def post(self):
"""
"""
self.set_post_header() # [设置]，请求头.
reqData = self.request.body # [取]，请求参数.
mylog.info("[Train_Choiced_DataSource.Request]...[%s]/[%s]" % (type(reqData), reqData))
# [校验]，请求参数.
errno, errMsg, reqDict = Optim_Public.verify_request(
reqData=reqData,
paramList=["modelType", "dataSourceName"]
)
mylog.info("[Train_Choiced_DataSource.Verify]...errno=[%s]...errMsg=[%s]...reqDict=[%s]" % (
errno, errMsg, reqDict
))
if errno < 0: # 校验异常，返回.
self.write(json.dumps({"errorNo": errno, "errorMsg": errMsg}))
return
# [Mysql]，取[ma_train]modeName列表.
modelNameList = []
modelNameRows = Mysql_MA_Train.find_modelNames_by_dataSouce(
modelType=reqDict["modelType"],
dataSourceName=reqDict["dataSourceName"],
)
print("..........", len(modelNameRows))
if modelNameRows:
for row in modelNameRows:
# print("row...", row)
modelNameList.append(row.modelName)
# [Mysql]，取数据源参数.
paramRow = Mysql_MA_DataSource.find_one_by_name(
dsName=reqDict["dataSourceName"],
)
if not paramRow: # 无匹配记录，返回.
self.write(json.dumps({
"errorNo": -1,
"errorMsg": "获取数据源参数异常！"
}))
return
# [返回], 给前端数据.
data = json.dumps({
"errorNo": 0,
"errorMsg": "成功",
"dataSourceParmas": json.loads(paramRow.paramsJson),
"modelNameList": modelNameList
})
print(data)
self.write(data)
def post(self):
"""
"""
self.set_post_header() # [设置]，请求头.
reqData = self.request.body # [取]，请求参数.
mylog.info("[Predict_Compare_Result.Request]...[%s]/[%s]" %
(type(reqData), reqData))
# [校验]，请求参数.
errno, errMsg, reqDict = Optim_Public.verify_request(
reqData=reqData, paramList=["modelType", "modelNames"])
mylog.info(
"[Predict_Compare_Result.Verify]...errno=[%s]...errMsg=[%s]...reqDict=[%s]"
% (errno, errMsg, reqDict))
if errno < 0: # 校验异常，返回.
self.write(json.dumps({"errorNo": errno, "errorMsg": errMsg}))
return
compareRes = Mysql_MA_Result.soft_result_Compare()
if not compareRes: # 记录不存在，返回
self.write(json.dumps({"errorNo": -1, "errorMsg": "没有结果更新！"}))
return
df = pd.read_sql(
"""SELECT a.modelName,a.time,a.prediction,b.Sample_TestResult,c.test_code
FROM soft_predict_result a,lims_data b,lim_dict c WHERE a.optColid=b.DICTIONARYID AND
b.DICTIONARYID=c.dictionaryid AND a.time=b.Sampling_Date ORDER BY a.time""",
engine.raw_connection())
resultList = []
if len(df) != 0:
for modelName in reqDict["modelNames"]:
resultDict = {}
resJson = df[df['modelName'] == modelName][[
'time', 'prediction', 'Sample_TestResult', 'test_code'
]].to_json(orient='records')
resultDict['modelName'] = modelName
resultDict['data'] = json.loads(resJson)
resultList.append(resultDict)
else:
self.write(json.dumps({"errorNo": -1, "errorMsg": "没有数据更新！"}))
data = json.dumps({
"errorNo": 0,
"errorMsg": "成功",
"Result": resultList
})
print(data)
self.write(data)
def post(self):
"""
"""
self.set_post_header() # [设置]，请求头.
reqData = self.request.body # [取]，请求参数.
