def save_predict_file(self, node_id, df):
_predict_path = utils.get_source_predict_path(node_id.split('_')[0], node_id.split('_')[1], 'predict')
predict_result_dir = utils.make_and_exist_directory(_predict_path + "/" + "result" + "/")
predict_result_filename = predict_result_dir + str(node_id) +"_" + strftime("%Y-%m-%d-%H:%M:%S",
gmtime()) + ".csv"
df.to_csv(predict_result_filename)
def predict(self, node_id, ver, parm, data=None, result=None):
""" Wdnn predict
batchlist info에서 active flag가 Y인 Model을 가져와서 예측을 함
Args:
params:
* node_id
* conf_data
Returns:
none
Raises:
Example
"""
try:
logging.info("wdnn predict_start nnid : {0}".format(node_id))
_node_id = node_id + "_" + ver + "_" + "netconf_node"
_data_conf_id = node_id + "_" + ver + "_dataconf_node"
self._init_node_parm(_node_id)
#self.cls_pool_all = conf_data['cls_pool'] # Data feeder
config = {
"type": self.model_type,
"labels": self.label_values,
"nn_id": node_id,
"nn_wf_ver_id": ver
}
train = TrainSummaryInfo(conf=config)
#print(config)
self.batch = self.get_active_batch(_node_id)
#print(train)
self.model_predict_path = ''.join(
[self.model_path + '/' + self.batch])
self.multi_node_flag = False
conf_data = {}
conf_data['node_id'] = _node_id
#conf_data['cls_pool'].get('nn00001_1_pre_feed_fr2wdnn_test')
print("model_path : " + str(self.model_path))
print("hidden_layers : " + str(self.hidden_layers))
print("activation_function : " + str(self.activation_function))
print("batch_size : " + str(self.batch_size))
print("epoch : " + str(self.epoch))
print("model_type : " + str(self.model_type))
# data_store_path = WorkFlowDataFrame(conf_data['nn_id']+"_"+conf_data['wf_ver']+"_"+ "data_node").step_store
data_conf_info = self.data_conf
# make wide & deep model
wdnn = NeuralCommonWdnn()
wdnn_model = wdnn.wdnn_build(self.model_type, node_id,
self.hidden_layers,
str(self.activation_function),
data_conf_info,
str(self.model_predict_path))
# feed
# TODO file이 여러개면 어떻하지?
filelist = sorted(parm.items())
#train_data_set = self.cls_pool # get filename
#file_queue = str(train_data_set.input_paths[0]) # get file_name
# file을 돌면서 최대 Row를 전부 들고 옴 tfrecord 총 record갯수 가져오는 방법필요
_batch_size = self.batch_size
_num_tfrecords_files = 0
# multi Feeder modified
multi_read_flag = self.multi_read_flag
# Todo H5
# train per files in folder h5용
# if multi_file flag = no이면 기본이 h5임
results = dict()
ori_list = list()
pre_list = list()
#self.batch_size = 5
for filename in filelist:
print("h5")
#feeder = PreNodeFeedFr2Wdnn().set_for_predict(_data_conf_id)
feeder = PreNodeFeedFr2Wdnn()
#_data_conf_id
#set_for_predict
feeder.set_for_predict(_data_conf_id)
data_node = DataNodeFrame()
train_data_set = data_node.load_csv_by_pandas(
self.predict_path + "/" + filename[1].name)
#feeder.set_input_paths([self.predict_path + "/" + filename[1].name])
