def spearman_evaluate(vectors, standard, language='en', verbose=0):
"""
Tests assoc_space's ability to recognize word correlation. This function
computes the spearman correlation between assoc_space's reported word
correlation and the expected word correlation according to 'standard'.
"""
gold_scores = []
our_scores = []
for term1, term2, gold_score in standard:
uri1 = standardized_uri(language, term1)
uri2 = standardized_uri(language, term2)
if isinstance(vectors, VectorSpaceWrapper):
our_score = vectors.get_similarity(uri1, uri2)
else:
our_score = cosine_similarity(get_vector(vectors, uri1), get_vector(vectors, uri2))
if verbose > 1:
print('%s\t%s\t%3.3f\t%3.3f' % (term1, term2, gold_score, our_score))
gold_scores.append(gold_score)
our_scores.append(our_score)
correlation = spearmanr(np.array(gold_scores), np.array(our_scores))[0]
if verbose:
print("Spearman correlation: %s" % (correlation,))
return confidence_interval(correlation, len(gold_scores))
def spearman_evaluate(vectors, standard, language='en', verbose=0):
"""
Tests assoc_space's ability to recognize word correlation. This function
computes the spearman correlation between assoc_space's reported word
correlation and the expected word correlation according to 'standard'.
"""
gold_scores = []
our_scores = []
for term1, term2, gold_score in standard:
uri1 = standardized_uri(language, term1)
uri2 = standardized_uri(language, term2)
if isinstance(vectors, VectorSpaceWrapper):
our_score = vectors.get_similarity(uri1, uri2)
else:
our_score = cosine_similarity(get_vector(vectors, uri1),
get_vector(vectors, uri2))
if verbose > 1:
print('%s\t%s\t%3.3f\t%3.3f' %
(term1, term2, gold_score, our_score))
gold_scores.append(gold_score)
our_scores.append(our_score)
correlation = spearmanr(np.array(gold_scores), np.array(our_scores))[0]
if verbose:
print("Spearman correlation: %s" % (correlation, ))
return confidence_interval(correlation, len(gold_scores))
def measure_bias(frame):
"""
Return a DataFrame that measures biases in a semantic space, on four
data sets:
- Gender
- Fine-grained ethnicity
- Coarse-grained ethnicity
- Religious beliefs
"""
vsw = VectorSpaceWrapper(frame=frame)
vsw.load()
gender_binary_axis = normalize_vec(
get_category_axis(frame, FEMALE_WORDS) -
get_category_axis(frame, MALE_WORDS))
gender_bias_numbers = []
for female_biased_word, male_biased_word in GENDER_BIAS_PAIRS:
female_biased_uri = standardized_uri('en', female_biased_word)
male_biased_uri = standardized_uri('en', male_biased_word)
diff = normalize_vec(
vsw.get_vector(female_biased_uri) -
vsw.get_vector(male_biased_uri)).dot(gender_binary_axis)
gender_bias_numbers.append(diff)
mean = np.mean(gender_bias_numbers)
sem = scipy.stats.sem(gender_bias_numbers)
gender_bias = pd.Series([mean, mean - sem * 2, mean + sem * 2],
index=['bias', 'low', 'high'])
stereotype_vecs_1 = get_vocabulary_vectors(frame, PEOPLE_BY_ETHNICITY)
stereotype_vecs_2 = get_vocabulary_vectors(frame, ETHNIC_STEREOTYPE_TERMS)
fine_ethnic_bias = correlation_bias(stereotype_vecs_1, stereotype_vecs_2)
stereotype_vecs_1 = get_vocabulary_vectors(frame, COARSE_ETHNICITY_TERMS)
stereotype_vecs_2 = get_vocabulary_vectors(frame, ETHNIC_STEREOTYPE_TERMS)
coarse_ethnic_bias = correlation_bias(stereotype_vecs_1, stereotype_vecs_2)
stereotype_vecs_1 = pd.DataFrame(
np.vstack(
[get_category_axis(frame, names) for names in ETHNIC_NAME_SETS]))
stereotype_vecs_2 = get_vocabulary_vectors(frame, ETHNIC_STEREOTYPE_TERMS)
name_ethnic_bias = correlation_bias(stereotype_vecs_1, stereotype_vecs_2)
stereotype_vecs_1 = get_vocabulary_vectors(frame, PEOPLE_BY_BELIEF)
stereotype_vecs_2 = get_vocabulary_vectors(frame, BELIEF_STEREOTYPE_TERMS)
belief_bias = correlation_bias(stereotype_vecs_1, stereotype_vecs_2)
return pd.DataFrame({
'gender': gender_bias,
'ethnicity-fine': fine_ethnic_bias,
'ethnicity-coarse': coarse_ethnic_bias,
'ethnicity-names': name_ethnic_bias,
'beliefs': belief_bias
}).T
def read_bats(category):
"""
Read BATS dataset pairs for a specific category. Turn them into questions.