mylog.info("[Predict_Get_DataSource.Request]...[%s]/[%s]" %
(type(reqData), reqData))
# [校验]，请求参数.
errno, errMsg, reqDict = Optim_Public.verify_request(
reqData=reqData, paramList=["modelType"])
mylog.info(
"[Predict_Get_DataSource.Verify]...errno=[%s]...errMsg=[%s]...reqDict=[%s]"
% (errno, errMsg, reqDict))
if errno < 0: # 校验异常，返回.
self.write(json.dumps({"errorNo": errno, "errorMsg": errMsg}))
return
# [Mysql]，取记录.
rows = Mysql_MA_DataSource.find_predict_dataSourceList(
modelType=reqDict["modelType"])
if not rows: # 无匹配记录，返回.
self.write(json.dumps({"errorNo": -1, "errorMsg": "没有匹配的数据源."}))
return
# [生成]，数据源名称列表.
dataSourceList = []
for row in rows:
# print(row)
dataSourceList.append({
"modelName": row.modelName,
"dataSource": row.dsName
})
# [返回], 给前端数据.
data = json.dumps({
"errorNo": 0,
"errorMsg": "成功",
"dataSourceList": dataSourceList,
})
print(data)
self.write(data)
def post(self):
"""
"""
self.set_post_header() # [设置]，请求头.
reqData = self.request.body # [取]，请求参数.
mylog.info("[Train_Get_ModelNameList.Request]...[%s]/[%s]" %
(type(reqData), reqData))
# [校验]，请求参数.
errno, errMsg, reqDict = Optim_Public.verify_request(
reqData=reqData, paramList=["modelType"])
mylog.info(
"[Train_Get_ModelNameList.Verify]...errno=[%s]...errMsg=[%s]...reqDict=[%s]"
% (errno, errMsg, reqDict))
if errno < 0: # 校验异常，返回.
self.write(json.dumps({"errorNo": errno, "errorMsg": errMsg}))
return
# [Mysql]，取[ma_train]记录.
trainRow = Mysql_MA_Train.find_modelNames_by_modelType(
modelType=reqDict["modelType"])
# modify @2019-11-25，没有匹配modelName返回空列表.
# if not trainRow: # 记录不存在，返回
# self.write(json.dumps({
# "errorNo": -1,
# "errorMsg": "模型名称不存在异常！"
# }))
# return
modelNameList = []
if trainRow:
for row in trainRow:
modelNameList.append(row.modelName)
# [返回], 给前端数据.
data = json.dumps({
"errorNo": 0,
"errorMsg": "成功",
"modelNameList": modelNameList
})
print(data)
self.write(data)
def post(self):
"""
"""
self.set_post_header() # [设置]，请求头.
reqData = self.request.body # [取]，请求参数.
mylog.info("[Predict_Add_DataSource.Request]...[%s]/[%s]" %
(type(reqData), reqData))
# [校验]，请求参数.
errno, errMsg, reqDict = self.verify_request(reqData=reqData)
mylog.info(
"[Predict_Add_DataSource.Verify]...errno=[%s]...errMsg=[%s]...reqDict=[%s]"
% (errno, errMsg, reqDict))
if errno < 0: # 校验异常，返回.
self.write(json.dumps({"errorNo": errno, "errorMsg": errMsg}))
return
# [Mysql]，判断[ma_data_source]记录是否存在.
row = Mysql_MA_DataSource.find_one_by_name(
dsName=reqDict.get('dataSourceName'))
if row: # 记录已存在，返回
self.write(json.dumps({"errorNo": -1, "errorMsg": "该数据源已存在！"}))
return
# [Mysql]，插入[ma_data_source]记录.
row = Mysql_MA_DataSource.insert(
modelType=reqDict.get('modelType'),
dsName=reqDict.get('dataSourceName'),
dsDesc=reqDict.get('dataSourceDesc'),
useType=2, # 用途类型。1训练，2预测
dsDir=reqDict.get('dataSourceDir'),
dsFile=reqDict.get('dataFileName'),
mylog=mylog)
if not row: # 插入Mysql，异常.
