#ReduceLRonPlateau : 개선이 없으면 Learningrate 반으로 줄인다.

import numpy as np
import pandas as pd
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split as tts

#1. 데이터

path='./_data/ddarung/'      # .=현 폴더, study    /= 하위폴더
path_save='./_save/ddarung/'      # .=현 폴더, study    /= 하위폴더
train_csv = pd.read_csv(path + 'train.csv',
                        index_col=0)                
test_csv = pd.read_csv(path + 'test.csv',
                       index_col=0)

train_csv = train_csv.dropna()   #dropna = 결측치 삭제 함수*****

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

x_train, x_test, y_train, y_test = tts(
    x,y, train_size=0.8, random_state = 337, shuffle = True,
)

#2. 모델
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

model = Sequential()
model.add(Dense(64, input_dim=x.shape[1]))
model.add(Dense(32))
model.add(Dense(32))
model.add(Dense(32))
model.add(Dense(1))

#3. 컴파일, 훈련

from tensorflow.keras.optimizers import Adam
learning_rate = 0.01
optimizer = Adam(learning_rate=learning_rate,)
model.compile(loss='mse', optimizer=optimizer)

from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau

es = EarlyStopping(monitor = 'val_loss',
                   patience = 20, mode = 'min',
                   verbose = 1)

rlr = ReduceLROnPlateau(monitor = 'val_loss',
                        patience = 2,
                        mode='auto',
                        verbose = 1,
                        factor=0.5)

model.fit(x_train, y_train,
          epochs = 200, 
          batch_size = 32, 
          validation_split=0.2, 
          verbose=1, 
          callbacks=[es, rlr])

#4. 평가, 예측
results = model.evaluate(x_test, y_test)

print("loss :", results)

# loss : 2953.94921875