diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..4d29575 --- /dev/null +++ b/.gitignore @@ -0,0 +1,23 @@ +# See https://help.github.com/articles/ignoring-files/ for more about ignoring files. + +# dependencies +/node_modules +/.pnp +.pnp.js + +# testing +/coverage + +# production +/build + +# misc +.DS_Store +.env.local +.env.development.local +.env.test.local +.env.production.local + +npm-debug.log* +yarn-debug.log* +yarn-error.log* diff --git a/Backend/.gitignore b/Backend/.gitignore new file mode 100644 index 0000000..ae47356 --- /dev/null +++ b/Backend/.gitignore @@ -0,0 +1,24 @@ +# common +*.pyc +*.csv +*.json +*.log +*.cfg +*.db +*.out +*.pth + +# dir +Ignore_to_Push/ +__pycache__/ + +# airflow +Airflow/logs/ +airflow-webserver.pid +airflow.cfg +webserver_config.py +Airflow/dags/practice.py + +# Backend +FastAPI/migrations +alembic.ini diff --git a/Backend/Airflow/dags/Import_Dataset_SQL.py b/Backend/Airflow/dags/Import_Dataset_SQL.py new file mode 100644 index 0000000..1603a32 --- /dev/null +++ b/Backend/Airflow/dags/Import_Dataset_SQL.py @@ -0,0 +1,78 @@ +from datetime import datetime, timedelta + +from airflow import DAG +from airflow.models.variable import Variable + +from airflow.operators.bash import BashOperator +from airflow.operators.python import PythonOperator + +import os +import sys +sys.path.append(os.path.abspath('../')) + +from utils.databases_import import get_CSV, import_dataset, get_citation_reference +from utils.paper_models_train import model_train_save + +default_args = { + 'depends_on_past' : True, + 'owner' : 'dohyun', + 'retries' : 3, + 'retry_delay' : timedelta(minutes=5) +} + +kaggle_api_key = Variable.get("kaggle_api_key") +kaggle_user = Variable.get("kaggle_username") + +api_key = Variable.get("semantic_api_key") # Airflow - site - variables 추가 +sql_user = Variable.get("sql_user") +sql_password = Variable.get("sql_password") +sql_port = Variable.get("sql_port") + +with DAG( + dag_id = 'Import_Dataset_SQL', + description = 'Import SQL with Target_date', + start_date = datetime(2010, 1, 1), + schedule_interval = '@monthly', + default_args = default_args, + tags = ['my_dags'], +) as dag : + + t1 = BashOperator( + task_id = 'Get_Json', + bash_command = f""" + export KAGGLE_USERNAME={kaggle_user} + export KAGGLE_KEY={kaggle_api_key} + kaggle datasets download Cornell-University/arxiv \-p /home/dohyun/Final_P/arxiv/ --unzip + """, + + ) + + t2 = PythonOperator( + task_id = 'Get_Csv', + python_callable = get_CSV, + op_args = ['{{execution_date}}'], + + ) + + t3 = PythonOperator( + task_id = 'Get_Citation_Reference', + python_callable = get_citation_reference, + op_args = ['{{execution_date}}', api_key], + + ) + + t4 = PythonOperator( + task_id = 'Import_MySQL', + python_callable = import_dataset, + op_args = ['{{execution_date}}', sql_user, sql_password, sql_port], + + ) + + t5 = PythonOperator( + task_id = 'Model_retraining', + python_callable = model_train_save, + op_args = ['{{execution_date}}', sql_user, sql_password, sql_port], + + ) + + t1 >> t2 >> t3 >> t4 >> t5 diff --git a/Backend/FastAPI/database.py b/Backend/FastAPI/database.py new file mode 100644 index 0000000..80eb916 --- /dev/null +++ b/Backend/FastAPI/database.py @@ -0,0 +1,12 @@ +from sqlalchemy import create_engine +from sqlalchemy.ext.declarative import declarative_base +from sqlalchemy.orm import sessionmaker + +SQLALCHEMY_DATABASE_URL = 'mysql+pymysql://dohyun:Dhyoon96!@localhost:3306/final_project' + +engine = create_engine( + SQLALCHEMY_DATABASE_URL, +) +SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine) + +Base = declarative_base() \ No newline at end of file diff --git a/Backend/FastAPI/domain/chat_data/chat_data.py b/Backend/FastAPI/domain/chat_data/chat_data.py new file mode 100644 index 0000000..ad55a73 --- /dev/null +++ b/Backend/FastAPI/domain/chat_data/chat_data.py @@ -0,0 +1,100 @@ +from typing import Annotated +from pydantic import BaseModel, Field +from datetime import datetime +from sqlalchemy.orm import Session +from fastapi import APIRouter, Depends, HTTPException +from starlette import status +from models import Chat, Message, PaperInfo +from database import SessionLocal +from domain.login_data.login_data import get_current_user +from sqlalchemy import asc, desc; + +router = APIRouter( + prefix='/chat', + tags=['chat'] +) + +def get_db(): + db = SessionLocal() + try: + yield db + finally: + db.close() + +db_dependency = Annotated[Session, Depends(get_db)] +user_dependency = Annotated[dict, Depends(get_current_user)] + +class ChatRequest(BaseModel): + paper_id : str + +class MessageRequest(BaseModel): + content : str + paper_id : str + time : datetime = Field(default_factory=datetime.now) + user_com : bool # 사용자면 0 / chatgpt면 1 + +@router.get("/room", status_code=status.HTTP_200_OK) +async def read_all_chat(user: user_dependency, db: db_dependency): + if user is None: + raise HTTPException(status_code=401, detail='Authentication Failed') + + return db.query(Chat).filter(Chat.user_id == user.get('id')).all() + +@router.get("/", status_code=status.HTTP_200_OK) +async def determine_chat(user: user_dependency, db: db_dependency, paper_id: str): + if user is None: + raise HTTPException(status_code=401, detail='Authentication Failed') + + Chat_model = db.query(Chat).filter(Chat.user_id == user.get('id')).filter(Chat.paper_id == paper_id).first() + + if Chat_model: + return db.query(Message).filter(Chat_model.chat_id == Message.chat_id).order_by(asc(Message.time)).all() + else: + return False + +@router.post("/", status_code=status.HTTP_201_CREATED) +async def create_chat(user: user_dependency, db: db_dependency, + chat_request: ChatRequest): + if user is None: + raise HTTPException(status_code=401, detail='Authentication Failed') + + Chat_model = db.query(Chat).filter(Chat.user_id == user.get('id')).filter(Chat.paper_id == chat_request.paper_id).first() + + if Chat_model is not None: + raise HTTPException(status_code=409, detail="Chatroom already exists") + else: + paper_title_model = db.query(PaperInfo).filter(PaperInfo.id == chat_request.paper_id) + + chat_model = Chat(paper_id = chat_request.paper_id, user_id=user.get('id'), paper_title = paper_title_model[0].title) + + db.add(chat_model) + db.commit() + +# text, paper_id, user_com(bool) 0이면 / 사용자 1이면 챗봇 +@router.post("/message", status_code=status.HTTP_201_CREATED) +async def create_message(user: user_dependency, db: db_dependency, + message_request: MessageRequest): + if user is None: + raise HTTPException(status_code=401, detail='Authentication Failed') + + chat_model = db.query(Chat).filter(Chat.paper_id == message_request.paper_id).first() + message_model = Message(content = message_request.content, chat_id = chat_model.chat_id, user_com = message_request.user_com) + + db.add(message_model) + db.commit() + +@router.delete("/", status_code=status.HTTP_200_OK) +async def delete_chat(user: user_dependency, db: db_dependency, + paper_id: str): + if user is None: + raise HTTPException(status_code=401, detail='Authentication Failed') + + Chat_model = db.query(Chat).filter(Chat.user_id == user.get('id')).filter(Chat.paper_id == paper_id).first() + if Chat_model is not None: + db.query(Message).filter(Message.chat_id == Chat_model.chat_id).delete() + db.delete(Chat_model) + db.commit() + else: + raise HTTPException(status_code=204, detail="NO_CONTENT") + + diff --git a/Backend/FastAPI/domain/get_data/get_data.py b/Backend/FastAPI/domain/get_data/get_data.py new file mode 100644 index 0000000..12bc3e7 --- /dev/null +++ b/Backend/FastAPI/domain/get_data/get_data.py @@ -0,0 +1,70 @@ +from fastapi import APIRouter, Depends, HTTPException + +from database import SessionLocal +from models import PaperInfo + +import sys +sys.path.append("/home/dohyun/Final_P/myapi") + +import torch +import torch.nn as nn + +from typing import Annotated +from starlette import status +from models import PaperInfo +from domain.login_data.login_data import get_current_user + +from sqlalchemy import or_ + +from paper_models_get import get_models_id + +######################################################################################################################## + +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + +class RNN(nn.Module): # Define the RNN model + + def __init__(self, input_size, hidden_size, num_layers, output_size): + super(RNN, self).__init__() + self.rnn = nn.RNN(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, batch_first=True) + self.fc = nn.Linear(hidden_size, output_size) + + def forward(self, x): + h0 = torch.zeros(self.rnn.num_layers, x.size(0), self.rnn.hidden_size).to(device) # Move initial hidden state to device + out, _ = self.rnn(x, h0) + out = self.fc(out[:, -1, :]) + return out + +model_rnn = RNN(input_size=1, hidden_size=128, num_layers=1, output_size=1).to(device) # Move model to device + +model_rnn.load_state_dict(torch.load('/home/dohyun/Final_P/paper_models/model_rnn_state_dict.pth', map_location=device)) + +router = APIRouter( + prefix="/api/data", +) +user_dependency = Annotated[dict, Depends(get_current_user)] + +@router.get("/get_data/{user_question}", status_code=status.HTTP_200_OK) +async def get_data(user: user_dependency, user_question: str): + db = SessionLocal() + + if user is None: + raise HTTPException(status_code=401, detail='Authentication Failed') + + user_question = user_question.strip() + + _data_list = db.query(PaperInfo).filter( + or_( + PaperInfo.title.like(f"%{user_question}%"), + PaperInfo.categories.like(f"%{user_question}%"), + ) + ).all() + + sorted_ids = get_models_id(model_rnn, _data_list) + sorted_ids = sorted_ids.values.tolist() + + print(sorted_ids) # 10개 + matched_papers = db.query(PaperInfo).filter(PaperInfo.id.in_(sorted_ids)).all() + + return matched_papers + \ No newline at end of file diff --git a/Backend/FastAPI/domain/login_data/login_data.py b/Backend/FastAPI/domain/login_data/login_data.py new file mode 100644 index 0000000..7feedd5 --- /dev/null +++ b/Backend/FastAPI/domain/login_data/login_data.py @@ -0,0 +1,96 @@ +from datetime import timedelta, datetime +from typing import Annotated +from fastapi import APIRouter, Depends, HTTPException +from pydantic import BaseModel,EmailStr +from sqlalchemy.orm import Session +from starlette import status +from database import SessionLocal +from models import Users +from passlib.context import CryptContext +from fastapi.security import OAuth2PasswordRequestForm, OAuth2PasswordBearer +from jose import jwt, JWTError + +router = APIRouter( + prefix="/api/login", +) + +SECRET_KEY = '197b2c37c391bed93fe80344fe73b806947a65e36206e05a1a23c2fa12702fe3' +ALGORITHM = 'HS256' + +bcrypt_context = CryptContext(schemes=['bcrypt'], deprecated='auto') +oauth2_bearer = OAuth2PasswordBearer(tokenUrl='/api/login/login') + +class CreateUserRequest(BaseModel): + email: EmailStr + password: str + username: str + +class Token(BaseModel): + access_token: str + token_type: str + +def get_db(): + db = SessionLocal() + try: + yield db + finally: + db.close() + +db_dependency = Annotated[Session, Depends(get_db)] + +def authenticate_user(email: str, password: str, db): + user = db.query(Users).filter(Users.email == email).first() + if not user: + return False + if not bcrypt_context.verify(password, user.hashed_password): + return False + return user + +def create_access_token(email:str, user_id: int, expires_delta: timedelta): + encode = {'sub': email, 'id': user_id} + expires = datetime.utcnow() + expires_delta + encode.update({'exp': expires}) + return jwt.encode(encode, SECRET_KEY, algorithm=ALGORITHM) + +async def get_current_user(token: Annotated[str, Depends(oauth2_bearer)]): + try: + payload = jwt.decode(token, SECRET_KEY, algorithms=[ALGORITHM]) + email: str = payload.get('sub') + user_id: int = payload.get('id') + if email is None or user_id is None: + raise HTTPException(status_code=status.HTTP_401_UNAUTHORIZED, + detail='Could not validate user.') + return {'email': email, 'id': user_id} + except JWTError: + raise HTTPException(status_code=status.HTTP_401_UNAUTHORIZED, + detail='Could not validate user.') + +@router.post("/signup", status_code=status.HTTP_201_CREATED) +async def create_user(db: db_dependency, + create_user_request: CreateUserRequest): + create_user_model = Users( + email=create_user_request.email, + username=create_user_request.username, + hashed_password=bcrypt_context.hash(create_user_request.password), + ) + + db.add(create_user_model) + db.commit() + +@router.post("/login", response_model=Token) +async def login_for_access_token(form_data: Annotated[OAuth2PasswordRequestForm, Depends()], + db: db_dependency): + user = authenticate_user(form_data.username, form_data.password, db) + if not user: + raise HTTPException(status_code=status.HTTP_401_UNAUTHORIZED, + detail='Could not validate user.') + token = create_access_token(user.email, user.user_id, timedelta(days=1)) + + return {'access_token': token, 'token_type': 'bearer'} + + + + + + + diff --git a/Backend/FastAPI/main.py b/Backend/FastAPI/main.py new file mode 100644 index 0000000..a152ff1 --- /dev/null +++ b/Backend/FastAPI/main.py @@ -0,0 +1,30 @@ +from fastapi import FastAPI +from starlette.middleware.cors import CORSMiddleware + +from domain.get_data import get_data +from domain.login_data import login_data +from domain.chat_data import chat_data + +app = FastAPI() + +origins = [ + "http://localhost:3000", + "http://localhost:3001", + "http://175.45.201.130:3000" +] + +app.add_middleware( + CORSMiddleware, + allow_origins=origins, + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + +@app.get("/") +def hello(): + return '안녕하세요?' + +app.include_router(get_data.router) +app.include_router(login_data.router) +app.include_router(chat_data.router) \ No newline at end of file diff --git a/Backend/FastAPI/models.py b/Backend/FastAPI/models.py new file mode 100644 index 0000000..943f508 --- /dev/null +++ b/Backend/FastAPI/models.py @@ -0,0 +1,45 @@ +from sqlalchemy import Column, Integer, String, Text, DateTime, JSON, ForeignKey, Boolean +from sqlalchemy.sql import func + +from database import Base + +class PaperInfo(Base): # 논문 + __tablename__ = "PaperInfo" + id = Column(String(255), nullable = False, primary_key=True) + title = Column(Text, nullable=False) + published_year = Column(Integer, nullable=False) + updated_year = Column(Integer, nullable=False) + abstract = Column(Text, nullable=False) + categories = Column(Text, nullable=False) + author1 = Column(Text, nullable=False) + citation_count = Column(Integer, nullable=False) + reference_count = Column(Integer, nullable=False) + citation_graph = Column(JSON) + +class Users(Base): # 사용자 login 관련 + __tablename__ = 'users' + user_id = Column(Integer, primary_key=True, index=True, unique=True) + email = Column(String(255), unique=True, nullable=False) + username = Column(String(255),nullable=False) + hashed_password = Column(String(255),nullable=False) + +class Keyword(Base): # keyword 저장 + __tablename__ = 'keyword' + keyword_id = Column(Integer, primary_key=True, index=True, unique=True) + content = Column(String(255),nullable=False) + user_id = Column(Integer, ForeignKey("users.user_id"),nullable=False) + +class Chat(Base): # 채팅 저장 + __tablename__ = 'chat' + chat_id = Column(Integer, primary_key=True, index=True, unique=True) + paper_id = Column(String(255), ForeignKey("PaperInfo.id"),nullable=False) + user_id = Column(Integer, ForeignKey("users.user_id"),nullable=False) + paper_title = Column(Text, nullable=False) + +class Message(Base): # 채팅방에서 했던 대화 내용 저장 + __tablename__ = 'message' + message_id = Column(Integer, primary_key=True, index=True, unique=True) + content = Column(Text,nullable=False) + chat_id = Column(Integer, ForeignKey("chat.chat_id"),nullable=False) + time = Column(DateTime(timezone=True), server_default=func.now()) + user_com = Column(Boolean, default=False) # 사용자면 0 / chatgpt면 1 \ No newline at end of file diff --git a/Backend/FastAPI/paper_models_get.py b/Backend/FastAPI/paper_models_get.py new file mode 100644 index 0000000..af4743b --- /dev/null +++ b/Backend/FastAPI/paper_models_get.py @@ -0,0 +1,136 @@ +import warnings +warnings.filterwarnings('ignore') + +import json +import pandas as pd + +import torch + +from tqdm import tqdm +from datetime import datetime +from collections import defaultdict + +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +now = datetime.now() + +target_start = '2010-01-01' +target_end = now.strftime('%Y-%m-%d') +interval = 1 + +def get_model_score(model, test_data) : + texst_sequences = torch.tensor(test_data, dtype=torch.float32).unsqueeze(-1) + texst_sequences1 = texst_sequences.unsqueeze(0).to(device) + + return model(texst_sequences1) + +def interval_change(str1) : + str1_dict = json.loads(str1) + return list(str1_dict.values()) + +def citation_list_interval(list1, int1) : + quote_dates = list1 + + quarterly_quotes = defaultdict(int) + max_quotes = 0 + + for date_str in quote_dates: + date = datetime.strptime(date_str, '%Y-%m-%d') + + year_quarter = (date.year, (date.month - 1) // int1 + 1) + quarterly_quotes[year_quarter] += int(list1.get(date_str)) - max_quotes + max_quotes = int(list1.get(date_str)) + + accumulated_quotes = defaultdict(int) + total_quotes = 0 + + start_year = min(quarterly_quotes.keys())[0] + start_quarter = min(quarterly_quotes.keys(), key=lambda x: (x[0], x[1]))[1] + end_year = max(quarterly_quotes.keys())[0] + end_quarter = max(quarterly_quotes.keys(), key=lambda x: (x[0], x[1]))[1] + + current_year = start_year + current_quarter = start_quarter + + while (current_year, current_quarter) <= (end_year, end_quarter): + total_quotes += quarterly_quotes[(current_year, current_quarter)] + if len(str(current_quarter)) == 1 : + keys_name = f'{current_year}-0{current_quarter}' + else : + keys_name = f'{current_year}-{current_quarter}' + accumulated_quotes[keys_name] = total_quotes + + current_quarter += 1 + if current_quarter > (12//int1): + current_year += 1 + current_quarter = 1 + + return accumulated_quotes + +def get_models_id(model, _data_list) : + id = [] + citation_graph = [] + + for a in _data_list : + id.append(a.id) + citation_graph.append(json.dumps(a.citation_graph)) + + citation_graph_list = pd.DataFrame({ + 'id': id, + 'citation_graph': citation_graph + }) + + for i, data in enumerate(tqdm(citation_graph_list['citation_graph'])) : + data = json.loads(data) + + last_key = max(data.keys()) + last_value = data[last_key] + + if target_end not in data.keys() : + data[target_end] = last_value + + data[target_start] = 0 + data = dict(sorted(data.items())) + + if min(data.keys()) != target_start : + + previous_key = None + for key in list(data.keys()): + if key == target_start : + data[key] = data[previous_key] + break + previous_key = key + + to_remove_start = [key for key in data if key < target_start] + for key in to_remove_start: + del data[key] + + if max(data.keys()) != target_end : + + previous_key = None + for key in list(data.keys())[::-1] : + if key == target_end : + data[key] = data[previous_key] + break + previous_key = key + + to_remove_end = [key for key in data if key > target_end] + for key in to_remove_end: + del data[key] + + data = citation_list_interval(data, interval) + + citation_graph_list.loc[i, f'citation_graph_interval'] = json.dumps(data) + citation_graph_list.loc[i, f'last_value'] = last_value + citation_graph_list.loc[i, f'data_value'] = json.dumps(list(data.values())) + + for i in range(len(citation_graph_list)) : + values = json.loads(citation_graph_list.loc[i, 'data_value']) + values = get_model_score(model, values) + + citation_graph_list.loc[i, 'model_value'] = values.item() + + sorted_df = citation_graph_list.sort_values(by='model_value', ascending=False) + sorted_ids = sorted_df['id'] + + return sorted_ids[:10] + diff --git a/Backend/utils/databases_import.py b/Backend/utils/databases_import.py new file mode 100644 index 0000000..a3652b7 --- /dev/null +++ b/Backend/utils/databases_import.py @@ -0,0 +1,483 @@ +import warnings +warnings.filterwarnings('ignore') + +import os +import json +import random +import pandas as pd +import requests + +import numpy as np +import matplotlib.pyplot as plt +import seaborn as sns + +import pymysql + +import re + +from pytz import timezone +from datetime import datetime +from collections import defaultdict +from tqdm import tqdm + +from keybert import KeyBERT +from sqlalchemy import create_engine + +FILE = '/home/dohyun/Final_P/arxiv/arxiv-metadata-oai-snapshot.json' + +def target_date_convert(str1) : + return str1[:7] + +def show_time(str1) : + now1 = datetime.now(timezone('Asia/Seoul')) + now2 = str(now1.strftime('%Y.