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fix eval_sick error #37

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fix eval_sick error #37

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40 changes: 21 additions & 19 deletions eval_sick.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,12 +20,13 @@ def evaluate(model, seed=1234, evaltest=False):
"""
print 'Preparing data...'
train, dev, test, scores = load_data()
train[0], train[1], scores[0] = shuffle(train[0], train[1], scores[0], random_state=seed)

train[0], train[1], scores[0] = shuffle(
train[0], train[1], scores[0], random_state=seed)

print 'Computing training skipthoughts...'
trainA = skipthoughts.encode(model, train[0], verbose=False, use_eos=True)
trainB = skipthoughts.encode(model, train[1], verbose=False, use_eos=True)

print 'Computing development skipthoughts...'
devA = skipthoughts.encode(model, dev[0], verbose=False, use_eos=True)
devB = skipthoughts.encode(model, dev[1], verbose=False, use_eos=True)
Expand All @@ -46,14 +47,16 @@ def evaluate(model, seed=1234, evaltest=False):

if evaltest:
print 'Computing test skipthoughts...'
testA = skipthoughts.encode(model, test[0], verbose=False, use_eos=True)
testB = skipthoughts.encode(model, test[1], verbose=False, use_eos=True)
testA = skipthoughts.encode(
model, test[0], verbose=False, use_eos=True)
testB = skipthoughts.encode(
model, test[1], verbose=False, use_eos=True)

print 'Computing feature combinations...'
testF = np.c_[np.abs(testA - testB), testA * testB]

print 'Evaluating...'
r = np.arange(1,6)
r = np.arange(1, 6)
yhat = np.dot(bestlrmodel.predict_proba(testF, verbose=2), r)
pr = pearsonr(yhat, scores[2])[0]
sr = spearmanr(yhat, scores[2])[0]
Expand All @@ -70,7 +73,7 @@ def prepare_model(ninputs=9600, nclass=5):
Set up and compile the model architecture (Logistic regression)
"""
lrmodel = Sequential()
lrmodel.add(Dense(ninputs, nclass))
lrmodel.add(Dense(nclass, input_dim=ninputs))
lrmodel.add(Activation('softmax'))
lrmodel.compile(loss='categorical_crossentropy', optimizer='adam')
return lrmodel
Expand All @@ -82,11 +85,12 @@ def train_model(lrmodel, X, Y, devX, devY, devscores):
"""
done = False
best = -1.0
r = np.arange(1,6)
r = np.arange(1, 6)

while not done:
# Every 100 epochs, check Pearson on development set
lrmodel.fit(X, Y, verbose=2, shuffle=False, validation_data=(devX, devY))
lrmodel.fit(X, Y, verbose=2, shuffle=False,
validation_data=(devX, devY))
yhat = np.dot(lrmodel.predict_proba(devX, verbose=2), r)
score = pearsonr(yhat, devscores)[0]
if score > best:
Expand All @@ -100,7 +104,7 @@ def train_model(lrmodel, X, Y, devX, devY, devscores):
score = pearsonr(yhat, devscores)[0]
print 'Dev Pearson: ' + str(score)
return bestlrmodel


def encode_labels(labels, nclass=5):
"""
Expand All @@ -109,19 +113,19 @@ def encode_labels(labels, nclass=5):
Y = np.zeros((len(labels), nclass)).astype('float32')
for j, y in enumerate(labels):
for i in range(nclass):
if i+1 == np.floor(y) + 1:
Y[j,i] = y - np.floor(y)
if i+1 == np.floor(y):
Y[j,i] = np.floor(y) - y + 1
if i + 1 == np.floor(y) + 1:
Y[j, i] = y - np.floor(y)
if i + 1 == np.floor(y):
Y[j, i] = np.floor(y) - y + 1
return Y


def load_data(loc='./data/'):
"""
Load the SICK semantic-relatedness dataset
"""
trainA, trainB, devA, devB, testA, testB = [],[],[],[],[],[]
trainS, devS, testS = [],[],[]
trainA, trainB, devA, devB, testA, testB = [], [], [], [], [], []
trainS, devS, testS = [], [], []

with open(loc + 'SICK_train.txt', 'rb') as f:
for line in f:
Expand All @@ -147,5 +151,3 @@ def load_data(loc='./data/'):
testS = [float(s) for s in testS[1:]]

return [trainA[1:], trainB[1:]], [devA[1:], devB[1:]], [testA[1:], testB[1:]], [trainS, devS, testS]