def clusterFunc(factory, device, measurePoint, timeRange, hashname):
P_total, device_index = Tool.getP_totalBySQL(factory, device, measurePoint,
timeRange)
kmeans_hour, labels_hour, kmeans_day, labels_day = cluster(
P_total, device_index, 96)
hourList = kmeans_hour.tolist()
dayList = kmeans_day.tolist()
# print(dayList)
hourX = len(hourList[0])
dayX = len(dayList[0])
resultDict = {
'hourX': list(range(0, hourX)),
'dayX': list(range(0, dayX)),
'hourList': hourList,
'dayList': dayList
}
try:
resultJson = json.dumps(resultDict, ensure_ascii=False)
except Exception as e:
e.with_traceback()
sql = "insert into clusterresult (hashstr,json)values ('%s', '%s')" % (
hashname, resultJson)
Tool.excuteSQL(sql)
return hashname
def baseLine(factory,
device,
measurePoint,
year,
month,
day,
hashname,
day_point=96):
date = datetime.datetime(year, month, day)
start = date - datetime.timedelta(days=8)
end = date + datetime.timedelta(days=2)
print(start)
print(date)
data, device_index = Tool.getP_totalBySQL(
factory, device, measurePoint,
[start.strftime("%Y-%m-%d"),
end.strftime("%Y-%m-%d")])
# data, device_index = Tool.getP_totalBySQL(factory, device, measurePoint)
baseValue, trueValue = baseline(data, device_index, year, month, day,
day_point)
resultDict = {'baseValue': list(baseValue), 'trueValue': list(trueValue)}
try:
resultJson = json.dumps(resultDict, ensure_ascii=False)
except Exception as e:
e.with_traceback()
sql = "insert into baseline (hashstr,json)values ('%s','%s')" % (
hashname, resultJson)
Tool.excuteSQL(sql)
return hashname
def correlationFunc(factory, device, measurePoint, timeRange, hashname):
if device == "-1":
P_total, device_index = Tool.getP_totalBySQL(-1, device, measurePoint,
timeRange)
else:
P_total, device_index = Tool.getP_totalBySQL(factory, device,
measurePoint, timeRange)
corr_device = correlation(P_total, device_index, 3, 96)
addSelfDeviceCorrIndex = corr_device.index.tolist()
addSelfDeviceCorrIndex.append(device)
corrData = P_total.loc[:, addSelfDeviceCorrIndex]
corrDataDict = {
'timestamp':
corrData.index.strftime("%Y-%m-%d %H:%M:%S").values.tolist(),
}
for i in addSelfDeviceCorrIndex:
corrDataDict[i] = corrData.loc[:, i].values.tolist()
corrDataJson = json.dumps(corrDataDict, ensure_ascii=False)
# keys:带有设备的文件名 values:相似度
a, b = corr_device.keys(), corr_device.values
resultDict = {}
for i, j in zip(a, b):
resultDict[i] = j
resultJson = json.dumps(resultDict, ensure_ascii=False)
sql = ''' insert into sjtudb.correlation values('%s','%s','%s') ''' % (
hashname, resultJson, corrDataJson)
Tool.excuteSQL(sql)
return hashname
def olapRotate(user=None,
device=None,
timeRange=None,
metric=None,
groups: list = None,
agg=None,
hashname=None):
data = olapSlice(user, device, timeRange, metric, groups, agg, 1)
data = data.T
resultJson = getRe(data)
sql = "insert into olapresult (hashname,json)values ('%s','%s')" % (
hashname, resultJson)
Tool.excuteSQL(sql)
return hashname
def olapDrill(user=None,
device=None,
timeRange=None,
metric=None,
timeMode=0,
zoneMode=0,
hashname=None):
totalData, deviceList, metricList = Tool.olapData(timeRange)
dataDrill = Drill(totalData, deviceList, metricList, user, device,
timeRange, metric, timeMode, zoneMode)
print("drill")
resultJson = getRe(dataDrill)
sql = "insert into olapresult (hashname,json)values ('%s','%s')" % (
hashname, resultJson)
Tool.excuteSQL(sql)
# dataDrill.to_csv(os.path.join(os.path.dirname(os.path.abspath(__file__)), hashname+".csv"))
return hashname
def predictFunc(factory, device, measurePoint, timeRange, hashname):
resultFileName = hashname[0:15]
