def main():
semeval_dir = 'semeval'
train2013 = "task-B-train.20140221.tsv"
dev2013 = "task-B-dev.20140225.tsv"
test2013_sms = "task-B-test2013-sms.tsv"
test2013_twitter = "task-B-test2013-twitter.tsv"
test2014_twitter = "task-B-test2014-twitter.tsv"
test2014_livejournal = "task-B-test2014-livejournal.tsv"
test2014_sarcasm = "test_2014_sarcasm.tsv"
test15 = "task-B-test2015-twitter.tsv"
train16 = "task-A-train-2016.tsv"
dev2016 = "task-A-dev-2016.tsv"
devtest2016 = "task-A-devtest-2016.tsv"
test2016 = "SemEval2016-task4-test.subtask-A.tsv"
data_dir = 'ACL/ep15params'
models = ['L1A','L2A','L2B','L3A','L3B','L3C','L3E','L3F','L3G','L3H']
pred_prob = 'predictions_probs'
pred_pred = 'predictions_pred'
files_training = [train2013,dev2013,train16,dev2016,devtest2016]
sentpos_train = [2,2,1,1,1]
files_test = [test2016,test15,test2014_twitter,test2013_twitter,test2014_livejournal,test2014_sarcasm]
sentpos_test = [1,2,2,2,2,2]
X_train,_ = extract_data(data_dir,files_training,models)
y_train = extract_labels(semeval_dir,files_training,sentpos_train)
testSets = {}
goldSets = {}
predModel = {}
for file,spos in zip(files_test,sentpos_test):
X_test,y_pred_model = extract_data(data_dir,[file],models)
y_test = extract_labels(semeval_dir,[file],[spos])
testSets[file] = X_test
goldSets[file] = y_test
predModel[file] = y_pred_model
print X_train.shape
print y_train.shape
print 'Fit model'
model = RandomForestClassifier(n_estimators=300,max_depth=3,max_features=15,bootstrap=True,n_jobs=4)
model.fit(X_train,y_train)
print 'Compute score'
for file in files_test:
X_test = testSets[file]
y_test = goldSets[file]
y_pred = model.predict(X_test)
print 'Set:\t{}\tRF\tScore:\t\t{}'.format(file,semeval_f1_taskA(y_test,y_pred))
for m in models:
y_pred = predModel[file][m]
print 'Set:\t{}\t{}\tScore:\t\t{}'.format(file,m,semeval_f1_taskA(y_test,y_pred))
print '\n'
def main(argv):
##########
# LAYERS #
#########
HOME_DIR = "semeval_parsed"
input_fname = '200M'
data_dir = HOME_DIR + '_' + input_fname
update_type = 'adadelta'
batch_size = 1000
test_type = ''
use_reg = False
compute_paramdist = False
max_norm = 0
reg = None
rho=0.95
eps=1e-6
argv = map(lambda x: x.replace('\r',''),argv)
try:
opts, args = getopt.getopt(argv,"t:u:r:pb:m:e:",["test_type","update=","rho=","batch_size=",'max_norm=','eps='])
except getopt.GetoptError as e:
print e
sys.exit(2)
for opt, arg in opts:
if opt in ("-u", "--update"):
update_type = arg
elif opt in ("-b", "--batch_size"):
batch_size = int(arg)
elif opt in ("-t", "--testtype"):
test_type = arg
elif opt in ("-m", "--max_norm"):
max_norm = int(arg)
elif opt in ("-r", "--rho"):
rho = float(arg)
elif opt in ("-e", "--eps"):
eps = float(arg)
elif opt == "-p":
compute_paramdist = True
print update_type
print batch_size
print max_norm
print rho
print eps
printeps = abs(int(numpy.floor(numpy.log10(numpy.abs(eps)))))
print data_dir+'/supervised_results_{}{}{}rho{}eps{}.p'.format(test_type,update_type,max_norm,int(rho*100),printeps)
numpy_rng = numpy.random.RandomState(123)
print "Load Parameters"
parameter_map = cPickle.load(open(data_dir+'/parameters_distant_{}.p'.format(test_type), 'rb'))
input_shape = parameter_map['inputShape']
filter_width = parameter_map['filterWidth']
n_in = parameter_map['n_in']
st = parameter_map['st']
batch_size = input_shape[0]
def relu(x):
return x * (x > 0)
activation = relu
tweets = T.imatrix('tweets_train')
y = T.lvector('y')
batch_tweets= T.imatrix('batch_x_q')
batch_y = T.lvector('batch_y')
lookup_table_words = nn_layers.LookupTableFast(
W=parameter_map['LookupTableFastStaticW'].get_value(),
pad=filter_width-1
)
parameter_map['LookupTableFastStaticW'] = lookup_table_words.W
filter_shape = parameter_map['FilterShape']
conv_layers = []
conv = nn_layers.Conv2dLayer(
W=parameter_map['Conv2dLayerW'],
rng=numpy_rng,
filter_shape=filter_shape,
input_shape=input_shape
)
parameter_map['Conv2dLayerW'] = conv.W
non_linearity = nn_layers.NonLinearityLayer(
b=parameter_map['NonLinearityLayerB'],
b_size=filter_shape[0],
activation=activation
)
parameter_map['NonLinearityLayerB'] = non_linearity.b
shape1 = parameter_map['PoolingShape1']
pooling = nn_layers.KMaxPoolLayerNative(shape=shape1,ignore_border=True,st=st)
input_shape2 = parameter_map['input_shape2']
filter_shape2 = parameter_map['FilterShape2']
con2 = nn_layers.Conv2dLayer(
W=parameter_map['Conv2dLayerW2' ],
rng=numpy_rng,
input_shape=input_shape2,
filter_shape=filter_shape2
)
parameter_map['Conv2dLayerW2'] = con2.W
non_linearity2 = nn_layers.NonLinearityLayer(
b=parameter_map['NonLinearityLayerB2'],
b_size=filter_shape2[0],
activation=activation
)
parameter_map['NonLinearityLayerB2'] = non_linearity2.b
shape2 = parameter_map['PoolingShape2']
st2 = parameter_map['st2']
pooling2 = nn_layers.KMaxPoolLayerNative(shape=shape2,st=st2,ignore_border=True)
input_shape3 = parameter_map['input_shape3']
filter_shape3 = parameter_map['FilterShape3']
con3 = nn_layers.Conv2dLayer(
W=parameter_map['Conv2dLayerW3'],
rng=numpy_rng,
input_shape=input_shape3,
filter_shape=filter_shape3
)
parameter_map['Conv2dLayerW3'] = con3.W
non_linearity3 = nn_layers.NonLinearityLayer(
b=parameter_map['NonLinearityLayerB3'],
b_size=filter_shape3[0],
activation=activation
)
parameter_map['NonLinearityLayerB3'] = non_linearity3.b
shape3 = parameter_map['PoolingShape3']
pooling3 = nn_layers.KMaxPoolLayerNative(shape=shape3,ignore_border=True)
conv2dNonLinearMaxPool = nn_layers.FeedForwardNet(layers=[
conv,
non_linearity,
pooling,
con2,
non_linearity2,
pooling2,
con3,
non_linearity3,
pooling3
])
conv_layers.append(conv2dNonLinearMaxPool)
join_layer = nn_layers.ParallelLayer(layers=conv_layers)
flatten_layer = nn_layers.FlattenLayer()
hidden_layer = nn_layers.LinearLayer(
W=parameter_map['LinearLayerW'],
b=parameter_map['LinearLayerB'],
rng=numpy_rng,
n_in=n_in,
n_out=n_in,
activation=activation
)
parameter_map['LinearLayerW'] = hidden_layer.W
parameter_map['LinearLayerB'] = hidden_layer.b
n_outs = 3
classifier = nn_layers.LogisticRegression(n_in=n_in, n_out=n_outs)
nnet_tweets = nn_layers.FeedForwardNet(layers=[
lookup_table_words,
join_layer,
flatten_layer,
hidden_layer,
classifier
])
inputs_train = [batch_tweets,
batch_y]
givens_train = {tweets: batch_tweets,
y: batch_y}
inputs_pred = [batch_tweets,
]
givens_pred = {tweets:batch_tweets,
}
nnet_tweets.set_input(tweets)
print nnet_tweets
params = nnet_tweets.params
cost = nnet_tweets.layers[-1].training_cost(y)
predictions = nnet_tweets.layers[-1].y_pred
l2 = 0
for param in params:
l2 += (param**2).sum()
if reg:
cost += reg*l2
if update_type == 'adadelta':
updates = sgd_trainer.get_adadelta_updates(cost, params,rho=rho,eps=eps,max_norm=max_norm,word_vec_name='None')
elif update_type == 'grad':
updates = sgd_trainer.get_sgd_updates(cost,params,max_norm=max_norm)
train_fn = theano.function(
inputs=inputs_train,
outputs=cost,
updates=updates,
givens=givens_train,
)
pred_fn = theano.function(inputs=inputs_pred,
outputs=predictions,
givens=givens_pred)
def predict_batch(batch_iterator):
