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 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 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 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 getAcc(model, words, f, params=[]):
f = open(f, 'r')
lines = f.readlines()
preds = []
golds = []
seq1 = []
seq2 = []
ct = 0
for i in lines:
i = i.split("\t")
p1 = i[0]
p2 = i[1]
score = i[2]
X1, X2 = data_io.getSeqs(p1, p2, words)
seq1.append(X1)
seq2.append(X2)
ct += 1
if ct % 100 == 0:
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)
scores = np.squeeze(scores)
preds.extend(scores.tolist())
seq1 = []
seq2 = []
golds.append(score)
if len(seq1) > 0:
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)
scores = np.squeeze(scores)
preds.extend(scores.tolist())
return acc(preds, golds)
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]
