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weat.py
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weat.py
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import numpy as np
import random
from itertools import filterfalse
from itertools import combinations
import codecs
import utils
import os
import pickle
import logging
import argparse
import time
from collections import OrderedDict
import math
from sklearn.metrics.pairwise import euclidean_distances
listofwarnings =[]
class XWEAT(object):
"""
Perform WEAT (Word Embedding Association Test) bias tests on a language model.
Follows from Caliskan et al 2017 (10.1126/science.aal4230).
Credits: Basic implementation based on https://gist.github.com/SandyRogers/e5c2e938502a75dcae25216e4fae2da5
"""
def __init__(self):
self.embd_dict = None
self.vocab = None
self.embedding_matrix = None
def set_embd_dict(self, embd_dict):
self.embd_dict = embd_dict
def _build_vocab_dict(self, vocab): #we look for every word in our vocab and we check if they have embeddings output is term as a key and index as value
self.vocab = OrderedDict()
#vocab = set(vocab) this cause shuffling
vocab = list(dict.fromkeys(vocab))
index = 0
for term in vocab:
if term in self.embd_dict:
self.vocab[term] = index
index += 1
else:
logging.warning("Not in vocab %s", term)
listofwarnings.append(term)
def convert_by_vocab(self, items): #givin a list (T1) we convert the list from (boy, man, etc ) to (1 , 2 ,3 )
"""Converts a sequence of [tokens|ids] using the vocab."""
output = []
for item in items:
if item in self.vocab:
output.append(self.vocab[item])
else:
continue
return output
def _build_embedding_matrix(self): # Create a matr
self.embedding_matrix = []
for term, index in self.vocab.items():
if term in self.embd_dict:
self.embedding_matrix.append(self.embd_dict[term])
else:
raise AssertionError("This should not happen.")
self.embd_dict = None
def mat_normalize(self, mat, norm_order=2, axis=1):
return mat / np.transpose([np.linalg.norm(mat, norm_order, axis)])
def cosine(self, a, b):
norm_a = self.mat_normalize(a)
norm_b = self.mat_normalize(b)
cos = np.dot(norm_a, np.transpose(norm_b))
return cos
def euclidean(self, a, b):
norm_a = self.mat_normalize(a)
norm_b = self.mat_normalize(b)
distances = euclidean_distances(norm_a, norm_b)
eucl = 1 / (1 + distances)
return eucl
def csls(self, a, b, k=10): #another similarity measurment
norm_a = self.mat_normalize(a)
norm_b = self.mat_normalize(b)
sims_local_a = np.dot(norm_a, np.transpose(norm_a))
sims_local_b = np.dot(norm_b, np.transpose(norm_b))
csls_norms_a = np.mean(np.sort(sims_local_a, axis=1)[:, -k - 1:-1], axis=1)
csls_norms_b = np.mean(np.sort(sims_local_b, axis=1)[:, -k - 1:-1], axis=1)
loc_sims = np.add(np.transpose(np.tile(csls_norms_a, (len(csls_norms_b), 1))),
np.tile(csls_norms_b, (len(csls_norms_a), 1)))
return 2 * np.dot(norm_a, np.transpose(norm_b)) - loc_sims
def _init_similarities(self, similarity_type):
if similarity_type == "cosine":
self.similarities = self.cosine(self.embedding_matrix, self.embedding_matrix)
elif similarity_type == "csls":
self.similarities = self.csls(self.embedding_matrix, self.embedding_matrix)
elif similarity_type == "euclidean":
self.similarities = self.euclidean(self.embedding_matrix, self.embedding_matrix)
else:
raise NotImplementedError()
def weat_1(self):
"""
WEAT 1 (universally accepted): Targets 1=Flowers, Targets 2=Insects, Attributes 1=pleasant, Attributes 2=unpleasant
:return: targets_1, targets_2, attributes_1, attributes_2
"""
