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%% Cell type:code id: tags:
```
from google.colab import drive
drive.mount('/content/gdrive')
```
%% Output
Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount("/content/gdrive", force_remount=True).
%% Cell type:markdown id: tags:
# 1. Data Collection & 2. Data Preprocessing(Cleaning and Integration)
%% Cell type:code id: tags:
```
import pandas
import re
import random
from googleapiclient.discovery import build
def cleansing(text):
pattern = '[0-9]+:[0-9]+' # comment timestamp
text = re.sub(pattern=pattern, repl='', string=text)
pattern = '([a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+)' # E-mail제거
text = re.sub(pattern=pattern, repl='', string=text)
pattern = '(http|ftp|https)://(?:[-\w.]|(?:%[\da-fA-F]{2}))+' # URL제거
text = re.sub(pattern=pattern, repl='', string=text)
pattern = '([ㄱ-ㅎㅏ-ㅣ]+)' # 한글 자음, 모음 제거
text = re.sub(pattern=pattern, repl='', string=text)
pattern = '<[^>]*>' # HTML 태그 제거
text = re.sub(pattern=pattern, repl='', string=text)
pattern = '[^\w\s]' # 특수기호제거
text = re.sub(pattern=pattern, repl='', string=text)
return text
def collection(videoId, outputName):
# key
_developerKey = ""
_videoId = videoId # 테스트 할 영상 ID
# get video information
comments = list()
api_obj = build('youtube', 'v3', developerKey=_developerKey)
video_info = api_obj.videos().list(part='statistics, snippet', id=_videoId).execute()
print("\n==========")
print("title: ", video_info['items'][0]['snippet']['title'])
print("view: ", video_info['items'][0]['statistics']['viewCount'])
print("like: ", video_info['items'][0]['statistics']['likeCount'], " / dislike: ", video_info['items'][0]['statistics']['dislikeCount'])
response = api_obj.commentThreads().list(part='snippet,replies', videoId=_videoId, maxResults=100, textFormat='plainText').execute()
while response:
for item in response['items']:
comment = item['snippet']['topLevelComment']['snippet']
text = cleansing(comment['textDisplay']).strip()
if text and (not text.isspace()):
comments.append(text)
if len(comments) >= 10000:
break
if 'nextPageToken' in response:
response = api_obj.commentThreads().list(part='snippet,replies', videoId=_videoId,
pageToken=response['nextPageToken'], maxResults=100).execute()
else:
break
if len(comments) > 1000:
comments = random.sample(comments, 1000)
print(len(comments))
print("실제 좋싫비: ", int(video_info['items'][0]['statistics']['likeCount']) / (int(video_info['items'][0]['statistics']['likeCount']) + int(video_info['items'][0]['statistics']['dislikeCount'])))
df = pandas.DataFrame(comments)
df.to_excel(outputName+'_data.xlsx', header=False, index=None)
return video_info['items'][0]['statistics']['likeCount']
```
%% Cell type:code id: tags:
```
count_like = collection("gSIQM6cipNs", "results")
```
%% Output
title: 펄어비스 도깨비 - 첫 공개 게임플레이
view: 1649019
like: 19846 / dislike: 444
1000
%% Cell type:markdown id: tags:
# 2. Data Preprocessing (Feature Extraction)
%% Cell type:markdown id: tags:
### 2-1. 준비 사항
%% Cell type:code id: tags:
```
# Hugging Face의 트랜스포머 모델을 설치
!pip install transformers
```
%% Output
Requirement already satisfied: transformers in /usr/local/lib/python3.7/dist-packages (4.12.5)
Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.19.5)
Requirement already satisfied: sacremoses in /usr/local/lib/python3.7/dist-packages (from transformers) (0.0.46)
Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)
Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)
Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (6.0)
Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.8.2)
Requirement already satisfied: huggingface-hub<1.0,>=0.1.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.2.1)
Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.4.0)
Requirement already satisfied: tokenizers<0.11,>=0.10.1 in /usr/local/lib/python3.7/dist-packages (from transformers) (0.10.3)
Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.62.3)
Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2019.12.20)
Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.1.0->transformers) (3.10.0.2)
Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers) (3.0.6)
Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.6.0)
Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)
Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2021.10.8)
Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)
Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)
Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.1.0)
Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.15.0)
Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (7.1.2)
%% Cell type:code id: tags:
```
import tensorflow as tf
import torch
from transformers import BertTokenizer
from transformers import BertForSequenceClassification, AdamW, BertConfig
from transformers import get_linear_schedule_with_warmup
from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
from keras.preprocessing.sequence import pad_sequences
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
import pandas as pd
import numpy as np
import random
import time
import datetime
```
%% Cell type:markdown id: tags:
### 2-2. 데이터 로드
%% Cell type:code id: tags:
```
# 네이버 영화리뷰 감정분석 데이터 다운로드
!git clone https://github.com/bab2min/corpus.git
# 디렉토리의 파일 목록
!ls corpus/sentiment -la
```
%% Output
fatal: destination path 'corpus' already exists and is not an empty directory.
