#따릉이 파일 모델링 과정

#임포트

import numpy as np
import pandas as pd
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score, mean_squared_error

#1. 데이터

path = './_data/ddarung/'
train_csv = pd.read_csv(path+'train.csv', index_col=0)
test_csv = pd.read_csv(path+'test.csv', index_col=0)
submission = pd.read_csv(path + 'submission.csv')

# print(train_csv.isnull().sum())
train_csv = train_csv.dropna()
# print(train_csv.isnull().sum())

x = train_csv.drop(['count'], axis=1)
y = train_csv['count']

# print(x.columns)     #(1328, 9)

x_train,x_test,y_train,y_test = train_test_split(
    x,y,
    train_size=0.8,
    random_state=1002,
    shuffle=True
)

print(x_train.shape, x_test.shape)  #(1062, 9) (266, 9)
print(y_train.shape, y_test.shape)  #(1062,) (266,)

#2. 모델구성

model = Sequential()
model.add(Dense(10,input_dim=9))
model.add(Dense(20))
model.add(Dense(1))

#3. 컴파일, 훈련

model.compile(loss= 'mse', optimizer= 'adam')
model.fit(x_train, y_train,
          epochs=100,
          batch_size=15,verbose=1)

#4. 평가, 예측

loss = model.evaluate(x_test, y_test,verbose=1)
print('loss :', loss)

y_predict = model.predict(x_test)

r2 = r2_score(y_test, y_predict)
print('r2 :', r2)

def RMSE(y_test, y_predict) :
    return np.sqrt(mean_squared_error(y_test, y_predict))

rmse = RMSE(y_test, y_predict)

print('rmse :', rmse)

y_submit = model.predict(test_csv)

submission['count']= y_submit

# print(submission)

submission.to_csv(path + 'submission12387.csv')

#rmse : 61.99368140717433