def regularization_using_grid_search (alphas, counts, class_counts, allkeys, tr_outfile='nb.alpha.tr.txt', dv_outfile='nb.alpha.dv.txt'):
tr_accs = []
dv_accs = []
# Choose your alphas here
weights_nb_alphas = dict()
for alpha in alphas:
weights_nb_alphas[alpha] = learnNBWeights(counts, class_counts, allkeys, alpha)
confusion = evalClassifier(weights_nb_alphas[alpha],tr_outfile,TRAINKEY)
tr_accs.append(scorer.accuracy(confusion))
confusion = evalClassifier(weights_nb_alphas[alpha],dv_outfile,DEVKEY)
dv_accs.append(scorer.accuracy(confusion))
return weights_nb_alphas, tr_accs, dv_accs
def regularization_using_grid_search(alphas, netas, N_its,inst_generator, outfile, devkey, learning_rate=1e-4, regularizer=1e-2, tr_outfile='logreg.alpha.tr.txt', dv_outfile='logreg.alpha.dv.txt'):
tr_accs = []
dv_accs = []
# Choose your alphas here
weights_log_reg_alphas = dict()
for alpha in alphas:
for neta in netas:
weights_log_reg_alphas[(alpha, neta)] = trainLRbySGD(N_its,inst_generator, outfile, devkey, learning_rate=neta, regularizer=alpha)
confusion = evalClassifier(weights_log_reg_alphas[(alpha, neta)],tr_outfile,TRAINKEY)
tr_accs.append(scorer.accuracy(confusion))
confusion = evalClassifier(weights_log_reg_alphas[(alpha, neta)],dv_outfile,DEVKEY)
dv_accs.append(scorer.accuracy(confusion))
return weights_log_reg_alphas, tr_accs, dv_accs
def test_wlc_dev_almost_there_accuracy ():
global weights_wlc
global WLC_FILE
mat = evalClassifier (weights_wlc, WLC_FILE, DEVKEY)
actual = scorer.accuracy(mat)
expected = 0.40
ok_ (expected <= actual, msg="UNEQUAL Expected:%f, Actual:%f" %(expected,actual))
def test_custom_str_perceptron():
# w,tr_acc,dv_acc = str_perceptron.trainAvgStructPerceptron(10,tr_all,features.yourHMMFeatures,alltags)
# confusion = tagger_base.evalTagger(lambda words,alltags : viterbi.viterbiTagger(words,features.yourHMMFeatures,w,alltags)[0],'custom_str_classifier')
confusion = scorer.getConfusion(DEV_FILE, 'str_avg_perceptron_custom.response')
expected_acc = 0.810
actual_acc = scorer.accuracy(confusion)
ok_ (expected_acc < actual_acc, msg="NOT_IN_RANGE Expected:%f, Actual:%f" %(expected_acc, actual_acc))
def trainAvgStructPerceptron(N_its,inst_generator,featfunc,tagset):
"""
:param N_its: number of iterations
:param inst_generator: A generator of (words,tags) tuples
:param tagger: A function from (words, weights) to tags
:param features: A function from (words, tags) to a dict of features and weights
"""
tr_acc = [None]*N_its
dv_acc = [None]*N_its
T = 0
weights = defaultdict(float)
wsum = defaultdict(float)
avg_weights = defaultdict(float)
for i in xrange(N_its):
# your code here
weights, wsum, tr_acc_i, num_instances = oneItAvgStructPerceptron(inst_generator, featfunc, weights, wsum, tagset, T)
# note that I call evalTagger to produce the dev set results
T += num_instances
for w in wsum:
avg_weights[w] = weights[w] - wsum[w] / float(T)
confusion = evalTagger(lambda words,tags : viterbiTagger(words,featfunc,avg_weights,tags)[0],'sp.txt')
dv_acc[i] = scorer.accuracy(confusion)
tr_acc[i] = tr_acc_i#1. - tr_err/float(sum([len(s) for s,t in inst_generator]))
print i,'dev:',dv_acc[i],'train:',tr_acc[i]
return avg_weights, tr_acc, dv_acc
def trainAvgPerceptron(N_its,inst_generator,featfunc,tagset):
"""
:param N_its: number of iterations
:param inst_generator: generate words,tags pairs
:param featfunc: feature function
:param tagset: set of all possible tags
:returns average weights, training accuracy, dev accuracy
"""
tr_acc = [None]*N_its
dv_acc = [None]*N_its