total 31520
drwxr-xr-x 2 root root 4096 Dec 9 05:17 .
drwxr-xr-x 4 root root 4096 Dec 9 05:17 ..
-rw-r--r-- 1 root root 20623547 Dec 9 05:17 naver_shopping.txt
-rw-r--r-- 1 root root 4617 Dec 9 05:17 README.md
-rw-r--r-- 1 root root 11631607 Dec 9 05:17 steam.txt
%% Cell type:code id: tags:
```
# 데이터 로드
total_data = pd.read_table('corpus/sentiment/steam.txt',names=['label','reviews'])
print('전체 리뷰 개수 : ',len(total_data))
```
%% Output
전체 리뷰 개수 : 100000
%% Cell type:code id: tags:
```
# # 훈련셋의 앞부분 출력
# total_data.head(10)
# # total_data['label'] = np.select([total_data.ratings > 3], [1], default=0)
# total_data[:5]
```
%% Cell type:code id: tags:
```
# 총 데이터
total_data['reviews'].nunique(), total_data['label'].nunique()
total_data.drop_duplicates(subset=['reviews'], inplace=True) # reviews 열에서 중복인 내용이 있다면 중복 제거
print('총 샘플의 수 :',len(total_data))
```
%% Output
총 샘플의 수 : 99892
%% Cell type:code id: tags:
```
# 훈련 데이터와 테스트 데이터를 3:1 비율로 분리
train_data, test_data = train_test_split(total_data, test_size = 0.25, random_state = 42)
print('훈련용 리뷰의 개수 :', len(train_data))
print('테스트용 리뷰의 개수 :', len(test_data))
```
%% Output
훈련용 리뷰의 개수 : 74919
테스트용 리뷰의 개수 : 24973
%% Cell type:markdown id: tags:
### 2-3. 전처리 - 훈련셋
%% Cell type:code id: tags:
```
# 리뷰 문장 추출
sentences = train_data['reviews']
#sentences[:10]
```
%% Cell type:code id: tags:
```
# BERT의 입력 형식에 맞게 변환
sentences = ["[CLS] " + str(sentence) + " [SEP]" for sentence in sentences]
#sentences[:10]
```
%% Cell type:code id: tags:
```
# import matplotlib.pyplot as plt
# fig,(ax1,ax2) = plt.subplots(1,2,figsize=(10,5))
# text_len = train_data[train_data['label']==1]['tokenized'].map(lambda x: len(x))
# ax1.hist(text_len, color='red')
# ax1.set_title('Positive Reviews')
# ax1.set_xlabel('length of samples')
# ax1.set_ylabel('number of samples')
# print('긍정 리뷰의 평균 길이 :', np.mean(text_len))
# text_len = train_data[train_data['label']==0]['tokenized'].map(lambda x: len(x))
# ax2.hist(text_len, color='blue')
# ax2.set_title('Negative Reviews')
# fig.suptitle('Words in texts')
# ax2.set_xlabel('length of samples')
# ax2.set_ylabel('number of samples')
# print('부정 리뷰의 평균 길이 :', np.mean(text_len))
# plt.show()
```
%% Cell type:code id: tags:
```
# 라벨 추출
labels = train_data['label'].values
labels
```
%% Output
array([1, 0, 0, ..., 1, 0, 1])
%% Cell type:code id: tags:
```
# BERT의 토크나이저로 문장을 토큰으로 분리
tokenizer = BertTokenizer.from_pretrained('bert-base-multilingual-cased', do_lower_case=False)
