def get_pc(data, We, weight4ind, params):
"Comput the principal component"
def get_weighted_average(We, x, w):
"Compute the weighted average vectors"
n_samples = x.shape[0]
emb = np.zeros((n_samples, We.shape[1]))
for i in range(n_samples):
emb[i, :] = w[i, :].dot(We[x[i, :], :]) / np.count_nonzero(w[i, :])
return emb
for i in data:
i[0].populate_embeddings(words)
if not params.task == "sentiment":
i[1].populate_embeddings(words)
if params.task == "ent":
(scores, g1x, g1mask, g2x, g2mask) = data_io.getDataEntailment(data)
if params.weightfile:
g1mask = data_io.seq2weight(g1x, g1mask, weight4ind)
elif params.task == "sim":
(scores, g1x, g1mask, g2x, g2mask) = data_io.getDataSim(data, -1)
if params.weightfile:
g1mask = data_io.seq2weight(g1x, g1mask, weight4ind)
elif params.task == "sentiment":
(scores, g1x, g1mask) = data_io.getDataSentiment(data)
if params.weightfile:
g1mask = data_io.seq2weight(g1x, g1mask, weight4ind)
emb = get_weighted_average(We, g1x, g1mask)
svd = TruncatedSVD(n_components=params.npc, n_iter=7, random_state=0)
svd.fit(emb)
return svd.components_
def getAccSentiment(model,words,f, params=[]):
f = open(f,'r')
lines = f.readlines()
preds = []
golds = []
seq1 = []
ct = 0
for i in lines:
i = i.split("\t")
p1 = i[0]; score = i[1]
X1 = data_io.getSeq(p1,words)
seq1.append(X1)
ct += 1
if ct % 100 == 0:
x1,m1 = data_io.prepare_data(seq1)
if params and params.weightfile:
m1 = data_io.seq2weight(x1, m1, params.weight4ind)
scores = model.scoring_function(x1,m1)
scores = np.squeeze(scores)
preds.extend(scores.tolist())
seq1 = []
golds.append(score)
if len(seq1) > 0:
x1,m1 = data_io.prepare_data(seq1)
if params and params.weightfile:
m1 = data_io.seq2weight(x1, m1, params.weight4ind)
scores = model.scoring_function(x1,m1)
scores = np.squeeze(scores)
preds.extend(scores.tolist())
return accSentiment(preds,golds)
def sim_getCorrelation(We, words, f, weight4ind, scoring_function, params):
f = open(f, 'r')
lines = f.readlines()
golds = []
seq1 = []
seq2 = []
for i in lines:
i = i.split("\t")
p1 = i[0]
p2 = i[1]
score = float(i[2])
X1, X2 = data_io.getSeqs(p1, p2, words)
seq1.append(X1)
seq2.append(X2)
golds.append(score)
x1, m1 = data_io.prepare_data(seq1)
x2, m2 = data_io.prepare_data(seq2)
m1 = data_io.seq2weight(x1, m1, weight4ind)
m2 = data_io.seq2weight(x2, m2, weight4ind)
scores = scoring_function(We, x1, x2, m1, m2, params)
print seq1[0]
print seq2[0]
print scores[0]
preds = np.squeeze(scores)
return pearsonr(preds, golds)[0], spearmanr(preds, golds)[0]
def return_sif(sentences, words, weight4ind, param, Weights):
# x is the array of word indices, m is the binary mask indicating whether there is a word in that location
x, m = data_io.sentences2idx(sentences, words)
w = data_io.seq2weight(x, m, weight4ind) # get word weights
# get SIF embedding
embeddings = SIF_embedding.SIF_embedding(Weights, x, w, param) # embedding[i,:] is the embedding for sentence i
return embeddings
def fit(self, sentences, We, lowercase_tokens, embeddings_format, embeddings_filepath, params, word_map, weight4ind):
# store these off for pickling or extra transforms
self.word_map = word_map
self.weight4ind = weight4ind
self.params = params
self.lowercase_tokens = lowercase_tokens
self.embeddings_format = embeddings_format
self.embeddings_filepath = embeddings_filepath
self.sentence_count = len(sentences)
x, m = data_io.sentences2idx(sentences, self.word_map) # x is the array of word indices, m is the binary mask indicating whether there is a word in that location
w = data_io.seq2weight(x, m, self.weight4ind) # get word weights
# now let's do some of what happens in src/SIF_embedding.py
# but also keep some pieces along the way
#weighted_emb = get_weighted_average(We, x, w)
weighted_emb = get_weighted_average_alternate(We, x, w)
self.compute_pc(weighted_emb)
self.trained = True
return self.remove_pc(weighted_emb)
def main(sentences,
wordfile: str,
weightfile: str,
weightpara: float = 1e-3,
rmpc: int = 1):
# load word vectors
(words, We) = data_io.getWordmap(wordfile)
# load word weights
word2weight = data_io.getWordWeight(