T = 0
weights = defaultdict(float)
wsum = defaultdict(float)
avg_weights = defaultdict(float)
for i in xrange(N_its):
# your code here
weights, wsum, tr_acc_i, num_instances = oneItAvgPerceptron(inst_generator, featfunc, weights, wsum, tagset, T)
T += num_instances
for w in wsum:
avg_weights[w] = weights[w] - wsum[w] / float(T)
confusion = evalTagger(lambda words, alltags: classifierTagger(words,featfunc,avg_weights,tagset),'perc')
dv_acc[i] = scorer.accuracy(confusion)
tr_acc[i] = tr_acc_i
print i,'dev:',dv_acc[i],'train:',tr_acc[i]
return avg_weights, tr_acc, dv_acc
def trainAvgStructPerceptron(N_its, inst_generator, featfunc, tagset):
"""
:param N_its: number of iterations
:param inst_generator: A generator of (words,tags) tuples
:param tagger: A function from (words, weights) to tags
:param features: A function from (words, tags) to a dict of features and weights
"""
tr_acc = [None] * N_its
dv_acc = [None] * N_its
T = 0
weights = defaultdict(float)
wsum = defaultdict(float)
avg_weights = defaultdict(float)
for i in xrange(N_its):
# your code here
weights, wsum, tr_acc_i, num_instances = oneItAvgStructPerceptron(
inst_generator, featfunc, weights, wsum, tagset, T)
# note that I call evalTagger to produce the dev set results
T += num_instances
for w in wsum:
avg_weights[w] = weights[w] - wsum[w] / float(T)
confusion = evalTagger(
lambda words, tags: viterbiTagger(words, featfunc, avg_weights,
tags)[0], 'sp.txt')
dv_acc[i] = scorer.accuracy(confusion)
tr_acc[
i] = tr_acc_i #1. - tr_err/float(sum([len(s) for s,t in inst_generator]))
print i, 'dev:', dv_acc[i], 'train:', tr_acc[i]
return avg_weights, tr_acc, dv_acc
def trainLRbySGD(N_its,inst_generator, outfile, devkey, learning_rate=1e-4, regularizer=1e-2):
weights = defaultdict(float)
dv_acc = [None]*N_its
tr_acc = [None]*N_its
# this block is all to take care of regularization
ratereg = learning_rate * regularizer
def regularize(base_feats,t):
for base_feat in base_feats:
for label in ALL_LABELS:
weights[(label,base_feat)] *= (1 - ratereg) ** (t-last_update[base_feat])
last_update[base_feat] = t
for it in xrange(N_its):
tr_err = 0
last_update = defaultdict(int) # reset, since we regularize at the end of every iteration
for i,(inst,true_label) in enumerate(inst_generator):
# apply "just-in-time" regularization to the weights for features in this instance
regularize(inst,i)
# compute likelihood gradient from this instance
probs = computeLabelProbs(inst,weights,ALL_LABELS)
if true_label != argmax(probs): tr_err += 1
# your code for updating the weights goes here
# regularize all features at the end of each iteration
regularize([base_feature for label,base_feature in weights.keys()],i)
dv_acc[it] = scorer.accuracy(evalClassifier(weights, outfile, devkey))
tr_acc[it] = 1. - tr_err/float(i)
print it,'dev:',dv_acc[it],'train:',tr_acc[it]
return weights,tr_acc,dv_acc
def test_custom_feat_avg_perceptron():
# w, tr_acc, dv_acc = avg_perceptron.trainAvgPerceptron(10,tr_all,features.yourFeatures,alltags)
# confusion = tagger_base.evalTagger(lambda words,alltags : tagger_base.classifierTagger(words,features.yourFeatures,w,alltags),'classifier')
confusion = scorer.getConfusion(constants.DEV_FILE, 'avg_perceptron_custom.response')
expected_acc = 0.810
actual_acc = scorer.accuracy(confusion)
ok_ (expected_acc < actual_acc, msg="NOT_IN_RANGE Expected:%f, Actual:%f" %(expected_acc, actual_acc))
def test_wlc_dev_exact_accuracy():
global weights_wlc
global WLC_FILE
mat = evalClassifier(weights_wlc, WLC_FILE, DEVKEY)
