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performanceQuantiles.py
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#!/usr/bin/env python3
"""
Digraph3 collection of python3 modules for
Algorithmic Decision Theory applications
Module for incremental performance quantiles computation
Copyright (C) 2016-2025 Raymond Bisdorff
This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR ANY PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
######################################
__version__ = "$Revision: Python 3.13.2"
"""
from time import time
from decimal import Decimal
from perfTabs import PerformanceTableau
class PerformanceQuantiles(PerformanceTableau):
"""
Implements the incremental performance quantiles representation of a
given performance tableau.
*Parameters*:
* *perfTab*: may be either a PerformanceTableau object or the name of a previously saved PerformanceQuantiles instance
* *NumberOfBins* may be either 'quartiles', 'deciles', ... or 'n', the integer number of bins.
Example python session:
>>> import performanceQuantiles
>>> from randomPerfTabs import RandomCBPerformanceTableau
>>> from randomPerfTabs import RandomPerformanceGenerator as PerfTabGenerator
>>> nbrActions=1000
>>> nbrCrit = 7
>>> tp = RandomCBPerformanceTableau(numberOfActions=nbrActions,
... numberOfCriteria=nbrCrit,seed=105)
>>> pq = performanceQuantiles.PerformanceQuantiles(tp,'quartiles',
... LowerClosed=True,Debug=False)
>>> pq.showLimitingQuantiles(ByObjectives=True)
*---- performance quantiles -----*
Costs
criteria | weights | '0.0' '0.25' '0.5' '0.75' '1.0'
---------|--------------------------------------------------
'c1' | 6 | -97.12 -65.70 -46.08 -24.96 -1.85
Benefits
criteria | weights | '0.0' '0.25' '0.5' '0.75' '1.0'
---------|--------------------------------------------------
'b1' | 1 | 2.11 27.92 48.76 68.94 98.69
'b2' | 1 | 0.00 3.00 5.00 7.00 10.00
'b3' | 1 | 1.08 30.41 50.57 69.01 97.23
'b4' | 1 | 0.00 3.00 5.00 7.00 10.00
'b5' | 1 | 1.84 29.77 50.62 70.14 96.40
'b6' | 1 | 0.00 3.00 5.00 7.00 10.00
>>> tpg = PerfTabGenerator(tp,seed=105)
>>> newActions = tpg.randomActions(100)
>>> pq.updateQuantiles(newActions,historySize=None)
>>> pq.showHTMLLimitingQuantiles(Transposed=True)
.. image:: examplePerfQuantiles.png
:alt: Example limiting quantiles html show method
:width: 400 px
:align: center
"""
def __repr__(self):
"""
Default presentation method for PerformanceQuantiles instances
"""
reprString = '*------- PerformanceQuantiles instance description ------*\n'
reprString += 'Instance class : %s\n' % self.__class__.__name__
try:
reprString += 'Seed : %s\n' % str(self.randomSeed)
except:
pass
reprString += 'Instance name : %s\n' % self.name
#reprString += '# Actions : %d\n' % len(self.actions)
try:
reprString += 'Objectives : %d\n' % len(self.objectives)
except:
pass
reprString += 'Criteria : %d\n' % len(self.criteria)
reprString += 'Quantiles : %d\n' % (len(self.quantilesFrequencies)-1)
reprString += 'History sizes : %s\n' % self.historySizes
reprString += 'Attributes : %s\n' % list(self.__dict__.keys())
return reprString
def __init__(self,perfTab=None,numberOfBins=4,
LowerClosed=True,
Debug=False):
from copy import deepcopy
from collections import OrderedDict, defaultdict
from randomPerfTabs import RandomCBPerformanceTableau,\
Random3ObjectivesPerformanceTableau,\
RandomPerformanceTableau
## if not LowerClosed:
## LowerClosed=True
## print('Note: Only lower-closed quantile classes are implemented')
if type(perfTab) != str:
self.perfTabType = perfTab.__class__.__name__
try:
self.valueDigits = perfTab.valueDigits
except:
self.valueDigits = 2
actionsTypeStatistics = {}
for x in perfTab.actions:
if type(perfTab) == RandomCBPerformanceTableau:
xType = perfTab.actions[x]['type']
elif type(perfTab) == Random3ObjectivesPerformanceTableau:
self.objectiveSupportingTypes = perfTab.objectiveSupportingTypes
xType = 'Soc' + perfTab.actions[x]['profile']['Soc']
xType += 'Eco' + perfTab.actions[x]['profile']['Eco']
xType += 'Env' + perfTab.actions[x]['profile']['Env']
else:
xType = 'NA'
try:
actionsTypeStatistics[xType] += 1
except:
actionsTypeStatistics[xType] = 1
self.actionsTypeStatistics = actionsTypeStatistics
try:
self.objectives = perfTab.objectives
except:
self.objectives = None
try:
self.commonScale = perfTab.commonScale
except:
pass
try:
self.OrdinalScales = perfTab.OrdinalScales
except:
pass
try:
self.BigData = perfTab.BigData
except:
pass
try:
self.missingDataProbability = perfTab.missingDataProbability
except:
pass
try:
self.NA = perfTab.NA
except:
self.NA = Decimla('-999.00')
self.criteria = deepcopy(perfTab.criteria)
self.LowerClosed = LowerClosed
self.quantilesFrequencies = self._computeQuantilesFrequencies(numberOfBins,Debug=Debug)
np = len(self.quantilesFrequencies)
limitingQuantiles = {}
cdf = {}
self.historySizes = {}
for g in self.criteria:
self.historySizes[g] = 0
limitingQuantiles[g] = self._computeLimitingQuantiles(perfTab,g)
if Debug:
print(g,limitingQuantiles[g])
cdf[g] = OrderedDict([(limitingQuantiles[g][i],self.quantilesFrequencies[i]) for i in range(np)])
self.limitingQuantiles = limitingQuantiles
self.cdf = cdf
elif type(perfTab) == str: # a stored instance file name is given
fileName = perfTab + '.py'
argDict = {}
fi = open(fileName,'r')
exec(compile(fi.read(), fileName, 'exec'),argDict)
fi.close()
self.name = str(perfTab)
try:
self.objectives = argDict['objectives']
except:
pass
try:
self.objectiveSupportingTypes = argDict['objectiveSupportingTypes']
except:
pass
try:
self.commonScale = argDict['commonScale']
except:
pass
try:
self.OrdinalScales = argDict['OrdinalScales']
except:
pass
try:
self.BigData = argDict['BigData']
except:
pass
try:
self.missingDataProbability = argDict['missingDataProbability']
except:
pass
try:
self.NA = argDict['NA']
except:
pass
self.criteria = argDict['criteria']
self.quantilesFrequencies = argDict['quantilesFrequencies']
self.historySizes = argDict['historySizes']
self.LowerClosed = argDict['LowerClosed']
self.NA = argDict['NA']
self.limitingQuantiles = argDict['limitingQuantiles']
cdf = {}
np = len(self.quantilesFrequencies)
for g in self.criteria:
cdf[g] = {}
for i in range(np):
cdf[g][self.limitingQuantiles[g][i]] = self.quantilesFrequencies[i]
#cdf[g] = OrderedDict([(self.limitingQuantiles[g][i],self.quantilesFrequencies[i]) for i in range(np)])
self.cdf = cdf
self.perfTabType = argDict['perfTabType']
#--------- private class methods
def _computeQuantilesFrequencies(self,x,Debug=False):
"""
renders the quantiles frequencies
"""
from math import floor
if isinstance(x,int):
n = x
elif x == 'quartiles':
n = 4
elif x == 'quintiles':
n = 5
elif x == 'sextiles':
n = 6
elif x == 'heptiles':
n = 7
elif x == 'octiles':
n = 8
elif x == 'deciles':
n = 10
elif x == 'dodeciles':
n = 20
elif x == 'centiles':
n = 100
else:
print("""Error: numberOfBins must be either an integer, None or
a string out of ['quartiles','quintiles','sextiles','heptiles
