def handleImbalanceProblem(self, dataset, labels, model, epochs):
model.getCompiledModel()
model = model.model
test_labels_orig = test_labels_pred = None
train_images, test_images, train_labels, test_labels = DataProcessor.trainTestSplit(
dataset, labels)
train_images, test_images, train_labels, test_labels = DataProcessor.prepareDatasetForFit(
train_images, test_images, train_labels, test_labels)
dataSequence = DatasetSequence(train_images, train_labels, test_images,
test_labels, model, epochs)
history = model.fit(dataSequence,
epochs=epochs,
validation_data=(test_images, test_labels))
predictions = model.predict(dataSequence.test_data)
if predictions.shape[1] > 1:
test_labels_orig = np.argmax(dataSequence.test_labels, axis=1)
test_labels_pred = np.argmax(predictions, axis=1)
else:
test_labels_orig = dataSequence.test_labels
test_labels_pred = np.where(predictions > 0.5, 1, 0)
logger.info(
classification_report(y_true=test_labels_orig,
y_pred=test_labels_pred,
zero_division=1))
logger.info(
precision_recall_fscore_support(y_true=test_labels_orig,
y_pred=test_labels_pred,
average='weighted'))
return history
def handleImbalanceProblem(self, dataset, labels, model, epochs):
model.getCompiledModel(loss=self.meanFalseError, runEagerly=True)
model = model.model
train_images, test_images, train_labels, test_labels = DataProcessor.trainTestSplit(
dataset, labels)
train_images, test_images, train_labels, test_labels = DataProcessor.prepareDatasetForFit(
train_images, test_images, train_labels, test_labels)
history = model.fit(train_images,
train_labels,
epochs=epochs,
validation_data=(test_images, test_labels))
predictions = model.predict(test_images)
if predictions.shape[1] > 1:
test_labels_orig = np.argmax(test_labels, axis=1)
test_labels_pred = np.argmax(predictions, axis=1)
else:
test_labels_orig = test_labels
test_labels_pred = np.where(predictions > 0.5, 1, 0)
logger.info(
classification_report(y_true=test_labels_orig,
y_pred=test_labels_pred))
logger.info(
precision_recall_fscore_support(y_true=test_labels_orig,
y_pred=test_labels_pred,
average='weighted'))
return history
def __init__(self, topology_model):
self.topology = topology_model
# create a workspace
self.base_path = Compiler.generate_basic_workspace()
# code file path
file_name = os.path.join(self.base_path, 'topology.json')
try:
try:
with open(file_name, 'w') as text_file:
t_dict = self.build_backend_json(self.topology)
t_json = json.dumps(t_dict)
logger.info(t_json)
text_file.write(t_json)
except OSError as e:
raise TopologyWriteException(e.message)
self.generate_pom_file(
base_path=self.base_path,
name=self.topology[TopologyConsts.FIELD_NAME],
version='1.0',
dependencies=self.build_dependencies())
except ModuleWriteException as Ex:
raise Ex
except ModuleErrorCompilingException as Ex:
raise Ex
def get_log(topology_name, num_lines=10):
filenames = StormUI.getWorkersByTopologyName(topology_name)
lines = ""
if filenames:
logger.debug("Filenames for topology " + topology_name + " -> " +
str(filenames))
for filename in filenames:
logger.info("Topology :" + topology_name + " - LogFilename: " +
filename)
# get log file from storm cluster
n_lines = int(num_lines) * 100
content = StormUI.getFile(filename, n_lines)
try:
# Remove HTML tags from Storm Log 8000 port
logcontent = BeautifulSoup(content, "lxml").find(
"pre", {"id": "logContent"})
if logcontent:
lines += "\n###################################################\n"
(hostname, port,
name) = LogViewer.parse_worker_info(filename)
lines += "Log from worker: " + hostname + " - " + name + "\n"
lines += "###################################################\n"
lines += logcontent.getText()
logger.debug("Getting " +
str(len(lines.splitlines())) + " lines.")
except Exception as e:
return "Error parsing data from Storm UI:" + str(e)
return lines
def copyID(self,keyModel,username=None,host=None):
from logger.Logger import logger
self.keyModel=keyModel
self.pubkey=self.keyModel.getPubKey()
# Use of a singleton here means that we should be able to do SSO on any AAF/Shibolleth web service. However we might have to guess the IdP.
