def main():
train, test, word2idx = get_ptb_data()
for t in train:
add_idx_to_tree(t, 0)
train = [tree2list(t, -1, is_binary=True) for t in train]
train = [t for t in train if t[3][-1] >= 0] # for filtering binary labels
for t in test:
add_idx_to_tree(t, 0)
test = [tree2list(t, -1, is_binary=True) for t in test]
test = [t for t in test if t[3][-1] >= 0] # for filtering binary labels
train = shuffle(train)
train = train[:1000]
test = shuffle(test)
test = test[:500]
V = len(word2idx)
print "vocab size:", V
D = 80
K = 5
model = RecursiveNN(V, D, K)
model.fit(train, reg=0, activation=T.nnet.relu)
print "train accuracy:", model.score(train)
print "test accuracy:", model.score(test)
def main():
train, test, word2idx = get_ptb_data()
for t in train:
add_idx_to_tree(t, 0)
train = [tree2list(t, -1, is_binary=True) for t in train]
train = [t for t in train if t[3][-1] >= 0] # for filtering binary labels
for t in test:
add_idx_to_tree(t, 0)
test = [tree2list(t, -1, is_binary=True) for t in test]
test = [t for t in test if t[3][-1] >= 0] # for filtering binary labels
train = shuffle(train)
train = train[:1000]
test = shuffle(test)
test = test[:500]
V = len(word2idx)
print "vocab size:", V
D = 80
K = 5
model = RecursiveNN(V, D, K)
model.fit(train, reg=0, activation=T.nnet.relu)
print "train accuracy:", model.score(train)
print "test accuracy:", model.score(test)
def main(is_binary=True):
train, test, word2idx = get_ptb_data()
for t in train:
add_idx_to_tree(t, 0)
train = [tree2list(t, -1, is_binary) for t in train]
if is_binary:
train = [t for t in train if t[3][-1] >= 0] # for filtering binary labels
for t in test:
add_idx_to_tree(t, 0)
test = [tree2list(t, -1, is_binary) for t in test]
if is_binary:
test = [t for t in test if t[3][-1] >= 0] # for filtering binary labels
train = shuffle(train)
train = train[:5000]
# n_pos = sum(t[3][-1] for t in train)
# print "n_pos train:", n_pos
test = shuffle(test)
test = test[:1000]
# n_pos = sum(t[3][-1] for t in test)
# print "n_pos test:", n_pos
V = len(word2idx)
print("vocab size:", V)
D = 20
K = 2 if is_binary else 5
model = RecursiveNN(V, D, K)
model.fit(train)
print("train accuracy:", model.score(train))
print("test accuracy:", model.score(test))
print("train f1:", model.f1_score(train))
print("test f1:", model.f1_score(test))
def main(is_binary=True):
train, test, word2idx = get_ptb_data()
for t in train:
add_idx_to_tree(t, 0)
train = [tree2list(t, -1, is_binary) for t in train]
if is_binary:
train = [t for t in train if t[3][-1] >= 0] # for filtering binary labels
for t in test:
add_idx_to_tree(t, 0)
test = [tree2list(t, -1, is_binary) for t in test]
if is_binary:
test = [t for t in test if t[3][-1] >= 0] # for filtering binary labels
train = shuffle(train)
train = train[:5000]
# n_pos = sum(t[3][-1] for t in train)
# print "n_pos train:", n_pos
test = shuffle(test)
test = test[:1000]
# n_pos = sum(t[3][-1] for t in test)
# print "n_pos test:", n_pos
V = len(word2idx)
print "vocab size:", V
D = 20
K = 2 if is_binary else 5
model = RecursiveNN(V, D, K)
model.fit(train)
print "train accuracy:", model.score(train)
print "test accuracy:", model.score(test)
print "train f1:", model.f1_score(train)
print "test f1:", model.f1_score(test)
def main():
train, test, word2idx = get_ptb_data()
for t in train:
add_idx_to_tree(t, 0)
train = [tree2list(t, -1, True) for t in train]
train = [t for t in train if t[3][-1] >= 0] # for filtering binary labels
for t in test:
add_idx_to_tree(t, 0)
test = [tree2list(t, -1, True) for t in test]
test = [t for t in test if t[3][-1] >= 0] # for filtering binary labels
train = shuffle(train)
train = train[:1000]
# n_pos = sum(t[3][-1] for t in train)
# print "n_pos train:", n_pos
test = shuffle(test)
test = test[:100]
# n_pos = sum(t[3][-1] for t in test)
# print "n_pos test:", n_pos
V = len(word2idx)
print "vocab size:", V
D = 80
K = 5
model = RecursiveNN(V, D, K)
model.fit(train, epochs=3, activation=T.nnet.relu)
print "train accuracy:", model.score(train)
print "test accuracy:", model.score(test)
print "train f1:", model.f1_score(train)