weightfile,
weightpara) # word2weight['str'] is the weight for the word 'str'
weight4ind = data_io.getWeight(
words, word2weight) # weight4ind[i] is the weight for the i-th word
# load sentences
x, m, _ = data_io.sentences2idx(
sentences, words
) # x is the array of word indices, m is the binary mask indicating whether there is a word in that location
w = data_io.seq2weight(x, m, weight4ind) # get word weights
# set parameters
params = params.params()
params.rmpc = rmpc
# get SIF embedding
embedding = SIF_embedding.SIF_embedding(
We, x, w, params) # embedding[i,:] is the embedding for sentence i
def getSIFscore(sentences: list, words, weight4ind, rmpc, We, params, sx: int,
sy: int):
# load sentences
x, m = data_io.sentences2idx(
sentences, words
) # x is the array of word indices, m is the binary mask indicating whether there is a word in that location
w = data_io.seq2weight(x, m, weight4ind) # get word weights
# print('load sentences finished')
# set parameters
params = params.params()
params.rmpc = rmpc
# get SIF embedding
embedding = SIF_embedding.SIF_embedding(
We, x, w, params) # embedding[i,:] is the embedding for sentence i
embeddingSize = len(embedding)
# print('embeddingSize= ',embeddingSize)
emb = list()
for x in range(embeddingSize):
emb.append(embedding[x, :])
emb1 = emb[sx]
emb2 = emb[sy]
inn = (emb1 * emb2).sum()
emb1norm = numpy.sqrt((emb1 * emb1).sum())
emb2norm = numpy.sqrt((emb2 * emb2).sum())
score = inn / emb1norm / emb2norm
# print(sentences[sx],'--------',sentences[sy],' = ',score,'\n')
return score
def compute_sif_emb(self, sentences):
# load sentences
x1, m = data_io.sentences2idx(sentences, self.words)
w1 = data_io.seq2weight(x1, m, self.weight4ind)
result = get_emb(self.We, x1, w1)
return result
def get_embedding(sentence, words, weight4ind, params, We):
# load sentences
xx, mm = data_io.sentences2idx(sentence, words)
ww = data_io.seq2weight(xx, mm, weight4ind) # get word weights
# get SIF embedding
em = SIF_embedding.SIF_embedding(
We, xx, ww, params) # embedding[i,:] is the embedding for sentence i
return em
def sim_badSents(We, words, weight4ind, scoring_function, params, fpc, sent1,
sent2):
seq1 = []
seq2 = []
X1, X2 = data_io.getSeqs(sent1, sent2, words)
seq1.append(X1)
seq2.append(X2)
x1, m1 = data_io.prepare_data(seq1)
x2, m2 = data_io.prepare_data(seq2)
m1 = data_io.seq2weight(x1, m1, weight4ind)
m2 = data_io.seq2weight(x2, m2, weight4ind)
scores = scoring_function(We, x1, x2, m1, m2, params, fpc)
preds = np.squeeze(scores)
preds = preds * 2 + 3
return preds
def get_embeddings(words, We, word2weight, weight4ind, filename, params): # load sentences x, m, _ = data_io.sentiment2idx(filename, words) # x is the array of word indices, m is a mask w = data_io.seq2weight(x, m, weight4ind) # get word weights # get SIF embedding embedding = SIF_embedding.SIF_embedding(We, x, w, params) # embedding[i,:] is the embedding for sentence i return embedding
def sim_getCorrelation(We, words, f, weight4ind, scoring_function, params, fpc,
test_name):
f = open(f, 'r')
lines = f.readlines()
golds = []
seq1 = []
seq2 = []
index = []
idx = 0
for i in lines:
i = i.split("\t")
p1 = i[0]
p2 = i[1]
score = float(i[2])
X1, X2 = data_io.getSeqs(p1, p2, words)
seq1.append(X1)
seq2.append(X2)
golds.append(score)
index.append(idx)
idx += 1
x1, m1 = data_io.prepare_data(seq1)
x2, m2 = data_io.prepare_data(seq2)
m1 = data_io.seq2weight(x1, m1, weight4ind)
m2 = data_io.seq2weight(x2, m2, weight4ind)
golds = np.asarray(golds)
scores = scoring_function(We, x1, x2, m1, m2, params, fpc)
# scores = scoring_function(We, x1, x2, m1, m2, golds, params, fpc)
# preds = np.squeeze(scores).reshape(-1, 1)
preds = np.squeeze(scores)
# print('the prediction list is {}'.format(preds))
# add SVM predictor
# clf = pickle.load(open('../score_predictor/model_svm', 'rb'))
# clf.fit(preds, golds)
# preds = clf.predict(preds)
print(preds)
# np.save(open("../pred_list", 'wb'), preds)
# np.save(open("../gold_list", 'wb'), golds)
# show_result_image(preds, golds, index, fpc, test_name)
# find_bad_scores(preds.tolist(), lower_threshold=2.5, higher_threshold=3.8)
MSE = sqrt(mean_squared_error(golds, preds))
