def trainPerceptron(N_its,inst_generator,labels, outfile, devkey):
tr_acc = [None]*N_its #holder for training accuracy
dv_acc = [None]*N_its #holder for dev accuracy
weights = defaultdict(float)
for i in xrange(N_its):
weights,tr_err,tr_tot = oneItPerceptron(inst_generator,weights,labels) #call your function for a single iteration
confusion = evalClassifier(weights,outfile, devkey) #evaluate on dev data
dv_acc[i] = scorer.accuracy(confusion) #compute accuracy
tr_acc[i] = 1. - tr_err/float(tr_tot) #compute training accuracy from output
print i,'dev: ',dv_acc[i],'train: ',tr_acc[i]
return weights, tr_acc, dv_acc
def trainPerceptron(N_its, inst_generator, labels, outfile, devkey):
tr_acc = [None] * N_its #holder for training accuracy
dv_acc = [None] * N_its #holder for dev accuracy
weights = defaultdict(float)
for i in xrange(N_its):
weights, tr_err, tr_tot = oneItPerceptron(
inst_generator, weights,
labels) #call your function for a single iteration
confusion = evalClassifier(weights, outfile,
devkey) #evaluate on dev data
dv_acc[i] = scorer.accuracy(confusion) #compute accuracy
tr_acc[i] = 1. - tr_err / float(
tr_tot) #compute training accuracy from output
print i, 'dev: ', dv_acc[i], 'train: ', tr_acc[i]
return weights, tr_acc, dv_acc
def trainAvgPerceptron(N_its, inst_generator, labels, outfile, devkey):
tr_acc = [None] * N_its # holder for training accuracy
dv_acc = [None] * N_its # holder for dev accuracy
weights = defaultdict(float)
avg_weights = defaultdict(float)
wsum = defaultdict(float)
cur_T = 1
for i in xrange(N_its):
weights, wsum, tr_err, tr_tot = oneItAvgPerceptron(
inst_generator, weights, wsum, labels, cur_T
) # call your function for a single iteration
cur_T += tr_tot
for w in wsum:
avg_weights[w] = weights[w] - wsum[w] / float(cur_T)
confusion = evalClassifier(avg_weights, outfile, devkey) # evaluate on dev data
dv_acc[i] = scorer.accuracy(confusion) # compute accuracy
tr_acc[i] = 1.0 - tr_err / float(tr_tot) # compute training accuracy from output
print i, "dev: ", dv_acc[i], "train: ", tr_acc[i]
return avg_weights, tr_acc, dv_acc
def trainAvgPerceptron(N_its, inst_generator, labels, outfile, devkey):
tr_acc = [None] * N_its #holder for training accuracy
dv_acc = [None] * N_its #holder for dev accuracy
weights = defaultdict(float)
avg_weights = defaultdict(float)
wsum = defaultdict(float)
cur_T = 1
for i in xrange(N_its):
weights, wsum, tr_err, tr_tot = oneItAvgPerceptron(
inst_generator, weights, wsum, labels,
cur_T) #call your function for a single iteration
cur_T += tr_tot
for w in wsum:
avg_weights[w] = weights[w] - wsum[w] / float(cur_T)
confusion = evalClassifier(avg_weights, outfile,
devkey) #evaluate on dev data
dv_acc[i] = scorer.accuracy(confusion) #compute accuracy
tr_acc[i] = 1. - tr_err / float(
tr_tot) #compute training accuracy from output
print i, 'dev: ', dv_acc[i], 'train: ', tr_acc[i]
return avg_weights, tr_acc, dv_acc