actual = scorer.accuracy(mat)
expected = 0.4467
assert_almost_equals(expected, actual, places=4, msg="UNEQUAL Expected:%f, Actual:%f" % (expected, actual))
def test_mcc_dev_accuracy ():
global weights_mcc
global MCC_FILE
mat = evalClassifier (weights_mcc, MCC_FILE, DEVKEY)
actual = scorer.accuracy(mat)
expected = 0.3756
assert_almost_equals (expected, actual, places=4, msg="UNEQUAL Expected:%f, Actual:%f" %(expected,actual))
def test_wlc_dev_exact_accuracy ():
global weights_wlc
global WLC_FILE
mat = evalClassifier (weights_wlc, WLC_FILE, DEVKEY)
actual = scorer.accuracy(mat)
expected = 0.4467
assert_almost_equals (expected, actual, places=4, msg="UNEQUAL Expected:%f, Actual:%f" %(expected,actual))
def test_get_most_common_tag():
expected = 0.63
weights = most_common.get_most_common_weights(TRAIN_FILE)
confusion = tagger_base.evalTagger(tagger_base.makeClassifierTagger(weights),'mcc')
actual = scorer.accuracy(confusion)
ok_(expected < actual, msg="NOT_IN_RANGE Expected:%f, Actual:%f" %(expected, actual))
def test_str_perceptron():
# w,tr_acc,dv_acc = str_perceptron.trainAvgStructPerceptron(10,tr_all,features.wordTransFeatures,alltags)
# confusion = tagger_base.evalTagger(lambda words,alltags : viterbi.viterbiTagger(words,features.wordTransFeatures,w,alltags)[0],'str_classifier')
confusion = scorer.getConfusion(DEV_FILE, 'str_avg_perceptron.response')
expected_acc = 0.749
actual_acc = scorer.accuracy(confusion)
ok_(expected_acc < actual_acc,
msg="NOT_IN_RANGE Expected:%f, Actual:%f" % (expected_acc, actual_acc))
def test_hmm_weights_accuracy():
confusion = tagger_base.evalTagger(
lambda words, alltags: viterbi.viterbiTagger(
words, viterbi.hmm_feats, hmm_weights, alltags)[0], 'hmm')
actual = scorer.accuracy(confusion)
expected = 0.74
ok_(expected < actual,
msg="NOT_IN_RANGE Expected:%f, Actual:%f" % (expected, actual))
def test_get_most_common_tag():
expected = 0.63
weights = most_common.get_most_common_weights(TRAIN_FILE)
confusion = tagger_base.evalTagger(
tagger_base.makeClassifierTagger(weights), 'mcc')
actual = scorer.accuracy(confusion)
ok_(expected < actual,
msg="NOT_IN_RANGE Expected:%f, Actual:%f" % (expected, actual))
def test_mcc_tagger_accuracy():
global tagger_mc, all_tags
expected = 0.811124
confusion = tagger_base.eval_tagger(tagger_mc,'most-common.preds',all_tags=all_tags)
actual = scorer.accuracy(confusion)
ok_(expected < actual, msg="NOT_IN_RANGE Expected:%f, Actual:%f" %(expected, actual))
def test_avg_perceptron():
# w, tr_acc, dv_acc = avg_perceptron.trainAvgPerceptron(10,tr_all,features.wordCharFeatures,alltags)
# confusion = tagger_base.evalTagger(lambda words,alltags : tagger_base.classifierTagger(words,features.wordCharFeatures,w,alltags),'classifier')
confusion = scorer.getConfusion(constants.DEV_FILE,
'avg_perceptron.response')
expected_acc = 0.740
actual_acc = scorer.accuracy(confusion)
ok_(expected_acc < actual_acc,
msg="NOT_IN_RANGE Expected:%f, Actual:%f" % (expected_acc, actual_acc))
def test_wlc_dev_exact_accuracy ():
global weights_wlc
global WLC_FILE
mat = evalClassifier (weights_wlc, WLC_FILE, DEVKEY)
actual = scorer.accuracy(mat)
expected_lower = 0.4440
expected_higher = 0.4470
ok = (actual >=expected_lower) and (actual <= expected_higher)
ok_(ok, msg="NOT IN RANGE Expected:%f,%f, Actual:%f" %(expected_lower,expected_higher,actual))
def test_classifier_tagger():
expected = 0.136844287788
noun_weights = most_common.get_noun_weights()
noun_tagger = tagger_base.makeClassifierTagger(noun_weights)
