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feat kookmin-sw#1 - 초기값 설정
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여자든 남자든 아무도 없을때의 default 초기값 삽입
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@cheesecrust
cheesecrust committed May 11, 2024
1 parent d0cce65 commit add3243
Showing 1 changed file with 79 additions and 26 deletions.
105 changes: 79 additions & 26 deletions main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -73,23 +73,35 @@ async def lifespan(app: FastAPI):
)

def extract_features(data):

if data is None:
return []
return '{"options": "[]", "smoking": "상관없어요", "room_sharing_option": "상관없어요"}'

if data == "null":
return '{"options": "[]", "smoking": "상관없어요", "room_sharing_option": "상관없어요"}'

parsed_data = json.loads(data)
# "options" 키의 값 추출
options_value = parsed_data['options']

# 문자열에서 배열로 변환
options_array = json.loads(options_value)

options_array = []

if options_value is not None:
options_array = ast.literal_eval(options_value)

# 추가할 키의 값들 추출
smoking_value = parsed_data['smoking']
mate_age_value = parsed_data['mate_age']
mate_age_value = None
if mate_age_value in parsed_data:
mate_age_value = parsed_data['mate_age']
room_sharing_option_value = parsed_data['room_sharing_option']

# 추가할 값들을 배열에 추가
options_array.extend([smoking_value, mate_age_value, room_sharing_option_value])
options_array.extend([smoking_value, room_sharing_option_value])
if mate_age_value is not None:
options_array.append(mate_age_value)
return options_array

def generate_df_data(data):
Expand All @@ -99,7 +111,6 @@ def generate_df_data(data):

df["features"] = df["features"].apply(extract_features)


features = (
df["features"]
.apply(pd.Series)
Expand Down Expand Up @@ -178,41 +189,46 @@ async def fetch_data():
return user_male_cards, user_female_cards, post_male_cards, post_female_cards

def fill_missing_values(df):
print("fill_missing_values")
imputer = SimpleImputer(strategy='mean')
return imputer.fit_transform(df)

def clustering(user_male_cards, user_female_cards, post_male_cards, post_female_cards):
async def clustering(user_male_cards, user_female_cards, post_male_cards, post_female_cards):

male_cards = [*user_male_cards, *post_male_cards]

if male_cards == []:
male_cards = [{'id': 'default', 'features': None, 'gender': 'MALE', 'card_type': 'my', 'birth_year': '1999'},
{'id': 'default', 'features': None, 'gender': 'MALE', 'card_type': 'mate', 'birth_year': '1999'}]

male_df = generate_df_data(male_cards)
print(male_df)

female_cards = [*user_female_cards, *post_female_cards]
female_df = generate_df_data(female_cards)


# 여기의 fit 이 뭔데 값으로??
# 결측값 우선 처리
if female_cards == []:
female_cards = [{'id': 'default', 'features': None, 'gender': 'MALE', 'card_type': 'my', 'birth_year': '1999'},
{'id': 'default', 'features': None, 'gender': 'MALE', 'card_type': 'mate', 'birth_year': '1999'}]

female_df = generate_df_data(female_cards)

male_cluster_model = DBSCAN(eps=0.2, min_samples=2)
male_cluster_model.fit(
convert_fit_data(male_df)
)

print(male_df)

female_cluster_model = DBSCAN(eps=0.2, min_samples=2)
female_cluster_model.fit(
convert_fit_data(female_df)
)

print("convert")
male_cluster = defaultdict(lambda: [])

find_male_user_cluster = defaultdict(lambda: {"my": None, "mate": None})

for index, cluster in enumerate(
male_cluster_model.fit_predict(convert_fit_data(male_df))
):
print(cluster)
card = male_cards[index]

male_cluster[cluster].append(male_cards[index])
Expand Down Expand Up @@ -269,6 +285,7 @@ def clustering(user_male_cards, user_female_cards, post_male_cards, post_female_
}
)

print("here?")
female_recommendation_result = defaultdict(
lambda: {"user": {"my": [], "mate": []}, "post": {"my": [], "mate": []}}
)
Expand Down Expand Up @@ -304,9 +321,50 @@ def clustering(user_male_cards, user_female_cards, post_male_cards, post_female_
"cardType": other_card["card_type"],
}
)
print(male_recommendation_result)
print(female_recommendation_result)


# print("male key : ", male_recommendation_result.keys())
# print("male result : ", male_recommendation_result.values())

"""
user_id <-> id, user_card_type, score, id_type
"""

for recommendation_result in (male_recommendation_result, female_recommendation_result):
for user_id in recommendation_result:
# print(user_id, recommendation_result[user_id])

my_card_result = recommendation_result[user_id]["user"]["my"]
mate_card_result = recommendation_result[user_id]["user"]["mate"]

for result in my_card_result:
id = result["id"]
score = result["score"]
card_type = result["cardType"]

# query = """
# insert into recommend (user_id, card_type, recommendation_id, recommendation_card_type, score)
# values (:user_id, 'my', :id, :card_type, :score)
# """

# # print("------------------------------- recommend_ result",user_id, id, score, card_type)
# await database.execute(query, {"user_id": user_id, "id": id, "score": score, "card_type": card_type})
# pass

for result in mate_card_result:
pass

post_my_card_result = recommendation_result[user_id]["post"]["my"]
post_mate_card_result = recommendation_result[user_id]["post"]["mate"]

for result in post_my_card_result:
pass

for result in post_mate_card_result:
pass


print()
print("female result : ", female_recommendation_result.values())
# 여기에 insert


Expand All @@ -321,7 +379,7 @@ async def update():
await fetch_data()
)

clustering(user_male_cards, user_female_cards, post_male_cards, post_female_cards)
await clustering(user_male_cards, user_female_cards, post_male_cards, post_female_cards)
print("clustering complete")

return {"detail": "ok"}
Expand All @@ -331,14 +389,9 @@ async def fetch():
user_male_cards, user_female_cards, post_male_cards, post_female_cards = (
await fetch_data()
)
# print("user male cards : ", user_male_cards)
# print()
# print("user female cards : ", user_female_cards)
# print()
# print(generate_df_data(user_male_cards))
# print()
print(user_female_cards)
print(generate_df_data(user_female_cards))

print(user_male_cards)
print(generate_df_data(user_male_cards))

@app.get("/insert")
async def insert():
Expand Down

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