%m.%d - %H:%M:%S')) + print('------------ ' + str1 + '_' + now2 + ' ------------') + +def get_data(): + with open(FILE) as f: + for line in f: + yield line + +category_ch = { + 'cs.AI' : 'Artificial Intelligence', + 'cmp-lg' : 'Computation and Language', + 'cs.AR' : 'Hardware Architecture', + 'cs.CC' : 'Computational Complexity', + 'cs.CE' : 'Computational Engineering, Finance and Science', + 'cs.CG' : 'Computational Geometry', + 'cs.CL' : 'Computational and Language', + 'cs.CR' : 'Cryptography and Security', + 'cs.CV' : 'Computer Vision and Pattern Recognition', + 'cs.CY' : 'Computers and Society', + 'cs.DB' : 'Databases', + 'cs.DC' : 'Distributed, Parallel and Cluster Computing', + 'cs.DL' : 'Digital Libraries', + 'cs.DM' : 'Discrete Mathematics', + 'cs.DS' : 'Data Structures and Algorithms', + 'cs.ET' : 'Emerging Technologies', + 'cs.FL' : 'Formal Languages and Automata Theory', + 'cs.GL' : 'General Literature', + 'cs.GR' : 'Graphics', + 'cs.GT' : 'Computer Science and Game Theory', + 'cs.HC' : 'Human-Computer Interaction', + 'cs.IR' : 'Information Retrieval', + 'cs.IT' : 'Information Theory', + 'cs.LG' : 'Machine Learning', + 'cs.LO' : 'Logic in Computer Science', + 'cs.MA' : 'Multiagent Systems', + 'cs.MM' : 'Multimedia', + 'cs.MS' : 'Mathematical Software', + 'cs.NA' : 'Numerical Analysis', + 'cs.NE' : 'Neural and Evolutionary Computing', + 'cs.NI' : 'Networking and Internet Architecture', + 'cs.OH' : 'Other Computer Science', + 'cs.OS' : 'Operating Systems', + 'cs.PF' : 'Performance', + 'cs.PL' : 'Programming Language', + 'cs.RO' : 'RObotics', + 'cs.SC' : 'Symbolic Computation', + 'cs.SD' : 'Sound', + 'cs.SE' : 'Software Engineering', + 'cs.SI' : 'Social and Information Networks', + 'cs.SY' : 'Systems and Control', + 'econ.EM' : 'Econometrics', + 'econ.GN' : 'General Economics', + 'econ.TH' : 'Theoretical Economics', + 'eess.AS' : 'Audio and Speech Processing', + 'eess.IV' : 'Image and Video Processing', + 'eess.SP' : 'Signal Processing', + 'eess.SY' : 'Systems and Control', + 'math.AC' : 'Commutative Algebra', + 'math.AG' : 'Algebraic Geometry', + 'alg-geom' : 'Algebraic Geometry', + 'math.AP' : 'Analysis of PDEs', + 'math.AT' : 'Algebraic Topology', + 'math.CA' : 'Classical Analysis and ODEs', + 'math.CO' : 'Combinatorics', + 'math.CT' : 'Category Theory', + 'math.CV' : 'Complex Variables', + 'math.DG' : 'Differential Geometry', + 'dg-ga' : 'Differential Geometry', + 'math.DS' : 'Dynamical Systems', + 'math.FA' : 'Functional Analysis', + 'funct-an' : 'Functional Analysis', + 'math.GM' : 'General Mathematics', + 'math.GN' : 'General Topology', + 'math.GR' : 'Group Theory', + 'math.GT' : 'Geometric Topology', + 'math.HO' : 'History and Overview', + 'math.IT' : 'Information Theory', + 'math.KT' : 'K-Theory and Homology', + 'math.LO' : 'Logic', + 'math.MG' : 'Metirc Geometry', + 'math.MP' : 'Mathematical Physics', + 'math.NA' : 'Numerical Analysis', + 'math.NT' : 'Number Theory', + 'math.OA' : 'Operator Algebras', + 'math.OC' : 'Optimization and Control', + 'math.PR' : 'Probability', + 'math.QA' : 'Quantum ALgebra', + 'q-alg' : 'Quantum Algebra and Topology', + 'math.RA' : 'Rings and Algebras', + 'math.RT' : 'Representation Theory', + 'math.SG' : 'Symplectic Geometry', + 'math.SP' : 'Spectral Theory', + 'math.ST' : 'Statistics Theory', + 'astro-ph' : 'Astrophysics', + 'astro-ph.CO' : 'Cosmology and Nongalactic Astrophysics', + 'astro-ph.EP' : 'Earth and Planetary Astrophysics', + 'astro-ph.GA' : 'Astrophysics of Galaxies', + 'astro-ph.HE' : 'High Energy Astrophysical Phenomena', + 'astro-ph.IM' : 'Instrumentation and Methods for Astrophysics', + 'astro-ph.SR' : 'Solar and Stellar Astrophysics', + 'cond-mat' : 'Condensed Matter', + 'cond-mat.dis-nn' : 'Disordered Systems and Neural Networks', + 'cond-mat.mes-hall' : 'Mesoscale and Nanoscale Physics', + 'cond-mat.mtrl-sci' : 'Materials Science', + 'mtrl-th' : 'Materials Theory', + 'cond-mat.other' : 'Other Condensed Matter', + 'cond-mat.quant-gas' : 'Quantum Gases', + 'cond-mat.soft' : 'Soft Condensed Matter', + 'cond-mat.stat-mech' : 'Statistical Mechanics', + 'cond-mat.str-el' : 'Strongly Correlated Electrons', + 'cond-mat.supr-con' : 'Superconductivity', + 'supr-con' : 'Superconductivity', + 'gr-qc' : 'General Relativity and Quantum Cosmology', + 'hep-ex' : 'High Energy Physics - Experiment', + 'hep-lat' : 'High Energy Physics - Lattice', + 'hep-ph' : 'High Energy Physics - Phenomenology', + 'hep-th' : 'High Energy Physics - Theory', + 'math-ph' : 'Mathematical Physics', + 'nlin.AO' : 'Adaptation and Self-Organizing Systems', + 'adap-org' : 'Adaptation and Self-Organizing Systems', + 'nlin.CD' : 'Chaotic Dynamics', + 'chao-dyn' : 'Chaotic Dynamics', + 'nlin.CG' : 'Cellular Automata and Lattice Gases', + 'comp-gas' : 'Cellular Automata and Lattice Gases', + 'nlin.PS' : 'Pattern Formation and Solitons', + 'patt-sol': 'Pattern Formation and Solitons', + 'nlin.SI' : 'Exactly Solvable and Integrable Systems', + 'solv-int' : 'Exactly Solvable and Integrable Systems', + 'nucl-ex' : 'Nuclear Experiment', + 'nucl-th' : 'Nuclear Theory', + 'physics.acc-ph' : 'Accelerator Physics', + 'acc-phys' : 'Accelerator Physics', + 'physics.ao-ph' : 'Atmospheric and Oceanic Physics', + 'physics.app-ph' : 'Applied Physics', + 'physics.atm-clus' : 'Atomic and Molecular Clusters', + 'physics.atom-ph' : 'Atomic Physics', + 'atom-ph' : 'Atomic Physics', + 'physics.bio-ph' : 'Biological Physics', + 'physics.chem-ph' : 'Chemical Physics', + 'chem-ph' : 'Chemical Physics', + 'physics.class-ph' : 'Classical Physics', + 'physics.comp-ph' : 'Computational Physics', + 'physics.data-an' : 'Data Analysis, Statistics and Probability', + 'physics.ed-ph' : 'Physics Education', + 'physics.flu-dyn' : 'Fluid Dynamics', + 'physics.gen-ph' : 'General Physics', + 'physics.geo-ph' : 'Geophysics', + 'physics.hist-ph' : 'History and Philosophy of Physics', + 'physics.ins-det' : 'Instrumentation and Detectors', + 'physics.med-ph' : 'Medical Physics', + 'physics.optics' : 'Optics', + 'physics.plasm-ph' : 'Plasma Physics', + 'plasm-ph' : 'Plasma Physics', + 'physics.pop-ph' : 'Popular Physics', + 'ao-sci' : 'Atmospheric-Oceanic Sciences', + 'bayes-an' : 'Bayesian Analysis', + 'physics.soc-ph' : 'Physics and Society', + 'physics.space-ph' : 'Space Physics', + 'quant-ph' : 'Quantum Physics', + 'q-bio.BM' : 'Biomolecules', + 'q-bio.CB' : 'Cell Behavior', + 'q-bio.GN' : 'Genomics', + 'q-bio.MN' : 'Molecular Networks', + 'q-bio.NC' : 'Neurons and Cognition', + 'q-bio.OT' : 'Other Quantitative Biology', + 'q-bio.PE' : 'Populations and Evolution', + 'q-bio.QM' : 'Quantitative Methods', + 'q-bio.SC' : 'Subcellular Process', + 'q-bio.TO' : 'Tissues and Organs', + 'q-fin.CP' : 'Computational Finance', + 'q-fin.EC' : 'Economics', + 'q-fin.GN' : 'General Finance', + 'q-fin.MF' : 'Mathematical Finance', + 'q-fin.PM' : 'Portfolio Management', + 'q-fin.PR' : 'Pricing of Securities', + 'q-fin.RM' : 'Risk Management', + 'q-fin.ST' : 'Statistical Finance', + 'q-fin.TR' : 'Trading and Market Microstructure', + 'stat.AP' : 'Applications', + 'stat.CO' : 'Computation', + 'stat.ME' : 'Methodology', + 'stat.ML' : 'Machine Learning', + 'stat.OT' : 'Other Statistics', + 'stat.TH' : 'Statistics Theory' + } + +def category_change(str1) : + return category_ch[str1] + +def id_list(str1) : + return f'ARXIV:{str1}' + +def get_citation(list1, api_key) : + + r = requests.post( + 'https://api.semanticscholar.org/graph/v1/paper/batch', + params={'fields':'citationCount,referenceCount,citations.publicationDate,year'}, + headers = {'x-api-key':api_key}, + json={"ids": list1} + ) + + return r.json() + +def get_citation_graph(data) : + temp = {} + + if data['citations'] : + + for citation in data['citations'] : + + if citation['publicationDate'] : + citation_year_month = citation['publicationDate'][:7] + citation_week = str((int(citation['publicationDate'][8:])//7)+1) + citation_time = citation_year_month + '-' + citation_week + + if citation_time not in temp : + temp[citation_time] = 1 + else : + temp[citation_time] += 1 + + temp = dict(sorted(temp.items())) + + time_values = list(temp.values()) + time_values_cumsum = np.cumsum(time_values) + + for a, b in zip(temp.keys(), time_values_cumsum) : + temp[a] = str(b) + + result = json.dumps(temp) + + return result + +def citation_list_interval(list1, int1) : + + quote_dates = json.loads(list1) + + quarterly_quotes = defaultdict(int) + max_quotes = 0 + + for date_str in quote_dates: + date = datetime.strptime(date_str, '%Y-%m-%d') + + year_quarter = (date.year, (date.month - 1) // int1 + 1) + quarterly_quotes[year_quarter] += int(quote_dates.get(date_str)) - max_quotes + max_quotes = int(quote_dates.get(date_str)) + + accumulated_quotes = defaultdict(int) + total_quotes = 0 + + start_year = min(quarterly_quotes.keys())[0] + start_quarter = min(quarterly_quotes.keys(), key=lambda x: (x[0], x[1]))[1] + end_year = max(quarterly_quotes.keys())[0] + end_quarter = max(quarterly_quotes.keys(), key=lambda x: (x[0], x[1]))[1] + + current_year = start_year + current_quarter = start_quarter + + while (current_year, current_quarter) <= (end_year, end_quarter): + total_quotes += quarterly_quotes[(current_year, current_quarter)] + if len(str(current_quarter)) == 1 : + keys_name = f'{current_year}-0{current_quarter}' + else : + keys_name = f'{current_year}-{current_quarter}' + accumulated_quotes[keys_name] = total_quotes + + current_quarter += 1 + if current_quarter > (12//int1): + current_year += 1 + current_quarter = 1 + + return accumulated_quotes + +def str_change(str1) : + return f'"{str1}"' + +################################ +def get_CSV(target_date1) : + + show_time('CSV START!!!') + + target_date = target_date_convert(target_date1) + + dataframe = { + 'id' : [], + 'title': [], + 'updated_year': [], + 'abstract': [], + 'categories' : [], + 'author1' : [], + } + + data = get_data() + + for i, paper in enumerate(data): + paper = json.loads(paper) + try: + date = paper['update_date'][:7] + title = paper['title'].replace('\n ', '') + category = paper['categories'].split(' ')[0] + author = ' '.join(paper['authors_parsed'][0][-2::-1]).strip() + + if date == target_date : + dataframe['id'].append(paper['id']) + dataframe['title'].append(title) + dataframe['updated_year'].append(int(date[:4])) + dataframe['abstract'].append(paper['abstract']) + dataframe['categories'].append(category_change(category)) + dataframe['author1'].append(author) + + except: pass + + df = pd.DataFrame(dataframe) + print(df.columns) + print(len(df)) + + del dataframe + + df_name = f'/home/dohyun/Final_P/csv_files/databases_{target_date}.csv' + df.to_csv(df_name, header = True, index = False ) + print(df_name) + + del df + + show_time('CSV DONE!!!') +################################ + +################################ +def get_citation_reference(target_date1, semantic_api_key) : + + show_time('Citation & Reference START!!!') + + target_date = target_date_convert(target_date1) + df_name = f'/home/dohyun/Final_P/csv_files/databases_{target_date}.csv' + + index_list = [] + + df = pd.read_csv(df_name) + print(df_name) + + for i in range(0, len(df), 500) : + df1 = df[i: i+500] + + df1_id_list = list(df1['id'].apply(id_list)) # + df1_id_result = get_citation(df1_id_list, semantic_api_key) + + for j, data in enumerate(df1_id_result) : + + if data : + result = get_citation_graph(data) + + df.loc[i+j, 'citation_count'] = int(data['citationCount']) + df.loc[i+j, 'reference_count'] = int(data['referenceCount']) + df.loc[i+j, 'published_year'] = int(data['year']) + df.loc[i+j, 'citation_graph'] = result + + else : + index_list.append(i+j) + + print(f'Wait! {i+500}th data Done!') + os.system('sleep 3') + + df = df.drop(index_list) + df = df.dropna(subset=['citation_count'], how='any', axis=0) + df = df.dropna(subset=['reference_count'], how='any', axis=0) + df = df.dropna(subset=['published_year'], how='any', axis=0) + + print(f'Before Length of DF : {len(df)}') + + condition = df['citation_graph'] == '{}' + + print(f'No citation data : {condition.sum()}') + + df.drop(df[condition].index, inplace=True) + + print(f'After Length of DF : {len(df)}') + + print(df.columns) + + df.to_csv(df_name, header = True, index = False ) + + del df + + show_time('Citation & Reference Done!!!') +################################ + +################################# +def import_dataset(target_date1, sql_user, sql_password, sql_port) : + + show_time('IMPORT START!!!') + + target_date = target_date_convert(target_date1) + + engine = create_engine(f'mysql+pymysql://{sql_user}:{sql_password}@localhost:{sql_port}/final_project') + conn = engine.connect() + + db = pymysql.connect( + host = 'localhost', # DATABASE_HOST + port = int(sql_port), + user = sql_user, # DATABASE_USERNAME + passwd = sql_password, # DATABASE_PASSWORD + db = 'final_project', # DATABASE_NAME + charset = 'utf8' + ) + + table_name = 'PaperInfo' + + table_sql = f'SELECT id FROM {table_name};' + table_id = pd.read_sql(table_sql, db) + + cursor = db.cursor() + + file_path = f'/home/dohyun/Final_P/csv_files/databases_{target_date}.csv' + data = pd.read_csv(file_path) + print(file_path) + + data.drop_duplicates(inplace = True) + + data_id = pd.DataFrame(data['id']) + + temp_id = pd.concat([table_id, data_id]) + + if temp_id.duplicated().any(): + show_time('Duplicated Database Start!!!') + print(temp_id[temp_id.duplicated()]) + + duplicated_id_df = pd.DataFrame(temp_id[temp_id.duplicated()]) + duplicated_id_df['id'] = duplicated_id_df['id'].apply(str_change) + duplicated_id_list = ','.join(duplicated_id_df['id']) + + duplicated_sql = f'Delete FROM {table_name} where id in ({duplicated_id_list})' + cursor.execute(duplicated_sql) + + db.commit() + + show_time('Duplicated Database Done!!!') + + print(len(data)) + + data.to_sql(table_name, con = conn, if_exists='append', index=False) + + del data + + db.commit() + + conn.close() + db.close() + + os.remove(file_path) + + show_time('IMPORT DONE!!!') +################################ \ No newline at end of file diff --git a/Backend/utils/paper_models_train.py b/Backend/utils/paper_models_train.py new file mode 100644 index 0000000..722f719 --- /dev/null +++ b/Backend/utils/paper_models_train.py @@ -0,0 +1,235 @@ +import warnings +warnings.filterwarnings('ignore') + +import json +import pandas as pd +import argparse + +import torch +import torch.nn as nn +import torch.optim as optim + +import pymysql + +import multiprocessing as mp + +from pytz import timezone +from datetime import datetime + +from torch.utils.data import DataLoader, TensorDataset +from collections import defaultdict + +device = "cuda" +torch.manual_seed(777) +torch.cuda.manual_seed_all(777) + +def target_date_convert(str1) : + return str1[:10] + +def show_time(str1) : + now1 = datetime.now(timezone('Asia/Seoul')) + now2 = str(now1.strftime('%Y.%m.%d - %H:%M:%S')) + print('------------ ' + str1 + '_' + now2 + ' ------------') + +def citation_list_interval(list1, int1) : + quote_dates = list1 + + quarterly_quotes = defaultdict(int) + max_quotes = 0 + + for date_str in quote_dates: + date = datetime.strptime(date_str, '%Y-%m-%d') + + year_quarter = (date.year, (date.month - 1) // int1 + 1) + quarterly_quotes[year_quarter] += int(list1.get(date_str)) - max_quotes + max_quotes = int(list1.get(date_str)) + + accumulated_quotes = defaultdict(int) + total_quotes = 0 + + start_year = min(quarterly_quotes.keys())[0] + start_quarter = min(quarterly_quotes.keys(), key=lambda x: (x[0], x[1]))[1] + end_year = max(quarterly_quotes.keys())[0] + end_quarter = max(quarterly_quotes.keys(), key=lambda x: (x[0], x[1]))[1] + + current_year = start_year + current_quarter = start_quarter + + while (current_year, current_quarter) <= (end_year, end_quarter): + total_quotes += quarterly_quotes[(current_year, current_quarter)] + if len(str(current_quarter)) == 1 : + keys_name = f'{current_year}-0{current_quarter}' + else : + keys_name = f'{current_year}-{current_quarter}' + accumulated_quotes[keys_name] = total_quotes + + current_quarter += 1 + if current_quarter > (12//int1): + current_year += 1 + current_quarter = 1 + + return accumulated_quotes + +def interval_change(str1) : + str1_dict = json.loads(str1) + + return list(str1_dict.values()) + +def create_sequences(data, sequence_length) : + sequences = [] + targets = [] + + for sequence in data: + input = sequence[:sequence_length] + label = sequence[sequence_length] + sequences.append(input) + targets.append(label) + + return sequences, targets + +def model_train_save(target_end, sql_user, sql_password, sql_port) : + + target_start = '2010-01-01' + target_end = target_date_convert(target_end) + interval = 1 + + print(target_end) + + db = pymysql.connect( + host = 'localhost', # DATABASE_HOST + port = int(sql_port), + user = sql_user, # DATABASE_USERNAME + passwd = sql_password, # DATABASE_PASSWORD + db = 'final_project', # DATABASE_NAME + charset = 'utf8' + ) + + citation_graph_sql = f'SELECT id, citation_graph FROM PaperInfo;' + citation_graph_list = pd.read_sql(citation_graph_sql, db) + + db.close() + + show_time(f'Wait!! {interval}_interval citation convert Start!!') + + print('target_start', target_start) + print('target_end', target_end) + print('interval', interval) + + for i, data in enumerate(citation_graph_list['citation_graph']) : + data = json.loads(data) + + last_key = max(data.keys()) + last_value = data[last_key] + + if target_end not in data.keys() : + data[target_end] = last_value + + data[target_start] = 0 + data = dict(sorted(data.items())) + + if min(data.keys()) != target_start : + + previous_key = None + for key in list(data.keys()): + if key == target_start : + data[key] = data[previous_key] + break + previous_key = key + + to_remove_start = [key for key in data if key < target_start] + for key in to_remove_start: + del data[key] + + if max(data.keys()) != target_end : + + previous_key = None + for key in list(data.keys())[::-1] : + if key == target_end : + data[key] = data[previous_key] + break + previous_key = key + + to_remove_end = [key for key in data if key > target_end] + for key in to_remove_end: + del data[key] + + data = citation_list_interval(data, interval) + citation_graph_list.loc[i, f'citation_graph_interval'] = json.dumps(data) + + if i % 100000 == 0 : + show_time(f'Wait!! {i}th data_{interval}_interval citation convert Done!!') + + show_time(f'Wait!! {interval}_interval citation convert Done!!') + + data = citation_graph_list['citation_graph_interval'].apply(interval_change) + + n = len(data[0]) + + sequence_length = n-1 + sequences, targets = create_sequences(data, sequence_length) + + sequences_tensor = torch.tensor(sequences, dtype=torch.float32).unsqueeze(-1) + targets_tensor = torch.tensor(targets, dtype=torch.float32).unsqueeze(-1) + + sequences_tensor = sequences_tensor.to(device) + targets_tensor = targets_tensor.to(device) + + print("sequences_tensor size:", sequences_tensor.size()) + print("targets_tensor size:", targets_tensor.size()) + + dataset = TensorDataset(sequences_tensor, targets_tensor) + + train_size = int(0.8 * len(dataset)) + val_size = len(dataset) - train_size + train_dataset, val_dataset = torch.utils.data.random_split(dataset, [train_size, val_size]) + + train_dataloader = DataLoader(train_dataset, batch_size=16, shuffle=True) + val_dataloader = DataLoader(val_dataset, batch_size=16, shuffle=False) + + class RNN(nn.Module) : + + def __init__(self, input_size, hidden_size, num_layers, output_size) : + super(RNN, self).