print "test f1:", model.f1_score(test)
def main(is_binary=True):
train, test, word2idx = get_ptb_data()
for t in train:
add_idx_to_tree(t, 0)
train = [tree2list(t, -1, is_binary) for t in train]
if is_binary:
train = [t for t in train
if t[3][-1] >= 0] # for filtering binary labels
for t in test:
add_idx_to_tree(t, 0)
test = [tree2list(t, -1, is_binary) for t in test]
if is_binary:
test = [t for t in test
if t[3][-1] >= 0] # for filtering binary labels
# check imbalance
# pos = 0
# neg = 0
# mid = 0
# label_counts = np.zeros(5)
# for t in train + test:
# words, left_child, right_child, labels = t
# # for l in labels:
# # if l == 0:
# # neg += 1
# # elif l == 1:
# # pos += 1
# # else:
# # mid += 1
# for l in labels:
# label_counts[l] += 1
# # print("pos / total:", float(pos) / (pos + neg + mid))
# # print("mid / total:", float(mid) / (pos + neg + mid))
# # print("neg / total:", float(neg) / (pos + neg + mid))
# print("label proportions:", label_counts / label_counts.sum())
# exit()
train = shuffle(train)
# train = train[:5000]
# n_pos = sum(t[3][-1] for t in train)
# print("n_pos train:", n_pos)
test = shuffle(test)
smalltest = test[:1000]
# n_pos = sum(t[3][-1] for t in test)
# print("n_pos test:", n_pos)
V = len(word2idx)
print("vocab size:", V)
D = 20
K = 2 if is_binary else 5
model = RecursiveNN(V, D, K)
model.fit(train, smalltest, epochs=20, train_inner_nodes=True)
print("train accuracy:", model.score(train))
print("test accuracy:", model.score(test))
print("train f1:", model.f1_score(train))
print("test f1:", model.f1_score(test))
def main(is_binary=True):
train, test, word2idx = get_ptb_data()
for t in train:
add_idx_to_tree(t, 0)
train = [tree2list(t, -1, is_binary) for t in train]
if is_binary:
train = [t for t in train if t[3][-1] >= 0] # for filtering binary labels
for t in test:
add_idx_to_tree(t, 0)
test = [tree2list(t, -1, is_binary) for t in test]
if is_binary:
test = [t for t in test if t[3][-1] >= 0] # for filtering binary labels
# check imbalance
# pos = 0
# neg = 0
# mid = 0
# label_counts = np.zeros(5)
# for t in train + test:
# words, left_child, right_child, labels = t
# # for l in labels:
# # if l == 0:
# # neg += 1
# # elif l == 1:
# # pos += 1
# # else:
# # mid += 1
# for l in labels:
# label_counts[l] += 1
# # print("pos / total:", float(pos) / (pos + neg + mid))
# # print("mid / total:", float(mid) / (pos + neg + mid))
# # print("neg / total:", float(neg) / (pos + neg + mid))
# print("label proportions:", label_counts / label_counts.sum())
# exit()
train = shuffle(train)
# train = train[:5000]
# n_pos = sum(t[3][-1] for t in train)
# print("n_pos train:", n_pos)
test = shuffle(test)
smalltest = test[:1000]
# n_pos = sum(t[3][-1] for t in test)
# print("n_pos test:", n_pos)
V = len(word2idx)
print("vocab size:", V)
D = 20
K = 2 if is_binary else 5
model = RecursiveNN(V, D, K)
model.fit(train, smalltest, epochs=20, train_inner_nodes=True)
print("train accuracy:", model.score(train))
print("test accuracy:", model.score(test))
print("train f1:", model.f1_score(train))
print("test f1:", model.f1_score(test))
def main():
train, test, word2idx = get_ptb_data()
train = train[:100]
test = test[:100]
V = len(word2idx)
D = 80
K = 5
model = TNN(V, D, K, tf.nn.relu)
model.fit(train)
def main(is_binary=True):
train, test, word2idx = get_ptb_data()
for t in train:
add_idx_to_tree(t, 0)
train = [tree2list(t, -1, is_binary) for t in train]
if is_binary:
train = [t for t in train
if t[3][-1] >= 0] # for filtering binary labels
# sanity check
# check that last node has no parent
# for t in train:
# assert(t[1][-1] == -1 and t[2][-1] == -1)
for t in test:
add_idx_to_tree(t, 0)
test = [tree2list(t, -1, is_binary) for t in test]
if is_binary:
test = [t for t in test
if t[3][-1] >= 0] # for filtering binary labels
train = shuffle(train)
# train = train[:2000]
n_pos = sum(t[3][-1] for t in train)
# print("num pos train:", n_pos)
# idx2word = {v:k for k, v in word2idx.items()}
# for i in range(4):
# words, _, _, labels = train[i]
# print_sentence(words, idx2word)
# print("label:", labels[-1])
test = shuffle(test)