'octiles','deciles','dodeciles','centiles']""")
return
quantilesFrequencies = []
for i in range(n+1):
freqStr = '%.6f' % (Decimal(str(i)) / Decimal(str(n)))
quantilesFrequencies.append(Decimal(freqStr))
self.name = '%d-tiled_performances' % n
if Debug:
print(x,quantilesFrequencies)
return quantilesFrequencies
def _computeLimitingQuantiles(self,perfTab,g,Debug=False):
"""
Renders the list of limiting quantiles *q(p)* on criteria *g* for *p* in *frequencies*
"""
from math import floor
from copy import copy, deepcopy
critg = self.criteria[g]
NA = self.NA
gValues = []
for x in perfTab.actions:
if Debug:
print('g,x,evaluation[g][x]',g,x,perfTab.evaluation[g][x])
if perfTab.evaluation[g][x] != NA:
if critg['weight'] > Decimal('0'):
gValues.append(perfTab.evaluation[g][x])
else:
gValues.append(-perfTab.evaluation[g][x])
gValues.sort()
if critg['weight'] > Decimal('0'):
self.criteria[g]['minValue'] = gValues[0]
self.criteria[g]['maxValue'] = gValues[-1]
else:
self.criteria[g]['minValue'] = -gValues[0]
self.criteria[g]['maxValue'] = -gValues[-1]
## if PrefThresholds:
## try:
## gPrefThrCst = self.criteria[g]['thresholds']['pref'][0]
## gPrefThrSlope = self.criteria[g]['thresholds']['pref'][1]
## except:
## gPrefThrCst = Decimal('0')
## gPrefThrSlope = Decimal('0')
n = len(gValues)
self.historySizes[g] = n
if Debug:
print('g,n,gValues',g,n,gValues)
## if n > 0:
## nf = Decimal(str(n+1))
nf = Decimal(str(n))
quantilesFrequencies = self.quantilesFrequencies
#limitingQuantiles = [Decimal(str(q)) for q in frequencies]
#limitingQuantiles.sort()
#self.limitingQuantiles = limitingQuantiles
if Debug:
print(quantilesFrequencies)
# computing the quantiles on criterion g
gQuantiles = []
if self.LowerClosed:
# we ignore the 1.00 quantile and replace it with +infty
for q in quantilesFrequencies:
r = (Decimal(str(nf)) * q)
rq = int(floor(r))
if Debug:
print('r,rq',r,rq, end=' ')
if rq < (n-1):
quantile = gValues[rq]\
+ ((r-Decimal(str(rq)))*(gValues[rq+1]-gValues[rq]))
## if rq > 0 and PrefThresholds:
## quantile += gPrefThrCst + quantile*gPrefThrSlope
else :
#if self.criteria[g]['preferenceDirection'] == 'min':
#quantile = Decimal('100.0')
quantile = gValues[-1]
#else:
#quantile = Decimal('200.0')
#quantile = gValues[-1]
if Debug:
print('quantile',quantile)
gQuantiles.append(quantile)
else: # upper closed categories
# we ignore the quantile 0.0 and replace it with -\infty
for q in quantilesFrequencies:
r = (Decimal(str(nf)) * q)
rq = int(floor(r))
if Debug:
print('r,rq',r,rq, end=' ')
if rq == 0:
#if self.criteria[g]['preferenceDirection'] == 'min':
#quantile = Decimal('-200.0')
quantile = gValues[0]
#else:
#quantile = Decimal('-100.0')
#quantile = gValues[0]
elif rq < (n-1):
quantile = gValues[rq]\
+ ((r-Decimal(str(rq)))*(gValues[rq+1]-gValues[rq]))
## if PrefThresholds:
## quantile -= gPrefThrCst - quantile*gPrefThrSlope
else:
if n > 0:
quantile = gValues[n-1]
else:
#if self.criteria[g]['preferenceDirection'] == 'min':
quantile = gValues[-1]
#else:
#quantile = gValues[-1]
if Debug:
print('quantile',quantile)
gQuantiles.append(quantile)
## else:
## gQuantiles = []
#if critg['weight'] < Decimal('0'):
# gQuantiles = [-quantile for quantile in gQuantiles]
if Debug:
print(g,self.LowerClosed,self.criteria[g]['preferenceDirection'],gQuantiles)
return gQuantiles
def _htmlLimitingQuantiles(self,Sorted=False,
Transposed=False,ndigits=2,
ContentCentered=True,
title=None):
"""
Renders the limiting quantiles in table format: citerion x limitss in html format.
