', 'kat', 'kam', 'kea', 'kaz', 'khm', 'kan', 'kor', 'ltz', 'lao', 'lit', 'lvs', 'mri', 'mkd', 'xng', 'mar', 'zsm', 'mlt', 'oci', 'ory', 'pan', 'pst', 'ron', 'snd', 'slk', 'slv', 'sna', | |
| 'som', 'srp', 'swe', 'tam', 'tel', 'tgk', 'tur', 'ukr', 'umb', 'urd', 'uzn', 'bhd', 'kfs', 'dgo', 'gbk', 'bgc', 'xnr', 'kfx', 'mjl', 'bfz', 'acf', 'bss', 'inb', 'nca', 'quh', 'wap', 'acr', 'bus', 'dgr', 'maz', 'nch', 'qul', 'tav', 'wmw', 'acu', 'byr', 'dik', 'iou', 'mbb', 'ncj', 'qvc', 'tbc', 'xed', 'agd', | |
| 'bzh', 'djk', 'ipi', 'mbc', 'ncl', 'qve', 'tbg', 'xon', 'agg', 'bzj', 'dop', 'jac', 'mbh', 'ncu', 'qvh', 'tbl', 'xtd', 'agn', 'caa', 'jic', 'mbj', 'ndj', 'qvm', 'tbz', 'xtm', 'agr', 'cab', 'emp', 'jiv', 'mbt', 'nfa', 'qvn', 'tca', 'yaa', 'agu', 'cap', 'jvn', 'mca', 'ngp', 'qvs', 'tcs', 'yad', 'aia', 'car', | |
| 'ese', 'mcb', 'ngu', 'qvw', 'yal', 'cax', 'kaq', 'mcd', 'nhe', 'qvz', 'tee', 'ycn', 'ake', 'cbc', 'far', 'mco', 'qwh', 'yka', 'alp', 'cbi', 'kdc', 'mcp', 'nhu', 'qxh', 'ame', 'cbr', 'gai', 'kde', 'mcq', 'nhw', 'qxn', 'tew', 'yre', 'amf', 'cbs', 'gam', 'kdl', 'mdy', 'nhy', 'qxo', 'tfr', 'yva', 'amk', 'cbt', | |
| 'geb', 'kek', 'med', 'nin', 'rai', 'zaa', 'apb', 'cbu', 'glk', 'ken', 'mee', 'nko', 'rgu', 'zab', 'apr', 'cbv', 'meq', 'tgo', 'zac', 'arl', 'cco', 'gng', 'kje', 'met', 'nlg', 'rop', 'tgp', 'zad', 'grc', 'klv', 'mgh', 'nnq', 'rro', 'zai', 'ata', 'cek', 'gub', 'kmu', 'mib', 'noa', 'ruf', 'tna', 'zam', 'atb', | |
| 'cgc', 'guh', 'kne', 'mie', 'not', 'rug', 'tnk', 'zao', 'atg', 'chf', 'knf', 'mih', 'npl', 'tnn', 'zar', 'awb', 'chz', 'gum', 'knj', 'mil', 'sab', 'tnp', 'zas', 'cjo', 'guo', 'ksr', 'mio', 'obo', 'seh', 'toc', 'zav', 'azg', 'cle', 'gux', 'kue', 'mit', 'omw', 'sey', 'tos', 'zaw', 'azz', 'cme', 'gvc', 'kvn', | |
| 'miz', 'ood', 'sgb', 'tpi', 'zca', 'bao', 'cni', 'gwi', 'kwd', 'mkl', 'shp', 'tpt', 'zga', 'bba', 'cnl', 'gym', 'kwf', 'mkn', 'ote', 'sja', 'trc', 'ziw', 'bbb', 'cnt', 'gyr', 'kwi', 'mop', 'otq', 'snn', 'ttc', 'zlm', 'cof', 'hat', 'kyc', 'mox', 'pab', 'snp', 'tte', 'zos', 'bgt', 'con', 'kyf', 'mpm', 'pad', | |