test = test[:1000]
V = len(word2idx)
print("vocab size:", V)
D = 10
K = 2 if is_binary else 5
model = RecursiveNN(V, D, K)
model.fit(train,
learning_rate=1e-2,
reg=1e-2,
mu=0,
epochs=20,
activation=T.tanh,
train_inner_nodes=False)
print("train accuracy:", model.score(train))
print("test accuracy:", model.score(test))
# make sure program doesn't end until we close the plot
plt.show()
def main():
train, test, word2idx = get_ptb_data()
train = train[:100]
test = test[:100]
V = len(word2idx)
D = 80
K = 5
model = TNN(V, D, K, tf.nn.relu)
model.fit(train)
print "train accuracy:", model.score(None)
print "test accuracy:", model.score(test)
def main():
train, test, word2idx = get_ptb_data()
train = train[:100]
test = test[:100]
V = len(word2idx)
D = 80
K = 5
model = TNN(V, D, K, tf.nn.relu)
model.fit(train)
print "train accuracy:", model.score(None)
print "test accuracy:", model.score(test)
def main():
train, test, word2idx = get_ptb_data()
train = train[:5]
# print (word2idx)
exit()
test = test[:5]
V = len(word2idx)
D = 20
K = 5
model = TNN(V, D, K, tf.nn.relu)
model.fit(train)
def main(is_binary=True):
train, test, word2idx = get_ptb_data()
for t in train:
add_idx_to_tree(t, 0)
train = [tree2list(t, -1, is_binary) for t in train]
if is_binary:
train = [t for t in train if t[3][-1] >= 0] # for filtering binary labels
for t in test:
add_idx_to_tree(t, 0)
test = [tree2list(t, -1, is_binary) for t in test]
if is_binary:
test = [t for t in test if t[3][-1] >= 0] # for filtering binary labels
train = shuffle(train)
def main(is_binary=True):
train, test, word2idx = get_ptb_data()
for t in train:
add_idx_to_tree(t, 0)
train = [tree2list(t, -1, is_binary) for t in train]
if is_binary:
train = [t for t in train if t[3][-1] >= 0] # for filtering binary labels
# sanity check
# check that last node has no parent
# for t in train:
# assert(t[1][-1] == -1 and t[2][-1] == -1)
for t in test:
add_idx_to_tree(t, 0)
test = [tree2list(t, -1, is_binary) for t in test]
if is_binary:
test = [t for t in test if t[3][-1] >= 0] # for filtering binary labels
train = shuffle(train)
# train = train[:2000]
n_pos = sum(t[3][-1] for t in train)
# print("num pos train:", n_pos)
# idx2word = {v:k for k, v in word2idx.items()}
# for i in range(4):
# words, _, _, labels = train[i]
# print_sentence(words, idx2word)
# print("label:", labels[-1])
test = shuffle(test)
test = test[:1000]
V = len(word2idx)
print("vocab size:", V)
D = 10
K = 2 if is_binary else 5
model = RecursiveNN(V, D, K)
model.fit(train, learning_rate=1e-2, reg=1e-2, mu=0, epochs=20, activation=T.tanh, train_inner_nodes=False)
print("train accuracy:", model.score(train))
print("test accuracy:", model.score(test))
# make sure program doesn't end until we close the plot
plt.show()
def main(is_binary=True):
train, test, word2idx = get_ptb_data()
for t in train:
add_idx_to_tree(t, 0)
train = [tree2list(t, -1, is_binary) for t in train]
if is_binary:
train = [t for t in train if t[3][-1] >= 0] # for filtering binary labels
# sanity check
# check that last node has no parent
# for t in train:
# assert(t[1][-1] == -1 and t[2][-1] == -1)
for t in test:
add_idx_to_tree(t, 0)
test = [tree2list(t, -1, is_binary) for t in test]
if is_binary:
test = [t for t in test if t[3][-1] >= 0] # for filtering binary labels
train = shuffle(train)
wordSequence.append(reverseLookUp[element])
else:
wordSequence.append('.')
return wordSequence
datasetPath = "trees/treeSentences.txt"
lookUp, reverseLookUp = getLookUps(datasetPath)
sentences = getSentences(datasetPath, lookUp)
#print(lookUp)
#print(reverseLookUp)
#print(sentences)
print("hello")
vocabsize = len(lookUp)
train, test, word2idx = get_ptb_data()
# vocabsize = len(word2idx)
ans = []
print(word_yield(train[0]))
exit()
#print("vocab size: "+str(len(lookUp))+" number of sentences: "+str(len(sentences)))
# word vector dimensions
dime = 200
# word embedding
# all variables will get changed during back propogation
embeddings = tf.Variable(tf.random_uniform([vocabsize, dime], -1.0, 1.0),
