def histoWeighted(matrix):
if matrix.__class__ != Matrix:
HandleError.exit("In Histogram.histo: incorrect type: " +
str(type(matrix)))
if matrix.cols > 2:
HandleError.exit("In Histogram.histo: matrix cols > 2")
events = {}
counts = {}
for r in range(matrix.rows):
row = matrix.getRow(r)
if row[0] in events.keys():
events[row[0]] = events[row[0]] + row[1]
counts[row[0]] = counts[row[0]] + 1
else:
events[row[0]] = row[1]
counts[row[0]] = 1
m = Matrix(len(events.keys()), 2)
m.addColumn(events.keys())
n1 = NumColumn(events.values())
n2 = NumColumn(counts.values())
m.addColumn(n1.scalarDivision(n2))
return m
def euclideanDistance(self, win2):
if not isinstance(win2, Window):
HandleError.exit("In euclideanDistance: argument is not a window")
d = self.colB.substract(win2.colB)
p = d.dotProduct(d)
return math.sqrt(p)
def createHyperSphere(matrix, winSize, winType='SLIDING'):
c1 = matrix.getCol(0)
c2 = matrix.getCol(1)
rows = matrix.rows
s = set([])
if winType is 'REGULAR':
for i in range(rows - winSize - 1):
x = i
y = i + winSize - 1
subCol1 = c1.getSubColumn(x, y)
subCol2 = c2.getSubColumn(x, y)
w = Window(subCol1, subCol2)
s.add(w)
elif winType is 'SLIDING':
numWins = int(math.floor(float(rows) / winSize))
for i in range(numWins):
x = i * winSize
y = x + winSize - 1
subCol1 = c1.getSubColumn(x, y)
subCol2 = c2.getSubColumn(x, y)
w = Window(subCol1, subCol2)
s.add(w)
else:
HandleError.exit("In createHyperSphere: incorrect window type")
return s
def getMetric(options):
metric_id = ''
if options.METRIC is None:
HandleError.exit('No abnormal metric is given.\nUse -h option for help.')
else:
metric_name = options.METRIC
# Find metric number in the file
normalFile = options.NFILE
file = open(normalFile, 'r')
metrics = file.readline()[:-1].split(',')
del(metrics[0])
file.close()
metric_found = False
for i in range(len(metrics)):
if metric_name == metrics[i]:
metric_found = True
metric_id = i + 1
break
if metric_found == False:
HandleError.exit('Unknown metric name.')
return metric_id
def getMetric(options):
metric_id = ''
if options.METRIC is None:
HandleError.exit('No abnormal metric is given.\nUse -h option for help.')
else:
metric_name = options.METRIC
# Find metric number in the file
normalFile = options.NFILE
file = open(normalFile, 'r')
metrics = file.readline()[:-1].split(',')
del(metrics[0])
file.close()
metric_found = False
for i in range(len(metrics)):
if metric_name == metrics[i]:
metric_found = True
metric_id = i + 1
break
if metric_found == False:
HandleError.exit('Unknown metric name.')
return metric_id
def normalizeAggregates(self, averageList, stdList):
s = len(self.aggr)
if (len(averageList) != s or len(stdList) != s):
HandleError.exit("In normalizeAggregates: incorrect list sizes")
for i in range(len(self.aggr)):
if (stdList[i] != 0.0):
self.aggr[i] = (self.aggr[i] - averageList[i]) / stdList[i]
else:
self.aggr[i] = 0
def getMatricsFromCorrelationIndex(index, n):
count = 0
for i in range(n - 1):
for j in range(n - i - 1):
x = i
y = x + j + 1
if index == count:
return (x, y)
count = count + 1
HandleError.exit('Could not find correlation index')
def __init__(self, col1, col2):
if isinstance(col1, StringColumn) and isinstance(col2, NumColumn):
if col1.size() != col2.size():
HandleError.exit("In window: cols of different sizes")
self.colA = col1
self.colB = col2
else:
if len(col1) != len(col2):
HandleError.exit("In window: cols of different sizes")
self.colA = StringColumn(col1)
self.colB = NumColumn(col2)
self.aggr = self.colB.getAggregates()
def removeColumnsButKeep(self, list=[]):
if len(list) == 0:
return
for i in list:
if i > self.cols - 1:
HandleError.exit("Incorrect column index: " + str(i))
newList = []
for i in range(self.cols):
if i in list:
newList.append(self.listOfCols[i])
self.listOfCols = newList
self.cols = len(self.listOfCols)
def findAnomalousPoints(windowsList, method):
occurrenceNumber = {}
subNameOccurr = {}
for win in windowsList:
obsSet = win.getUniqueObservations()
for o in obsSet:
# Parse observation
if method == 'CLASSNAME_ONLY':
