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 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 sentences2idx(texts, words):
"""
Take in data, output array of word indices that can be fed into the algorithms.
:param texts: List of texts
:return: x1, m1. x1[i, :] is the word indices in sentence i, m1[i,:] is the mask for sentence i (0 means no word at the location)
"""
seq = []
for t in texts:
# Doing some cleaning of the text
stopwords = utils.get_stopwords()
text = t.strip().strip('"')
text_clean = utils.clean_text(text)
s = [w for w in text_clean.split(" ") if w not in stopwords]
s = s[0:MAX_WORDS]
seq.append(data_io.getSeq(' '.join(s), words))
x1, m1 = data_io.prepare_data(seq)
return x1, m1