preds = numpy.hstack([pred_fn(batch_x_q[0]) for batch_x_q in batch_iterator])
return preds[:batch_iterator.n_samples]
#######
#Names#
#######
test_2016n = 'Test 2016'
test_2015n = 'Test 2015'
test_2014n = 'Test 2014'
test_2013n = 'Test 2013'
test_2014ljn = 'Test 2014 LiveJournal'
test_2014srcn = 'Test 2014 Sarcasm'
test_2013_smsn = 'Test 2013 SMS'
train_fulln = 'Training Score'
pdist_n = 'parameter distance'
ep_pred = {}
ep_pred[test_2016n] = []
ep_pred[test_2015n] = []
ep_pred[test_2014n] = []
ep_pred[test_2013n] = []
ep_pred[test_2014ljn] = []
ep_pred[test_2014srcn] = []
ep_pred[test_2013_smsn] = []
ep_pred[train_fulln] = []
ep_pred[pdist_n] = []
#######################
# Supervised Learining#
######################
training2013_tids = numpy.load(os.path.join(data_dir, 'task-B-train.20140221.tids.npy'))
training2013_tweets = numpy.load(os.path.join(data_dir, 'task-B-train.20140221.tweets.npy'))
training2013_sentiments = numpy.load(os.path.join(data_dir, 'task-B-train.20140221.sentiments.npy'))
dev_2013_tids = numpy.load(os.path.join(data_dir, 'task-B-dev.20140225.tids.npy'))
dev_2013_tweets = numpy.load(os.path.join(data_dir, 'task-B-dev.20140225.tweets.npy'))
dev_2013_sentiments = numpy.load(os.path.join(data_dir, 'task-B-dev.20140225.sentiments.npy'))
trainingA_2016_tids = numpy.load(os.path.join(data_dir, 'task-A-train-2016.tids.npy'))
trainingA_2016_tweets = numpy.load(os.path.join(data_dir, 'task-A-train-2016.tweets.npy'))
trainingA_2016_sentiments = numpy.load(os.path.join(data_dir, 'task-A-train-2016.sentiments.npy'))
devA_2016_tids = numpy.load(os.path.join(data_dir, 'task-A-dev-2016.tids.npy'))
devA_2016_tweets = numpy.load(os.path.join(data_dir, 'task-A-dev-2016.tweets.npy'))
devA_2016_sentiments = numpy.load(os.path.join(data_dir, 'task-A-dev-2016.sentiments.npy'))
devtestA_2016_tids = numpy.load(os.path.join(data_dir, 'task-A-devtest-2016.tids.npy'))
devtestA_2016_tweets = numpy.load(os.path.join(data_dir, 'task-A-devtest-2016.tweets.npy'))
devtestA_2016_sentiments = numpy.load(os.path.join(data_dir, 'task-A-devtest-2016.sentiments.npy'))
test_2016_tids = numpy.load(os.path.join(data_dir, 'task-A-test2016.tids.npy'))
test_2016_tweets = numpy.load(os.path.join(data_dir, 'task-A-test2016.tweets.npy'))
test_2016_sentiments = numpy.load(os.path.join(data_dir, 'task-A-test2016.sentiments.npy'))
test_2013_tids = numpy.load(os.path.join(data_dir, 'task-B-test2013-twitter.tids.npy'))
test_2013_tweets = numpy.load(os.path.join(data_dir, 'task-B-test2013-twitter.tweets.npy'))
test_2013_sentiments = numpy.load(os.path.join(data_dir, 'task-B-test2013-twitter.sentiments.npy'))
test_2014_tids = numpy.load(os.path.join(data_dir, 'task-B-test2014-twitter.tids.npy'))
test_2014_tweets = numpy.load(os.path.join(data_dir, 'task-B-test2014-twitter.tweets.npy'))
test_2014_sentiments = numpy.load(os.path.join(data_dir, 'task-B-test2014-twitter.sentiments.npy'))
test_2015_tids = numpy.load(os.path.join(data_dir, 'task-B-test2015-twitter.tids.npy'))
test_2015_tweets = numpy.load(os.path.join(data_dir, 'task-B-test2015-twitter.tweets.npy'))
test_2015_sentiments = numpy.load(os.path.join(data_dir, 'task-B-test2015-twitter.sentiments.npy'))
test_2013_sms_tids = numpy.load(os.path.join(data_dir, 'task-B-test2013-sms.tids.npy'))
test_2013_sms_tweets = numpy.load(os.path.join(data_dir, 'task-B-test2013-sms.tweets.npy'))
test_2013_sms_sentiments = numpy.load(os.path.join(data_dir, 'task-B-test2013-sms.sentiments.npy'))
test_2014_livejournal_tids = numpy.load(os.path.join(data_dir, 'task-B-test2014-livejournal.tids.npy'))
test_2014_livejournal_tweets = numpy.load(os.path.join(data_dir, 'task-B-test2014-livejournal.tweets.npy'))
test_2014_livejournal_sentiments = numpy.load(os.path.join(data_dir, 'task-B-test2014-livejournal.sentiments.npy'))
test_2014_sarcasm_tids = numpy.load(os.path.join(data_dir, 'task-B-test2014-twittersarcasm.tids.npy'))
test_2014_sarcasm_tweets = numpy.load(os.path.join(data_dir, 'task-B-test2014-twittersarcasm.tweets.npy'))
test_2014_sarcasm_sentiments = numpy.load(os.path.join(data_dir, 'task-B-test2014-twittersarcasm.sentiments.npy'))
rand_tweets_tids = numpy.load(os.path.join(data_dir, 'random_tweet.tids.npy'))
rand_tweets_tweets = numpy.load(os.path.join(data_dir, 'random_tweet.tweets.npy'))
rand_tweets_sentiments = numpy.load(os.path.join(data_dir, 'random_tweet.sentiments.npy'))
rand_tweets_neg_tids = numpy.load(os.path.join(data_dir, 'random_tweet_neg.tids.npy'))
rand_tweets_neg_tweets = numpy.load(os.path.join(data_dir, 'random_tweet_neg.tweets.npy'))
rand_tweets_neg_sentiments = numpy.load(os.path.join(data_dir, 'random_tweet_neg.sentiments.npy'))
rand_tweets_neut_tids = numpy.load(os.path.join(data_dir, 'random_tweet_neut.tids.npy'))
rand_tweets_neut_tweets = numpy.load(os.path.join(data_dir, 'random_tweet_neut.tweets.npy'))
rand_tweets_neut_sentiments = numpy.load(os.path.join(data_dir, 'random_tweet_neut.sentiments.npy'))
training_full_id = numpy.concatenate((training2013_tids,dev_2013_tids),axis=0)
training_full_id = numpy.concatenate((training_full_id,trainingA_2016_tids),axis=0)
training_full_id = numpy.concatenate((training_full_id,devA_2016_tids),axis=0)
training_full_id = numpy.concatenate((training_full_id,devtestA_2016_tids),axis=0)
training_full_tweets = numpy.concatenate((training2013_tweets,dev_2013_tweets),axis=0)
training_full_tweets = numpy.concatenate((training_full_tweets,trainingA_2016_tweets),axis=0)
training_full_tweets = numpy.concatenate((training_full_tweets,devA_2016_tweets),axis=0)
training_full_tweets = numpy.concatenate((training_full_tweets,devtestA_2016_tweets),axis=0)
training_full_sentiments = numpy.concatenate((training2013_sentiments,dev_2013_sentiments),axis=0)
training_full_sentiments = numpy.concatenate((training_full_sentiments,trainingA_2016_sentiments),axis=0)
training_full_sentiments = numpy.concatenate((training_full_sentiments,devA_2016_sentiments),axis=0)
training_full_sentiments = numpy.concatenate((training_full_sentiments,devtestA_2016_sentiments),axis=0)
train_set_iterator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng,
[training_full_tweets,training_full_sentiments],
batch_size=batch_size,
randomize=True
)
train_err_iterator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng,
[training_full_tweets],
batch_size=batch_size,
randomize=False
)
test_2015_iterator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng,
[test_2015_tweets],
batch_size=batch_size,
randomize=False
)
dev2016_iterator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng,
[devA_2016_tweets],
batch_size=batch_size,
randomize=False
)
test_2016_iterator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng,
[test_2016_tweets],
batch_size=batch_size,
randomize=False
)
train2016_iterator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng,
[trainingA_2016_tweets],
batch_size=batch_size,
randomize=False
)
test2016_iterator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng,
[test_2016_tweets],
batch_size=batch_size,
randomize=False
