# 사이킷런 load_digits 데이터 

import pandas as pd
import numpy as np
from tensorflow.python.keras.models import Sequential, Model
from tensorflow.python.keras.layers import Dense, Input
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import tensorflow as tf
from sklearn.datasets import load_digits
from tensorflow.python.keras.callbacks import EarlyStopping
from keras.utils import to_categorical
from sklearn.preprocessing import MinMaxScaler, MaxAbsScaler, StandardScaler, RobustScaler

#1. 데이터 

data_sets= load_digits()

x=data_sets.data

y=data_sets['target']
# .reshape(-1, 1)

#판다스 원핫 해보기

y = to_categorical(y) #

print(x.shape, y.shape)  #(1797, 64) (1797, 10)

x_train, x_test, y_train, y_test = train_test_split(x, y,
                                                    train_size=0.8,
                                                    random_state=443,
                                                    stratify=y,
                                                    shuffle=True,
                                                    )

scaler=MinMaxScaler()
# scaler=MaxAbsScaler()
# scaler=StandardScaler()
# scaler=RobustScaler()
scaler.fit(x_train)
x_train = scaler.transform(x_train)
x_test = scaler.transform(x_test)

# print(np.unique(y_train, return_counts=True))

# #2. 모델 구성

# model = Sequential()
# model.add(Dense(100, input_dim=64))
# model.add(Dense(80,activation='relu'))
# model.add(Dense(60,activation='relu'))
# model.add(Dense(40,activation='relu'))
# model.add(Dense(20,activation='relu'))
# model.add(Dense(10,activation='softmax'))

input1 = Input(shape=(64,))
dense1 = Dense(100,activation='relu')(input1)
dense2 = Dense(80,activation='relu')(dense1)
dense3 = Dense(60,activation='relu')(dense2)
dense4 = Dense(40,activation='relu')(dense3)
dense5 = Dense(20,activation='relu')(dense4)
output1 = Dense(10,activation='relu')(dense5)

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

#3. 컴파일, 훈련
model.compile(loss='categorical_crossentropy', optimizer='adam',
              metrics=['acc']
              )

es = EarlyStopping(monitor='val_loss',
                   mode='min',
                   patience=150,
                   verbose=1)

hist = model.fit(x_train, y_train,
                 epochs = 1000,
                 validation_split=0.2,
                 batch_size=300,
                 verbose=1,
                 callbacks=[es])

#4. 평가, 예측

result = model.evaluate(x_test, y_test)
print('result :', result)
y_predict = model.predict(x_test)
y_predict=np.argmax(y_predict, axis=-1)
print(y_predict.shape)

y_test = np.argmax(y_test, axis=1)
acc = accuracy_score(y_test, y_predict)
print('acc :', acc)

# acc : 0.9666666666666667 맥스앱스 스케일러 적용

# acc : 0.9694444444444444 민맥스 스케일러 적용