TeamJCIM_AI/python.py at main · TeamJCIM/TeamJCIM_AI · GitHub

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importjoblib
importnumpyasnp
importpandasaspd
fromsklearn.ensembleimportRandomForestRegressor
fromsklearn.ensembleimportRandomForestClassifier
fromsklearn.model_selectionimporttrain_test_split
importmatplotlib.pyplotasplt
importseabornassns
fromIPython.displayimportImage
fromsklearn.preprocessingimportStandardScaler
fromsklearn.preprocessingimportLabelEncoder

data=pd.read_csv('data.csv', encoding='cp949')

input_num=pd.DataFrame(data, columns= ['평균기온(℃)', '평균풍속(m/s)', '평균습도(%rh)', '평균일강수량(mm)', '일사합',
'평균전력사용량', '코스피', '미세먼지', '최저기온(℃)','최고기온(℃)',
'평균 현지기압(hPa)','평균지면온도(℃)', '합계 소형증발량(mm)'])
input_day=pd.DataFrame(data, columns= ['요일'])
input_holi=pd.DataFrame(data, columns= ['공휴일/국경일'])
input_wek=pd.DataFrame(data, columns= ['평일/주말'])
y=pd.DataFrame(data, columns= ['전력'])

# StandardScaler 객체 생성
scaler=StandardScaler()

# 데이터 세트 변환
scaler.fit(input_num)
input_num=scaler.transform(input_num)

# ndarray -> dataframe
input_num=pd.DataFrame(input_num, columns= ['평균기온(℃)', '평균풍속(m/s)', '평균습도(%rh)', '평균일강수량(mm)', '일사합',
'평균전력사용량', '코스피', '미세먼지', '최저기온(℃)','최고기온(℃)',
'평균 현지기압(hPa)','평균지면온도(℃)', '합계 소형증발량(mm)'])

# 요일 변환
encoder=LabelEncoder()
encoder.fit(input_day)
input_day=encoder.transform(input_day)
input_day=pd.DataFrame(input_day, columns= ['요일'])

input_num['일시'] =data['일시']
input_day['일시'] =data['일시']
input_holi['일시'] =data['일시']
input_wek['일시'] =data['일시']
input_num=input_num.set_index('일시')
input_day=input_day.set_index('일시')
input_holi=input_holi.set_index('일시')
input_wek=input_wek.set_index('일시')
y['일시'] =data['일시']
y=y.set_index('일시')
y=np.ravel(y, order='C')

x=input_num.join(input_day)
x=x.join(input_holi)
x=x.join(input_wek)

saved_model=joblib.load('./model.pkl')

pre=saved_model.predict(x)
print(pre)

# print('heool')