)
test2013_itarator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng,
[test_2013_tweets],
batch_size=batch_size,
randomize=False
)
test_2014_iterator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng,
[test_2014_tweets],
batch_size=batch_size,
randomize=False
)
test_2014_sarcasm_iterator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng,
[test_2014_sarcasm_tweets],
batch_size=batch_size,
randomize=False
)
train2013_iterator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng,
[training2013_tweets],
batch_size=batch_size,
randomize=False
)
dev_2013_iterator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng,
[dev_2013_tweets],
batch_size=batch_size,
randomize=False
)
test_2013_sms_iterator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng,
[test_2013_sms_tweets],
batch_size=batch_size,
randomize=False
)
test_2014_livejournal_iterator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng,
[test_2014_livejournal_tweets],
batch_size=batch_size,
randomize=False
)
W_emb_list = [w for w in params if w.name == 'W_emb']
zerout_dummy_word = theano.function([], updates=[(W, T.set_subtensor(W[-1:], 0.)) for W in W_emb_list])
epoch = 0
n_epochs = 0
early_stop = 250
check_freq = 4
timer_train = time.time()
no_best_dev_update = 0
best_dev_acc = -numpy.inf
num_train_batches = len(train_set_iterator)
best_params = [numpy.copy(p.get_value(borrow=True)) for p in params]
old_params = [numpy.copy(p.get_value(borrow=True)) for p in params]
while epoch < n_epochs:
timer = time.time()
for i, (tweet,y_label) in enumerate(tqdm(train_set_iterator,ascii=True), 1):
train_fn(tweet,y_label)
if i % check_freq == 0 or i == num_train_batches:
y_pred_dev_2015 = predict_batch(test_2015_iterator)
y_pred_test_2014 = predict_batch(test_2014_iterator)
y_pred_test_2013 = predict_batch(test2013_itarator)
y_pred_test_sms_2013 = predict_batch(test_2013_sms_iterator)
y_pred_test_livejournal_2014 = predict_batch(test_2014_livejournal_iterator)
y_pred_test_sarcasm_2014 = predict_batch(test_2014_sarcasm_iterator)
y_pred_test_2016 = predict_batch(test_2016_iterator)
y_train_score = predict_batch(train_err_iterator)
dev_acc_2015 = semeval_f1_taskA(test_2015_sentiments,y_pred_dev_2015)
dev_acc_2014 = semeval_f1_taskA(test_2014_sentiments,y_pred_test_2014)
dev_acc_2014_lj = semeval_f1_taskA(test_2014_livejournal_sentiments,y_pred_test_livejournal_2014)
dev_acc_2014_srcs = semeval_f1_taskA(test_2014_sarcasm_sentiments,y_pred_test_sarcasm_2014)
dev_acc_2013 = semeval_f1_taskA(test_2013_sentiments,y_pred_test_2013)
dev_acc_2013_sms = semeval_f1_taskA(test_2013_sms_sentiments,y_pred_test_sms_2013)
dev_acc_2016_test = semeval_f1_taskA(test_2016_sentiments,y_pred_test_2016)
dev_acc_train_err = semeval_f1_taskA(training_full_sentiments,y_train_score)
ep_pred[test_2016n].append(dev_acc_2016_test)
ep_pred[test_2015n].append(dev_acc_2015)
ep_pred[test_2014n].append(dev_acc_2014)
ep_pred[test_2013n].append(dev_acc_2013)
ep_pred[test_2014ljn].append(dev_acc_2014_lj)
ep_pred[test_2014srcn].append(dev_acc_2014_srcs)
ep_pred[test_2013_smsn].append(dev_acc_2013_sms)
ep_pred[train_fulln].append(dev_acc_train_err)
if dev_acc_2016_test > best_dev_acc:
best_dev_acc = dev_acc_2016_test
best_params = [numpy.copy(p.get_value(borrow=True)) for p in params]
no_best_dev_update = 0
print('2016 epoch: {} chunk: {} best_chunk_auc: {:.4f};'.format(epoch, i, dev_acc_2016_test))
print('2015 epoch: {} chunk: {} best_chunk_auc: {:.4f};'.format(epoch, i, dev_acc_2015))
print('2014 epoch: {} chunk: {} best_chunk_auc: {:.4f};'.format(epoch, i, dev_acc_2014))
print('2013 epoch: {} chunk: {} best_chunk_auc: {:.4f};'.format(epoch, i, dev_acc_2013))
print('2014lj epoch: {} chunk: {} best_chunk_auc: {:.4f};'.format(epoch, i, dev_acc_2014_lj))
print('2014src epoch: {} chunk: {} best_chunk_auc: {:.4f};'.format(epoch, i, dev_acc_2014_srcs))
print('2013sms epoch: {} chunk: {} best_chunk_auc: {:.4f};'.format(epoch, i, dev_acc_2013_sms))
print('Train epoch: {} chunk: {} best_chunk_auc: {:.4f};'.format(epoch, i, dev_acc_train_err))
zerout_dummy_word()
print('epoch {} took {:.4f} seconds'.format(epoch, time.time() - timer))
epoch += 1
no_best_dev_update += 1
if no_best_dev_update >= early_stop:
print "Quitting after of no update of the best score on dev set", no_best_dev_update
break
print('Training took: {:.4f} seconds'.format(time.time() - timer_train))
cPickle.dump(ep_pred,open(data_dir+'/supervised_results_{}{}{}eps{}rho{}.p'.format(test_type,update_type,max_norm,printeps,int(rho*100)), 'wb'))
if n_epochs > 0:
cPickle.dump(best_params, open(data_dir+'/best_param_{}_{}.p'.format('supervised',test_type), 'wb'))
#######################
# Get Sentence Vectors#
######################
inputs_senvec = [batch_tweets]
givents_senvec = {tweets:batch_tweets}
sets = [
(rand_tweets_tids,rand_tweets_tweets,'random_tweets'),
(rand_tweets_neg_tids,rand_tweets_neg_tweets,'random_tweets_neg'),
(rand_tweets_neut_tids,rand_tweets_neut_tweets,'random_tweets_neut'),
(test_2016_tids,test_2016_tweets,'SemEval2016-task4-test.subtask-A'),
(test_2014_tids,test_2014_tweets,'task-B-test2014-twitter'),
(test_2015_tids,test_2015_tweets,'task-B-test2015-twitter'),
(test_2013_tids,test_2013_tweets,'task-B-test2013-twitter'),
(test_2014_livejournal_tids,test_2014_livejournal_tweets,'task-B-test2014-livejournal'),
(test_2014_sarcasm_tids,test_2014_sarcasm_tweets,'test_2014_sarcasm'),
(test_2013_sms_tids,test_2013_sms_tweets,'task-B-test2013-sms'),
(training2013_tids,training2013_tweets,'task-B-train.20140221'),
(devA_2016_tids,devA_2016_tweets,'task-A-dev-2016'),
(trainingA_2016_tids,trainingA_2016_tweets,'task-A-train-2016'),
(devtestA_2016_tids,devtestA_2016_tweets,'task-A-devtest-2016'),
(dev_2013_tids,dev_2013_tweets,'task-B-dev.20140225'),
(training_full_id,training_full_tweets,'training_full_set')
]
get_senvec = False
if get_senvec:
batch_size = input_shape[0]
output = nnet_tweets.layers[-2].output
output_fn = function(inputs=inputs_senvec, outputs=output,givens=givents_senvec)
for (fids,fset,name) in sets:
test_set_iterator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng,
[fset],
batch_size=batch_size,
randomize=False
)
counter = 0
fname_prob = open(os.path.join(data_dir,'sentence-vecs/{}.txt'.format(name)), 'w+')
for i, tweet in enumerate(tqdm(test_set_iterator), 1):
o = output_fn(tweet[0])
for vec in o:
fname_prob.write(fids[counter])
for el in numpy.nditer(vec):
fname_prob.write(" %f" % el)
fname_prob.write("\n")
counter+=1
if counter == test_set_iterator.n_samples:
break
##############################
# Get Predictions Probabilites#
#############################
print 'Store Predictions'
best_params = cPickle.load(open(data_dir+'/best_param_supervised_{}.p'.format(test_type), 'rb'))
for i, param in enumerate(best_params):
params[i].set_value(param, borrow=True)
y_pred_test_2016 = predict_batch(test_2016_iterator)
dev_acc_2016_test = semeval_f1_taskA(test_2016_sentiments,y_pred_test_2016)
print dev_acc_2016_test
batch_size = input_shape[0]