For some questions, BATS contains multiple answers. For example, the answer to an
analogy question Nicaragua:Spanish::Switzerland:? could be German, French, or Italian. These
will all be supplied as a list if they are an answer (b2). However, if they are a part of a
question (b1), only the first one will be used.
"""
filename = 'bats/{}.txt'.format(category)
pairs = []
with open(get_support_data_filename(filename)) as file:
for line in file:
if '\t' in line:
left, right = line.lower().split('\t')
else:
left, right = line.lower().split()
right = right.strip()
if '/' in right:
right = [i.strip() for i in right.split('/')]
else:
right = [i.strip() for i in right.split(',')]
pairs.append([left, right])
quads = []
for i in range(len(pairs)):
first_pair = pairs[i]
first_pair[1] = first_pair[1][
0] # select only one term for b1, even if more may be available
second_pairs = [pair for j, pair in enumerate(pairs) if j != i]
for second_pair in second_pairs:
quad = []
# the first three elements of a quad are the two terms in first_pair and the first
# term of the second_pair
quad.extend([
standardized_uri('en', term)
for term in first_pair + second_pair[:1]
])
# if the second element of the second pair (b2) is a list, it means there are multiple
# correct answers for b2. We want to keep all of them.
if isinstance(second_pair[1], list):
quad.append(
[standardized_uri('en', term) for term in second_pair[1]])
else:
quad.append(standardized_uri('en', second_pair[1]))
quads.append(quad)
return quads
def read_turney_analogies(filename):
"""
Read Turney and Littman's dataset of SAT analogy questions. This data
requires permission to redistribute, so you have to ask Peter Turney
for the file.
"""
questions = []
question_lines = []
with open(filename, encoding='utf-8') as file:
for line in file:
line = line.rstrip()
if line and not line.startswith('#'):
if len(line) == 1:
# A single letter on a line indicates the answer to a question.
answer_index = ord(line) - ord('a')
# Line 0 is a header we can discard.
raw_pairs = [qline.split(' ')[:2] for qline in question_lines[1:]]
concept_pairs = [tuple(standardized_uri('en', term) for term in pair) for pair in raw_pairs]
# The first of the pairs we got is the prompt pair. The others are
# answers (a) through (e).
questions.append((concept_pairs[0], concept_pairs[1:], answer_index))
question_lines.clear()
else:
question_lines.append(line)
return questions
def eval_analogies(frame):
filename = get_support_data_filename('google-analogies/questions-words.txt')
quads = read_google_analogies(filename)
vocab = [
standardized_uri('en', word)
for word in wordfreq.top_n_list('en', 200000)
]
wrap = VectorSpaceWrapper(frame=frame)
vecs = np.vstack([wrap.get_vector(word) for word in vocab])
tframe = pd.DataFrame(vecs, index=vocab)
total = 0
correct = 0
seen_mistakes = set()
for quad in quads:
prompt = quad[:3]
answer = quad[3]
vector = analogy_func(frame, *prompt)
similar = similar_to_vec(tframe, vector)
result = None
for match in similar.index:
if match not in prompt:
result = match
break
if result == answer:
correct += 1
else:
if result not in seen_mistakes:
print(
"%s : %s :: %s : [%s] (should be %s)"
% (quad[0], quad[1], quad[2], result, answer)
)
seen_mistakes.add(result)
total += 1
low, high = proportion_confint(correct, total)
return pd.Series([correct / total, low, high], index=['acc', 'low', 'high'])
def eval_analogies(frame):
filename = get_support_data_filename('google-analogies/questions-words.txt')
quads = read_google_analogies(filename)
vocab = [
standardized_uri('en', word)
for word in wordfreq.top_n_list('en', 200000)
]
wrap = VectorSpaceWrapper(frame=frame)
vecs = np.vstack([wrap.get_vector(word) for word in vocab])
tframe = pd.DataFrame(vecs, index=vocab)
total = 0
correct = 0
seen_mistakes = set()
for quad in quads:
prompt = quad[:3]
answer = quad[3]
vector = analogy_func(frame, *prompt)
similar = similar_to_vec(tframe, vector)
result = None
for match in similar.index:
if match not in prompt:
result = match
break
if result == answer:
correct += 1
else:
if result not in seen_mistakes:
print(
"%s : %s :: %s : [%s] (should be %s)"
% (quad[0], quad[1], quad[2], result, answer)
)
seen_mistakes.add(result)
total += 1
low, high = proportion_confint(correct, total)
return pd.Series([correct / total, low, high], index=['acc', 'low', 'high'])
def get_vocabulary_vectors(frame, vocab):
"""
Given a vocabulary (as a list of English terms), get a sub-frame of the
given DataFrame containing just the known vectors for that vocabulary.
