def main():
util.line()
print("PEP300 - Metaheuristics Techniques for Combinatorial Optimization")
print("TSPLib")
util.line("-")
sourceFile = ''
resultFile = ''
try:
sourceFile = sys.argv[1]
except:
print("Usage: tsp.py <sourceFile>")
sys.exit(2)
name, dimension, distances = problem.readTSPLibFiles(sourceFile)
# print("Distances", distances)
# pcb442 -> cost: 221440 -> 1:n
# testSolution = [*range(1, dimension + 1)]
# print(testSolution)
# cost = problem.calculateCost(testSolution, distances)
# print(cost)
menu(name, dimension, distances)
def move():
global T
global cnt
head = snake[-1].copy()
head.move(aim)
if head in walls:
rectangle(-200, -195, 60, 20, 'white') f
timer()
ontimer(move, 100)
return
if head in pwalls:
if pwalls.get(head) != None:
pwalls[head] -= 1
if pwalls[head] == 0:
pwalls.pop(head)
walls.add(head)
if head in thorns:
again()
return
for body in snake:
for i in range(len(lasers)):
if cnt > 0 and lasers[i][0].x <= body.x <= lasers[i][1].x and lasers[i][0].y <= body.y <= lasers[i][1].y:
again()
return
if len(foods) == 0:
nextstage()
return
if head in teledict:
head = teledict[head]
if head in foods:
foods.remove(head)
else:
snake.pop(0)
snake.append(head)
clear()
for body in snake:
square(body.x, body.y, 9, 'black')
for telep in teledict:
square(telep.x, telep.y, 9, 'magenta')
square(teledict[telep].x, teledict[telep].y, 9, 'purple')
for food in foods:
square(food.x, food.y, 9, 'green')
for i in range(len(lasers)):
if cnt > 0 : line(lasers[i][0].x, lasers[i][0].y+4.5, lasers[i][1].x, lasers[i][1].y+4.5)
cnt += 1
if cnt == 10: cnt = -5
for wall in walls:
square(wall.x, wall.y, 9, 'gray')
for pwall in pwalls:
squarenum(pwall.x, pwall.y, 9, 'blue', pwalls[pwall])
for thorn in thorns:
triangle(thorn.x, thorn.y, 9, 'red')
timer()
update()
ontimer(move, 100)
def isvalid(x_new, x_near, constraints, obstacles):
l = line(x_new, x_near)
if isinstance(constraints, list):
isinside = any(contains(c, x_new) for c in constraints)
else:
isinside = contains(constraints, x_new)
return isinside and \
all(not obs.contains(l) for obs in obstacles)
def _extend_slope(cls, h, m, n_fit_points=10):
for i in range(m.shape[0]):
j = util.arg_first_not_nan(m[i])
popt, _ = so.curve_fit(util.line, h[i, j:j + n_fit_points],
m[i, j:j + n_fit_points])
m[i, 0:j] = util.line(h[i, 0:j], *popt)
return
def menuConstructiveMethods(dimension, distances, solution):
util.line()
print("Métodos construtivos:")
util.line("-")
print("1 - Construção aleatória")
print("2 - Construção gulosa - vizinho mais próximo")
print("3 - Construção parcialmente gulosa")
print("0 - Voltar")
choice = int(input("Opção: "))
util.line()
if choice == 0:
return solution
elif choice == 1:
solution = constructive.createRandom(dimension)
elif choice == 2:
solution = constructive.nearestNeighbour(dimension, distances)
elif choice == 3:
alpha = float(input("Informe um valor para alpha [0; 1]: "))
solution = constructive.semiGreedyNearNeighbour(
dimension, distances, alpha)
else:
print("Metodo ainda não implementado.")
print("Solução: ", solution)
return solution
def menu(name, dimension, distances):
solution = []
cost = sys.maxsize
while True:
util.line()
print("Defina a operação: ")
util.line()
print("1 - Construir solução")
print("2 - Refinar solução")
print("3 - Aplicar meta-heurística")
print("4 - Calcular custo")
print("0 - Sair")
util.line("-")
choice = int(input("Opção: "))
util.line()
if choice == 0:
print("The end.")
sys.exit(0)
elif choice == 1:
solution = menuConstructiveMethods(dimension, distances, solution)
elif choice == 2:
solution, cost = menuRefiningHeuristics(dimension, distances,
solution)
elif choice == 3:
solution, cost = menuMetaHeuristics(dimension, distances)
elif choice == 4:
print("Solução: ", solution)
cost = problem.calculateCost(solution, distances)
util.line("-")
print("Custo: ", cost)
else:
print("Metodo ainda não implementado.")
