process engines and learners @TODO: Bootup & revisit detailprocess class

src/api/index.py

@@ -2,13 +2,17 @@
 	This is a RESTful interface implemented using Flask micro framework.
 	The API is driven by configuration that is organized in terms of the monitoring classes
 	
+	The API is both restful and websocket/socketio enabled.
+
 	We designed the classes to be reusable (and powered by labels):
 	'monitoring-type':
 		'class':'<class-name>'
 		'config':<labeled-class-specific-configuration>'
 """
+
 from flask import Flask, session, request, redirect, Response
 from flask.templating import render_template
+
 from flask_session import Session
 import time
 import sys
@@ -18,6 +22,7 @@ import re
 import monitor
 import Queue
 from utils.transport import *
+
 PARAMS  = {'context':''}
 if len(sys.argv) > 1:
 	
@@ -36,6 +41,10 @@ if len(sys.argv) > 1:
 		
 
 app = Flask(__name__)
+app.config['SECRET_KEY'] = '!h8-[0v8]247-4-360'
+#app.secret_key = 'A0Zr98j/3yX R~XHH!jmN]LWX=?RT'
+
+
 f = open(PARAMS['path'])
 CONFIG 	= json.loads(f.read())
 HANDLERS= {}
@@ -152,11 +161,17 @@ def learn():
 	label = ML.Extract(['status'],r)
 	r = ML.Extract(['cpu_usage','memory_usage'],r)
 
+@app.route('/anomalies/status')
+def anomalies_status():
+	pass
+
+@app.route('/anomalies/get')
+def anomalies_get():
+	pass
+	
 if __name__== '__main__':
 	
 	mthread.start()
-
-	app.secret_key = 'A0Zr98j/3yX R~XHH!jmN]LWX=?RT'
 	app.run(host='0.0.0.0',debug=True,threaded=True)
 
 	

src/api/ml-index.py

@@ -0,0 +1,9 @@
+from flask import Flask, render_template
+from flask_socketio import SocketIO
+
+app = Flask(__name__)
+app.config['SECRET_KEY'] = '[0v8-247]-4qdm-h8r5!'
+socketio = SocketIO(app)
+
+if __name__ == '__main__':
+    socketio.run(app)

src/api/static/index.html

@@ -1,5 +1,6 @@
 <link type="text/css" rel="stylesheet" href="{{ context }}/js/jsgrid/jsgrid.min.css" />
 <link type="text/css" rel="stylesheet" href="{{ context }}/js/jsgrid/jsgrid-theme.min.css" />
+<script src="https://cdn.socket.io/socket.io-1.4.5.js"></script>
 <script src="{{ context }}/static/js/jsgrid.js"></script>
 <script src="{{ context }}/static/js/jquery/jquery.min.js"></script>
 

src/api/static/js/dashboard.js

@@ -334,3 +334,7 @@ monitor.sandbox.render = function (logs) {
 	jx.dom.show('inspect_sandbox')
 	
 }
+
+/**
+ * Socket handler, check for learning status
+ */

src/api/templates/dashboard.html

@@ -26,7 +26,9 @@
 		
 		<div id="menu" class="menu"></div>
 	</div>
-		
+	<div class="left">
+		{% include "prediction.html" %}
+	</div>		
 	<div class="left info ">
 		
 		<div class="" style="text-transform:capitalize; ">

src/api/templates/prediction.html

@@ -1,31 +1,29 @@
 <meta charset="UTF-8">
 <meta http-equiv="cache-control" content="no-cache">
 <meta name="viewport" content="width=device-width, initial-scale=1,maximum-scale=1">
+<script src="https://cdn.socket.io/socket.io-1.4.5.js"></script>
 <!--
 	*
 	* This section is to handle predictions, i.e multivariate anomaly detection
 	*
 -->
 
