def get(self):
gql_query = Metadata.gql("ORDER BY receivedDateTime DESC");
metadata_list = gql_query.fetch(10)
for metadata in metadata_list:
assert isinstance(metadata, Metadata)
assert isinstance(metadata.sender, Sender)
all_data = {}
all_data_string = ""
for data_key in metadata.dataList:
assert isinstance(data_key, Key)
data = Data.get(data_key)
assert isinstance(data, Data)
logger.debug(data.field)
logger.debug(data.string)
all_data_string = all_data_string + data.field + " = " + data.string + "<br/>"
all_data[data.field] = data.string
if all_data.has_key("longitude") and all_data.has_key("latitude"):
break
all_data_string = "received at " + str(metadata.receivedDateTime) + "<br/>" + all_data_string
template_values = {"longitude": all_data["longitude"], "latitude": all_data["latitude"], "all_data":all_data, "all_data_string": all_data_string}
logging.debug(str(template_values))
self.writeWithTemplate(template_values, "Last")
def get(self):
gql_query = Metadata.gql("ORDER BY receivedDateTime DESC")
metadata_list = gql_query.fetch(10)
for metadata in metadata_list:
assert isinstance(metadata, Metadata)
assert isinstance(metadata.sender, Sender)
all_data = {}
all_data_string = ""
for data_key in metadata.dataList:
assert isinstance(data_key, Key)
data = Data.get(data_key)
assert isinstance(data, Data)
logger.debug(data.field)
logger.debug(data.string)
all_data_string = all_data_string + data.field + " = " + data.string + "<br/>"
all_data[data.field] = data.string
if all_data.has_key("longitude") and all_data.has_key("latitude"):
break
all_data_string = "received at " + str(
metadata.receivedDateTime) + "<br/>" + all_data_string
template_values = {
"longitude": all_data["longitude"],
"latitude": all_data["latitude"],
"all_data": all_data,
"all_data_string": all_data_string
}
logging.debug(str(template_values))
self.writeWithTemplate(template_values, "Last")
def get(self):
self.sender = GetSender(self.request)
self.raw_data = putRawData(self.request)
# self.data_list = putDataList(self.request)
self.data_list = Data.storeRequest(self.request)
self.metadata = putMetadata(self.sender, self.raw_data, self.data_list)
assert isinstance(self.response, Response)
self.response.headers['Content-Type'] = "text/plain"
for key in self.data_list:
data = db.get(key)
self.response.out.write("field:" + data.field + " string:" + data.string + "\n")
def get(self):
template_values = {}
template_values["all_data"] = []
gql = Data.gql("ORDER BY dataId DESC LIMIT 100")
recent = gql.run()
#all_raw_data = RawData.all()
logging.info(recent)
for data in recent:
data_dict = {"dataId": data.dataId,
"field": data.field,
"string": data.string }
logging.info(data_dict)
template_values["all_data"].append(data_dict)
self.writeWithTemplate(template_values, "Data")
def get(self):
template_values = {}
template_values["all_data"] = []
gql = Data.gql("ORDER BY dataId DESC LIMIT 100")
recent = gql.run()
#all_raw_data = RawData.all()
logging.info(recent)
for data in recent:
data_dict = {
"dataId": data.dataId,
"field": data.field,
"string": data.string
}
logging.info(data_dict)
template_values["all_data"].append(data_dict)
self.writeWithTemplate(template_values, "Data")
def get(self):
self.sender = GetSender(self.request)
self.raw_data = putRawData(self.request)
# self.data_list = putDataList(self.request)
self.data_list = Data.storeRequest(self.request)
self.metadata = putMetadata(self.sender, self.raw_data, self.data_list)
assert isinstance(self.response, webapp.Response)
self.response.headers["Content-Type"] = "text/plain"
for key in self.data_list:
data = db.get(key)
if data.field == "productName":
self.productName = data.string
if data.field == "serialNumber":
self.serialNumber = data.string
if data.field == "moduleId":
self.moduleId = data.string
# self.response.out.write("field:" + data.field + " string:" + data.string + "\n")
try:
relays = Relays(self.productName, self.serialNumber, self.moduleId)