targets_1 = ["aster", "clover", "hyacinth", "marigold", "poppy", "azalea", "crocus", "iris", "orchid", "rose",
"blue-bell", "daffodil", "lilac", "pansy", "tulip", "buttercup", "daisy", "lily", "peony",
"violet",
"carnation", "gladiola", "magnolia", "petunia", "zinnia"]
targets_2 = ["ant", "caterpillar", "flea", "locust", "spider", "bedbug", "centipede", "fly", "maggot",
"tarantula",
"bee", "cockroach", "gnat", "mosquito", "termite", "beetle", "cricket", "hornet", "moth", "wasp",
"blackfly",
"dragonfly", "horsefly", "roach", "weevil"]
attributes_1 = ["caress", "freedom", "health", "love", "peace", "cheer", "friend", "heaven", "loyal",
"pleasure",
"diamond", "gentle", "honest", "lucky", "rainbow", "diploma", "gift", "honor", "miracle",
"sunrise",
"family",
"happy", "laughter", "paradise", "vacation"]
attributes_2 = ["abuse", "crash", "filth", "murder", "sickness", "accident", "death", "grief", "poison",
"stink",
"assault", "disaster", "hatred", "pollute", "tragedy", "divorce", "jail", "poverty", "ugly",
"cancer",
"kill", "rotten",
"vomit", "agony", "prison"]
return targets_1, targets_2, attributes_1, attributes_2
# WEAT 2: Target 1=Musical instruments, Target 2= Weapons, Attributes 1: pleasant, Attributes 2=unpleasant
def weat_2(self):
targets_1 = ["bagpipe", "cello", "guitar", "lute", "trombone", "banjo", "clarinet", "harmonica", "mandolin",
"trumpet", "bassoon", "drum", "harp", "oboe", "tuba", "bell", "fiddle", "harpsichord", "piano",
"viola", "bongo",
"flute", "horn", "saxophone", "violin"]
targets_2 = ["arrow", "club", "gun", "missile", "spear", "axe", "dagger", "harpoon", "pistol", "sword", "blade",
"dynamite", "hatchet", "rifle", "tank", "bomb", "firearm", "knife", "shotgun", "teargas", "cannon",
"grenade",
"mace", "slingshot", "whip"]
attributes_1 = ["caress", "freedom", "health", "love", "peace", "cheer", "friend", "heaven", "loyal",
"pleasure",
"diamond", "gentle", "honest", "lucky", "rainbow", "diploma", "gift", "honor", "miracle",
"sunrise",
"family", "happy", "laughter", "paradise", "vacation"]
attributes_2 = ["abuse", "crash", "filth", "murder", "sickness", "accident", "death", "grief", "poison",
"stink",
"assault", "disaster", "hatred", "pollute", "tragedy", "divorce", "jail", "poverty", "ugly",
"cancer",
"kill", "rotten",
"vomit", "agony", "prison"]
return targets_1, targets_2, attributes_1, attributes_2
# Here they deleted the infrequent african american names, and the same number randomly choosen from the european american names
def weat_3(self):
# excluded in the original paper: Chip, Ian, Fred, Jed, Todd, Brandon, Wilbur, Sara, Amber, Crystal, Meredith, Shannon, Donna,
# Bobbie-Sue, Peggy, Sue-Ellen, Wendy
targets_1 = ["Adam", "Harry", "Josh", "Roger", "Alan", "Frank", "Justin", "Ryan", "Andrew", "Jack", "Matthew",
"Stephen",
"Brad", "Greg", "Paul", "Hank", "Jonathan", "Peter", "Amanda", "Courtney", "Heather", "Melanie",
"Katie", "Betsy", "Kristin", "Nancy", "Stephanie", "Ellen", "Lauren", "Colleen", "Emily", "Megan",
"Rachel",
"Chip", "Ian", "Fred", "Jed", "Todd", "Brandon", "Wilbur", "Sara", "Amber", "Crystal", "Meredith",
"Shannon",
"Donna", "Bobbie-Sue", "Peggy", "Sue-Ellen", "Wendy"]
# excluded: Lerone, Percell, Rasaan, Rashaun, Everol, Terryl, Aiesha, Lashelle, Temeka, Tameisha, Teretha, Latonya, Shanise,
# Sharise, Tashika, Lashandra, Shavonn, Tawanda,
targets_2 = ["Alonzo", "Jamel", "Theo", "Alphonse", "Jerome", "Leroy", "Torrance", "Darnell", "Lamar", "Lionel",
"Tyree", "Deion", "Lamont", "Malik", "Terrence", "Tyrone", "Lavon", "Marcellus", "Wardell",
"Nichelle",
"Shereen", "Ebony", "Latisha", "Shaniqua", "Jasmine", "Tanisha", "Tia", "Lakisha", "Latoya",