tokenized_texts = [tokenizer.tokenize(sent) for sent in sentences]
# print (sentences[0])
# print (tokenized_texts[0])
```
%% Cell type:code id: tags:
```
# 입력 토큰의 최대 시퀀스 길이
MAX_LEN = 80
# 토큰을 숫자 인덱스로 변환
input_ids = [tokenizer.convert_tokens_to_ids(x) for x in tokenized_texts]
# 문장을 MAX_LEN 길이에 맞게 자르고, 모자란 부분을 패딩 0으로 채움
input_ids = pad_sequences(input_ids, maxlen=MAX_LEN, dtype="long", truncating="post", padding="post")
input_ids[0]
```
%% Output
array([ 101, 66402, 9836, 12692, 12965, 9955, 9137, 18382,
77039, 9415, 118884, 11664, 9709, 12692, 12508, 9523,
119081, 48345, 9670, 89523, 9654, 73306, 9913, 119228,
66402, 58303, 48345, 9765, 38631, 33188, 48345, 102,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0])
%% Cell type:code id: tags:
```
# 어텐션 마스크 초기화
attention_masks = []
# 어텐션 마스크를 패딩이 아니면 1, 패딩이면 0으로 설정
# 패딩 부분은 BERT 모델에서 어텐션을 수행하지 않아 속도 향상
for seq in input_ids:
seq_mask = [float(i>0) for i in seq]
attention_masks.append(seq_mask)
print(attention_masks[0])
```
%% Output
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
%% Cell type:code id: tags:
```
# 훈련셋과 검증셋으로 분리
train_inputs, validation_inputs, train_labels, validation_labels = train_test_split(input_ids,
labels,
random_state=2018,
test_size=0.1)
# 어텐션 마스크를 훈련셋과 검증셋으로 분리
train_masks, validation_masks, _, _ = train_test_split(attention_masks,
input_ids,
random_state=2018,
test_size=0.1)
# 데이터를 파이토치의 텐서로 변환
train_inputs = torch.tensor(train_inputs)
train_labels = torch.tensor(train_labels)
train_masks = torch.tensor(train_masks)
validation_inputs = torch.tensor(validation_inputs)
validation_labels = torch.tensor(validation_labels)
validation_masks = torch.tensor(validation_masks)
print(train_inputs[0])
print(train_labels[0])
print(train_masks[0])
print(validation_inputs[0])
print(validation_labels[0])
print(validation_masks[0])
```
%% Output
tensor([ 101, 9955, 118627, 12092, 9555, 11664, 8982, 19105, 8924,
56710, 12508, 9283, 31401, 118632, 28578, 100, 9546, 118837,
14153, 9960, 21711, 14843, 12508, 39420, 31401, 118632, 11903,
9407, 118624, 10003, 14863, 12453, 9954, 35465, 12092, 100,
9405, 12508, 23811, 24982, 48549, 102, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0])
tensor(0)
tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0.])
tensor([ 101, 9954, 118663, 119383, 18622, 119212, 9960, 16323, 24982,
48549, 119, 102, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0])
tensor(1)
tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0.])
