data = pd.read_csv('../data/water_heart.csv')
# 读取用水事件记录
sj = pd.read_csv('../data/sj.csv')
# 转换时间格式
data["发生时间"] = pd.to_datetime(data["发生时间"],format="%Y%m%d%H%M%S")

# 构造特征：总用水时长
timeDel = pd.Timedelta("0.5 sec")
sj["事件开始时间"] = data.iloc[sj["事件起始编号"]-1,0].values- timeDel
sj["事件结束时间"] = data.iloc[sj["事件终止编号"]-1,0].values + timeDel
sj['洗浴时间点'] = [i.hour for i in sj["事件开始时间"]]
sj["总用水时长"] = np.int64(sj["事件结束时间"] - sj["事件开始时间"])/1000000000 + 1

# 构造用水停顿事件
# 构造特征“停顿开始时间”、“停顿结束时间”
# 停顿开始时间指从有水流到无水流，停顿结束时间指从无水流到有水流
for i in range(len(data)-1):
    if (data.loc[i,"水流量"] != 0) & (data.loc[i + 1,"水流量"] == 0) :
        data.loc[i + 1,"停顿开始时间"] = data.loc[i +1, "发生时间"] - timeDel
    if (data.loc[i,"水流量"] == 0) & (data.loc[i + 1,"水流量"] != 0) :
        data.loc[i,"停顿结束时间"] = data.loc[i , "发生时间"] + timeDel
        
# 提取停顿开始时间与结束时间所对应行号，放在数据框Stop中
indStopStart = data.index[data["停顿开始时间"].notnull()]+1
indStopEnd = data.index[data["停顿结束时间"].notnull()]+1
Stop = pd.DataFrame(data={"停顿开始编号":indStopStart[:-1],
                            "停顿结束编号":indStopEnd[1:]}) 
# 计算停顿时长，并放在数据框stop中，停顿时长=停顿结束时间-停顿结束时间
Stop["停顿时长"] = np.int64(data.loc[indStopEnd[1:]-1,"停顿结束时间"].values-
                     data.loc[indStopStart[:-1]-1,"停顿开始时间"].values)/1000000000
# 将每次停顿与事件匹配,停顿的开始时间要大于事件的开始时间，
# 且停顿的结束时间要小于事件的结束时间
for i in range(len(sj)):
    Stop.loc[(Stop["停顿开始编号"] > sj.loc[i,"事件起始编号"]) & 
           (Stop["停顿结束编号"] < sj.loc[i,"事件终止编号"]),"停顿归属事件"]=i+1
             
# 删除停顿次数为0的事件
Stop = Stop[Stop["停顿归属事件"].notnull()]

# 构造特征 用水事件停顿总时长、停顿次数、停顿平均时长、
# 用水时长，用水/总时长
stopAgg =  Stop.groupby("停顿归属事件").agg({"停顿时长":sum,"停顿开始编号":len})
sj.loc[stopAgg.index - 1,"总停顿时长"] = stopAgg.loc[:,"停顿时长"].values
sj.loc[stopAgg.index-1,"停顿次数"] = stopAgg.loc[:,"停顿开始编号"].values
sj.fillna(0,inplace=True)  # 对缺失值用0插补
stopNo0 = sj["停顿次数"] != 0  # 判断用水事件是否存在停顿
sj.loc[stopNo0,"平均停顿时长"] = sj.loc[stopNo0,"总停顿时长"]/sj.loc[stopNo0,"停顿次数"] 
sj.fillna(0,inplace=True)  # 对缺失值用0插补
sj["用水时长"] = sj["总用水时长"] - sj["总停顿时长"]  # 定义特征用水时长
sj["用水/总时长"] = sj["用水时长"] / sj["总用水时长"]  # 定义特征 用水/总时长
print('用水事件用水时长与频率特征构造完成后数据的特征为：\n',sj.columns)
print('用水事件用水时长与频率特征构造完成后数据的前5行5列特征为：\n',
      sj.iloc[:5,:5])

data["水流量"] = data["水流量"] / 60 # 原单位L/min，现转换为L/sec
sj["总用水量"] = 0 # 给总用水量赋一个初始值0
for i in range(len(sj)):
    Start = sj.loc[i,"事件起始编号"]-1
    End = sj.loc[i,"事件终止编号"]-1
    if Start != End:
        for j in range(Start,End):
            if data.loc[j,"水流量"] != 0:
                sj.loc[i,"总用水量"] = (data.loc[j + 1,"发生时间"] - 
                                    data.loc[j,"发生时间"]).seconds* \
                                    data.loc[j,"水流量"] + sj.loc[i,"总用水量"]
        sj.loc[i,"总用水量"] = sj.loc[i,"总用水量"] + data.loc[End,"水流量"] * 2
    else:
        sj.loc[i,"总用水量"] = data.loc[Start,"水流量"] * 2
        