"Yolanda",
"Malika", "Yvette", "Lerone", "Percell", "Rasaan", "Rashaun", "Everol", "Terryl", "Aiesha",
"Lashelle",
"Temeka", "Tameisha", "Teretha", "Latonya", "Shanise", "Sharise", "Tashika", "Lashandra",
"Shavonn", "Tawanda"]
attributes_1 = ["caress", "freedom", "health", "love", "peace", "cheer", "friend", "heaven", "loyal",
"pleasure",
"diamond", "gentle", "honest", "lucky", "rainbow", "diploma", "gift", "honor", "miracle",
"sunrise",
"family", "happy", "laughter", "paradise", "vacation"]
# same as before but agony and prison are replaced with bomb and evil
attributes_2 = ["abuse", "crash", "filth", "murder", "sickness", "accident", "death", "grief", "poison",
"stink",
"assault", "disaster", "hatred", "pollute", "tragedy", "divorce", "jail", "poverty", "ugly",
"cancer",
"kill", "rotten",
"vomit", "bomb", "evil"]
return targets_1, targets_2, attributes_1, attributes_2
# again: african american names vs. european american names and pleasant vs unpleasant but with different names
def weat_4(self):
# excluded as in the original paper: Jay, Kristen, (here only excluded in the glove experiments)
targets_1 = ["Brad", "Brendan", "Geoffrey", "Greg", "Brett", "Matthew", "Neil", "Todd", "Allison", "Anne",
"Carrie", "Emily", "Jill", "Laurie", "Meredith", "Sarah", "Jay", "Kristen"]
# excluded in GloVe experiments: Tremayne, Latonya,
targets_2 = ["Darnell", "Hakim", "Jermaine", "Kareem", "Jamal", "Leroy", "Rasheed", "Tyrone", "Aisha", "Ebony",
"Keisha", "Kenya", "Lakisha", "Latoya", "Tamika", "Tanisha", "Tremayne", "Latonya"]
attributes_1 = ["caress", "freedom", "health", "love", "peace", "cheer", "friend", "heaven", "loyal",
"pleasure",
"diamond", "gentle", "honest", "lucky", "rainbow", "diploma", "gift", "honor", "miracle",
"sunrise",
"family", "happy", "laughter", "paradise", "vacation"]
# same as before but agony and prison are replaced with bomb and evil
attributes_2 = ["abuse", "crash", "filth", "murder", "sickness", "accident", "death", "grief", "poison",
"stink",
"assault", "disaster", "hatred", "pollute", "tragedy", "divorce", "jail", "poverty", "ugly",
"cancer",
"kill", "rotten",
"vomit", "bomb", "evil"]
return targets_1, targets_2, attributes_1, attributes_2
# again african american vs european american names, but with different attributes
def weat_5(self):
# excluded as in the original paper: Jay, Kristen, (here only excluded in the glove experiments)
targets_1 = ["Brad", "Brendan", "Geoffrey", "Greg", "Brett", "Matthew", "Neil", "Todd", "Allison", "Anne",
"Carrie", "Emily", "Jill", "Laurie", "Meredith", "Sarah", "Jay", "Kristen"]
# excluded in GloVe experiments: Tremayne, Latonya,
targets_2 = ["Darnell", "Hakim", "Jermaine", "Kareem", "Jamal", "Leroy", "Rasheed", "Tyrone", "Aisha", "Ebony",
"Keisha", "Kenya", "Lakisha", "Latoya", "Tamika", "Tanisha", "Tremayne", "Latonya"]
attributes_1 = ["joy", "love", "peace", "wonderful", "pleasure", "friend", "laughter", "happy"]
attributes_2 = ["agony", "terrible", "horrible", "nasty", "evil", "war", "awful", "failure"]
return targets_1, targets_2, attributes_1, attributes_2
# sex- career
def weat_6(self):
targets_1 = ["John", "Paul", "Mike", "Kevin", "Steve", "Greg", "Jeff", "Bill"]
targets_2 = ["Amy", "Joan", "Lisa", "Sarah", "Diana", "Kate", "Ann", "Donna"]
attributes_1 = ["executive", "management", "professional", "corporation", "salary", "office", "business",
"career"]
attributes_2 = ["home", "parents", "children", "family", "cousins", "marriage", "wedding", "relatives"]
return targets_1, targets_2, attributes_1, attributes_2