-<div class="small">
-	
-</div>
-<div>
+<div class="left border info">
 	<div id="predict_chart" class="left"></div>
 	<div class="class">
 		<div>
 			<div></div>
 			<div>Accuracy</div>
 		</div>
-		<div>
+		<div class="left">
 			<div id="precision">00</div>
 			<div class="small">Precision</div>
 		</div>
-		<div>
+		<div class="left">
 			<div id="recall">00</div>
 			<div class="small">Recall</div>
 		</div>
-		<div>
+		<div class="left">
 			<div id="fscore">00</div>
 			<div class="small"></div>
 		</div>

src/monitor.py

@@ -221,68 +221,24 @@ class Monitor (Thread):
 		self.keep_running = True
 		lock = RLock()
 		while self.keep_running:
-			
-			for label in self.mconfig:
 			lock.acquire()
+			for label in self.mconfig:
+				
 				self.handler.init(self.mconfig[label])
 				r = self.handler.composite()
 				self.writer.write(label=label,row = r)
-				lock.release()		
-				time.sleep(2)
 				
+				time.sleep(2)
+			lock.release()		
 			
 			self.prune()
 			HALF_HOUR = 60*25
 			time.sleep(HALF_HOUR)
 		print "Stopped ..."
 	def prune(self) :
+		
 		MAX_ENTRIES = 100
 		if len(self.logs) > MAX_ENTRIES :
 			BEG = len(self.logs) - MAX_SIZE -1 
 			self.logs = self.logs[BEG:]
 
-class mapreducer:
-	def __init__(self):
-		self.store = {}
-	def filter (self,key,dataset):
-		return [row[key] for row in dataset if key in row]
-	def run(self,dataset,mapper,reducer):
-		r = None
-		if mapper is not None:
-			if isinstance(dataset,list) :
-				[mapper(row,self.emit) for row in dataset]
-			
-			
-		if reducer is not None:
-			r = self.store
-			# r = [reducer(self.store[key]) for key in self.store]
-		else:
-			r = self.store
-		return r
-	def mapper(self,row,emit):
-		[emit(_matrix['label'],_matrix) for _matrix in row ]
-	def reducer(self,values):
-		beg = len(values)-101 if len(values) > 100 else 0
-		return values[beg:]
-
-	def emit(self,key,content):
-		if key not in self.store:
-			self.store[key] = []
-		self.store[key].append(content)
-# 	#
-# 	# We need to generate the appropriate dataset here
-# 	# map/reduce is a well documented technique for generating datasets
-# 	#
-# 	def map(self,key,id,rows):
-		
-# 		#r =  [row[key] for row in rows if key in row]
-# 		for row in rows:
-# 			if key in row :
-# 				for xr in row[key]:
-# 					self.emit(xr['label'],xr)
-		
-		
-
-# def reduce(keys,values):
-# 	print values[0]
-# 	return r

src/utils/ml.py

@@ -1,6 +1,10 @@
 """
 	This file is intended to perfom certain machine learning tasks based on numpy
 	We are trying to keep it lean that's why no sklearn involved yet
+
+	@TODO:
+	Create factory method for the learners implemented here
+	Improve preconditions (size of the dataset, labels)
 """
 from __future__ import division
 import numpy as np
@@ -16,6 +20,8 @@ class ML:
 	@staticmethod
 	def Extract(lattr,data):
 		return [[row[id] for id in lattr] for row in data]
+
+	
 """
 	Implements a multivariate anomaly detection
 	@TODO: determine computationally determine epsilon
@@ -41,7 +47,8 @@ class AnomalyDetection:
 	"""
 	def learn(self,data,key,value,features,label):
 		xo = ML.Filter(key,value,data)
-		
+		if len(xo) < 100 :
+			return None
 		# attr = conf['features']
 		# label= conf['label']
 		yo= ML.Extract([label['name']],xo)
@@ -55,7 +62,8 @@ class AnomalyDetection:
 		
 		px =  self.gPx(p['mean'],p['cov'],xo['test'])
 		
