def main():
print("Loading...")
x, y = load_dataset("data/amazon_reviews_multilingual_JP_v1_00.tsv",
n=5000)
x = [clean_html(text, strip=True) for text in x]
x_train, x_test, y_train, y_test = train_test_split(x,
y,
test_size=0.2,
random_state=42)
print("Vectorizing")
vectorizer = CountVectorizer(tokenizer=tokenize)
x_train = vectorizer.fit_transform(x_train)
x_test = vectorizer.transform(x_test)
print(x_train.shape)
print(x_test.shape)
print("Selecting features...")
selector = SelectKBest(k=7000, score_func=mutual_info_classif)
selector.fit(x_train, y_train)
x_train_new = selector.transform(x_train)
x_test_new = selector.transform(x_test)
print(x_train_new.shape)
print(x_test_new.shape)
print("Evaluating...")
clf = LogisticRegression(solver="liblinear")
clf.fit(x_train_new, y_train)
y_pred = clf.predict(x_test_new)
score = accuracy_score(y_test, y_pred)
print("{:4f}".format(score))
def main():
logger = logging.getLogger("__name__")
x, y = load_dataset("data/amazon_reviews_multilingual_JP_v1_00.tsv", n=5000)
x = [clean_html(text, strip=True) for text in x]
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=44)
vectorizer = TfidfVectorizer(tokenizer=tokenize)
x_train_vec = vectorizer.fit_transform(x_train)
x_test_vec = vectorizer.transform(x_test)
@stop_watch
def k_fold_cv(clf, x_train, y_train, k_cv=5) -> None:
logger = logging.getLogger("kfoldcvLigging")
scores = cross_val_score(clf, x_train, y_train, cv=k_cv)
logger.debug(f"CV scores (k={k_cv}): {scores}")
logger.debug("Accuracy: {:.4f} (+/- {:.4f})".format(scores.mean(), scores.std()*2))
return None
clf = LogisticRegression(solver="liblinear")
for k in [3,4,5]:
k_fold_cv(clf=clf, x_train=x_train_vec, y_train=y_train, k_cv=k)
clf.fit(x_train_vec, y_train)
y_pred = clf.predict(x_test_vec)
score = accuracy_score(y_pred, y_test)
logger.debug("Accuracy score (test): {:.4f}".format(score))
def main():
x, y = load_dataset('data/amazon_reviews_multilingual_JP_v1_00.tsv',
n=5000)
print('Tokenization')
x = [clean_html(text, strip=True) for text in x]
x = [' '.join(tokenize(text)) for text in x]
x_train, x_test, y_train.y_test = train_test_split(x,
y,
test_size=0.2,
random_state=42)
print('Binary')
vectorizer = CountVectorizer(binary=True)
train_and_eval(x_train, y_train, x_test, y_test, vectorizer)
print('COunt')
vectorizer = CountVectorizer(binary=False)
train_and_eval(x_train, y_train, x_test, y_test, vectorizer)
print('TF-IDF')
vectorizer = TfidfVectorizer()
train_and_eval(x_train, y_train, x_test, y_test, vectorizer)
print('Bigram')
vectorizer = TfidfVectorizer(ngram_range=(1, 2))
train_and_eval(x_train, y_train, x_test, y_test, vectorizer)
def main():
logger = logging.getLogger('__name__')
import MeCab
path_neologd = '/usr/lib/x86_64-linux-gnu/mecab/dic/mecab-ipadic-neologd'
t_mecab = MeCab.Tagger("-Owakati && -d {}".format(path_neologd))
def tokenize_by_mecab(text):
return list(t_mecab.parse(text).strip().split(" "))
use_tokens = [tokenize, tokenize_by_mecab]
t_names = ["janome", "MeCab"]
logger.debug("Loading dataset...")
