import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.wrappers.scikit_learn import KerasClassifier
from keras.utils import np_utils
from sklearn.model_selection import train_test_split, KFold, cross_val_score
from sklearn.preprocessing import LabelEncoder
from keras.optimizers import SGD
from keras.layers import LSTM

# load dataset
dataframe = pd.read_csv("./data/iris1.csv", header=None)
dataset = dataframe.values
X = dataset[:, 0:19].astype(float)
dummy_y1 = dataset[:, 19]
m,n=1682,6
dum_imax=np.zeros((m,n))
# print(type(dum_imax))
for i in range(m):
    # print(i)
    # exit()
    if dummy_y1[i]!=0:
        dum_imax[i][dummy_y1[i]-1]=1
    else:
        dum_imax[i][5]=1
# print(dum_imax)
dummy_y =dum_imax
print(dummy_y)
print(type(dummy_y[0][0]))

def baseline_model():
    model = Sequential()
    model.add(Dense(output_dim=50, input_dim=19, activation='relu'))
    # model.add(LSTM(128))
    model.add(Dropout(0.4))
    model.add(Dense(output_dim=6, input_dim=50, activation='softmax'))
    # Compile model
    sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
    # model.compile(loss='categorical_crossentropy', optimizer=sgd)
    #编译模型。由于我们做的是二元分类，所以我们指定损失函数为binary_crossentropy，以及模式为binary
    #另外常见的损失函数还有mean_squared_error、categorical_crossentropy等，请阅读帮助文件。
    #求解方法我们指定用adam，还有sgd、rmsprop等可选
    model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
    return model
estimator = KerasClassifier(build_fn=baseline_model, nb_epoch=40, batch_size=256)
print(estimator)

# splitting data into training set and test set. If random_state is set to an integer, the split datasets are fixed.
X_train, X_test, Y_train, Y_test = train_test_split(X, dummy_y, test_size=0.2, random_state=0)#train_test_split是交叉验证中常用的函数，功能是从样本中随机的按比例选取train data和testdata，
print(len(X_train[0]))
print(len(Y_train[0]))
estimator.fit(X_train, Y_train,nb_epoch = 100)#训练模型，学习一百次

# make predictions
print(X_test)
pred = estimator.predict(X_test)
print(pred)
# init_lables = encoder.inverse_transform(pred)
# print(init_lables)

# inverse numeric variables to initial categorical labels
# init_lables = encoder.inverse_transform(pred)
# print(init_lables)

# k-fold cross-validate
# seed = 42
# np.random.seed(seed)
'''
n_splits : 默认3，最小为2；K折验证的K值
shuffle : 默认False;shuffle会对数据产生随机搅动(洗牌)
random_state :默认None，随机种子
'''
kfold = KFold(n_splits=5, shuffle=True)#定义5折，在对数据进行划分之前，对数据进行随机混洗

results = cross_val_score(estimator, X, dummy_y, cv=kfold)#在数据集上，使用k fold交叉验证，对估计器estimator进行评估。
print("baseline:%.2f%%(%.2f%%)"%(results.mean()*100,results.std()*100))#返回的结果，是10次数据集划分后，每次的评估结果。评估结果包括平均准确率和标准差