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[DACON] 소설 작가 분류 AI 경진대회 6일차! 본문

DACON/소설 작가 분류 AI 경진대회

[DACON] 소설 작가 분류 AI 경진대회 6일차!

솜씨좋은장씨 2020. 11. 4. 19:45
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소설 작가 분류 AI 경진대회

출처 : DACON - Data Science Competition

dacon.io

소설 작가 분류 AI 경진대회 6일차!

 

오늘도 퇴근 후 즐거운 DACON 도전의 시간이 다가왔습니다.

 

오늘은 전처리 방법에서 Stemmer를 LancasterStemmer 에서 Porterstemmer로 바꾸고 

임베딩 차원을 16 -> 128로 늘렸습니다.

 

import pandas as pd
import re

train_dataset = pd.read_csv("./train.csv")
test_dataset = pd.read_csv("./test_x.csv")

먼저 데이터를 불러옵니다.

from nltk.corpus import stopwords

def alpha_num(text):
    return re.sub(r"[^A-Za-z0-9\']", ' ', text)
    
stopwords_list = [ "a", "about", "above", "after", "again", "against", "all", "am", "an", "and", "any", "are", "as", 
             "at", "be", "because", "been", "before", "being", "below", "between", "both", "but", "by", "could", "will",
             "did", "do", "does", "doing", "down", "during", "each", "few", "for", "from", "further", "had", "has", 
             "have", "having", "he", "he'd", "he'll", "he's", "her", "here", "here's", "hers", "herself", "him", "himself", 
             "his", "how", "how's", "i", "i'd", "i'll", "i'm", "i've", "if", "in", "into", "is", "it", "it's", "its", "itself", 
             "let's", "me", "more", "most", "my", "myself", "nor", "of", "on", "once", "only", "or", "other", "ought", "our", "ours", 
             "ourselves", "out", "over", "own", "same", "she", "she'd", "she'll", "she's", "should", "so", "some", "such", "than", "that", 
             "that's", "the", "their", "theirs", "them", "themselves", "then", "there", "there's", "these", "they", "they'd", "they'll", 
             "they're", "they've", "this", "those", "through", "to", "too", "under", "until", "up", "very", "was", "we", "we'd", "we'll", 
             "we're", "we've", "were", "what", "what's", "when", "when's", "where", "where's", "which", "while", "who", "who's", "whom", 
             "why", "why's", "with", "would", "you", "you'd", "you'll", "you're", "you've", "your", "yours", "yourself", "yourselves" ]
             
stopwords_list = stopwords_list + stopwords.words('english')

' 를 제외한 특수문자를 제거하는 함수와 불용어를 만들어줍니다.

 

from tqdm import tqdm
import re

def get_clean_text_list(data_df):
    plain_text_list = list(data_df['text'])
    
    clear_text_list = []
    
    for i in tqdm(range(len(plain_text_list))):
        plain_text = plain_text_list[i].lower()
        
        plain_text = alpha_num(plain_text)
        
        plain_split = plain_text.split()
        
        plain_split = [word.strip() for word in plain_split if word.strip() not in stopwords_list]
        
        clear_text = " ".join(plain_split).replace("'", "")
        
        clear_text_list.append(clear_text)
        
    return clear_text_list
train_dataset['clear_text'] = get_clean_text_list(train_dataset)
test_dataset['clear_text'] = get_clean_text_list(test_dataset)

이를 바탕으로 전처리를 한 번 진행합니다.

 

from nltk.tokenize import word_tokenize
# from nltk.stem.lancaster import LancasterStemmer
from nltk.stem.porter import PorterStemmer
stemmer = PorterStemmer()
import re

X_train = []

train_clear_text = list(train_dataset['clear_text'])

for i in tqdm(range(len(train_clear_text))):
    temp = word_tokenize(train_clear_text[i])
    temp = [stemmer.stem(word) for word in temp]
    temp = [word for word in temp if len(word) > 1]
    
    X_train.append(temp)

X_test = []

test_clear_text = list(test_dataset['clear_text'])

for i in tqdm(range(len(test_clear_text))):
    temp = word_tokenize(test_clear_text[i])
    temp = [stemmer.stem(word) for word in temp]
    temp = [word for word in temp if len(word) > 1]
    
    X_test.append(temp)

5일차에서 LancasterStemmer를 활용했던 것을 PorterStemmer로 변경하여 시도해보았습니다.