x, y = load_dataset("data/amazon_reviews_multilingual_JP_v1_00.tsv",
n=5000)
x = [clean_html(text, strip=True) for text in x]
for use_token, t_name in zip(use_tokens, t_names):
logger.debug("●○ {} ○●".format(t_name))
x_train, x_test, y_train, y_test = train_test_split(x,
y,
test_size=0.2,
random_state=44)
logger.debug("Count vectorizing...")
vectorizer = CountVectorizer(tokenizer=use_token)
x_train = vectorizer.fit_transform(x_train)
x_test = vectorizer.transform(x_test)
logger.debug(f"x_train's shape is {x_train.shape}")
logger.debug(f" x_test's shape is {x_test.shape}")
logger.debug("Selecting features...")
selector = SelectKBest(k=7000, score_func=mutual_info_classif)
# k; number of used features, mutal_info_classif; 相互情報量
selector.fit(x_train, y_train)
x_train_new = selector.transform(x_train)
x_test_new = selector.transform(x_test)
logger.debug(f"x_train_new's shape is {x_train_new.shape}")
logger.debug(f" x_test_new's shape is {x_test_new.shape}")
logger.debug("Evaluating...")
clf = LogisticRegression(solver="liblinear")
clf.fit(x_train_new, y_train)
y_pred = clf.predict(x_test_new)
score = accuracy_score(y_test, y_pred)
logger.debug("{:.4f}".format(score))
def main():
x, y = load_dataset("data/amazon_reviews_multilingual_JP_v1_00.tsv",
n=5000)
x = [clean_html(text, strip=True) for text in x]
x_train, x_test, y_train, y_test = train_test_split(x,
y,
test_size=0.2,
random_state=42)
vectorizer = TfidfVectorizer(tokenizer=tokenize)
x_train_vec = vectorizer.fit_transform(x_train)
x_test_vec = vectorizer.transform(x_test)
title = "Learning Curves"
cv = ShuffleSplit(n_splits=5, test_size=0.2, random_state=0)
clf = LogisticRegression(solver="liblinear")
plot_learning_curve(clf, title, x_train_vec, y_train, cv=cv)
def main():
x, y = load_dataset("data/amazon_reviews_multilingual_JP_v1_00.tsv", n=5000)
print("Tokenization")
x = [clean_html(text, strip=True) for text in x]
x_train, x_test, y_train, y_test = train_test_split(x, y,
test_size=0.2,
random_state=42)
vectorizer = TfidfVectorizer(tokenizer=tokenize)
x_train_vec = vectorizer.fit_transform(x_train)
x_test_vec = vectorizer.transform(x_test)
clf = LogisticRegression(solver="liblinear")
clf.fit(x_train_vec, y_train)
y_pred = clf.predict(x_test_vec)
score = accuracy_score(y_test, y_pred)
print("{:.4f}".format(score))
def main():
x, y = load_dataset('data/amazon_reviews_multilingual_JP_v1_00.tsv', n=5000)
x = [clean_html(text, strip=True) for text in x]
x_train, x_test, y_train. y_test = train_test_split(x, y,
test_size=0.2,
random_state=42)
vectorizer = TfidfVectorizer(tokenizer=tokenize)
x_train_vec = vectorizer.fit_transform(x_train)
x_test_vec = vectorizer.transform(x_test)
clf = LogisticRegression(solver='liblinear')
scores = cross_val_score(clf, x_train_vec, y_train, cv=5)
print('scores')
print('Accuracy(test): {:4f} (+/- {:4f})'.format(scores.mean(), scores.std() *2))
clf.fit(x_train_vec, y_train)
y_pred = clf.pred(x_test_vec)
score = accuracy_score(y_test, y_pred)
print('Accuracy(test): {:4f}'.format(score))
def main():
x, y = load_dataset("data/amazon_reviews_multilingual_JP_v1_00.tsv", n=5000)
x = [clean_html(text, strip=True) for text in x]
x_train, x_test, y_train, y_test = train_test_split(x, y,
test_size=0.2,
random_state=42)
vectorizer = TfidfVectorizer(tokenizer=tokenize)
x_train_vec = vectorizer.fit_transform(x_train)
x_test_vec = vectorizer.transform(x_test)
parameters = {"penalty": ["l1", "l2"],
"C": [0.01, 0.03, 0.1, 0.3, 0.7, 1, 1.01, 1.03, 1.07, 1.1, 1.3, 1.7, 3]}
lr = LogisticRegression(solver="liblinear")
clf = GridSearchCV(lr, parameters, cv=5, n_jobs=-1)