sj["平均水流量"] = sj["总用水量"] / sj["用水时长"] # 定义特征 平均水流量
# 构造特征：水流量波动
# 水流量波动=∑(((单次水流的值-平均水流量)^2)*持续时间)/用水时长
sj["水流量波动"] = 0 # 给水流量波动赋一个初始值0
for i in range(len(sj)):
    Start = sj.loc[i,"事件起始编号"] - 1
    End = sj.loc[i,"事件终止编号"] - 1
    for j in range(Start,End + 1):
        if data.loc[j,"水流量"] != 0:
            slbd = (data.loc[j,"水流量"] - sj.loc[i,"平均水流量"])**2
            slsj = (data.loc[j + 1,"发生时间"] - data.loc[j,"发生时间"]).seconds
            sj.loc[i,"水流量波动"] = slbd * slsj + sj.loc[i,"水流量波动"]
    sj.loc[i,"水流量波动"] = sj.loc[i,"水流量波动"] / sj.loc[i,"用水时长"]   

# 构造特征：停顿时长波动
# 停顿时长波动=∑(((单次停顿时长-平均停顿时长)^2)*持续时间)/总停顿时长
sj["停顿时长波动"] = 0 # 给停顿时长波动赋一个初始值0
for i in range(len(sj)):
    if sj.loc[i,"停顿次数"] > 1: # 当停顿次数为0或1时，停顿时长波动值为0，故排除
        for j in Stop.loc[Stop["停顿归属事件"] == (i+1),"停顿时长"].values:
            sj.loc[i,"停顿时长波动"] = ((j - sj.loc[i,"平均停顿时长"])**2) * j + \
                                     sj.loc[i,"停顿时长波动"]
        sj.loc[i,"停顿时长波动"] = sj.loc[i,"停顿时长波动"] / sj.loc[i,"总停顿时长"]

print('用水量和波动特征构造完成后数据的特征为：\n',sj.columns)
print('用水量和波动特征构造完成后数据的前5行5列特征为：\n',sj.iloc[:5,:5])

sj_bool = (sj['用水时长'] >100) & (sj['总用水时长'] > 120) & (sj['总用水量'] > 5)
sj_final = sj.loc[sj_bool,:]
sj_final.to_csv('./sj_final.csv',index=False)
print('筛选出候选洗浴事件前的数据形状为：',sj.shape)
print('筛选出候选洗浴事件后的数据形状为：',sj_final.shape)

import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.neural_network import MLPClassifier
import joblib


# 读取数据
Xtrain = pd.read_csv('../data/sj_final.csv')
ytrain = pd.read_excel('../data/water_heater_log.xlsx')
test = pd.read_excel('../data/test_data.xlsx')
# 训练集测试集区分
x_train, x_test, y_train, y_test = Xtrain.iloc[:,5:],test.iloc[:,4:-1],\
                                   ytrain.iloc[:,-1],test.iloc[:,-1]
# 标准化
stdScaler = StandardScaler().fit(x_train)
x_stdtrain = stdScaler.transform(x_train)
x_stdtest = stdScaler.transform(x_test)
# 建立模型
bpnn = MLPClassifier(hidden_layer_sizes = (17,10), max_iter = 200, solver = 'lbfgs',random_state=50)
bpnn.fit(x_stdtrain, y_train)
# 保存模型
joblib.dump(bpnn,'../data/water_heater_nnet.m')
print('构建的模型为：\n',bpnn)

# 模型评价
from sklearn.metrics import classification_report
from sklearn.metrics import roc_curve
import joblib
import matplotlib.pyplot as plt

bpnn = joblib.load('../data/water_heater_nnet.m')  # 加载模型
y_pred = bpnn.predict(x_stdtest)  # 返回预测结果
print('神经网络预测结果评价报告：\n',classification_report(y_test,y_pred))
# 绘制roc曲线图
fpr, tpr, thresholds = roc_curve(y_pred,y_test)  # 求出TPR和FPR
plt.figure(figsize=(6,4))  # 创建画布
plt.plot(fpr,tpr)  # 绘制曲线
plt.title('用户用水事件识别ROC曲线(3144)')  # 标题
plt.xlabel('FPR')  # x轴标签
plt.ylabel('TPR')  # y轴标签
plt.savefig('../data/ROC.png')  # 保存图片