def test():
data = LoadTestData()
untrained_models = []
config = {'ngram': 3, 'est': 'add-delta', 'delta': 0.3}
untrained_models.append((HMM(config), 'HMM. config: {}'.format(config)))
config = {
'ftrs': ('IS_FIRST', 'IS_LAST', 'VAL', 'PRV_VAL', 'NXT_VAL',
'FRST_VAL', 'LST_VAL', 'SCND_VAL', 'SCND_LST_VAL')
}
untrained_models.append((MEMM(config), 'MEMM. config: {}'.format(config)))
config = {
'ftrs': ('IS_FIRST', 'IS_LAST', 'IDX', 'VAL', 'PRV_VAL', 'NXT_VAL',
'FRST_VAL', 'LST_VAL', 'SCND_VAL', 'SCND_LST_VAL')
}
untrained_models.append(
(CRF_WORD(config), 'CRF. config: {}'.format(config)))
trained_models = [(model.prep_data().shuffle(0xfab1e).split(0).train(),
name) for model, name in untrained_models]
config = {
'n_layers': 3,
'hidden_dim': 32,
'embedding': 'mds',
'win_len': 4,
"device": "cpu"
}
rnn = RNN(config)
trained_models.append((rnn.prep_model().load('rnn_model.bin'),
'RNN. config: {}'.format(config)))
for model, name in trained_models:
trained_model = model
conf_mat, dist = TestModel(trained_model, data)
print('\n')
print(name)
print('=' * 80)
print('Vowel metrics:')
print('-' * 50)
PrintConfMat(conf_mat)
print('-' * 50)
print('Edit distance:')
print('-' * 50)
for stage in range(1, 4):
print('Stage = {}:'.format(stage_names[stage]))
print(' Average = {}\n Median = {}\n Min = {}\n Max = {}'.
format(dist[stage][0], dist[stage][1], dist[stage][2],
dist[stage][3]))
all_tag = []
# for t in tag1:
# if t not in punctuations:
# all_tag.append(t)
all_tag = list(set(tag))
param = [0 for i in range(24)]
# print 'Profiling started'
# prof = cProfile.Profile()
# prof.enable()
memm = MEMM(
data,
tag,
all_tag,
param,
wsj.tagged_sents()[:no_of_sentences],
[f1, f2, f3, f4, f5, f6, f7, f8, f9, f10, f11, f12, f13, f14],
0,
)
memm.train()
print "For", memm.num_calls_cost, "calls to cost total time taken is", memm.tot_time_cost
print "Per call avg time taken is", memm.tot_time_cost / memm.num_calls_cost
print "For", memm.num_calls_gradient, "calls to gradient total time taken is", memm.tot_time_gradient
print "Per call avg time taken is", memm.tot_time_gradient / memm.num_calls_gradient
# dt1 = datetime.datetime.now()
# print 'before training: ', dt1
# memm.cost(param)
# dt2 = datetime.datetime.now()
print "testing set feats:", test_feat.shape print "moving to a sparse representation..." train_feat = sparsify(train_feat) test_feat = sparsify(test_feat) train_labl = [s.labels for s in train_sequences] train_labl = [item for sublist in train_labl for item in sublist] obsr_labl = [s.labels for s in test_sequences] test_seq = [" ".join(x) for x in obsr_labl] test_tok = [item for sublist in obsr_labl for item in sublist] obsr_list = get_observations(test_feat, test_sequences) memm = MEMM(10, 0.0001) memm.fit(train_feat, train_labl, fe.num_feats) pred_tok, pred_seq = memm.predict_sequences(obsr_list) # Structured Perceptron using viterbi in the inference step """percep = StructuredPerceptron(10, fe, 0.1) percep.fit(train_sequences) pred_tok, pred_seq = percep.predict_sequences(test_sequences) obsr_labl = [s.labels for s in test_sequences] test_seq = [" ".join(x) for x in obsr_labl] test_tok = [item for sublist in obsr_labl for item in sublist]"""
def f9(x, y):
return round(random.random())
def f10(x, y):
return round(random.random())
if __name__ == '__main__':
data, tag = create_dataset()
tag1 = list(set(tag))
punctuations = ['.', ',', ':', ';', '\"', '\'', '``', '\'\'']
all_tag = []
for t in tag1:
if t not in punctuations:
all_tag.append(t)
param = [0 for i in range(10)]
print 'Profiling started'
# prof = cProfile.Profile()
# prof.enable()
memm = MEMM(data, tag, all_tag, param,
[f1, f2, f3, f4, f5, f6, f7, f8, f9, f10], 0)
memm.train()
# prof.disable()
# s = StringIO.StringIO()
# sortby = 'cumulative'
# ps = pstats.Stats(prof, stream=s).sort_stats(sortby)
# ps.print_stats()
# print s.getvalue()
print "testing set feats:", test_feat.shape print "moving to a sparse representation..." train_feat = sparsify(train_feat) test_feat = sparsify(test_feat) train_labl = [s.labels for s in train_sequences] train_labl = [item for sublist in train_labl for item in sublist] obsr_labl = [s.labels for s in test_sequences] test_seq = [" ".join(x) for x in obsr_labl] test_tok = [item for sublist in obsr_labl for item in sublist] obsr_list = get_observations(test_feat, test_sequences) memm = MEMM(10, 0.0001) memm.fit(train_feat, train_labl, fe.num_feats) pred_tok, pred_seq = memm.predict_sequences(obsr_list) # Structured Perceptron using viterbi in the inference step '''percep = StructuredPerceptron(10, fe, 0.1) percep.fit(train_sequences) pred_tok, pred_seq = percep.predict_sequences(test_sequences) obsr_labl = [s.labels for s in test_sequences] test_seq = [" ".join(x) for x in obsr_labl] test_tok = [item for sublist in obsr_labl for item in sublist]''' # print results print metrics.classification_report(test_tok, pred_tok) print metrics.accuracy_score(test_tok, pred_tok)