self.write(json.dumps({"errorNo": -1, "errorMsg": "Mysql插入失败！"}))
return
# [返回], 返回给前端数据.
data = json.dumps({"errorNo": 0, "errorMsg": "成功"})
print(data)
self.write(data)
def post(self):
"""
"""
self.set_post_header() # [设置]，请求头.
reqData = self.request.body # [取]，请求参数.
mylog.info("[Train_Get_Model_Params.Request]...[%s]/[%s]" %
(type(reqData), reqData))
# [校验]，请求参数.
errno, errMsg, reqDict = Optim_Public.verify_request(
reqData=reqData, paramList=["modelType", "modelName"])
mylog.info(
"[Train_Get_Model_Params.Verify]...errno=[%s]...errMsg=[%s]...reqDict=[%s]"
% (errno, errMsg, reqDict))
if errno < 0: # 校验异常，返回.
self.write(json.dumps({"errorNo": errno, "errorMsg": errMsg}))
return
# [Mysql]，取[ma_train]记录.
trainRow = Mysql_MA_Train.find_one(modelType=reqDict["modelType"],
modelName=reqDict["modelName"])
if not trainRow: # 记录不存在，返回
self.write(json.dumps({"errorNo": -1, "errorMsg": "模型记录不存在异常！"}))
return
# [Mysql]，取[ma_data_source]记录.
dsRow = Mysql_MA_DataSource.find_one_by_id(dsId=trainRow.dsId, )
if not dsRow: # 记录不存在，返回.
self.write(json.dumps({"errorNo": -1, "errorMsg": "数据源记录不存在异常！"}))
return
# [返回], 给前端数据.
data = json.dumps({
"errorNo": 0,
"errorMsg": "成功",
"dataSourceParams": json.loads(dsRow.paramsJson),
"modelParams": json.loads(trainRow.modelParams),
"trainState": int(trainRow.trainState)
})
print(data)
self.write(data)
def post(self):
"""
"""
self.set_post_header() # [设置]，请求头.
reqData = self.request.body # [取]，请求参数.
mylog.info("[Predict_Get_ModelNameList.Request]...[%s]/[%s]" % (type(reqData), reqData))
# [校验]，请求参数.
errno, errMsg, reqDict = Optim_Public.verify_request(
reqData=reqData, paramList=["modelType", "predictDataSourceName"]
)
mylog.info("[Predict_Get_ModelNameList.Verify]...errno=[%s]...errMsg=[%s]...reqDict=[%s]" % (
errno, errMsg, reqDict
))
if errno < 0: # 校验异常，返回.
self.write(json.dumps({"errorNo": errno, "errorMsg": errMsg}))
return
# [Mysql]，取[ma_predict]记录.
predictRow = Mysql_MA_Predict.find_modelNames_by_modelType_dsName(
modelType=reqDict["modelType"], dataSourceName=reqDict["predictDataSourceName"]
)
modelNameList = []
if predictRow:
for row in predictRow:
modelNameList.append(row.modelName)
# [返回], 给前端数据.
data = json.dumps({
"errorNo": 0,
"errorMsg": "成功",
"modelNameList": modelNameList
})
print(data)
self.write(data)
def post(self):
"""
"""
self.set_post_header() # [设置]，请求头.
reqData = self.request.body # [取]，请求参数.
mylog.info("[Predict_Commit_Task.Request]...[%s]/[%s]" %
(type(reqData), reqData))
# [校验]，请求参数.
# errno, errMsg, reqDict = Optim_Public.verify_request(
# reqData=reqData,
# paramList=["modelType", "modelName"]
# )
# mylog.info("[Predict_Commit_Task.Verify]...errno=[%s]...errMsg=[%s]...reqDict=[%s]" % (
# errno, errMsg, reqDict
# ))
# if errno < 0: # 校验异常，返回.
# self.write(json.dumps({"errorNo": errno, "errorMsg": errMsg}))
# return
# stop
val = os.popen('jps | grep maPredict | wc -l').read() # 执行结果包含在val中
if int(val) <= 0:
self.write(json.dumps({"errorNo": 0, "errorMsg": "当前没有任务运行"}))
else:
res = os.system(
"jps | grep maPredict | awk '{print $1}' | xargs kill -9")
if res == 0:
self.write(json.dumps({"errorNo": 0, "errorMsg": "stop执行成功!"}))
else:
self.write(json.dumps({
"errorNo": -1,
"errorMsg": "stop执行失败!"
