def SGD_train(train_labels, train_sentences, max_epoch, validate_labels, validate_sentences, method, lr=0.1):
"""
The main subroutine for training by SGD training, train_labels and
train_sentence are list of lists of corresponding (y,x) pairs.
validate_labels and validate_sentences are similar list of lists for doing
early stopping regularization.
"""
# max_epoch = 5
converged = 0
# lr = 0.1
avg_time_per_epoch = 0.0
score_list = []
num_training = len(train_sentences)
order = np.arange(0, num_training, dtype="int") # ordering of training ex's
J = ff.calcJ()
# two weightvectors for bookkeeping, and a dw vector for updates, we initialize
# the weightvector at zero
# w_traj = np.zeros(shape=(max_epoch+1,J))
weights = np.zeros(J)
# w_traj[0,:] = weights
old_weights = np.zeros(J)
dw = np.zeros(J)
score = 0.0
score_list.append(score)
for epoch in range(max_epoch):
time1 = time.time()
# print "epoch number {}".format(epoch)
i = 0
# put something in here about learning rate?
np.random.shuffle(order) # randomly shuffle test data
old_weights = np.copy(weights)
for ind_ex in order:
# print "Now processing sample {} of {}".format(i,num_training)
x = train_sentences[ind_ex]
y = train_labels[ind_ex]
dw = compute_gradient(x, y, weights, dw)
weights += lr * dw
i += 1
time2 = time1 - time.time()
avg_time_per_epoch += time2
# convergence test, remove this commentary when we have a score function
# in collins module
new_score = sr.general_score(weights, validate_labels, validate_sentences, method, 0)
score_list.append(new_score)
if new_score > score:
# the validation score has increased
score = new_score
else:
# validation score has decreased, early stopping dictates we stop
# training and use the old weights
converged = 1
weights = np.copy(old_weights)
break
avg_time_per_epoch /= float(epoch)
# score = 0.0
return weights, score_list, epoch, avg_time_per_epoch
def collins(train_labels, train_sentences, validation_labels,
validation_sentences, pct_train=0.5, Nex=None):
"""
Runs the Collins perceptron training on the input training data.
labels - All training, validation labels.
sentences - All training, validation sentences.
pct_train - Percentage of examples from data set to use as training data.
The rest are used as validation data.
"""
# get J, the total number of feature functions
J = ffs.calcJ()
print 'J = ',J
# now run it
scores = []
w0 = np.zeros(J)
print 'Calculating initial score...'
scores.append(sr.score_by_word(w0,validation_labels,validation_sentences))
print 'Done!\n'
# run until converged, according to score on validation set
nep = 1
epoch_time = []
print 'Initiating Collins perceptron training.'
while True:
print 'Epoch #',nep,'...'
t0 = time.time()
# get the new weights & score
print 'Training...'
w1 = collins_epoch(train_labels, train_sentences, w0)
print 'Done.\n'
epoch_time.append([time.time() - t0])
t0 = time.time()
print 'Calculating new score...'
scores.append(sr.general_score(w1,validation_labels,validation_sentences,'word',0))
print 'Done.\n'
epoch_time[nep-1].append(time.time() - t0)
# decide if converged
if scores[nep] < scores[nep-1]:
break
else:
w0 = w1
nep += 1
print 'Training complete!\n'
"""
# make a prediction on a dummy sentence
#dummy = ['FIRSTWORD','I','like','cheese','but','I','also','like','bread','LASTWORD']
dummy = ['FIRSTWORD','Do','you','like','cheese','LASTWORD']
g_dummy = sr.g(w,dummy)
U_dummy = sr.U(g_dummy)
y_best = sr.bestlabel(U_dummy,g_dummy)
"""
# now return final weights, score time series, and epoch timing
return w0, scores, epoch_time
