def copied_code_from_translate_Akkadian():
train_texts, dev_texts, sign_to_id, tran_to_id, id_to_sign, id_to_tran = preprocess(
)
# Run the HMM.
hmm_train(train_texts, dev_texts)
# lambda1, lambda2 = hmm_train(train_texts, dev_texts)
(lambda1, lambda2, _, _, _,
_) = load_object_from_file(Path("../output/hmm_model.pkl"))
memm_path = Path("../output/memm_model.pkl")
memm_from_file = load_object_from_file(memm_path)
(logreg, vec, idx_to_tag_dict) = memm_from_file
extra_decoding_arguments = build_extra_decoding_arguments(train_texts)
#dump_object_to_file(predictor, "predictor")
pred_path = Path("../output/predictor_lr_03_test_96_8.pkl")
predictor_from_file = load_object_from_file(pred_path)
#dump_object_to_file(model, "model")
model_path = Path("../output/model_lr_03_test_96_8.pkl")
model_from_file = load_object_from_file(model_path)
return lambda1, lambda2, logreg, vec, idx_to_tag_dict, extra_decoding_arguments, sign_to_id, id_to_tran, \
predictor_from_file, model_from_file
def memm_train_and_store(corpora):
"""
Trains MEMM model and stores all the data needed for using MEMM
:return: nothing, stores everything in memm_model.pkl
"""
train_texts, dev_texts, test_texts, _, _, _, _ = preprocess(corpora)
logreg, vec, idx_to_tag_dict = memm_train(train_texts, dev_texts)
dump_object_to_file((logreg, vec, idx_to_tag_dict, test_texts), memm_path)
def hmm_train_and_store(corpora):
"""
Trains HMM model and stores all the data needed for using HMM
:return: nothing, stores everything in hmm_model.pkl
"""
train_texts, dev_texts, test_texts, _, _, _, _ = preprocess(corpora)
most_common_tag, possible_tags, q, e, S, total_tokens, q_bi_counts, q_uni_counts, lambda1, lambda2 = \
hmm_train(train_texts, dev_texts)
dump_object_to_file((most_common_tag, possible_tags, q, e, S, total_tokens, q_bi_counts, q_uni_counts, lambda1,
lambda2, test_texts), hmm_path)
def biLSTM_train_and_store(corpora):
"""
Trains biLSTM model and stores all the data needed for using biLSTM
:return: nothing, stores everything in bilstm_model.pkl
"""
train_texts, dev_texts, test_texts, sign_to_id, _, _, id_to_tran = preprocess(corpora)
model, vocab, train_dataset, validation_dataset, cuda_device, reader = prepare1()
trainer, model, reader, vocab = prepare2(model, vocab, train_dataset, validation_dataset, cuda_device, reader)
trainer.train()
predictor = SentenceTaggerPredictor(model, dataset_reader=reader)
dump_object_to_file((model, predictor, sign_to_id, id_to_tran, test_texts), bilstm_path)
def main():
"""
Tests the run of HMM
:return: nothing
"""
train_texts, dev_texts, test_texts, sign_to_id, tran_to_id, id_to_sign, id_to_tran = \
preprocess(['rinap/rinap1', 'rinap/rinap3', 'rinap/rinap4', 'rinap/rinap5'])
most_common_tag, possible_tags, q, e, S, total_tokens, q_bi_counts, q_uni_counts, lambda1, lambda2 = \
hmm_train(train_texts, dev_texts)
print("Done training, now computing accuracy!")
print(compute_accuracy(train_texts, hmm_viterbi, total_tokens, q_bi_counts, q_uni_counts, q, e, S, most_common_tag,
possible_tags, lambda1, lambda2))
print(compute_accuracy(dev_texts, hmm_viterbi, total_tokens, q_bi_counts, q_uni_counts, q, e, S, most_common_tag,
possible_tags, lambda1, lambda2))
print(compute_accuracy(test_texts, hmm_viterbi, total_tokens, q_bi_counts, q_uni_counts, q, e, S, most_common_tag,
possible_tags, lambda1, lambda2))
def main():
"""
Check the biLSTM model
:return: nothing
"""
train_texts, dev_texts, test_texts, sign_to_id, tran_to_id, id_to_sign, id_to_tran = preprocess(['rinap/rinap1', 'rinap/rinap3', 'rinap/rinap4', 'rinap/rinap5'])
model, vocab, train_dataset, validation_dataset, cuda_device, reader = prepare1()
trainer, model, reader, vocab = prepare2(model, vocab, train_dataset, validation_dataset, cuda_device, reader)
train(trainer, model, reader)
check_results(train_texts, dev_texts, test_texts, sign_to_id, id_to_tran)
def main():
"""
Test the run of MEMM and HMM
:return: nothing
"""
full_flow_start = time.time()
train_sents, dev_sents, _, _, _, _ = preprocess(
['rinap/rinap1', 'rinap/rinap3', 'rinap/rinap4', 'rinap/rinap5'])
print(len(train_sents))
print(len(dev_sents))
vocab = compute_vocab_count(train_sents)
extra_decoding_arguments = build_extra_decoding_arguments(train_sents)
total_tokens, q_tri_counts, q_bi_counts, q_uni_counts, e_word_tag_counts, e_tag_counts = hmm_preprocess(
train_sents)
print("HMM trained")
tag_to_idx_dict = build_tag_to_idx_dict(train_sents)
index_to_tag_dict = invert_dict(tag_to_idx_dict)
vec = DictVectorizer()
print("Create train examples")
train_examples, train_labels = create_examples(train_sents,
tag_to_idx_dict)
num_train_examples = len(train_examples)
print("#example: " + str(num_train_examples))
print("Done")
print("Create dev examples")
dev_examples, dev_labels = create_examples(dev_sents, tag_to_idx_dict)
num_dev_examples = len(dev_examples)
print("#example: " + str(num_dev_examples))
print("Done")
all_examples = train_examples
all_examples.extend(dev_examples)
print("Vectorize examples")
all_examples_vectorized = vec.fit_transform(all_examples)
train_examples_vectorized = all_examples_vectorized[:num_train_examples]
dev_examples_vectorized = all_examples_vectorized[num_train_examples:]
print("Done")
logreg = linear_model.LogisticRegression(multi_class='multinomial',
max_iter=1,
solver='lbfgs',
C=100000,
verbose=1,
n_jobs=2)
print("Fitting...")
start = time.time()
logreg.fit(train_examples_vectorized, train_labels)
end = time.time()
print("End training, elapsed " + str(end - start) + " seconds")
# End of log linear model training
start = time.time()
print("Start evaluation on dev set")
acc_viterbi, acc_greedy, acc_hmm = memm_hmm_eval(
dev_sents, logreg, vec, index_to_tag_dict, extra_decoding_arguments,
total_tokens, q_tri_counts, q_bi_counts, q_uni_counts,
e_word_tag_counts, e_tag_counts)
end = time.time()
print("Dev: Accuracy hmm : " + acc_hmm)
print("Dev: Accuracy greedy memm : " + acc_greedy)
print("Dev: Accuracy Viterbi memm : " + acc_viterbi)
print("Evaluation on dev set elapsed: " + str(end - start) + " seconds")