return pearsonr(preds, golds)[0], MSE
def get_embs(sentences, params):
# load word vectors
(words, We) = data_io.getWordmap(wordfile)
# load word weights
word2weight = data_io.getWordWeight(weightfile, weightpara) # word2weight['str'] is the weight for the word 'str'
weight4ind = data_io.getWeight(words, word2weight) # weight4ind[i] is the weight for the i-th word
# load sentences
x, m = data_io.sentences2idx(sentences, words) # x is the array of word indices, m is the binary mask indicating whether there is a word in that location
w = data_io.seq2weight(x, m, weight4ind) # get word weights
# get SIF embedding
embedding = SIF_embedding.SIF_embedding(We, x, w, params) # embedding[i,:] is the embedding for sentence i
return embedding
def prepare_first_pc(We, words, weight4ind, generation_function, params, fpc):
print("reading file: {}.".format(fpc))
# pre_calculate_first_pc(We, words, fpc, weight4ind, generation_function, params)
file_name = fpc
f = os.path.join("../data/", fpc)
f = open(f, 'r')
seq = []
for i in f.readlines():
X = data_io.getSeq(i, words)
seq.append(X)
x, m = data_io.prepare_data(seq)
m = data_io.seq2weight(x, m, weight4ind)
generation_function(We, x, m, params, file_name)
def getCorrelation(model,words,f, params=[]):
f = open(f,'r')
lines = f.readlines()
preds = []
golds = []
seq1 = []
seq2 = []
for i in lines:
i = i.split("\t")
p1 = i[0]; p2 = i[1]; score = float(i[2])
X1, X2 = data_io.getSeqs(p1,p2,words)
seq1.append(X1)
seq2.append(X2)
golds.append(score)
x1,m1 = data_io.prepare_data(seq1)
x2,m2 = data_io.prepare_data(seq2)
if params and params.weightfile:
m1 = data_io.seq2weight(x1, m1, params.weight4ind)
m2 = data_io.seq2weight(x2, m2, params.weight4ind)
scores = model.scoring_function(x1,x2,m1,m2)
preds = np.squeeze(scores)
return pearsonr(preds,golds)[0], spearmanr(preds,golds)[0]
def sim_getCorrelation1(We, words, file_index, weight4ind, scoring_function,
params):
f = open(file_index[0], 'r')
#print(f)
line = f.readlines()
lines = [lin for lin in line]
f = open(file_index[1], 'r')
#print(f)
score_line = f.readlines()
score_lines = [score for score in score_line]
golds = []
seq1 = []
seq2 = []
for index in range(len(lines)):
i = lines[index]
j = score_lines[index]
i = i.split("\t")
#print(i)
#print(i)
p1 = i[0].lower()
p2 = i[1].lower()
try:
score = float(j)
X1, X2 = data_io.getSeqs(p1, p2, words)
seq1.append(X1)
seq2.append(X2)
golds.append(score)
except:
pass
x1, m1 = data_io.prepare_data(seq1)
x2, m2 = data_io.prepare_data(seq2)
m1 = data_io.seq2weight(x1, m1, weight4ind)
m2 = data_io.seq2weight(x2, m2, weight4ind)
# print(x1,x2,m1,m2)
# print(x1.shape,x2.shape,m1.shape,m2.shape)
scores = scoring_function(We, x1, x2, m1, m2, params)
preds = np.squeeze(scores)
return pearsonr(preds, golds)[0]
def generate_vecs(models, document):
words, weight4ind, rmpc, We = models
x, m = data_io.sentences2idx(document, words)
# x is the array of word indices, m is the binary mask indicating whether there is a word in that location
w = data_io.seq2weight(x, m, weight4ind) # get word weights
# set parameters
param = params.params()
param.rmpc = rmpc
# get SIF embedding
embedding = SIF_embedding.SIF_embedding(
We, x, w, param) # embedding[i,:] is the embedding for sentence i
return embedding
def SIFSentEmbedding(weighttxt,
docfile,
words,
We,
weight4ind,
weightpara=1e-3,
paramm=1):
# the parameter in the SIF weighting scheme, usually in the range [3e-5, 3e-3]
# number of principal components to remove in SIF weighting scheme
sentences = sent_tokenize(docfile)
x, m = data_io.sentences2idx(sentences, words)
w = data_io.seq2weight(x, m, weight4ind) # get word weights
paramm = params.params()
paramm = paramm.LC
embedding = SIF_embedding.SIF_embedding(
We, x, w, paramm) # embedding[i,:] is the embedding for sentence i
return embedding
def sif_embedding(sen):
import sys
#sys.path.append("../src")
#sys.path.append("../data")
import data_io, params, SIF_embedding