self.session=cvlsshutils.RequestsSessionSingleton.RequestsSessionSingleton().GetSession()
destURL='https://portal.synchrotron.org.au:443/api/v1/oauth/token'
auth=cvlsshutils.ASyncAuth.ASyncAuth(self.session,destURL,parent=self.parent,extraParams=self.extraParams,**self.kwargs)
apitoken=auth.gettoken()
self.updateDict=auth.getUpdateDict()
try:
self.postKey(apitoken)
except Exception as e:
raise e
logger.info('copied pub key %s to user account %s'%(self.pubkey,self.username))
def balanceDataset(self, dataset, labels):
classes, classesCount = np.unique(labels, return_counts=True)
logger.info(classesCount)
highestClassCount = np.amax(classesCount)
for classId, classCount in zip(classes, classesCount):
if classCount < highestClassCount:
amountToCopy = highestClassCount - classCount
logger.info("Oversampling class {} with {} samples".format(
classId, amountToCopy))
classIndexes = np.where(labels == classId)[0].tolist()
indexesToCopy = random.choices(classIndexes, k=amountToCopy)
copiedDataset = np.copy(dataset[indexesToCopy, ])
copiedLabels = np.copy(labels[indexesToCopy, ])
dataset = np.concatenate((dataset, copiedDataset))
labels = np.concatenate((labels, copiedLabels))
return dataset, labels
def add_java_module(module_name, module_type):
cmd_launch = [
"cd", conf.CLASSES_TMP_PATH + ";", conf.JAVA_JAR_BIN, "uf",
conf.SINFONIER_LAST_JAR, module_type + module_name + ".class"
]
os.remove(conf.CLASSES_TMP_PATH + module_type + module_name + ".class")
result = CommandExecutor.execute(cmd_launch, capture_out=True)
f = open(conf.CLASSES_TMP_PATH + module_type + module_name + ".info")
listclasses = f.read().splitlines()
f.close()
for classjava in listclasses:
logger.info("Updating JAR with class " + classjava)
cmd_launch = [
"cd", conf.CLASSES_TMP_PATH + ";", conf.JAVA_JAR_BIN, "uf",
conf.SINFONIER_LAST_JAR,
module_type + classjava.replace("$", "\$")
]
CommandExecutor.execute(cmd_launch, capture_out=True)
os.remove(conf.CLASSES_TMP_PATH + module_type + classjava)
os.remove(conf.CLASSES_TMP_PATH + module_type + module_name + ".info")
def request_job():
start_time = time.time()
content = request.get_json(silent=True)
priority = content['priority']
cpu = content['cpu']
logger.info('Priority : %s, CPU : %s' % (priority, cpu))
if priority == 'None':
print('Priority is None.')
priority = -1
output = check_output('bash /STREAM/run.sh', shell=True, stderr=STDOUT)
else:
output = check_output('bash /STREAM/run.sh %s' % (priority),
shell=True,
stderr=STDOUT)
output = output.decode('utf-8').replace('\n', '')
end_time = time.time()
store_result(priority, output, cpu, start_time, end_time)
return json.dumps({'status': 'success'})
def handleImbalanceProblem(self, dataset, labels, model, epochs):
model.getCompiledModel()
model = model.model
flatten = labels.flatten()
class_weights = class_weight.compute_class_weight(
'balanced', classes=np.unique(labels), y=flatten)
classWeightsMap = {
idx: ratio
for idx, ratio in enumerate(class_weights)
}
train_images, test_images, train_labels, test_labels = DataProcessor.trainTestSplit(
dataset, labels)
train_images, test_images, train_labels, test_labels = DataProcessor.prepareDatasetForFit(
train_images, test_images, train_labels, test_labels)
history = model.fit(train_images,
train_labels,
epochs=epochs,
validation_data=(test_images, test_labels),
class_weight=classWeightsMap)
predictions = model.predict(test_images)
if predictions.shape[1] > 1:
test_labels_orig = np.argmax(test_labels, axis=1)
test_labels_pred = np.argmax(predictions, axis=1)
else:
test_labels_orig = test_labels
test_labels_pred = np.where(predictions > 0.5, 1, 0)
logger.info(
classification_report(y_true=test_labels_orig,
y_pred=test_labels_pred))
logger.info(
precision_recall_fscore_support(y_true=test_labels_orig,
y_pred=test_labels_pred,
average='weighted'))
return history
def on_epoch_end(self):
self.counter += 1
if self.counter == 10:
logger.info("return")
return
predictions = self.model.predict(self.train_data)
loss = tfa.losses.sigmoid_focal_crossentropy(
tf.cast(self.train_labels, tf.float32), predictions)
if predictions.shape[1] > 1:
y_true = np.argmax(self.train_labels, axis=1)
y_pred = np.argmax(predictions, axis=1)
else:
y_true = self.train_labels
y_pred = np.where(predictions > 0.5, 1, 0)
classes, classesCount = np.unique(y_true, return_counts=True)
highestClassCount = np.amax(classesCount)
for classId, classCount in zip(classes, classesCount):