output = nnet_tweets.layers[-1].p_y_given_x
output_fn = function(inputs=inputs_senvec, outputs=output,givens=givents_senvec)
for line in open('semeval/phrases'):
fids = numpy.load(os.path.join(data_dir, 'random_tweet_{}.tids.npy'.format(line.replace(' ','_').replace('\r','').replace('\n',''))))
fset = numpy.load(os.path.join(data_dir, 'random_tweet_{}.tweets.npy'.format(line.replace(' ','_').replace('\r','').replace('\n',''))))
name = 'random_tweet_{}'.format(line.replace(' ','_').replace('\r','').replace('\n',''))
test_set_iterator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng,
[fset],
batch_size=batch_size,
randomize=False
)
opath_prob = os.path.join(data_dir,'{}/{}/predictions_probs'.format('err',test_type))
if not os.path.exists(opath_prob):
os.makedirs(opath_prob)
print 'Created Path',opath_prob
opath_pred = os.path.join(data_dir,'{}/{}/predictions_pred'.format('err',test_type))
if not os.path.exists(opath_pred):
os.makedirs(opath_pred)
print 'Created Path',opath_pred
counter = 0
fname_prob = open(os.path.join(opath_prob,'{}.txt'.format(name)), 'w+')
fname_pred = open(os.path.join(opath_pred,'{}.txt'.format(name)), 'w+')
for i, tweet in enumerate(tqdm(test_set_iterator), 1):
o = output_fn(tweet[0])
for vec in o:
#save pred_prob
for el in numpy.nditer(vec):
fname_prob.write("%f\t" % el)
fname_prob.write("\n")
#save pred
pred = numpy.argmax(vec)
sentiments = {
0 : 'negative',
1 : 'neutral',
2 : 'positive'
}
fname_pred.write('{}\n'.format(sentiments[pred]))
counter+=1
if counter == test_set_iterator.n_samples:
break
def main():
##########
# LAYERS #
#########
HOME_DIR = "semeval_parsed"
input_fname = '200M'
test_type = ''
if len(sys.argv) > 1:
test_type = sys.argv[1]
data_dir = HOME_DIR + '_' + input_fname
numpy_rng = numpy.random.RandomState(123)
print "Load Parameters"
parameter_map = cPickle.load(
open(data_dir + '/parameters_distant_{}.p'.format(test_type), 'rb'))
input_shape = parameter_map['inputShape']
filter_width = parameter_map['filterWidth']
n_in = parameter_map['n_in']
st = parameter_map['st']
def relu(x):
return x * (x > 0)
activation = relu
tweets = T.imatrix('tweets_train')
y = T.lvector('y')
batch_tweets = T.imatrix('batch_x_q')
batch_y = T.lvector('batch_y')
lookup_table_words = nn_layers.LookupTableFast(
W=parameter_map['LookupTableFastStaticW'].get_value(),
pad=filter_width - 1)
filter_shape = parameter_map['FilterShape']
conv_layers = []
conv = nn_layers.Conv2dLayer(W=parameter_map['Conv2dLayerW'],
rng=numpy_rng,
filter_shape=filter_shape,
input_shape=input_shape)
non_linearity = nn_layers.NonLinearityLayer(
b=parameter_map['NonLinearityLayerB'],
b_size=filter_shape[0],
activation=activation)
shape1 = parameter_map['PoolingShape1']
pooling = nn_layers.KMaxPoolLayerNative(shape=shape1,
ignore_border=True,
st=st)
input_shape2 = parameter_map['input_shape2']
filter_shape2 = parameter_map['FilterShape2']
con2 = nn_layers.Conv2dLayer(W=parameter_map['Conv2dLayerW2'],
rng=numpy_rng,
input_shape=input_shape2,
filter_shape=filter_shape2)
non_linearity2 = nn_layers.NonLinearityLayer(
b=parameter_map['NonLinearityLayerB2'],
b_size=filter_shape2[0],
activation=activation)
shape2 = parameter_map['PoolingShape2']
st2 = parameter_map['st2']
pooling2 = nn_layers.KMaxPoolLayerNative(shape=shape2,
st=st2,
ignore_border=True)
input_shape3 = parameter_map['input_shape3']
filter_shape3 = parameter_map['FilterShape3']
con3 = nn_layers.Conv2dLayer(W=parameter_map['Conv2dLayerW3'],
rng=numpy_rng,
input_shape=input_shape3,
filter_shape=filter_shape3)
non_linearity3 = nn_layers.NonLinearityLayer(
b=parameter_map['NonLinearityLayerB3'],
b_size=filter_shape3[0],
activation=activation)
shape3 = parameter_map['PoolingShape3']
pooling3 = nn_layers.KMaxPoolLayerNative(shape=shape3, ignore_border=True)
conv2dNonLinearMaxPool = nn_layers.FeedForwardNet(layers=[
conv, non_linearity, pooling, con2, non_linearity2, pooling2, con3,
non_linearity3, pooling3
])
conv_layers.append(conv2dNonLinearMaxPool)
join_layer = nn_layers.ParallelLayer(layers=conv_layers)
flatten_layer = nn_layers.FlattenLayer()
hidden_layer = nn_layers.LinearLayer(W=parameter_map['LinearLayerW'],
b=parameter_map['LinearLayerB'],
rng=numpy_rng,
n_in=n_in,
n_out=n_in,
activation=activation)
n_outs = 3
classifier = nn_layers.LogisticRegression(n_in=n_in, n_out=n_outs)
nnet_tweets = nn_layers.FeedForwardNet(layers=[
lookup_table_words, join_layer, flatten_layer, hidden_layer, classifier
])
inputs_train = [batch_tweets, batch_y]
givens_train = {tweets: batch_tweets, y: batch_y}
inputs_pred = [
batch_tweets,
]
givens_pred = {
tweets: batch_tweets,
}
nnet_tweets.set_input(tweets)
print nnet_tweets
params = nnet_tweets.params
cost = nnet_tweets.layers[-1].training_cost(y)
predictions = nnet_tweets.layers[-1].y_pred
updates = sgd_trainer.get_adadelta_updates(cost,
params,
rho=0.95,
eps=1e-6,
max_norm=0,
word_vec_name='None')
train_fn = theano.function(
inputs=inputs_train,
outputs=cost,
updates=updates,
givens=givens_train,
)
pred_fn = theano.function(inputs=inputs_pred,
outputs=predictions,
givens=givens_pred)
def predict_batch(batch_iterator):
preds = numpy.hstack(
[pred_fn(batch_x_q[0]) for batch_x_q in batch_iterator])
return preds[:batch_iterator.n_samples]
#######
#Names#
#######
test_2016n = 'Test 2016'
test_2015n = 'Test 2015'
test_2014n = 'Test 2014'
test_2013n = 'Test 2013'
test_2014ljn = 'Test 2014 LiveJournal'
test_2014srcn = 'Test 2014 Sarcasm'
test_2013_smsn = 'Test 2013 SMS'
ep_pred = {}
ep_pred[test_2016n] = []
ep_pred[test_2015n] = []
ep_pred[test_2014n] = []
ep_pred[test_2013n] = []
ep_pred[test_2014ljn] = []
ep_pred[test_2014srcn] = []
ep_pred[test_2013_smsn] = []
#######################
# Supervised Learining#
######################
batch_size = 1000
training2013_tids = numpy.load(
os.path.join(data_dir, 'task-B-train.20140221.tids.npy'))
training2013_tweets = numpy.load(
os.path.join(data_dir, 'task-B-train.20140221.tweets.npy'))
training2013_sentiments = numpy.load(
os.path.join(data_dir, 'task-B-train.20140221.sentiments.npy'))
dev_2013_tids = numpy.load(
os.path.join(data_dir, 'task-B-dev.20140225.tids.npy'))
dev_2013_tweets = numpy.load(
os.path.join(data_dir, 'task-B-dev.20140225.tweets.npy'))
dev_2013_sentiments = numpy.load(
os.path.join(data_dir, 'task-B-dev.20140225.sentiments.npy'))
trainingA_2016_tids = numpy.load(
os.path.join(data_dir, 'task-A-train-2016.tids.npy'))
trainingA_2016_tweets = numpy.load(
os.path.join(data_dir, 'task-A-train-2016.tweets.npy'))
trainingA_2016_sentiments = numpy.load(
os.path.join(data_dir, 'task-A-train-2016.sentiments.npy'))
devA_2016_tids = numpy.load(
os.path.join(data_dir, 'task-A-dev-2016.tids.npy'))
devA_2016_tweets = numpy.load(
os.path.join(data_dir, 'task-A-dev-2016.tweets.npy'))
devA_2016_sentiments = numpy.load(
os.path.join(data_dir, 'task-A-dev-2016.sentiments.npy'))
devtestA_2016_tids = numpy.load(
os.path.join(data_dir, 'task-A-devtest-2016.tids.npy'))
devtestA_2016_tweets = numpy.load(
os.path.join(data_dir, 'task-A-devtest-2016.tweets.npy'))