def plot_nnoutput(plotObject, kwargs):
savename = kwargs['cname'] + '.pdf'
cut = kwargs['cut']
from ROOT import TH1F, TCanvas, gROOT, gStyle, kTRUE
gROOT.SetBatch(kTRUE)
from ROOT import kCyan, kRed, kGreen, kBlue, kBlack, kMagenta, gPad
from RingerCore.util import Roc_to_histogram
from util import setBox, line
gStyle.SetOptStat(1111)
curve = plotObject[0][kwargs['rocname']]
signal, background = Roc_to_histogram(curve, kwargs['nsignal'],
kwargs['nbackground'])
hist_signal = TH1F('Signal', 'dist output;output;count', 100, -1, 1)
hist_background = TH1F('Background', 'dist output;output;count', 100, -1,
1)
for out in signal:
hist_signal.Fill(out)
for out in background:
hist_background.Fill(out)
canvas = TCanvas('canvas', 'canvas', 800, 600)
hist_signal.SetStats(1)
hist_background.SetStats(1)
hist_signal.SetLineColor(kBlack)
hist_background.SetLineColor(kRed)
#hist_signal.GetXaxis().SetTitleSize(0.05);
#hist_signal.GetYaxis().SetTitleSize(0.05);
#hist_background.GetXaxis().SetTitleSize(0.05);
#hist_background.GetYaxis().SetTitleSize(0.05);
if hist_signal.GetEntries() > hist_background.GetEntries():
hist_signal.Draw()
hist_background.Draw('sames')
else:
hist_background.Draw()
hist_signal.Draw('sames')
canvas.SetLogy()
setBox(gPad, [hist_signal, hist_background])
l = line(cut, 0, cut, 1000, kBlue, 2, 2)
l.Draw()
canvas.SaveAs(savename)
return savename
def plot_nnoutput(plotObject, kwargs):
savename = kwargs["cname"] + ".pdf"
cut = kwargs["cut"]
from ROOT import TH1F, TCanvas, gROOT, gStyle, kTRUE
gROOT.SetBatch(kTRUE)
from ROOT import kCyan, kRed, kGreen, kBlue, kBlack, kMagenta, gPad
from RingerCore.util import Roc_to_histogram
from util import setBox, line
gStyle.SetOptStat(1111)
curve = plotObject[0][kwargs["rocname"]]
signal, background = Roc_to_histogram(curve, kwargs["nsignal"], kwargs["nbackground"])
hist_signal = TH1F("Signal", "dist output;output;count", 100, -1, 1)
hist_background = TH1F("Background", "dist output;output;count", 100, -1, 1)
for out in signal:
hist_signal.Fill(out)
for out in background:
hist_background.Fill(out)
canvas = TCanvas("canvas", "canvas", 800, 600)
hist_signal.SetStats(1)
hist_background.SetStats(1)
hist_signal.SetLineColor(kBlack)
hist_background.SetLineColor(kRed)
# hist_signal.GetXaxis().SetTitleSize(0.05);
# hist_signal.GetYaxis().SetTitleSize(0.05);
# hist_background.GetXaxis().SetTitleSize(0.05);
# hist_background.GetYaxis().SetTitleSize(0.05);
if hist_signal.GetEntries() > hist_background.GetEntries():
hist_signal.Draw()
hist_background.Draw("sames")
else:
hist_background.Draw()
hist_signal.Draw("sames")
canvas.SetLogy()
setBox(gPad, [hist_signal, hist_background])
l = line(cut, 0, cut, 1000, kBlue, 2, 2)
l.Draw()
canvas.SaveAs(savename)
return savename
def menuMetaHeuristics(dimension, distances):
util.line()
print("Meta-heurísticas:")
util.line("-")
print("1 - Multi-start")
print("2 - GRASP")
print("3 - Simulated Annealing")
print("4 - Busca Tabu")
print("5 - VNS")
print("6 - Iterated Local Search")
print("7 - Algoritmos genéticos")
print("8 - Colônia de Formigas")
print("9 - Scatter Search")
print("0 - Voltar")
choice = int(input("Opção: "))
util.line()
solution = []
cost = sys.maxsize
if choice == 1: # Multi-start
iterMax = int(
input(
"Defina o número máximo de iterações sem melhora (critério de parada): "
))
solution, cost = multistart.multiStart(dimension, distances, iterMax)
print("Solução: ", solution)
print("Custo: ", cost)
elif choice == 2: # GRASP
graspMax = int(
input("Defina o número máximo de iterações (GRASPmax): "))
alpha = float(input("Informe um valor para alpha [0; 1]: "))
solution, cost = grasp.grasp(dimension, distances, graspMax, alpha)
print("Solução: ", solution)
print("Custo: ", cost)
else:
print("Metodo ainda não implementado.")
