analysis of anomalies @TODO: road test

src/api/index.py

@@ -23,7 +23,7 @@ import monitor
 import Queue
 from utils.transport import *
 from utils.workers import ThreadManager, Factory
-from utils.ml import ML,AnomalyDetection
+from utils.ml import ML,AnomalyDetection,AnalyzeAnomaly
 import utils.params as SYS_ARGS
 import atexit
 
@@ -51,7 +51,7 @@ def procs(id):
 	try:
 		gReader = factory.instance(type=class_read,args=p)
 		data =  gReader.read()
-		ahandler = AnomalyDetection()
+		ahandler = AnalyzeAnomaly()
 		learn = {}
 		for row in data['learn'] :
 			label = row['label']
@@ -66,15 +66,19 @@ def procs(id):
 				# Let us determine if this is a normal operation or not
 				# We will update the status of the information ...
 				#
+				
 				for row in r[label] :	
-								
+					index = r[label].index(row)			
 					if row['label'] in learn:
 						id = row['label']
 						px = ahandler.predict([row],learn[id])
 						if px :
-							px = px[0]
-							row['anomaly'] = px[1]==1
-							print row
+							
+							# row['anomaly'] = px[1]==1
+							print ""
+							print label,' *** ',index
+							row = dict(row,**px)
+							r[label][index] =row
 							#
 							# @TODO:
 							# Compile a report here that will be sent to the mailing list
@@ -243,7 +247,7 @@ if __name__== '__main__':
 #	ThreadManager.start(CONFIG)	
 	if 'port' not in SYS_ARGS.PARAMS :
 		SYS_ARGS.PARAMS['port'] = 5000
-	PORT = SYS_ARGS.PARAMS['port']
+	PORT = int(SYS_ARGS.PARAMS['port'])
 	app.run(host='0.0.0.0',port=PORT,debug=True,threaded=True)
 
 	

src/api/static/js/dashboard.js

@@ -7,7 +7,8 @@ monitor.processes.fetch = function(){
 	
 }
 
-monitor.processes.init = function(x){
+monitor.processes.init = function (x) {
+	
 	var r = JSON.parse(x.responseText)
 	monitor.processes.summary.init(r) 
 	var keys = jx.utils.keys(r)	
@@ -79,6 +80,7 @@ monitor.processes.render = function(label,data) {
 		var id 	= jx.dom.get.value('latest_processes_label')
 		var app = item.label
 		monitor.processes.trend.init(id, app)
+		
 		if (item.anomaly == true) {
 			jx.dom.show('has_anomaly')
 		} else {
@@ -155,7 +157,7 @@ monitor.processes.trend.render = function (logs, key,label) {
 	// var _y = {}
 	var cpu = {yAxisID:'0', label: 'CPU Usage (%)', data: [] ,backgroundColor:'transparent',borderColor:COLORS[187],fill:false,borderWidth:1}
 	var mem = {yAxisID:'0',label : 'Memory Usage(%)',data:[],backgroundColor:'transparent',borderColor:COLORS[32],fill:false,borderWidth:1}
-	var proc= {yAxisID:'1',label : 'Proc Count',data:[],backgroundColor:'transparent',borderColor:COLORS[42],fill:false,borderWidth:1}
+	var proc= {yAxisID:'1',label : 'Proc Count',data:[],backgroundColor:'transparent',borderColor:COLORS[542],fill:false,borderWidth:1}
 	jx.utils.patterns.visitor(logs,function(item){
 		x = new Date(item.year,item.month-1,item.day,item.hour,item.minute)
 		y = item[key]
@@ -280,6 +282,7 @@ monitor.sandbox.init = function () {
 	jx.dom.hide('inspect_sandbox')
 	var httpclient = HttpClient.instance()
 	httpclient.get('/sandbox', function (x) {
+		
 		var r = JSON.parse(x.responseText)
 		if (r.length > 0){
 			monitor.sandbox.render(r);

test/TestML.py

@@ -1,5 +1,5 @@
 from utils import transport
-from utils.ml import ML, AnomalyDetection
+from utils.ml import ML, AnomalyDetection, AnalyzeAnomaly
 import unittest
 import json
 import os
@@ -57,7 +57,7 @@ class TestML(unittest.TestCase):
 		features = CONFIG['learner']['anomalies']['features']
 		label	= CONFIG['learner']['anomalies']['label']
 		x = lhandler.learn(data,'label',app,features,label)
-		print x
+		
 		
 	def test_Predict(self):
 		ref = CONFIG['store']['class']['read']
@@ -68,14 +68,16 @@ class TestML(unittest.TestCase):
 			info = data['learn']
 
 			app = CONFIG['monitor']['processes']['config']['apps'][0]
-			print [app]
-			lhandler = AnomalyDetection()
+			
+			lhandler = AnalyzeAnomaly()
 			features = CONFIG['learner']['anomalies']['features']
 			label	= CONFIG['learner']['anomalies']['label']
 		#x = lhandler.learn(data,'label',app,features,label)
 			data = data['apps']
 			xo = ML.Filter('label',app,data)
-			print app,xo
+			info = ML.Filter('label',app,info)
+			
+			lhandler.predict(xo,info[0])
 
 if __name__ == '__main__' :
 	unittest.main()