def main():
batch_size = 128
epochs = 100
maxlen = 300
model_path = "cnn_model.h5"
num_words = 40000
num_label = 2
x, y = load_dataset("data/amazon_reviews_multilingual_JP_v1_00.tsv")
x = preprocess_dataset(x)
x_train, x_test, y_train, y_test = train_test_split(x,
y,
test_size=0.2,
random_state=42)
vocab = build_vocabulary(x_train, num_words)
x_train = vocab.texts_to_sequences(x_train)
x_test = vocab.texts_to_sequences(x_test)
x_train = pad_sequences(x_train, maxlen=maxlen, truncating="post")
x_test = pad_sequences(x_test, maxlen=maxlen, truncating="post")
wv = load_fasttext("data/cc.ja.300.vec.gz")
wv = filter_embeddings(wv, vocab.word_index, num_words)
model = CNNModel(num_words, num_label, embeddings=wv).build()
model.compile(optimizer="adam",
loss="sparse_categorical_crossentropy",
metrics=["acc"])
callbakcs = [
EarlyStopping(patience=3),
ModelCheckpoint(model_path, save_best_only=True)
]
model.fit(x=x_train,
y=y_train,
batch_size=batch_size,
epochs=epochs,
validation_split=0.2,
callbacks=callbakcs,
shuffle=True)
model = load_model(model_path)
api = InferenceAPI(model, vocab, preprocess_dataset)
y_pred = api.predict_from_sequence(x_test)
print("precision: {:.4f}".format(
precision_score(y_test, y_pred, average="binary")))
print("recall: {:.4f}".format(
recall_score(y_test, y_pred, average="binary")))
print("f1: {:.4f}".format(f1_score(y_test, y_pred, average="binary")))
def train():
df_tweets = pd.read_csv("data/df_tweets", index_col=0)
df_tweets["text"] = preprocess_dataset(df_tweets["text"])
df_tweets = df_tweets.dropna(how='any')
df_tweets = df_tweets.drop(df_tweets.index[df_tweets["Irrelevant"] == 1])
x = df_tweets["text"]
# y = df_tweets[["posi_and_nega", "posi", "nega", "neutral", "Irrelevant"]]
y = df_tweets[["posi_and_nega", "posi", "nega", "neutral"]]
y = np.asarray(y)
x_train, x_test, y_train, y_test = train_test_split(x,
y,
test_size=0.2,
random_state=42)
vocab = build_vocabulary(x_train, num_words)
with open('model/tokenizer.pickle', 'wb') as handle:
pickle.dump(vocab, handle, protocol=pickle.HIGHEST_PROTOCOL)
x_train = vocab.texts_to_sequences(x_train)
x_test = vocab.texts_to_sequences(x_test)
x_train = pad_sequences(x_train, maxlen=maxlen, truncating="post")
x_test = pad_sequences(x_test, maxlen=maxlen, truncating="post")
wv = KeyedVectors.load("model/word2vec.model", mmap='r')
wv = filter_embeddings(wv, vocab.word_index, num_words)
model = CNNModel(num_words, num_label, embeddings=wv).build()
model.compile(optimizer="adam",
loss="binary_crossentropy",
metrics=["acc"])
callbakcs = [
EarlyStopping(patience=3),
ModelCheckpoint(model_path, save_best_only=True)
]
model.fit(x=x_train,
y=y_train,
batch_size=batch_size,
epochs=epochs,
validation_split=0.5,
callbacks=callbakcs,
shuffle=True)