return solution, cost
def view (id, fileName=''):
log.debug("Viewing Gist with ID: {0} and fileName: '{1}'".format(id,fileName))
if id[0] in _cmds['#']:
id = _get_id_for_index(id)
if id:
gist = _get_gist(id)
# display line delims only if more than one file exists. facilitates piping file content
noDelim = len(gist['files']) == 1 or fileName != ''
for (file, data) in gist['files'].items():
content = data['content']
if not noDelim:
util.line()
print 'Gist: {0} File: {1}'.format(id, file)
util.line()
if fileName != '':
if fileName.strip().lower() == file.strip().lower():
print content
else:
print content
if not noDelim:
util.line()
def menuRefiningHeuristics(dimension, distances, solution):
util.line()
print("Heurísticas de refinamento:")
util.line("-")
print("1 - First improvement descent")
print("2 - Best improvement descent")
print("3 - Random descent")
print("0 - Voltar")
choice = int(input("Opção: "))
util.line()
if choice == 1:
solution, cost = refining.firstImprovementDescent(
dimension, distances, solution)
print("Solução: ", solution)
print("Custo: ", cost)
elif choice == 2:
solution, cost = refining.bestImprovementDescent(
dimension, distances, solution)
print("Solução: ", solution)
print("Custo: ", cost)
elif choice == 3:
iterMax = int(
input("Defina o número máximo de iterações sem melhora: "))
solution, cost = refining.randomDescent(dimension, distances, solution,
iterMax)
print("Solução: ", solution)
print("Custo: ", cost)
else:
print("Metodo ainda não implementado.")
return solution, cost
def view(id, fileName=''):
log.debug("Viewing Gist with ID: {0} and fileName: '{1}'".format(
id, fileName))
if id[0] in _cmds['#']:
id = _get_id_for_index(id)
if id:
gist = _get_gist(id)
# display line delims only if more than one file exists. facilitates piping file content
noDelim = len(gist['files']) == 1 or fileName != ''
for (file, data) in list(gist['files'].items()):
content = data['content']
if not noDelim:
util.line()
print('Gist: {0} File: {1}'.format(id, file))
util.line()
if fileName != '':
if fileName.strip().lower() == file.strip().lower():
print(content)
else:
print(content)
if not noDelim:
util.line()
label = kp[-1]
xs = [int(float(i)) for i in kp[2:-1:2]]
ys = [int(float(i)) for i in kp[3:-1:2]]
# Show the keypoints
if SHOW:
plt.scatter(xs, ys)
# Sort the keypoints to head, tail, and wings of plane
pH = (xs[0], ys[0])
pT = (xs[1], ys[1])
pW1 = (xs[2], ys[2])
pW2 = (xs[3], ys[3])
# Find the major/longest axis for the plane and the points of the major and minor axis
LMaj = u.line(pH, pT)
pMaj = (pH, pT)
pMin = (pW1, pW2)
# if u.longer((pW1, pW2), (pH, pT)):
# LMaj = u.line(pW1, pW2)
# pMaj = (pW1, pW2)
# pMin = (pH, pT)
# Find the equations of the long and short edges of the bounding box
le = (u.linePointSlope(LMaj, pMin[0]),
u.linePointSlope(LMaj, pMin[1]))
se = (u.linePointSlopeInverted(LMaj, pMaj[0]),
u.linePointSlopeInverted(LMaj, pMaj[1]))
# Find the vertices of the bounding box from the lines
v = (u.intersection(le[0], se[0]),
def plot_rocs(plotObjects, kwargs):
from ROOT import kCyan, kRed, kGreen, kBlue, kBlack, kMagenta, kGray, kWhite, kYellow
Colors = [kBlue, kRed, kMagenta, kBlack, kCyan, kGreen]
from RingerCore import StdPair as std_pair
from util import line, minmax
dset = kwargs['set']
ref = kwargs['reference']
refVal = kwargs['refVal']