-		print self.gPerformance(px,yo['test'])
+		perf = self.gPerformance(px,yo['test'])
+		return {"parameters":p,"performance":perf}
 	def getLabel(self,yo,label_conf):
 		return [ int(len(set(item) & set(label_conf["1"]))>0) for item in yo ]
 

src/utils/params.py

@@ -0,0 +1,17 @@
+import sys
+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
+		

src/utils/workers.py

@@ -0,0 +1,96 @@
+import multiprocessing
+from utils import transport
+import time
+import monitor
+import sys
+"""
+	This class is intended to collect data given a configuration
+
+"""
+class Top(multiprocessing.Process):
+	def __init__(self,_config,lock):
+		multiprocessing.Process.__init__(self)
+		self.lock = lock
+		self.reader_class	= _config['store']['class']['read']
+		self.write_class	= _config['store']['class']['write']
+		self.rw_args		= _config['store']['args']
+		self.factory 		= transport.DataSourceFactory()
+		
+		self.name = 'Zulu-Top'
+		self.quit = False
+		print sys.argv
+		sys.argv[0] = self.name
+		print sys.argv
+		# multiprocessing.current_process().name = 'Zulu-Top'
+		self.exit = multiprocessing.Event()
+		
+		
+		className = ''.join(['monitor.',_config['monitor']['processes']['class'],'()'])
+		self.handler = eval(className)
+		self.config = _config['monitor']['processes']['config']
+	def stop(self):
+		self.quit = True
+	def run(self):
+		while self.quit == False:
+			for label in self.config :
+				self.lock.acquire()
+				gwriter = self.factory.instance(type=self.write_class,args=self.rw_args)
+				for app in self.config[label] :
+					self.handler.init(app)	
+					r = self.handler.composite()
+					gwriter.write(label=label,row=r)
+					time.sleep(5)
+				self.lock.release()
+			ELLAPSED_TIME = 60*30
+			time.sleep(ELLAPSED_TIME)
+		print "Exiting ",self.name
+				
+class Learner(multiprocessing.Process) :
+	
+	"""
+		This function expects paltform config (store,learner)
+		It will leverage store and learner in order to operate
+	"""
+	def __init__(self,config,lock):
+		multiprocessing.Process.__init__(self)
+		self.name='Zulu-Learner'
+		self.lock = lock
+		self.reader_class	= config['store']['class']['read']
+		self.write_class	= config['store']['class']['write']
+		self.rw_args	= config['store']['args']
+		self.features = config['learner']['anomalies']['features']
+		self.yo	= config['learner']['anomalies']['label']
+		self.apps = config['learner']['anomalies']['apps']
+		self.factory 		= transport.DataSourceFactory()
+	"""
+		This function will initiate learning every (x-hour)
+		If there is nothing to learn the app will simply go to sleep
+	"""
+	def run(self):
+		reader = self.factory.instance(type=self.reader_class,args=self.rw_args)
+		data = reader.read()
+		#
+		# This is the motherload of innefficiency ...
+		#
+		while True:
+			r = {}
+			for key in data :
+				logs = data[key]
+				r[key] = {}
+				for app in self.apps:
+					handler = AnomalyDetection()
+					r[key][app] = lhandler.learn(data,'label',app,self.features,self.yo)
+			#
+			# At this point we've already learnt every thing we need to learn
+			#
+			self.lock.aquire()
+			writer = sef.factory.instance(type.self.write_class,args=self.rw_args)
+			writer.write('learn',r)
+			self.lock.release()
+
+			TIME_ELLAPSED = 60*120	#-- Every 2 hours
+			time.sleep(TIME_ELLAPSED)
+			
+
+
+

src/utils/zulu-learn/__init__.py

@@ -0,0 +1 @@
+

src/utils/zulu-learn/__main__.py

@@ -0,0 +1 @@
+

src/utils/zulu-top/__main__.py

@@ -0,0 +1,14 @@
+
+import os
+import json
+from utils.workers import *
+from utils.params import PARAMS
+f = open(PARAMS['path'])
+config = json.loads(f.read())
+f.close()
+from multiprocessing import Lock
+lock = Lock()
+p = Top(config,lock)
+p.daemon = True
+p.start()
+p.join()