devtestA_2016_sentiments = numpy.load(
os.path.join(data_dir, 'task-A-devtest-2016.sentiments.npy'))
test_2016_tids = numpy.load(
os.path.join(data_dir, 'task-A-test2016.tids.npy'))
test_2016_tweets = numpy.load(
os.path.join(data_dir, 'task-A-test2016.tweets.npy'))
test_2016_sentiments = numpy.load(
os.path.join(data_dir, 'task-A-test2016.sentiments.npy'))
test_2013_tids = numpy.load(
os.path.join(data_dir, 'task-B-test2013-twitter.tids.npy'))
test_2013_tweets = numpy.load(
os.path.join(data_dir, 'task-B-test2013-twitter.tweets.npy'))
test_2013_sentiments = numpy.load(
os.path.join(data_dir, 'task-B-test2013-twitter.sentiments.npy'))
test_2014_tids = numpy.load(
os.path.join(data_dir, 'task-B-test2014-twitter.tids.npy'))
test_2014_tweets = numpy.load(
os.path.join(data_dir, 'task-B-test2014-twitter.tweets.npy'))
test_2014_sentiments = numpy.load(
os.path.join(data_dir, 'task-B-test2014-twitter.sentiments.npy'))
test_2015_tids = numpy.load(
os.path.join(data_dir, 'task-B-test2015-twitter.tids.npy'))
test_2015_tweets = numpy.load(
os.path.join(data_dir, 'task-B-test2015-twitter.tweets.npy'))
test_2015_sentiments = numpy.load(
os.path.join(data_dir, 'task-B-test2015-twitter.sentiments.npy'))
test_2013_sms_tids = numpy.load(
os.path.join(data_dir, 'task-B-test2013-sms.tids.npy'))
test_2013_sms_tweets = numpy.load(
os.path.join(data_dir, 'task-B-test2013-sms.tweets.npy'))
test_2013_sms_sentiments = numpy.load(
os.path.join(data_dir, 'task-B-test2013-sms.sentiments.npy'))
test_2014_livejournal_tids = numpy.load(
os.path.join(data_dir, 'task-B-test2014-livejournal.tids.npy'))
test_2014_livejournal_tweets = numpy.load(
os.path.join(data_dir, 'task-B-test2014-livejournal.tweets.npy'))
test_2014_livejournal_sentiments = numpy.load(
os.path.join(data_dir, 'task-B-test2014-livejournal.sentiments.npy'))
test_2014_sarcasm_tids = numpy.load(
os.path.join(data_dir, 'task-B-test2014-twittersarcasm.tids.npy'))
test_2014_sarcasm_tweets = numpy.load(
os.path.join(data_dir, 'task-B-test2014-twittersarcasm.tweets.npy'))
test_2014_sarcasm_sentiments = numpy.load(
os.path.join(data_dir,
'task-B-test2014-twittersarcasm.sentiments.npy'))
training_full_tweets = numpy.concatenate(
(training2013_tweets, dev_2013_tweets), axis=0)
training_full_tweets = numpy.concatenate(
(training_full_tweets, trainingA_2016_tweets), axis=0)
training_full_tweets = numpy.concatenate(
(training_full_tweets, devA_2016_tweets), axis=0)
training_full_tweets = numpy.concatenate(
(training_full_tweets, devtestA_2016_tweets), axis=0)
training_full_sentiments = numpy.concatenate(
(training2013_sentiments, dev_2013_sentiments), axis=0)
training_full_sentiments = numpy.concatenate(
(training_full_sentiments, trainingA_2016_sentiments), axis=0)
training_full_sentiments = numpy.concatenate(
(training_full_sentiments, devA_2016_sentiments), axis=0)
training_full_sentiments = numpy.concatenate(
(training_full_sentiments, devtestA_2016_sentiments), axis=0)
train_set_iterator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng, [training_full_tweets, training_full_sentiments],
batch_size=batch_size,
randomize=True)
test_2015_iterator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng, [test_2015_tweets], batch_size=batch_size, randomize=False)
dev2016_iterator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng, [devA_2016_tweets], batch_size=batch_size, randomize=False)
test_2016_iterator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng, [test_2016_tweets], batch_size=batch_size, randomize=False)
train2016_iterator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng, [trainingA_2016_tweets],
batch_size=batch_size,
randomize=False)
test2016_iterator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng, [test_2016_tweets], batch_size=batch_size, randomize=False)
test2013_itarator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng, [test_2013_tweets], batch_size=batch_size, randomize=False)
test_2014_iterator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng, [test_2014_tweets], batch_size=batch_size, randomize=False)
test_2014_sarcasm_iterator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng, [test_2014_sarcasm_tweets],
batch_size=batch_size,
randomize=False)
train2013_iterator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng, [training2013_tweets],
batch_size=batch_size,
randomize=False)
dev_2013_iterator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng, [dev_2013_tweets], batch_size=batch_size, randomize=False)
test_2013_sms_iterator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng, [test_2013_sms_tweets],
batch_size=batch_size,
randomize=False)
test_2014_livejournal_iterator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng, [test_2014_livejournal_tweets],
batch_size=batch_size,
randomize=False)
W_emb_list = [w for w in params if w.name == 'W_emb']
zerout_dummy_word = theano.function([],
updates=[(W,
T.set_subtensor(W[-1:], 0.))
for W in W_emb_list])
epoch = 0
n_epochs = 50
early_stop = 50
check_freq = 4
timer_train = time.time()
no_best_dev_update = 0
best_dev_acc = -numpy.inf
num_train_batches = len(train_set_iterator)
while epoch < n_epochs:
timer = time.time()
for i, (tweet,
y_label) in enumerate(tqdm(train_set_iterator, ascii=True), 1):
train_fn(tweet, y_label)
if i % check_freq == 0 or i == num_train_batches:
y_pred_dev_2015 = predict_batch(test_2015_iterator)
y_pred_test_2014 = predict_batch(test_2014_iterator)
y_pred_test_2013 = predict_batch(test2013_itarator)
y_pred_test_sms_2013 = predict_batch(test_2013_sms_iterator)
y_pred_test_livejournal_2014 = predict_batch(
test_2014_livejournal_iterator)
y_pred_test_sarcasm_2014 = predict_batch(
test_2014_sarcasm_iterator)
y_pred_test_2016 = predict_batch(test_2016_iterator)
dev_acc_2015 = semeval_f1_taskA(test_2015_sentiments,
y_pred_dev_2015)
dev_acc_2014 = semeval_f1_taskA(test_2014_sentiments,
y_pred_test_2014)
dev_acc_2014_lj = semeval_f1_taskA(
test_2014_livejournal_sentiments,
y_pred_test_livejournal_2014)
dev_acc_2014_srcs = semeval_f1_taskA(
test_2014_sarcasm_sentiments, y_pred_test_sarcasm_2014)
dev_acc_2013 = semeval_f1_taskA(test_2013_sentiments,
y_pred_test_2013)
dev_acc_2013_sms = semeval_f1_taskA(test_2013_sms_sentiments,
y_pred_test_sms_2013)
dev_acc_2016_test = semeval_f1_taskA(test_2016_sentiments,
y_pred_test_2016)
ep_pred[test_2016n].append(dev_acc_2016_test)
ep_pred[test_2015n].append(dev_acc_2015)
ep_pred[test_2014n].append(dev_acc_2014)
ep_pred[test_2013n].append(dev_acc_2013)
ep_pred[test_2014ljn].append(dev_acc_2014_lj)
ep_pred[test_2014srcn].append(dev_acc_2014_srcs)
ep_pred[test_2013_smsn].append(dev_acc_2013_sms)
if dev_acc_2016_test > best_dev_acc:
best_dev_acc = dev_acc_2016_test
best_params = [
numpy.copy(p.get_value(borrow=True)) for p in params
]
no_best_dev_update = 0
print('2016 epoch: {} chunk: {} best_chunk_auc: {:.4f};'.
format(epoch, i, dev_acc_2016_test))
print('2015 epoch: {} chunk: {} best_chunk_auc: {:.4f};'.
format(epoch, i, dev_acc_2015))
print('2014 epoch: {} chunk: {} best_chunk_auc: {:.4f};'.