assert isinstance(relays, Relays)
l = []
for k, v in relays.iteritems():
assert isinstance(v, Relay)
r = {
"relayId": v.relayId,
# "scheduledDateTime" : v.scheduledDateTime,
"scheduledEpoch": nativeToEpoch(v.scheduledDateTime),
"expectedState": v.expectedState,
}
l.append(r)
o = {"relayStates": l}
self.response.out.write(dumps(o))
except AttributeError, e:
l = map(lambda key: db.get(key), self.data_list)
j = dumps(l)
self.response.out.write(j)
def get(self):
self.sender = GetSender(self.request)
self.raw_data = putRawData(self.request)
# self.data_list = putDataList(self.request)
self.data_list = Data.storeRequest(self.request)
self.metadata = putMetadata(self.sender, self.raw_data, self.data_list)
assert isinstance(self.response, webapp.Response)
self.response.headers['Content-Type'] = "text/plain"
for key in self.data_list:
data = db.get(key)
if data.field == "productName":
self.productName = data.string
if data.field == "serialNumber":
self.serialNumber = data.string
if data.field == "moduleId":
self.moduleId = data.string
#self.response.out.write("field:" + data.field + " string:" + data.string + "\n")
try:
relays = Relays(self.productName, self.serialNumber, self.moduleId)
assert isinstance(relays, Relays)
l = []
for k, v in relays.iteritems():
assert isinstance(v, Relay)
r = {
"relayId": v.relayId,
#"scheduledDateTime" : v.scheduledDateTime,
"scheduledEpoch": nativeToEpoch(v.scheduledDateTime),
"expectedState": v.expectedState
}
l.append(r)
o = {"relayStates": l}
self.response.out.write(dumps(o))
except AttributeError, e:
l = map(lambda key: db.get(key), self.data_list)
j = dumps(l)
self.response.out.write(j)
for idx in range(batch_size):
pred = [
data.reverse_tag_to_idx[get_instance(pred_tag[idx][idy])]
for idy in range(seq_len) if mask[idx][idy] != 0
]
pred_label.append(pred)
return pred_label
seed_num = 123
random.seed(seed_num)
torch.manual_seed(seed_num)
np.random.seed(seed_num)
if __name__ == '__main__':
data = Data()
data.load('./data/PoSTagger.data')
predict_config_path = './predict.config'
data.readConfig(predict_config_path)
printParameterSummary(data)
predict_instances = getDataLoader(data.infer_path, data)
device = torch.device("cuda:" +
data.GPU if torch.cuda.is_available() else "cpu")
model = SequenceModel(data)
model = torch.load(data.model_save_path)
model.eval()
words = pd.read_csv(data.infer_path,
def create_data(data_id, field, string):
data = DataDb()
data.dataId = data_id
data.field = field
data.string = string
return data.put()
def create_data(data_id, field, string):
data = DataDb()
data.dataId = data_id
data.field = field
data.string = string
return data.put()
import os
from controller.Controller import Controller
from util.util import read
from flask import Flask
from flask_cors import CORS
from dotenv import load_dotenv
load_dotenv()
logging.basicConfig()
logging.root.setLevel(logging.INFO)
data = None
if os.environ.get('CACHE', True) == True:
data = read('data.pickle')
if data is None:
data = Data()
dataService = DataService(data)
dataService.getAndCacheAllData()
else:
data = Data()
dataService = DataService(data)
dataService.getAllData()
app = Flask(__name__)
CORS(app)
Controller(app, data)
if data.NER:
print("Training finished. Best f_score is {:0.4f}.".format(best_score))
else:
print("Training finished. Best accuracy is {:0.4f}.".format(best_score))
seed_num = 123
random.seed(seed_num)
torch.manual_seed(seed_num)
np.random.seed(seed_num)
if __name__ == '__main__':
data = Data()
train_config_path = './train.config'
data.readConfig(train_config_path)
data.buildDictionary()
data.getPretrainedEmbedding()
# print parameter summary
printParameterSummary(data)
# build dataloaders
training_instances = getDataLoader(data.training_path, data)
validation_instances = getDataLoader(data.validation_path, data)
evaluation_instances = getDataLoader(data.evaluation_path, data)
data.saveData()
device = torch.device("cuda:"+data.GPU if torch.cuda.is_available() else "cpu")