import params
import SIF_embedding
# input
wordfile = 'data/dic_files.txt' # word vector file, can be downloaded from GloVe website
weightfile = 'data/dic_freq.txt' # each line is a word and its frequency
weightpara = 1e-3 # the parameter in the SIF weighting scheme, usually in the range [3e-5, 3e-3]
rmpc = 1 # number of principal components to remove in SIF weighting scheme
# sentences = ['这是一个例句', '这是一个更长一些的例句']
# sentences = ['昨天天气不错', '这是一个更长一些的例句']
sentences = sen
# sentences = ['this is an example sentence', 'this is another sentence that is slightly longer']
# load word vectors
(words, We) = data_io.getWordmap(wordfile)
# print(words,We) #单词，和词向量
# load word weights
word2weight = data_io.getWordWeight(
weightfile,
weightpara) # word2weight['str'] is the weight for the word 'str'
weight4ind = data_io.getWeight(
words, word2weight) # weight4ind[i] is the weight for the i-th word
# load sentences
# x, m, _ = data_io.sentences2idx(sentences, words) # x is the array of word indices, m is the binary mask indicating whether there is a word in that location
x, m = data_io.sentences2idx(
sentences, words
) # x is the array of word indices, m is the binary mask indicating whether there is a word in that location
# print(x,m)
w = data_io.seq2weight(x, m, weight4ind) # get word weights
# print('word weight:',w)
# set parameters
# params = params.params()
params = params.params_all() # name 'params' is not defined
params.rmpc = rmpc
# get SIF embedding
embedding = SIF_embedding.SIF_embedding(
We, x, w, params) # embedding[i,:] is the embedding for sentence i
return embedding
def vectorize_sif(filename):
class params(object):
def __init__(self):
self.LW = 1e-5
self.LC = 1e-5
self.eta = 0.05
def __str__(self):
t = "LW", self.LW, ", LC", self.LC, ", eta", self.eta
t = map(str, t)
return ' '.join(t)
# input
wordfile = 'glove.6B.100d.txt' # word vector file, can be downloaded from GloVe website
weightfile = 'enwiki_vocab_min200.txt' # each line is a word and its frequency
weightpara = 1e-3 # the parameter in the SIF weighting scheme, usually in the range [3e-5, 3e-3]
rmpc = 1 # number of principal components to remove in SIF weighting scheme
#sentiment_file = '../data/sentiment-test' # sentiment data file
#cleanfile = "2/D1026-A.M.100.E.10.segs.cl"
#sentiment_file = '../data/clean-5.txt'
# load word vectors
(words, We) = data_io.getWordmap(wordfile)
# load word weights
word2weight = data_io.getWordWeight(
weightfile,
weightpara) # word2weight['str'] is the weight for the word 'str'
weight4ind = data_io.getWeight(
words, word2weight) # weight4ind[i] is the weight for the i-th word
# load sentences (here use sentiment data as an example)
#x, m, _ = data_io.sentiment2idx(sentiment_file, words) # x is the array of word indices, m is the binary mask indicating whether there is a word in that location
x, m = data_io.sentiment2idx(filename, words)
w = data_io.seq2weight(x, m, weight4ind) # get word weights
# parameters
params = params()
#params = params.params()
params.rmpc = rmpc
# get SIF embedding
embedding = SIF_embedding_lib.SIF_embedding(
We, x, w, params) # embedding[i,:] is the embedding for sentence i
return embedding
def sentences2embeddings(sentences): """ Input: sentences - a list of sentences Output: sentence_embeddings - a list of sentence embeddings (numpy vectors of shape (1,300)) """ # load sentences x, m = data_io.sentences2idx(sentences, words) # x is the array of word indices, m is the binary mask indicating whether there is a word in that location w = data_io.seq2weight(x, m, weight4ind) # get word weights # set parameters parameters = params.params() parameters.rmpc = rmpc # get SIF embedding embedding = SIF_embedding.SIF_embedding(We, x, w, parameters) # embedding[i,:] is the embedding for sentence i sentence_embeddings = [] for i in range(len(sentences)): e = np.array(embedding[i,:]).reshape((1,300)) # reshape to fit into the function semantic_similarity sentence_embeddings.append(e) return sentence_embeddings
def get_sent_vec(sentences):
import params