format(epoch, i, dev_acc_2014))
print('2013 epoch: {} chunk: {} best_chunk_auc: {:.4f};'.
format(epoch, i, dev_acc_2013))
print('2014lj epoch: {} chunk: {} best_chunk_auc: {:.4f};'.
format(epoch, i, dev_acc_2014_lj))
print(
'2014src epoch: {} chunk: {} best_chunk_auc: {:.4f};'.
format(epoch, i, dev_acc_2014_srcs))
print(
'2013sms epoch: {} chunk: {} best_chunk_auc: {:.4f};'.
format(epoch, i, dev_acc_2013_sms))
zerout_dummy_word()
print('epoch {} took {:.4f} seconds'.format(epoch,
time.time() - timer))
epoch += 1
no_best_dev_update += 1
if no_best_dev_update >= early_stop:
print "Quitting after of no update of the best score on dev set", no_best_dev_update
break
print('Training took: {:.4f} seconds'.format(time.time() - timer_train))
for i, param in enumerate(best_params):
params[i].set_value(param, borrow=True)
cPickle.dump(
ep_pred,
open(data_dir + '/supervised_results_{}.p'.format(test_type), 'wb'))
return
#######################
# Get Sentence Vectors#
######################
batch_size = input_shape[0]
inputs_senvec = [batch_tweets]
givents_senvec = {
tweets: batch_tweets,
}
output = nnet_tweets.layers[-2].output
output_fn = function(inputs=inputs_senvec,
outputs=output,
givens=givents_senvec)
sets = [(test_2014_tids, test_2014_tweets, 'task-B-test2014-twitter'),
(test_2015_tids, test_2015_tweets, 'task-B-test2015-twitter'),
(training2013_tids, training2013_tweets, 'task-BD-train-2013'),
(test_2013_sms_tids, test_2013_sms_tweets, 'task-B-test2013-sms'),
(devA_2016_tids, devA_2016_tweets, 'task-A-dev-2016'),
(trainingA_2016_tids, trainingA_2016_tweets, 'task-A-train-2016'),
(devtestA_2016_tids, devtestA_2016_tweets, 'task-A-devtest-2016'),
(test_2016_tids, test_2016_tweets,
'SemEval2016-task4-test.subtask-A'),
(test_2014_sarcasm_tids, test_2014_sarcasm_tweets,
'test_2014_sarcasm'),
(test_2014_livejournal_tids, test_2014_livejournal_tweets,
'task-B-test2014-livejournal'),
(test_2013_tids, test_2013_tweets, 'task-BD-train-2013'),
(dev_2013_tids, dev_2013_tweets, 'task-BD-dev-2013')]
for (fids, fset, name) in sets:
test_set_iterator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng, [fset], batch_size=batch_size, randomize=False)
counter = 0
fname = open(
os.path.join(data_dir, 'sentence-vecs/{}.txt'.format(name)), 'w+')
for i, tweet in enumerate(tqdm(test_set_iterator), 1):
o = output_fn(tweet[0])
for vec in o:
fname.write(fids[counter])
for el in numpy.nditer(vec):
fname.write(" %f" % el)
fname.write("\n")
counter += 1
if counter == test_set_iterator.n_samples:
break
##############################
# Get Predictions Probabilites#
#############################
batch_size = input_shape[0]
output = nnet_tweets.layers[-1].p_y_given_x
output_fn = function(inputs=inputs_senvec,
outputs=output,
givens=givents_senvec)
for (fids, fset, name) in sets:
test_set_iterator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng, [fset], batch_size=batch_size, randomize=False)
counter = 0
fname = open(
os.path.join(data_dir, 'prob_predictions/{}.txt'.format(name)),
'w+')
for i, tweet in enumerate(tqdm(test_set_iterator), 1):
o = output_fn(tweet[0])
for vec in o:
for el in numpy.nditer(vec):
fname.write(" %f" % el)
fname.write("\n")
counter += 1
if counter == test_set_iterator.n_samples:
break
def main():
##########
# LAYERS #
#########
HOME_DIR = "semeval_parsed"
input_fname = '200M'
test_type = ''
if len(sys.argv) > 1:
test_type = sys.argv[1]
data_dir = HOME_DIR + '_' + input_fname
numpy_rng = numpy.random.RandomState(123)
print "Load Parameters"
parameter_map = cPickle.load(open(data_dir+'/parameters_distant_{}.p'.format(test_type), 'rb'))
input_shape = parameter_map['inputShape']
filter_width = parameter_map['filterWidth']
n_in = parameter_map['qLogisticIn']
k_max = parameter_map['kmax']
def relu(x):
return x * (x > 0)
activation = relu
tweets = T.imatrix('tweets_train')
y = T.lvector('y')
batch_tweets= T.imatrix('batch_x_q')
batch_y = T.lvector('batch_y')
lookup_table_words = nn_layers.LookupTableFast(
W=parameter_map['LookupTableFastStaticW'].get_value(),
pad=filter_width-1
)
filter_shape = parameter_map['FilterShape' + str(filter_width)]
conv_layers = []
conv = nn_layers.Conv2dLayer(
W=parameter_map['Conv2dLayerW' + str(filter_width)],
rng=numpy_rng,
filter_shape=filter_shape,
input_shape=input_shape
)
non_linearity = nn_layers.NonLinearityLayer(
b=parameter_map['NonLinearityLayerB' + str(filter_width)],
b_size=filter_shape[0],
activation=activation
)
pooling = nn_layers.KMaxPoolLayer(k_max=k_max)
conv2dNonLinearMaxPool = nn_layers.FeedForwardNet(layers=[
conv,
non_linearity,
pooling
])
conv_layers.append(conv2dNonLinearMaxPool)
join_layer = nn_layers.ParallelLayer(layers=conv_layers)
flatten_layer = nn_layers.FlattenLayer()
hidden_layer = nn_layers.LinearLayer(
W=parameter_map['LinearLayerW'],
b=parameter_map['LinearLayerB'],
rng=numpy_rng,
n_in=n_in,
n_out=n_in,
activation=activation
)
n_outs = 3
classifier = nn_layers.LogisticRegression(n_in=n_in, n_out=n_outs)
nnet_tweets = nn_layers.FeedForwardNet(layers=[
lookup_table_words,
join_layer,
flatten_layer,
hidden_layer,
classifier
])
inputs_train = [batch_tweets,
batch_y]
givens_train = {tweets: batch_tweets,
y: batch_y}
inputs_pred = [batch_tweets,
]
givens_pred = {tweets:batch_tweets,
}
nnet_tweets.set_input(tweets)
print nnet_tweets
params = nnet_tweets.params
cost = nnet_tweets.layers[-1].training_cost(y)
predictions = nnet_tweets.layers[-1].y_pred
updates = sgd_trainer.get_adadelta_updates(
cost,
params,
rho=0.95,
eps=1e-6,
max_norm=0,
word_vec_name='None'
)
train_fn = theano.function(
inputs=inputs_train,
outputs=cost,
updates=updates,
givens=givens_train,
)
pred_fn = theano.function(inputs=inputs_pred,
outputs=predictions,
givens=givens_pred)
def predict_batch(batch_iterator):
preds = numpy.hstack([pred_fn(batch_x_q[0]) for batch_x_q in batch_iterator])
return preds[:batch_iterator.n_samples]
#######
#Names#
#######
test_2016n = 'Test 2016'
test_2015n = 'Test 2015'
test_2014n = 'Test 2014'
test_2013n = 'Test 2013'
test_2014ljn = 'Test 2014 LiveJournal'
test_2014srcn = 'Test 2014 Sarcasm'
test_2013_smsn = 'Test 2013 SMS'
train_fulln = 'Training Score'
ep_pred = {}
ep_pred[test_2016n] = []
ep_pred[test_2015n] = []
ep_pred[test_2014n] = []
ep_pred[test_2013n] = []
ep_pred[test_2014ljn] = []
ep_pred[test_2014srcn] = []
ep_pred[test_2013_smsn] = []
ep_pred[train_fulln] = []
#######################
# Supervised Learining#
######################
batch_size = 1000
training2013_tids = numpy.load(os.path.join(data_dir, 'task-B-train.20140221.tids.npy'))
training2013_tweets = numpy.load(os.path.join(data_dir, 'task-B-train.20140221.tweets.npy'))
training2013_sentiments = numpy.load(os.path.join(data_dir, 'task-B-train.20140221.sentiments.npy'))
dev_2013_tids = numpy.load(os.path.join(data_dir, 'task-B-dev.20140225.tids.npy'))
dev_2013_tweets = numpy.load(os.path.join(data_dir, 'task-B-dev.20140225.tweets.npy'))
dev_2013_sentiments = numpy.load(os.path.join(data_dir, 'task-B-dev.20140225.sentiments.npy'))
trainingA_2016_tids = numpy.load(os.path.join(data_dir, 'task-A-train-2016.tids.npy'))