"""
uris = [standardized_uri('en', term) for term in vocab]
return frame.reindex(uris).dropna()
def get_vocabulary_vectors(frame, vocab):
"""
Given a vocabulary (as a list of English terms), get a sub-frame of the
given DataFrame containing just the known vectors for that vocabulary.
"""
uris = [standardized_uri('en', term) for term in vocab]
return frame.reindex(uris).dropna()
def read_turney_analogies(filename):
"""
Read Turney and Littman's dataset of SAT analogy questions. This data
requires permission to redistribute, so you have to ask Peter Turney
for the file.
"""
questions = []
question_lines = []
with open(filename, encoding='utf-8') as file:
for line in file:
line = line.rstrip()
if line and not line.startswith('#'):
if len(line) == 1:
# A single letter on a line indicates the answer to a question.
answer_index = ord(line) - ord('a')
# Line 0 is a header we can discard.
raw_pairs = [
qline.split(' ')[:2] for qline in question_lines[1:]
]
concept_pairs = [
tuple(standardized_uri('en', term) for term in pair)
for pair in raw_pairs
]
# The first of the pairs we got is the prompt pair. The others are
# answers (a) through (e).
questions.append(
(concept_pairs[0], concept_pairs[1:], answer_index))
question_lines.clear()
else:
question_lines.append(line)
return questions
def get_category_axis(frame, category_examples):
"""
Get a vector representing the average of several example terms, where
the terms are specified as plain English text.
"""
return get_weighted_vector(frame, [(standardized_uri('en', term), 1.)
for term in category_examples])
def standardize_row_labels(frame, language='en', forms=True):
"""
Convert a frame whose row labels are bare English terms (e.g. of the
form 'en/term') to one whose row labels are standardized ConceptNet URIs
(e.g. of the form '/c/en/term'; and with some extra word2vec-style
normalization of digits). Rows whose labels get the same standardized
URI get combined, with earlier rows given more weight.
"""
# Check for en/term format we use to train fastText on OpenSubtitles data
if all(label.count('/') == 1 for label in frame.index[0:5]):
tuples = [label.partition('/') for label in frame.index]
frame.index = [
uri_prefix(standardized_uri(language, text))
for language, _slash, text in tuples
]
# Re-label the DataFrame with standardized, non-unique row labels
frame.index = [
uri_prefix(standardized_uri(language, label)) for label in frame.index
]
# Assign row n a weight of 1/(n+1) for weighted averaging
nrows = frame.shape[0]
weights = 1.0 / np.arange(1, nrows + 1)
label_weights = pd.Series(weights, index=frame.index)
# groupby(level=0).sum() means to add rows that have the same label
relabeled = frame.mul(weights,
axis='rows').sort_index().groupby(level=0).sum()
combined_weights = label_weights.sort_index().groupby(level=0).sum()
# Optionally adjust words to be more like their word forms
if forms:
for label in relabeled.index:
lemmatized = lemmatize_uri(label)
if lemmatized != label and lemmatized in relabeled.index:
relabeled.loc[lemmatized] += relabeled.loc[label] / 2
combined_weights.loc[
lemmatized] += combined_weights.loc[label] / 2
scaled = relabeled.div(combined_weights, axis='rows')
# Rearrange the items in descending order of weight, similar to the order
# we get them in from word2vec and GloVe
combined_weights.sort_values(inplace=True, ascending=False)
result = scaled.loc[combined_weights.index]
return result
def read_google_analogies(filename):
"""
Read the 'questions-words.txt' file that comes with the word2vec package.