}))
def post(self):
"""
"""
self.set_post_header() # [设置]，请求头.
reqData = self.request.body # [取]，请求参数.
mylog.info("[Predict_Commit_Task.Request]...[%s]/[%s]" % (type(reqData), reqData))
# [校验]，请求参数.
errno, errMsg, reqDict = Optim_Public.verify_request(
reqData=reqData,
paramList=["modelType", "modelName", "predictDataSourceName"]
)
mylog.info("[Predict_Commit_Task.Verify]...errno=[%s]...errMsg=[%s]...reqDict=[%s]" % (
errno, errMsg, reqDict
))
if errno < 0: # 校验异常，返回.
self.write(json.dumps({"errorNo": errno, "errorMsg": errMsg}))
return
# [Mysql]，[ma_predict]表记录是否存在.
row = Mysql_MA_Predict.find_one(
modelType=reqDict["modelType"],
modelName=reqDict["modelName"],
dataSourceName=reqDict["predictDataSourceName"],
)
if row:
# 记录已存在,直接计算
self.start_predict_compute(reqDict.get('modelType'), reqDict.get('modelName'))
return
# [Mysql]，取[ma_data_source]数据源记录.
dsRow = Mysql_MA_DataSource.find_one_by_name(
dsName=reqDict["predictDataSourceName"],
)
if not dsRow: # 无匹配记录，返回.
self.write(json.dumps({
"errorNo": -1,
"errorMsg": "获取数据源记录异常！"
}))
return
# [Mysql]，取[ma_train]数据源记录.
trainRow = Mysql_MA_Train.find_one(
modelType=reqDict["modelType"],
modelName=reqDict["modelName"],
)
if not trainRow: # 无匹配记录，返回.
self.write(json.dumps({
"errorNo": -1,
"errorMsg": "获取模型训练记录异常！"
}))
return
# # [创建]，目录
# predictDir = Optim_Public_Predict.create_predict_dir(
# modelType=reqDict.get('modelType'),
# modelName=reqDict.get('modelName'),
# dsDataFileName="%s/%s" % (dsRow.dsDir, dsRow.dsFile),
# )
# if predictDir is None: # 创建目录，异常.
# self.write(json.dumps({
# "errorNo": -1,
# "errorMsg": "创建预测目录异常！"
# }))
# return
# [插入]，Mysql记录.
print("dsId={}...trainId={}".format(dsRow.id, trainRow.id))
row = Mysql_MA_Predict.insert(
trainId=trainRow.id,
dsId=dsRow.id,
modelType=reqDict.get('modelType'),
modelName=reqDict.get('modelName'),
dsName=dsRow.dsName,
# predictDir=predictDir,
predictState=0, # 0未开始，1进行中，2已完成
mylog=mylog
)
if not row: # 插入Mysql，异常.
self.write(json.dumps({
"errorNo": -1,
"errorMsg": "Mysql插入失败！"
}))
return
self.start_predict_compute(reqDict.get('modelType'), reqDict.get('modelName'))
def post(self):
"""
"""
global trainResult0000
self.set_post_header() # [设置]，请求头.
reqData = self.request.body # [取]，请求参数.
mylog.info("[Train_Query_Result.Request]...[%s]/[%s]" % (type(reqData), reqData))
# [校验]，请求参数.
errno, errMsg, reqDict = Optim_Public.verify_request(
reqData=reqData,
paramList=["modelType", "modelName"]
)
mylog.info("[Train_Get_Model_Params.Verify]...errno=[%s]...errMsg=[%s]...reqDict=[%s]" % (
errno, errMsg, reqDict
))
if errno < 0: # 校验异常，返回.