"""
criteria = self.criteria
if title is None:
html = '<h1>Performance quantiles</h1>'
else:
html = '<h1>%s</h1>' % title
historySizes = [self.historySizes[g] for g in self.historySizes]
html += '<p>Sampling sizes between %d and %d.</p>' % ( min(historySizes),max(historySizes))
## if self.LowerClosed:
## html += '<p>Quantile bins %s.</p>' % ('lowerclosed')
## else:
## html += '<p>Quantile bins %s.</p>' % ('upperclosed')
criteriaKeys = [g for g in self.criteria]
if Sorted:
criteriaKeys.sort()
limitingQuantiles = self.limitingQuantiles
quantilesFrequencies = self.quantilesFrequencies
nq = len(quantilesFrequencies)
if ContentCentered:
alignFormat = 'center'
else:
alignFormat = 'right'
if Transposed:
html += '<table style="background-color:White;" border="1">'
html += '<tr bgcolor="#9acd32"><th>criterion</th>'
for x in quantilesFrequencies:
xName = '%.2f' % x
html += '<th bgcolor="#FFF79B">%s</th>' % (xName)
html += '</tr>'
for g in criteriaKeys:
try:
gName = criteria[g]['shortName']
except:
gName = str(g)
html += '<tr><th bgcolor="#FFF79B">%s</th>' % (gName)
for i in range(nq):
formatString = '<td align="%s">%% .%df</td>' % (alignFormat,ndigits)
if criteria[g]['preferenceDirection'] == 'max':
value = min(criteria[g]['scale'][1],max(criteria[g]['scale'][0],limitingQuantiles[g][i]))
else:
if criteria[g]['weight'] > Decimal('0'):
value = max(-criteria[g]['scale'][1],min(-criteria[g]['scale'][0],limitingQuantiles[g][i]))
else:
value = max(criteria[g]['scale'][0],min(criteria[g]['scale'][1],-limitingQuantiles[g][i]))
html += formatString % (value)
html += '</tr>'
html += '</table>'
else:
html += '<table style="background-color:White;" border="1">'
html += '<tr bgcolor="#9acd32"><th>criterion</th>'
for g in criteriaKeys:
try:
gName = criteria[g]['shortName']
except:
gName = str(g)
html += '<th bgcolor="#FFF79B">%s</th>' % (gName)
html += '</tr>'
for i in range(nq):
xName = '%.2f' % quantilesFrequencies[i]
html += '<tr><th bgcolor="#FFF79B">%s</th>' % (xName)
for g in criteriaKeys:
formatString = '<td align="%s">%% .%df</td>' % (alignFormat,ndigits)
if criteria[g]['preferenceDirection'] == 'max':
value = min(criteria[g]['scale'][1],max(criteria[g]['scale'][0],limitingQuantiles[g][i]))
else:
if criteria[g]['weight'] > Decimal('0'):
value = max(-criteria[g]['scale'][1],min(-criteria[g]['scale'][0],limitingQuantiles[g][i]))
else:
value = max(criteria[g]['scale'][0],min(criteria[g]['scale'][1],-limitingQuantiles[g][i]))
html += formatString % (value)
html += '</tr>'
html += '</table>'
return html
def _interpolateQuantile(self,x,newq,newp,Debug=False):
#Debug = self.Debug
#Debug = True
if Debug:
print('==>>?')
print('x')
print(x)
print('newq')
print(newq)
print('newp')
print(newp)
np = len(newp)
i = 0
while i < np-1:
if Debug:
print(i)
if x < newp[i]:
ix = i+1
if Debug:
print('x < newp[i]', x, newp[i],ix, newp[i-1],newq[ix-1],newp[i],newq[ix])
# nsq[0]
diffp = newp[i]-newp[i-1]
if diffp > Decimal('0.0'):
res = newq[ix-1]+ ((x-newp[i-1])/diffp)*(newq[ix]-newq[ix-1])
else: # avoid dividing by 0
res = newq[ix-1]
if Debug:
print('res', res)
return res
#i = np
elif x == newp[i+1]:
ix = i+1
res = newq[ix]
if Debug:
print('x == newp[i]',ix, newp[ix],newq[ix],res)
return res
else: # x > newp[i]
ix = i+1
i += 1
if Debug:
print('x > newp[i]', i,ix)
# if x is in the last quantile class
if i == np-1:
if x < newp[i]:
ix = i
if Debug:
print('x < newp[i]', x, newp[i],ix, newp[i-1],newq[ix-1],newp[i],newq[ix])
# nsq[0]
diffp = newp[i]-newp[i-1]
if diffp > Decimal('0.0'):
res = newq[ix-1]+ ((x-newp[i-1])/diffp)*(newq[ix]-newq[ix-1])
else: # avoid dividing by 0
res = newq[ix-1]
if Debug:
print('res', res)
return res
i = np
else: # x == newp[i]:
ix = i
res = newq[ix]
if Debug:
print('x == newp[i]',ix, newp[ix],newq[ix],res)
return res
def _updateCriterionQuantiles(self,g,newValues,historySize=None,Debug=False):
"""
See lecture about the iq-agent of the MICS-3 Computational Statistics Course.