eps = kwargs['eps']
savename = kwargs['cname'] + '.pdf'
#Some protection
if not ('operation' in dset or 'tst' in dset):
raise ValueError('Option set must be: tst (test) or val (validation)')
if not ('SP' in ref or 'Pd' in ref or 'Pf' in ref):
raise ValueError('Option reference must be: SP, Pd or Pf')
#Create dict to hold all list plots
curves = dict()
#list of dicts to dict of lists
for name in plotObjects.keys():
curves[name] = plotObjects.tolist(name)
paintIdx = kwargs['paintListIdx'] # [best, worst]
paintCurves = [
std_pair(plotObjects.index_correction(paintIdx[0]), kBlack),
std_pair(plotObjects.index_correction(paintIdx[1]), kRed)
]
curves['roc'] = curves['roc_' + dset]
#Start to build all ROOT objects
from ROOT import TCanvas, gROOT, kTRUE
gROOT.SetBatch(kTRUE)
canvas = TCanvas('canvas', 'canvas', 1600, 1300)
x_limits = [0.00, 0.40]
y_limits = [0.6, 1.03]
#create dummy graph
dummy = curves['roc'][0]
dummy.SetTitle('Receive Operation Curve')
dummy.GetXaxis().SetTitle('False Alarm')
dummy.GetYaxis().SetTitle('Detection')
dummy.GetHistogram().SetAxisRange(y_limits[0], y_limits[1], 'Y')
dummy.GetHistogram().SetAxisRange(x_limits[0], x_limits[1], 'X')
dummy.Draw('AL')
corredor = None
target = None
from ROOT import TBox
if ref == 'Pf':
corredor = TBox(refVal - eps, y_limits[0], refVal + eps, y_limits[1])
target = line(refVal, y_limits[0], refVal, y_limits[1], kBlack, 2, 1,
'')
elif ref == 'Pd':
corredor = TBox(x_limits[0], refVal - eps, x_limits[1], refVal + eps)
target = line(x_limits[0], refVal, x_limits[1], refVal, kBlack, 2, 1,
'')
if ref != 'SP':
corredor.SetFillColor(kYellow - 9)
corredor.Draw('same')
target.Draw('same')
canvas.Modified()
canvas.Update()
#Plot curves
for c in curves['roc']:
c.SetLineColor(kGray + 1)
#c.SetMarkerStyle(7)
#c.SetMarkerColor(kBlue)
c.SetLineWidth(1)
c.SetLineStyle(3)
#c.Draw('PLsame')
c.Draw('same')
marker = list()
#Paint a specifical curve
for pair in paintCurves:
curves['roc'][pair.first].SetLineWidth(1)
curves['roc'][pair.first].SetLineStyle(1)
#curves['roc'][pair.first].SetMarkerStyle(7)
#curves['roc'][pair.first].SetMarkerColor(kBlue)
curves['roc'][pair.first].SetLineColor(pair.second)
#curves['roc'][pair.first].Draw('PLsame')
curves['roc'][pair.first].Draw('same')
if ref == 'SP':
faVec = curves['roc'][pair.first].GetX()
detVec = curves['roc'][pair.first].GetY()
from RingerCore import calcSP
spVec = [
calcSP(detVec[i], 1 - faVec[i])
for i in range(curves['roc'][pair.first].GetN())
]
imax = spVec.index(max(spVec))
from ROOT import TMarker
marker.append(TMarker(faVec[imax], detVec[imax], 4))
marker[-1].SetMarkerColor(pair.second)
marker[-1].Draw('same')
#Update Canvas
canvas.Modified()
canvas.Update()
canvas.SaveAs(savename)
del canvas
return savename
def plot_4c(plotObjects, kwargs):
"""
kwargs is a dict with all kwargsion needed to config the figure and the
curves. The kwargsions will be:
reference: Pd, SP or Pf
operation: True or False
set: tst or val
plot 4 curves
"""
from ROOT import kCyan, kRed, kGreen, kBlue, kBlack, kMagenta, kGray
Colors = [kBlue, kRed, kMagenta, kBlack, kCyan, kGreen]
from RingerCore import StdPair as std_pair
from util import line
ref = kwargs['reference']
refVal = kwargs['refVal']
dset = kwargs['set']
isOperation = kwargs['operation']
detailed = True if plotObjects.size() == 1 else False
percent = 0.03 #(default for now)