__init__() + self.rnn = nn.RNN(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, batch_first=True) + self.fc = nn.Linear(hidden_size, output_size) + + def forward(self, x) : + h0 = torch.zeros(self.rnn.num_layers, x.size(0), self.rnn.hidden_size).to(device) + out, _ = self.rnn(x, h0) + out = self.fc(out[:, -1, :]) + return out + + rnn = RNN(input_size=1, hidden_size=128, num_layers=1, output_size=1).to(device) + criterion = nn.MSELoss() + optimizer = optim.Adam(rnn.parameters(), lr=0.001) + + num_epochs = 10 + for epoch in range(num_epochs) : + rnn.train() + train_loss = 0.0 + for seqs, targets in train_dataloader : + optimizer.zero_grad() + outputs = rnn(seqs) + loss = criterion(outputs, targets) + loss.backward() + optimizer.step() + train_loss += loss.item() * seqs.size(0) + train_loss /= len(train_dataloader.dataset) + + rnn.eval() + val_loss = 0.0 + with torch.no_grad() : + for seqs, targets in val_dataloader : + outputs = rnn(seqs) + loss = criterion(outputs, targets) + val_loss += loss.item() * seqs.size(0) + val_loss /= len(val_dataloader.dataset) + + print(f'Epoch {epoch+1}/{num_epochs}, Train Loss: {train_loss:.4f}, Val Loss: {val_loss:.4f}') + + path = '/home/dohyun/Final_P/paper_models/' + torch.save(rnn.state_dict(), path + 'model_rnn_state_dict.pth') + +if __name__ == '__main__': + mp.set_start_method('spawn', force=True) + model_train_save(target_end, sql_user, sql_password, sql_port) diff --git a/Frontend/back/__pycache__/main.cpython-38.pyc b/Frontend/back/__pycache__/main.cpython-38.pyc new file mode 100644 index 0000000..63e9d74 Binary files /dev/null and b/Frontend/back/__pycache__/main.cpython-38.pyc differ diff --git a/Frontend/back/create_json.py b/Frontend/back/create_json.py new file mode 100644 index 0000000..4b9cb6b --- /dev/null +++ b/Frontend/back/create_json.py @@ -0,0 +1,60 @@ +import json +import os + +r""" +nodes: [ + { # paper에 대한 정보 + id: str -> xxxx.xxxx # 챗봇 연결링크 생성시 필요 + title: str -> Attention is ~ # 유저한테 보여줄 논문 제목 + size: int # 인용수 + hieght: int # 부모 노드에 연결될 Edge 굵기: 1hop 2hop 구분으로 사용? + color: int # 노드 특성 반영한 한가지 값으로 주면 투명도로 조절해서 표시할 수 있음. + }, ... +], +edges: [ + { + source: str -> title # 자식 노드 제목 + to: str -> title # 부모 노드 제목 + distance -> int [0 ~ 1] # 모델 output 유사도 + }, ... +] +""" + +# Example Output +data = { + "nodes": [ + { + "id": "검색 논문", # 논문 제목이나 고유 ItemID나 상관 없는데 제목이면 보이는건 좀 더 나을 수 있음 + "height": 2, # 연결된 Edge들의 두께 -> Edge 하나하나 Custom 안되는 것 같으니 당장은 꾸미는 용으로만 + "size": 32, # 노드의 크기 -> 중요도 순? 인용수? 사용 가능할듯 + "color": "rgb(244, 117, 96)" # default로 설정하면 간선이 안보이니까 너무 이상한 값만 아니면 상관 X + }, + { + "id": "추천 1", + "height": 1, + "size": 24, + "color": "rgb(97, 205, 187)" + }, + { + "id": "추천 1.1", + "height": 0, + "size": 12, + "color": "rgb(232, 193, 160)" + } + ], + "links": [ + { + "source": "검색 논문", # from + "target": "추천 1", # to + "distance": 80 # 노드 사이의 거리 -> 유사도 순으로 사용 가능할 듯. 모델 디벨롭 한 뒤에 결정하면 될 듯 + }, + { + "source": "추천 1", + "target": "추천 1.1", + "distance": 50 + } + ] +} + +with open(os.path.join('data', 'test.json'), 'w') as json_file: + json.dump(data, json_file, ensure_ascii=False) # ensure_ascii True 하면 한글이 안보임 ㅋㅋ \ No newline at end of file diff --git a/Frontend/back/data/data.json b/Frontend/back/data/data.json new file mode 100644 index 0000000..01301c5 --- /dev/null +++ b/Frontend/back/data/data.json @@ -0,0 +1,237 @@ +{ + "nodes": [ + { + "id": "검색 논문", + "height": 2, + "size": 32, + "color": "rgb(244, 117, 96)" + }, + { + "id": "추천 1", + "height": 1, + "size": 24, + "color": "rgb(97, 205, 187)" + }, + { + "id": "추천 2", + "height": 1, + "size": 24, + "color": "rgb(97, 205, 187)" + }, + { + "id": "추천 3", + "height": 1, + "size": 24, + "color": "rgb(97, 205, 187)" + }, + { + "id": "추천 4", + "height": 1, + "size": 24, + "color": "rgb(97, 205, 187)" + }, + { + "id": "추천 5", + "height": 1, + "size": 24, + "color": "rgb(97, 205, 187)" + }, + { + "id": "추천 1.0", + "height": 0, + "size": 12, + "color": "rgb(232, 193, 160)" + }, + { + "id": "추천 1.1", + "height": 0, + "size": 12, + "color": "rgb(232, 193, 160)" + }, + { + "id": "추천 1.2", + "height": 0, + "size": 12, + "color": "rgb(232, 193, 160)" + }, + { + "id": "추천 2.0", + "height": 0, + "size": 12, + "color": "rgb(232, 193, 160)" + }, + { + "id": "추천 2.1", + "height": 0, + "size": 12, + "color": "rgb(232, 193, 160)" + }, + { + "id": "추천 2.2", + "height": 0, + "size": 12, + "color": "rgb(232, 193, 160)" + }, + { + "id": "추천 3.0", + "height": 0, + "size": 12, + "color": "rgb(232, 193, 160)" + }, + { + "id": "추천 3.1", + "height": 0, + "size": 12, + "color": "rgb(232, 193, 160)" + }, + { + "id": "추천 3.2", + "height": 0, + "size": 12, + "color": "rgb(232, 193, 160)" + }, + { + "id": "추천 4.0", + "height": 0, + "size": 12, + "color": "rgb(232, 193, 160)" + }, + { + "id": "추천 4.1", + "height": 0, + "size": 12, + "color": "rgb(232, 193, 160)" + }, + { + "id": "추천 4.2", + "height": 0, + "size": 12, + "color": "rgb(232, 193, 160)" + }, + { + "id": "추천 5.0", + "height": 0, + "size": 12, + "color": "rgb(232, 193, 160)" + }, + { + "id": "추천 5.1", + "height": 0, + "size": 12, + "color": "rgb(232, 193, 160)" + }, + { + "id": "추천 5.2", + "height": 0, + "size": 12, + "color": "rgb(232, 193, 160)" + } + ], + "links": [ + { + "source": "검색 논문", + "target": "추천 1", + "distance": 100 + }, + { + "source": "추천 1", + "target": "추천 4", + "distance": 100 + }, + { + "source": "추천 1", + "target": "추천 1.0", + "distance": 50 + }, + { + "source": "추천 1", + "target": "추천 1.1", + "distance": 50 + }, + { + "source": "추천 1", + "target": "추천 1.2", + "distance": 50 + }, + { + "source": "검색 논문", + "target": "추천 2", + "distance": 100 + }, + { + "source": "추천 2", + "target": "추천 2.0", + "distance": 50 + }, + { + "source": "추천 2", + "target": "추천 2.1", + "distance": 50 + }, + { + "source": "추천 2", + "target": "추천 2.2", + "distance": 50 + }, + { + "source": "검색 논문", + "target": "추천 3", + "distance": 100 + }, + { + "source": "추천 3", + "target": "추천 3.0", + "distance": 50 + }, + { + "source": "추천 3", + "target": "추천 3.1", + "distance": 50 + }, + { + "source": "추천 3", + "target": "추천 3.2", + "distance": 50 + }, + { + "source": "검색 논문", + "target": "추천 4", + "distance": 80 + }, + { + "source": "추천 4", + "target": "추천 4.0", + "distance": 50 + }, + { + "source": "추천 4", + "target": "추천 4.1", + "distance": 50 + }, + { + "source": "추천 4", + "target": "추천 4.2", + "distance": 50 + }, + { + "source": "검색 논문", + "target": "추천 5", + "distance": 100 + }, + { + "source": "추천 5", + "target": "추천 5.0", + "distance": 50 + }, + { + "source": "추천 5", + "target": "추천 5.1", + "distance": 50 + }, + { + "source": "추천 5", + "target": "추천 5.2", + "distance": 50 + } + ] +} \ No newline at end of file diff --git a/Frontend/back/data/mkdata.json b/Frontend/back/data/mkdata.json new file mode 100644 index 0000000..e8d17a2 --- /dev/null +++ b/Frontend/back/data/mkdata.json @@ -0,0 +1 @@ +{"nodes": [{"title": "Four-dimensional differential equations for the leading divergences of\n dimensionally-regulated loop integrals", "author": "Rourou Ma", "year": 2023, "id": "2211.13967", "height": 2, "size": 88, "color": "rgb(11, 96, 176, 0.4)"}, {"title": "Algorithm for differential equations for Feynman integrals in general\n dimensions", "author": "Pierre Vanhove", "year": 2024, "id": "2401.09908", "height": 1, "size": 51, "color": "rgb(11, 96, 176, 0.45)"}, {"title": "UV Divergence and Tensor Reduction", "author": "Qingjun Jin", "year": 2022, "id": "2203.11554", "height": 1, "size": 41, "color": "rgb(11, 96, 176, 0.35)"}, {"title": "The SAGEX Review on Scattering Amplitudes, Chapter 3: Mathematical\n structures in Feynman integrals", "author": "Ruth Britto", "year": 2023, "id": "2203.13014", "height": 1, "size": 61, "color": "rgb(11, 96, 176, 0.4)"}, {"title": "$D$-Module Techniques for Solving Differential Equations in the Context\n of Feynman Integrals", "author": "Anna-Laura Sattelberger", "year": 2023, "id": "2303.11105", "height": 1, "size": 33, "color": "rgb(11, 96, 176, 0.4)"}, {"title": "Calculation of one-loop integrals for four-photon amplitudes by\n functional reduction method", "author": "Oleg Tarasov Dr.", "year": 2023, "id": "2211.15535", "height": 1, "size": 31, "color": "rgb(11, 96, 176, 0.4)"}], "links": [{"source": "2401.09908", "target": "2211.13967", "distance": 65}, {"source": "2203.11554", "target": "2211.13967", "distance": 92}, {"source": "2203.13014", "target": "2211.13967", "distance": 4}, {"source": "2303.11105", "target": "2211.13967", "distance": 2}, {"source": "2211.15535", "target": "2211.13967", "distance": 3}]} \ No newline at end of file diff --git a/Frontend/back/data/test.json b/Frontend/back/data/test.json new file mode 100644 index 0000000..03bdfa1 --- /dev/null +++ b/Frontend/back/data/test.json @@ -0,0 +1 @@ +{"nodes": [{"id": "검색 논문", "height": 2, "size": 32, "color": "rgb(244, 117, 96)"}, {"id": "추천 1", "height": 1, "size": 24, "color": "rgb(97, 205, 187)"}, {"id": "추천 1.1", "height": 0, "size": 12, "color": "rgb(232, 193, 160)"}], "links": [{"source": "검색 논문", "target": "추천 1", "distance": 80}, {"source": "추천 1", "target": "추천 1.1", "distance": 50}]} \ No newline at end of file diff --git a/Frontend/back/main.py b/Frontend/back/main.py new file mode 100644 index 0000000..ec10483 --- /dev/null +++ b/Frontend/back/main.py @@ -0,0 +1,68 @@ +from fastapi import FastAPI, File, UploadFile, Form +from fastapi.middleware.cors import CORSMiddleware +from fastapi.responses import JSONResponse, FileResponse, Response +from fastapi.encoders import jsonable_encoder +import json +from pydantic import BaseModel +import requests +import os +import shutil +import base64 + +app = FastAPI() + +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], + allow_credentials=True, + allow_methods=["GET", "POST", "PUT", "DELETE"], + allow_headers=["*"], +) + +UPLOAD_DIRECTORY = './' +img_directory = "./" + +DATA_PATH = '/dev/shm/back/data' +@app.get("/graph/{file}") +async def get_graph(file: str = 'data'): + # retrival 위해 저장한 값 불러오는 식으로 구성했지만 실제 서빙때는 걍 실시간으로 만들어서 보내주기만 하면 됨. + try: + with open(os.path.join(DATA_PATH, f"{file}.json"), "r") as f: + data = json.load(f) + # print(data) + return JSONResponse(status_code=200, content=data) + except Exception as e: + print(e) + + +@app.get('/api/data/chatbot/{paper_id}/{query}') +async def get_chatbot(paper_id: str, query: str): + data ={'paper_id': paper_id, 'query': query} + data = requests.post("http://223.130.162.53:8000/predict", json=data) + + data = data.json() + print(data) + + q_and_a = data['prediction'].split('\n\nReferences\n\n')[0] + + _answer = "\n".join(q_and_a.split("\n\n")[1:]) + try: + _Reference = "".join(data['prediction'].split('\n\nReferences\n\n')[1]) + except: + _Reference = str(data['prediction'].split('\n\nReferences\n\n')[1]) + + + chatbot = {'answer' : _answer, 'Reference' : _Reference} + print(_Reference) + + return chatbot + + +papers = [] +@app.get("/api/data/sidebar/{paper_id}") +async def get_sidebar(paper_id: str): + print(papers, paper_id) + if paper_id in papers: return papers + papers.append(paper_id) + + return papers \ No newline at end of file diff --git a/Frontend/back/text.txt b/Frontend/back/text.txt new file mode 100644 index 0000000..e69de29 diff --git a/Frontend/back/uploads/text.txt b/Frontend/back/uploads/text.txt new file mode 100644 index 0000000..e69de29 diff --git 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0000000..5a60068 Binary files /dev/null and b/Frontend/front/public/Alex.webp differ diff --git a/Frontend/front/public/PDF_1.png b/Frontend/front/public/PDF_1.png new file mode 100644 index 0000000..bf5baf5 Binary files /dev/null and b/Frontend/front/public/PDF_1.png differ diff --git a/Frontend/front/public/book.jpg b/Frontend/front/public/book.jpg new file mode 100644 index 0000000..c7e60ce Binary files /dev/null and b/Frontend/front/public/book.jpg differ diff --git a/Frontend/front/public/chatbot.png b/Frontend/front/public/chatbot.png new file mode 100644 index 0000000..bc538be Binary files /dev/null and b/Frontend/front/public/chatbot.png differ diff --git a/Frontend/front/public/delete_1.png b/Frontend/front/public/delete_1.png new file mode 100644 index 0000000..8cd757b Binary files /dev/null and b/Frontend/front/public/delete_1.png differ diff --git a/Frontend/front/public/favicon.ico b/Frontend/front/public/favicon.ico new file mode 100644 index 0000000..a11777c Binary files /dev/null and b/Frontend/front/public/favicon.ico differ diff --git a/Frontend/front/public/index.html b/Frontend/front/public/index.html new file mode 100644 index 0000000..aa069f2 --- /dev/null +++ b/Frontend/front/public/index.html @@ -0,0 +1,43 @@ + + + + + + + + + + + + + React App + + + +
+ + + diff --git a/Frontend/front/public/logo_4.png b/Frontend/front/public/logo_4.png new file mode 100644 index 0000000..5e26bfe Binary files /dev/null and b/Frontend/front/public/logo_4.png differ diff --git a/Frontend/front/public/manifest.json b/Frontend/front/public/manifest.json new file mode 100644 index 0000000..080d6c7 --- /dev/null +++ b/Frontend/front/public/manifest.json @@ -0,0 +1,25 @@ +{ + "short_name": "React App", + "name": "Create React App Sample", + "icons": [ + { + "src": "favicon.ico", + "sizes": "64x64 32x32 24x24 16x16", + "type": "image/x-icon" + }, + { + "src": "logo192.png", + "type": "image/png", + "sizes": "192x192" + }, + { + "src": "logo512.png", + "type": "image/png", + "sizes": "512x512" + } + ], + "start_url": ".", + "display": "standalone", + "theme_color": "#000000", + "background_color": "#ffffff" +} diff --git a/Frontend/front/public/notice.png b/Frontend/front/public/notice.png new file mode 100644 index 0000000..60131c7 Binary files /dev/null and b/Frontend/front/public/notice.png differ diff --git a/Frontend/front/public/opacity30.png b/Frontend/front/public/opacity30.png new file mode 100644 index 0000000..6b446f8 Binary files /dev/null and b/Frontend/front/public/opacity30.png differ diff --git a/Frontend/front/public/robots.txt b/Frontend/front/public/robots.txt new file mode 100644 index 0000000..e9e57dc --- /dev/null +++ b/Frontend/front/public/robots.txt @@ -0,0 +1,3 @@ +# https://www.robotstxt.org/robotstxt.html +User-agent: * +Disallow: diff --git a/Frontend/front/src/App.css b/Frontend/front/src/App.css new file mode 100644 index 0000000..f7bf749 --- /dev/null +++ b/Frontend/front/src/App.css @@ -0,0 +1,46 @@ +@import url('https://fonts.googleapis.com/css2?family=Roboto:wght@400;900&display=swap'); + +body { + box-sizing: border-box; + margin: 0 auto; + padding: 0; + font-family: 'Roboto', sans-serif; + color: #000000; + background-image: url('../public/opacity30.png'); + background-size: cover; /* 배경 이미지를 화면에 맞게 조절합니다. */ + isolation: isolate; +} + +body::after { + content: ""; + z-index: -1; + inset: 0; + background: white; + opacity: .5; +} + +.menu { + margin-top: 10px; + margin-left: 10px; + font-size: 30px; +} + +ul li a { + color: antiquewhite; + text-decoration: none; + font-size: 16px; + padding: 5px; + border-radius: 0; +} + +header svg { + font-size: 30px; + color: antiquewhite; +} + +.horizontal-list { + list-style: none; + padding: 0; + flex-direction: row; + position: relative; +} diff --git a/Frontend/front/src/App.js b/Frontend/front/src/App.js new file mode 100644 index 0000000..f429dc5 --- /dev/null +++ b/Frontend/front/src/App.js @@ -0,0 +1,30 @@ +import "./App.css"; +import "./styles.css"; +import UploadForm from './pages/UploadForm'; +import Chat from "./pages/chat"; +import Login from './pages/Login'; +import SignUp from './pages/SignUp'; +import { AuthProvider } from './components/AuthContext'; +import Navbar from "./Navbar"; + +import { Routes, Route, BrowserRouter } from "react-router-dom"; + +export default function App() { + + return ( +
+ + + + + } /> + } /> + } /> + } /> + + + +
+ ); +} +// paperId, query -> query only \ No newline at end of file diff --git a/Frontend/front/src/App.test.js b/Frontend/front/src/App.test.js new file mode 100644 index 0000000..1f03afe --- /dev/null +++ b/Frontend/front/src/App.test.js @@ -0,0 +1,8 @@ +import { render, screen } from '@testing-library/react'; +import App from './App'; + +test('renders learn react link', () => { + render(); + const linkElement = screen.getByText(/learn react/i); + expect(linkElement).toBeInTheDocument(); +}); diff --git a/Frontend/front/src/Navbar.css b/Frontend/front/src/Navbar.css new file mode 100644 index 0000000..1be922a --- /dev/null +++ b/Frontend/front/src/Navbar.css @@ -0,0 +1,88 @@ +@font-face { + font-family: 'yg-jalnan'; + src: url('https://cdn.jsdelivr.net/gh/projectnoonnu/noonfonts_four@1.2/JalnanOTF00.woff') format('woff'); + font-weight: normal; + font-style: normal; +} + .font_logo{ + font-family: 'yg-jalnan', sans-serif; + font-weight: 200; + color: #007bff; + text-decoration: none; /* 밑줄 제거 */ + display: inline-block; /* 인라인 블록 요소로 변경하여 여백 지정 가능 */ + padding: 10px 15px; /* 위아래 왼쪽오른쪽 여백 설정 */ + position: relative; /* 요소를 상대적으로 위치시킵니다. */ + top: -15px; /* 위쪽으로 5px 이동 */ +} + +.navbar { + display: flex; + flex-wrap: wrap; + justify-content: space-between; /* 로고와 버튼 사이의 간격을 최대한 벌립니다. */ + align-items: center; /* 로고와 버튼을 수직으로 정렬합니다. */ + top: 0; /* 화면 맨 위에 위치 */ + left: 0; /* 왼쪽에 위치 */ + width: 100%; /* 화면 전체 너비를 차지 */ + background-color: #333; /* 배경색 지정 */ + height: 80px; + } + .logo { + width: 60px; + height: 60px; + margin-left: 20px; + +} + .signinup-button { + font-family: 'yg-jalnan', sans-serif; + font-weight: 100; + display: inline-block; + padding: 10px 20px; + background-color: #007bff; + color: #fff; + border: none; + border-radius: 5px; + cursor: pointer; + margin-right: 30px; /* 각 버튼 사이의 간격을 조절할 수 있습니다. */ + margin-left: auto; + position: relative; /* 요소를 상대적으로 위치시킵니다. */ + top: -15px; /* 위쪽으로 5px 이동 */ + } + .logout-button { + font-family: 'yg-jalnan', sans-serif; + font-weight: 100; + font-size: 16px; + display: inline-block; + padding: 10px 20px; + background-color: #ff6a00; + color: #fff; + border: none; + border-radius: 5px; + cursor: pointer; + margin-right: 30px; /* 각 버튼 사이의 간격을 조절할 수 있습니다. */ + margin-left: auto; + position: relative; /* 요소를 상대적으로 위치시킵니다. */ + top: -15px; /* 위쪽으로 5px 이동 */ + } + + .navbar ul { + list-style-type: none; /* 목록 기호 제거 */ + margin: 0.5%; + padding: 0; + } + + .navbar ul li { + display: inline; /* 가로로 배치 */ + } + + .navbar ul li a { + display: inline-block; /* 인라인 블록 요소로 변경하여 여백 지정 가능 */ + padding: 10px 15px; /* 위아래 왼쪽오른쪽 여백 설정 */ + color: #fff; /* 글자색 지정 */ + text-decoration: none; /* 밑줄 제거 */ + } + + /* 마우스 호버시 배경색 변경 */ + .navbar ul li a:hover { + background-color: #555; + } + diff --git a/Frontend/front/src/Navbar.js b/Frontend/front/src/Navbar.js new file mode 100644 index 0000000..b00a8da --- /dev/null +++ b/Frontend/front/src/Navbar.js @@ -0,0 +1,40 @@ +import React from 'react'; +import { Link } from 'react-router-dom'; +import './Navbar.css'; // Navbar 스타일을 정의하는 CSS 파일을 import +import { useAuth } from './components/AuthContext'; + +const Navbar = () => { + const { logout, token } = useAuth(); + const handleLogout = async () => { + logout(); + } + return ( +
+
+ + +

PRQAS

+ +
+ {token ? ( +
+ + + +
+ ) : ( +
+ +
+ Sign in/Sign up +
+ +
+ )} +