"""
#Debug = self.Debug
#Debug = True
if Debug:
print('==>>', g,newValues)
from collections import OrderedDict
# get present state of the quantiles
p = self.quantilesFrequencies
np = len(p)
q = self.limitingQuantiles[g]
cdf = self.cdf[g]
if historySize is None:
t = self.historySizes[g]
else:
t = historySize
oldfrq = [p[i]*(t+1) for i in range(np)]
if Debug:
print('q', q)
print('p', p)
print('cdf', cdf)
print('oldfrq', oldfrq)
print(t)
# new observations
nv = []
for x in newValues:
if x != self.NA:
## if self.criteria[g]['weight'] < Decimal('0'):
## nv.append(-x)
## else:
nv.append(x)
nv.sort()
nt = len(nv)
if Debug:
print('nv,nt', nv,nt)
###
if nt > 0: # there may be solely missing values observed on g
# Init results newq and newp
# Init indexes: i in q & p & oldfrq, j in nv, ins = # insertions
newp = [Decimal('0.0')]
if nv[0] < q[0]:
self.criteria[g]['minValue'] = nv[0]
newq= [nv[0]]
ins = 1
j = 1
i = 0
elif nv[0] > q[0] :
newq = [q[0]]
j = 0
ins = 0
i = 1
else:
newq = [q[0]]
j = 1
ins = 0
i = 1
if nv[-1] > q[-1]:
self.criteria[g]['maxValue'] = nv[-1]
# compute new cumulative densities
while i < np:
while j < nt and i < np:
#print(i,j)
if nv[j] > q[i]:
newq.append(q[i])
# ins += 0
newp.append(cdf[q[i]]+ins)
i += 1
elif nv[j] < q[i]:
if nv[j] > newq[-1]:
newq.append(nv[j])
# ins += 1
# interpolate cdf of nv[j]
cdfnv = cdf[q[i-1]] + (nv[j]-q[i-1])/(q[i]-q[i-1])*cdf[q[i]] + ins
newp.append(cdfnv)
else:
newp[-1] += 1
ins += 1
j +=1
else: # nv[j] = q[i]
newp[-1] += 1
ins += 1
j += 1
if j == nt:
for ni in range(i,np):
newq.append(q[ni])
newp.append(cdf[q[ni]]+ins)
#ins += 0
i = np
for nj in range(j,nt):
ins += 1
if newq[-1] < nv[nj]:
newq.append(nv[nj])
newp.append(newp[-1]+1)
else:
newp[-1] += 1
if Debug:
self.newp = newp
self.newq = newq
print('newp', newp)
print('newq', newq)
# renormalising frequencies
if Debug:
print('#inserts = %d' % ins)
t += len(nv)
for i in range(len(newq)):
newp[i] /= newp[-1]
if Debug:
print('p q (t+1)*q')
for i in range(len(newq)):
print('%.3f \t %.2f \t %.2f' % (newp[i],newq[i],newp[i]*(t)) )
print('t = %d' % t)
# compute new state by interpolation
nstate = {p[0]:newq[0]}
if Debug:
print('interpolate ==>> ',p)
np = len(p)
for i in range(1,np):
x = p[i]
if Debug:
print('pi', i, x)
nstate[x] = self._interpolateQuantile(x,newq,newp,Debug=Debug)
if Debug:
print('nstate', nstate)
# store new state
q = [nstate[x] for x in nstate]
q.sort()
self.limitingQuantiles[g] = q
cdf = {}
for p in nstate:
cdf[nstate[p]] = p
self.cdf[g] = cdf
self.historySizes[g] = t
#------------- public class methods
def computeQuantileProfile(self,p,qFreq=None,Debug=False):
"""
Renders the quantile *q(p)* on all the criteria.