trainingA_2016_tweets = numpy.load(os.path.join(data_dir, 'task-A-train-2016.tweets.npy'))
trainingA_2016_sentiments = numpy.load(os.path.join(data_dir, 'task-A-train-2016.sentiments.npy'))
devA_2016_tids = numpy.load(os.path.join(data_dir, 'task-A-dev-2016.tids.npy'))
devA_2016_tweets = numpy.load(os.path.join(data_dir, 'task-A-dev-2016.tweets.npy'))
devA_2016_sentiments = numpy.load(os.path.join(data_dir, 'task-A-dev-2016.sentiments.npy'))
devtestA_2016_tids = numpy.load(os.path.join(data_dir, 'task-A-devtest-2016.tids.npy'))
devtestA_2016_tweets = numpy.load(os.path.join(data_dir, 'task-A-devtest-2016.tweets.npy'))
devtestA_2016_sentiments = numpy.load(os.path.join(data_dir, 'task-A-devtest-2016.sentiments.npy'))
test_2016_tids = numpy.load(os.path.join(data_dir, 'task-A-test2016.tids.npy'))
test_2016_tweets = numpy.load(os.path.join(data_dir, 'task-A-test2016.tweets.npy'))
test_2016_sentiments = numpy.load(os.path.join(data_dir, 'task-A-test2016.sentiments.npy'))
test_2013_tids = numpy.load(os.path.join(data_dir, 'task-B-test2013-twitter.tids.npy'))
test_2013_tweets = numpy.load(os.path.join(data_dir, 'task-B-test2013-twitter.tweets.npy'))
test_2013_sentiments = numpy.load(os.path.join(data_dir, 'task-B-test2013-twitter.sentiments.npy'))
test_2014_tids = numpy.load(os.path.join(data_dir, 'task-B-test2014-twitter.tids.npy'))
test_2014_tweets = numpy.load(os.path.join(data_dir, 'task-B-test2014-twitter.tweets.npy'))
test_2014_sentiments = numpy.load(os.path.join(data_dir, 'task-B-test2014-twitter.sentiments.npy'))
test_2015_tids = numpy.load(os.path.join(data_dir, 'task-B-test2015-twitter.tids.npy'))
test_2015_tweets = numpy.load(os.path.join(data_dir, 'task-B-test2015-twitter.tweets.npy'))
test_2015_sentiments = numpy.load(os.path.join(data_dir, 'task-B-test2015-twitter.sentiments.npy'))
test_2013_sms_tids = numpy.load(os.path.join(data_dir, 'task-B-test2013-sms.tids.npy'))
test_2013_sms_tweets = numpy.load(os.path.join(data_dir, 'task-B-test2013-sms.tweets.npy'))
test_2013_sms_sentiments = numpy.load(os.path.join(data_dir, 'task-B-test2013-sms.sentiments.npy'))
test_2014_livejournal_tids = numpy.load(os.path.join(data_dir, 'task-B-test2014-livejournal.tids.npy'))
test_2014_livejournal_tweets = numpy.load(os.path.join(data_dir, 'task-B-test2014-livejournal.tweets.npy'))
test_2014_livejournal_sentiments = numpy.load(os.path.join(data_dir, 'task-B-test2014-livejournal.sentiments.npy'))
test_2014_sarcasm_tids = numpy.load(os.path.join(data_dir, 'task-B-test2014-twittersarcasm.tids.npy'))
test_2014_sarcasm_tweets = numpy.load(os.path.join(data_dir, 'task-B-test2014-twittersarcasm.tweets.npy'))
test_2014_sarcasm_sentiments = numpy.load(os.path.join(data_dir, 'task-B-test2014-twittersarcasm.sentiments.npy'))
rand_tweets_tids = numpy.load(os.path.join(data_dir, 'random_tweet.tids.npy'))
rand_tweets_tweets = numpy.load(os.path.join(data_dir, 'random_tweet.tweets.npy'))
rand_tweets_sentiments = numpy.load(os.path.join(data_dir, 'random_tweet.sentiments.npy'))
training_full_id = numpy.concatenate((training2013_tids,dev_2013_tids),axis=0)
training_full_id = numpy.concatenate((training_full_id,trainingA_2016_tids),axis=0)
training_full_id = numpy.concatenate((training_full_id,devA_2016_tids),axis=0)
training_full_id = numpy.concatenate((training_full_id,devtestA_2016_tids),axis=0)
training_full_tweets = numpy.concatenate((training2013_tweets,dev_2013_tweets),axis=0)
training_full_tweets = numpy.concatenate((training_full_tweets,trainingA_2016_tweets),axis=0)
training_full_tweets = numpy.concatenate((training_full_tweets,devA_2016_tweets),axis=0)
training_full_tweets = numpy.concatenate((training_full_tweets,devtestA_2016_tweets),axis=0)
training_full_sentiments = numpy.concatenate((training2013_sentiments,dev_2013_sentiments),axis=0)
training_full_sentiments = numpy.concatenate((training_full_sentiments,trainingA_2016_sentiments),axis=0)
training_full_sentiments = numpy.concatenate((training_full_sentiments,devA_2016_sentiments),axis=0)
training_full_sentiments = numpy.concatenate((training_full_sentiments,devtestA_2016_sentiments),axis=0)
train_set_iterator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng,
[training_full_tweets,training_full_sentiments],
batch_size=batch_size,
randomize=True
)
train_err_iterator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng,
[training_full_tweets],
batch_size=batch_size,
randomize=False
)
test_2015_iterator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng,
[test_2015_tweets],
batch_size=batch_size,
randomize=False
)
dev2016_iterator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng,
[devA_2016_tweets],
batch_size=batch_size,
randomize=False
)
devtestA2016_iterator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng,
[devtestA_2016_tweets],
batch_size=batch_size,
randomize=False
)
train2016_iterator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng,
[trainingA_2016_tweets],
batch_size=batch_size,
randomize=False
)
test2016_iterator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng,
[test_2016_tweets],
batch_size=batch_size,
randomize=False
)
test2013_itarator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng,
[test_2013_tweets],
batch_size=batch_size,
randomize=False
)
test_2014_iterator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng,
[test_2014_tweets],
batch_size=batch_size,
randomize=False
)
test_2014_sarcasm_iterator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng,
[test_2014_sarcasm_tweets],
batch_size=batch_size,
randomize=False
)
train2013_iterator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng,
[training2013_tweets],
batch_size=batch_size,
randomize=False
)
dev_2013_iterator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng,
[dev_2013_tweets],
batch_size=batch_size,
randomize=False
)
test_2013_sms_iterator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng,
[test_2013_sms_tweets],
batch_size=batch_size,
randomize=False
)
test_2014_livejournal_iterator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng,
[test_2014_livejournal_tweets],
batch_size=batch_size,
randomize=False
)
W_emb_list = [w for w in params if w.name == 'W_emb']
zerout_dummy_word = theano.function([], updates=[(W, T.set_subtensor(W[-1:], 0.)) for W in W_emb_list])
epoch = 0
n_epochs = 50
early_stop = 50
check_freq = 4
timer_train = time.time()
no_best_dev_update = 0
best_dev_acc = -numpy.inf
num_train_batches = len(train_set_iterator)
saved12 = False
while epoch < n_epochs:
timer = time.time()
for i, (tweet,y_label) in enumerate(tqdm(train_set_iterator,ascii=True), 1):
train_fn(tweet,y_label)
if i % check_freq == 0 or i == num_train_batches:
y_pred_dev_2015 = predict_batch(test_2015_iterator)
#y_pred_train_2013 = predict_batch(train2013_iterator)
#y_pred_train_2016 = predict_batch(train2016_iterator)
#y_pred_dev2016 = predict_batch(dev2016_iterator)
#y_pred_dev2013 = predict_batch(dev_2013_iterator)
y_pred_test_2016 = predict_batch(test2016_iterator)
y_pred_test_2014 = predict_batch(test_2014_iterator)
y_pred_test_2013 = predict_batch(test2013_itarator)