%% Cell type:code id: tags:
```
# 배치 사이즈
batch_size = 60
# 파이토치의 DataLoader로 입력, 마스크, 라벨을 묶어 데이터 설정
# 학습시 배치 사이즈 만큼 데이터를 가져옴
train_data = TensorDataset(train_inputs, train_masks, train_labels)
train_sampler = RandomSampler(train_data)
train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=batch_size)
validation_data = TensorDataset(validation_inputs, validation_masks, validation_labels)
validation_sampler = SequentialSampler(validation_data)
validation_dataloader = DataLoader(validation_data, sampler=validation_sampler, batch_size=batch_size)
```
%% Cell type:markdown id: tags:
### 2-4. 전처리 - 테스트셋
%% Cell type:code id: tags:
```
# 리뷰 문장 추출
sentences = test_data['reviews']
#sentences[:10]
```
%% Cell type:code id: tags:
```
# BERT의 입력 형식에 맞게 변환
sentences = ["[CLS] " + str(sentence) + " [SEP]" for sentence in sentences]
#sentences[:10]
```
%% Cell type:code id: tags:
```
# 라벨 추출
labels = test_data['label'].values
labels
```
%% Output
array([0, 0, 1, ..., 1, 1, 0])
%% Cell type:code id: tags:
```
# BERT의 토크나이저로 문장을 토큰으로 분리
tokenizer = BertTokenizer.from_pretrained('bert-base-multilingual-cased', do_lower_case=False)
tokenized_texts = [tokenizer.tokenize(sent) for sent in sentences]
#print (sentences[0])
#print (tokenized_texts[0])
```
%% Cell type:code id: tags:
```
# 입력 토큰의 최대 시퀀스 길이
MAX_LEN = 80
# 토큰을 숫자 인덱스로 변환
input_ids = [tokenizer.convert_tokens_to_ids(x) for x in tokenized_texts]
# 문장을 MAX_LEN 길이에 맞게 자르고, 모자란 부분을 패딩 0으로 채움
input_ids = pad_sequences(input_ids, maxlen=MAX_LEN, dtype="long", truncating="post", padding="post")
input_ids[0]
```
%% Output
array([ 101, 9640, 48446, 66402, 10892, 9405, 11664, 8982,
14867, 10003, 14863, 33188, 48345, 119, 9995, 118992,
108436, 23969, 28911, 9029, 119266, 77884, 100, 102,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0])
%% Cell type:code id: tags:
```
# 어텐션 마스크 초기화
attention_masks = []
# 어텐션 마스크를 패딩이 아니면 1, 패딩이면 0으로 설정
# 패딩 부분은 BERT 모델에서 어텐션을 수행하지 않아 속도 향상
for seq in input_ids:
seq_mask = [float(i>0) for i in seq]
attention_masks.append(seq_mask)
print(attention_masks[0])
```
%% Output
[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
%% Cell type:code id: tags:
```
# 데이터를 파이토치의 텐서로 변환
test_inputs = torch.tensor(input_ids)
test_labels = torch.tensor(labels)
test_masks = torch.tensor(attention_masks)
print(test_inputs[0])
print(test_labels[0])
print(test_masks[0])
```
%% Output
tensor([ 101, 9640, 48446, 66402, 10892, 9405, 11664, 8982, 14867,
10003, 14863, 33188, 48345, 119, 9995, 118992, 108436, 23969,
28911, 9029, 119266, 77884, 100, 102, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0])
tensor(0)
tensor([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,
1., 1., 1., 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 0., 0., 0., 0., 0., 0., 0.])
%% Cell type:code id: tags:
```
# 배치 사이즈
batch_size = 60
# 파이토치의 DataLoader로 입력, 마스크, 라벨을 묶어 데이터 설정
# 학습시 배치 사이즈 만큼 데이터를 가져옴
test_data = TensorDataset(test_inputs, test_masks, test_labels)
test_sampler = RandomSampler(test_data)
test_dataloader = DataLoader(test_data, sampler=test_sampler, batch_size=batch_size)
```
%% Cell type:markdown id: tags:
### 2-5. 모델 생성
%% Cell type:code id: tags:
```
# GPU 디바이스 이름 구함
device_name = tf.test.gpu_device_name()
# GPU 디바이스 이름 검사
if device_name == '/device:GPU:0':
print('Found GPU at: {}'.format(device_name))
else:
raise SystemError('GPU device not found')
```
%% Output
Found GPU at: /device:GPU:0
%% Cell type:code id: tags:
```
# 디바이스 설정
if torch.cuda.is_available():
device = torch.device("cuda")
print('There are %d GPU(s) available.' % torch.cuda.device_count())
print('We will use the GPU:', torch.cuda.get_device_name(0))
else:
device = torch.device("cpu")
print('No GPU available, using the CPU instead.')
```
%% Output
There are 1 GPU(s) available.
