def main():
assert args.working_directory is not None
try:
os.mkdir(args.working_directory)
except:
pass
# 学習に用いるテキストデータを準備
corpus_l = build_corpus(args.train_filename_l,
args.train_directory_l,
supervised=True)
corpus_u = build_corpus(args.train_filename_u,
args.train_directory_u,
supervised=False)
# 辞書
dictionary = nlp.dictionary()
# 訓練データ・検証データに分けてデータセットを作成
# 同時に辞書が更新される
dataset_l = nlp.dataset(corpus_l, dictionary, args.train_dev_split,
args.seed) # 教師あり
dataset_u = nlp.dataset(corpus_u, dictionary, args.train_dev_split,
args.seed) # 教師なし
# 辞書を保存
dictionary.save(os.path.join(args.working_directory, "char.dict"))
# 確認
size_train_l = dataset_l.get_size_train()
size_dev_l = dataset_l.get_size_dev()
size_train_u = dataset_u.get_size_train()
size_dev_u = dataset_u.get_size_dev()
table = [
["Labeled", size_train_l, size_dev_l, size_train_l + size_dev_l],
["Unlabeled", size_train_u, size_dev_u, size_train_u + size_dev_u],
[
"Total", size_train_u + size_train_l, size_dev_u + size_dev_l,
size_train_u + size_train_l + size_dev_u + size_dev_l
],
]
print(tabulate(table, headers=["Data", "Train", "Dev", "Total"]))
num_character_ids = dictionary.get_num_characters()
# モデル
crf = nlp.crf(
dataset_labeled=dataset_l,
num_character_ids=num_character_ids,
feature_x_unigram_start=args.crf_feature_x_unigram_start,
feature_x_unigram_end=args.crf_feature_x_unigram_end,
feature_x_bigram_start=args.crf_feature_x_bigram_start,
feature_x_bigram_end=args.crf_feature_x_bigram_end,
feature_x_identical_1_start=args.crf_feature_x_identical_1_start,
feature_x_identical_1_end=args.crf_feature_x_identical_1_end,
feature_x_identical_2_start=args.crf_feature_x_identical_2_start,
feature_x_identical_2_end=args.crf_feature_x_identical_2_end,
initial_lambda_0=args.crf_lambda_0,
sigma=args.crf_prior_sigma)
npylm = nlp.npylm(max_word_length=args.max_word_length,
g0=1.0 / num_character_ids,
initial_lambda_a=args.lambda_a,
initial_lambda_b=args.lambda_b,
vpylm_beta_stop=args.vpylm_beta_stop,
vpylm_beta_pass=args.vpylm_beta_pass)
npycrf = nlp.npycrf(npylm=npylm, crf=crf)
num_features = crf.get_num_features()
print(
tabulate([["#characters", num_character_ids],
["#features", num_features]]))
# 学習の準備
trainer = nlp.trainer(dataset_labeled=dataset_l,
dataset_unlabeled=dataset_u,
dictionary=dictionary,
npycrf=npycrf,
crf_regularization_constant=1.0)
# 文字列の単語IDが衝突しているかどうかをチェック
# 時間の無駄なので一度したらしなくてよい
# メモリを大量に消費します
if False:
print("ハッシュの衝突を確認中 ...")