y_pred_test_sms_2013 = predict_batch(test_2013_sms_iterator)
y_pred_test_livejournal_2014 = predict_batch(test_2014_livejournal_iterator)
y_pred_test_sarcasm_2014 = predict_batch(test_2014_sarcasm_iterator)
#y_pred_devtest_2016 = predict_batch(devtestA2016_iterator)
y_train_score = predict_batch(train_err_iterator)
dev_acc_2015 = semeval_f1_taskA(test_2015_sentiments,y_pred_dev_2015)
dev_acc_2014 = semeval_f1_taskA(test_2014_sentiments,y_pred_test_2014)
dev_acc_2014_lj = semeval_f1_taskA(test_2014_livejournal_sentiments,y_pred_test_livejournal_2014)
dev_acc_2014_srcs = semeval_f1_taskA(test_2014_sarcasm_sentiments,y_pred_test_sarcasm_2014)
dev_acc_2013 = semeval_f1_taskA(test_2013_sentiments,y_pred_test_2013)
dev_acc_2013_sms = semeval_f1_taskA(test_2013_sms_sentiments,y_pred_test_sms_2013)
dev_acc_2016 = semeval_f1_taskA(test_2016_sentiments,y_pred_test_2016)
dev_acc_train_err = semeval_f1_taskA(training_full_sentiments,y_train_score)
ep_pred[test_2016n].append(dev_acc_2016)
ep_pred[test_2015n].append(dev_acc_2015)
ep_pred[test_2014n].append(dev_acc_2014)
ep_pred[test_2013n].append(dev_acc_2013)
ep_pred[test_2014ljn].append(dev_acc_2014_lj)
ep_pred[test_2014srcn].append(dev_acc_2014_srcs)
ep_pred[test_2013_smsn].append(dev_acc_2013_sms)
ep_pred[train_fulln].append(dev_acc_train_err)
if dev_acc_2016 > best_dev_acc:
best_dev_acc = dev_acc_2016
best_params = [numpy.copy(p.get_value(borrow=True)) for p in params]
no_best_dev_update = 0
print('2016 epoch: {} chunk: {} best_chunk_auc: {:.4f};'.format(epoch, i, dev_acc_2016))
print('2015 epoch: {} chunk: {} best_chunk_auc: {:.4f};'.format(epoch, i, dev_acc_2015))
print('2014 epoch: {} chunk: {} best_chunk_auc: {:.4f};'.format(epoch, i, dev_acc_2014))
print('2013 epoch: {} chunk: {} best_chunk_auc: {:.4f};'.format(epoch, i, dev_acc_2013))
print('2014lj epoch: {} chunk: {} best_chunk_auc: {:.4f};'.format(epoch, i, dev_acc_2014_lj))
print('2014src epoch: {} chunk: {} best_chunk_auc: {:.4f};'.format(epoch, i, dev_acc_2014_srcs))
print('2013sms epoch: {} chunk: {} best_chunk_auc: {:.4f};'.format(epoch, i, dev_acc_2013_sms))
print('Train Err: {} chunk: {} best_chunk_auc: {:.4f};'.format(epoch, i, dev_acc_train_err))
if epoch == 15 and not saved12:
saved12 = True
ep_12_params = [numpy.copy(p.get_value(borrow=True)) for p in params]
zerout_dummy_word()
print('epoch {} took {:.4f} seconds'.format(epoch, time.time() - timer))
epoch += 1
no_best_dev_update += 1
if no_best_dev_update >= early_stop:
print "Quitting after of no update of the best score on dev set", no_best_dev_update
break
print('Training took: {:.4f} seconds'.format(time.time() - timer_train))
cPickle.dump(ep_pred,open(data_dir+'/supervised_results_{}.p'.format(test_type), 'wb'))
#######################
# Get Sentence Vectors#
######################
inputs_senvec = [batch_tweets]
givents_senvec = {tweets:batch_tweets}
sets = [
(rand_tweets_tids,rand_tweets_tweets,'random_tweets')
(test_2016_tids,test_2016_tweets,'SemEval2016-task4-test.subtask-A'),
(test_2014_tids,test_2014_tweets,'task-B-test2014-twitter'),
(test_2015_tids,test_2015_tweets,'task-B-test2015-twitter'),
(test_2013_tids,test_2013_tweets,'task-B-test2013-twitter'),
(test_2014_livejournal_tids,test_2014_livejournal_tweets,'task-B-test2014-livejournal'),
(test_2014_sarcasm_tids,test_2014_sarcasm_tweets,'test_2014_sarcasm'),
(test_2013_sms_tids,test_2013_sms_tweets,'task-B-test2013-sms'),
(training2013_tids,training2013_tweets,'task-B-train.20140221'),
(devA_2016_tids,devA_2016_tweets,'task-A-dev-2016'),
(trainingA_2016_tids,trainingA_2016_tweets,'task-A-train-2016'),
(devtestA_2016_tids,devtestA_2016_tweets,'task-A-devtest-2016'),
(dev_2013_tids,dev_2013_tweets,'task-B-dev.20140225'),
(training_full_id,training_full_tweets,'training_full_set')
]
get_senvec = False
if get_senvec:
batch_size = input_shape[0]
output = nnet_tweets.layers[-2].output
output_fn = function(inputs=inputs_senvec, outputs=output,givens=givents_senvec)
for (fids,fset,name) in sets:
test_set_iterator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng,
[fset],
batch_size=batch_size,
randomize=False
)
counter = 0
fname = open(os.path.join(data_dir,'sentence-vecs/{}.txt'.format(name)), 'w+')
for i, tweet in enumerate(tqdm(test_set_iterator), 1):
o = output_fn(tweet[0])
for vec in o:
fname.write(fids[counter])
for el in numpy.nditer(vec):
fname.write(" %f" % el)
fname.write("\n")
counter+=1
if counter == test_set_iterator.n_samples:
break
##############################
# Get Predictions Probabilites#
#############################
print 'Store Predictions'
get_params = [
('best_params',best_params),
('ep15params',ep_12_params)
]
for pname, gparams in get_params:
for i, param in enumerate(gparams):
params[i].set_value(param, borrow=True)
batch_size = input_shape[0]
output = nnet_tweets.layers[-1].p_y_given_x
output_fn = function(inputs=inputs_senvec, outputs=output,givens=givents_senvec)
for (fids,fset,name) in sets:
test_set_iterator = sgd_trainer.MiniBatchIteratorConstantBatchSize(
numpy_rng,
[fset],
batch_size=batch_size,
randomize=False
)
opath_prob = os.path.join(data_dir,'{}/{}/predictions_probs'.format(pname,test_type))
if not os.path.exists(opath_prob):
os.makedirs(opath_prob)
print 'Created Path',opath_prob
opath_pred = os.path.join(data_dir,'{}/{}/predictions_pred'.format(pname,test_type))
if not os.path.exists(opath_pred):
os.makedirs(opath_pred)
print 'Created Path',opath_pred
counter = 0
fname_prob = open(os.path.join(opath_prob,'{}.txt'.format(name)), 'w+')
fname_pred = open(os.path.join(opath_pred,'{}.txt'.format(name)), 'w+')
for i, tweet in enumerate(tqdm(test_set_iterator), 1):
o = output_fn(tweet[0])
for vec in o:
#save pred_prob
for el in numpy.nditer(vec):
fname_prob.write("%f\t" % el)
fname_prob.write("\n")
#save pred
pred = numpy.argmax(vec)
sentiments = {
0 : 'negative',
1 : 'neutral',
2 : 'positive'
}
fname_pred.write('{}\n'.format(sentiments[pred]))
counter+=1
if counter == test_set_iterator.n_samples:
break
print('Load Supervised Model')
model.load_weights(super_weight_path)
for tids, X_test, y_test, name in test_sets:
raw_data = open(os.path.join('semeval', '{}.tsv'.format(name)),
'r').readlines()
raw_data = map(lambda x: x.replace('\n', '').split('\t'), raw_data)
raw_tweets = map(lambda x: (x[0], x[-1]), raw_data)
raw_lables = map(lambda x: (x[0], x[-2]), raw_data)
raw_data_dict = dict(raw_tweets)
raw_lables_dict = dict(raw_lables)
ofile = open(os.path.join(res_dir, name), 'w')
y_pred = model.predict(X_test)
y_pred = probas_to_classes(y_pred)
score = semeval_f1_taskA(y_test, y_pred)
scores = f1_score(y_test, y_pred, average=None)
output += '{}: {}\t'.format(name, score)
output += 'neg_f1: {}\t neut_f1: {}\t pos_f1: {}'.format(
scores[0], scores[1], scores[2])
for tid, label in zip(tids, y_pred):
tweet = raw_data_dict[tid].replace('\n', '')
truth = raw_lables_dict[tid]
l = {0: 'negative', 1: 'neutral', 2: 'positive'}.get(label)
outline = '{}\t{}\t{}\n'.format(tweet, truth, l)
ofile.write(outline)
open(os.path.join('results', 'results_log.tsv'), 'a').write(output + '\n')