We will use the GPU: Tesla K80
%% Cell type:code id: tags:
```
# 분류를 위한 BERT 모델 생성
model = BertForSequenceClassification.from_pretrained("bert-base-multilingual-cased", num_labels=2)
model.cuda()
```
%% Output
Some weights of the model checkpoint at bert-base-multilingual-cased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.dense.weight', 'cls.seq_relationship.weight', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.bias']
- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-multilingual-cased and are newly initialized: ['classifier.bias', 'classifier.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
BertForSequenceClassification(
(bert): BertModel(
(embeddings): BertEmbeddings(
(word_embeddings): Embedding(119547, 768, padding_idx=0)
(position_embeddings): Embedding(512, 768)
(token_type_embeddings): Embedding(2, 768)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(encoder): BertEncoder(
(layer): ModuleList(
(0): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(1): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(2): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(3): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(4): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(5): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(6): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(7): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(8): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(9): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(10): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(11): BertLayer(
(attention): BertAttention(
(self): BertSelfAttention(
(query): Linear(in_features=768, out_features=768, bias=True)
(key): Linear(in_features=768, out_features=768, bias=True)
(value): Linear(in_features=768, out_features=768, bias=True)
(dropout): Dropout(p=0.1, inplace=False)
)
(output): BertSelfOutput(
(dense): Linear(in_features=768, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
(intermediate): BertIntermediate(
(dense): Linear(in_features=768, out_features=3072, bias=True)
)
(output): BertOutput(
(dense): Linear(in_features=3072, out_features=768, bias=True)
(LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)
(dropout): Dropout(p=0.1, inplace=False)
)
)
)
)
(pooler): BertPooler(
(dense): Linear(in_features=768, out_features=768, bias=True)
(activation): Tanh()
)
)
(dropout): Dropout(p=0.1, inplace=False)
(classifier): Linear(in_features=768, out_features=2, bias=True)
)
%% Cell type:markdown id: tags:
### 2-6. Prediction
%% Cell type:code id: tags:
```
# 옵티마이저 설정
optimizer = AdamW(model.parameters(),
lr = 2e-5, # 학습률
eps = 1e-8 # 0으로 나누는 것을 방지하기 위한 epsilon 값
)
# 에폭수
epochs = 1
# 총 훈련 스텝 : 배치반복 횟수 * 에폭
total_steps = len(train_dataloader) * epochs
# 처음에 학습률을 조금씩 변화시키는 스케줄러 생성
scheduler = get_linear_schedule_with_warmup(optimizer,
num_warmup_steps = 0,
num_training_steps = total_steps)
```
%% Cell type:markdown id: tags:
### 2-7. 모델 학습
%% Cell type:code id: tags:
```
# 정확도 계산 함수
def flat_accuracy(preds, labels):
pred_flat = np.argmax(preds, axis=1).flatten()
labels_flat = labels.flatten()
return np.sum(pred_flat == labels_flat) / len(labels_flat)
def precision(preds, labels):
# 실제 맞은거 / 내가 맞다고 한거
pred_flat = np.argmax(preds, axis=1).flatten() # 예측한 결과물 리스트
labels_flat = labels.flatten() # 정답 리스트
a=np.sum(pred_flat==True) + np.sum(labels_flat==True) - np.sum(pred_flat!=labels_flat)
b=np.sum(pred_flat==True) + np.sum(labels_flat==False) - np.sum(pred_flat==labels_flat)
return a/(a+b)
def recall(preds,lables):
# 실제 맞은거/ 실제 맞은거 + 아니라고 했는데 맞은거
pred_flat = np.argmax(preds, axis=1).flatten()
labels_flat = labels.flatten()
a= np.sum(pred_flat==True) + np.sum(labels_flat==True) - np.sum(pred_flat!=labels_flat)
b= np.sum(pred_flat==False) + np.sum(labels_flat ==True) - np.sum(pred_flat==labels_flat)
return a/(a+b)
```
%% Cell type:code id: tags:
```
# 시간 표시 함수
def format_time(elapsed):
# 반올림
elapsed_rounded = int(round((elapsed)))
# hh:mm:ss으로 형태 변경
return str(datetime.timedelta(seconds=elapsed_rounded))
```
%% Cell type:code id: tags:
```
# 재현을 위해 랜덤시드 고정
seed_val = 42
random.seed(seed_val)
np.random.seed(seed_val)
torch.manual_seed(seed_val)
torch.cuda.manual_seed_all(seed_val)
# 그래디언트 초기화
model.zero_grad()
# 에폭만큼 반복
for epoch_i in range(0, epochs):
# ========================================
# Training
# ========================================
print("")
print('======== Epoch {:} / {:} ========'.format(epoch_i + 1, epochs))
print('Training...')