| 'tue', 'zpc', 'bjr', 'cot', 'kyg', 'mpp', 'soy', 'tuf', 'zpl', 'bjv', 'cpa', 'kyq', 'mpx', 'pao', 'tuo', 'zpm', 'bjz', 'cpb', 'hlt', 'kyz', 'mqb', 'pib', 'spp', 'zpo', 'bkd', 'cpu', 'hns', 'lac', 'mqj', 'pir', 'spy', 'txq', 'zpu', 'blz', 'crn', 'hto', 'lat', 'msy', 'pjt', 'sri', 'txu', 'zpz', 'bmr', 'cso', | |
| 'hub', 'lex', 'mto', 'pls', 'srm', 'udu', 'ztq', 'bmu', 'ctu', 'lgl', 'muy', 'poi', 'srn', 'zty', 'bnp', 'cuc', 'lid', 'mxb', 'stp', 'upv', 'zyp', 'boa', 'cui', 'huu', 'mxq', 'sus', 'ura', 'boj', 'cuk', 'huv', 'llg', 'mxt', 'poy', 'suz', 'urb', 'box', 'cwe', 'hvn', 'prf', 'urt', 'bpr', 'cya', 'ign', 'lww', | |
| 'myk', 'ptu', 'usp', 'bps', 'daa', 'ikk', 'maj', 'myy', 'vid', 'bqc', 'dah', 'nab', 'qub', 'tac', 'bqp', 'ded', 'imo', 'maq', 'nas', 'quf', 'taj', 'vmy'], | |
| k=5) | |
| ss.find_closest("tree", | |
| "aym", | |
| ['eng', 'deu', 'fra', 'spa', 'cmn', 'por', 'pol', 'ita', 'nld', 'ell', 'fin', 'vie', 'rus', 'hun', 'bem', 'swh', 'amh', 'wol', 'mal', 'chv', 'iba', 'jav', 'fon', 'hau', 'lbb', 'kik', 'lin', 'lug', 'luo', 'sxb', 'yor', 'nya', 'loz', 'toi', 'afr', 'arb', 'asm', 'ast', 'azj', 'bel', 'bul', 'ben', 'bos', 'cat', | |
| 'ceb', 'sdh', 'ces', 'cym', 'dan', 'ekk', 'pes', 'fil', 'gle', 'glg', 'guj', 'heb', 'hin', 'hrv', 'hye', 'ind', 'ibo', 'isl', 'kat', 'kam', 'kea', 'kaz', 'khm', 'kan', 'kor', 'ltz', 'lao', 'lit', 'lvs', 'mri', 'mkd', 'xng', 'mar', 'zsm', 'mlt', 'oci', 'ory', 'pan', 'pst', 'ron', 'snd', 'slk', 'slv', 'sna', | |
| 'som', 'srp', 'swe', 'tam', 'tel', 'tgk', 'tur', 'ukr', 'umb', 'urd', 'uzn', 'bhd', 'kfs', 'dgo', 'gbk', 'bgc', 'xnr', 'kfx', 'mjl', 'bfz', 'acf', 'bss', 'inb', 'nca', 'quh', 'wap', 'acr', 'bus', 'dgr', 'maz', 'nch', 'qul', 'tav', 'wmw', 'acu', 'byr', 'dik', 'iou', 'mbb', 'ncj', 'qvc', 'tbc', 'xed', 'agd', | |
| 'bzh', 'djk', 'ipi', 'mbc', 'ncl', 'qve', 'tbg', 'xon', 'agg', 'bzj', 'dop', 'jac', 'mbh', 'ncu', 'qvh', 'tbl', 'xtd', 'agn', 'caa', 'jic', 'mbj', 'ndj', 'qvm', 'tbz', 'xtm', 'agr', 'cab', 'emp', 'jiv', 'mbt', 'nfa', 'qvn', 'tca', 'yaa', 'agu', 'cap', 'jvn', 'mca', 'ngp', 'qvs', 'tcs', 'yad', 'aia', 'car', | |
