recommend_course.py

import pandas as pd
import numpy as np
from catboost import CatBoostRegressor
from bson import ObjectId
from base_response import baseresponse  
import time

# 거리 계산 함수 (반경 필터링)
def haversine(lon1, lat1, lon2, lat2):
    R = 6371  # 지구 반지름 (km)
    lon1, lat1, lon2, lat2 = map(np.radians, [lon1, lat1, lon2, lat2])
    dlon = lon2 - lon1
    dlat = lat2 - lat1
    a = np.sin(dlat / 2) ** 2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon / 2) ** 2
    c = 2 * np.arcsin(np.sqrt(a))
    return R * c * 1000  # meters

# 반경 내 후보지를 필터링
def filter_by_radius(user_longitude, user_latitude, radius, area_data):
    filter_start = time.time()
    distances = haversine(
        user_longitude,
        user_latitude,
        area_data['LONGITUDE'].values,
        area_data['LATITUDE'].values
    )
    print(f"Distance Calculate Time: {time.time() - filter_start:.3f} seconds")
    return area_data[distances <= radius]

# 추천 생성 함수
def generate_recommendations(user_input, df, model):
    # 반경 내 데이터 필터링 및 중복 제거
    filtered_area_data = filter_by_radius(user_input['LONGITUDE'], user_input['LATITUDE'], user_input['RADIUS'], df)
    unique_area_data = filtered_area_data.drop_duplicates(subset=['VISIT_AREA_NM'])

    user_data = {
        'VISIT_AREA_TYPE_CD': user_input['VISIT_AREA_TYPE_CD'],
        'REVISIT_YN': 'Y',
        'REVISIT_INTENTION': 5,
        'RCMDTN_INTENTION': 5,
        'GENDER': user_input['GENDER'],
        'AGE_GRP': user_input['AGE_GRP'],
        'TRAVEL_STYL_1': user_input['TRAVEL_STYL_1'],
        'TRAVEL_STYL_2': user_input['TRAVEL_STYL_2'],
        'TRAVEL_STYL_3': user_input['TRAVEL_STYL_3'],
        'TRAVEL_STYL_4': user_input['TRAVEL_STYL_4'],
        'TRAVEL_STYL_5': user_input['TRAVEL_STYL_5'],
        'TRAVEL_STYL_6': user_input['TRAVEL_STYL_6'],
        'TRAVEL_STATUS_ACCOMPANY': user_input['TRAVEL_STATUS_ACCOMPANY']
    }

    rows = []
    for area_nm in unique_area_data['VISIT_AREA_NM']:
        input_data = [area_nm] + list(user_data.values())
        try:
            score = model.predict(input_data, thread_count=1)
        except Exception as e:
            print(f"Prediction error: {e}")
            return baseresponse(False, 500, "Prediction failed")
        rows.append([area_nm, score])

    
    result = pd.DataFrame(rows, columns=['AREA_NM', 'SCORE'])
    return result.sort_values('SCORE', ascending=False).head(10)

# ObjectId 변환 함수
def convert_object_id(data):
    if isinstance(data, list):
        return [convert_object_id(item) for item in data]
    elif isinstance(data, dict):
        return {k: str(v) if isinstance(v, ObjectId) else v for k, v in data.items()}
    return data