+ ); +}; + +export default Navbar; \ No newline at end of file diff --git a/Frontend/front/src/components/AuthContext.js b/Frontend/front/src/components/AuthContext.js new file mode 100644 index 0000000..14f643c --- /dev/null +++ b/Frontend/front/src/components/AuthContext.js @@ -0,0 +1,33 @@ +import React, { createContext, useContext, useState } from 'react'; +import { useNavigate } from 'react-router-dom'; + +const AuthContext = createContext(); + +export const AuthProvider = ({ children }) => { + const navigate = useNavigate(); + const [token, setToken] = useState(() => { + const storedToken = localStorage.getItem('accessToken'); + return storedToken ? JSON.parse(storedToken) : null; + }); + const login = (newToken) => { + setToken(newToken); + localStorage.setItem('accessToken', JSON.stringify(newToken)); + navigate('/'); + }; + + const logout = () => { + setToken(null); + localStorage.removeItem('accessToken'); + navigate('/'); + }; + + return ( + + {children} + + ); +}; + +export const useAuth = () => { + return useContext(AuthContext); +}; \ No newline at end of file diff --git a/Frontend/front/src/components/ExampleChatBotWithBackend.js b/Frontend/front/src/components/ExampleChatBotWithBackend.js new file mode 100644 index 0000000..6fb4cfb --- /dev/null +++ b/Frontend/front/src/components/ExampleChatBotWithBackend.js @@ -0,0 +1,301 @@ +import PropTypes from 'prop-types'; +import ChatBot, { Loading } from 'react-simple-chatbot'; +import axios from 'axios'; +import React, { Component } from 'react'; +import './ExampleChatbotWithBackend.css'; +import Latex from 'react-latex-next'; +import 'katex/dist/katex.min.css'; +import './chat.css' +// import ReactHtmlParser from 'react-html-parser'; + +class PreviousSessionLogs extends Component { + constructor(props) { + super(props); + + this.state = { + loading: true, + logs: [], + }; + + this.exampleQuestions = [ + { 'content': 'History not found: Lead to sample questions!' }, + ]; + } + + componentDidMount() { + const accessToken = JSON.parse(localStorage.getItem('accessToken')); + const params = new URLSearchParams(window.location.search); + const paperId = params.get('paper_id'); + + const axiosInstance = axios.create({ + headers: { + Authorization: `Bearer ${accessToken.access_token}` + } + }); + + axiosInstance.get(`http://223.130.141.170:8000/chat/?paper_id=${paperId}`) + .then(response => { + if (response.data === false) { + axiosInstance.post(`http://223.130.141.170:8000/chat/`, + { + "paper_id": paperId + } + ).then(() => { + window.location.reload(); + }).catch(error => { + console.error('Error posting initial message:', error); + this.setState({ loading: false }); + }); + } + else { + let logs = response.data; + if (logs.length === 0) { + logs = this.exampleQuestions; // 클래스 속성으로 정의된 exampleQuestions 사용 + } + this.setState({ logs, loading: false }); + } + }) + .catch(error => { + alert("Error Fetching Data"); + const logs = this.exampleQuestions; // 클래스 속성으로 정의된 exampleQuestions 사용 + console.error('Error fetching previous logs:', error); + this.setState({ logs, loading: false }); + }); + } + + render() { + const { loading, logs } = this.state; + const params = new URLSearchParams(window.location.search); + const paperId = params.get('paper_id'); + console.log(logs); + + return ( +
+ {loading ?

Loading...

: + + } +
+ To see an original paper PDF, Click: + + PDF2 + +
+
+ ); + } +} + +class ChatBotWithBackend extends Component { + constructor(props) { + super(props); + + this.state = { + loading: true, + result: '', + trigger: false, + }; + + this.triggetNext = this.triggetNext.bind(this); + } + + componentDidMount() { + const queryString = window.location.search; + const urlParams = new URLSearchParams(queryString); + const paperId = urlParams.get('paper_id'); + const accessToken = JSON.parse(localStorage.getItem('accessToken')); + + axios.get(`http://223.130.128.44:8000/api/data/chatbot/${paperId}/${this.props.previousStep.message}`) + .then(response => { + const data = response.data; + if (data) { + this.setState({ loading: false, result: data }); + + const userMessage = { + "content": this.props.previousStep.message, + "paper_id": paperId, + "user_com": false + }; + + const botMessage = { + "content": removeQuotes(JSON.stringify(data.answer)), + "paper_id": paperId, + "user_com": true + }; + + axios.post(`http://223.130.141.170:8000/chat/message`, userMessage, { + headers: { + Authorization: `Bearer ${accessToken.access_token}` + } + }) + .then(response => { + console.log(response.data); + axios.post(`http://223.130.141.170:8000/chat/message`, botMessage, { + headers: { + Authorization: `Bearer ${accessToken.access_token}` + } + }) + .then(response => { + console.log('Data successfully sent:', response.data); + }) + .catch(error => { + console.error('Error sending machine answer data:', error); + }); + }) + .catch(error => { + console.error('Error sending user question data:', error); + }); + + } else { + this.setState({ loading: false, result: 'Not found.' }); + } + }) + .catch(error => { + console.error('Error fetching data:', error); + this.setState({ loading: false, result: 'Error fetching data.' }); + }); + + this.props.triggerNextStep(); + } + + triggetNext() { + this.setState({ trigger: true }, () => { + this.props.triggerNextStep(); + }); + } + + render() { + const { trigger, loading, result } = this.state; + + return ( +
+ {loading ? :
+ {removeQuotes(JSON.stringify(result.answer, null, 2)).replace(/\\["n\\]/g, match => { + if (match === '\\n') return '\n'; // 대체 패턴이 \\n인 경우 줄 바꿈으로 대체 + if (match === '\\"') return '"'; // 대체 패턴이 \\"인 경우 따옴표로 대체 + if (match === '\\\\') return '\\'; // 대체 패턴이 \인 경우는 그대로 출력 + }) + } + +
+ {removeQuotes(JSON.stringify(result.Reference)).replace(/\\["n\\]/g, match => { + if (match === '\\n') return '\n'; // 대체 패턴이 \\n인 경우 줄 바꿈으로 대체 + if (match === '\\"') return '"'; // 대체 패턴이 \\"인 경우 따옴표로 대체 + if (match === '\\\\') return '\\'; // 대체 패턴이 \인 경우는 그대로 출력 + }) + } +
+
} +
+ ); + } +} + +ChatBotWithBackend.propTypes = { + triggerNextStep: PropTypes.func, +}; + +ChatBotWithBackend.defaultProps = { + triggerNextStep: undefined, +}; + +function removeQuotes(str) { + if (!str) { return "no response found" } + if (str.charAt(0) === '"' && str.charAt(str.length - 1) === '"') { + return str.slice(1, -1); + } + return str; +} + +const ExampleChatBotWithBackend = () => { + const params = new URLSearchParams(window.location.search); + const paperId = params.get('paper_id'); + const accessToken = JSON.parse(localStorage.getItem('accessToken')); + let showOption = true; + axios.get(`http://223.130.141.170:8000/chat/?paper_id=${paperId}`, { + headers: { + Authorization: `Bearer ${accessToken.access_token}` + } + }) + .then(response => { + if (response.data === false || response.data.length === 0) { + showOption = true; + } + else { + showOption = false; + } + }).catch(error => { + showOption = true; + console.log(error); + alert('Fail to connect with Server!'); + }) + + return ( + , + trigger: () => showOption ? 'option' : 'query' // 로그가 로드되었는지 여부에 따라 다음 단계를 결정 + }, + { + id: 'option', + options: [ + { value: 1, label: 'Who is the author of this paper?', trigger: '3' }, + { value: 2, label: 'Can you brief on this paper?', trigger: '3' }, + { value: 3, label: 'What is the proposal of this paper?', trigger: '3' }, + ], + }, + { + id: 'query', + user: true, + trigger: '3', + }, + { + id: '3', + component: , + waitAction: true, + trigger: 'query', + }, + ]} + /> + ); +} +export default ExampleChatBotWithBackend; \ No newline at end of file diff --git a/Frontend/front/src/components/ExampleChatbotWithBackend.css b/Frontend/front/src/components/ExampleChatbotWithBackend.css new file mode 100644 index 0000000..c0a66fb --- /dev/null +++ b/Frontend/front/src/components/ExampleChatbotWithBackend.css @@ -0,0 +1,7 @@ +@font-face { + font-family: 'NanumSquareRound'; + src: url('https://cdn.jsdelivr.net/gh/projectnoonnu/noonfonts_two@1.0/NanumSquareRound.woff') format('woff'); + font-weight: normal; + font-style: normal; +} + diff --git a/Frontend/front/src/components/MyResponsiveNetwork.js b/Frontend/front/src/components/MyResponsiveNetwork.js new file mode 100644 index 0000000..31c532b --- /dev/null +++ b/Frontend/front/src/components/MyResponsiveNetwork.js @@ -0,0 +1,44 @@ +import { ResponsiveNetwork } from "@nivo/network"; +import { useNavigate } from 'react-router-dom'; + +export function MyResponsiveNetwork(props) { + const navigate = useNavigate(); + return ( + { + return navigate(`/chatbot?paper_id=${e.id}`); + }} + linkDistance={function (e) { + return e.distance + 100; + }} + distanceMin={8} + nodeTooltip={(e) => { + return {e.node.data.title} + }} + centeringStrength={0.7} + repulsivity={15} + nodeSize={function (n) { + return n.size * 2; + }} + activeNodeSize={function (n) { + return 2.5 * n.size; + }} + inactiveNodeSize={function (n) { + return n.size; + }} + nodeColor={function (e) { + return e.color; + }} + linkThickness={function (n) { + return 2 + 2 * n.target.data.height; + }} + linkBlendMode="multiply" + motionConfig="wobbly" + /> + ); +} + +export default { MyResponsiveNetwork }; \ No newline at end of file diff --git a/Frontend/front/src/components/PaperList.js b/Frontend/front/src/components/PaperList.js new file mode 100644 index 0000000..d2c30a9 --- /dev/null +++ b/Frontend/front/src/components/PaperList.js @@ -0,0 +1,56 @@ +import React from 'react'; +import { useNavigate } from 'react-router-dom'; +import './chat.css' +import Latex from 'react-latex-next'; +import 'katex/dist/katex.min.css'; +import TeX from '@matejmazur/react-katex' +import ReactHtmlParser from 'react-html-parser'; + + +function PaperList(props) { + const { papers } = props; + const navigate = useNavigate(); + + // papers가 배열이 아니거나 null 또는 undefined인 경우 빈 배열로 초기화합니다. + const paperArray = Array.isArray(papers) ? papers : []; + + return ( +
+ + + + + + + + + + + {paperArray.map(paper => ( + + + + + + {/* */} + + ))} + +
TitleAuthorsYearAsk to Alex
navigate(`/chatbot?paper_id=${paper.id}`)} style={{ padding: "5px", cursor: "pointer" }}> + {ReactHtmlParser(paper.title)} + {paper.author1}{paper.published_year} navigate(`/chatbot?paper_id=${paper.id}`)} style={{ padding: "5px", cursor: "pointer" }}> + PDF + navigate(`/chatbot?paper_id=${paper.id}`)} style={{ padding: "5px", cursor: "pointer" }}> + PDF +
+
+ ); +} + +export default PaperList; diff --git a/Frontend/front/src/components/Sidebar.js b/Frontend/front/src/components/Sidebar.js new file mode 100644 index 0000000..2458076 --- /dev/null +++ b/Frontend/front/src/components/Sidebar.js @@ -0,0 +1,113 @@ +import { Sidebar, Menu, MenuItem } from 'react-pro-sidebar'; +import { useState, useEffect } from 'react'; +import { useNavigate } from 'react-router-dom'; +import axios from 'axios'; +import './chat.css'; + +function CustomSidebar() { + const [searchHistory, setSearchHistory] = useState([]); + const [paperId, setPaperId] = useState(); + const navigate = useNavigate(); + const accessToken = JSON.parse(localStorage.getItem('accessToken')); + + useEffect(() => { + const params = new URLSearchParams(window.location.search); + const paperIdFromUrl = params.get('paper_id'); + const accessToken = JSON.parse(localStorage.getItem('accessToken')) + + if (paperIdFromUrl) { + setPaperId(paperIdFromUrl); + axios.get(`http://223.130.141.170:8000/chat/room`, { + headers: { + Authorization: `Bearer ${accessToken.access_token}` + } + }) + .then(response => { + console.log(response.data); + setSearchHistory(response.data); + }) + .catch(error => { + console.error('Error fetching data:', error); + }); + } + }, [paperId]); + + const handleMenuItemClick = (item) => { + navigate(`/chatbot?paper_id=${item}`); + window.location.reload(); + }; + + const handleDelete = async (token_1, paperId) => { + try { + await axios.delete(`http://223.130.141.170:8000/chat/?paper_id=${paperId}`, { + headers: { + Authorization: `Bearer ${token_1}` + } + }); + const currentIndex = searchHistory.findIndex(item => item.paper_id === paperId); + let prevItem = searchHistory[currentIndex - 1]; + let length = searchHistory.length; + if (currentIndex === 0){ + prevItem = searchHistory[currentIndex + 1]; + if (length === 1){ + prevItem = searchHistory[currentIndex]; + } + } + // 다음 아이템 가져오기 + const updatedHistory = searchHistory.filter(item => item.paper_id !== paperId); + + // 새로운 배열로 상태 업데이트 + setSearchHistory(updatedHistory); + console.log(length) + if (length === 0){ + navigate('/'); + } + else{ + navigate(`/chatbot?paper_id=${prevItem.paper_id}`); + window.location.reload(); + } + } catch (error) { + console.error("Error deleting paper:", error); + } + }; + + return ( + + + {/* 데이터를 매핑하여 MenuItem을 동적으로 생성 */} + {searchHistory.map((item, index) => ( +
+ handleMenuItemClick(item.paper_id)}> + Delete { + e.stopPropagation(); // 이벤트 전파 방지 + handleDelete(accessToken.access_token, item.paper_id); + }} + style={{ position: 'absolute', top: '50%', right: '0', transform: 'translateY(-50%)', cursor: 'pointer' }} // 이미지 스타일 + /> + {item.paper_title} + + +
+ ))} + + + ); +}; + +export default CustomSidebar; diff --git a/Frontend/front/src/components/chat.css b/Frontend/front/src/components/chat.css new file mode 100644 index 0000000..738bcdf --- /dev/null +++ b/Frontend/front/src/components/chat.css @@ -0,0 +1,17 @@ +.delete{ + width: 20px; + height: 20px; + margin-left: 5px; +} + +.PDF1{ + width: 50px; + height: 50px; + margin-left: 5px; +} + +.PDF2{ + width: 30px; + height: 30px; + margin-left: 5px; +} \ No newline at end of file diff --git a/Frontend/front/src/index.css b/Frontend/front/src/index.css new file mode 100644 index 0000000..626f80a --- /dev/null +++ b/Frontend/front/src/index.css @@ -0,0 +1,13 @@ +/* body { + margin: 0; + font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', 'Roboto', 'Oxygen', + 'Ubuntu', 'Cantarell', 'Fira Sans', 'Droid Sans', 'Helvetica Neue', + sans-serif; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; +} + +code { + font-family: source-code-pro, Menlo, Monaco, Consolas, 'Courier New', + monospace; +} */ diff --git a/Frontend/front/src/index.js b/Frontend/front/src/index.js new file mode 100644 index 0000000..26a2c71 --- /dev/null +++ b/Frontend/front/src/index.js @@ -0,0 +1,15 @@ +import React from 'react'; +import ReactDOM from 'react-dom/client'; +import './index.css'; +import App from './App'; +import reportWebVitals from './reportWebVitals'; + +const root = ReactDOM.createRoot(document.getElementById('root')); +root.render( + +); + +// If you want to start measuring performance in your app, pass a function +// to log results (for example: reportWebVitals(console.log)) +// or send to an analytics endpoint. Learn more: https://bit.ly/CRA-vitals +reportWebVitals(); diff --git a/Frontend/front/src/pages/Login.css b/Frontend/front/src/pages/Login.css new file mode 100644 index 0000000..1006de0 --- /dev/null +++ b/Frontend/front/src/pages/Login.css @@ -0,0 +1,129 @@ +@font-face { + font-family: 'yg-jalnan'; + src: url('https://cdn.jsdelivr.net/gh/projectnoonnu/noonfonts_four@1.2/JalnanOTF00.woff') format('woff'); + font-weight: normal; + font-style: normal; +} +@font-face { + font-family: 'Cafe24Ssurround'; + src: url('https://cdn.jsdelivr.net/gh/projectnoonnu/noonfonts_2105_2@1.0/Cafe24Ssurround.woff') format('woff'); + font-weight: normal; + font-style: normal; +} +@font-face { + font-family: 'NanumSquareRound'; + src: url('https://cdn.jsdelivr.net/gh/projectnoonnu/noonfonts_two@1.0/NanumSquareRound.woff') format('woff'); + font-weight: normal; + font-style: normal; +} +@font-face { + font-family: 'Pretendard-Regular'; + src: url('https://cdn.jsdelivr.net/gh/Project-Noonnu/noonfonts_2107@1.1/Pretendard-Regular.woff') format('woff'); + font-weight: 400; + font-style: normal; +} +@font-face { + font-family: 'NPSfontBold'; + src: url('https://cdn.jsdelivr.net/gh/projectnoonnu/noonfonts_2310@1.0/NPSfontBold.woff2') format('woff2'); + font-weight: 700; + font-style: normal; +} +.font_big{ + font-family: 'yg-jalnan', sans-serif; + font-weight: 200; + color: #007bff; +} + +.font_email{ + font-family: 'NanumSquareRound', sans-serif; + margin-right: 270px; + margin-top: 50px; +} +.font_password{ + font-family: 'NanumSquareRound', sans-serif; + margin-right: 240px; + margin-top: 50px; +} + +.container { + display: flex; + justify-content: space-between;; + align-items: center; + height: 100vh; + +} + +.login-container { + width: 500px; + height: 530px; + margin: 0 0; + margin-left: auto; /* 중앙 정렬 */ + padding: 20px; + background-color: #ffffff; + border-top-left-radius: 10px; + border-bottom-left-radius: 10px; + box-shadow: 0px 0px 10px rgba(0, 0, 0, 0.1); + } + .sign-container { + display: flex; + flex-direction: column; + justify-content: center; /* 수평 가운데 정렬 */ + align-items: center; /* 수직 가운데 정렬 */ + height: 100vh; /* 뷰포트의 높이를 기준으로 사용 */ + width: 350px; + height: 530px; + margin: 0 0; + margin-right: auto; /* 중앙 정렬 */ + padding: 20px; + background-color: #ffffff; + border-top-right-radius: 10px; + border-bottom-right-radius: 10px; + box-shadow: 0px 0px 10px rgba(0, 0, 0, 0.1); + } + .leftbox { + margin-right: 220px; + margin-top: 30px; + } + .summary { + font-size: 20px; + color: #a7a7a7; + margin-bottom: 40px; + } + /* 입력란 */ + .login-input { + width: calc(100% - 170px); /* 오른쪽에 패딩이 있으므로 너비 조정 */ + padding: 10px; + margin-bottom: 15px; + border: 1px solid #f0f0f0; + background-color: #f0f0f0; + border-radius: 5px; + font-size: 16px; + } + + /* 로그인 버튼 */ + .login-button { + width: calc(100% - 250px); /* 오른쪽에 패딩이 있으므로 너비 조정 */ + padding: 10px; + margin-top: 40px; + background-color: #007bff; + border: none; + border-radius: 5px; + color: #fff; + font-size: 20px; + cursor: pointer; + border-radius: 20px; + font-weight: 700; + } + .sign-button { + width: calc(100% - 200px); /* 오른쪽에 패딩이 있으므로 너비 조정 */ + padding: 10px; + margin-top: 60px; + background-color: #007bff; + border: none; + border-radius: 5px; + color: #fff; + font-size: 20px; + cursor: pointer; + border-radius: 20px; + font-weight: 700; + } \ No newline at end of file diff --git a/Frontend/front/src/pages/Login.js b/Frontend/front/src/pages/Login.js new file mode 100644 index 0000000..f48724d --- /dev/null +++ b/Frontend/front/src/pages/Login.js @@ -0,0 +1,114 @@ +import React, { useState, useEffect } from 'react'; +import axios from 'axios'; +import { useAuth } from '../components/AuthContext'; +import './Login.css'; +import { useNavigate } from 'react-router-dom'; + +const Login = () => { + const [username, setUsername] = useState(''); + const [password, setPassword] = useState(''); + const [rememberMe, setRememberMe] = useState(false); // 추가 + const { login } = useAuth(); + const navigate = useNavigate(); + // Remember Me 체크박스 변경 핸들러 + const handleRememberMeChange = () => { + setRememberMe(!rememberMe); + }; + useEffect(() => { + // 페이지가 로드될 때 로컬 스토리지에서 이메일과 비밀번호를 가져와 설정합니다. + const rememberedEmail = localStorage.getItem('rememberedEmail'); + const rememberedPassword = localStorage.getItem('rememberedPassword'); + if (rememberedEmail && rememberedPassword) { + setUsername(rememberedEmail); + setPassword(rememberedPassword); + setRememberMe(true); + } + }, []); + const handleSignUp = () => { + navigate('/signup') + }; + const handleLogin = async () => { + try { + const response = await axios.post( + 'http://223.130.141.170:8000/api/login/login', + { + username, + password, + }, + { + headers: { + 'Content-Type': 'application/x-www-form-urlencoded', + }, + } + ); + + // Handle successful login + const { access_token, token_type } = response.data; + login(response.data); + + if (rememberMe) { + // Remember Me가 체크되어 있으면 이메일과 비밀번호를 로컬 스토리지에 저장 + localStorage.setItem('rememberedEmail', username); + localStorage.setItem('rememberedPassword', password); + } else { + // Remember Me가 체크되어 있지 않으면 로컬 스토리지에서 저장된 이메일과 비밀번호 삭제 + localStorage.removeItem('rememberedEmail'); + localStorage.removeItem('rememberedPassword'); + } + + // Pass the token to the parent component or perform other actions + } catch (error) { + // Handle login failure + console.error('Error logging in:', error); + alert('Fail to login.'); + } + }; + + return ( +
+
+

ACCOUNT  LOGIN

+
+

E-mail

+ setUsername(e.target.value)} + /> +
+
+

Password

+ setPassword(e.target.value)} + /> +
+
+ + +
+ +
+
+ + +
+