| 'ese', 'mcb', 'ngu', 'qvw', 'yal', 'cax', 'kaq', 'mcd', 'nhe', 'qvz', 'tee', 'ycn', 'ake', 'cbc', 'far', 'mco', 'qwh', 'yka', 'alp', 'cbi', 'kdc', 'mcp', 'nhu', 'qxh', 'ame', 'cbr', 'gai', 'kde', 'mcq', 'nhw', 'qxn', 'tew', 'yre', 'amf', 'cbs', 'gam', 'kdl', 'mdy', 'nhy', 'qxo', 'tfr', 'yva', 'amk', 'cbt', | |
| 'geb', 'kek', 'med', 'nin', 'rai', 'zaa', 'apb', 'cbu', 'glk', 'ken', 'mee', 'nko', 'rgu', 'zab', 'apr', 'cbv', 'meq', 'tgo', 'zac', 'arl', 'cco', 'gng', 'kje', 'met', 'nlg', 'rop', 'tgp', 'zad', 'grc', 'klv', 'mgh', 'nnq', 'rro', 'zai', 'ata', 'cek', 'gub', 'kmu', 'mib', 'noa', 'ruf', 'tna', 'zam', 'atb', | |
| 'cgc', 'guh', 'kne', 'mie', 'not', 'rug', 'tnk', 'zao', 'atg', 'chf', 'knf', 'mih', 'npl', 'tnn', 'zar', 'awb', 'chz', 'gum', 'knj', 'mil', 'sab', 'tnp', 'zas', 'cjo', 'guo', 'ksr', 'mio', 'obo', 'seh', 'toc', 'zav', 'azg', 'cle', 'gux', 'kue', 'mit', 'omw', 'sey', 'tos', 'zaw', 'azz', 'cme', 'gvc', 'kvn', | |
| 'miz', 'ood', 'sgb', 'tpi', 'zca', 'bao', 'cni', 'gwi', 'kwd', 'mkl', 'shp', 'tpt', 'zga', 'bba', 'cnl', 'gym', 'kwf', 'mkn', 'ote', 'sja', 'trc', 'ziw', 'bbb', 'cnt', 'gyr', 'kwi', 'mop', 'otq', 'snn', 'ttc', 'zlm', 'cof', 'hat', 'kyc', 'mox', 'pab', 'snp', 'tte', 'zos', 'bgt', 'con', 'kyf', 'mpm', 'pad', | |
| 'tue', 'zpc', 'bjr', 'cot', 'kyg', 'mpp', 'soy', 'tuf', 'zpl', 'bjv', 'cpa', 'kyq', 'mpx', 'pao', 'tuo', 'zpm', 'bjz', 'cpb', 'hlt', 'kyz', 'mqb', 'pib', 'spp', 'zpo', 'bkd', 'cpu', 'hns', 'lac', 'mqj', 'pir', 'spy', 'txq', 'zpu', 'blz', 'crn', 'hto', 'lat', 'msy', 'pjt', 'sri', 'txu', 'zpz', 'bmr', 'cso', | |
| 'hub', 'lex', 'mto', 'pls', 'srm', 'udu', 'ztq', 'bmu', 'ctu', 'lgl', 'muy', 'poi', 'srn', 'zty', 'bnp', 'cuc', 'lid', 'mxb', 'stp', 'upv', 'zyp', 'boa', 'cui', 'huu', 'mxq', 'sus', 'ura', 'boj', 'cuk', 'huv', 'llg', 'mxt', 'poy', 'suz', 'urb', 'box', 'cwe', 'hvn', 'prf', 'urt', 'bpr', 'cya', 'ign', 'lww', | |
| 'myk', 'ptu', 'usp', 'bps', 'daa', 'ikk', 'maj', 'myy', 'vid', 'bqc', 'dah', 'nab', 'qub', 'tac', 'bqp', 'ded', 'imo', 'maq', 'nas', 'quf', 'taj', 'vmy'], | |
| k=10, find_furthest=True) | |