self.write(json.dumps({"errorNo": errno, "errorMsg": errMsg}))
return
# [Mysql]，取[ma_train]记录.
trainRow = Mysql_MA_Train.find_one(
modelType=reqDict["modelType"],
modelName=reqDict["modelName"]
)
if not trainRow: # 记录不存在，返回
self.write(json.dumps({
"errorNo": -1,
"errorMsg": "训练记录不存在异常！"
}))
return
# [Mysql]，取[ma_data_source]记录.
dsRow = Mysql_MA_DataSource.find_one_by_id(
dsId=trainRow.dsId,
)
if not dsRow: # 记录不存在，返回.
self.write(json.dumps({
"errorNo": -1,
"errorMsg": "数据源记录不存在异常！"
}))
return
if trainRow.modelType == "优化分析":
# [训练结果查询]，未完成，或处理中。[0未开始，1进行中，2已完成]
if trainRow.trainState == 0 or trainRow.trainState == 1:
trainResult1 = None
trainResult2 = None
trainResult3 = None
else:
if conf.OS_TYPE == "window":
# trainResult1 = None
# trainResult3 = None
trainResult1 = Optim_Public_Train.fetch_train_result(
fileDir=trainRow.trainDir + r"/train_result/result_1", mylog=mylog)
trainResult2 = Optim_Public_Train.fetch_train_result(
fileDir=trainRow.trainDir + r"/train_result/result_2", paramOriJson=dsRow.paramOriJson, mylog=mylog)
trainResult3 = Optim_Public_Train.fetch_train_result(
fileDir=trainRow.trainDir + r"/train_result/result_3", paramOriJson=dsRow.paramOriJson, mylog=mylog)
else:
trainResult1 = Optim_Public_Train.fetch_train_result(
fileDir=trainRow.trainDir + r"/train_result/\result_1", mylog=mylog)
trainResult2 = Optim_Public_Train.fetch_train_result(
fileDir=trainRow.trainDir + r"/train_result/\result_2", paramOriJson=dsRow.paramOriJson, mylog=mylog)
trainResult3 = Optim_Public_Train.fetch_train_result(
fileDir=trainRow.trainDir + r"/train_result/\result_3", paramOriJson=dsRow.paramOriJson, mylog=mylog)
# [返回], 给前端数据.
data = json.dumps({
"errorNo": 0,
"errorMsg": "成功",
"dataSourceParams": json.loads(dsRow.paramsJson),
"modelParams": json.loads(trainRow.modelParams),
"trainState": int(trainRow.trainState),
"trainResult1": trainResult1,
"trainResult2": trainResult2,
"trainResult3": trainResult3,
})
mylog.debug(data)
self.write(data)
elif trainRow.modelType == "产品质量软测量":
if trainRow.trainState == 0 or trainRow.trainState == 1:
trainResult = None
else:
trainResult = Optim_Public_Train.fetch_train_result(
fileDir=trainRow.trainDir + r"/train_result/\predict_xiaoguo.csv", mylog=mylog)
# [返回], 给前端数据.
data = json.dumps({
"errorNo": 0,
"errorMsg": "成功",
"dataSourceParams": json.loads(dsRow.paramsJson),
"modelParams": json.loads(trainRow.modelParams),
"trainState": int(trainRow.trainState),
"trainResult": trainResult
})
mylog.debug(data)
self.write(data)
elif trainRow.modelType == "生产预警分析" or trainRow.modelType == "三次平滑指数预警分析":
if trainRow.trainState == 0 or trainRow.trainState == 1:
predictSucessSta = None
TimeSeq = None
else:
predictSucessSta = Optim_Public_Train.fetch_train_result(
fileDir=trainRow.trainDir + r"/train_result/predictSucessSta", mylog=mylog)
TimeSeq = Optim_Public_Train.fetch_train_result(
fileDir=trainRow.trainDir + r"/train_result/TimeSeq", mylog=mylog)
# [返回], 给前端数据.
data = json.dumps({
"errorNo": 0,
"errorMsg": "成功",
"dataSourceParams": json.loads(dsRow.paramsJson),
"modelParams": json.loads(trainRow.modelParams),
"trainState": int(trainRow.trainState),
"predictSucessSta": predictSucessSta,
"TimeSeq": TimeSeq
})
mylog.debug(data)
self.write(data)
def post(self):
"""
"""
self.set_post_header() # [设置]，请求头.
