""" ########################################################## ### @Author Joe Krall ############################### ### @copyright see below ############################### This file is part of JMOO, Copyright Joe Krall, 2014. JMOO is free software: you can redistribute it and/or modify it under the terms of the GNU Lesser General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. JMOO is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more details. You should have received a copy of the GNU Lesser General Public License along with JMOO. If not, see . ### ############################### ########################################################## """ "Brief notes" "Report tool for keeping track of stats during MOEAs" import math import jmoo_algorithms from jmoo_individual import * from jmoo_properties import * from utility import * from deap.tools.support import ParetoFront class jmoo_stats: "A single stat box - a simple record" def __init__(stats, population, fitnesses, fitnessMedians, fitnessSpreads, numEval, gen, IBD, IBS, changes): stats.population = population stats.fitnesses = fitnesses stats.fitnessMedians = fitnessMedians stats.fitnessSpreads = fitnessSpreads stats.numEval = numEval stats.gen = gen stats.IBD = IBD stats.IBS = IBS stats.changes = changes class jmoo_stats_box: "Management of one stat box per generation" def __init__(statBox, problem, alg, foam=None): statBox.problem = problem statBox.numEval = 0 statBox.box = [] statBox.alg = alg statBox.foam = [{} for o in problem.objectives] statBox.bests = [100.0 for o in problem.objectives] statBox.bests_actuals = [0 for o in problem.objectives] statBox.lives = 3 def update(statBox, population, gen, numNewEvals, initial = False, printOption=True): "add a stat box - compute the statistics first" fa = open("data/results_"+statBox.problem.name+"_"+statBox.alg.name+".datatable", 'a') # Calculate percentage of violations violationsPercent = sum([ 1 for pop in population if statBox.problem.evalConstraints(pop.decisionValues)])/float(len(population)) # Update Number of Evaluations statBox.numEval += numNewEvals #front = population #for pop in population: # if not pop.valid: pop.evaluate() population = [pop for pop in population if pop.fitness.valid] #population = jmoo_algorithms.deap_format(statBox.problem, population) #front = ParetoFront() #front.update(population) """ fitnesses = [] population = [] for i,dIndividual in enumerate(front): cells = [] for j in range(len(dIndividual)): cells.append(dIndividual[j]) fit = [] for k in range(len(statBox.problem.objectives)): fit.append(dIndividual.fitness.values[k]) population.append( jmoo_individual(statBox.problem, cells, fit) ) """ # Evaluate Fitnesses #for individual in population: # if not individual.valid: individual.evaluate() fitnesses = [individual.fitness.fitness for individual in population if individual.valid] # Split Columns into Lists fitnessColumns = [[fit[i] for fit in fitnesses] for i,obj in enumerate(statBox.problem.objectives)] # Calculate Medians and Spreads fitnessMedians = [median(fitCol) for fitCol in fitnessColumns] fitnessSpreads = [spread(fitCol) for fitCol in fitnessColumns] # Initialize Reference Point on Initial Run if initial == True: #statBox.referencePoint = fitnessMedians #statBox.referencePoint = statBox.problem.referencePoint #statBox.referencePoint = statBox.problem.evaluate(population[0].decisionValues) statBox.referencePoint = [o.med for o in statBox.problem.objectives] print [(o.low, o.up) for o in statBox.problem.objectives] # Calculate IBD & IBS norms = [[min(fitnessColumns[i]+[statBox.referencePoint[i]]), max(fitnessColumns[i]+[statBox.referencePoint[i]])] for i,obj in enumerate(statBox.problem.objectives)] lossInQualities = [loss_in_quality(statBox.problem, [statBox.referencePoint], fit, norms) for fit in fitnesses] IBD = median(lossInQualities) IBS = spread(lossInQualities) if initial == True: IBD = 1.0 statBox.referenceIBD = 1.0 changes = [] # Print Option if printOption == True: outString = "" if initial: outString += str(statBox.numEval) + "," for med,spr,initmed,obj,o in zip(statBox.referencePoint, [0 for x in statBox.problem.objectives], statBox.referencePoint,statBox.problem.objectives,range(len(statBox.problem.objectives))): change = percentChange(med, initmed, obj.lismore, obj.low, obj.up) changes.append(float(change.strip("%"))) statBox.bests[o] = changes[-1] statBox.bests_actuals[o] = med outString += str("%8.4f" % med) + "," + change + "," + str("%8.4f" % spr) + "," if statBox.numEval in statBox.foam[o]: statBox.foam[o][statBox.numEval].append(change) else: statBox.foam[o][statBox.numEval] = [change] outString += str("%8.4f" % IBD) + "," + percentChange(statBox.referenceIBD, statBox.referenceIBD, True, 0, 1) + "," + str("%8.4f" % IBS) else: outString += str(statBox.numEval) + "," for med,spr,initmed,obj,o in zip(fitnessMedians, fitnessSpreads, statBox.referencePoint,statBox.problem.objectives,range(len(statBox.problem.objectives))): change = percentChange(med, initmed, obj.lismore, obj.low, obj.up) changes.append(float(change.strip("%"))) if changes[-1] < statBox.bests[o]: statBox.bests[o] = changes[-1] statBox.bests_actuals[o] = med outString += str("%8.4f" % med) + "," + change + "," + str("%8.4f" % spr) + "," if statBox.numEval in statBox.foam[o]: statBox.foam[o][statBox.numEval].append(change) else: statBox.foam[o][statBox.numEval] = [change] outString += str("%8.4f" % IBD) + "," + percentChange(IBD, statBox.referenceIBD, True, 0, 1) + "," + str("%8.4f" % IBS) print outString + ", violations: " + str("%4.1f" % violationsPercent) fa.write(outString + "\n") # Add Stat to the Stat Box trunk = [] for i,pop in enumerate(population): trunk.append(jmoo_individual(statBox.problem, pop.decisionValues, pop.fitness.fitness)) #if i < 5: print trunk[-1].decisionValues, statBox.problem.evalConstraints(trunk[-1].decisionValues) statBox.box.append(jmoo_stats(trunk, fitnesses, fitnessMedians, fitnessSpreads, statBox.numEval, gen, IBD, IBS, changes)) fa.close() ########### ### Utility Functions ########### def percentChange(new, old, lismore, low, up): return str("%1.1f" % changeFromOld(new, old, lismore, low, up)) + "%" def changeFromOld(new, old, lismore, low, up): if new < 0 or old < 0: ourlismore = not lismore new = abs(new) old = abs(old) else: ourlismore = lismore #if new == 0 or old == 0: return 0 if ourlismore else 110 new = normalize(new, low, up) old = normalize(old, low, up) if old == 0: x = 0 else: x = (new/float(old)) if x == 0: if ourlismore: return 0 else: return 1 else: return 100.0*x**(1 if ourlismore else -1) def median(list): return getPercentile(list, 50) def spread(list): return getPercentile(list, 75) - getPercentile(list, 25) def getPercentile(list, percentile): if len(list) == 0: return 0 #sort the list list.sort() k = (len(list) - 1) * (percentile/100.0) f = math.floor(k) c = math.ceil(k) if f == c: val = list[int(k)] else: d0 = list[int(f)] * (c-k) d1 = list[int(c)] * (k-f) val = d0+d1 return val def normalize(x, min, max): tmp = float((x - min)) / \ (max - min + 0.000001) if tmp > 1 : return 1 elif tmp < 0 : return 0 else : return tmp def loss_in_quality(problem, pop, fit1, norms): "Loss in Quality Indicator" weights = [-1 if o.lismore else +1 for o in problem.objectives] k = len(weights) # Calculate the loss in quality of removing fit1 from the population F = [] for X2 in pop: F.append(-k/(sum([-math.exp(-w*(normalize(p2,n[0],n[1]) - normalize(p1,n[0],n[1]))/k) for w,p1,p2,n in zip(weights,fit1, X2, norms)]))) F1 = sum(F) return F1