num_checked_words = trainer.detect_hash_collision(args.max_word_length)
print("衝突はありません (総単語数 {})".format(num_checked_words))
learning_rate = args.crf_learning_rate
batchsize = 32
start = time.time()
# 初期化
trainer.add_labeled_data_to_npylm() # 教師データをNPYLMに追加
trainer.sgd(learning_rate, batchsize, pure_crf=True) # NPYLMを除いてCRF単体を最適化
print("Iteration {} / {} - {:.3f} sec".format(0, args.epochs,
time.time() - start))
for epoch in range(1, args.epochs + 1):
start = time.time()
# 学習
## NPYLMのパラメータをギブスサンプリング
## 教師データも含めた方がいい気がする
trainer.gibbs(include_labeled_data=True)
## CRFを最適化
trainer.sgd(learning_rate, batchsize)
# 各種サンプリング
## HPYLMとVPYLMのハイパーパラメータの更新
trainer.sample_hpylm_vpylm_hyperparameters()
## 単語長のポアソン分布のパラメータλの更新
trainer.sample_npylm_lambda()
# この推定は数イテレーション後にやるほうが精度が良い
if epoch > 3:
# VPYLMから長さkの単語が生成される確率P(k|VPYLM)を推定
trainer.update_p_k_given_vpylm()
# ログ
print("Iteration {} / {} - {:.3f} sec".format(epoch, args.epochs,
time.time() - start))
# log_likelihood_l = trainer.compute_log_likelihood_labeled_dev()
# log_likelihood_u = trainer.compute_log_likelihood_unlabeled_dev()
# table = [
# ["Labeled", log_likelihood_l],
# ["Unlabeled", log_likelihood_u]
# ]
# print(tabulate(table, headers=["Log-likelihood", "Dev"]))
trainer.print_segmentation_labeled_dev(10) # ランダムに分割を表示
trainer.print_segmentation_unlabeled_dev(10) # ランダムに分割を表示
precision, recall = trainer.compute_precision_and_recall_labeled_dev()
f_measure = 2 * precision * recall / (precision + recall)
print(
tabulate([["Labeled", precision, recall, f_measure]],
headers=["Precision", "Recall", "F-measure"]))
# モデルの保存
npylm.save(os.path.join(args.working_directory, "npylm.model"))
crf.save(os.path.join(args.working_directory, "crf.model"))
# trainer.print_p_k_vpylm()
print("lambda_0:", crf.get_lambda_0())
def main():
assert args.working_directory is not None
try:
os.mkdir(args.working_directory)
except:
pass
# 辞書
dictionary = nlp.dictionary(
os.path.join(args.working_directory, "char.dict"))
# モデル
crf = nlp.crf(os.path.join(args.working_directory, "crf.model"))
npylm = nlp.npylm(os.path.join(args.working_directory, "npylm.model"))
npycrf = nlp.npycrf(npylm=npylm, crf=crf)
num_features = crf.get_num_features()
num_character_ids = dictionary.get_num_characters()
print(
tabulate([["#characters", num_character_ids],
["#features", num_features]]))
# ビタビアルゴリズムによる最尤分解を求める
assert args.test_filename is not None or args.test_directory is not None
sentence_list = []
def preprocess(sentence):
sentence = re.sub(r"[0-9.,]+", "#", sentence)
sentence = sentence.strip()
return sentence
if args.test_filename is not None:
with codecs.open(args.test_filename, "r", "utf-8") as f:
for sentence_str in f:
sentence_str = preprocess(sentence_str)
sentence_list.append(sentence_str)
if args.test_directory is not None:
for filename in os.listdir(args.test_directory):
with codecs.open(os.path.join(args.test_directory, filename), "r",
"utf-8") as f:
for sentence_str in f:
sentence_str = preprocess(sentence_str)
sentence_list.append(sentence_str)
# 教師データはMeCabによる分割
tagger = MeCab.Tagger() if args.neologd_path is None else MeCab.Tagger(
"-d " + args.neologd_path)
tagger.parse("") # バグ回避のため空データを分割
for sentence_str in sentence_list:
sentence_str = sentence_str.strip()
m = tagger.parseToNode(sentence_str) # 形態素解析
words_true = []
while m:
word = m.surface
if len(word) > 0:
words_true.append(word)
m = m.next
if len(words_true) > 0:
words_npycrf = npycrf.parse(sentence_str, dictionary)
words_npylm = npylm.parse(sentence_str, dictionary)
print("MeCab+NEologd")
print(" / ".join(words_true))
print("NPYCRF")
print(" / ".join(words_npycrf))
print("NPYLM")
print(" / ".join(words_npylm))
print()