"""
from collections import OrderedDict
from decimal import Decimal
x = Decimal('%.2f' % p)
if qFreq is None:
qFreq = self.quantilesFrequencies
quantiles = OrderedDict()
for g in self.criteria:
q = self.limitingQuantiles[g]
quantiles[g] = self._interpolateQuantile(x,q,qFreq)
if Debug:
print(x, quantiles[g], q)
return quantiles
def save(self,fileName='tempPerfQuant',valueDigits=2):
"""
Persistant storage of a PerformanceQuantiles instance.
"""
print('*--- Saving performance quantiles in file: <' + str(fileName) + '.py> ---*')
valueString = 'Decimal("%%.%df"),\n' % (valueDigits)
objectives = self.objectives
fileNameExt = str(fileName)+str('.py')
fo = open(fileNameExt, 'w')
fo.write('# Saved performance quantiles: \n')
fo.write('from decimal import Decimal\n')
fo.write('from collections import OrderedDict\n')
# perfTabType
fo.write('perfTabType = \'%s\'\n' % self.perfTabType)
# objectives
try:
fo.write('objectiveSupportingTypes = %s\n' % str(self.objectiveSupportingTypes) )
except:
pass
fo.write('objectives = OrderedDict([\n')
if objectives is not None:
for obj in objectives:
fo.write('(\'%s\', {\n' % str(obj))
for it in self.objectives[obj].keys():
fo.write('\'%s\': %s,\n' % (it,repr(self.objectives[obj][it])))
fo.write('}),\n')
fo.write('])\n')
# criteria
try:
fo.write('OrdinalScales = %s\n' % str(self.OrdinalScales))
except:
pass
try:
fo.write('BigData = %s\n' % str(self.BigData))
except:
pass
try:
fo.write('missingDataProbability = %s\n' % str(self.missingDataProbability))
except:
pass
try:
fo.write('NA = Decimal(\'%s\')\n' % str(self.NA))
except:
pass
try:
fo.write('commonScale = %s\n' % str(self.commonScale))
except:
pass
criteria = self.criteria
fo.write('criteria = OrderedDict([\n')
for g in criteria:
fo.write('(\'%s\', {\n' % str(g))
for it in self.criteria[g].keys():
fo.write('\'%s\': %s,\n' % (it,repr(self.criteria[g][it])))
fo.write('}),\n')
fo.write('])\n')
# quanties frequencies
quantilesFrequencies = self.quantilesFrequencies
np = len(quantilesFrequencies)
fo.write('quantilesFrequencies = [\n')
for i in range(np):
fo.write(valueString % quantilesFrequencies[i] )
fo.write( ']\n')
# history sizes
historySizes = self.historySizes
fo.write('historySizes = {\n')
for g in criteria:
fo.write('\'%s\': %d,' % (g,historySizes[g]) )
fo.write( '}\n')
# quantile limits
fo.write('LowerClosed = %s\n' % repr(self.LowerClosed))
limitingQuantiles = self.limitingQuantiles
fo.write('limitingQuantiles = {\n')
for g in criteria:
fo.write('\'%s\': [\n' % g )
for i in range(np):
fo.write(valueString % limitingQuantiles[g][i] )
fo.write('],\n')
fo.write( '}\n')
fo.close()
def showActions(self):
print("""No decision actions are actually being stored!
Only the cumulated density function per criteria of so far
observed performance evaluations are kept.
The number of so far observed evaluations per criteria are the following:
""" )
for g in self.criteria:
print(g,self.historySizes[g])
def showCriteria(self,IntegerWeights=False,Alphabetic=False,ByObjectives=True,Debug=False):
"""
print Criteria with thresholds and weights.