clf.fit(x_train_vec, y_train)
best_clf = clf.best_estimator_
print("clf.best_params")
print("Accuracy(best): {:.4f}".format(clf.best_score_))
y_pred = best_clf.predict(x_test_vec)
score = accuracy_score(y_test, y_pred)
print("Accuracy(test): {:.4f}".format(score))
def main():
logger = logging.getLogger("__name__")
# load dataset
x, y = load_dataset("data/amazon_reviews_multilingual_JP_v1_00.tsv",
n=5000)
# feature engineering
x = [clean_html(text, strip=True) for text in x]
x_train, x_test, y_train, y_test = train_test_split(x,
y,
test_size=0.2,
random_state=44)
# vectorization
vectorizer = CountVectorizer(tokenizer=tokenize)
x_train = vectorizer.fit_transform(x_train)
x_test = vectorizer.transform(x_test)
x_train = x_train.toarray()
x_test = x_test.toarray()
# setting hyperparameters
vocab_size = len(vectorizer.vocabulary_)
label_size = len(set(y_train))
# build model
model = create_model(vocab_size, label_size)
model.compile(loss="sparse_categorical_crossentropy",
optimizer="adam",
metrics=["accuracy"])
# declare callbacks
filepath = "./log/model.h5"
cb_early = EarlyStopping(
monitor=
"val_loss", # monitored parameter, default: val_loss (validation loss)
min_delta=0, # default: 0
patience=3, # epochs for stopping, default: 0
verbose=0, # default: 0
mode="auto", # default: auto
baseline=None, # default: None
restore_best_weights=False # default: False
)
cb_modelcp = ModelCheckpoint(
filepath=
filepath, # path for save model, default: nothing; NECCESARY ARGUMENT
monitor=
"val_loss", # monitored parameter, default: val_loss (validation loss)
verbose=0, # default: 0
save_best_only=
True, # if True, save best model only (save HDD usage), default: False
save_weights_only=
False, # if True, save weights parameter only (not include model architecture), default: False
mode="auto", # default: False
period=1 # default: False
)
# when use Tensorboard, run commands on terminal as shown below;
# ``` tensorboard --logdir=./logs (this is default) --bind_all ```
# after training model, u can access to http://localhost:6006 and use tensorboard.
# if u wanna exit tensorboard, press Ctrl+C on terminal.
cb_tensorb = TensorBoard(
log_dir=
"./log", # path for save params plotting on tensorboard, delfault: ./logs
histogram_freq=0, # default: 0
batch_size=32, # default: 32
write_graph=True, # default: True
write_grads=False, # default: False
write_images=False, # default: False
embeddings_freq=0, # default: 0
embeddings_layer_names=None, # default: None
embeddings_metadata=None, # default: None
embeddings_data=None, # default: None
update_freq="epoch" # default: epoch
)
"""callbacks = [
cb_early,
cb_modelcp,
cb_tensorb
]"""
callbacks = [
EarlyStopping(
patience=3, # epochs for stopping, default: 0
),
ModelCheckpoint(
filepath=
filepath, # path for save model, default: nothing; NECCESARY ARGUMENT
save_best_only=
True, # if True, save best model only (save HDD usage), default: False
),
TensorBoard(
log_dir=
"./log", # path for save params plotting on tensorboard, delfault: ./logs
)
]
# training model
#@stop_watch
def train_model(x_train, y_train):
return model.fit(x_train,
y_train,
validation_split=0.2,
epochs=100,
batch_size=32,
callbacks=callbacks)
history = train_model(x_train, y_train)
# load saved model
model = load_model(filepath)
# describe model
# if fail to run this, try command on terminal;
# pip install pydot==1.2.3 pydot_ng && apt-get install graphviz
plot_model(model, to_file="./log/model.png")
# predict by model
text = "このアプリ超最高！"
vec = vectorizer.transform([text])
y_pred = model.predict(vec.toarray())
logger.debug(f"""input text is "{text}".""")
