""" ########################################################## ### @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 on decision bins used." from pylab import * import csv from jmoo_problems import * from jmoo_algorithms import * from jmoo_stats_box import * from jmoo_properties import * import random, utility from time import * import os class histobin: def __init__(self, numBins, decision): self.numBins = numBins self.bins = [] spread = decision.up - decision.low step = spread/float(numBins) self.bins = [bin(i, decision.low + step*i, decision.low + step*(i+1)) for i in range(numBins)] def getTotal(self): total = 0 for bin in self.bins: total += bin.count return total def __repr__(self): s = "[" total = self.getTotal() if total == 0: return str([0 for bin in self.bins]) for bin in self.bins: s += str("%3.0f" % (100.0*bin.count/float(total))) + " " s += "]" return s def populate(self, data): for item in data: for b,bin in enumerate(self.bins): if b == 0: if item >= bin.low and item < bin.up: self.bins[b].add(item) break elif b == (len(self.bins)-1): if item > bin.low and item <= bin.up: self.bins[b].add(item) break else: if item > bin.low and item < bin.up: self.bins[b].add(item) break def sort(self, bywhat=None): if bywhat == "count": bywhat = lambda bin: bin.count else: bywhat = lambda bin: bin.index self.bins = sorted(self.bins, key=bywhat) class bin: def __init__(self, ind, low, up): self.index = ind self.low = low self.up = up self.count = 0 self.items = [] def add(self, item): self.count += 1 self.items.append(item) def joes_decision_reporter(problems, algorithms, tag=""): date_folder_prefix = strftime("%m-%d-%Y") if not os.path.isdir('reports/' + date_folder_prefix): os.makedirs('reports/' + date_folder_prefix) fignum = len([name for name in os.listdir('reports/' + date_folder_prefix)]) + 1 #optional tag name for the file fa = open('reports/' + date_folder_prefix + "/decision_bin_rules_report" + "_" + tag + str("%02d" % fignum) + ".txt", 'w') decision_data = [] objective_data = [] f, axarr = plt.subplots(max(2,len(problems)), max(2,len(algorithms))) f.set_size_inches(6.75, 10.25) for p,problem in enumerate(problems): decision_data.append([]) objective_data.append([]) for a,algorithm in enumerate(algorithms): finput = open(DATA_PREFIX + DECISION_BIN_TABLE + "_" + problem.name + "_" + algorithm.name + DATA_SUFFIX, 'rb') reader = csv.reader(finput, delimiter=',') decision_data[p].append([]) objective_data[p].append([]) for i,row in enumerate(reader): if i == 0: objective_data[p][a] = [[] for obj in problem.objectives] decision_data[p][a] = [[] for dec in problem.decisions] elements = row for d,dec in enumerate(problem.decisions): decision_data[p][a][d].append(float(elements[d])) d = len(problem.decisions) for o,obj in enumerate(problem.objectives): if not elements[d+o].strip() == "?": objective_data[p][a][o].append(float(elements[d+o])) # histogram recommendations = [] lows = [] ups = [] ranges = [] numAttributes = len(problem.decisions) numBins = 10 for c,col in enumerate(decision_data[p][a]): histobins = [histobin(numBins, problem.decisions[i]) for i in range(numAttributes)] histobins[c].populate(col) histo,bin = numpy.histogram(col, bins=10) bins = [] total = float(sum(histo)) s = "" for h in histo: bins.append( ((h/total)*100) ) s += str( ("%4.0f" % bins[-1] ) ) + "," print histobins[c], algorithm.name, ",", problem.name, ", ", problem.decisions[c].name maxbin = 0 biggest = 0 for ix,b in enumerate(bins): if b > biggest: biggest = b maxbin = ix lows.append(bin[maxbin]) ups.append(bin[maxbin+1]) recommendations.append( "[" + str( bin[maxbin]) + "~" + str(bin[maxbin+1]) + "]" ) ranges.append( [ bin[maxbin], bin[maxbin+1] ] ) #print s, algorithm.name, ",", problem.name, ", ", problem.decisions[c].name, ",", str(maxbin), ", ", bin[maxbin], ", ", bin[maxbin+1], ", [", bin[maxbin], "~", bin[maxbin+1], "]" """ for d,dec in enumerate(problem.decisions): z = algorithm.name + " on " + problem.name + " recommends the following range for " + dec.name + ":" + recommendations[d] print z fa.write(z + "\n") """ z = '{0: <16}'.format(problem.name) + "," + '{0: <16}'.format(algorithm.name) + "," + '{0: <16}'.format("DecisionNames, ") for d in problem.decisions: z += '{0: >8}'.format(d.name) + "," z += "\n" + '{0: <16}'.format(problem.name) + "," + '{0: <16}'.format(algorithm.name) + "," + '{0: <16}'.format("LowerBounds, ") for l in lows: z += str("%8.2f" % l) + "," z += "\n" + '{0: <16}'.format(problem.name) + "," + '{0: <16}'.format(algorithm.name) + "," + '{0: <16}'.format("UpperBounds, ") for u in ups: z += str("%8.2f" % u) + "," z += "\n" + '{0: <16}'.format(problem.name) + "," + '{0: <16}'.format(algorithm.name) + "," + '{0: <16}'.format("ObjectiveNames, ") for o in problem.objectives: z += '{0: >8}'.format(o.name) + "," #read baseline f2input = open("data/" + problem.name + str(MU) + "dataset.txt", 'rb') reader2 = csv.reader(f2input, delimiter=',') referencePoint = [] for i,row in enumerate(reader2): if i > MU: referencePoint.append(float(row[1])) trials = [] for repeat in range(20): novel_candidate = [ random.uniform(rng[0], rng[1]) for rng in ranges ] trials.append(problem.evaluate(novel_candidate)) validation_vector = utility.matrix_avg(trials) z += "\n" + '{0: <16}'.format(problem.name) + "," + '{0: <16}'.format(algorithm.name) + "," + '{0: <16}'.format("Validation, ") for vv in validation_vector: z += str("%8.2f" % vv) + "," # Split Columns into Lists fitnessColumns = [[fit[i] for fit in trials] for i,obj in enumerate(problem.objectives)] # Calculate Medians and Spreads fitnessMedians = [median(fitCol) for fitCol in fitnessColumns] fitnessSpreads = [spread(fitCol) for fitCol in fitnessColumns] # Calculate IBD & IBS norms = [[min(fitnessColumns[i]+[referencePoint[i]]), max(fitnessColumns[i]+[referencePoint[i]])] for i,obj in enumerate(problem.objectives)] lossInQualities = [loss_in_quality(problem, [referencePoint], fit, norms) for fit in trials] IBD = median(lossInQualities) IBS = spread(lossInQualities) z += str("%8.2f" % IBD) + "," + str("%8.2f" % IBS) z += "\n" + '{0: <16}'.format(problem.name) + "," + '{0: <16}'.format(",") + "," + '{0: <16}'.format("Baseline, ") for i,row in enumerate(referencePoint): z += str("%8.2f" % float(row)) + "," #print z fa.write(z + "\n") for c,col in enumerate(objective_data[p][a]): histo,bin = numpy.histogram(col, bins=10) total = float(sum(histo)) s = "" for h in histo: s += str( ("%4.0f" % ((h/total)*100)) ) + "," #print s, algorithm.name, ",", problem.name, ", ", problem.objectives[c].name