"""
## if self.Debug:
## Debug = True
criteria = self.criteria
try:
objectives = self.objectives
except:
ByObjectives = False
print('*---- criteria -----*')
## sumWeights = Decimal('0.0')
## for g in criteria:
## sumWeights += criteria[g]['weight']
sumWeights = sum([criteria[g]['weight'] for g in criteria])
if ByObjectives:
for obj in objectives.keys():
criteriaList = [g for g in criteria if criteria[g]['objective']==obj]
for g in criteriaList:
try:
criterionName = '%s/' % objectives[criteria[g]['objective']]['name']
except:
criterionName = ''
try:
criterionName += criteria[g]['name']
except:
pass
print(g, repr(criterionName))
print(' Scale =', criteria[g]['scale'])
if IntegerWeights:
print(' Weight = %d ' % (criteria[g]['weight']))
else:
weightg = criteria[g]['weight']/sumWeights
print(' Weight = %.3f ' % (weightg))
try:
for th in criteria[g]['thresholds']:
if Debug:
print('-->>>', th,criteria[g]['thresholds'][th][0],criteria[g]['thresholds'][th][1])
print(' Threshold %s : %.2f + %.2fx' %\
(th,criteria[g]['thresholds'][th][0],criteria[g]['thresholds'][th][1]))
#print self.criteria[g]['thresholds'][th]
#print('; percentile: ',self.computeVariableThresholdPercentile(g,th,Debug))
except:
pass
#print(self.limitingQuantiles[g])
print(' history size: %d' % self.historySizes[g])
print(' p : quantile(p)')
if self.LowerClosed:
nq = len(self.quantilesFrequencies)
for i in range(nq):
q = self.quantilesFrequencies[i]
#print(q,self.limitingQuantiles[g])
if i < (nq-1):
print('%.2f : %.2f' % (q,self.limitingQuantiles[g][i]) )
else:
print('%.2f : %.2f' % (q,self.criteria[g]['maxValue']) )
else: # upperclosed quantile bins
nq = len(self.quantilesFrequencies)
for i in range(nq):
q = self.quantilesFrequencies[i]
#print(q,self.limitingQuantiles[g])
if i > 0:
print('%.2f : %.2f' % (q,self.limitingQuantiles[g][i]) )
else:
print('%.2f : %.2f' % (q,self.criteria[g]['minValue']) )
## for i in range(len(self.quantilesFrequencies)):
## q = self.quantilesFrequencies[i]
## if i > 0:
## print('%.2f : %.2f' % (q,self.limitingQuantiles[g][i]) )
## else:
## print('%.2f : %.2f' % (q,self.criteria[g]['minValue']) )
print()
else:
criteriaList = list(self.criteria.keys())
if Alphabetic:
criteriaList.sort()
for g in criteriaList:
try:
criterionName = '%s/' % objectives[criteria[g]['objective']]['name']
except:
criterionName = ''
try:
criterionName += criteria[g]['name']
except:
pass
print(g, repr(criterionName))
print(' Scale =', criteria[g]['scale'])
if IntegerWeights:
print(' Weight = %d ' % (criteria[g]['weight']))
else:
weightg = criteria[g]['weight']/sumWeights
print(' Weight = %.3f ' % (weightg))
try:
for th in criteria[g]['thresholds']:
if Debug:
print('-->>>', th,criteria[g]['thresholds'][th][0],criteria[g]['thresholds'][th][1])
print(' Threshold %s : %.2f + %.2fx'\
% (th,criteria[g]['thresholds'][th][0],criteria[g]['thresholds'][th][1]))
#print self.criteria[g]['thresholds'][th]
#print('; percentile: ',self.computeVariableThresholdPercentile(g,th,Debug))
except:
pass
#print(self.limitingQuantiles[g])
print(' history size: %d' % self.historySizes[g])
print(' p : qantile(p')
if self.LowerClosed:
nq = len(self.quantilesFrequencies)
for i in range(nq):
q = self.quantilesFrequencies[i]
#print(q,self.limitingQuantiles[g])
if i < (nq-1):
print('%.2f : %.2f' % (q,self.limitingQuantiles[g][i]) )
else:
print('%.2f : %.2f' % (q,self.criteria[g]['maxValue']) )
else: # upperclosed quantile bins
nq = len(self.quantilesFrequencies)
for i in range(nq):
q = self.quantilesFrequencies[i]
#print(q,self.limitingQuantiles[g])
if i > 0:
print('%.2f : %.2f' % (q,self.limitingQuantiles[g][i]) )
else:
print('%.2f : %.2f' % (q,self.criteria[g]['minValue']) )
print()
def showHTMLLimitingQuantiles(self,Sorted=True,
Transposed=False,ndigits=2,
ContentCentered=True,title=None,
htmlFileName=None):
"""
shows the html version of the limiting quantiles in a browser window.
