def insert_many():
service = MemberPro()
Session = openSession()
session = Session()
df = service.hook()
print(df.head())
# 저장된 모델로 멤버 이탈 확률 구하기
mmdp = MemberModelingDataPreprocessing()
refined_members = mmdp.hook_process(df)
print(f'REFINED_MEMBERS: \n{refined_members}')
refined_members = refined_members.drop('exited', axis=1)
refined_members = [np.array(refined_members, dtype=np.float32)]
print(f'REFINED_MEMBERS AFTER NUMPY ARRAY: \n{refined_members}')
path = os.path.join(os.path.abspath(os.path.dirname(__file__)),
'models', 'member')
new_model = tf.keras.models.load_model(os.path.join(
path, 'member_churn.h5'),
compile=False)
model_pred = new_model.predict(refined_members)
print(f'MODEL PREDICTION: {model_pred}')
df['probability_churn'] = model_pred
print(f'LAST DATAFRAME: {df}')
# admin = {'email': '*****@*****.**', 'password': '******', 'name': 'Admin', 'profile': 'noimage.png', 'geography': 'Spain',
# 'gender': '', 'age': '', 'tenure': 100, 'stock_qty': 0, 'balance': 0, 'as_credit': 0, 'credit_score': 0, 'is_active_member': 1,
# 'estimated_salary': 0, 'role': 'ROLE_ADMIN', 'probability_churn': -1, 'exited': 0}
session.bulk_insert_mappings(MemberDto, df.to_dict(orient="records"))
session.commit()
session.close()
def modify_recommend_stock(recommend_stock):
Session = openSession()
session = Session()
trading = session.query(RecommendStockDto)\
.filter(RecommendStockDto.id==recommend_stock.id)\
.update({RecommendStockDto.stock_type: recommend_stock['stock_type'], RecommendStockDto.stock_id: recommend_stock['stock_id']})
session.commit()
session.close()
def modify_comment(comment):
Session = openSession()
session = Session()
member = session.query(CommentDto)\
.filter(CommentDto.email == comment.email)\
.update({CommentDto.comment: comment['comment']})
session.commit()
session.close()
def modify_board(cls, board):
Session = openSession()
session = Session()
board = session.query(BoardDto)\
.filter(BoardDto.id==board.id)\
.update({BoardDto.title: board['title'], BoardDto.content: board['content']})
session.commit()
session.close()
def modify_trading(trading):
Session = openSession()
session = Session()
trading = session.query(TradingDto)\
.filter(TradingDto.id==trading['id'])\
.update({TradingDto.stock_qty: trading['stock_qty'], TradingDto.price: trading['price'], TradingDto.trading_date: trading['trading_date']})
session.commit()
session.close()
def insert_many():
service = BoardPro()
Session = openSession()
session = Session()
df = service.process()
print(df.head())
session.bulk_insert_mappings(BoardDto, df.to_dict(orient="records"))
session.commit()
session.close()
def insert_many():
service = MemberPro()
Session = openSession()
session = Session()
df = service.hook()
print(df.head())
session.bulk_insert_mappings(MemberDto, df.to_dict(orient="records"))
session.commit()
session.close()
def update(member):
Session = openSession()
session = Session()
mcp = MemberChurnPredService()
mcp.assign(member)
prediction = mcp.predict()
prediction = round(prediction[0, 0], 5)
print(f'PREDICTION: {prediction}')
member.probability_churn = float(prediction)
print(member)
member = session.query(MemberDto)\
.filter(MemberDto.email==member.email)\
.update({MemberDto.password: member.password, MemberDto.name: member.name, MemberDto.profile: member.profile, MemberDto.geography: member.geography,
MemberDto.gender: member.gender, MemberDto.age: member.age, MemberDto.tenure: member.tenure, MemberDto.stock_qty: member.stock_qty, MemberDto.balance: member.balance,
MemberDto.has_credit: member.has_credit, MemberDto.credit_score: member.credit_score, MemberDto.is_active_member: member.is_active_member, MemberDto.estimated_salary: member.estimated_salary,
MemberDto.role: member.role, MemberDto.probability_churn: member.probability_churn, MemberDto.exited: member.exited})
session.commit()
session.close()
geography: str = ''
gender: str = ''
age: int = 0
tenure: int = 0
stock_qty: int = 0
balance: float = 0.0
has_credit: int = 0
credit_score: int = 0
is_active_member: int = 1
estimated_salary: float = 0.0
role: str = 'ROLE_USER'
probability_churn: float = 0.0
exited: int = 0
Session = openSession()
session = Session()
class MemberDao(MemberDto):
def __init__(self):
...
@classmethod
def count(cls):
return session.query(func.count(MemberDto.email)).one()
@classmethod
def find_all(cls):
sql = cls.query.filter(cls.exited != 1)
df = pd.read_sql(sql.statement, sql.session.bind)
