@TODO: compute slope

src/api/index.py

@@ -241,6 +241,9 @@ def anomalies_status():
 if __name__== '__main__':
 	
 #	ThreadManager.start(CONFIG)	
-	app.run(host='0.0.0.0',debug=True,threaded=True)
+	if 'port' not in SYS_ARGS.PARAMS :
+		SYS_ARGS.PARAMS['port'] = 5000
+	PORT = SYS_ARGS.PARAMS['port']
+	app.run(host='0.0.0.0',port=PORT,debug=True,threaded=True)
 
 	

src/utils/ml.py

@@ -18,7 +18,17 @@ class ML:
 		#
 		value = ML.CleanupName(value)
 		#return [item[0] for item in data if item and attr in item[0] and item[0][attr] == value]
-		return [[item for item in row if item[attr] == value][0] for row in data]
+		#return [[item for item in row if item[attr] == value][0] for row in data]
+		#
+		# We are making the filtering more rescillient, i.e if an item doesn't exist we don't have to throw an exception
+		# This is why we expanded the loops ... fully expressive but rescilient
+		#
+		r = []
+		for row in data :
+			for item in row :
+				if attr in item and item[attr] == value:
+					r.append(item)
+		return r
 	@staticmethod
 	def Extract(lattr,data):
 		if isinstance(lattr,basestring):
@@ -67,7 +77,9 @@ class AnomalyDetection:
 		yo= ML.Extract([label['name']],xo)
 		xo = ML.Extract(features,xo)
 		yo = self.getLabel(yo,label)
-		
+		#
+		# @TODO: Insure this can be finetuned, training size matters for learning. It's not obvious to define upfront
+		# 
 		xo = self.split(xo)
 		yo = self.split(yo)
 		p = self.gParameters(xo['train'])
@@ -214,7 +226,28 @@ class AnomalyDetection:
 		sigma = [ list(row) for row in sigma]
 		return {"cov":sigma,"mean":list(u)}
 
-
+class AnalyzeAnomalies(AnomalyDetection):
+	"""
+		This analysis function will include a predicted status because an anomaly can either be 
+			- A downtime i.e end of day 
+			- A spike and thus a potential imminent crash
+		@param xo	matrix of variables
+		@param info	information about what was learnt 
+	"""
+	def predict(self,xo,info):
+		x = xo[len(xo)-1]
+		r = AnomalyDetection.predict(x,info)
+		#
+		# In order to determine what the anomaly is we compute the slope (idle or crash)
+		# The slope is computed using the covariance / variance of features
+		#
+		N = len(info['features'])
+		xy = ML.Extract(info['features'],xo)
+		xy = np.matrix(xy)
+		vxy= [xy[:,i] for i in range(0,N)]
+		print N,vxy.shape
+		alpha = info['cov'] / vxy
+		return r
 class Regression:
 	parameters = {}
 	@staticmethod

test/TestML.py

@@ -55,12 +55,27 @@ class TestML(unittest.TestCase):
 		app = CONFIG['monitor']['processes']['config']['apps'][0]
 		lhandler = AnomalyDetection()
 		features = CONFIG['learner']['anomalies']['features']
-		print features
-		print app
 		label	= CONFIG['learner']['anomalies']['label']
 		x = lhandler.learn(data,'label',app,features,label)
 		print x
 		
+	def test_Predict(self):
+		ref = CONFIG['store']['class']['read']
+		p	= CONFIG['store']['args']
+		greader = factory.instance(type=ref,args=p)
+		data = greader.read()
+		if 'learn' in data :	
+			info = data['learn']
+
+			app = CONFIG['monitor']['processes']['config']['apps'][0]
+			print [app]
+			lhandler = AnomalyDetection()
+			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
 
 if __name__ == '__main__' :
 	unittest.main()