reqData = self.request.body # [取]，请求参数.
mylog.info("[Predict_Commit_Task.Request]...[%s]/[%s]" %
(type(reqData), reqData))
# [校验]，请求参数.
errno, errMsg, reqDict = Optim_Public.verify_request(
reqData=reqData,
paramList=["modelType", "modelNames", "predictDataSourceName"])
mylog.info(
"[Predict_Commit_Task.Verify]...errno=[%s]...errMsg=[%s]...reqDict=[%s]"
% (errno, errMsg, reqDict))
if errno < 0: # 校验异常，返回.
self.write(json.dumps({"errorNo": errno, "errorMsg": errMsg}))
return
# add by sip
res = Mysql_MA_Predict.find_by_model_names(
modelType=reqDict["modelType"],
modelNames=reqDict["modelNames"],
dataSourceName=reqDict["predictDataSourceName"],
)
# 将已经存在的模型名称存入列表
exist_list = []
for item in res:
exist_list.append(item.modelName)
# 新建模型名称列表
new_mode_list = list(set(reqDict["modelNames"]) - set(exist_list))
print(new_mode_list)
# 没有新模型，直接启动算法进行计算
if not new_mode_list:
self.start_compute_via_modeNamelist(reqDict["modelType"],
reqDict["modelNames"])
return
for modeName in new_mode_list:
# [Mysql]，取[ma_data_source]数据源记录.
dsRow = Mysql_MA_DataSource.find_one_by_name(
dsName=reqDict["predictDataSourceName"], )
if not dsRow: # 无匹配记录，返回.
self.write(
json.dumps({
"errorNo": -1,
"errorMsg": "获取数据源记录异常！"
}))
return
# [Mysql]，取[ma_train]数据源记录.
trainRow = Mysql_MA_Train.find_one(
modelType=reqDict["modelType"],
modelName=modeName,
)
if not trainRow: # 无匹配记录，返回.
self.write(
json.dumps({
"errorNo": -1,
"errorMsg": "获取模型训练记录异常！"
}))
return
# # [创建]，目录
# predictDir = Optim_Public_Predict.create_predict_dir(
# modelType=reqDict.get('modelType'),
# modelName=modeName,
# dsDataFileName="%s/%s" % (dsRow.dsDir, dsRow.dsFile),
# )
# if predictDir is None: # 创建目录，异常.
# self.write(json.dumps({
# "errorNo": -1,
# "errorMsg": "创建预测目录异常！"
# }))
# return
# [插入]，Mysql记录.
print("dsId={}...trainId={}".format(dsRow.id, trainRow.id))
row = Mysql_MA_Predict.insert(
trainId=trainRow.id,
dsId=dsRow.id,
modelType=reqDict.get('modelType'),
modelName=modeName,
dsName=dsRow.dsName,
predictState=0, # 0未开始，1进行中，2已完成
mylog=mylog)
if not row: # 插入Mysql，异常.
self.write(
json.dumps({
"errorNo": -1,
"errorMsg": "Mysql插入失败！"
}))
return
# 启动算法进行计算
self.start_compute_via_modeNamelist(reqDict["modelType"],
reqDict["modelNames"])
def post(self):
"""
"""
self.set_post_header() # [设置]，请求头.
reqData = self.request.body # [取]，请求参数.