confusion = tagger_base.evalTagger(noun_tagger,'nouns')
actual = scorer.accuracy(confusion)
assert_almost_equals(expected, actual,places=3, msg="UNEQUAL Expected:%s, Actual:%s" %(expected, actual))
def test_nb2_tagger_is_good():
global theta_nb_fixed
tagger = tagger_base.make_classifier_tagger(theta_nb_fixed)
confusion = tagger_base.eval_tagger(tagger,'nb2')
dev_acc = scorer.accuracy(confusion)
expected_acc = 0.84
ok_(expected_acc < dev_acc, msg="NOT_IN_RANGE Expected:%f, Actual:%f" %(expected_acc, dev_acc))
def test_model_crf_nr_dev_accuracies():
confusion = scorer.get_confusion(DEV_FILE, '../bilstm-dev-en.preds')
acc = scorer.accuracy(confusion)
print("Acc: " + str(acc))
# ok_(acc > .86, "Accuracy Obt: " + str(acc))
confusion = scorer.get_confusion(DEV_FILE, '../bilstm_crf-dev-en.preds')
acc = scorer.accuracy(confusion)
print("Acc: " + str(acc))
# ok_(acc > .86, "Accuracy Obt: " + str(acc))
confusion = scorer.get_confusion(NR_DEV_FILE, '../bilstm_crf-dev-nr.preds')
acc = scorer.accuracy(confusion)
print("Acc: " + str(acc))
# ok_(acc > .86, "Accuracy Obt: " + str(acc))
confusion = scorer.get_confusion(NR_DEV_FILE, '../bilstm-dev-nr.preds')
acc = scorer.accuracy(confusion)
print("Acc: " + str(acc))
def eval_tagging_model(testfile,tagger_func,features,weights,all_tags,output_file=None):
tagger = lambda words, all_tags : tagger_func(words,
features,
weights,
all_tags)[0]
confusion = eval_tagger(tagger,
output_file,
testfile=testfile,
all_tags=all_tags)
return scorer.accuracy(confusion)
def test_nb2_tagger_is_good():
global theta_nb_fixed
tagger = tagger_base.make_classifier_tagger(theta_nb_fixed)
confusion = tagger_base.eval_tagger(tagger, 'nb2')
dev_acc = scorer.accuracy(confusion)
expected_acc = 0.84
ok_(expected_acc < dev_acc,
msg="NOT_IN_RANGE Expected:%f, Actual:%f" % (expected_acc, dev_acc))
def test_str_perceptron_small():
w, tr_acc, dv_acc = str_perceptron.trainAvgStructPerceptron(
5, tr_all[:50], features.wordTransFeatures, alltags)
confusion = tagger_base.evalTagger(
lambda words, alltags: viterbi.viterbiTagger(
words, features.wordTransFeatures, w, alltags)[0],
'str_classifier_small')
expected_acc = 0.506
actual_acc = scorer.accuracy(confusion)
ok_(expected_acc < actual_acc,
msg="NOT_IN_RANGE Expected:%f, Actual:%f" % (expected_acc, actual_acc))
def test_classifier_tagger():
global all_tags
expected = 0.1668919993637665
noun_weights = most_common.get_noun_weights()
noun_tagger = tagger_base.make_classifier_tagger(noun_weights)
confusion = tagger_base.eval_tagger(noun_tagger,'all_nouns.preds',all_tags=all_tags)
actual = scorer.accuracy(confusion)
assert_almost_equals(expected, actual,places=3, msg="UNEQUAL Expected:%s, Actual:%s" %(expected, actual))
def test_classifier():
global all_tags
expected = 0.1527613022274944
noun_weights = most_common.get_noun_weights()
noun_tagger = tagger_base.make_classifier_tagger(noun_weights)
confusion = tagger_base.eval_tagger(noun_tagger,'all_nouns.preds',all_tags=all_tags)
actual = scorer.accuracy(confusion)
assert_almost_equal(expected, actual,places=3, msg="UNEQUAL Expected:%s, Actual:%s" %(expected, actual))
def test_classifier_tagger():
expected = 0.136844287788
noun_weights = most_common.get_noun_weights()
noun_tagger = tagger_base.makeClassifierTagger(noun_weights)
confusion = tagger_base.evalTagger(noun_tagger, 'nouns')
actual = scorer.accuracy(confusion)
assert_almost_equals(expected,
actual,
places=3,
msg="UNEQUAL Expected:%s, Actual:%s" %
(expected, actual))
def test_basic_classifer():
test_weights = defaultdict(float)