+ ); +}; + +export default Login; \ No newline at end of file diff --git a/Frontend/front/src/pages/SignUp.css b/Frontend/front/src/pages/SignUp.css new file mode 100644 index 0000000..e9a8acc --- /dev/null +++ b/Frontend/front/src/pages/SignUp.css @@ -0,0 +1,86 @@ +@font-face { + font-family: 'yg-jalnan'; + src: url('https://cdn.jsdelivr.net/gh/projectnoonnu/noonfonts_four@1.2/JalnanOTF00.woff') format('woff'); + font-weight: normal; + font-style: normal; +} +@font-face { + font-family: 'Cafe24Ssurround'; + src: url('https://cdn.jsdelivr.net/gh/projectnoonnu/noonfonts_2105_2@1.0/Cafe24Ssurround.woff') format('woff'); + font-weight: normal; + font-style: normal; +} +@font-face { + font-family: 'NanumSquareRound'; + src: url('https://cdn.jsdelivr.net/gh/projectnoonnu/noonfonts_two@1.0/NanumSquareRound.woff') format('woff'); + font-weight: normal; + font-style: normal; +} +@font-face { + font-family: 'Pretendard-Regular'; + src: url('https://cdn.jsdelivr.net/gh/Project-Noonnu/noonfonts_2107@1.1/Pretendard-Regular.woff') format('woff'); + font-weight: 400; + font-style: normal; +} +@font-face { + font-family: 'NPSfontBold'; + src: url('https://cdn.jsdelivr.net/gh/projectnoonnu/noonfonts_2310@1.0/NPSfontBold.woff2') format('woff2'); + font-weight: 700; + font-style: normal; +} +.sign-input { + width: calc(100% - 170px); /* 오른쪽에 패딩이 있으므로 너비 조정 */ + padding: 10px; + margin: 15px; + border: 1px solid #f0f0f0; + background-color: #f0f0f0; + border-radius: 5px; + font-size: 16px; + } +.font_label{ + font-family: 'yg-jalnan', sans-serif; + margin: 50px; + font-weight: 200; + color: #000000; +} +.font_email{ + font-family: 'NanumSquareRound', sans-serif; + margin-right: 270px; + margin-top: 50px; +} +.font_password{ + font-family: 'NanumSquareRound', sans-serif; + margin-right: 240px; + margin-top: 50px; +} + +.container { + display: flex; + justify-content: space-between;; + align-items: center; + height: 100vh; +} + +.signup-container { + width: 450px; + height: 530px; + margin: 0 auto; + margin-left: auto; /* 중앙 정렬 */ + padding: 20px; + background-color: #ffffff; + border-radius: 10px; + box-shadow: 0px 0px 10px rgba(0, 0, 0, 0.1); + } + .signup-button { + width: calc(100% - 200px); /* 오른쪽에 패딩이 있으므로 너비 조정 */ + padding: 10px; + margin-top: 50px; + background-color: #007bff; + border: none; + border-radius: 5px; + color: #fff; + font-size: 20px; + cursor: pointer; + border-radius: 20px; + font-weight: 700; + } diff --git a/Frontend/front/src/pages/SignUp.js b/Frontend/front/src/pages/SignUp.js new file mode 100644 index 0000000..8410e6c --- /dev/null +++ b/Frontend/front/src/pages/SignUp.js @@ -0,0 +1,56 @@ +import React, { useState } from 'react'; +import axios from 'axios'; +import { useNavigate } from 'react-router-dom'; +import './SignUp.css'; + +const SignUp = () => { + const [username, setUsername] = useState(''); + const [password, setPassword] = useState(''); + const [email, setEmail] = useState(''); + const navigate = useNavigate(); + const handleSignUp = async () => { + try { + const response = await axios.post('http://223.130.141.170:8000/api/login/signup', { + email, + password, + username + }); + + // Handle successful signup + navigate('/login'); + } catch (error) { + // Handle signup failure + console.error('Error signing up:', error); + alert('Fail to sign up'); + } + }; + + return ( +
+
+

Create Account

+ setEmail(e.target.value)} /> + setPassword(e.target.value)} /> + setUsername(e.target.value)} /> + +
+
+ ); +}; + +export default SignUp; \ No newline at end of file diff --git a/Frontend/front/src/pages/UploadForm.css b/Frontend/front/src/pages/UploadForm.css new file mode 100644 index 0000000..0faf60c --- /dev/null +++ b/Frontend/front/src/pages/UploadForm.css @@ -0,0 +1,20 @@ +.file-input { + width: 0px; + height: 0px; + padding: 0px; + margin: 0px auto; + color: #f0f000; +} + +.inputField { + margin: 0 auto; + border-radius: 20px; + width: 500px; + height: 30px; + border: 3px solid; + border-color: #0B60B0; +} + +.response-container { + margin: 0 auto; +} diff --git a/Frontend/front/src/pages/UploadForm.js b/Frontend/front/src/pages/UploadForm.js new file mode 100644 index 0000000..c6ec749 --- /dev/null +++ b/Frontend/front/src/pages/UploadForm.js @@ -0,0 +1,147 @@ +import React, { useState, useCallback } from 'react'; +import axios from 'axios'; +import './UploadForm.css'; +import '../App.css'; +import PaperList from '../components/PaperList'; +import { useAuth } from '../components/AuthContext'; +import { MyResponsiveNetwork } from '../components/MyResponsiveNetwork'; + +const DefaultStyle = () => { + return ( +
+ +
+ ); +}; + +const UploadForm = () => { + const [user_question, setUserQuestion] = useState(''); + const [showPaperList, setShowPaperList] = useState(false); + const [data, setData] = useState(''); // 전체 데이터를 담는 상태 + const [graph, setGraph] = useState(null); + const [annotations, setAnnotations] = useState([]); + const { token } = useAuth(); + + const handleSubmit = useCallback(async () => { + try { + const response = await axios.get(`http://223.130.141.170:8000/api/data/get_data/${user_question}`, { + headers: { + Authorization: `Bearer ${token.access_token}`, + } + }); + + setData(response.data.matched_papers); + + function nodeExists(id, nodesArray) { + return nodesArray.some(node => node.id === id); + } + + const baseNodes = response.data.matched_papers; + const maxCitationCount = Math.max(...baseNodes.map(item => item.citation_count)); + + const nodes1 = baseNodes.map(item => ({ + id: item.id, + title: item.title, + author: item.author1, + year: item.published_year, + height: 2, + size: Math.max(0.3, (item.citation_count / maxCitationCount)) * 80, + color: `rgb(11, 96, 176, ${Math.max(0, (item.published_year - 1920) / (2024 - 1920)) * 0.7})` + })); + + const childNodes = response.data.targets_list; + const nodes2 = childNodes.map(item => ({ + id: item.id, + title: item.title, + author: item.author1, + year: item.published_year, + height: 1, + size: Math.max(0.3, (item.citation_count / maxCitationCount)) * 40, + color: `rgb(150,75,0, ${Math.max(0, (item.published_year - 1920) / (2024 - 1920)) * 0.7})` + })); + + const nodes = [...nodes1]; + + nodes2.forEach(node => { + if (!nodes.some(existingNode => existingNode.id === node.id)) { + nodes.push(node); + } + }); + + const links = []; + if (response.data.top_5_results) { + for (const key in response.data.top_5_results) { + if (response.data.top_5_results.hasOwnProperty(key)) { + response.data.top_5_results[key].forEach(item => { + if (nodeExists(item.source, nodes) && nodeExists(item.target, nodes)) { + links.push({ + source: item.source, + target: item.target, + distance: (1 - item.distance) * 4 + }); + } + }); + } + } + } + + const newAnnotations = []; + for (let i = 0; i < nodes.length; i++) { + newAnnotations[i] = { + type: 'circle', + match: { + id: nodes[i].id + }, + note: `${nodes[i].author}, ${nodes[i].year}`, + noteX: 75, + noteY: 36, + offset: 6, + noteTextOffset: 5 + }; + } + + setGraph({ nodes, links }); + setAnnotations(newAnnotations); + setShowPaperList(true); + } catch (error) { + console.error('Error Rised During Search', error); + alert('Failed to connect with server.'); + } + }, [user_question, token]); + + const handleButtonClick = async () => { + if (user_question && token) { + setShowPaperList(false); + await handleSubmit(); + } + }; + + const handleTextChange = (event) => { + setUserQuestion(event.target.value); + }; + + return ( +
+ {!showPaperList && } +
+ + +
+
+ {showPaperList && } +
+ {showPaperList && } +
+
+
+ ); +}; + +export default UploadForm; diff --git a/Frontend/front/src/pages/chat.js b/Frontend/front/src/pages/chat.js new file mode 100644 index 0000000..39a5818 --- /dev/null +++ b/Frontend/front/src/pages/chat.js @@ -0,0 +1,17 @@ +import ExampleChatBotWithBackend from '../components/ExampleChatBotWithBackend'; +import CustomSidebar from '../components/Sidebar'; + +function Chat() { + return ( +
+
+ +
+
+ +
+
+ ); +} + +export default Chat; diff --git a/Frontend/front/src/reportWebVitals.js b/Frontend/front/src/reportWebVitals.js new file mode 100644 index 0000000..5253d3a --- /dev/null +++ b/Frontend/front/src/reportWebVitals.js @@ -0,0 +1,13 @@ +const reportWebVitals = onPerfEntry => { + if (onPerfEntry && onPerfEntry instanceof Function) { + import('web-vitals').then(({ getCLS, getFID, getFCP, getLCP, getTTFB }) => { + getCLS(onPerfEntry); + getFID(onPerfEntry); + getFCP(onPerfEntry); + getLCP(onPerfEntry); + getTTFB(onPerfEntry); + }); + } +}; + +export default reportWebVitals; diff --git a/Frontend/front/src/setupTests.js b/Frontend/front/src/setupTests.js new file mode 100644 index 0000000..8f2609b --- /dev/null +++ b/Frontend/front/src/setupTests.js @@ -0,0 +1,5 @@ +// jest-dom adds custom jest matchers for asserting on DOM nodes. +// allows you to do things like: +// expect(element).toHaveTextContent(/react/i) +// learn more: https://github.com/testing-library/jest-dom +import '@testing-library/jest-dom'; diff --git a/Frontend/front/src/styles.css b/Frontend/front/src/styles.css new file mode 100644 index 0000000..d3b3b35 --- /dev/null +++ b/Frontend/front/src/styles.css @@ -0,0 +1,12 @@ +.App { + font-family: sans-serif; + text-align: center; + } + + .responsive-network { + height: 700px; + width: auto; + align-content: center; + justify-content: center; + } + \ No newline at end of file diff --git a/Frontend/graph/baseKNN.ipynb b/Frontend/graph/baseKNN.ipynb new file mode 100644 index 0000000..02d0f05 --- /dev/null +++ b/Frontend/graph/baseKNN.ipynb @@ -0,0 +1,1250 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'1.12.1+cu113'" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import torch\n", + "import json\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "torch.__version__" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "torch.cuda.is_available()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "FILE = '../../arxiv-metadata-oai-snapshot.json'\n", + "\n", + "def get_data():\n", + " with open(FILE) as f:\n", + " for line in f:\n", + " yield line" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### TODO: dataframe에 JSON LOAD Columns 모두 추가해서 csv 저장" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "dataframe = {\n", + " 'id': [],\n", + " 'title': [],\n", + " 'year': [],\n", + " 'abstract': [],\n", + " 'author': []\n", + "}\n", + "\n", + "data = get_data()\n", + "for i, paper in enumerate(data):\n", + " paper = json.loads(paper)\n", + " # print(paper)\n", + " # break\n", + " try:\n", + " date = int(paper['update_date'].split('-')[0])\n", + " if date > 2019:\n", + " dataframe['id'].append(paper['id'])\n", + " dataframe['title'].append(paper['title'])\n", + " dataframe['year'].append(date)\n", + " dataframe['abstract'].append(paper['abstract'])\n", + " dataframe['author'].append(paper['submitter'])\n", + " except: pass" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "데이터 너무 많음 -> cs 관련?\n", + "인용수 -> 적은 애들 neg sample로 사용 가능\n", + "\n", + "graph -> 인접행렬" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame(dataframe)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "del dataframe" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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10704.0038The discrete dipole approximation: an overview...2022We present a review of the discrete dipole a...Maxim A. Yurkin
20704.0275Mapping radii of metric spaces2021It is known that every closed curve of lengt...George M. Bergman
30704.0348What can emission lines tell us?20231 Generalities\\n 2 Empirical diagnostics ba...Grazyna Stasinska
40704.0479The affine part of the Picard scheme2021We describe the maximal torus and maximal un...Thomas Geisser H
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886395solv-int/9502003The Hamiltonian structure of the dispersionles...2020The Hamiltonian structure of the two-dimensi...David Fairlie
886396solv-int/9807001DNA Transcription Mechanism with a Moving Enzyme2021Previous numerical investigations of an one-...Juhi-Lian Julian Ting
886397solv-int/9807004Degenerate Frobenius manifolds and the bi-Hami...2020The bi-Hamiltonian structure of certain mult...I. A. B. Strachan
886398solv-int/9808019Hypercomplex Integrable Systems2020In this paper we study hypercomplex manifold...James D. E. Grant
886399supr-con/9508005Nonlinear Flux Diffusion and ac Susceptibility...2022The ac response of a slab of material with e...Zbigniew J. Koziol
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idtitleyearabstractauthor
2286442009.14794Rethinking Attention with Performers2022We introduce Performers, Transformer archite...Valerii Likhosherstov
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KNeighborsClassifier(n_neighbors=6)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ], + "text/plain": [ + "KNeighborsClassifier(n_neighbors=6)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nn = KNeighborsClassifier(n_neighbors=6)\n", + "nn.fit(output, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "import random" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "맨처음 시나리오 있으면 좋겠다." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sample:\n", + "Attention Is All You Need\n", + "\n", + "Recommendation author-0:\n", + "Fixed Encoder Self-Attention Patterns in Transformer-Based Machine\n", + " Translation\n", + "\n", + "Recommendation author-1:\n", + "Transformer++\n", + "\n", + "Recommendation author-2:\n", + "An Efficient Transformer Decoder with Compressed Sub-layers\n", + "\n", + "Recommendation author-3:\n", + "Context- and Sequence-Aware Convolutional Recurrent Encoder for Neural\n", + " Machine Translation\n", + "\n", + "Recommendation author-4:\n", + "A Cost-Efficient FPGA Implementation of Tiny Transformer Model using\n", + " Neural ODE\n", + "\n", + "===============\n", + "\n", + "Sample:\n", + "CCNet: Criss-Cross Attention for Semantic Segmentation\n", + "\n", + "Recommendation author-0:\n", + "CTNet: Context-based Tandem Network for Semantic Segmentation\n", + "\n", + "Recommendation author-1:\n", + "TriangleNet: Edge Prior Augmented Network for Semantic Segmentation\n", + " through Cross-Task Consistency\n", + "\n", + "Recommendation author-2:\n", + "Real-time Semantic Segmentation via Spatial-detail Guided Context\n", + " Propagation\n", + "\n", + "Recommendation author-3:\n", + "BiCANet: Bi-directional Contextual Aggregating Network for Image\n", + " Semantic Segmentation\n", + "\n", + "Recommendation author-4:\n", + "HCNet: Hierarchical Context Network for Semantic Segmentation\n", + "\n", + "===============\n", + "\n", + "Sample:\n", + "Heterogeneous Graph Attention Network\n", + "\n", + "Recommendation author-0:\n", + "SHGNN: Structure-Aware Heterogeneous Graph Neural Network\n", + "\n", + "Recommendation author-1:\n", + "ISHNE: Influence Self-attention for Heterogeneous Network Embedding\n", + "\n", + "Recommendation author-2:\n", + "Simple and Efficient Heterogeneous Graph Neural Network\n", + "\n", + "Recommendation author-3:\n", + "Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous\n", + " Graphs\n", + "\n", + "Recommendation author-4:\n", + "SGAT: Simplicial Graph Attention Network\n", + "\n", + "===============\n", + "\n", + "Sample:\n", + "DeBERTa: Decoding-enhanced BERT with Disentangled Attention\n", + "\n", + "Recommendation author-0:\n", + "DrBERT: Unveiling the Potential of Masked Language Modeling Decoder in\n", + " BERT pretraining\n", + "\n", + "Recommendation author-1:\n", + "DecBERT: Enhancing the Language Understanding of BERT with Causal\n", + " Attention Masks\n", + "\n", + "Recommendation author-2:\n", + "Optimizing small BERTs trained for German NER\n", + "\n", + "Recommendation author-3:\n", + "Data-Efficient French Language Modeling with CamemBERTa\n", + "\n", + "Recommendation author-4:\n", + "What the [MASK]? Making Sense of Language-Specific BERT Models\n", + "\n", + "===============\n", + "\n", + "Sample:\n", + "Rethinking Attention with Performers\n", + "\n", + "Recommendation author-0:\n", + "Masked Language Modeling for Proteins via Linearly Scalable Long-Context\n", + " Transformers\n", + "\n", + "Recommendation author-1:\n", + "cosFormer: Rethinking Softmax in Attention\n", + "\n", + "Recommendation author-2:\n", + "Random Feature Attention\n", + "\n", + "Recommendation author-3:\n", + "Superiority of Softmax: Unveiling the Performance Edge Over Linear\n", + " Attention\n", + "\n", + "Recommendation author-4:\n", + "An Attention Matrix for Every Decision: Faithfulness-based Arbitration\n", + " Among Multiple Attention-Based Interpretations of Transformers in Text\n", + " Classification\n", + "\n", + "===============\n", + "\n" + ] + } + ], + "source": [ + "data = {i:[] for i in df.columns}\n", + "for idx in [16667, 36734, 45683, 170515, 228644]:\n", + " sample = df[\"title\"][idx]\n", + " for i in df.columns: data[i].append(df[i][idx])\n", + " dist, index = nn.kneighbors(X=output[idx,:].reshape(1,-1))\n", + " print(f\"Sample:\\n{sample}\\n\")\n", + " # for i in range(1,6):\n", + " # print(f\"Recommendation {i}:\\n{df['title'][index[0][i]]}\\n\")\n", + " # for j in df.columns: data[j].append(df[j][index[0][i]])\n", + " for k in range(1,6):\n", + " print(f\"Recommendation {i}-{k-1}:\\n{df['title'][index[0][k]]}\\n\")\n", + " for j in df.columns: data[j].append(df[j][index[0][k]])\n", + "\n", + " print(\"===============\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "30" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(data['id'])" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "import random" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "metadata": {}, + "outputs": [], + "source": [ + "graph = {\n", + " 'nodes': [\n", + " {\n", + " 'title': data['title'][i], \n", + " 'author': data['author'][i],\n", + " 'year': int(data['year'][i]),\n", + " 'id': data['id'][i],\n", + " 'height': 2 if i==0 else 1,\n", + " 'size': random.randint(10,20), # citation num\n", + " 'color': f'rgb(240, 237, 207, {(data[\"year\"][i] - 2015) / 20})' # publish year\n", + " }\n", + " for i in range(30)], \n", + " 'links': [\n", + " {\n", + " 'source': data['id'][i],\n", + " 'target': data['id'][j],\n", + " 'distance': int(random.randrange(1, 100)) # 100 * similarity\n", + " }\n", + " for i in range(0, 30, 6) for j in range(i+1, i+6)]\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [], + "source": [ + "iter = 0\n", + "for i in graph['nodes']:\n", + " if iter % 6 == 0:\n", + " i['size'] = 80\n", + " i['color'] = f'rgb(11, 96, 176, 0.8)'\n", + " iter += 1\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': ['1706.03762',\n", + " '2002.10260',\n", + " '2003.04974',\n", + " '2101.00542',\n", + " '2101.04030',\n", + " '2401.02721',\n", + " '1811.11721',\n", + " '2104.09805',\n", + " '2210.05152',\n", + " '2005.11034',\n", + " '2003.09669',\n", + " '2010.04962',\n", + " '1903.07293',\n", + " '2112.06244',\n", + " '2103.00118',\n", + " '2207.02547',\n", + " '2007.08294',\n", + " '2207.11761',\n", + " '2006.03654',\n", + " '2401.15861',\n", + " '2204.08688',\n", + " '2104.11559',\n", + " '2306.01497',\n", + " '2003.02912',\n", + " '2009.14794',\n", + " '2006.03555',\n", + " '2202.08791',\n", + " '2103.02143',\n", + " '2310.11685',\n", + " '2209.10876'],\n", + " 'title': ['Attention Is All You Need',\n", + " 'Fixed Encoder Self-Attention Patterns in Transformer-Based Machine\\n Translation',\n", + " 'Transformer++',\n", + " 'An Efficient Transformer Decoder with Compressed Sub-layers',\n", + " 'Context- and Sequence-Aware Convolutional Recurrent Encoder for Neural\\n Machine Translation',\n", + " 'A Cost-Efficient FPGA Implementation of Tiny Transformer Model using\\n Neural ODE',\n", + " 'CCNet: Criss-Cross Attention for Semantic Segmentation',\n", + " 'CTNet: Context-based Tandem Network for Semantic Segmentation',\n", + " 'TriangleNet: Edge Prior Augmented Network for Semantic Segmentation\\n through Cross-Task Consistency',\n", + " 'Real-time Semantic Segmentation via Spatial-detail Guided Context\\n Propagation',\n", + " 'BiCANet: Bi-directional Contextual Aggregating Network for Image\\n Semantic Segmentation',\n", + " 'HCNet: Hierarchical Context Network for Semantic Segmentation',\n", + " 'Heterogeneous Graph Attention Network',\n", + " 'SHGNN: Structure-Aware Heterogeneous Graph Neural Network',\n", + " 'ISHNE: Influence Self-attention for Heterogeneous Network Embedding',\n", + " 'Simple and Efficient Heterogeneous Graph Neural Network',\n", + " 'Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous\\n Graphs',\n", + " 'SGAT: Simplicial Graph Attention Network',\n", + " 'DeBERTa: Decoding-enhanced BERT with Disentangled Attention',\n", + " 'DrBERT: Unveiling the Potential of Masked Language Modeling Decoder in\\n BERT pretraining',\n", + " 'DecBERT: Enhancing the Language Understanding of BERT with Causal\\n Attention Masks',\n", + " 'Optimizing small BERTs trained for German NER',\n", + " 'Data-Efficient French Language Modeling with CamemBERTa',\n", + " 'What the [MASK]? Making Sense of Language-Specific BERT Models',\n", + " 'Rethinking Attention with Performers',\n", + " 'Masked Language Modeling for Proteins via Linearly Scalable Long-Context\\n Transformers',\n", + " 'cosFormer: Rethinking Softmax in Attention',\n", + " 'Random Feature Attention',\n", + " 'Superiority of Softmax: Unveiling the Performance Edge Over Linear\\n Attention',\n", + " 'An Attention Matrix for Every Decision: Faithfulness-based Arbitration\\n Among Multiple Attention-Based Interpretations of Transformers in Text\\n Classification'],\n", + " 'year': [2023,\n", + " 2020,\n", + " 2020,\n", + " 2023,\n", + " 2021,\n", + " 2024,\n", + " 2020,\n", + " 2021,\n", + " 2023,\n", + " 2022,\n", + " 2020,\n", + " 2020,\n", + " 2021,\n", + " 2021,\n", + " 2022,\n", + " 2023,\n", + " 2021,\n", + " 2022,\n", + " 2021,\n", + " 2024,\n", + " 2022,\n", + " 2021,\n", + " 2023,\n", + " 2020,\n", + " 2022,\n", + " 2020,\n", + " 2022,\n", + " 2021,\n", + " 2023,\n", + " 2022],\n", + " 'abstract': [' The dominant sequence transduction models are based on complex recurrent or\\nconvolutional neural networks in an encoder-decoder configuration. The best\\nperforming models also connect the encoder and decoder through an attention\\nmechanism. We propose a new simple network architecture, the Transformer, based\\nsolely on attention mechanisms, dispensing with recurrence and convolutions\\nentirely. Experiments on two machine translation tasks show these models to be\\nsuperior in quality while being more parallelizable and requiring significantly\\nless time to train. Our model achieves 28.4 BLEU on the WMT 2014\\nEnglish-to-German translation task, improving over the existing best results,\\nincluding ensembles by over 2 BLEU. On the WMT 2014 English-to-French\\ntranslation task, our model establishes a new single-model state-of-the-art\\nBLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction\\nof the training costs of the best models from the literature. We show that the\\nTransformer generalizes well to other tasks by applying it successfully to\\nEnglish constituency parsing both with large and limited training data.