# sex - math
def weat_7(self):
targets_1 = ["math", "algebra", "geometry", "calculus", "equations", "computation", "numbers", "addition"]
targets_2 = ["poetry", "art", "dance", "literature", "novel", "symphony", "drama", "sculpture"]
attributes_1 = ["male", "man", "boy", "brother", "he", "him", "his", "son"]
attributes_2 = ["female", "woman", "girl", "sister", "she", "her", "hers", "daughter"]
return targets_1, targets_2, attributes_1, attributes_2
# sex - science
def weat_8(self):
targets_1 = ["science", "technology", "physics", "chemistry", "Einstein", "NASA", "experiment", "astronomy"]
targets_2 = ["poetry", "art", "Shakespeare", "dance", "literature", "novel", "symphony", "drama"]
attributes_1 = ["brother", "father", "uncle", "grandfather", "son", "he", "his", "him"]
attributes_2 = ["sister", "mother", "aunt", "grandmother", "daughter", "she", "hers", "her"]
return targets_1, targets_2, attributes_1, attributes_2
# desease - uncontrollability
def weat_9(self):
targets_1 = ["sad", "hopeless", "gloomy", "tearful", "miserable", "depressed"]
targets_2 = ["sick", "illness", "influenza", "disease", "virus", "cancer"]
# in w2v experiments, the've replaced short-term with short
attributes_1 = ["impermanent", "unstable", "variable", "fleeting", "short-term", "brief", "occasional"]
attributes_2 = ["stable", "always", "constant", "persistent", "chronic", "prolonged", "forever"]
return targets_1, targets_2, attributes_1, attributes_2
# old - pleasant
def weat_10(self):
targets_1 = ["Tiffany", "Michelle", "Cindy", "Kristy", "Brad", "Eric", "Joey", "Billy"]
targets_2 = ["Ethel", "Bernice", "Gertrude", "Agnes", "Cecil", "Wilbert", "Mortimer", "Edgar"]
attributes_1 = ["joy", "love", "peace", "wonderful", "pleasure", "friend", "laughter", "happy"]
attributes_2 = ["agony", "terrible", "horrible", "nasty", "evil", "war", "awful", "failure"]
return targets_1, targets_2, attributes_1, attributes_2
# missing from the original IAT: arab-muslim
# occupations
def wefat_1(self):
# occupations derived from th bureau of labor statistics
targets = ["technician", "accountant", "supervisor", "engineer", "worker", "educator", "clerk", "counselor",
"inspector",
"mechanic", "manager", "therapist", "administrator", "salesperson", "receptionist", "librarian",
"advisor", "pharmacist",
"janitor", "psychologist", "physician", "carpenter", "nurse", "investigator", "bartender",
"specialist", "electrician",
"officer", "pathologist", "teacher", "lawyer", "planner", "practitioner", "plumber", "instructor",
"surgeon", "veterinarian", "paramedic",
"examiner", "chemist", "machinist", "appraiser", "nutritionist", "architect", "hairdresser", "baker",
"programmer", "paralegal", "hygienist", "scientist"]
attributes_1 = ["male", "man", "boy", "brother", "he", "him", "his", "son"]
attributes_2 = ["female", "woman", "girl", "sister", "she", "her", "hers", "daughter"]
return targets, attributes_1, attributes_2
# androgynous names
def wefat_2(self):
targets = ["Kelly", "Tracy", "Jamie", "Jackie", "Jesse", "Courtney", "Lynn", "Taylor", "Leslie", "Shannon",
"Stacey", "Jessie", "Shawn", "Stacy", "Casey", "Bobby", "Terry", "Lee", "Ashley", "Eddie", "Chris",
"Jody", "Pat",
"Carey", "Willie", "Morgan", "Robbie", "Joan", "Alexis", "Kris", "Frankie", "Bobbie", "Dale",
"Robin", "Billie",
"Adrian", "Kim", "Jaime", "Jean", "Francis", "Marion", "Dana", "Rene", "Johnnie", "Jordan", "Carmen",
"Ollie",
"Dominique", "Jimmie", "Shelby"]
attributes_1 = ["male", "man", "boy", "brother", "he", "him", "his", "son"]
attributes_2 = ["female", "woman", "girl", "sister", "she", "her", "hers", "daughter"]
