else:
weights_0p25 = np.load('training_output/collins_w_0p25.npy')
weights_0p5 = np.load('training_output/collins_w_0p5.npy')
weights_0p75 = np.load('training_output/collins_w_0p75.npy')
weights = [weights_0p25, weights_0p5, weights_0p75]
# load the test data
test_labels = dp.labels_as_ints('dataset/testLabels.txt')
test_sentences = dp.import_sentences('dataset/testSentences.txt')
# calculate the scores
score = []
for w in weights:
if scoretype == 'word':
score.append(sr.score_by_word(w,test_labels,test_sentences,dummy))
elif scoretype == 'sentence':
score.append(sr.score_by_sentence(w,test_labels,test_sentences,dummy))
elif scoretype == 'mark':
score.append(sr.score_by_mark(w,test_labels,test_sentences,dummy))
else:
print 'Not a valid method!\n'
exit(0)
# save to file
f = open('scores/'+method+scoretype+'.txt', 'w')
f.write('Method: ' + method + ', Scoretype: ' + scoretype + '\n')
if method != 'mark':
f.write('25\%: ' + str(score[0]) + '\n')
f.write('50\%: ' + str(score[1]) + '\n')
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