test_tags = ['N','V','V','N']
for i in range(len(sent)):
for feat in features.wordFeatures(sent,test_tags[i],'X',i):
test_weights[feat] = 1
for feat in features.wordFeatures(sent,'X','X',i):
test_weights[feat] = 1
expected = test_tags
actual = tagger_base.classifierTagger(sent,features.wordFeatures,test_weights,alltags)
eq_ (expected, actual, msg="UNEQUAL Expected:%s, Actual:%s" %(expected, actual) )
expected_acc = 0.139539705577
confusion = tagger_base.evalTagger(lambda words,alltags : tagger_base.classifierTagger(words,features.wordFeatures,test_weights,alltags),'test')
actual_acc =scorer.accuracy(confusion)
assert_almost_equals(expected_acc ,actual_acc,places = 3)
def trainLRbyAdaGrad(N_its,inst_generator, outfile, devkey, learning_rate=1e-4, regularizer=1e-2):
weights = defaultdict(float)
dv_acc = [None]*N_its
tr_acc = [None]*N_its
running_value = defaultdict(float)
num_inst = len(inst_generator)
# this block is all to take care of regularization
ratereg = learning_rate * regularizer
def regularize(base_feats, t):
for base_feat in base_feats:
for label in ALL_LABELS:
weights[(label, base_feat)] *= (1 - ratereg) ** (t-last_update[base_feat])
last_update[base_feat] = t
for it in xrange(N_its):
tr_err = 0
last_update = defaultdict(int) # reset, since we regularize at the end of every iteration
for i, (inst, true_label) in enumerate(inst_generator):
# apply "just-in-time" regularization to the weights for features in this instance
regularize(inst, i)
# compute likelihood gradient from this instance
probs = computeLabelProbs(inst, weights, ALL_LABELS)
label_pred = argmax(probs)
if true_label != label_pred:tr_err += 1
for word, value in inst.items():
weights[(true_label, word)] += num_inst * learning_rate * value / running_value.get((true_label, word), 1)
for label in ALL_LABELS:
weights[(label, word)] -= num_inst * probs[label] * learning_rate * value / running_value.get((label, word), 1)
running_value[(true_label, word)] = value**2
# regularize all features at the end of each iteration
regularize([base_feature for label,base_feature in weights.keys()], i)
dv_acc[it] = scorer.accuracy(evalClassifier(weights, outfile, devkey))
tr_acc[it] = 1. - tr_err/float(i)
print it,'dev:',dv_acc[it],'train:',tr_acc[it]
return weights,tr_acc,dv_acc
def test_basic_classifer():
test_weights = defaultdict(float)
test_tags = ['N', 'V', 'V', 'N']
for i in range(len(sent)):
for feat in features.wordFeatures(sent, test_tags[i], 'X', i):
test_weights[feat] = 1
for feat in features.wordFeatures(sent, 'X', 'X', i):
test_weights[feat] = 1
expected = test_tags
actual = tagger_base.classifierTagger(sent, features.wordFeatures,
test_weights, alltags)
eq_(expected,
actual,
msg="UNEQUAL Expected:%s, Actual:%s" % (expected, actual))
expected_acc = 0.139539705577
confusion = tagger_base.evalTagger(
lambda words, alltags: tagger_base.classifierTagger(
words, features.wordFeatures, test_weights, alltags), 'test')
actual_acc = scorer.accuracy(confusion)
assert_almost_equals(expected_acc, actual_acc, places=3)
def test_bakeoff_acc_d3_4_ja_beat_the_prof():
acc = scorer.accuracy(
scorer.get_confusion(JA_TEST_FILE, 'sp-best-te.ja.preds'))
assert_greater(acc, .879926)
def test_bakeoff_acc_d3_4_en_half_credit():
acc = scorer.accuracy(scorer.get_confusion(DEV_FILE,'sp-best.preds'))
assert_greater(acc,.885)
def test_bakeoff_acc_d3_4_en_beat_the_prof():
acc = scorer.accuracy(scorer.get_confusion(TEST_FILE, 'sp-best-te.preds'))
assert_greater(acc,
.88735) # same as with the classification-based tagger!