logger.debug("predict: ", y_pred)
# plot acc and loss graphs
plot_history(history)
def main():
logger = logging.getLogger('__name__')
x, y = load_dataset("data/amazon_reviews_multilingual_JP_v1_00.tsv",
n=5000)
logger.debug("●○ Tokenization ○●")
x = [clean_html(text, strip=True) for text in x]
x = [" ".join(tokenize(text)) for text in x]
x_train, x_test, y_train, y_test = train_test_split(x,
y,
test_size=0.2,
random_state=44)
logger.debug("●○ Binary ○●")
vectorizer = CountVectorizer(binary=True)
train_and_eval(x_train, y_train, x_test, y_test, vectorizer)
logger.debug("●○ Count ○●")
vectorizer = CountVectorizer(binary=False)
train_and_eval(x_train, y_train, x_test, y_test, vectorizer)
logger.debug("●○ TF-IDF; Uni-gram ○●")
vectorizer = TfidfVectorizer(ngram_range=(1, 1))
train_and_eval(x_train, y_train, x_test, y_test, vectorizer)
logger.debug("●○ TF-IDF; Bi-gram ○●")
vectorizer = TfidfVectorizer(ngram_range=(2, 2))
train_and_eval(x_train, y_train, x_test, y_test, vectorizer)
logger.debug("●○ TF-IDF; Uni- and Bi-grams ○●")
vectorizer = TfidfVectorizer(ngram_range=(1, 2))
train_and_eval(x_train, y_train, x_test, y_test, vectorizer)
logger.debug("●○ TF-IDF; Uni-, Bi-, and Tri-grams ○●")
vectorizer = TfidfVectorizer(ngram_range=(1, 3))
train_and_eval(x_train, y_train, x_test, y_test, vectorizer)
logger.debug("●○ Use MaCab; TF-IDF; Uni-gram ○●") # not written in text
import MeCab
path_neologd = '/usr/lib/x86_64-linux-gnu/mecab/dic/mecab-ipadic-neologd'
t_mecab = MeCab.Tagger("-Owakati && -d {}".format(path_neologd))
def tokenize_by_mecab(text):
return list(t_mecab.parse(text).strip().split(" "))
x, y = load_dataset("data/amazon_reviews_multilingual_JP_v1_00.tsv",
n=5000)
x = [clean_html(text, strip=True) for text in x]
x = [" ".join(tokenize_by_mecab(text)) for text in x]
x_train, x_test, y_train, y_test = train_test_split(x,
y,
test_size=0.2,
random_state=44)
vectorizer = TfidfVectorizer(ngram_range=(1, 1))
train_and_eval(x_train, y_train, x_test, y_test, vectorizer)
logger.debug(
"●○ Use MaCab; TF-IDF; Uni- and Bi-grams ○●") # not written in text
vectorizer = TfidfVectorizer(ngram_range=(1, 2))
train_and_eval(x_train, y_train, x_test, y_test, vectorizer)
logger.debug("●○ Use MaCab; TF-IDF; Uni-, Bi-, and Tri-grams ○●"
) # not written in text
vectorizer = TfidfVectorizer(ngram_range=(1, 3))
train_and_eval(x_train, y_train, x_test, y_test, vectorizer)