# 시작 시간 설정
t0 = time.time()
# 로스 초기화
total_loss = 0
# 훈련모드로 변경
model.train()
# 데이터로더에서 배치만큼 반복하여 가져옴
for step, batch in enumerate(train_dataloader):
# 경과 정보 표시
if step % 500 == 0 and not step == 0:
elapsed = format_time(time.time() - t0)
print(' Batch {:>5,} of {:>5,}. Elapsed: {:}.'.format(step, len(train_dataloader), elapsed))
# 배치를 GPU에 넣음
batch = tuple(t.to(device) for t in batch)
# 배치에서 데이터 추출
b_input_ids, b_input_mask, b_labels = batch
# Forward 수행
outputs = model(b_input_ids,
token_type_ids=None,
attention_mask=b_input_mask,
labels=b_labels)
# 로스 구함
loss = outputs[0]
# 총 로스 계산
total_loss += loss.item()
# Backward 수행으로 그래디언트 계산
loss.backward()
# 그래디언트 클리핑
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
# 그래디언트를 통해 가중치 파라미터 업데이트
optimizer.step()
# 스케줄러로 학습률 감소
scheduler.step()
# 그래디언트 초기화
model.zero_grad()
# 평균 로스 계산
avg_train_loss = total_loss / len(train_dataloader)
print("")
print(" Average training loss: {0:.2f}".format(avg_train_loss))
print(" Training epcoh took: {:}".format(format_time(time.time() - t0)))
# ========================================
# Validation
# ========================================
print("")
print("Running Validation...")
#시작 시간 설정
t0 = time.time()
# 평가모드로 변경
model.eval()
# 변수 초기화
eval_loss, eval_accuracy = 0, 0
eval_precision, eval_recall = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
# 데이터로더에서 배치만큼 반복하여 가져옴
for batch in validation_dataloader:
# 배치를 GPU에 넣음
batch = tuple(t.to(device) for t in batch)
# 배치에서 데이터 추출
b_input_ids, b_input_mask, b_labels = batch
# 그래디언트 계산 안함
with torch.no_grad():
# Forward 수행
outputs = model(b_input_ids,
token_type_ids=None,
attention_mask=b_input_mask)
# 로스 구함
logits = outputs[0]
# CPU로 데이터 이동
logits = logits.detach().cpu().numpy()
label_ids = b_labels.to('cpu').numpy()
# 출력 로짓과 라벨을 비교하여 정확도 계산
tmp_eval_accuracy = flat_accuracy(logits, label_ids)
eval_accuracy += tmp_eval_accuracy
temp_eval_precision = precision(logits,label_ids)
eval_precision += temp_eval_precision
temp_eval_recall = recall(logits,label_ids)
eval_recall += temp_eval_recall
nb_eval_steps += 1
print(" Accuracy: {0:.5f}".format(eval_accuracy/nb_eval_steps))
print(" Precision: {0:.5f}".format(eval_precision/nb_eval_steps))
print(" Recall: {0:.5f}".format(eval_recall/nb_eval_steps))
print(" Validation took: {:}".format(format_time(time.time() - t0)))
print("")
print("Training complete!")
```
%% Output
======== Epoch 1 / 1 ========
Training...
Batch 500 of 1,124. Elapsed: 0:13:09.
Batch 1,000 of 1,124. Elapsed: 0:26:18.
Average training loss: 0.52
Training epcoh took: 0:29:34
Running Validation...
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:21: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:22: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.
Accuracy: 0.77688
Precision: 0.78540
Recall: 0.49996
Validation took: 0:01:13
Training complete!
%% Cell type:markdown id: tags:
에폭마다 훈련셋과 검증셋을 반복하여 학습을 수행합니다.