mylog.info("[Train_Save_Model.Request]...{}...{}".format(type(reqData), reqData))
# [校验]，请求参数.
errno, errMsg, reqDict = Optim_Public.verify_request(
reqData=reqData,
paramList=["modelType", "dataSourceName", "modelName", "modelParams"]
)
mylog.info("[Train_Save_Model.Verify]...errno=[{}]...errMsg=[{}]...reqDict=[{}]".format(
errno, errMsg, reqDict
))
if errno < 0: # 校验异常，返回.
self.write(json.dumps({"errorNo": errno, "errorMsg": errMsg}))
return
# [校验], [优化分析]“op_w”权重值之和应该是1.
if reqDict["modelType"] == "优化分析":
sumVal = self.verify_op_w(modelParams=reqDict["modelParams"])
if sumVal != 1:
self.write(json.dumps({
"errorNo": -1,
"errorMsg": "(优化目标)权重值之和应该等于1！"
}))
return
# [Mysql]，[ma_train]表modelName记录是否存在.
row = Mysql_MA_Train.find_one(
modelType=reqDict["modelType"],
modelName=reqDict["modelName"]
)
if row: # 记录已存在，返回
self.write(json.dumps({
"errorNo": -1,
"errorMsg": "模型名称已存在！"
}))
return
# [Mysql]，取[ma_data_source]数据源记录.
dsRow = Mysql_MA_DataSource.find_one_by_name(
dsName=reqDict["dataSourceName"],
)
if not dsRow: # 无匹配记录，返回.
self.write(json.dumps({
"errorNo": -1,
"errorMsg": "获取数据源异常！"
}))
return
# [创建]，训练目录
trainDir = Optim_Public_Train.create_train_dir(
modelType=reqDict.get('modelType'),
modelName=reqDict.get('modelName'),
modelParams=reqDict.get("modelParams"),
dsDataFileName = "%s/%s" % (dsRow.dsDir, dsRow.dsFile),
dsParamFileName = "%s/%s" % (dsRow.dsDir, dsRow.paramsFile),
)
if trainDir is None: # 创建目录，异常.
self.write(json.dumps({
"errorNo": -1,
"errorMsg": "创建训练目录异常！"
}))
return
# [插入]，Mysql记录.
# print(dsRow.id)
# 将模型参数转换为 算法API可用参数.
modelParams4ml = Optim_Public_Train.convert_modelParams_4ml(
modelType=reqDict.get('modelType'),
modelParams=reqDict.get('modelParams')
)
print("...........", modelParams4ml)
# 插入DB.
row = Mysql_MA_Train.insert(
modelType=reqDict.get('modelType'),
modelName=reqDict.get('modelName'),
dsId=dsRow.id,
modelParams=json.dumps(reqDict.get('modelParams')),
modelParams4ml=modelParams4ml,
trainDir=trainDir,
trainState=0, # 0未开始，1进行中，2已完成
mylog=mylog
)
if not row: # 插入Mysql，异常.
self.write(json.dumps({
"errorNo": -1,
"errorMsg": "Mysql插入失败！"
}))
return
# [返回], 给前端数据.
data = json.dumps({
"errorNo": 0,
"errorMsg": "成功"
})
print(data)
self.write(data)
def post(self):
"""
"""
self.set_post_header() # [设置]，请求头.
reqData = self.request.body # [取]，请求参数.
mylog.info("[Predict_Query_Result.Request]...[%s]/[%s]" %
(type(reqData), reqData))
# [校验]，请求参数.
errno, errMsg, reqDict = Optim_Public.verify_request(
reqData=reqData,
paramList=["modelType", "modelNames", "predictDataSourceName"])
mylog.info(
"[Predict_Query_Result.Verify]...errno=[%s]...errMsg=[%s]...reqDict=[%s]"
% (errno, errMsg, reqDict))
if errno < 0: # 校验异常，返回.