"""
quads = [[
standardized_uri('en', term) for term in line.rstrip().split(' ')
] for line in open(filename, encoding='utf-8') if not line.startswith(':')]
return quads
def text_to_vector(self, language, text):
"""
Used in Story Cloze Test to create a vector for text.
"""
tokens = wordfreq.simple_tokenize(text)
weighted_terms = [(uri_prefix(standardized_uri(language, token)), 1.)
for token in tokens]
return self.get_vector(weighted_terms, oov_vector=False)
def de_bias_category(frame, category_examples, bias_examples):
"""
Remove correlations between a class of words that should have biases
removed (category_examples) and a set of words reflecting those biases
(bias_examples). For example, the `category_examples` may be ethnicities,
and `bias_examples` may be stereotypes about them.
The check for whether a word should be de-biased works like
`de_bias_binary`, where the category words are positive examples and the
bias words are negative examples (because the words that define the bias
presumably should not be de-biased).
The words that should be de-biased will have their correlations with
each of the bias words removed.
"""
# Make an SVM that distinguishes words that are in the category to be
# de-biased from words that are not.
category_predictor = two_class_svm(frame, category_examples, bias_examples)
# Predict the probability of each word in the vocabulary being in the
# category. This is done on shards, to reduce peak memory consumption.
applicability = np.zeros(shape=(len(frame), ), dtype=np.float32)
for shard_start, shard_end in make_shard_endpoints(len(frame)):
applicability[
shard_start:shard_end] = category_predictor.predict_proba(
frame[shard_start:shard_end])[:, 1]
del category_predictor
# Make a matrix of vectors representing the correlations to remove.
vocab = [standardized_uri('en', term) for term in bias_examples]
components_to_reject = frame.reindex(vocab).dropna().values
# Make a modified version of the space that projects the bias vectors to 0.
# Then weight each row of that space by "applicability", the probability
# that each row should be de-biased. This is also done on shards.
for shard_start, shard_end in make_shard_endpoints(len(frame)):
shard_len = shard_end - shard_start
modified_component = reject_subspace(
frame[shard_start:shard_end],
components_to_reject).mul(applicability[shard_start:shard_end],
axis=0).values
# Make another component representing the vectors that should not be
# de-biased: the original space times (1 - applicability).
np.multiply(1 - applicability[shard_start:shard_end].reshape(
(shard_len, 1)),
frame.values[shard_start:shard_end, :],
out=frame.values[shard_start:shard_end, :])
# The sum of these two components is the de-biased space, where
# de-biasing applies to each row proportional to its applicability.
np.add(frame.values[shard_start:shard_end, :],
modified_component,
out=frame.values[shard_start:shard_end, :])
del modified_component
# L_2-normalize the resulting rows in-place.
normalize(frame.values, norm='l2', copy=False)
def standardize_row_labels(frame, language='en', forms=True):
"""
Convert a frame whose row labels are bare English terms (e.g. of the
form 'en/term') to one whose row labels are standardized ConceptNet URIs
(e.g. of the form '/c/en/term'; and with some extra word2vec-style
normalization of digits). Rows whose labels get the same standardized
URI get combined, with earlier rows given more weight.
"""
# Check for en/term format we use to train fastText on OpenSubtitles data
if all(label.count('/') == 1 for label in frame.index[0:5]):
tuples = [label.partition('/') for label in frame.index]
frame.index = [
uri_prefix(standardized_uri(language, text))
for language, _slash, text in tuples
]
# Re-label the DataFrame with standardized, non-unique row labels
frame.index = [
uri_prefix(standardized_uri(language, label)) for label in frame.index
]
# Assign row n a weight of 1/(n+1) for weighted averaging
nrows = frame.shape[0]
weights = 1.0 / np.arange(1, nrows + 1)
label_weights = pd.Series(weights, index=frame.index)
# groupby(level=0).sum() means to add rows that have the same label
relabeled = frame.mul(weights, axis='rows').sort_index().groupby(level=0).sum()
combined_weights = label_weights.sort_index().groupby(level=0).sum()
# Optionally adjust words to be more like their word forms
if forms:
for label in relabeled.index:
lemmatized = lemmatize_uri(label)
if lemmatized != label and lemmatized in relabeled.index:
relabeled.loc[lemmatized] += relabeled.loc[label] / 2
combined_weights.loc[lemmatized] += combined_weights.loc[label] / 2
scaled = relabeled.div(combined_weights, axis='rows')
# Rearrange the items in descending order of weight, similar to the order
# we get them in from word2vec and GloVe
combined_weights.sort_values(inplace=True, ascending=False)
result = scaled.loc[combined_weights.index]
return result
def text_to_vector(self, language, text):
"""
Used in Story Cloze Test to create a vector for text.
