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fixing learning bugs on integration

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master
Steve L. Nyemba 8 years ago
parent b787ee0608
commit cbef913877
3 changed files with 129 additions and 67 deletions
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79
src/api/index.py
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@ -23,40 +23,17 @@ import monitor
import Queue
from utils.transport import *
from utils.workers import ThreadManager, Factory
from utils.ml import ML,AnomalyDetection
import utils.params as SYS_ARGS
import atexit
PARAMS = {'context':''}
if len(sys.argv) > 1:
N = len(sys.argv)
for i in range(1,N):
value = None
if sys.argv[i].startswith('--'):
key = sys.argv[i].replace('-','')
if i + 1 < N:
value = sys.argv[i + 1] = sys.argv[i+1].strip()
if key and value:
PARAMS[key] = value
i += 2
app = Flask(__name__)
app.config['SECRET_KEY'] = '!h8-[0v8]247-4-360'
#app.secret_key = 'A0Zr98j/3yX R~XHH!jmN]LWX=?RT'
PARAMS = SYS_ARGS.PARAMS
f = open(PARAMS['path'])
CONFIG = json.loads(f.read())
#HANDLERS= {}
#for key in CONFIG['monitor'] :
#className = CONFIG['monitor'][key]['class']
#ref = "".join(["monitor.",className,"()"])
#ref = eval(ref)
#HANDLERS[key] = {"class":ref,"config":CONFIG['monitor'][key]["config"]}
f.close()
#
@ -66,12 +43,7 @@ p = CONFIG['store']['args']
class_read = CONFIG['store']['class']['read']
class_write= CONFIG['store']['class']['write']
factory = DataSourceFactory()
#gWriter = factory.instance(type='CouchdbWritera',args=p)
#gReader = factory.instance(type='CouchdbReader',args=p)
#p['qid'] = HANDLERS['processes']['config'].keys()
gReader = factory.instance(type=class_read,args=p)
#gWriter = factory.instance(type=class_write,args=p)
#mthread = monitor.Monitor(HANDLERS,gWriter,'processes',)
atexit.register(ThreadManager.stop)
@app.route('/get/<id>')
@ -82,8 +54,12 @@ def procs(id):
r = {}
for label in d :
if label not in ['learn'] :
index = len(d[label]) - 1
r[label] = d[label][index]
#for row in r[label] :
#yo = ML.Extract(['status'],row)
#xo = ML.Extract(['cpu_usage','memory_usage'],row)
except Exception, e:
print e
@ -122,13 +98,14 @@ def trends ():
p = CONFIG['store']['args']
class_read = CONFIG['store']['class']['read']
p['qid'] =[id] #HANDLERS['processes']['config'].keys()
gReader = factory.instance(type=class_read,args=p)
r = gReader.read()
if id in r:
r = r[id] #--matrix
series = []
for row in r:
series += [item for item in row if str(item['label'])== app]
@ -156,19 +133,37 @@ def dashboard():
"""
@app.route('/learn')
def learn():
app = request.args.get('app')
id = request.args.get('id')
global CONFIG
p = CONFIG['store']['args']
class_read = CONFIG['store']['class']['read']
p['qid'] =[id] #HANDLERS['processes']['config'].keys()
gReader = factory.instance(type=class_read,args=p)
d = gReader.read()
if 'learn' in d :
logs = d['learn']
del d['learn']
else :
logs = []
r = []
if 'id' in request.args:
id = request.args['id']
d = d[id]
print CONFIG['monitor']['processes']['config'][id]
print (apps)
#apps = list(set(ML.Extract(['label'],d)))
p = AnomalyDetection()
#for row in d :
#xo = ML.Filter('label',app,d)
#info = ML.Filter('label',app,logs)
#value = p.predict(xo,info)
#print app,value
#if value is not None:
# r.append(value)
print r
return json.dumps("[]")
r = gReader.read()
r = r[id]
r = ML.Filter('label',app,r)
label = ML.Extract(['status'],r)
r = ML.Extract(['cpu_usage','memory_usage'],r)
@app.route('/anomalies/status')
def anomalies_status():
@ -180,7 +175,7 @@ def anomalies_get():
if __name__== '__main__':
ThreadManager.start(CONFIG)
#ThreadManager.start(CONFIG)
app.run(host='0.0.0.0',debug=True,threaded=True)

