def __init__(self, config):
pass
if config == None:
self.configFile = "acm.config.dev.yml"
self.configManager = ConfigManager(
params={"config.file": self.configFile})
self.config = self.configManager.read()
def get(self, request, format=None):
print ("django-root:", settings.BASE_DIR)
self.configFile= os.path.join(settings.BASE_DIR, "acm.config.dev.yml")
self.configManager = ConfigManager(params={"config.file":self.configFile})
self.config = self.configManager.read()
self.hdfsServerUrl = "hdfs://"+self.config.acm.servers.hdfs.host+":"+str(self.config.acm.servers.hdfs.port)
#jsonFile = '/home/halil/gitlab/acm/pyspark/acm-text-classification-rest/jsontordd.test.json'
jsonFile = os.path.join(settings.BASE_DIR, self.config.acm.test.test001.jsonRequestPath)
self.modelHdfsPath = self.hdfsizePath(self.config.acm.test.test001.modelHdfsPath)
self.pipelineHdfsPath = self.hdfsizePath(self.config.acm.test.test001.pipelineHdfsPath)
json_ = None
with open(jsonFile, 'r') as f:
content = f.read()
json_ = json.loads(content)
ret_ = self.classify(json_)
return Response(["hello","world!", ret_])
class KafkaClassificationModelConsumerExecutor(object):
def __init__(self, config):
pass
if config == None:
self.configFile = "acm.config.dev.yml"
self.configManager = ConfigManager(
params={"config.file": self.configFile})
self.config = self.configManager.read()
def start(self):
pass
k = KafkaClassificationModelConsumerProcess(self.config.toJson())
k.start()
k.join()
class MulticlassClassifierExecutor(object):
def __init__(self, config=None):
pass
if config == None:
self.configFile = "acm.config.dev.yml"
self.configManager = ConfigManager(
params={"config.file": self.configFile})
self.config = self.configManager.read()
# this method run as a child process
# lr: logistic regression
def lrModelTrainer(self, q, parentEnv, configJsonStr):
pass
classifier = MulticlassLogisticRegressionModelTrainer()
classifier.start(q, parentEnv, configJsonStr)
q.put(["hello parent!", "I am lrModelTrainer"])
# this is the start point of parent process
def start(self):
pass
fs = [
self.lrModelTrainer,
self.lrModelTrainer,
] #functions
ps = [] #processes
qs = [] # queues
#passing objects to child process via queue
env_ = json.dumps(os.environ.copy())
for f in fs:
q = Queue()
configJsonStr = self.config.toJson()
p = Process(target=f, args=(q, env_, configJsonStr))
qs.append(q)
p.start()
ps.append(p)
p.join()
for q in qs:
print(q.get())
class AcmTextLogisticRegressionClassifierView(APIView):
"""
List all snippets, or create a new snippet.
"""
def hdfsizePath(self, path):
return self.hdfsServerUrl+path
def classify(self, inputJson):
pass
self.hdfs = PyWebHdfsClient(host=self.config.acm.servers.hdfs.host,port=self.config.acm.servers.hdfs.restPort, user_name=self.config.acm.servers.hdfs.fileOwner)
self.hdfsServerUrl = "hdfs://"+self.config.acm.servers.hdfs.host+":"+str(self.config.acm.servers.hdfs.port)
if hasattr(self, 'sc')==False:
self.sc =SparkContext()
if hasattr(self, 'sqlContext')==False:
self.sqlContext = SQLContext(self.sc)
schema = StructType([StructField('Category', StringType(), True),
StructField('Descript', StringType(), True),
StructField('Dates', StringType(), True),
StructField('DayOfWeek', StringType(), True),
StructField('PdDistrict', StringType(), True),
StructField('Resolution', StringType(), True),
StructField('Address', StringType(), True),
StructField('X', DoubleType(), True),
StructField('Y', DoubleType(), True)
])
test = self.sqlContext.createDataFrame(inputJson, schema)
#pipeline= PipelineModel.load("/home/halil/gitlab/acm/pyspark/acm-text-classification-rest/lr.model.pipeline.savepoint")
pipeline= PipelineModel.load(self.pipelineHdfsPath)
testData = pipeline.transform(test)
print("Test Dataset Count: " + str(testData.count()))
##########################################################
################## Train/load the model ##################
##########################################################
#lrModel = LogisticRegressionModel.load("/home/halil/gitlab/acm/pyspark/acm-text-classification-rest/lr.model.savepoint")
lrModel = LogisticRegressionModel.load(self.modelHdfsPath)
predictions = lrModel.transform(testData)
predictions.filter(predictions['prediction'] == 7) \
.select("Descript","Category","probability","label","prediction") \
.orderBy("probability", ascending=False) \
.show(n = 10, truncate = 30)
#.select("probability","label","prediction") \
resultJson = predictions.filter(predictions['prediction'] == 7) \
.select("prediction") \
.orderBy("probability", ascending=False) \
.toJSON().collect()
self.sc.stop()
return ["al sana ML!", resultJson]
def get(self, request, format=None):
print ("django-root:", settings.BASE_DIR)
self.configFile= os.path.join(settings.BASE_DIR, "acm.config.dev.yml")
self.configManager = ConfigManager(params={"config.file":self.configFile})
self.config = self.configManager.read()
self.hdfsServerUrl = "hdfs://"+self.config.acm.servers.hdfs.host+":"+str(self.config.acm.servers.hdfs.port)
#jsonFile = '/home/halil/gitlab/acm/pyspark/acm-text-classification-rest/jsontordd.test.json'
jsonFile = os.path.join(settings.BASE_DIR, self.config.acm.test.test001.jsonRequestPath)
self.modelHdfsPath = self.hdfsizePath(self.config.acm.test.test001.modelHdfsPath)
self.pipelineHdfsPath = self.hdfsizePath(self.config.acm.test.test001.pipelineHdfsPath)
json_ = None
with open(jsonFile, 'r') as f:
content = f.read()
json_ = json.loads(content)
ret_ = self.classify(json_)
return Response(["hello","world!", ret_])
def post(self, request, format=None):
return self.get(request, format)