return targets, attributes_1, attributes_2
def similarity_precomputed_sims(self, w1, w2, type="cosine"):
return self.similarities[w1, w2]
def word_association_with_attribute_precomputed_sims(self, w, A, B):
return np.mean([self.similarity_precomputed_sims(w, a) for a in A]) - np.mean(
[self.similarity_precomputed_sims(w, b) for b in B])
def differential_association_precomputed_sims(self, T1, T2, A1, A2):
return np.sum([self.word_association_with_attribute_precomputed_sims(t1, A1, A2) for t1 in T1]) \
- np.sum([self.word_association_with_attribute_precomputed_sims(t2, A1, A2) for t2 in T2])
def weat_effect_size_precomputed_sims(self, T1, T2, A1, A2):
return (
np.mean([self.word_association_with_attribute_precomputed_sims(t1, A1, A2) for t1 in T1]) -
np.mean([self.word_association_with_attribute_precomputed_sims(t2, A1, A2) for t2 in T2])
) / np.std([self.word_association_with_attribute_precomputed_sims(w, A1, A2) for w in T1 + T2])
def _random_permutation(self, iterable, r=None):
pool = tuple(iterable)
r = len(pool) if r is None else r
return tuple(random.sample(pool, r))
def weat_p_value_precomputed_sims(self, T1, T2, A1, A2, sample):
logging.info("Calculating p value ... ")
size_of_permutation = min(len(T1), len(T2))
T1_T2 = T1 + T2
observed_test_stats_over_permutations = []
total_possible_permutations = math.factorial(len(T1_T2)) / math.factorial(size_of_permutation) / math.factorial(
(len(T1_T2) - size_of_permutation))
logging.info("Number of possible permutations: %d", total_possible_permutations)
listofwarnings.append(total_possible_permutations)
if not sample or sample >= total_possible_permutations:
permutations = combinations(T1_T2, size_of_permutation)
else:
logging.info("Computing randomly first %d permutations", sample)
permutations = set()
while len(permutations) < sample:
permutations.add(tuple(sorted(self._random_permutation(T1_T2, size_of_permutation))))
for Xi in permutations:
Yi = filterfalse(lambda w: w in Xi, T1_T2)
observed_test_stats_over_permutations.append(self.differential_association_precomputed_sims(Xi, Yi, A1, A2))
if len(observed_test_stats_over_permutations) % 100000 == 0:
logging.info("Iteration %s finished", str(len(observed_test_stats_over_permutations)))
unperturbed = self.differential_association_precomputed_sims(T1, T2, A1, A2)
is_over = np.array([o > unperturbed for o in observed_test_stats_over_permutations])
return is_over.sum() / is_over.size
def weat_stats_precomputed_sims(self, T1, T2, A1, A2, sample_p=None):
test_statistic = self.differential_association_precomputed_sims(T1, T2, A1, A2)
effect_size = self.weat_effect_size_precomputed_sims(T1, T2, A1, A2)
p = self.weat_p_value_precomputed_sims(T1, T2, A1, A2, sample=sample_p)
return test_statistic, effect_size, p
def _create_vocab(self): #we dont need it
"""
>>> weat = XWEAT(None); weat._create_vocab()
:return: all
"""
all = []
for i in range(1, 10):
t1, t2, a1, a2 = getattr(self, "weat_" + str(i))()
all = all + t1 + t2 + a1 + a2
for i in range(1, 2):
t1, a1, a2 = getattr(self, "wefat_" + str(i))()
all = all + t1 + a1 + a2
all = set(all)
return all
def _output_vocab(self, path="./data/vocab_en.txt"):
"""
>>> weat = XWEAT(None); weat._output_vocab()
"""
vocab = self._create_vocab()
with codecs.open(path, "w", "utf8") as f:
for w in vocab:
f.write(w)
f.write("\n")
f.close()
def run_test_precomputed_sims(self, target_1, target_2, attributes_1, attributes_2, sample_p=None,
similarity_type="cosine"):
"""Run the WEAT test for differential association between two
sets of target words and two sets of attributes.