# 详见data_io.py
(words, We) = data_io.getWordmap(wordfile)
# 详见data_io.py
word2weight = data_io.getWordWeight(weightfile, weightpara)
weight4ind = data_io.getWeight(words, word2weight)
# 详见data_io.py
x, m = data_io.sentences2idx(sentences, words)
w = data_io.seq2weight(x, m, weight4ind)
# 参数设置
params = params.params()
params.rmpc = rmpc
# 调用SIF核心算法计算句向量，详见SIF_core
embedding = SIF_core.SIF_embedding(We, x, w, params)
get_sent_vec = {}
for i in range(len(embedding)):
get_sent_vec[sentences[i]] = embedding[i]
return get_sent_vec
def get_sent_vec(sentences):
'''
通过SIF算法计算句向量
:param sentences: 类型为列表，列表内的元素为字句子（句子类型为字符串，不用通过jieba.cut分词处理）
:return: 输出类型为字典，key为句子字符串，value为句向量列表
'''
import params
# 详见data_io.py
x, m = data_io.sentences2idx(sentences, words)
w = data_io.seq2weight(x, m, weight4ind)
# 参数设置
rmpc = 1 # number of principal components to remove in SIF weighting scheme
params = params.params()
params.rmpc = rmpc
# 调用SIF核心算法计算句向量，详见SIF_core
embedding = SIF_core.SIF_embedding(We, x, w, params)
get_sent_vec = {}
for i in range(len(embedding)):
get_sent_vec[sentences[i]] = embedding[i]
return get_sent_vec
def sentences2vecs(sentences, We, words, weight4ind):
x, m = sentences2idx(sentences, words)
w = data_io.seq2weight(x, m, weight4ind)
return SIF_embedding.get_weighted_average(We, x, w)
def top_filtering(logits,
words,
weight4ind,
We,
tokenizer,
history,
args,
params,
embedding1,
top_k=0,
top_p=0.0,
threshold=-float('Inf'),
filter_value=-float('Inf'),
current_output=None):
""" Filter a distribution of logits using top-k, top-p (nucleus) and/or threshold filtering
Args:
logits: logits distribution shape (vocabulary size)
top_k: <=0: no filtering, >0: keep only top k tokens with highest probability.
top_p: <=0.0: no filtering, >0.0: keep only a subset S of candidates, where S is the smallest subset
whose total probability mass is greater than or equal to the threshold top_p.
In practice, we select the highest probability tokens whose cumulative probability mass exceeds
the threshold top_p.
threshold: a minimal threshold to keep logits
"""
if current_output is None:
current_output = []
assert logits.dim(
) == 1 # Only work for batch size 1 for now - could update but it would obfuscate a bit the code
top_k = min(top_k, logits.size(-1))
if top_k > 0:
# Remove all tokens with a probability less than the last token in the top-k tokens
indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1,
None]
logits[indices_to_remove] = filter_value
if top_p > 0.0:
# Compute cumulative probabilities of sorted tokens
sorted_logits, sorted_indices = torch.sort(logits, descending=True)
cumulative_probabilities = torch.cumsum(F.softmax(sorted_logits,
dim=-1),
dim=-1)
# Remove tokens with cumulative probability above the threshold
sorted_indices_to_remove = cumulative_probabilities > top_p
# Shift the indices to the right to keep also the first token above the threshold
sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[
..., :-1].clone()
sorted_indices_to_remove[..., 0] = 0
# Back to unsorted indices and set them to -infinity
indices_to_remove = sorted_indices[sorted_indices_to_remove]
indices_to_use = sorted_indices[~sorted_indices_to_remove]
logits[indices_to_remove] = filter_value
if len(indices_to_use) > 1:
cands = [current_output + [idx] for idx in indices_to_use.tolist()]
raw_cands = [
tokenizer.decode(cand, skip_special_tokens=True)
for cand in cands
]
scores = []
for i in raw_cands:
sentences = [i]
x, m = data_io.sentences2idx(sentences, words)
w = data_io.seq2weight(x, m, weight4ind)
embedding2 = SIF_embedding.SIF_embedding(We, x, w, params)
inn = (embedding1 * embedding2).sum(axis=1)
emb1norm = np.sqrt((embedding1 * embedding1).sum(axis=1))
emb2norm = np.sqrt((embedding2 * embedding2).sum(axis=1))
scores.append(inn / emb1norm / emb2norm)
#print(sentences)
for idx, sim in zip(indices_to_use, scores):
logits[idx] += sim.item()
"""
probs = F.softmax(logits, dim=-1)
index = []
for i in probs:
if i > 0:
index.append(i)
prev = torch.topk(probs, 1)[1] if args.no_sample else torch.multinomial(probs, len(index))
text = []
