from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from tensorflow.keras.models import Sequential, Model
from tensorflow.keras.layers import Dense, Dropout, Input, Conv2D, MaxPooling2D, Flatten, SimpleRNN, LSTM, GRU
import numpy as np
from sklearn.metrics import r2_score
from sklearn.preprocessing import MinMaxScaler,StandardScaler
from tensorflow.keras.callbacks import EarlyStopping
import time
import tensorflow as tf

#1. 데이터

datasets = load_boston()
x= datasets.data
y= datasets['target']

# print(x.shape, y.shape)  #(506, 13) (506,)
x_train, x_test, y_train, y_test = train_test_split(x, y,
                                                    train_size=0.8,
                                                    random_state=221,
                                                    )

scaler=MinMaxScaler()
scaler.fit(x_train)
x_train = scaler.transform(x_train)
x_test = scaler.transform(x_test)
# print(np.min(x_test), np.max(x_test))
print(x_train.shape, x_test.shape)

x_train = x_train.reshape(404, 13, 1)
x_test = x_test.reshape(102, 13, 1)

#2. 모델 구성

input1 = Input(shape=(13, 1))
dltjdgh = LSTM(20,activation='linear')(input1)
dense1 = Dense(10, activation='relu')(dltjdgh)
dense2 = Dense(10, activation='relu')(dense1)
dense3 = Dense(10, activation='relu')(dense2)
output1 = Dense(1)(dense3)

model=Model(inputs=input1, outputs=output1)

#3. 컴파일, 훈련
start_time=time.time()
model.compile(loss='mse', optimizer='adam')
es = EarlyStopping(monitor = 'loss',
                   patience=20,
                   restore_best_weights=True,
                   verbose=1)
hist = model.fit(x_train, y_train,
                 epochs =1000,
                 batch_size=160,
                #  validation_split = 0.2,
                 callbacks=[es])

end_time=time.time()

#4. 평가, 예측
result = model.evaluate(x_test, y_test)
print('result :', result)

y_pred=model.predict(x_test)
r2 = r2_score(y_test, y_pred)
print('r2 :', r2)

# result : 19.00429916381836
# r2 : 0.7454836664304496

# result : 22.428110122680664
# r2 : 0.6996300554059725   LSTM 적용