RETURNS:
(d, e, p). A tuple of floats, where d is the WEAT Test statistic,
e is the effect size, and p is the one-sided p-value measuring the
(un)likeliness of the null hypothesis (which is that there is no
difference in association between the two target word sets and
the attributes).
If e is large and p small, then differences in the model between
the attribute word sets match differences between the targets.
"""
vocab = target_1 + target_2 + attributes_1 + attributes_2
self._build_vocab_dict(vocab)
T1 = self.convert_by_vocab(target_1)
T2 = self.convert_by_vocab(target_2)
A1 = self.convert_by_vocab(attributes_1)
A2 = self.convert_by_vocab(attributes_2)
while len(T1) < len(T2):
logging.info("Popped T2 %d", T2[-1])
T2.pop(-1)
while len(T2) < len(T1):
logging.info("Popped T1 %d", T1[-1])
T1.pop(-1)
while len(A1) < len(A2):
logging.info("Popped A2 %d", A2[-1])
A2.pop(-1)
while len(A2) < len(A1):
logging.info("Popped A1 %d", A1[-1])
A1.pop(-1)
assert len(T1) == len(T2)
assert len(A1) == len(A2)
self._build_embedding_matrix()
self._init_similarities(similarity_type)
return self.weat_stats_precomputed_sims(T1, T2, A1, A2, sample_p)
def _parse_translations(self, path="./data/vocab_en_de.csv", new_path="./data/vocab_dict_en_de.p",
is_russian=False):
"""
:param path: path of the csv file edited by our translators
:param new_path: path of the clean dict to save
>>> XWEAT()._parse_translations(is_russian=False)
293
"""
# This code probably does not work for the russian code, as dmitry did use other columns for his corrections
with codecs.open(path, "r", "utf8") as f:
translation_dict = {}
for line in f.readlines():
parts = line.split(",")
en = parts[0]
if en == "" or en[0].isupper():
continue
else:
if is_russian and parts[3] != "\n" and parts[3] != "\r\n" and parts[3] != "\r":
other_m = parts[2]
other_f = parts[3].strip()
translation_dict[en] = (other_m, other_f)
else:
other_m = parts[1].strip()
other_f = None
if len(parts) > 2 and parts[2] != "\n" and parts[2] != "\r\n" and parts[2] != "\r" and parts[
2] != '':
other_f = parts[2].strip()
translation_dict[en] = (other_m, other_f)
pickle.dump(translation_dict, open(new_path, "wb"))
return len(translation_dict)
def load_vocab_goran(path): #load pickle files
return pickle.load(open(path, "rb"))
def load_vectors_goran(path):
return np.load(path)
def load_embedding_dict(vocab_path="", vector_path="", embeddings_path="", glove=False, postspec=False): #i can ignore
"""
>>> _load_embedding_dict()
:param vocab_path:
:param vector_path:
:return: embd_dict
"""
if glove and postspec:
raise ValueError("Glove and postspec cannot both be true")
elif glove:
if os.name == "nt":
embd_dict = utils.load_embeddings("C:/Users/anlausch/workspace/embedding_files/glove.6B/glove.6B.300d.txt",
word2vec=False)
else:
embd_dict = utils.load_embeddings("/work/anlausch/glove.6B.300d.txt", word2vec=False)
return embd_dict
elif postspec:
embd_dict_temp = utils.load_embeddings("/work/anlausch/ft_postspec.txt", word2vec=False)
embd_dict = {}
for key, value in embd_dict_temp.items():
embd_dict[key.split("en_")[1]] = value
assert ("test" in embd_dict)
assert ("house" in embd_dict)
return embd_dict
elif embeddings_path.endswith("p"):
with open(embeddings_path, 'rb') as handle:
embd_dict = pickle.load(handle)
return embd_dict
elif embeddings_path != "":