#train_data_set = feeder
#_size = train_data_set
# 파일이 하나 돌때마다
# for 배치사이즈와 파일의 총갯수를 가져다가 돌린다. -> 마지막에 뭐가 있을지 구분한다.
# 파일에 iter를 넣으면 배치만큼 가져오는 fn이 있음 그걸 __itemd에 넣고
# Input 펑션에서 multi를 vk판단해서 col와 ca를 구분한다.(이걸 배치마다 할 필요가 있나?)
# -> 그러면서 피팅
#
# # Iteration is to improve for Model Accuracy
# Per Line in file
# eval should be one line predict
#self.batch_size = 2
#train_date를 어떻게 가져오냐가 문제
result_df = pd.DataFrame()
for i in range(0, len(train_data_set.index), self.batch_size):
data_set = train_data_set[i:i + self.batch_size]
#if i == 0:
#eval_data_Set = data_set
# input_fn2(self, mode, data_file, df, nnid, dataconf):
predict_value = wdnn_model.predict(
input_fn=lambda: feeder.input_fn2(
tf.contrib.learn.ModeKeys.TRAIN, filename,
data_set, data_conf_info))
data_set_count = len(data_set.index)
predict_val_list = [_pv for _pv in predict_value]
predict_val_count = len(predict_val_list)
if (data_set_count != predict_val_count):
logging.error(
"wdnn eval error check : dataframe count({0}) predict count({1})"
.format(data_set_count, predict_val_count))
raise ValueError(
'eval data validation check error : dataframe and predict count is different(neuralnet_node_wdnn.eval)'
)
data_set[
'predict_label'] = predict_val_list #list(predict_value)
#_predict = list(predict_value)
predict_y = list(data_set['predict_label'])
#pd.concat(result_df, data_set)
result_df = result_df.append(data_set)
ori_list.extend(data_set[self.label].values.tolist())
pre_list.extend(list(data_set['predict_label']))
# model fitting
print(len(ori_list))
print(len(pre_list))
#logging.error("wdnn eval ori list : {0}".format(ori_list) )
logging.info("wdnn eval ori list : {0}".format(
len(ori_list)))
#logging.info("wdnn eval ori list : {0}".format('info'))
#logging.debug("wdnn eval ori list : {0}".format('debug'))
#logging.critical("wdnn eval ori list : {0}".format('critical'))
#print("model fitting h5 " + str(data_set))
# #Select Next file
#train_data_set.next()
predict_result_dir = utils.make_and_exist_directory(
self.predict_path + "/" + "result" + "/")
predict_result_filename = predict_result_dir + "result_" + strftime(
"%Y-%m-%d-%H:%M:%S", gmtime()) + ".csv"
result_df.to_csv(predict_result_filename)
#os.remove(self.predict_path + "/" + filename[1].name)
# #TODO : 앞으로 옮기자
# train.set_nn_batch_ver_id(self.batch)
# if self.model_type == "regression":
# results['ori'] = ori_list
# results['pre'] = pre_list
# train.set_result_info(ori_list, pre_list)
#
# if self.model_type == "category":
# # tfrecord는 여기서 Label을 변경한다. 나중에 꺼낼때 답이 없음 Tensor 객체로 추출되기 때문에 그러나 H5는 feeder에서 변환해주자
# le = LabelEncoder()
# le.fit(self.label_values)
#
# for _i, _ori in enumerate(ori_list):
# #return_value = self.labels[np.argmax(model.predict(X_train))]
# train.set_result_info(str(_ori), str(le.inverse_transform(pre_list[_i])))
#return self.batch
logging.info("eval end")
return train
except Exception as e:
logging.error("Wdnn predict error {0}".format(e))
raise Exception(e)
def predict(self, node_id,ver, parm, data=None, result=None):
""" Wdnn predict
batchlist info에서 active flag가 Y인 Model을 가져와서 예측을 함
Args:
params:
* node_id
* conf_data
Returns:
none
Raises:INFO
Example
"""
try:
logging.info("wdnn predict_start nnid : {0}".format(node_id))
if ver == 'active':
self.batch, wf_ver = self.get_active_batch2(node_id)
#self.model_predict_path = ''.join([self.model_path + '/' + self.batch])
else:
wf_ver = ver
_nn_ver_id= node_id + "_" + wf_ver + "_" + "netconf_node"
self.batch = self.get_active_batch(_nn_ver_id) # Train이 Y인것 가져오기 Eval Flag가 Y인거 가져오기
_node_id = node_id + "_" + wf_ver+ "_" + "netconf_node"
_data_conf_id = node_id + "_" + wf_ver + "_dataconf_node"