45
src/utils/ml.py
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@ -16,9 +16,12 @@ class ML:
# @TODO: Make sure this approach works across all transport classes
# We may have a potential issue of how the data is stored ... it may not scale
#
return [item[0] for item in data if item and attr in item[0] and item[0][attr] == value]
@staticmethod
def Extract(lattr,data):
if isinstance(lattr,basestring):
lattr = [lattr]
return [[row[id] for id in lattr] for row in data]
@ -27,6 +30,7 @@ class ML:
@TODO: determine computationally determine epsilon
"""
class AnomalyDetection:
def split(self,data,index=-1,threshold=0.8) :
N = len(data)
# if N < LIMIT:
@ -64,12 +68,21 @@ class AnomalyDetection:
yo = self.split(yo)
if xo['train'] :
E = 0.01
for i in range(0,10):
Epsilon = E + (2*E*i)
p = self.gParameters(xo['train'])
px = self.gPx(p['mean'],p['cov'],xo['test'])
px = self.gPx(p['mean'],p['cov'],xo['test'],Epsilon)
perf = self.gPerformance(px,yo['test'])
return {"parameters":p,"performance":perf}
if perf['fscore'] > 0 :
perf['epsilon'] = Epsilon
break
return {"label":value,"parameters":p,"performance":perf}
return None
def getLabel(self,yo,label_conf):
return [ int(len(set(item) & set(label_conf["1"]))>0) for item in yo ]
@ -77,9 +90,10 @@ class AnomalyDetection:
"""
This function will compute the probability density function given a particular event/set of events
The return value is [px,yo]
@pre xu.shape[0] == sigma[0] == sigma[1]
"""
def gPx(self,xu,sigma,data,EPSILON=0.25):
def gPx(self,xu,sigma,data,EPSILON=0.01):
n = len(data[0])
r = []
@ -95,6 +109,21 @@ class AnomalyDetection:
px = float(b/a)
r.append([px,int(px < EPSILON)])
return r
"""
This function uses stored learnt information to predict on raw data
In this case it will determin if we have an anomaly or not
@param xo raw observations (matrix)
@param info stored information about this
"""
def predict(self,xo,info):
xo = ML.Filter(info['extract'],xo)
if not xo :
return None
sigma = info['parameters']['cov']
xu = info['parameters']['mean']
epsilon = info['performance']['epsilon']
return self.getPx(xu,sigma,xo,epsilon)
"""
This function computes performance metrics i.e precision, recall and f-score
for details visit https://en.wikipedia.org/wiki/Precision_and_recall
@ -134,3 +163,13 @@ class AnomalyDetection:
sigma = np.cov(m)
sigma = [ list(row) for row in sigma]
return {"cov":sigma,"mean":list(u)}
class Regression:
parameters = {}
@staticmethod
def predict(xo):
pass
def __init__(self,config):
pass

48
src/utils/workers.py
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@ -1,7 +1,7 @@
#import multiprocessing
from threading import Thread, RLock
from utils import transport
from utils.ml import AnomalyDetection
from utils.ml import AnomalyDetection,ML
import time
import monitor
import sys
@ -76,16 +76,38 @@ class Learner(Thread) :
def run(self):
reader = self.factory.instance(type=self.reader_class,args=self.rw_args)
data = reader.read()
#
# Let's make sure we extract that which has aleady been learnt
#
if 'learn' in data:
r = data['learn']
del data['learn']
r = ML.Extract('label',r)
logs = [row[0] for row in r]
logs = list(set(logs))
else:
logs = []
#
# This is the motherload of innefficiency ...
# In order to address the inefficiencies below, we chose to adopt the following policy
# We don't learn that which is already learnt, This measure consists in filtering out the list of the apps that already have learning data
#
self.apps = list(set(self.apps) - set(logs))
while self.quit == False:
r = {}
lapps = list(self.apps)
for key in data :
logs = data[key]
for app in self.apps:
#
# There poor design at this point, we need to make sure things tested don't get tested again
# This creates innefficiencies (cartesian product)
#
for app in lapps:
handler = AnomalyDetection()
value = handler.learn(logs,'label',app,self.features,self.yo)
@ -94,17 +116,23 @@ class Learner(Thread) :
if key not in r:
r[key] = {}
r[key][app] = value
i = lapps.index(app)
del lapps[i]
#
# At this point we've already learnt every thing we need to learn
# This offers a clean write to the data store upon value retrieved
# The removal of the application enables us to improve efficiency (among other things)
#
if r.keys() :
value = dict(value,**{"features":self.features})
self.lock.acquire()
writer = self.factory.instance(type=self.write_class,args=self.rw_args)
writer.write(label='learn',row=r)
writer.write(label='learn',row=value)
self.lock.release()
#
# Usually this is used for development
# @TODO : Remove this and find a healthy way to stop the server
#
if 'MONITOR_CONFIG_PATH' in os.environ:
#
# This suggests we are in development mode

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