def validation(z):
#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]))
validation_vector = utility.matrix_avg(trials)
z += str("%10.2f" % 0)
for vv in validation_vector: z += str("%10.2f" % 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("%10.2f" % IBD) + "," + str("%10.2f" % IBS)
z += str("%10.2f" % 0)
print z
return IBD
def validation():
#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]))
z = ""
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)
print z
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)) ) + ","
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))) + ","