\\n',\n", + " ' Transformer-based models have brought a radical change to neural machine\\ntranslation. A key feature of the Transformer architecture is the so-called\\nmulti-head attention mechanism, which allows the model to focus simultaneously\\non different parts of the input. However, recent works have shown that most\\nattention heads learn simple, and often redundant, positional patterns. In this\\npaper, we propose to replace all but one attention head of each encoder layer\\nwith simple fixed -- non-learnable -- attentive patterns that are solely based\\non position and do not require any external knowledge. Our experiments with\\ndifferent data sizes and multiple language pairs show that fixing the attention\\nheads on the encoder side of the Transformer at training time does not impact\\nthe translation quality and even increases BLEU scores by up to 3 points in\\nlow-resource scenarios.\\n',\n", + " ' Recent advancements in attention mechanisms have replaced recurrent neural\\nnetworks and its variants for machine translation tasks. Transformer using\\nattention mechanism solely achieved state-of-the-art results in sequence\\nmodeling. Neural machine translation based on the attention mechanism is\\nparallelizable and addresses the problem of handling long-range dependencies\\namong words in sentences more effectively than recurrent neural networks. One\\nof the key concepts in attention is to learn three matrices, query, key, and\\nvalue, where global dependencies among words are learned through linearly\\nprojecting word embeddings through these matrices. Multiple query, key, value\\nmatrices can be learned simultaneously focusing on a different subspace of the\\nembedded dimension, which is called multi-head in Transformer. We argue that\\ncertain dependencies among words could be learned better through an\\nintermediate context than directly modeling word-word dependencies. This could\\nhappen due to the nature of certain dependencies or lack of patterns that lend\\nthem difficult to be modeled globally using multi-head self-attention. In this\\nwork, we propose a new way of learning dependencies through a context in\\nmulti-head using convolution. This new form of multi-head attention along with\\nthe traditional form achieves better results than Transformer on the WMT 2014\\nEnglish-to-German and English-to-French translation tasks. We also introduce a\\nframework to learn POS tagging and NER information during the training of\\nencoder which further improves results achieving a new state-of-the-art of 32.1\\nBLEU, better than existing best by 1.4 BLEU, on the WMT 2014 English-to-German\\nand 44.6 BLEU, better than existing best by 1.1 BLEU, on the WMT 2014\\nEnglish-to-French translation tasks. We call this Transformer++.\\n',\n", + " ' The large attention-based encoder-decoder network (Transformer) has become\\nprevailing recently due to its effectiveness. But the high computation\\ncomplexity of its decoder raises the inefficiency issue. By examining the\\nmathematic formulation of the decoder, we show that under some mild conditions,\\nthe architecture could be simplified by compressing its sub-layers, the basic\\nbuilding block of Transformer, and achieves a higher parallelism. We thereby\\npropose Compressed Attention Network, whose decoder layer consists of only one\\nsub-layer instead of three. Extensive experiments on 14 WMT machine translation\\ntasks show that our model is 1.42x faster with performance on par with a strong\\nbaseline. This strong baseline is already 2x faster than the widely used\\nstandard baseline without loss in performance.\\n',\n", + " ' Neural Machine Translation model is a sequence-to-sequence converter based on\\nneural networks. Existing models use recurrent neural networks to construct\\nboth the encoder and decoder modules. In alternative research, the recurrent\\nnetworks were substituted by convolutional neural networks for capturing the\\nsyntactic structure in the input sentence and decreasing the processing time.\\nWe incorporate the goodness of both approaches by proposing a\\nconvolutional-recurrent encoder for capturing the context information as well\\nas the sequential information from the source sentence. Word embedding and\\nposition embedding of the source sentence is performed prior to the\\nconvolutional encoding layer which is basically a n-gram feature extractor\\ncapturing phrase-level context information. The rectified output of the\\nconvolutional encoding layer is added to the original embedding vector, and the\\nsum is normalized by layer normalization. The normalized output is given as a\\nsequential input to the recurrent encoding layer that captures the temporal\\ninformation in the sequence. For the decoder, we use the attention-based\\nrecurrent neural network. Translation task on the German-English dataset\\nverifies the efficacy of the proposed approach from the higher BLEU scores\\nachieved as compared to the state of the art.\\n',\n", + " ' Transformer is an emerging neural network model with attention mechanism. It\\nhas been adopted to various tasks and achieved a favorable accuracy compared to\\nCNNs and RNNs. While the attention mechanism is recognized as a general-purpose\\ncomponent, many of the Transformer models require a significant number of\\nparameters compared to the CNN-based ones. To mitigate the computational\\ncomplexity, recently, a hybrid approach has been proposed, which uses ResNet as\\na backbone architecture and replaces a part of its convolution layers with an\\nMHSA (Multi-Head Self-Attention) mechanism. In this paper, we significantly\\nreduce the parameter size of such models by using Neural ODE (Ordinary\\nDifferential Equation) as a backbone architecture instead of ResNet. The\\nproposed hybrid model reduces the parameter size by 94.6% compared to the\\nCNN-based ones without degrading the accuracy. We then deploy the proposed\\nmodel on a modest-sized FPGA device for edge computing. To further reduce FPGA\\nresource utilization, we quantize the model following QAT (Quantization Aware\\nTraining) scheme instead of PTQ (Post Training Quantization) to suppress the\\naccuracy loss. As a result, an extremely lightweight Transformer-based model\\ncan be implemented on resource-limited FPGAs. The weights of the feature\\nextraction network are stored on-chip to minimize the memory transfer overhead,\\nallowing faster inference. By eliminating the overhead of memory transfers,\\ninference can be executed seamlessly, leading to accelerated inference. The\\nproposed FPGA implementation achieves 12.8x speedup and 9.21x energy efficiency\\ncompared to ARM Cortex-A53 CPU.\\n',\n", + " ' Contextual information is vital in visual understanding problems, such as\\nsemantic segmentation and object detection. We propose a Criss-Cross Network\\n(CCNet) for obtaining full-image contextual information in a very effective and\\nefficient way. Concretely, for each pixel, a novel criss-cross attention module\\nharvests the contextual information of all the pixels on its criss-cross path.\\nBy taking a further recurrent operation, each pixel can finally capture the\\nfull-image dependencies. Besides, a category consistent loss is proposed to\\nenforce the criss-cross attention module to produce more discriminative\\nfeatures. Overall, CCNet is with the following merits: 1) GPU memory friendly.\\nCompared with the non-local block, the proposed recurrent criss-cross attention\\nmodule requires 11x less GPU memory usage. 2) High computational efficiency.\\nThe recurrent criss-cross attention significantly reduces FLOPs by about 85% of\\nthe non-local block. 3) The state-of-the-art performance. We conduct extensive\\nexperiments on semantic segmentation benchmarks including Cityscapes, ADE20K,\\nhuman parsing benchmark LIP, instance segmentation benchmark COCO, video\\nsegmentation benchmark CamVid. In particular, our CCNet achieves the mIoU\\nscores of 81.9%, 45.76% and 55.47% on the Cityscapes test set, the ADE20K\\nvalidation set and the LIP validation set respectively, which are the new\\nstate-of-the-art results. The source codes are available at\\n\\\\url{https://github.com/speedinghzl/CCNet}.\\n',\n", + " ' Contextual information has been shown to be powerful for semantic\\nsegmentation. This work proposes a novel Context-based Tandem Network (CTNet)\\nby interactively exploring the spatial contextual information and the channel\\ncontextual information, which can discover the semantic context for semantic\\nsegmentation. Specifically, the Spatial Contextual Module (SCM) is leveraged to\\nuncover the spatial contextual dependency between pixels by exploring the\\ncorrelation between pixels and categories. Meanwhile, the Channel Contextual\\nModule (CCM) is introduced to learn the semantic features including the\\nsemantic feature maps and class-specific features by modeling the long-term\\nsemantic dependence between channels. The learned semantic features are\\nutilized as the prior knowledge to guide the learning of SCM, which can make\\nSCM obtain more accurate long-range spatial dependency. Finally, to further\\nimprove the performance of the learned representations for semantic\\nsegmentation, the results of the two context modules are adaptively integrated\\nto achieve better results. Extensive experiments are conducted on three\\nwidely-used datasets, i.e., PASCAL-Context, ADE20K and PASCAL VOC2012. The\\nresults demonstrate the superior performance of the proposed CTNet by\\ncomparison with several state-of-the-art methods.\\n',\n", + " ' This paper addresses the task of semantic segmentation in computer vision,\\naiming to achieve precise pixel-wise classification. We investigate the joint\\ntraining of models for semantic edge detection and semantic segmentation, which\\nhas shown promise. However, implicit cross-task consistency learning in\\nmulti-task networks is limited. To address this, we propose a novel \"decoupled\\ncross-task consistency loss\" that explicitly enhances cross-task consistency.\\nOur semantic segmentation network, TriangleNet, achieves a substantial 2.88\\\\%\\nimprovement over the Baseline in mean Intersection over Union (mIoU) on the\\nCityscapes test set. Notably, TriangleNet operates at 77.4\\\\% mIoU/46.2 FPS on\\nCityscapes, showcasing real-time inference capabilities at full resolution.\\nWith multi-scale inference, performance is further enhanced to 77.8\\\\%.\\nFurthermore, TriangleNet consistently outperforms the Baseline on the FloodNet\\ndataset, demonstrating its robust generalization capabilities. The proposed\\nmethod underscores the significance of multi-task learning and explicit\\ncross-task consistency enhancement for advancing semantic segmentation and\\nhighlights the potential of multitasking in real-time semantic segmentation.\\n',\n", + " ' Nowadays, vision-based computing tasks play an important role in various\\nreal-world applications. However, many vision computing tasks, e.g. semantic\\nsegmentation, are usually computationally expensive, posing a challenge to the\\ncomputing systems that are resource-constrained but require fast response\\nspeed. Therefore, it is valuable to develop accurate and real-time vision\\nprocessing models that only require limited computational resources. To this\\nend, we propose the Spatial-detail Guided Context Propagation Network (SGCPNet)\\nfor achieving real-time semantic segmentation. In SGCPNet, we propose the\\nstrategy of spatial-detail guided context propagation. It uses the spatial\\ndetails of shallow layers to guide the propagation of the low-resolution global\\ncontexts, in which the lost spatial information can be effectively\\nreconstructed. In this way, the need for maintaining high-resolution features\\nalong the network is freed, therefore largely improving the model efficiency.\\nOn the other hand, due to the effective reconstruction of spatial details, the\\nsegmentation accuracy can be still preserved. In the experiments, we validate\\nthe effectiveness and efficiency of the proposed SGCPNet model. On the\\nCitysacpes dataset, for example, our SGCPNet achieves 69.5% mIoU segmentation\\naccuracy, while its speed reaches 178.5 FPS on 768x1536 images on a GeForce GTX\\n1080 Ti GPU card. In addition, SGCPNet is very lightweight and only contains\\n0.61 M parameters.\\n',\n", + " ' Exploring contextual information in convolution neural networks (CNNs) has\\ngained substantial attention in recent years for semantic segmentation. This\\npaper introduces a Bi-directional Contextual Aggregating Network, called\\nBiCANet, for semantic segmentation. Unlike previous approaches that encode\\ncontext in feature space, BiCANet aggregates contextual cues from a categorical\\nperspective, which is mainly consist of three parts: contextual condensed\\nprojection block (CCPB), bi-directional context interaction block (BCIB), and\\nmuti-scale contextual fusion block (MCFB). More specifically, CCPB learns a\\ncategory-based mapping through a split-transform-merge architecture, which\\ncondenses contextual cues with different receptive fields from intermediate\\nlayer. BCIB, on the other hand, employs dense skipped-connections to enhance\\nthe class-level context exchanging. Finally, MCFB integrates multi-scale\\ncontextual cues by investigating short- and long-ranged spatial dependencies.\\nTo evaluate BiCANet, we have conducted extensive experiments on three semantic\\nsegmentation datasets: PASCAL VOC 2012, Cityscapes, and ADE20K. The\\nexperimental results demonstrate that BiCANet outperforms recent\\nstate-of-the-art networks without any postprocess techniques. Particularly,\\nBiCANet achieves the mIoU score of 86.7%, 82.4% and 38.66% on PASCAL VOC 2012,\\nCityscapes and ADE20K testset, respectively.\\n',\n", + " ' Global context information is vital in visual understanding problems,\\nespecially in pixel-level semantic segmentation. The mainstream methods adopt\\nthe self-attention mechanism to model global context information. However,\\npixels belonging to different classes usually have weak feature correlation.\\nModeling the global pixel-level correlation matrix indiscriminately is\\nextremely redundant in the self-attention mechanism. In order to solve the\\nabove problem, we propose a hierarchical context network to differentially\\nmodel homogeneous pixels with strong correlations and heterogeneous pixels with\\nweak correlations. Specifically, we first propose a multi-scale guided\\npre-segmentation module to divide the entire feature map into different\\nclassed-based homogeneous regions. Within each homogeneous region, we design\\nthe pixel context module to capture pixel-level correlations. Subsequently,\\ndifferent from the self-attention mechanism that still models weak\\nheterogeneous correlations in a dense pixel-level manner, the region context\\nmodule is proposed to model sparse region-level dependencies using a unified\\nrepresentation of each region. Through aggregating fine-grained pixel context\\nfeatures and coarse-grained region context features, our proposed network can\\nnot only hierarchically model global context information but also harvest\\nmulti-granularity representations to more robustly identify multi-scale\\nobjects. We evaluate our approach on Cityscapes and the ISPRS Vaihingen\\ndataset. Without Bells or Whistles, our approach realizes a mean IoU of 82.8%\\nand overall accuracy of 91.4% on Cityscapes and ISPRS Vaihingen test set,\\nachieving state-of-the-art results.\\n',\n", + " ' Graph neural network, as a powerful graph representation technique based on\\ndeep learning, has shown superior performance and attracted considerable\\nresearch interest. However, it has not been fully considered in graph neural\\nnetwork for heterogeneous graph which contains different types of nodes and\\nlinks. The heterogeneity and rich semantic information bring great challenges\\nfor designing a graph neural network for heterogeneous graph. Recently, one of\\nthe most exciting advancements in deep learning is the attention mechanism,\\nwhose great potential has been well demonstrated in various areas. In this\\npaper, we first propose a novel heterogeneous graph neural network based on the\\nhierarchical attention, including node-level and semantic-level attentions.\\nSpecifically, the node-level attention aims to learn the importance between a\\nnode and its metapath based neighbors, while the semantic-level attention is\\nable to learn the importance of different meta-paths. With the learned\\nimportance from both node-level and semantic-level attention, the importance of\\nnode and meta-path can be fully considered. Then the proposed model can\\ngenerate node embedding by aggregating features from meta-path based neighbors\\nin a hierarchical manner. Extensive experimental results on three real-world\\nheterogeneous graphs not only show the superior performance of our proposed\\nmodel over the state-of-the-arts, but also demonstrate its potentially good\\ninterpretability for graph analysis.\\n',\n", + " ' Many real-world graphs (networks) are heterogeneous with different types of\\nnodes and edges. Heterogeneous graph embedding, aiming at learning the\\nlow-dimensional node representations of a heterogeneous graph, is vital for\\nvarious downstream applications. Many meta-path based embedding methods have\\nbeen proposed to learn the semantic information of heterogeneous graphs in\\nrecent years. However, most of the existing techniques overlook the graph\\nstructure information when learning the heterogeneous graph embeddings. This\\npaper proposes a novel Structure-Aware Heterogeneous Graph Neural Network\\n(SHGNN) to address the above limitations. In detail, we first utilize a feature\\npropagation module to capture the local structure information of intermediate\\nnodes in the meta-path. Next, we use a tree-attention aggregator to incorporate\\nthe graph structure information into the aggregation module on the meta-path.