 

word_list = []

for i in tqdm(range(len(X_train))):
    for j in range(len(X_train[i])):
        word_list.append(X_train[i][j])
len(list(set(word_list)))
20445

이렇게 전처리를 하고보니 총 20,445개의 유니크한 단어로 구성되어있었습니다.

import matplotlib.pyplot as plt 
print("최대 길이 :" , max(len(l) for l in X_train)) 
print("평균 길이 : ", sum(map(len, X_train))/ len(X_train)) 
plt.hist([len(s) for s in X_train], bins=50) 
plt.xlabel('length of Data') 
plt.ylabel('number of Data') 
plt.show()

#파라미터 설정
vocab_size = 20445
embedding_dim = 16
max_length = 204
padding_type='post'
from keras_preprocessing.text import Tokenizer
#tokenizer에 fit
tokenizer = Tokenizer(num_words = vocab_size)#, oov_token=oov_tok)
tokenizer.fit_on_texts(X_train)
word_index = tokenizer.word_index
from keras_preprocessing.sequence import pad_sequences
#데이터를 sequence로 변환해주고 padding 해줍니다.
train_sequences = tokenizer.texts_to_sequences(X_train)
train_padded = pad_sequences(train_sequences, padding=padding_type, maxlen=max_length)

test_sequences = tokenizer.texts_to_sequences(X_test)
test_padded = pad_sequences(test_sequences, padding=padding_type, maxlen=max_length)
import tensorflow as tf
#가벼운 NLP모델 생성
model23 = tf.keras.Sequential([
    tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),
    tf.keras.layers.GlobalAveragePooling1D(),
    tf.keras.layers.Dense(24, activation='relu'),
    tf.keras.layers.Dropout(0.1),
    tf.keras.layers.Dense(5, activation='softmax')
])

# compile model
model23.compile(loss='sparse_categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

# model summary
print(model23.summary())

# fit model
num_epochs = 40
history23 = model23.fit(train_padded, y_train, 
                    epochs=num_epochs, batch_size=256,
                    validation_split=0.2)
Model: "sequential_4"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
embedding_4 (Embedding)      (None, 204, 16)           327120    
_________________________________________________________________
global_average_pooling1d_4 ( (None, 16)                0         
_________________________________________________________________
dense_8 (Dense)              (None, 24)                408       
_________________________________________________________________
dropout_4 (Dropout)          (None, 24)                0         
_________________________________________________________________
dense_9 (Dense)              (None, 5)                 125       
=================================================================
Total params: 327,653
Trainable params: 327,653
Non-trainable params: 0
_________________________________________________________________
None
Train on 43903 samples, validate on 10976 samples
Epoch 1/40
43903/43903 [==============================] - 2s 43us/sample - loss: 1.5755 - accuracy: 0.2769 - val_loss: 1.5629 - val_accuracy: 0.2700
Epoch 2/40
43903/43903 [==============================] - 1s 32us/sample - loss: 1.5521 - accuracy: 0.2924 - val_loss: 1.5365 - val_accuracy: 0.3098
...
Epoch 39/40
43903/43903 [==============================] - 1s 30us/sample - loss: 0.4508 - accuracy: 0.8363 - val_loss: 0.7365 - val_accuracy: 0.7362
Epoch 40/40
43903/43903 [==============================] - 1s 30us/sample - loss: 0.4475 - accuracy: 0.8374 - val_loss: 0.7387 - val_accuracy: 0.7408

5일차에서 가장 좋은 성적을 냈던 모델과 같은 모델에 넣고 epoch은 40으로 늘려 학습시킨 후에

결과를 도출하고 제출해보았습니다.

 

결과 도출

# predict values
sample_submission = pd.read_csv("./sample_submission.csv")
pred = model23.predict_proba(test_padded)
sample_submission[['0','1','2','3','4']] = pred
sample_submission.to_csv('submission_16.csv', index = False, encoding = 'utf-8')

 

DACON 제출 결과

5일차보다 좋은 결과가 나오기를 기대했지만  0.5070571629 라는 조금은 아쉬운 점수를 얻을 수 있었습니다.

 

임베딩 레이어에서 차원이 너무 작아서 그런가...? 라는 생각을 하게되었고 임베딩 레이어에 embedding_dim을 128로 

변경하여 시도해 보았습니다.

import tensorflow as tf
#가벼운 NLP모델 생성
model24 = tf.keras.Sequential([
    tf.keras.layers.Embedding(vocab_size, 128, input_length=max_length),
    tf.keras.layers.GlobalAveragePooling1D(),
    tf.keras.layers.Dense(24, activation='relu'),
    tf.keras.layers.Dropout(0.1),
    tf.keras.layers.Dense(5, activation='softmax')
])