"""
tokens = wordfreq.tokenize(text, language)
weighted_terms = [
(uri_prefix(standardized_uri(language, token)), 1.) for token in tokens
]
return self.get_vector(weighted_terms, oov_vector=False)
def get_category_axis(frame, category_examples):
"""
Get a vector representing the average of several example terms, where
the terms are specified as plain English text.
"""
return get_weighted_vector(
frame,
[(standardized_uri('en', term), 1.)
for term in category_examples]
)
def read_google_analogies(filename):
"""
Read the 'questions-words.txt' file that comes with the word2vec package.
"""
quads = [
[standardized_uri('en', term) for term in line.rstrip().split(' ')]
for line in open(filename, encoding='utf-8')
if not line.startswith(':')
]
return quads
def de_bias_category(frame, category_examples, bias_examples):
"""
Remove correlations between a class of words that should have biases
removed (category_examples) and a set of words reflecting those biases
(bias_examples). For example, the `category_examples` may be ethnicities,
and `bias_examples` may be stereotypes about them.
The check for whether a word should be de-biased works like
`de_bias_binary`, where the category words are positive examples and the
bias words are negative examples (because the words that define the bias
presumably should not be de-biased).
The words that should be de-biased will have their correlations with
each of the bias words removed.
"""
# Make an SVM that distinguishes words that are in the category to be
# de-biased from words that are not.
category_predictor = two_class_svm(frame, category_examples, bias_examples)
# Predict the probability of each word in the vocabulary being in the
# category. This is done on shards, to reduce peak memory consumption.
applicability = np.zeros(shape=(len(frame),), dtype=np.float32)
for shard_start, shard_end in make_shard_endpoints(len(frame)):
applicability[shard_start:shard_end] = category_predictor.predict_proba(
frame[shard_start:shard_end])[:, 1]
del category_predictor
# Make a matrix of vectors representing the correlations to remove.
vocab = [
standardized_uri('en', term) for term in bias_examples
]
components_to_reject = frame.reindex(vocab).dropna().values
# Make a modified version of the space that projects the bias vectors to 0.
# Then weight each row of that space by "applicability", the probability
# that each row should be de-biased. This is also done on shards.
modified_component = np.zeros(shape=frame.values.shape, dtype=np.float32)
for shard_start, shard_end in make_shard_endpoints(len(frame)):
modified_component[shard_start:shard_end, :] = \
reject_subspace(frame[shard_start:shard_end], components_to_reject).mul(
applicability[shard_start:shard_end], axis=0).values
del components_to_reject
# Make another component representing the vectors that should not be
# de-biased: the original space times (1 - applicability).
np.multiply(1 - applicability.reshape((len(frame), 1)), frame.values,
out=frame.values)
# The sum of these two components is the de-biased space, where de-biasing
# applies to each row proportional to its applicability.
np.add(frame.values, modified_component, out=frame.values)
del modified_component
# L_2-normalize the resulting rows in-place.
normalize(frame.values, norm='l2', copy=False)
def test_load_vectors():
vectors = ai._load_vectors()
ok_(vectors.frame.index[0].startswith('/c/en/'))
# Test we have vectors for all Codenames words
wordlist = [
standardized_uri('en', line.strip()) for line in open(
resource_filename('codenames', 'data/codenames-words.txt'))
]
for word in wordlist:
ok_(word in vectors.frame.index)
def choose_vocab(quads, vocab_size):
"""
Google and Bats analogies are not multiple-choice; instead, you're supposed to pick
the best match out of your vector space's entire vocabulary, excluding the
three words used in the prompt. The vocabulary size can matter a lot: Set
it too high and you'll get low-frequency words that the data set wasn't
looking for as answers. Set it too low and the correct answers won't be
in the vocabulary.
Set vocab_size='cheat' to see the results for an unrealistically optimal
vocabulary (the vocabulary of the set of answer words).