last_utt = history[-1]
last = tokenizer.decode(last_utt, skip_special_tokens=True)
sentences = [last]
# load sentences
x, m = data_io.sentences2idx(sentences,
words) # x is the array of word indices, m is the binary mask indicating whether there is a word in that location
w = data_io.seq2weight(x, m, weight4ind) # get word weights
# set parameters
global params
params.rmpc = rmpc
# get SIF embedding
embedding1 = SIF_embedding.SIF_embedding(We, x, w, params) # embedding[i,:] is the embedding for sentence i
for i in prev:
text.append(i.item())
for i in text:
cand = current_output.copy()
cand.append(i)
indice = i
raw_text=tokenizer.decode(cand, skip_special_tokens=True)
sentences = [raw_text]
x, m= data_io.sentences2idx(sentences,
words)
w = data_io.seq2weight(x, m, weight4ind)
embedding2 = SIF_embedding.SIF_embedding(We, x, w, params)
inn = (embedding1 * embedding2 ).sum(axis=1)
emb1norm = np.sqrt((embedding1 * embedding1).sum(axis=1))
emb2norm = np.sqrt((embedding2 * embedding2 ).sum(axis=1))
scores = inn / emb1norm / emb2norm
#print(scores)
logits[indice] += scores.item()
cand.clear()
"""
indices_to_remove = logits < threshold
logits[indices_to_remove] = filter_value
return logits
def sample_sequence(personality,
history,
tokenizer,
model,
args,
words,
weight4ind,
We,
current_output=None):
special_tokens_ids = tokenizer.convert_tokens_to_ids(SPECIAL_TOKENS)
if current_output is None:
current_output = []
last_utt = history[-1]
last = tokenizer.decode(last_utt, skip_special_tokens=True)
sentences = [last]
# load sentences
x, m = data_io.sentences2idx(
sentences, words
) # x is the array of word indices, m is the binary mask indicating whether there is a word in that location
w = data_io.seq2weight(x, m, weight4ind) # get word weights
rmpc = 1 # number of principal components to remove in SIF weighting scheme
# set parameters
global params
params.rmpc = rmpc
# get SIF embedding
embedding1 = SIF_embedding.SIF_embedding(
We, x, w, params) # embedding[i,:] is the embedding for sentence i
for i in range(args.max_length):
instance, _ = build_input_from_segments(personality,
history,
current_output,
tokenizer,
with_eos=False)
input_ids = torch.tensor(instance["input_ids"],
device=args.device).unsqueeze(0)
token_type_ids = torch.tensor(instance["token_type_ids"],
device=args.device).unsqueeze(0)
temperature = 1.0
top_k = 0
top_p = 0.9
logits = model(input_ids, token_type_ids=token_type_ids)
if isinstance(logits, tuple): # for gpt2 and maybe others
logits = logits[0]
logits = logits[0, -1, :] / args.temperature
logits = top_filtering(logits,
words,
weight4ind,
We,
tokenizer,
history,
args,
params,
embedding1,
top_k=top_k,
top_p=top_p,
current_output=current_output)
probs = F.softmax(logits, dim=-1)
prev = torch.topk(
probs, 1)[1] if args.no_sample else torch.multinomial(probs, 1)
if i < args.min_length and prev.item() in special_tokens_ids:
while prev.item() in special_tokens_ids:
if probs.max().item() == 1:
warnings.warn(
"Warning: model generating special token with probability 1."
)
break # avoid infinitely looping over special token
prev = torch.multinomial(probs, num_samples=1)
if prev.item() in special_tokens_ids:
break
current_output.append(prev.item())
return current_output
def train_util(model, train_data, dev, test, train, words, params):
"utility function for training the model"
start_time = time()
try:
for eidx in range(params.epochs):
kf = data_io.get_minibatches_idx(len(train_data),
params.batchsize,
shuffle=True)
uidx = 0
for _, train_index in kf:
uidx += 1
batch = [train_data[t] for t in train_index]
# load the word ids
for i in batch:
i[0].populate_embeddings(words)
if not params.task == "sentiment":
i[1].populate_embeddings(words)
# load the data
if params.task == "ent":
(scores, g1x, g1mask, g2x,
g2mask) = data_io.getDataEntailment(batch)
elif params.task == "sim":
(scores, g1x, g1mask, g2x,
g2mask) = data_io.getDataSim(batch, model.nout)
elif params.task == "sentiment":
(scores, g1x, g1mask) = data_io.getDataSentiment(batch)
else:
raise ValueError('Task should be ent or sim.')