# compile model
model24.compile(loss='sparse_categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

# model summary
print(model24.summary())

# fit model
num_epochs = 15
history24 = model24.fit(train_padded, y_train, 
                    epochs=num_epochs, batch_size=256,
                    validation_split=0.2)
Model: "sequential_8"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
embedding_10 (Embedding)     (None, 204, 128)          2616960   
_________________________________________________________________
global_average_pooling1d_7 ( (None, 128)               0         
_________________________________________________________________
dense_15 (Dense)             (None, 24)                3096      
_________________________________________________________________
dropout_10 (Dropout)         (None, 24)                0         
_________________________________________________________________
dense_16 (Dense)             (None, 5)                 125       
=================================================================
Total params: 2,620,181
Trainable params: 2,620,181
Non-trainable params: 0
_________________________________________________________________
None
Train on 43903 samples, validate on 10976 samples
Epoch 1/15
43903/43903 [==============================] - 5s 114us/sample - loss: 1.5650 - accuracy: 0.2796 - val_loss: 1.5434 - val_accuracy: 0.2772
Epoch 2/15
43903/43903 [==============================] - 4s 96us/sample - loss: 1.4584 - accuracy: 0.3705 - val_loss: 1.3469 - val_accuracy: 0.4257
...
Epoch 14/15
43903/43903 [==============================] - 5s 105us/sample - loss: 0.5870 - accuracy: 0.7869 - val_loss: 0.7445 - val_accuracy: 0.7261
Epoch 15/15
43903/43903 [==============================] - 4s 101us/sample - loss: 0.5620 - accuracy: 0.7966 - val_loss: 0.7366 - val_accuracy: 0.7294

 

결과 도출

# predict values
sample_submission = pd.read_csv("./sample_submission.csv")
pred = model24.predict_proba(test_padded)
sample_submission[['0','1','2','3','4']] = pred
sample_submission.to_csv('submission_17.csv', index = False, encoding = 'utf-8')

 

DACON 제출 결과

 

오 이번에는 0.4829543261로 5일차의 최고 점수에 근접한 결과가 나왔습니다.

 

같은 방법으로 만든 모델로 여러번 재시도 해보면서 validation loss가 가장 잘 떨어지기를 확인해보면서 

가장 괜챃았던 모델을 활용하여 결과를 도출하고 제출해보았습니다.

 

Model: "sequential_12"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
embedding_14 (Embedding)     (None, 204, 128)          2616960   
_________________________________________________________________
global_average_pooling1d_11  (None, 128)               0         
_________________________________________________________________
dense_23 (Dense)             (None, 24)                3096      
_________________________________________________________________
dropout_14 (Dropout)         (None, 24)                0         
_________________________________________________________________
dense_24 (Dense)             (None, 5)                 125       
=================================================================
Total params: 2,620,181
Trainable params: 2,620,181
Non-trainable params: 0
_________________________________________________________________
None
Train on 43903 samples, validate on 10976 samples
Epoch 1/15
43903/43903 [==============================] - 5s 119us/sample - loss: 1.5651 - accuracy: 0.2718 - val_loss: 1.5515 - val_accuracy: 0.2710
Epoch 2/15
43903/43903 [==============================] - 5s 103us/sample - loss: 1.4956 - accuracy: 0.3691 - val_loss: 1.3967 - val_accuracy: 0.5198
...
Epoch 14/15
43903/43903 [==============================] - 4s 101us/sample - loss: 0.5399 - accuracy: 0.8054 - val_loss: 0.7070 - val_accuracy: 0.7365
Epoch 15/15
43903/43903 [==============================] - 4s 100us/sample - loss: 0.5214 - accuracy: 0.8114 - val_loss: 0.7027 - val_accuracy: 0.7421

결과 도출

# predict values
sample_submission = pd.read_csv("./sample_submission.csv")
pred = model27.predict_proba(test_padded)
sample_submission[['0','1','2','3','4']] = pred
sample_submission.to_csv('submission_18.csv', index = False, encoding = 'utf-8')

 

DACON 제출 결과

와우! 0.4455598506 으로 오늘도 5일차의 결과보다 더 좋은 결과가 나왔습니다.

 

그러나 아직 데이터를 EDA 해보거나 하지 않고 이 모델 저 모델 도전을 해보고 있는 실정이라

아직 마음에 들지 않습니다.

 

아직 해보기로 했던 표제어 추출 전처리도 시도해보아야하고 

각 라벨별로는 각각 몇 개의 데이터가 존재하는지도 확인해봐야하고

각 라벨별 그룹에는 어떤 단어들이 가장 많이 나왔는지도 확인해봐야합니다.

 

주말에 차차 차근차근 하나씩 해보고자 합니다.

 

읽어주셔서 감사합니다.

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