embd_dict = utils.load_embeddings(embeddings_path, word2vec=False)
return embd_dict
else:
embd_dict = {}
vocab = load_vocab_goran(vocab_path)
vectors = load_vectors_goran(vector_path)
for term, index in vocab.items():
embd_dict[term] = vectors[index]
assert len(embd_dict) == len(vocab)
return embd_dict
def translate(translation_dict, terms):
translation = []
for t in terms:
if t in translation_dict or t.lower() in translation_dict:
if t.lower() in translation_dict:
male, female = translation_dict[t.lower()]
elif t in translation_dict:
male, female = translation_dict[t]
if female is None or female is '':
translation.append(male)
else:
translation.append(male)
translation.append(female)
else:
translation.append(t)
translation = list(set(translation))
return translation
def compute_oov_percentage():
"""
>>> compute_oov_percentage()
:return:
"""
with codecs.open("./results/oov_short.txt", "w", "utf8") as f:
for test in range(1, 11):
f.write("Test %d \n" % test)
targets_1, targets_2, attributes_1, attributes_2 = XWEAT().__getattribute__("weat_" + str(test))()
vocab = targets_1 + targets_2 + attributes_1 + attributes_2
vocab = [t.lower() for t in vocab]
# f.write("English vocab: %s \n" % str(vocab))
for language in ["en", "es", "de", "tr", "ru", "hr", "it"]:
if language != "en":
# f.write("Translating terms from en to %s\n" % language)
translation_dict = load_vocab_goran("data/vocab_dict_en_" + language + ".p") #todo: add "." before /data
vocab_translated = translate(translation_dict, vocab)
vocab_translated = [t.lower() for t in vocab_translated]
# f.write("Translated terms %s\n" % str(vocab))
embd_dict = load_embedding_dict(
vocab_path="/work/gglavas/data/word_embs/yacle/fasttext/200K/npformat/ft.wiki." + language + ".300.vocab",
vector_path="/work/gglavas/data/word_embs/yacle/fasttext/200K/npformat/ft.wiki." + language + ".300.vectors")
ins = []
not_ins = []
if language != "en":
for term in vocab_translated:
if term in embd_dict:
ins.append(term)
else:
not_ins.append(term)
else:
for term in vocab:
if term in embd_dict:
ins.append(term)
else:
not_ins.append(term)
# f.write("OOVs: %s\n" % str(not_ins))
f.write("OOV Percentage for language %s: %s\n" % (language, (len(not_ins) / len(vocab))))
f.write("\n")
f.close()
def main():
def boolean_string(s):
if s not in {'False', 'True', 'false', 'true'}:
raise ValueError('Not a valid boolean string')
return s == 'True' or s == 'true'
parser = argparse.ArgumentParser(description="Running XWEAT")
parser.add_argument("--test_number", type=int, help="Number of the weat test to run", required=False)
parser.add_argument("--permutation_number", type=int, default=None,
help="Number of permutations (otherwise all will be run)", required=False)
parser.add_argument("--output_file", type=str, default=None, help="File to store the results)", required=True)
parser.add_argument("--lower", type=boolean_string, default=False, help="Whether to lower the vocab", required=True)
parser.add_argument("--similarity_type", type=str, default="cosine", help="Which similarity function to use",
required=False)
parser.add_argument("--embedding_vocab", type=str, help="Vocab of the embeddings")
parser.add_argument("--embedding_vectors", type=str, help="Vectors of the embeddings")
parser.add_argument("--use_glove", type=boolean_string, default=False, help="Use glove")