# insertSQL = '''insert into sjtudb.algorithmresult values('%s','%s',null)''' % (parameterHash, resultFileName)
# Tool.excuteSQL(insertSQL)
P_total, device_index = Tool.getP_totalBySQL(factory, device, measurePoint,
timeRange)
corr_device = correlation(P_total, device_index, 3, 96)
a, b = train_forecast(P_total, corr_device, device_index, 96)
lastResult = {'y_true': a, 'y_pred': b}
jsonStr = json.dumps(lastResult)
insertSQL = '''insert into sjtudb.algorithmresult values('%s','%s','%s')''' % (
hashname, resultFileName, jsonStr)
# print(insertSQL)
Tool.excuteSQL(insertSQL)
return hashname
def olapSlice(user=None,
device=None,
timeRange=None,
metric=None,
groups: list = None,
agg=None,
rotate=None,
parameterHash=None):
totalData, deviceList, metricList = Tool.olapData(timeRange)
dataSlice = pd.DataFrame()
if timeRange == None and metric == None and groups == None:
# 1.选定[用户+设备]切片
dataSlice = Slice(totalData, deviceList, metricList, user, device)
elif timeRange != None and metric == None and groups == None:
# 2.选定[用户+设备+时间段]切块
dataSlice = Slice(totalData, deviceList, metricList, user, device,
timeRange)
elif timeRange != None and metric != None and groups == None:
# 3.选定[用户+设备+时间段+属性]切块
dataSlice = Slice(totalData, deviceList, metricList, user, device,
timeRange, metric)
elif timeRange != None and metric != None and groups != None:
dataSlice = Slice(totalData, deviceList, metricList, user, device,
timeRange, metric, groups, agg)
print("slice")
if rotate == None:
resultJson = getRe(dataSlice)
sql = "insert into olapresult (hashname,json)values ('%s','%s')" % (
parameterHash, resultJson)
Tool.excuteSQL(sql)
return parameterHash
# dataSlice.to_csv(os.path.join(os.path.dirname(os.path.abspath(__file__)), parameterHash + '.csv'))
return dataSlice
def profileFeatureFunc(factory, device, measurePoint, timeRange, hashname):
P_total, device_index = Tool.getP_totalBySQL(factory, device, measurePoint,
timeRange)
kmeans_hour, labels_hour, kmeans_day, labels_day = cluster(
P_total, device_index, 96)
temp8760 = pd.read_csv(os.path.join(
os.path.dirname(os.path.abspath(__file__)), 'ShanghaiTemp8760.csv'),
header=None,
sep="[;,]",
engine='python')
staticFeatures, dynamicFeatures, tempload, temp, scattertemp, scatterdataunique = profileFeature(
P_total, device_index, kmeans_hour, kmeans_day, labels_hour,
labels_day, temp8760)
scatter = []
for i in range(len(scattertemp)):
if not (scatterdataunique.iloc[i] == 0):
scatter.append([scattertemp.iloc[i], scatterdataunique.iloc[i]])
staticFeatures[0]
staticFeatures[5] = str(staticFeatures[5])[1:-2]
staticFeatures[7] = staticFeatures[7].tolist()
staticFeatures[8] = staticFeatures[8].tolist()
dynamicFeatures[0] = dynamicFeatures[0].tolist()
dynamicFeatures[2] = dynamicFeatures[2].tolist()
staticFeaturesObj = {
'maxv': staticFeatures[0],
'minv': staticFeatures[1],
'median': staticFeatures[2],
'avg': staticFeatures[3],
'standard': staticFeatures[4],
'fftavg': staticFeatures[5],
'fftstandard': staticFeatures[6],
'featurelineh': staticFeatures[7], #典型特征模式曲线（小时尺度）
'linearfeaturelined': staticFeatures[8], #线性特征模式曲线（天尺度）
}
dynamicFeaturesObj = {
'transfermatrixh': dynamicFeatures[0], #基于聚类结果的马尔科夫转移矩阵（小时尺度）
'entropyh': dynamicFeatures[1],
'transfermatrixd': dynamicFeatures[2], #基于聚类结果的科尔科夫转移矩阵（天尺度）
'entropyd': dynamicFeatures[3]
}
resultDict = {
'static': staticFeaturesObj,
'dynamic': dynamicFeaturesObj,
"load": tempload.tolist(),
"temp": temp.tolist(),
"scatter": scatter
}
try:
resultJson = json.dumps(resultDict, ensure_ascii=False)
except Exception as e:
e.with_traceback()
sql = "insert into profilefeature (hashstr,json)values ('%s','%s')" % (
hashname, resultJson)
Tool.excuteSQL(sql)
return hashname