"""
if vocab_size == 'cheat':
vocab = [
standardized_uri('en', word)
for word in sorted(set([quad[3] for quad in quads]))
]
else:
vocab = [
standardized_uri('en', word)
for word in wordfreq.top_n_list('en', vocab_size)
]
return vocab
def _load_vectors():
frame = load_hdf(resource_filename('codenames', 'data/mini.h5'))
selections = [
label for label in frame.index
if label.startswith('/c/en/') and '_' not in label and '#' not in label
and wordfreq.zipf_frequency(label[6:], 'en') > 3.0
]
# Make sure all the words in Codenames are represented
wordlist = [
standardized_uri('en', line.strip()) for line in open(
resource_filename('codenames', 'data/codenames-words.txt'))
]
additions = [word for word in wordlist if word not in selections]
selections += additions
frame = l2_normalize_rows(frame.loc[selections].astype('f'))
return VectorSpaceWrapper(frame=frame)
def de_bias_category(frame, category_examples, bias_examples):
"""
Remove correlations between a class of words that should have biases
removed (category_examples) and a set of words reflecting those biases
(bias_examples). For example, the `category_examples` may be ethnicities,
and `bias_examples` may be stereotypes about them.
The check for whether a word should be de-biased works like
`de_bias_binary`, where the category words are positive examples and the
bias words are negative examples (because the words that define the bias
presumably should not be de-biased).
The words that should be de-biased will have their correlations with
each of the bias words removed.
"""
# Make an SVM that distinguishes words that are in the category to be
# de-biased from words that are not.
category_predictor = two_class_svm(frame, category_examples, bias_examples)
# Predict the probability of each word in the vocabulary being in the
# category.
applicability = category_predictor.predict_proba(frame)[:, 1]
del category_predictor
# Make a matrix of vectors representing the correlations to remove.
vocab = [standardized_uri('en', term) for term in bias_examples]
components_to_reject = frame.reindex(vocab).dropna().values
# Make a modified version of the space that projects the bias vectors to 0.
# Then weight each row of that space by "applicability", the probability
# that each row should be de-biased.
modified_component = reject_subspace(frame, components_to_reject).mul(
applicability, axis=0)
del components_to_reject
# Make another component representing the vectors that should not be
# de-biased: the original space times (1 - applicability).
result = frame.mul(1 - applicability, axis=0)
# The sum of these two components is the de-biased space, where de-biasing
# applies to each row proportional to its applicability.
np.add(result.values, modified_component.values, out=result.values)
del modified_component
# L_2-normalize the resulting rows in-place.
normalize(result.values, norm='l2', copy=False)
return result
def text_to_vector(self, language, text):
# TODO: docstring -- is this only used for Story Cloze Test?
tokens = wordfreq.tokenize(text, language)
weighted_terms = [(standardized_uri(language, token), 1.)
for token in tokens]
return self.get_vector(weighted_terms, include_neighbors=False)
def eval_semeval2012_analogies(vectors, weight_direct, weight_transpose,
subset, subclass):
"""
For a set of test pairs:
* Compute a Spearman correlation coefficient between the ranks produced by vectors and
gold ranks.
* Compute an accuracy score of answering MaxDiff questions.
"""
train_pairs = read_train_pairs_semeval2012(subset, subclass)
test_questions = read_test_questions_semeval2012(subset, subclass)
pairqnum2least, pairqnum2most = read_turk_answers_semeval2012(
subset, subclass, test_questions)
turk_rank = read_turk_ranks_semeval2012(subset, subclass)
pairs_to_rank = [pair for pair, score in turk_rank]
# Assign a score to each pair, according to pairwise_analogy_func
our_pair_scores = {}
for pair in pairs_to_rank:
rank_pair_scores = []
for train_pair in train_pairs:
pair_to_rank = pair.strip().replace('"', '').split(':')
score = pairwise_analogy_func(
vectors, standardized_uri('en', train_pair[0]),
standardized_uri('en', train_pair[1]),
standardized_uri('en', pair_to_rank[0]),
standardized_uri('en', pair_to_rank[1]), weight_direct,
weight_transpose)
rank_pair_scores.append(score)
our_pair_scores[pair] = np.mean(rank_pair_scores)
# Answer MaxDiff questions using the ranks from the previous step
correct_most = 0
correct_least = 0
total = 0
for i, question in enumerate(test_questions):