# train
if not params.task == "sentiment":
if params.weightfile:
g1mask = data_io.seq2weight(g1x, g1mask,
params.weight4ind)
g2mask = data_io.seq2weight(g2x, g2mask,
params.weight4ind)
cost = model.train_function(scores, g1x, g2x, g1mask,
g2mask)
else:
if params.weightfile:
g1mask = data_io.seq2weight(g1x, g1mask,
params.weight4ind)
cost = model.train_function(scores, g1x, g1mask)
if np.isnan(cost) or np.isinf(cost):
print('NaN detected')
# undo batch to save RAM
for i in batch:
i[0].representation = None
i[0].unpopulate_embeddings()
if not params.task == "sentiment":
i[1].representation = None
i[1].unpopulate_embeddings()
# evaluate
if params.task == "sim":
dp, ds = eval.supervised_evaluate(model, words, dev, params)
tp, ts = eval.supervised_evaluate(model, words, test, params)
rp, rs = eval.supervised_evaluate(model, words, train, params)
print(("evaluation: ", dp, ds, tp, ts, rp, rs))
elif params.task == "ent" or params.task == "sentiment":
ds = eval.supervised_evaluate(model, words, dev, params)
ts = eval.supervised_evaluate(model, words, test, params)
rs = eval.supervised_evaluate(model, words, train, params)
print(("evaluation: ", ds, ts, rs))
else:
raise ValueError('Task should be ent or sim.')
print(('Epoch ', (eidx + 1), 'Cost ', cost))
sys.stdout.flush()
except KeyboardInterrupt:
print("Training interupted")
end_time = time()
print(("total time:", (end_time - start_time)))
#in sentence_file or '5-15' in sentence_file:
batch_num = 0
with open(sentence_file, 'r', encoding='utf-8') as fr:
print('Processing file', sentence_file, '...')
p = hnswlib.Index(space='cosine', dim=dimension)
p.init_index(max_elements = num_elements, ef_construction = 2000, M = 80)
p.set_ef(1000)
# Set number of threads used during batch search/construction
# By default using all available cores
p.set_num_threads(30)
for n_lines in iter(lambda: tuple(islice(fr, batch_size)), ()):
sents = list(map(str.strip, n_lines))
sent_id = list(map(lambda x:int(x.split('\t')[0]), sents))
sentences = list(map(lambda x:x.split('\t')[-1], sents))
x, m = data_io.sentences2idx(sentences, words)
w = data_io.seq2weight(x, m, weight4ind)
# get SIF embedding
embedding = SIF_embedding.SIF_embedding(We, x, w, params) # embedding[i,:] is the embedding for sentence i
embeddings.normalize(embedding, ["unit", "center"])
p.add_items(embedding,sent_id)
print('Finished batch', batch_num, '.', end = '\r')
batch_num += 1
print('\nFinished loading', sentence_file, '.')
out_file = sentence_file+'.ann'
p.save_index(out_file)
print('Finished saving', out_file, '.')
del p
(glove_words, We) = data_io.getWordmap(wordfile)
print("shape of Word embedding is: " + str(We.shape))
# load word weights
word2weight = data_io.getWordWeight(
weightfile,
weightpara) # word2weight['str'] is the weight for the word 'str'
weight4ind = data_io.getWeight(
glove_words, word2weight) # weight4ind[i] is the weight for the i-th word
# set parameters
params = params.params()
params.rmpc = rmpc
# load sentences
print("reading the input sentences now & converting to indices .. \n")
sample_sents = read_NMT_data.read_data(sample_ara)
# AraSIF embedding for sample sentences
print("computing AraSIF embedding now ...\n")
# x is the array of word indices, m is the binary mask indicating whether there is a word in that location
x, m = data_io.sentences2idx(sample_sents, glove_words)
w = data_io.seq2weight(x, m, weight4ind) # get word weights
sample_embedding = SIF_embedding.SIF_embedding(
We, x, w, params) # embedding[i,:] is the embedding for sentence i
print("shape of sample sentence embedding is: " + str(sample_embedding.shape))
# serialize for future use
numpy.save('sample_sentence_embedding.npy', sample_embedding)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-d',
'--dataname',
default='t6',
help='dataset name',
choices=['t6', 't26', '2C'])
parser.add_argument('-c',
'--classifiername',
default='RF',
help='which classifier to use',
choices=['GaussianNB', 'RF', 'SVM', 'KNN'])
args = parser.parse_args()