self._init_node_parm(_node_id)
graph = NNCommonManager().get_nn_node_name(node_id)
for net in graph:
if net['fields']['graph_node'] == 'netconf_node':
netconf_node = net['fields']['graph_node_name']
self.model_path = utils.get_model_path(node_id, wf_ver, netconf_node)
config = {"type": self.model_type, "labels": self.label_values, "nn_id":node_id, "nn_wf_ver_id":ver}
#train = TrainSummaryInfo(conf=config)
self.model_predict_path = ''.join([self.model_path + '/' + self.batch])
self.multi_node_flag = False
self.predict_path = ''.join(['/hoya_src_root/'+node_id+'/common/predict'])
conf_data = {}
conf_data['node_id'] = _node_id
logging.info("model_path : " + str(self.model_path))
logging.info("hidden_layers : " + str(self.hidden_layers))
logging.info("activation_function : " + str(self.activation_function))
logging.info("batch_size : " + str(self.batch_size))
logging.info("epoch : " + str(self.epoch))
logging.info("model_type : " + str(self.model_type))
data_conf_info = self.data_conf
# make wide & deep model
wdnn = NeuralCommonWdnn()
wdnn_model = wdnn.wdnn_build(self.model_type, node_id, self.hidden_layers,
str(self.activation_function), data_conf_info, str(self.model_predict_path),dememsion_auto_flag = self.auto_demension)
# feed
le = LabelEncoder()
le.fit(self.label_values)
file_cnt = len(parm.FILES.keys())
dir = 'predict'
filelist = list()
if file_cnt > 0:
for key, requestSingleFile in parm.FILES.items():
filelist.append(str(requestSingleFile._name))
_batch_size = self.batch_size
_num_tfrecords_files = 0
# multi Feeder modified
multi_read_flag = self.multi_read_flag
results = dict()
ori_list = list()
pre_list = list()
#self.batch_size = 5
for filename in filelist:
print("h5")
#feeder = PreNodeFeedFr2Wdnn().set_for_predict(_data_conf_id)
feeder = PreNodeFeedFr2Wdnn()
feeder.set_for_predict(_data_conf_id)
data_node = DataNodeFrame()
train_data_set = data_node.load_csv_by_pandas(self.predict_path + "/" + filename)
result_df = pd.DataFrame()
for i in range(0, len(train_data_set.index), self.batch_size):
data_set = train_data_set[i:i + self.batch_size]
predict_value = wdnn_model.predict(
input_fn=lambda: feeder.input_fn2(tf.contrib.learn.ModeKeys.TRAIN, filename,
data_set, data_conf_info))
data_set_count = len(data_set.index)
predict_val_list = [_pv for _pv in predict_value]
predict_val_count = len(predict_val_list)
if (data_set_count != predict_val_count):
logging.error("wdnn eval error check : dataframe count({0}) predict count({1})".format(data_set_count, predict_val_count))
raise ValueError(
'eval data validation check error : dataframe and predict count is different(neuralnet_node_wdnn.eval)')
data_set['predict_label'] = list(le.inverse_transform(predict_val_list)) #list(predict_value)
#_predict = list(predict_value)
predict_y = list(data_set['predict_label'])
#pd.concat(result_df, data_set)
result_df = result_df.append(data_set)
ori_list.extend(data_set[self.label].values.tolist())
pre_list.extend(list(data_set['predict_label']))
logging.info("wdnn eval ori list : {0}".format(len(ori_list)))
logging.info("wdnn eval ori list : {0}".format(len(pre_list)))
#train_data_set.next()
predict_result_dir = utils.make_and_exist_directory(self.predict_path + "/" + "result" + "/")
predict_result_filename = predict_result_dir + "result_" + strftime("%Y-%m-%d-%H:%M:%S",
gmtime()) + ".csv"
result_df.to_csv(predict_result_filename)
logging.info("eval end")
return json.loads(result_df.to_json())
except Exception as e:
logging.error("Wdnn predict error {0}".format(e))
raise Exception(e)
def predict(self, node_id, ver, parm, data=None, result=None):
""" Wdnn predict