"""
import webbrowser
if htmlFileName == None:
from tempfile import NamedTemporaryFile
fileName = (NamedTemporaryFile(suffix='.html',
delete=False,dir='.')).name
else:
from os import getcwd
fileName = getcwd()+'/'+htmlFileName
fo = open(fileName,'w')
fo.write(self._htmlLimitingQuantiles(Sorted=Sorted,\
Transposed=Transposed,\
ndigits=ndigits,
ContentCentered=ContentCentered,
title=title))
fo.close()
url = 'file://'+fileName
webbrowser.open(url,new=2)
def showLimitingQuantiles(self,ByObjectives=False,Sorted=False,ndigits=2):
"""
Prints the performance quantile limits in table format: criteria x limits.
"""
criteria = self.criteria
print('*---- performance quantiles -----*')
quantiles = self.quantilesFrequencies
nq = len(quantiles)
limitingQuantiles = self.limitingQuantiles
if ByObjectives:
objectives = self.objectives
for obj in objectives:
print(objectives[obj]['name'])
criteriaList = [g for g in criteria if criteria[g]['objective']==obj]
if Sorted:
criteriaList.sort()
print('criteria | weights |', end=' ')
for x in quantiles:
print('\''+str(x)+'\' ', end=' ')
print('\n---------|-----------------------------------------')
for g in criteriaList:
print(' \''+str(g)+'\' | '+str(criteria[g]['weight'])+' | ', end=' ')
for i in range(nq):
formatString = '%% .%df ' % ndigits
if criteria[g]['preferenceDirection'] == 'max':
value = min(criteria[g]['scale'][1],max(criteria[g]['scale'][0],limitingQuantiles[g][i]))
else:
if criteria[g]['weight'] > Decimal('0'):
value = max(-criteria[g]['scale'][1],min(-criteria[g]['scale'][0],limitingQuantiles[g][i]))
else:
value = max(criteria[g]['scale'][0],min(criteria[g]['scale'][1],limitingQuantiles[g][i]))
print(formatString % (value), end=' ')
print()
else:
criteriaList = list(self.criteria)
if sorted:
criteriaList.sort()
print('criteria | weights |', end=' ')
for x in quantiles:
print('\''+str(x)+'\' ', end=' ')
print('\n---------|-----------------------------------------')
for g in criteriaList:
print(' \''+str(g)+'\' | '+str(self.criteria[g]['weight'])+' | ', end=' ')
for i in range(nq):
formatString = '%% .%df ' % ndigits
if criteria[g]['preferenceDirection'] == 'max':
value = min(criteria[g]['scale'][1],max(criteria[g]['scale'][0],limitingQuantiles[g][i]))
else:
if criteria[g]['weight'] > Decimal('0'):
value = max(-criteria[g]['scale'][1],min(-criteria[g]['scale'][0],limitingQuantiles[g][i]))
else:
value = max(criteria[g]['scale'][0],min(criteria[g]['scale'][1],-limitingQuantiles[g][i]))
print(formatString % (value), end=' ')
print()
def showCriterionStatistics(self,g,Debug=False):
"""
show statistics concerning the evaluation distributions
on each criteria.
"""
import math
criteria = self.criteria
qFreq = self.quantilesFrequencies
nc = len(qFreq)
frequencies = ['%.2f' % p for p in qFreq]
glimitingQuantiles = self.limitingQuantiles[g]
#actions = self.actions
#n = len(actions)
print('*-------- Performance Quantiles statistics -------*')
print('Instance name :', self.name)
print('Quantiles frequencies :', frequencies)
print('Summary for criterion : %s' % g)
print(' Criterion name : %s' % criteria[g]['name'])
print(' Comment : %s' % self.criteria[g]['comment'])
if criteria[g]['preferenceDirection'] == 'max':
print(' Performance range : [%.2f-%.2f]'\
% (self.criteria[g]['scale'][0],self.criteria[g]['scale'][1]) )
else:
print(' Performance range : [%.2f;%.2f]'\
% (-self.criteria[g]['scale'][1],-self.criteria[g]['scale'][0]) )
print(' Quantiles repartition :')