parser.add_argument("--postspec", type=boolean_string, default=False, help="Use postspecialized fasttext")
parser.add_argument("--is_vec_format", type=boolean_string, default=False,
help="Whether embeddings are in vec format")
parser.add_argument("--embeddings", type=str, help="Vectors and vocab of the embeddings")
parser.add_argument("--lang", type=str, default="en", help="Language to test")
args = parser.parse_args()
start = time.time()
logging.basicConfig(level=logging.INFO)
logging.info("XWEAT started")
weat = XWEAT()
if args.test_number == 1:
targets_1, targets_2, attributes_1, attributes_2 = weat.weat_1()
elif args.test_number == 2:
targets_1, targets_2, attributes_1, attributes_2 = weat.weat_2()
elif args.test_number == 3:
targets_1, targets_2, attributes_1, attributes_2 = weat.weat_3()
elif args.test_number == 4:
targets_1, targets_2, attributes_1, attributes_2 = weat.weat_4()
elif args.test_number == 5:
targets_1, targets_2, attributes_1, attributes_2 = weat.weat_5()
elif args.test_number == 6:
targets_1, targets_2, attributes_1, attributes_2 = weat.weat_6()
elif args.test_number == 7:
targets_1, targets_2, attributes_1, attributes_2 = weat.weat_7()
elif args.test_number == 8:
targets_1, targets_2, attributes_1, attributes_2 = weat.weat_8()
elif args.test_number == 9:
targets_1, targets_2, attributes_1, attributes_2 = weat.weat_9()
elif args.test_number == 10:
targets_1, targets_2, attributes_1, attributes_2 = weat.weat_10()
else:
raise ValueError("Only WEAT 1 to 10 are supported")
if args.lang != "en":
logging.info("Translating terms from en to %s", args.lang)
translation_dict = load_vocab_goran("/home/rtakiedd/projects/XWEAT/data/vocab_dict_en_" + args.lang + ".p") #todo: change back to ./data
targets_1 = translate(translation_dict, targets_1)
targets_2 = translate(translation_dict, targets_2)
attributes_1 = translate(translation_dict, attributes_1)
attributes_2 = translate(translation_dict, attributes_2)
if args.lower:
targets_1 = [t.lower() for t in targets_1]
targets_2 = [t.lower() for t in targets_2]
attributes_1 = [a.lower() for a in attributes_1]
attributes_2 = [a.lower() for a in attributes_2]
if args.use_glove:
logging.info("Using glove")
embd_dict = load_embedding_dict(glove=True)
elif args.postspec:
logging.info("Using postspecialized embeddings")
embd_dict = load_embedding_dict(postspec=True)
elif args.is_vec_format:
logging.info("Embeddings are in vec format")
embd_dict = load_embedding_dict(embeddings_path=args.embeddings, glove=False)
else:
embd_dict = load_embedding_dict(vocab_path=args.embedding_vocab, vector_path=args.embedding_vectors,
glove=False)
weat.set_embd_dict(embd_dict)
logging.info("Embeddings loaded")
logging.info("Running test %s", args.test_number)
result = weat.run_test_precomputed_sims(targets_1, targets_2, attributes_1, attributes_2, args.permutation_number,
args.similarity_type)
logging.info(result)
with codecs.open(args.output_file, "w", "utf8") as f:
f.write("Config: ")
f.write(str(args.test_number) + " and ")
f.write(str(args.lower) + " and ")
f.write(str(args.permutation_number) + "\n")
f.writelines(["%s\n" % item for item in listofwarnings]) #adds missing words to the log file
f.write("Result: ")
f.write(str(result))
f.write("\n")
end = time.time()
duration_in_hours = ((end - start) / 60) / 60
f.write(str(duration_in_hours))
f.close()
if __name__ == "__main__":
main()