batchlist info에서 active flag가 Y인 Model을 가져와서 예측을 함
Args:
params:
* node_id
* conf_data
Returns:
none
Raises:INFO
Example
"""
try:
logging.info("wdnn predict_start nnid : {0}".format(node_id))
if ver == 'active':
self.batch, wf_ver = self.get_active_batch2(node_id)
else:
wf_ver = ver
_nn_ver_id = node_id + "_" + wf_ver + "_" + "netconf_node"
self.batch = self.get_active_batch(
_nn_ver_id) # Train이 Y인것 가져오기 Eval Flag가 Y인거 가져오기
_node_id = node_id + "_" + wf_ver + "_" + "netconf_node"
_data_conf_id = node_id + "_" + wf_ver + "_dataconf_node"
self._init_node_parm(_node_id)
graph = NNCommonManager().get_nn_node_name(node_id)
for net in graph:
if net['fields']['graph_node'] == 'netconf_node':
netconf_node = net['fields']['graph_node_name']
self.model_path = utils.get_model_path(node_id, wf_ver,
netconf_node)
config = {
"type": self.model_type,
"labels": self.label_values,
"nn_id": node_id,
"nn_wf_ver_id": ver
}
self.model_predict_path = ''.join(
[self.model_path + '/' + self.batch])
self.multi_node_flag = False
self.predict_path = ''.join(
['/hoya_src_root/' + node_id + '/common/predict'])
conf_data = {}
conf_data['node_id'] = _node_id
logging.info("model_path : " + str(self.model_path))
logging.info("ml_class : " + str(self.ml_class))
logging.info("config : " + str(self.config))
data_conf_info = self.data_conf
# make ML model
clf = joblib.load(self.model_path + 'model.pkl')
# feed
le = LabelEncoder()
le.fit(self.label_values)
file_cnt = len(parm.FILES.keys())
dir = 'predict'
filelist = list()
if file_cnt > 0:
for key, requestSingleFile in parm.FILES.items():
filelist.append(str(requestSingleFile._name))
_batch_size = self.batch_size
_num_tfrecords_files = 0
# multi Feeder modified
multi_read_flag = self.multi_read_flag
results = dict()
ori_list = list()
pre_list = list()
#self.batch_size = 5
for filename in filelist:
print("h5")
feeder = PreNodeFeedFr2ML()
feeder.set_for_predict(_data_conf_id)
data_node = DataNodeFrame()
train_data_set = data_node.load_csv_by_pandas(
self.predict_path + "/" + filename)
result_df = pd.DataFrame()
for i in range(0, len(train_data_set.index), self.batch_size):
data_set = train_data_set[i:i + self.batch_size]
predict_value = clf.predict(
input_fn=lambda: feeder.input_fn2(
tf.contrib.learn.ModeKeys.TRAIN, filename,
data_set, data_conf_info))
data_set_count = len(data_set.index)
predict_val_list = [_pv for _pv in predict_value]
predict_val_count = len(predict_val_list)
if (data_set_count != predict_val_count):
logging.error(
"wdnn eval error check : dataframe count({0}) predict count({1})"
.format(data_set_count, predict_val_count))
raise ValueError(
'eval data validation check error : dataframe and predict count is different(neuralnet_node_wdnn.eval)'
)
data_set['predict_label'] = list(
le.inverse_transform(
predict_val_list)) #list(predict_value)
#_predict = list(predict_value)
predict_y = list(data_set['predict_label'])
#pd.concat(result_df, data_set)
result_df = result_df.append(data_set)
ori_list.extend(data_set[self.label].values.tolist())
pre_list.extend(list(data_set['predict_label']))
logging.info("wdnn eval ori list : {0}".format(
len(ori_list)))
logging.info("wdnn eval ori list : {0}".format(
len(pre_list)))
#train_data_set.next()
predict_result_dir = utils.make_and_exist_directory(
self.predict_path + "/" + "result" + "/")
predict_result_filename = predict_result_dir + "result_" + strftime(
"%Y-%m-%d-%H:%M:%S", gmtime()) + ".csv"
result_df.to_csv(predict_result_filename)
logging.info("eval end")
return json.loads(result_df.to_json())
except Exception as e:
logging.error("Wdnn predict error {0}".format(e))
raise Exception(e)