import numpy as np
import pandas as pd
from tensorflow.python.keras.models import Sequential
from tensorflow.python.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/'
path_save = './_save/ddarung/'
train_set = pd.read_csv(path + 'train.csv', index_col=0)
test_set = pd.read_csv(path+'test.csv', index_col=0)
submission = pd.read_csv(path+'submission.csv', index_col=0)

# print(train_set.shape)   #(1459, 10)
train_set = train_set.dropna()

x = train_set.drop(['count'], axis=1)
y = train_set['count']
print(x.shape)

x_val = x[1200:]
y_val = y[1200:]

x_test = x[1000:1200]
y_test = y[1000:1200]

x_train=x[:1000]
y_train=y[:1000]

#2. 모델구성
model=Sequential()
model.add(Dense(10, input_dim=9))
model.add(Dense(1))

#3. 컴파일, 훈련
model.compile(loss='mse',optimizer='adam')
model.fit(x_train,y_train,epochs=100, batch_size=100, validation_data=(x_val, y_val))

#4. 평가, 예측

loss = model.evaluate(x_test, y_test)
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_set)
submission['count']= y_submit
submission.to_csv(path_save + 'submission_validation_v2.csv')

#loss : 3081.24365234375
# r2 : 0.5225717070839723
# rmse : 55.50895164987975