%% Cell type:markdown id: tags:
### 2-8. 테스트셋 평가
%% Cell type:code id: tags:
```
#시작 시간 설정
t0 = time.time()
# 평가모드로 변경
model.eval()
# 변수 초기화
eval_loss, eval_accuracy = 0, 0
eval_precision, eval_recall = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
# 데이터로더에서 배치만큼 반복하여 가져옴
for step, batch in enumerate(test_dataloader):
# 경과 정보 표시
if step % 100 == 0 and not step == 0:
elapsed = format_time(time.time() - t0)
print(' Batch {:>5,} of {:>5,}. Elapsed: {:}.'.format(step, len(test_dataloader), elapsed))
# 배치를 GPU에 넣음
batch = tuple(t.to(device) for t in batch)
# 배치에서 데이터 추출
b_input_ids, b_input_mask, b_labels = batch
# 그래디언트 계산 안함
with torch.no_grad():
# Forward 수행
outputs = model(b_input_ids,
token_type_ids=None,
attention_mask=b_input_mask)
# 로스 구함
logits = outputs[0]
# CPU로 데이터 이동
logits = logits.detach().cpu().numpy()
label_ids = b_labels.to('cpu').numpy()
# 출력 로짓과 라벨을 비교하여 정확도 계산
tmp_eval_accuracy = flat_accuracy(logits, label_ids)
eval_accuracy += tmp_eval_accuracy
temp_eval_precision = precision(logits,label_ids)
eval_precision += temp_eval_precision
temp_eval_recall = recall(logits,label_ids)
eval_recall += temp_eval_recall
nb_eval_steps += 1
print("")
print("Accuracy: {0:.5f}".format(eval_accuracy/nb_eval_steps))
print(" Precision: {0:.5f}".format(eval_precision/nb_eval_steps))
print(" Recall: {0:.5f}".format(eval_recall/nb_eval_steps))
print("Test took: {:}".format(format_time(time.time() - t0)))
```
%% Output
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:21: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.
/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:22: DeprecationWarning: elementwise comparison failed; this will raise an error in the future.
Batch 100 of 417. Elapsed: 0:00:59.
Batch 200 of 417. Elapsed: 0:01:57.
Batch 300 of 417. Elapsed: 0:02:56.
Batch 400 of 417. Elapsed: 0:03:55.
Accuracy: 0.77245
Precision: 0.77396
Recall: 0.49996
Test took: 0:04:04
%% Cell type:markdown id: tags:
### **2-9. 새로운 문장 테스트**
%% Cell type:code id: tags:
```
# 입력 데이터 변환
def convert_input_data(sentences):
# BERT의 토크나이저로 문장을 토큰으로 분리
tokenized_texts = [tokenizer.tokenize(sent) for sent in sentences]
# 입력 토큰의 최대 시퀀스 길이
MAX_LEN = 128
# 토큰을 숫자 인덱스로 변환
input_ids = [tokenizer.convert_tokens_to_ids(x) for x in tokenized_texts]
# 문장을 MAX_LEN 길이에 맞게 자르고, 모자란 부분을 패딩 0으로 채움
input_ids = pad_sequences(input_ids, maxlen=MAX_LEN, dtype="long", truncating="post", padding="post")
# 어텐션 마스크 초기화
attention_masks = []
# 어텐션 마스크를 패딩이 아니면 1, 패딩이면 0으로 설정
# 패딩 부분은 BERT 모델에서 어텐션을 수행하지 않아 속도 향상
for seq in input_ids:
seq_mask = [float(i>0) for i in seq]
attention_masks.append(seq_mask)
# 데이터를 파이토치의 텐서로 변환
inputs = torch.tensor(input_ids)
masks = torch.tensor(attention_masks)
return inputs, masks
# 문장 테스트
def test_sentences(sentences):
# 평가모드로 변경
model.eval()
# 문장을 입력 데이터로 변환
inputs, masks = convert_input_data(sentences)
# 데이터를 GPU에 넣음
b_input_ids = inputs.to(device)
b_input_mask = masks.to(device)
# 그래디언트 계산 안함
with torch.no_grad():
# Forward 수행
outputs = model(b_input_ids,
token_type_ids=None,
attention_mask=b_input_mask)
# 로스 구함
logits = outputs[0]
# CPU로 데이터 이동
logits = logits.detach().cpu().numpy()
return logits
```
%% Cell type:code id: tags:
```
import openpyxl
def labeling(inputName):
sentences = pd.read_excel(inputName+'_data.xlsx', header = None)