\\nFinally, we leverage a meta-path aggregator to fuse the information aggregated\\nfrom different meta-paths. We conducted experiments on node classification and\\nclustering tasks and achieved state-of-the-art results on the benchmark\\ndatasets, which shows the effectiveness of our proposed method.\\n',\n", + " ' In recent years, Graph Neural Networks has received enormous attention from\\nacademia for its huge potential of modeling the network traits such as\\nmacrostructure and single node attributes. However, prior mainstream works\\nmainly focus on homogeneous network and lack the capacity to characterize the\\nnetwork heterogeneous property. Besides, most previous literature cannot model\\nthe influence under microscope vision, making it infeasible to model the joint\\nrelation between the heterogeneity and mutual interaction within multiple\\nrelation type. In this paper, we propose an Influence Self-attention network to\\naddress the difficulties mentioned above. To model heterogeneity and mutual\\ninteraction, we redesign attention mechanism with influence factor on the\\nsingle-type relation level, which learns the importance coefficient from its\\nadjacent neighbors under the same meta-path based patterns. To incorporate the\\nheterogeneous meta-path in a unified dimension, we developed a self-attention\\nbased framework for meta-path relation fusion according to the learned\\nmeta-path coefficient. Our experimental results demonstrate that our framework\\nnot only achieve higher results than current state-of-the-art baselines, but\\nalso show promising vision on depicting heterogeneous interactive relations\\nunder complicated network structure.\\n',\n", + " ' Heterogeneous graph neural networks (HGNNs) have powerful capability to embed\\nrich structural and semantic information of a heterogeneous graph into node\\nrepresentations. Existing HGNNs inherit many mechanisms from graph neural\\nnetworks (GNNs) over homogeneous graphs, especially the attention mechanism and\\nthe multi-layer structure. These mechanisms bring excessive complexity, but\\nseldom work studies whether they are really effective on heterogeneous graphs.\\nThis paper conducts an in-depth and detailed study of these mechanisms and\\nproposes Simple and Efficient Heterogeneous Graph Neural Network (SeHGNN). To\\neasily capture structural information, SeHGNN pre-computes the neighbor\\naggregation using a light-weight mean aggregator, which reduces complexity by\\nremoving overused neighbor attention and avoiding repeated neighbor aggregation\\nin every training epoch. To better utilize semantic information, SeHGNN adopts\\nthe single-layer structure with long metapaths to extend the receptive field,\\nas well as a transformer-based semantic fusion module to fuse features from\\ndifferent metapaths. As a result, SeHGNN exhibits the characteristics of simple\\nnetwork structure, high prediction accuracy, and fast training speed. Extensive\\nexperiments on five real-world heterogeneous graphs demonstrate the superiority\\nof SeHGNN over the state-of-the-arts on both accuracy and training speed.\\n',\n", + " ' Graph neural networks have shown superior performance in a wide range of\\napplications providing a powerful representation of graph-structured data.\\nRecent works show that the representation can be further improved by auxiliary\\ntasks. However, the auxiliary tasks for heterogeneous graphs, which contain\\nrich semantic information with various types of nodes and edges, have less\\nexplored in the literature. In this paper, to learn graph neural networks on\\nheterogeneous graphs we propose a novel self-supervised auxiliary learning\\nmethod using meta-paths, which are composite relations of multiple edge types.\\nOur proposed method is learning to learn a primary task by predicting\\nmeta-paths as auxiliary tasks. This can be viewed as a type of meta-learning.\\nThe proposed method can identify an effective combination of auxiliary tasks\\nand automatically balance them to improve the primary task. Our methods can be\\napplied to any graph neural networks in a plug-in manner without manual\\nlabeling or additional data. The experiments demonstrate that the proposed\\nmethod consistently improves the performance of link prediction and node\\nclassification on heterogeneous graphs.\\n',\n", + " \" Heterogeneous graphs have multiple node and edge types and are semantically\\nricher than homogeneous graphs. To learn such complex semantics, many graph\\nneural network approaches for heterogeneous graphs use metapaths to capture\\nmulti-hop interactions between nodes. Typically, features from non-target nodes\\nare not incorporated into the learning procedure. However, there can be\\nnonlinear, high-order interactions involving multiple nodes or edges. In this\\npaper, we present Simplicial Graph Attention Network (SGAT), a simplicial\\ncomplex approach to represent such high-order interactions by placing features\\nfrom non-target nodes on the simplices. We then use attention mechanisms and\\nupper adjacencies to generate representations. We empirically demonstrate the\\nefficacy of our approach with node classification tasks on heterogeneous graph\\ndatasets and further show SGAT's ability in extracting structural information\\nby employing random node features. Numerical experiments indicate that SGAT\\nperforms better than other current state-of-the-art heterogeneous graph\\nlearning methods.\\n\",\n", + " \" Recent progress in pre-trained neural language models has significantly\\nimproved the performance of many natural language processing (NLP) tasks. In\\nthis paper we propose a new model architecture DeBERTa (Decoding-enhanced BERT\\nwith disentangled attention) that improves the BERT and RoBERTa models using\\ntwo novel techniques. The first is the disentangled attention mechanism, where\\neach word is represented using two vectors that encode its content and\\nposition, respectively, and the attention weights among words are computed\\nusing disentangled matrices on their contents and relative positions,\\nrespectively. Second, an enhanced mask decoder is used to incorporate absolute\\npositions in the decoding layer to predict the masked tokens in model\\npre-training. In addition, a new virtual adversarial training method is used\\nfor fine-tuning to improve models' generalization. We show that these\\ntechniques significantly improve the efficiency of model pre-training and the\\nperformance of both natural language understanding (NLU) and natural langauge\\ngeneration (NLG) downstream tasks. Compared to RoBERTa-Large, a DeBERTa model\\ntrained on half of the training data performs consistently better on a wide\\nrange of NLP tasks, achieving improvements on MNLI by +0.9% (90.2% vs. 91.1%),\\non SQuAD v2.0 by +2.3% (88.4% vs. 90.7%) and RACE by +3.6% (83.2% vs. 86.8%).\\nNotably, we scale up DeBERTa by training a larger version that consists of 48\\nTransform layers with 1.5 billion parameters. The significant performance boost\\nmakes the single DeBERTa model surpass the human performance on the SuperGLUE\\nbenchmark (Wang et al., 2019a) for the first time in terms of macro-average\\nscore (89.9 versus 89.8), and the ensemble DeBERTa model sits atop the\\nSuperGLUE leaderboard as of January 6, 2021, out performing the human baseline\\nby a decent margin (90.3 versus 89.8).\\n\",\n", + " \" BERT (Bidirectional Encoder Representations from Transformers) has\\nrevolutionized the field of natural language processing through its exceptional\\nperformance on numerous tasks. Yet, the majority of researchers have mainly\\nconcentrated on enhancements related to the model structure, such as relative\\nposition embedding and more efficient attention mechanisms. Others have delved\\ninto pretraining tricks associated with Masked Language Modeling, including\\nwhole word masking. DeBERTa introduced an enhanced decoder adapted for BERT's\\nencoder model for pretraining, proving to be highly effective. We argue that\\nthe design and research around enhanced masked language modeling decoders have\\nbeen underappreciated. In this paper, we propose several designs of enhanced\\ndecoders and introduce DrBERT (Decoder-refined BERT), a novel method for\\nmodeling training. Typically, a pretrained BERT model is fine-tuned for\\nspecific Natural Language Understanding (NLU) tasks. In our approach, we\\nutilize the original BERT model as the encoder, making only changes to the\\ndecoder without altering the encoder. This approach does not necessitate\\nextensive modifications to the model's architecture and can be seamlessly\\nintegrated into existing fine-tuning pipelines and services, offering an\\nefficient and effective enhancement strategy. Compared to other methods, while\\nwe also incur a moderate training cost for the decoder during the pretraining\\nprocess, our approach does not introduce additional training costs during the\\nfine-tuning phase. We test multiple enhanced decoder structures after\\npretraining and evaluate their performance on the GLUE benchmark. Our results\\ndemonstrate that DrBERT, having only undergone subtle refinements to the model\\nstructure during pretraining, significantly enhances model performance without\\nescalating the inference time and serving budget.\\n\",\n", + " ' Since 2017, the Transformer-based models play critical roles in various\\ndownstream Natural Language Processing tasks. However, a common limitation of\\nthe attention mechanism utilized in Transformer Encoder is that it cannot\\nautomatically capture the information of word order, so explicit position\\nembeddings are generally required to be fed into the target model. In contrast,\\nTransformer Decoder with the causal attention masks is naturally sensitive to\\nthe word order. In this work, we focus on improving the position encoding\\nability of BERT with the causal attention masks. Furthermore, we propose a new\\npre-trained language model DecBERT and evaluate it on the GLUE benchmark.\\nExperimental results show that (1) the causal attention mask is effective for\\nBERT on the language understanding tasks; (2) our DecBERT model without\\nposition embeddings achieve comparable performance on the GLUE benchmark; and\\n(3) our modification accelerates the pre-training process and DecBERT w/ PE\\nachieves better overall performance than the baseline systems when pre-training\\nwith the same amount of computational resources.\\n',\n", + " ' Currently, the most widespread neural network architecture for training\\nlanguage models is the so called BERT which led to improvements in various\\nNatural Language Processing (NLP) tasks. In general, the larger the number of\\nparameters in a BERT model, the better the results obtained in these NLP tasks.\\nUnfortunately, the memory consumption and the training duration drastically\\nincreases with the size of these models. In this article, we investigate\\nvarious training techniques of smaller BERT models: We combine different\\nmethods from other BERT variants like ALBERT, RoBERTa, and relative positional\\nencoding. In addition, we propose two new fine-tuning modifications leading to\\nbetter performance: Class-Start-End tagging and a modified form of Linear Chain\\nConditional Random Fields. Furthermore, we introduce Whole-Word Attention which\\nreduces BERTs memory usage and leads to a small increase in performance\\ncompared to classical Multi-Head-Attention. We evaluate these techniques on\\nfive public German Named Entity Recognition (NER) tasks of which two are\\nintroduced by this article.\\n',\n", + " \" Recent advances in NLP have significantly improved the performance of\\nlanguage models on a variety of tasks. While these advances are largely driven\\nby the availability of large amounts of data and computational power, they also\\nbenefit from the development of better training methods and architectures. In\\nthis paper, we introduce CamemBERTa, a French DeBERTa model that builds upon\\nthe DeBERTaV3 architecture and training objective. We evaluate our model's\\nperformance on a variety of French downstream tasks and datasets, including\\nquestion answering, part-of-speech tagging, dependency parsing, named entity\\nrecognition, and the FLUE benchmark, and compare against CamemBERT, the\\nstate-of-the-art monolingual model for French. Our results show that, given the\\nsame amount of training tokens, our model outperforms BERT-based models trained\\nwith MLM on most tasks. Furthermore, our new model reaches similar or superior\\nperformance on downstream tasks compared to CamemBERT, despite being trained on\\nonly 30% of its total number of input tokens. In addition to our experimental\\nresults, we also publicly release the weights and code implementation of\\nCamemBERTa, making it the first publicly available DeBERTaV3 model outside of\\nthe original paper and the first openly available implementation of a DeBERTaV3\\ntraining objective. https://gitlab.inria.fr/almanach/CamemBERTa\\n\",\n", + " ' Recently, Natural Language Processing (NLP) has witnessed an impressive\\nprogress in many areas, due to the advent of novel, pretrained contextual\\nrepresentation models. In particular, Devlin et al. (2019) proposed a model,\\ncalled BERT (Bidirectional Encoder Representations from Transformers), which\\nenables researchers to obtain state-of-the art performance on numerous NLP\\ntasks by fine-tuning the representations on their data set and task, without\\nthe need for developing and training highly-specific architectures. The authors\\nalso released multilingual BERT (mBERT), a model trained on a corpus of 104\\nlanguages, which can serve as a universal language model. This model obtained\\nimpressive results on a zero-shot cross-lingual natural inference task. Driven\\nby the potential of BERT models, the NLP community has started to investigate\\nand generate an abundant number of BERT models that are trained on a particular\\nlanguage, and tested on a specific data domain and task. This allows us to\\nevaluate the true potential of mBERT as a universal language model, by\\ncomparing it to the performance of these more specific models. This paper\\npresents the current state of the art in language-specific BERT models,\\nproviding an overall picture with respect to different dimensions (i.e.\\narchitectures, data domains, and tasks). Our aim is to provide an immediate and\\nstraightforward overview of the commonalities and differences between\\nLanguage-Specific (language-specific) BERT models and mBERT. We also provide an\\ninteractive and constantly updated website that can be used to explore the\\ninformation we have collected, at https://bertlang.unibocconi.it.\\n',\n", + " ' We introduce Performers, Transformer architectures which can estimate regular\\n(softmax) full-rank-attention Transformers with provable accuracy, but using\\nonly linear (as opposed to quadratic) space and time complexity, without\\nrelying on any priors such as sparsity or low-rankness. To approximate softmax\\nattention-kernels, Performers use a novel Fast Attention Via positive\\nOrthogonal Random features approach (FAVOR+), which may be of independent\\ninterest for scalable kernel methods. FAVOR+ can be also used to efficiently\\nmodel kernelizable attention mechanisms beyond softmax. This representational\\npower is crucial to accurately compare softmax with other kernels for the first\\ntime on large-scale tasks, beyond the reach of regular Transformers, and\\ninvestigate optimal attention-kernels. Performers are linear architectures\\nfully compatible with regular Transformers and with strong theoretical\\nguarantees: unbiased or nearly-unbiased estimation of the attention matrix,\\nuniform convergence and low estimation variance. We tested Performers on a rich\\nset of tasks stretching from pixel-prediction through text models to protein\\nsequence modeling. We demonstrate competitive results with other examined\\nefficient sparse and dense attention methods, showcasing effectiveness of the\\nnovel attention-learning paradigm leveraged by Performers.\\n',\n", + " ' Transformer models have achieved state-of-the-art results across a diverse\\nrange of domains. However, concern over the cost of training the attention\\nmechanism to learn complex dependencies between distant inputs continues to\\ngrow. In response, solutions that exploit the structure and sparsity of the\\nlearned attention matrix have blossomed. However, real-world applications that\\ninvolve long sequences, such as biological sequence analysis, may fall short of\\nmeeting these assumptions, precluding exploration of these models. To address\\nthis challenge, we present a new Transformer architecture, Performer, based on\\nFast Attention Via Orthogonal Random features (FAVOR). Our mechanism scales\\nlinearly rather than quadratically in the number of tokens in the sequence, is\\ncharacterized by sub-quadratic space complexity and does not incorporate any\\nsparsity pattern priors. Furthermore, it provides strong theoretical\\nguarantees: unbiased estimation of the attention matrix and uniform\\nconvergence. It is also backwards-compatible with pre-trained regular\\nTransformers. We demonstrate its effectiveness on the challenging task of\\nprotein sequence modeling and provide detailed theoretical analysis.\\n',\n", + " ' Transformer has shown great successes in natural language processing,\\ncomputer vision, and audio processing. As one of its core components, the\\nsoftmax attention helps to capture long-range dependencies yet prohibits its\\nscale-up due to the quadratic space and time complexity to the sequence length.\\nKernel methods are often adopted to reduce the complexity by approximating the\\nsoftmax operator. Nevertheless, due to the approximation errors, their\\nperformances vary in different tasks/corpus and suffer crucial performance\\ndrops when compared with the vanilla softmax attention. In this paper, we\\npropose a linear transformer called cosFormer that can achieve comparable or\\nbetter accuracy to the vanilla transformer in both casual and cross attentions.\\ncosFormer is based on two key properties of softmax attention: i).\\nnon-negativeness of the attention matrix; ii). a non-linear re-weighting scheme\\nthat can concentrate the distribution of the attention matrix. As its linear\\nsubstitute, cosFormer fulfills these properties with a linear operator and a\\ncosine-based distance re-weighting mechanism. Extensive experiments on language\\nmodeling and text understanding tasks demonstrate the effectiveness of our\\nmethod. We further examine our method on long sequences and achieve\\nstate-of-the-art performance on the Long-Range Arena benchmark. The source code\\nis available at https://github.com/OpenNLPLab/cosFormer.\\n',\n", + " \" Transformers are state-of-the-art models for a variety of sequence modeling\\ntasks. At their core is an attention function which models pairwise\\ninteractions between the inputs at every timestep. While attention is powerful,\\nit does not scale efficiently to long sequences due to its quadratic time and\\nspace complexity in the sequence length. We propose RFA, a linear time and\\nspace attention that uses random feature methods to approximate the softmax\\nfunction, and explore its application in transformers. RFA can be used as a\\ndrop-in replacement for conventional softmax attention and offers a\\nstraightforward way of learning with recency bias through an optional gating\\nmechanism. Experiments on language modeling and machine translation demonstrate\\nthat RFA achieves similar or better performance compared to strong transformer\\nbaselines. In the machine translation experiment, RFA decodes twice as fast as\\na vanilla transformer. Compared to existing efficient transformer variants, RFA\\nis competitive in terms of both accuracy and efficiency on three long text\\nclassification datasets. Our analysis shows that RFA's efficiency gains are\\nespecially notable on long sequences, suggesting that RFA will be particularly\\nuseful in tasks that require working with large inputs, fast decoding speed, or\\nlow memory footprints.