data_name = args.dataname # t6 or t26, 2C, 4C
clf_name = args.classifiername # classfier
# Original SIF paper used glove.840B.300d, we use the ones that were trained on twitter.
embed_dims = [100] # can add 25, 50, 200 dimension if needed
wordfile_list = [
'../data/glove.twitter.27B.{}d.txt'.format(dim) for dim in embed_dims
]
# each line is a word and its frequency
weightfile = 'SIF-master/auxiliary_data/enwiki_vocab_min200.txt'
# the parameter in the SIF weighting scheme, usually in the range [3e-5, 3e-3]
weightpara = 1e-3
# number of principal components to remove in SIF weighting scheme
rmpc = 1
for wordfile, dim in zip(wordfile_list, embed_dims):
# load word vectors
(words, We) = data_io.getWordmap(wordfile)
# load word weights
# word2weight['str'] is the weight for the word 'str'
word2weight = data_io.getWordWeight(weightfile, weightpara)
# weight4ind[i] is the weight for the i-th word
weight4ind = data_io.getWeight(words, word2weight)
data_path = "../data/"
if data_name == "t6":
file_path = data_path + "CrisisLexT6_cleaned/"
disasters = [
"sandy", "queensland", "boston", "west_texas", "oklahoma",
"alberta"
]
test_list = [
"{}_glove_token.csv.unique.csv".format(disaster)
for disaster in disasters
]
train_list = [
"{}_training.csv".format(disaster) for disaster in disasters
]
if data_name == "t26":
file_path = data_path + "CrisisLexT26_cleaned/"
disasters = [
"2012_Colorado_wildfires", "2013_Queensland_floods",
"2013_Boston_bombings", "2013_West_Texas_explosion",
"2013_Alberta_floods", "2013_Colorado_floods",
"2013_NY_train_crash"
]
test_list = [
"{}-tweets_labeled.csv.unique.csv".format(disaster)
for disaster in disasters
]
train_list = [
"{}_training.csv".format(disaster) for disaster in disasters
]
if data_name == "2C":
file_path = data_path + "2CTweets_cleaned/"
disasters = [
"Memphis", "Seattle", "NYC", "Chicago", "SanFrancisco",
"Boston", "Brisbane", "Dublin", "London", "Sydney"
]
test_list = [
"{}2C.csv.token.csv.unique.csv".format(disaster)
for disaster in disasters
]
train_list = [
"{}2C_training.csv".format(disaster) for disaster in disasters
]
accu_list = []
roc_list = []
precision_list = []
recall_list = []
f1_list = []
for train, test in zip(train_list, test_list):
train_file = os.path.join(file_path, train)
test_file = os.path.join(file_path, test)
xtrain, ytrain = load_data(data_name, train_file)
xtest, ytest = load_data(data_name, test_file)
# load train
# xtrain_windx is the array of word indices, m_train is the binary mask indicating whether there is a word in that location
xtrain_windx, m_train = data_io.sentences2idx(xtrain, words)
w_train = data_io.seq2weight(xtrain_windx, m_train,
weight4ind) # get word weights
# set parameters
paramss = params.params()
paramss.rmpc = rmpc
# get SIF embedding
train_embed = SIF_embedding.SIF_embedding(
We, xtrain_windx, w_train,
paramss) # embedding[i,:] is the embedding for sentence i
# load target
# xtest_windx is the array of word indices, m_test is the binary mask indicating whether there is a word in that location
xtest_windx, m_test = data_io.sentences2idx(xtest, words)
# get word weights
w_test = data_io.seq2weight(xtest_windx, m_test, weight4ind)
# set parameters
paramsss = params.params()
paramsss.rmpc = rmpc
# get SIF embedding
test_embed = SIF_embedding.SIF_embedding(
We, xtest_windx, w_test,
paramsss) # embedding[i,:] is the embedding for sentence i
print(test)
accu, roc, precision, recall, f1 = run_classifier(
train_embed, ytrain, test_embed, ytest, clf_name, 100)
accu_list.append(accu)
roc_list.append(roc)
precision_list.append(precision)
recall_list.append(recall)
f1_list.append(f1)
print("{}_SIF_{}_LOO_accuracy {}".format(data_name,
clf_name + str(dim),
accu_list))
print("{}_SIF_{}_LOO_roc {}".format(data_name, clf_name + str(dim),
roc_list))
print("{}_SIF_{}_LOO_precision {}".format(data_name,
clf_name + str(dim),
precision_list))
print("{}_SIF_{}_LOO_recall {}".format(data_name, clf_name + str(dim),
recall_list))
print("{}_SIF_{}_LOO_f1 {}".format(data_name, clf_name + str(dim),
f1_list))
print(
"{0}_SIF_LOO_{1} {2:.4f} + {3:.4f} {4:.4f} + {5:.4f} {6:.4f} + {7:.4f} {8:.4f} + {9:.4f} {10:.4f} + {11:.4f}"
.format(data_name, clf_name + str(dim), np.mean(accu_list),
np.std(accu_list), np.mean(roc_list), np.std(roc_list),
np.mean(f1_list), np.std(f1_list), np.mean(precision_list),
np.std(precision_list), np.mean(recall_list),
np.std(recall_list)))