workbook = openpyxl.load_workbook(inputName+'_data.xlsx')
sheet = workbook['Sheet1']
result_label = []
for i in range(len(sentences)):
logits = test_sentences([sheet.cell(column=1, row=i+1).value])
result_label.append(np.argmax(logits))
#print(np.argmax(logits))
print(result_label)
df = pandas.DataFrame(result_label)
df.to_excel(inputName+'_label.xlsx', header=False, index=None)
return result_label
```
%% Cell type:code id: tags:
```
# logits = test_sentences(['인가니 가장 큰 공포를 느낀다는 시빌미터에서 뛰어내리는 모형탑훈련 제가 직접 한번 해보겠습니다 이야야야야야야야야 쯔아크아아아아아아아가아가아아갸갸'])
# print(logits)
# print(np.argmax(logits))
```
%% Cell type:code id: tags:
```
result_label = labeling(str(i))
```
%% Output
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%% Cell type:markdown id: tags:
# 3. ANALYTICAL PROCESSING
%% Cell type:code id: tags:
```
def prediction(count_like, result_label):
count_like_label = result_label.count(1)
count_dislike_label = result_label.count(0)
print("count_like_label: ", count_like_label)
print("count_dislike_label: ", count_dislike_label)
print("댓글 좋싫비: ", count_like_label / (count_like_label + count_dislike_label))
count_like = int(count_like)
print("count_like: ", count_like)
count_estimated_dislike = count_like * count_dislike_label / count_like_label / 17.7
print("count_estimated_dislike: ", int(count_estimated_dislike))
```
%% Cell type:code id: tags:
```
prediction(count_like, result_label)
```
%% Output
count_like_label: 160
count_dislike_label: 50
count_like: 19846
count_estimated_dislike: 6201
%% Cell type:markdown id: tags:
# 4. 통합 실행
%% Cell type:code id: tags:
```
videoIds = ["gSIQM6cipNs", "umG9aF-1BmM", "O7oVpVfpBvE", "gzIkbqA7p9k", "TRvUe_SRJ6g", "elGcWzBx0fQ", "Zq-taY1u7fw", "XkcOfZ83wrk", "R6OyrTHPrLY", "iGd7f1-ONSM", "Lcm0xqBgfRM"]
for i in range(len(videoIds)):
count_like = collection(videoIds[i], str(i))
result_label = labeling(str(i))
prediction(count_like, result_label)
# 실제 싫어요:댓글 싫어요= 17.70:1 정도
```
%% Output
==========
title: 펄어비스 도깨비 - 첫 공개 게임플레이
view: 1649177
like: 19846 / dislike: 444
1000
실제 좋싫비: 0.9781172991621488
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count_like_label: 506
count_dislike_label: 494
댓글 좋싫비: 0.506
count_like: 19846
count_estimated_dislike: 1094
==========
title: [배드워즈] 럭키블럭판 돌잡이 챌린지! / 로블록스
view: 142061
like: 1868 / dislike: 68
438
실제 좋싫비: 0.9648760330578512
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count_like_label: 269
count_dislike_label: 169
댓글 좋싫비: 0.6141552511415526
count_like: 1868
count_estimated_dislike: 66
==========
title: 독가스 방구가 몰려온다! 마을을 지켜라! -캐릭온 마크 애니
view: 143422
like: 2220 / dislike: 69
215
실제 좋싫비: 0.9698558322411533
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count_like_label: 145
count_dislike_label: 70
댓글 좋싫비: 0.6744186046511628
count_like: 2220
count_estimated_dislike: 60
==========
title: [롤]응답하라 2018 (feat.PTSD)
view: 287080
like: 4214 / dislike: 140
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실제 좋싫비: 0.9678456591639871
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실제 좋싫비: 0.9810035842293907
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count_like_label: 723
count_dislike_label: 277
댓글 좋싫비: 0.723
count_like: 13685
count_estimated_dislike: 296
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