\\n\",\n", + " ' Large transformer models have achieved state-of-the-art results in numerous\\nnatural language processing tasks. Among the pivotal components of the\\ntransformer architecture, the attention mechanism plays a crucial role in\\ncapturing token interactions within sequences through the utilization of\\nsoftmax function.\\n Conversely, linear attention presents a more computationally efficient\\nalternative by approximating the softmax operation with linear complexity.\\nHowever, it exhibits substantial performance degradation when compared to the\\ntraditional softmax attention mechanism.\\n In this paper, we bridge the gap in our theoretical understanding of the\\nreasons behind the practical performance gap between softmax and linear\\nattention. By conducting a comprehensive comparative analysis of these two\\nattention mechanisms, we shed light on the underlying reasons for why softmax\\nattention outperforms linear attention in most scenarios.\\n',\n", + " ' Transformers are widely used in natural language processing, where they\\nconsistently achieve state-of-the-art performance. This is mainly due to their\\nattention-based architecture, which allows them to model rich linguistic\\nrelations between (sub)words. However, transformers are difficult to interpret.\\nBeing able to provide reasoning for its decisions is an important property for\\na model in domains where human lives are affected. With transformers finding\\nwide use in such fields, the need for interpretability techniques tailored to\\nthem arises. We propose a new technique that selects the most faithful\\nattention-based interpretation among the several ones that can be obtained by\\ncombining different head, layer and matrix operations. In addition, two\\nvariations are introduced towards (i) reducing the computational complexity,\\nthus being faster and friendlier to the environment, and (ii) enhancing the\\nperformance in multi-label data. We further propose a new faithfulness metric\\nthat is more suitable for transformer models and exhibits high correlation with\\nthe area under the precision-recall curve based on ground truth rationales. We\\nvalidate the utility of our contributions with a series of quantitative and\\nqualitative experiments on seven datasets.\\n'],\n", + " 'author': ['Llion Jones',\n", + " 'Alessandro Raganato',\n", + " 'Prakhar Thapak',\n", + " 'Ye Lin',\n", + " 'Seba Susan',\n", + " 'Ikumi Okubo',\n", + " 'Yunchao Wei',\n", + " 'Yanpeng Sun',\n", + " 'Rui Zheng',\n", + " 'Yuan Zhou',\n", + " 'Dechun Cong',\n", + " 'Congchong Nie',\n", + " 'Houye Ji',\n", + " 'Wentao Xu',\n", + " 'Yang Yan',\n", + " 'Xiaocheng Yang',\n", + " 'Dasol Hwang',\n", + " 'See Hian Lee',\n", + " 'Pengcheng He',\n", + " 'Wen Liang',\n", + " 'Ziyang Luo',\n", + " 'Jochen Z\\\\\"ollner',\n", + " 'Wissam Antoun',\n", + " 'Federico Bianchi',\n", + " 'Valerii Likhosherstov',\n", + " 'Xingyou Song',\n", + " 'Yiran Zhong',\n", + " 'Hao Peng',\n", + " 'Yichuan Deng',\n", + " 'Ioannis Mollas']}" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "r\"\"\"\n", + "nodes: [\n", + " { # paper에 대한 정보\n", + " id: str -> xxxx.xxxx # 챗봇 연결링크 생성시 필요, Nullable: False\n", + " title: str -> Attention is ~ # 유저한테 보여줄 논문 제목 -> 만드는중\n", + " size: int # 인용수\n", + " hieght: int # 부모 노드에 연결될 Edge 굵기: 1hop 2hop 구분으로 사용?\n", + " color: int # 노드 특성 반영한 한가지 값. 투명도로 조절해서 표시할 수 있음.\n", + " }, ...\n", + "],\n", + "edges: [\n", + " {\n", + " source: str -> id # 자식 노드 제목\n", + " to: str -> id # 부모 노드 제목\n", + " distance -> int [0 ~ 1] # 모델 output 유사도\n", + " }, ...\n", + "]\n", + "\"\"\"" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'nodes': [{'title': 'Attention Is All You Need',\n", + " 'author': 'Llion Jones',\n", + " 'year': 2023,\n", + " 'id': '1706.03762',\n", + " 'height': 2,\n", + " 'size': 80,\n", + " 'color': 'rgb(240, 237, 207, 0.8)'},\n", + " {'title': 'Fixed Encoder Self-Attention Patterns in Transformer-Based Machine\\n Translation',\n", + " 'author': 'Alessandro Raganato',\n", + " 'year': 2020,\n", + " 'id': '2002.10260',\n", + " 'height': 1,\n", + " 'size': 77,\n", + " 'color': 'rgb(11, 96, 176, 0.25)'},\n", + " {'title': 'Transformer++',\n", + " 'author': 'Prakhar Thapak',\n", + " 'year': 2020,\n", + " 'id': '2003.04974',\n", + " 'height': 1,\n", + " 'size': 34,\n", + " 'color': 'rgb(11, 96, 176, 0.25)'},\n", + " {'title': 'An Efficient Transformer Decoder with Compressed Sub-layers',\n", + " 'author': 'Ye Lin',\n", + " 'year': 2023,\n", + " 'id': '2101.00542',\n", + " 'height': 1,\n", + " 'size': 43,\n", + " 'color': 'rgb(11, 96, 176, 0.4)'},\n", + " {'title': 'Context- and Sequence-Aware Convolutional Recurrent Encoder for Neural\\n Machine Translation',\n", + " 'author': 'Seba Susan',\n", + " 'year': 2021,\n", + " 'id': '2101.04030',\n", + " 'height': 1,\n", + " 'size': 39,\n", + " 'color': 'rgb(11, 96, 176, 0.3)'},\n", + " {'title': 'A Cost-Efficient FPGA Implementation of Tiny Transformer Model using\\n Neural ODE',\n", + " 'author': 'Ikumi Okubo',\n", + " 'year': 2024,\n", + " 'id': '2401.02721',\n", + " 'height': 1,\n", + " 'size': 80,\n", + " 'color': 'rgb(240, 237, 207, 0.8)'},\n", + " {'title': 'CCNet: Criss-Cross Attention for Semantic Segmentation',\n", + " 'author': 'Yunchao Wei',\n", + " 'year': 2020,\n", + " 'id': '1811.11721',\n", + " 'height': 1,\n", + " 'size': 39,\n", + " 'color': 'rgb(11, 96, 176, 0.25)'},\n", + " {'title': 'CTNet: Context-based Tandem Network for Semantic Segmentation',\n", + " 'author': 'Yanpeng Sun',\n", + " 'year': 2021,\n", + " 'id': '2104.09805',\n", + " 'height': 1,\n", + " 'size': 30,\n", + " 'color': 'rgb(11, 96, 176, 0.3)'},\n", + " {'title': 'TriangleNet: Edge Prior Augmented Network for Semantic Segmentation\\n through Cross-Task Consistency',\n", + " 'author': 'Rui Zheng',\n", + " 'year': 2023,\n", + " 'id': '2210.05152',\n", + " 'height': 1,\n", + " 'size': 31,\n", + " 'color': 'rgb(11, 96, 176, 0.4)'},\n", + " {'title': 'Real-time Semantic Segmentation via Spatial-detail Guided Context\\n Propagation',\n", + " 'author': 'Yuan Zhou',\n", + " 'year': 2022,\n", + " 'id': '2005.11034',\n", + " 'height': 1,\n", + " 'size': 93,\n", + " 'color': 'rgb(11, 96, 176, 0.35)'},\n", + " {'title': 'BiCANet: Bi-directional Contextual Aggregating Network for Image\\n Semantic Segmentation',\n", + " 'author': 'Dechun Cong',\n", + " 'year': 2020,\n", + " 'id': '2003.09669',\n", + " 'height': 1,\n", + " 'size': 80,\n", + " 'color': 'rgb(240, 237, 207, 0.8)'},\n", + " {'title': 'HCNet: Hierarchical Context Network for Semantic Segmentation',\n", + " 'author': 'Congchong Nie',\n", + " 'year': 2020,\n", + " 'id': '2010.04962',\n", + " 'height': 1,\n", + " 'size': 42,\n", + " 'color': 'rgb(11, 96, 176, 0.25)'},\n", + " {'title': 'Heterogeneous Graph Attention Network',\n", + " 'author': 'Houye Ji',\n", + " 'year': 2021,\n", + " 'id': '1903.07293',\n", + " 'height': 1,\n", + " 'size': 38,\n", + " 'color': 'rgb(11, 96, 176, 0.3)'},\n", + " {'title': 'SHGNN: Structure-Aware Heterogeneous Graph Neural Network',\n", + " 'author': 'Wentao Xu',\n", + " 'year': 2021,\n", + " 'id': '2112.06244',\n", + " 'height': 1,\n", + " 'size': 37,\n", + " 'color': 'rgb(11, 96, 176, 0.3)'},\n", + " {'title': 'ISHNE: Influence Self-attention for Heterogeneous Network Embedding',\n", + " 'author': 'Yang Yan',\n", + " 'year': 2022,\n", + " 'id': '2103.00118',\n", + " 'height': 1,\n", + " 'size': 46,\n", + " 'color': 'rgb(11, 96, 176, 0.35)'},\n", + " {'title': 'Simple and Efficient Heterogeneous Graph Neural Network',\n", + " 'author': 'Xiaocheng Yang',\n", + " 'year': 2023,\n", + " 'id': '2207.02547',\n", + " 'height': 1,\n", + " 'size': 80,\n", + " 'color': 'rgb(240, 237, 207, 0.8)'},\n", + " {'title': 'Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous\\n Graphs',\n", + " 'author': 'Dasol Hwang',\n", + " 'year': 2021,\n", + " 'id': '2007.08294',\n", + " 'height': 1,\n", + " 'size': 39,\n", + " 'color': 'rgb(11, 96, 176, 0.3)'},\n", + " {'title': 'SGAT: Simplicial Graph Attention Network',\n", + " 'author': 'See Hian Lee',\n", + " 'year': 2022,\n", + " 'id': '2207.11761',\n", + " 'height': 1,\n", + " 'size': 40,\n", + " 'color': 'rgb(11, 96, 176, 0.35)'},\n", + " {'title': 'DeBERTa: Decoding-enhanced BERT with Disentangled Attention',\n", + " 'author': 'Pengcheng He',\n", + " 'year': 2021,\n", + " 'id': '2006.03654',\n", + " 'height': 1,\n", + " 'size': 90,\n", + " 'color': 'rgb(11, 96, 176, 0.3)'},\n", + " {'title': 'DrBERT: Unveiling the Potential of Masked Language Modeling Decoder in\\n BERT pretraining',\n", + " 'author': 'Wen Liang',\n", + " 'year': 2024,\n", + " 'id': '2401.15861',\n", + " 'height': 1,\n", + " 'size': 49,\n", + " 'color': 'rgb(11, 96, 176, 0.45)'},\n", + " {'title': 'DecBERT: Enhancing the Language Understanding of BERT with Causal\\n Attention Masks',\n", + " 'author': 'Ziyang Luo',\n", + " 'year': 2022,\n", + " 'id': '2204.08688',\n", + " 'height': 1,\n", + " 'size': 80,\n", + " 'color': 'rgb(240, 237, 207, 0.8)'},\n", + " {'title': 'Optimizing small BERTs trained for German NER',\n", + " 'author': 'Jochen Z\\\\\"ollner',\n", + " 'year': 2021,\n", + " 'id': '2104.11559',\n", + " 'height': 1,\n", + " 'size': 72,\n", + " 'color': 'rgb(11, 96, 176, 0.3)'},\n", + " {'title': 'Data-Efficient French Language Modeling with CamemBERTa',\n", + " 'author': 'Wissam Antoun',\n", + " 'year': 2023,\n", + " 'id': '2306.01497',\n", + " 'height': 1,\n", + " 'size': 92,\n", + " 'color': 'rgb(11, 96, 176, 0.4)'},\n", + " {'title': 'What the [MASK]? Making Sense of Language-Specific BERT Models',\n", + " 'author': 'Federico Bianchi',\n", + " 'year': 2020,\n", + " 'id': '2003.02912',\n", + " 'height': 1,\n", + " 'size': 52,\n", + " 'color': 'rgb(11, 96, 176, 0.25)'},\n", + " {'title': 'Rethinking Attention with Performers',\n", + " 'author': 'Valerii Likhosherstov',\n", + " 'year': 2022,\n", + " 'id': '2009.14794',\n", + " 'height': 1,\n", + " 'size': 47,\n", + " 'color': 'rgb(11, 96, 176, 0.35)'},\n", + " {'title': 'Masked Language Modeling for Proteins via Linearly Scalable Long-Context\\n Transformers',\n", + " 'author': 'Xingyou Song',\n", + " 'year': 2020,\n", + " 'id': '2006.03555',\n", + " 'height': 1,\n", + " 'size': 80,\n", + " 'color': 'rgb(240, 237, 207, 0.8)'},\n", + " {'title': 'cosFormer: Rethinking Softmax in Attention',\n", + " 'author': 'Yiran Zhong',\n", + " 'year': 2022,\n", + " 'id': '2202.08791',\n", + " 'height': 1,\n", + " 'size': 89,\n", + " 'color': 'rgb(11, 96, 176, 0.35)'},\n", + " {'title': 'Random Feature Attention',\n", + " 'author': 'Hao Peng',\n", + " 'year': 2021,\n", + " 'id': '2103.02143',\n", + " 'height': 1,\n", + " 'size': 56,\n", + " 'color': 'rgb(11, 96, 176, 0.3)'},\n", + " {'title': 'Superiority of Softmax: Unveiling the Performance Edge Over Linear\\n Attention',\n", + " 'author': 'Yichuan Deng',\n", + " 'year': 2023,\n", + " 'id': '2310.11685',\n", + " 'height': 1,\n", + " 'size': 57,\n", + " 'color': 'rgb(11, 96, 176, 0.4)'},\n", + " {'title': 'An Attention Matrix for Every Decision: Faithfulness-based Arbitration\\n Among Multiple Attention-Based Interpretations of Transformers in Text\\n Classification',\n", + " 'author': 'Ioannis Mollas',\n", + " 'year': 2022,\n", + " 'id': '2209.10876',\n", + " 'height': 1,\n", + " 'size': 51,\n", + " 'color': 'rgb(11, 96, 176, 0.35)'}],\n", + " 'links': [{'source': '1706.03762', 'target': '2002.10260', 'distance': 80},\n", + " {'source': '1706.03762', 'target': '2003.04974', 'distance': 88},\n", + " {'source': '1706.03762', 'target': '2101.00542', 'distance': 22},\n", + " {'source': '1706.03762', 'target': '2101.04030', 'distance': 17},\n", + " {'source': '1706.03762', 'target': '2401.02721', 'distance': 61},\n", + " {'source': '1811.11721', 'target': '2104.09805', 'distance': 92},\n", + " {'source': '1811.11721', 'target': '2210.05152', 'distance': 39},\n", + " {'source': '1811.11721', 'target': '2005.11034', 'distance': 73},\n", + " {'source': '1811.11721', 'target': '2003.09669', 'distance': 21},\n", + " {'source': '1811.11721', 'target': '2010.04962', 'distance': 67},\n", + " {'source': '1903.07293', 'target': '2112.06244', 'distance': 47},\n", + " {'source': '1903.07293', 'target': '2103.00118', 'distance': 48},\n", + " {'source': '1903.07293', 'target': '2207.02547', 'distance': 38},\n", + " {'source': '1903.07293', 'target': '2007.08294', 'distance': 47},\n", + " {'source': '1903.07293', 'target': '2207.11761', 'distance': 22},\n", + " {'source': '2006.03654', 'target': '2401.15861', 'distance': 79},\n", + " {'source': '2006.03654', 'target': '2204.08688', 'distance': 10},\n", + " {'source': '2006.03654', 'target': '2104.11559', 'distance': 94},\n", + " {'source': '2006.03654', 'target': '2306.01497', 'distance': 14},\n", + " {'source': '2006.03654', 'target': '2003.02912', 'distance': 26},\n", + " {'source': '2009.14794', 'target': '2006.03555', 'distance': 81},\n", + " {'source': '2009.14794', 'target': '2202.08791', 'distance': 83},\n", + " {'source': '2009.14794', 'target': '2103.02143', 'distance': 61},\n", + " {'source': '2009.14794', 'target': '2310.11685', 'distance': 9},\n", + " {'source': '2009.14794', 'target': '2209.10876', 'distance': 48}]}" + ] + }, + "execution_count": 86, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "graph" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import json\n", + "with open(os.path.join('back/data', 'attentiongraph.json'), 'w') as json_file:\n", + " json.dump(graph, json_file, ensure_ascii=False) # ensure_ascii True 하면 한글이 안보임 ㅋㅋ" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Frontend/graph/test.ipynb b/Frontend/graph/test.ipynb new file mode 100644 index 0000000..94d30c5 --- /dev/null +++ b/Frontend/graph/test.ipynb @@ -0,0 +1,194 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " id embeddings\n", + "0 1609.04741 [-0.6698794364929199, -0.4727935194969177, 0.2...\n", + "1 1703.07738 [-0.2581411302089691, -0.012778624892234802, -...\n", + "2 1706.03762 [-0.5425103902816772, 0.18396970629692078, -0....\n", + "3 1710.08969 [-0.41298601031303406, 0.5831630229949951, -0....\n", + "4 1711.06149 [-0.7896791696548462, 0.07237350940704346, -0....\n", + "... ... ...\n", + "4064 2402.09329 [-0.4379268288612366, -0.23558062314987183, -0...\n", + "4065 2402.09812 [-0.6690561771392822, -0.07181265950202942, -0...\n", + "4066 2402.11639 [-0.7059850692749023, -0.26324883103370667, -0...\n", + "4067 2402.13512 [-0.5639562606811523, -0.09524745494127274, -0...\n", + "4068 2402.17417 [-0.6053261756896973, -0.2860853970050812, -0....\n", + "\n", + "[4069 rows x 2 columns]\n", + "변환된 텐서:\n", + "tensor([-0.6699, -0.4728, 0.2280, ..., -0.2286, -0.6570, 0.6681])\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import torch\n", + "\n", + "data = pd.read_csv('embedding.csv')\n", + "print(data)\n", + "series_data = data.embeddings[0]\n", + "number_list = [float(num_str) for num_str in series_data.strip('[]').split(',')]\n", + "\n", + "# 리스트를 PyTorch 텐서로 변환\n", + "tensor_data = torch.tensor(number_list)\n", + "\n", + "print(\"변환된 텐서:\")\n", + "print(tensor_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "import csv\n", + "import ast\n", + "import numpy as np\n", + "from sklearn.metrics.pairwise import cosine_similarity\n", + "\n", + "# 전역 변수로 데이터를 저장할 딕셔너리\n", + "vectors = {}\n", + "\n", + "def parse_csv(file_path):\n", + " global vectors\n", + " with open(file_path, newline='') as csvfile:\n", + " reader = csv.DictReader(csvfile)\n", + " for row in reader:\n", + " id = row['id']\n", + " vector_str = row['embeddings']\n", + " vector = ast.literal_eval(vector_str)\n", + " vectors[id] = vector\n", + "\n", + "def compute_similarity(base_ids):\n", + " similarity_results = []\n", + " for base_id in base_ids:\n", + " base_vector = np.array([vectors[base_id]])\n", + " for id, vector in vectors.items():\n", + " if id != base_id:\n", + " similarity = cosine_similarity(base_vector, [np.array(vector)])[0][0]\n", + " similarity_results.append({'source': id, 'target': base_id, 'distance': similarity})\n", + " return similarity_results\n", + "\n", + "def get_top_similarity_results(similarity_results, top_n=5):\n", + " similarity_results = sorted(similarity_results, key=lambda x: x['similarity'], reverse=True)\n", + " return similarity_results[:top_n]\n", + "\n", + "# CSV 파일에서 데이터 파싱\n", + "file_path = 'embedding.csv'\n", + "parse_csv(file_path)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Top 5 similarities for base ID 1609.04741:\n", + "{'from': '2105.03891', 'to': '1609.04741', 'similarity': 0.7967764055095873}\n", + "{'from': '2102.06361', 'to': '1609.04741', 'similarity': 0.7796217906946868}\n", + "{'from': '2005.08665', 'to': '1609.04741', 'similarity': 0.7540322177135702}\n", + "{'from': '2301.03634', 'to': '1609.04741', 'similarity': 0.7500096755111593}\n", + "{'from': '2103.16273', 'to': '1609.04741', 'similarity': 0.7476865946495533}\n", + "Top 5 similarities for base ID 1703.07738:\n", + "{'from': '2008.04378', 'to': '1703.07738', 'similarity': 0.6856879431382494}\n", + "{'from': '2101.08967', 'to': '1703.07738', 'similarity': 0.669291005697058}\n", + "{'from': '2010.00516', 'to': '1703.07738', 'similarity': 0.6687917998791476}\n", + "{'from': '2004.13621', 'to': '1703.07738', 'similarity': 0.668282168170491}\n", + "{'from': '2005.0665', 'to': '1703.07738', 'similarity': 0.6627738646226708}\n", + "Top 5 similarities for base ID 1806.05382:\n", + "{'from': '2306.01526', 'to': '1806.05382', 'similarity': 0.8868579787302365}\n", + "{'from': '2011.03891', 'to': '1806.05382', 'similarity': 0.8562002713592036}\n", + "{'from': '2112.10481', 'to': '1806.05382', 'similarity': 0.8542720670209276}\n", + "{'from': '2302.04368', 'to': '1806.05382', 'similarity': 0.8526240282440782}\n", + "{'from': '2006.11487', 'to': '1806.05382', 'similarity': 0.8505263314488972}\n" + ] + } + ], + "source": [ + "# 기준이 되는 여러 ID\n", + "base_ids = ['1609.04741', '1703.07738', '1806.05382']\n", + "\n", + "top_5_results = {}\n", + "for base_id in base_ids:\n", + " similarity_results = compute_similarity([base_id])\n", + " top_5_results[base_id] = get_top_similarity_results(similarity_results)\n", + "\n", + "# 결과 출력\n", + "results = []\n", + "for base_id, top_5 in top_5_results.items():\n", + " print(f\"Top 5 similarities for base ID {base_id}:\")\n", + " for result in top_5:\n", + " print(result)\n", + " results.append(result)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[{'from': '1609.04741', 'to': '2105.03891', 'similarity': 0.7967764055095873},\n", + " {'from': '1609.04741', 'to': '2102.06361', 'similarity': 0.7796217906946868},\n", + " {'from': '1609.04741', 'to': '2005.08665', 'similarity': 0.7540322177135702},\n", + " {'from': '1609.04741', 'to': '2301.03634', 'similarity': 0.7500096755111593},\n", + " {'from': '1609.04741', 'to': '2103.16273', 'similarity': 0.7476865946495533},\n", + " {'from': '1703.07738', 'to': '2008.04378', 'similarity': 0.6856879431382494},\n", + " {'from': '1703.07738', 'to': '2101.08967', 'similarity': 0.669291005697058},\n", + " {'from': '1703.07738', 'to': '2010.00516', 'similarity': 0.6687917998791476},\n", + " {'from': '1703.07738', 'to': '2004.13621', 'similarity': 0.668282168170491},\n", + " {'from': '1703.07738', 'to': '2005.0665', 'similarity': 0.6627738646226708},\n", + " {'from': '1806.05382', 'to': '2306.01526', 'similarity': 0.8868579787302365},\n", + " {'from': '1806.05382', 'to': '2011.03891', 'similarity': 0.8562002713592036},\n", + " {'from': '1806.05382', 'to': '2112.10481', 'similarity': 0.8542720670209276},\n", + " {'from': '1806.05382', 'to': '2302.04368', 'similarity': 0.8526240282440782},\n", + " {'from': '1806.05382', 'to': '2006.11487', 'similarity': 0.8505263314488972}]" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}