self.write(json.dumps({"errorNo": errno, "errorMsg": errMsg}))
return
# # [Mysql]，取[ma_predict]记录.
# predictRow = Mysql_MA_Predict.find_one(
# modelType=reqDict["modelType"],
# modelName=reqDict["modelName"],
# dataSourceName=reqDict["predictDataSourceName"],
# )
# if not predictRow: # 记录不存在，返回
# self.write(json.dumps({
# "errorNo": -1,
# "errorMsg": "模型预测记录不存在异常！"
# }))
# return
# add by sjp
predictRows = Mysql_MA_Predict.find_by_model_names(
modelType=reqDict["modelType"],
modelNames=reqDict["modelNames"],
dataSourceName=reqDict["predictDataSourceName"],
)
if not predictRows: # 记录不存在，返回
self.write(json.dumps({"errorNo": -1, "errorMsg": "模型预测记录不存在异常！"}))
return
Result = []
for predictRow in predictRows:
# [Mysql]，取[ma_train]记录.
trainRow = Mysql_MA_Train.find_one(modelType=reqDict["modelType"],
modelName=predictRow.modelName)
if not trainRow: # 记录不存在，返回
self.write(
json.dumps({
"errorNo": -1,
"errorMsg": "模型训练记录不存在异常！"
}))
return
# [Mysql]，取[ma_data_source]记录.
trainDSRow = Mysql_MA_DataSource.find_one_by_id(
dsId=trainRow.dsId, )
if not trainDSRow: # 记录不存在，返回.
self.write(
json.dumps({
"errorNo": -1,
"errorMsg": "数据源记录不存在异常！"
}))
return
predictDSRow = Mysql_MA_DataSource.find_one_by_id(
dsId=predictRow.dsId, )
if not predictDSRow: # 记录不存在，返回.
self.write(
json.dumps({
"errorNo": -1,
"errorMsg": "数据源记录不存在异常！"
}))
return
if trainRow.modelType == "优化分析":
# 判断目录是否存在
result_dir = predictRow.predictDir + r"/predict_result/\result_2"
if not os.path.isdir(result_dir):
predictResult = {"time": {}, "prediction": {}}
else:
Files = os.listdir(result_dir)
if not Files or len(Files) < 4:
predictResult = {}
else:
predictResult = Optim_Public_Predict.real_compute_fetch_predict_result(
result1_Dir=predictRow.predictDir +
r"/predict_result/\result_1",
result2_Dir=predictRow.predictDir +
r"/predict_result/\result_2",
paramOriJson=trainDSRow.paramOriJson,
mylog=mylog)
# [返回], 给前端数据.
data = json.dumps({
"errorNo":
0,
"errorMsg":
"成功",
"dataSourceParams":
json.loads(trainDSRow.paramsJson),
"modelParams":
json.loads(trainRow.modelParams),
"predictState":
int(predictRow.predictState),
"predictResult":
predictResult
})
print(data)
self.write(data)
elif trainRow.modelType == "产品质量软测量":
# if predictRow.predictState == 0 or predictRow.predictState == 1:
# predictResult = None
# ResultFileName = None
#
# else:
# predictResult, ResultFileName = Optim_Public_Predict.fetch_predict_result(
# predictRow.predictDir + r"/predict_result/\predict_result",
# mylog=mylog
# )
# 判断目录是否存在
result_dir = predictRow.predictDir + r"/predict_result/\predict_result"
if not os.path.isdir(result_dir):
predictResult = {"time": {}, "prediction": {}}
else:
Files = os.listdir(result_dir)
if not Files or len(Files) < 4:
predictResult = {"time": {}, "prediction": {}}
else:
predictResult, ResultFileName = Optim_Public_Predict.fetch_predict_result(
result_dir, mylog=mylog)
tempResult = {
"modelName": predictRow.modelName,
"dataSourceParams": json.loads(trainDSRow.paramsJson),
"modelParams": json.loads(trainRow.modelParams),
"predictState": int(predictRow.predictState),
"predictResult": predictResult,
}
Result.append(tempResult)
data = json.dumps({"errorNo": 0, "errorMsg": "成功", "Result": Result})
print(data)
self.write(data)