question_pairs_scores = []
for question_pair in question:
score = our_pair_scores[question_pair]
question_pairs_scores.append(score)
our_answer_most = question[np.argmax(question_pairs_scores)]
our_answer_least = question[np.argmin(question_pairs_scores)]
votes_guess_least = pairqnum2least[(i, our_answer_least)]
votes_guess_most = pairqnum2most[(i, our_answer_most)]
max_votes_least = 0
max_votes_most = 0
for question_pair in question:
num_votes_least = pairqnum2least[(i, question_pair)]
num_votes_most = pairqnum2most[(i, question_pair)]
if num_votes_least > max_votes_least:
max_votes_least = num_votes_least
if num_votes_most > max_votes_most:
max_votes_most = num_votes_most
# a guess is correct if it got the same number of votes as the most frequent turkers' answer
if votes_guess_least == max_votes_least:
correct_least += 1
if votes_guess_most == max_votes_most:
correct_most += 1
total += 1
# Compute Spearman correlation of our ranks and MT ranks
our_semeval_scores = [
score for pair, score in sorted(our_pair_scores.items())
]
turk_semeval_scores = [score for pair, score in turk_rank]
spearman = spearmanr(our_semeval_scores, turk_semeval_scores)[0]
spearman_results = confidence_interval(spearman, total)
# Compute an accuracy score on MaxDiff questions
maxdiff = (correct_least + correct_most) / (2 * total)
low_maxdiff, high_maxdiff = proportion_confint(
(correct_least + correct_most), (2 * total))
maxdiff_results = pd.Series([maxdiff, low_maxdiff, high_maxdiff],
index=['acc', 'low', 'high'])
return [maxdiff_results, spearman_results]
def text_to_vector(self, language, text):
tokens = wordfreq.tokenize(text, language)
weighted_terms = [(standardized_uri(language, token), 1.)
for token in tokens]
return self.get_vector(weighted_terms, include_neighbors=False)
def text_to_vector(self, language, text):
"""Used in Story Cloze Test to create a vector for text """
tokens = wordfreq.tokenize(text, language)
weighted_terms = [(standardized_uri(language, token), 1.)
for token in tokens]
return self.get_vector(weighted_terms, include_neighbors=False)
def eval_google_analogies(vectors,
subset='semantic',
vocab_size=200000,
verbose=False):
"""
Evaluate the Google Research analogies, released by Mikolov et al. along
with word2vec.
These analogies come in two flavors: semantic and syntactic. Numberbatch
is intended to be a semantic space, so we focus on semantic analogies.
The syntactic analogies are about whether you can inflect or conjugate a
particular word. The semantic analogies are about whether you can sort
words by their gender, and about geographic trivia.
I (Rob) think this data set is not very representative, but evaluating
against it is all the rage.
These analogies are not multiple-choice; instead, you're supposed to pick
the best match out of your vector space's entire vocabulary, excluding the
three words used in the prompt. The vocabulary size can matter a lot: Set
it too high and you'll get low-frequency words that the data set wasn't
looking for as answers. Set it too low and the correct answers won't be
in the vocabulary.
Set vocab_size='cheat' to see the results for an unrealistically optimal
vocabulary (the vocabulary of the set of answer words).
"""
filename = get_support_data_filename(
'google-analogies/{}-words.txt'.format(subset))
quads = read_google_analogies(filename)
if vocab_size == 'cheat':
vocab = [
standardized_uri('en', word)
for word in sorted(set([quad[3] for quad in quads]))
]
else:
vocab = [
standardized_uri('en', word)
for word in wordfreq.top_n_list('en', vocab_size)
]
vecs = np.vstack([vectors.get_vector(word) for word in vocab])
tframe = pd.DataFrame(vecs, index=vocab)
total = 0
correct = 0
seen_mistakes = set()
for quad in quads:
prompt = quad[:3]
answer = quad[3]
result = best_analogy_3cosmul(vectors, tframe, *prompt)
if result == answer:
correct += 1
else:
if verbose and result not in seen_mistakes:
print("%s : %s :: %s : [%s] (should be %s)" %
(quad[0], quad[1], quad[2], result, answer))
seen_mistakes.add(result)
total += 1
low, high = proportion_confint(correct, total)
result = pd.Series([correct / total, low, high],
index=['acc', 'low', 'high'])
if verbose:
print(result)
return result
def tag_en(word):
return standardized_uri('en', word)
def text_to_vector(self, language, text):
tokens = wordfreq.tokenize(text, language)
weighted_terms = [(standardized_uri(language, token), 1.0) for token in tokens]
return self.get_vector(weighted_terms, include_neighbors=False)