# Only split by '-' in Java applications.
if '-' in o:
name = o.split('-')[1].split('$')[0]
tmp = o.split('-')[1].split('$')[1:]
subName = "$".join(tmp)
else:
name = o
subName = o
if name not in subNameOccurr.keys():
tmp = {}
tmp[subName] = 1
subNameOccurr[name] = tmp
else:
if subName not in subNameOccurr[name].keys():
subNameOccurr[name][subName] = 1
else:
subNameOccurr[name][
subName] = subNameOccurr[name][subName] + 1
elif method == 'CLASSNAME_AND_METHOD':
if '-' in o:
name = o.split('-')[1]
else:
name = o
else:
HandleError.exit('in findAnomalousPoints: unknown method')
if name not in occurrenceNumber.keys():
occurrenceNumber[name] = 1
else:
occurrenceNumber[name] = occurrenceNumber[name] + 1
l = sorted(occurrenceNumber, key=occurrenceNumber.get)
l.reverse()
ret = []
for e in l:
ret.append((e, occurrenceNumber[e]))
#print "org/apache/hadoop/dfs/DFSClient", subNameOccurr['org/apache/hadoop/dfs/DFSClient']
#print "org/apache/hadoop/hbase/regionserver/HRegion", subNameOccurr['org/apache/hadoop/hbase/regionserver/HRegion']
return ret
def getMode(options):
select_metrics = options.SELECT_METRICS
select_regions = options.SELECT_REGIONS
# Can only specify one operational mode
if select_metrics is True and select_regions is True:
HandleError.exit('Cannot use these options together:\n --select-metrics & --select-regions. \nUse -h option for help.')
if select_metrics is False and select_regions is False:
HandleError.exit('Please use one of these options:\n--select-metrics OR --select-regions. \nUse -h option for help.')
if select_metrics is True:
mode = 'SELECT_METRICS'
elif select_regions is True:
mode = 'SELECT_REGIONS'
return mode
def normalize(matrix):
if matrix.__class__ != Matrix:
HandleError.exit("In Histogram.histo: incorrect type: " +
str(type(matrix)))
if matrix.cols > 2:
HandleError.exit("In Histogram.histo: matrix cols > 2")
maximum = float(matrix.max(1))
v = []
for r in range(matrix.rows):
row = matrix.getRow(r)
v.append(row[1] / maximum * 100)
ret = Matrix(matrix.rows, 2)
ret.addColumn(matrix.getCol(0))
ret.addColumn(v)
return ret
def calculateCorrelation(listX, listY):
if len(listX) != len(listY):
HandleError.exit("In calculateCorrelation: lists of different sizes")
avg_x = numpy.average(listX)
avg_y = numpy.average(listY)
std_x = numpy.std(listX)
std_y = numpy.std(listY)
ret = 0
for i in range(len(listX)):
tmp1 = (listX[i] - avg_x) / std_x
tmp2 = (listY[i] - avg_y) / std_y
ret = ret + (tmp1 * tmp2)
ret = ret / (len(listX) - 1)
if math.isnan(ret):
ret = 0
return ret
def addColumn(self, col):
# If column is a 'Column' class
if isinstance(col, Column):
self.listOfCols.append(col)
return
# If it's a list
if (len(col) != self.rows):
HandleError.exit("Invalid size of column")
if (len(self.listOfCols) + 1 > self.cols):
HandleError.exit("Too many columns")
if (len(self.listOfCols) == 0):
c = StringColumn(col)
else:
c = NumColumn(col)
self.listOfCols.append(c)
def getMode(options):
select_metrics = options.SELECT_METRICS
select_regions = options.SELECT_REGIONS
select_classname = options.SELECT_CLASSNAME
# Can only specify one operational mode
if (select_metrics is True and select_regions is True) or (select_metrics is True and select_classname is True):
HandleError.exit('Cannot use these options together:\n --select-metrics & --select-regions & --select-classname. \nUse -h option for help.')
if select_metrics is False and select_regions is False and select_classname is False:
HandleError.exit('Please use one of these options:\n--select-metrics OR --select-regions OR --select-classname. \nUse -h option for help.')
if select_metrics is True:
mode = 'SELECT_METRICS'
elif select_regions is True:
mode = 'SELECT_REGIONS'
elif select_classname is True:
mode = 'SELECT_CLASSNAME'
return mode
def histo(matrix):
if matrix.__class__ != Matrix:
HandleError.exit("In Histogram.histo: incorrect type: " +
str(type(matrix)))
if matrix.cols > 2:
HandleError.exit("In Histogram.histo: matrix cols > 2")
events = {}
for r in range(matrix.rows):
row = matrix.getRow(r)
if row[0] in events.keys():
events[row[0]] = events[row[0]] + row[1]
else:
events[row[0]] = row[1]
m = Matrix(len(events.keys()), 2)
m.addColumn(events.keys())
m.addColumn(events.values())
return m
def aggregatesDistance(self, win2):
if not isinstance(win2, Window):
HandleError.exit("In aggregateDistance: argument is not a window")
return Window.listDistance(win2.aggr, self.aggr)
def getPrintAbnormal(options):
if options.PRINT_ABNORMAL is None:
HandleError.exit('No print preference given.\nUse -h option for help.')
else:
return options.PRINT_ABNORMAL
def getClassName(options):
if options.CLASSNAME is None:
HandleError.exit('No classname given.\nUse -h option for help.')
else:
return options.CLASSNAME
def getNormalFile(options):
if options.NFILE is None:
HandleError.exit('No normal-traces file given.\nUse -h option for help.')
else:
return options.NFILE
def getNormalFile(options):
if options.NFILE is None:
HandleError.exit('No normal-traces file given.\nUse -h option for help.')
else:
return options.NFILE
