def test_srl_neural_model(train_file_path, valid_file_path):
train_corpora = Conll05Corpora()
train_corpora.load(train_file_path)
train_problem = SRLProblem(train_corpora)
valid_corpora = Conll05Corpora()
valid_corpora.load(valid_file_path)
valid_problem = SRLProblem(valid_corpora)
init_rng()
nn_architecture = SRLNetowrkArchitecture()
nn_architecture.word_feature_dim = 50
nn_architecture.pos_feature_dim = 50
nn_architecture.dist_feature_dim = 50
nn_architecture.conv_window_height = 3
nn_architecture.conv_output_dim = 500
nn_architecture.hidden_layer_output_dims = [500,500]
hyper_param = NeuralModelHyperParameter()
hyper_param.n_epochs = 1000
hyper_param.learning_rate = 0.001 #1
hyper_param.learning_rate_decay_ratio = 0.8
hyper_param.learning_rate_lowerbound = 0.0000
hyper_param.l1_reg = 0
hyper_param.l2_reg = 0
problem_character = train_problem.get_problem_property()
m = SRLNetwork(problem_character, nn_architecture)
optimizer = CGDOptimizer()
trained_batch_num = 0
valid_freq = 10
for iter in range(1000):
for sentence in train_problem.sentences():
for X, y in train_problem.get_dataset_for_sentence(sentence):
if trained_batch_num % valid_freq == 0:
evaluate(m, valid_problem, trained_batch_num/valid_freq)
optimizer.batch_size = X.shape[0]
optimizer.update_chunk(X, y)
param = optimizer.optimize(m, m.get_parameter())
m.set_parameter(param)
trained_batch_num += 1
def test_srl_label_formatter(data_file_path):
conll05corpora = Conll05Corpora()
conll05corpora.load(data_file_path)
print 'load done'
test_label_file_path = "test_label.txt"
pred_label_file_path = "pred_label.txt"
srl_problem = SRLProblem(conll05corpora)
for valid_sentence in srl_problem.sentences():
test_labels = []
pred_labels = []
test_label_file = open(test_label_file_path, "w")
pred_label_file = open(pred_label_file_path, "w")
for srl_x, srl_y in srl_problem.get_dataset_for_sentence(valid_sentence):
pred_y = [random.choice(SrlTypes.LABEL_SRLTYPE_MAP.keys()) for i in range(srl_y.size)]
test_labels.append(srl_y)
pred_labels.append(pred_y)
test_label_str = srl_problem.pretty_srl_test_label(valid_sentence, test_labels)
pred_label_str = srl_problem.pretty_srl_predict_label(valid_sentence, pred_labels)
test_label_file.write(test_label_str)
pred_label_file.write(pred_label_str)
test_label_file.close()
pred_label_file.close()
try:
valid_result = eval_srl(test_label_file_path, pred_label_file_path)
except:
print "label = "
print "\n".join([" ".join([SrlTypes.LABEL_SRLTYPE_MAP[x] for x in label]) for label in pred_labels])
print "formatted_label = "
print pred_label_str
def test_srl_problem(data_file_path):
conll05corpora = Conll05Corpora()
conll05corpora.load(data_file_path)
print 'load done'
srl_problem = SRLProblem(conll05corpora)
for sentence in srl_problem.sentences():
for X, y in srl_problem.get_dataset_for_sentence(sentence):
word_id = [word.id for word in sentence.words()]
print "word_id = ", word_id
print "X = ", X[0]
break
for srl in sentence.srl_structs():
print "verb = ", srl.verb.id
break