if classCount < highestClassCount:
# hard mining
amountToCopy = highestClassCount - classCount
classIndexes = np.where(y_true == classId)[0].tolist()
classesLoss = [(idx, loss[idx]) for idx in classIndexes]
classesLoss.sort(key=lambda x: x[1])
indexesToCopy = [
x[0] for x in classesLoss[-self.hardMiningSample:]
]
for i in range(int(amountToCopy / self.hardMiningSample)):
copiedData = np.copy(self.train_data[indexesToCopy, ])
copiedLabels = np.copy(self.train_labels[indexesToCopy, ])
self.train_data = np.concatenate(
(self.train_data, copiedData))
self.train_labels = np.concatenate(
(self.train_labels, copiedLabels))
logger.info(np.unique(self.train_data)[1])
logger.info(self.train_data.shape)
logger.info(self.train_labels.shape)
def on_epoch_end(self):
self.counter += 1
if self.counter == self.epochs:
logger.info("return")
return
predictions = self.model.predict(self.train_data)
y_true = None
y_pred = None
if self.train_labels.shape[1] > 1:
y_true = np.argmax(self.train_labels, axis=1)
y_pred = np.argmax(predictions, axis=1)
else:
y_true = self.train_labels
y_pred = np.where(predictions > 0.5, 1, 0)
report = classification_report(y_true=y_true,
y_pred=y_pred,
output_dict=True)
if self.counter == self.epochs - 1:
logger.info(classification_report(y_true=y_true, y_pred=y_pred))
classesCounts = np.unique(y_true, return_counts=True)[1]
averageClassSize = math.ceil(np.mean(classesCounts))
totalsScores = [
report[label]['f1-score'] for label in report
if label not in ["accuracy", "macro avg", "weighted avg"]
]
for ix, s in enumerate(totalsScores):
logger.info("{} : {}".format(ix, s))
totals = [
1 - report[label]['f1-score'] for label in report
if label not in ["accuracy", "macro avg", "weighted avg"]
]
f1ScoreTotal = np.sum([
1 - report[label]['f1-score'] for label in report
if label not in ["accuracy", "macro avg", "weighted avg"]
]) / len(classesCounts)
updatedSampleSize = {}
for label in report:
if label not in ["accuracy", "macro avg", "weighted avg"]:
updatedSampleSize[int(label)] = int(
((1 - report[label]['f1-score']) / f1ScoreTotal) *
averageClassSize)
logger.info(updatedSampleSize)
logger.info("mean class size: {}".format(averageClassSize))
classes, classesCount = np.unique(y_true, return_counts=True)
logger.info(classesCount)
for classId, classCount in zip(classes, classesCount):
if classCount > updatedSampleSize[classId]:
logger.info("Undersample class {}".format(classId))
amountToRemove = classCount - updatedSampleSize[classId]
classIndexes = np.where(y_true == classId)[0].tolist()
indexesToRemove = random.sample(classIndexes, amountToRemove)
self.train_data = np.delete(self.train_data, indexesToRemove,
0)
self.train_labels = np.delete(self.train_labels,
indexesToRemove, 0)
elif classCount < updatedSampleSize[classId]:
logger.info("Oversample class {}".format(classId))
amountToCopy = updatedSampleSize[classId] - classCount
classIndexes = np.where(y_true == classId)[0].tolist()
indexesToCopy = random.choices(classIndexes, k=amountToCopy)
copiedDataset = np.copy(self.train_data[indexesToCopy, ])
copiedLabels = np.copy(self.train_labels[indexesToCopy, ])
self.train_data = np.concatenate(
(self.train_data, copiedDataset))
self.train_labels = np.concatenate(
(self.train_labels, copiedLabels))
if self.train_labels.shape[1] > 1:
y_true = np.argmax(self.train_labels, axis=1)
else:
y_true = self.train_labels
parser.add_argument('--production', action='store_true')
parser.add_argument('--docker', action='store_true')
args = parser.parse_args()
if args.production:
os.environ[EnvConst.SINFONIER_ENV_KEY] = EnvConst.PROD_ENVIRONMENT
elif args.docker:
os.environ[EnvConst.SINFONIER_ENV_KEY] = EnvConst.DOCKER_ENVIRONMENT
else:
os.environ[EnvConst.SINFONIER_ENV_KEY] = EnvConst.DEVELOP_ENVIRONMENT
from config.config import conf
from config.Routes import Routes
from logger.Logger import logger
routes = Routes()
api = routes.api
from wsgiref import simple_server
httpd = simple_server.make_server(conf.SINFONIER_API_HOST,
conf.SINFONIER_API_PORT, api)
logger.info('Server up! running in ' + conf.SINFONIER_API_HOST + ':' +
str(conf.SINFONIER_API_PORT))
logger.info(os.environ[EnvConst.SINFONIER_ENV_KEY].upper() + ' mode')
httpd.serve_forever()
else:
from utils.SinfonierConstants import Environment
os.environ[Environment.SINFONIER_ENV_KEY] = Environment.PROD_ENVIRONMENT
app = running_server()
def log(self):
logger.info(self.stdout)
if self.stderr:
logger.error(self.stderr)