def test_bakeoff_acc_d3_4_ja_full_credit():
acc = scorer.accuracy(scorer.get_confusion(JA_DEV_FILE,
'sp-best.ja.preds'))
assert_greater(acc, .91)
def test_nr_hmm_dev_accuracy():
confusion = scorer.get_confusion(NR_DEV_FILE,'hmm-dev-nr.preds')
acc = scorer.accuracy(confusion)
ok_(acc > .861)
def test_bakeoff_acc_d3_4_en_half_credit():
acc = scorer.accuracy(scorer.get_confusion(DEV_FILE, 'sp-best.preds'))
assert_greater(acc, .885)
def test_bakeoff_acc_d2_6_ja_beat_the_prof():
acc = scorer.accuracy(
scorer.get_confusion(JA_TEST_FILE, 'avp-words-best-te.ja.preds'))
assert_greater(acc, .87882)
def test_bakeoff_acc_d3_4_ja_beat_the_prof():
acc = scorer.accuracy(scorer.get_confusion(JA_TEST_FILE,'sp-best-te.ja.preds'))
assert_greater(acc,.879926)
def test_bakeoff_acc_d3_4_en_beat_the_prof():
acc = scorer.accuracy(scorer.get_confusion(TEST_FILE,'sp-best-te.preds'))
assert_greater(acc,.88735) # same as with the classification-based tagger!
def test_bilstm_test_accuracy():
confusion = scorer.get_confusion(DEV_FILE,'bilstm-te-en.preds')
acc = scorer.accuracy(confusion)
ok_(acc > .83) #change the no's
def test_neighbor_acc_d2_5_ja():
confusion = scorer.get_confusion(JA_DEV_FILE,
'avp-words-neighbor.ja.preds')
acc = scorer.accuracy(confusion)
assert_greater(acc, .792) # should be .802
def test_hmm_dev_accuracy():
confusion = scorer.get_confusion(DEV_FILE,'hmm-dev-en.preds')
acc = scorer.accuracy(confusion)
ok_(acc > .840)
def test_nr_hmm_test_accuracy():
confusion = scorer.get_confusion(NR_TEST_FILE,'hmm-te-nr.preds')
acc = scorer.accuracy(confusion)
ok_(acc > .853)
def test_hmm_feat_acc_d3_3_ja():
confusion = scorer.get_confusion(JA_DEV_FILE,'sp-hmm.ja.preds')
acc = scorer.accuracy(confusion)
assert_greater(acc,.797) # should be .807
def test_hmm_feat_acc_d3_3_en():
confusion = scorer.get_confusion(DEV_FILE, 'sp-hmm.preds')
acc = scorer.accuracy(confusion)
assert_greater(acc, .862) # should be .872
def test_bakeoff_acc_d3_4_ja_full_credit():
acc = scorer.accuracy(scorer.get_confusion(JA_DEV_FILE,'sp-best.ja.preds'))
assert_greater(acc,.91)
def test_hmm_feat_acc_d3_3_ja():
confusion = scorer.get_confusion(JA_DEV_FILE, 'sp-hmm.ja.preds')
acc = scorer.accuracy(confusion)
assert_greater(acc, .797) # should be .807
def test_bakeoff_acc_d2_6_en_half_credit():
acc = scorer.accuracy(
scorer.get_confusion(DEV_FILE, 'avp-words-best.preds'))
assert_greater(acc, .87)
def test_hmm_test_accuracy():
confusion = scorer.get_confusion(TEST_FILE,'hmm-te-en.preds')
acc = scorer.accuracy(confusion)
ok_(acc > .840)
def test_bakeoff_acc_d2_6_ja_full_credit():
acc = scorer.accuracy(
scorer.get_confusion(JA_DEV_FILE, 'avp-words-best.ja.preds'))
assert_greater(acc, .90)
def test_hmm_weights_accuracy():
confusion = tagger_base.evalTagger(lambda words, alltags : viterbi.viterbiTagger(words,viterbi.hmm_feats,hmm_weights,alltags)[0],'hmm')
actual = scorer.accuracy(confusion)
expected = 0.74
ok_ (expected < actual, msg="NOT_IN_RANGE Expected:%f, Actual:%f" %(expected, actual))