savename = kwargs['cname'] + '.pdf'
lines = []
#Some protection
if not ('val' in dset or 'tst' in dset):
raise ValueError('Option set must be: tst (test) or val (validation)')
if not ('SP' in ref or 'Pd' in ref or 'Pf' in ref):
raise ValueError('Option reference must be: SP, Pd or Pf')
ylabel = {
'mse': 'MSE (' + dset + ')',
'sp': 'SP (' + dset + ')',
'det': 'Det (' + dset + ')',
'fa': 'FA (' + dset + ')'
}
#Create dict to hold all list plots
curves = dict()
#list of dicts to dict of lists
for name in plotObjects.keys():
curves[name] = plotObjects.tolist(name)
#Adapt reference into the validation set
#mse_trn, mse_val, mse_tst
if ref == 'Pd':
curves['sp_val'] = curves['det_point_sp_val']
curves['det_val'] = curves['det_point_det_val'] # det_fitted
curves['fa_val'] = curves['det_point_fa_val']
curves['sp_tst'] = curves['det_point_sp_tst']
curves['det_tst'] = curves['det_point_det_tst']
curves['fa_tst'] = curves['det_point_fa_tst']
elif ref == 'Pf':
curves['sp_val'] = curves['fa_point_sp_val']
curves['det_val'] = curves['fa_point_det_val']
curves['fa_val'] = curves['fa_point_fa_val'] # fa_fitted
curves['sp_tst'] = curves['fa_point_sp_tst']
curves['det_tst'] = curves['fa_point_det_tst']
curves['fa_tst'] = curves['fa_point_fa_tst']
else: # ref == 'SP'
curves['sp_val'] = curves['bestsp_point_sp_val'] # best SP curve
curves['det_val'] = curves['bestsp_point_det_val']
curves['fa_val'] = curves['bestsp_point_fa_val']
curves['sp_tst'] = curves['bestsp_point_sp_tst']
curves['det_tst'] = curves['bestsp_point_det_tst']
curves['fa_tst'] = curves['bestsp_point_fa_tst']
from util import minmax
#check if the test set is zeros
hasTst = True if curves['mse_tst'][0].GetMean(2) > 1e-10 else False
if dset == 'tst' and not hasTst:
print 'We dont have test set into plotObjects, abort plot!'
return False
#If only one plot per key, enabled analysis using all sets
if detailed:
#train, validation and test
paint_curves = [std_pair(i, Colors[i]) for i in range(3)]
curves['mse'] = [curves['mse_trn'][0], curves['mse_val'][0]]
curves['sp'] = [curves['sp_val'][0]]
curves['det'] = [curves['det_val'][0]]
curves['fa'] = [curves['fa_val'][0]]
if hasTst:
for key in ['mse', 'sp', 'det', 'fa']:
curves[key].append(curves[key + '_tst'][0])
ylabel = {'mse': 'MSE', 'sp': 'SP', 'det': 'Det', 'fa': 'FA'}
else: #Do analysis for each set type
paintIdx = kwargs['paintListIdx'] # [best, worst]
paint_curves = [
std_pair(plotObjects.index_correction(paintIdx[0]), kBlack),
std_pair(plotObjects.index_correction(paintIdx[1]), kRed)
]
curves['mse'] = curves['mse_' + dset]
curves['sp'] = curves['sp_' + dset]
curves['det'] = curves['det_' + dset]
curves['fa'] = curves['fa_' + dset]
#Adapt the legend and percent vec
pmask = [1, 1, 1, 1]
if ref == 'Pd':
ylabel['det'] = ylabel['det'] + ' [Reference]'
ylabel['fa'] = ylabel['fa'] + ' [benchmark]'
pmask = [1, 1, 0, 1]
elif ref == 'Pf':
ylabel['det'] = ylabel['det'] + ' [benchmark]'
ylabel['fa'] = ylabel['fa'] + ' [Reference]'
pmask = [1, 1, 1, 0]
else:
ylabel['sp'] = ylabel['sp'] + ' [benchmark]'
#Build lines
lines = {'mse': [], 'sp': [], 'det': [], 'fa': [], 'ref': None}
if detailed: # Hard code setting lines
y = dict()
x = dict()
for idx, key in enumerate(['mse', 'sp', 'det', 'fa']):
y[key] = minmax(curves[key], 8, pmask[idx] * percent)
x[key] = curves[key + '_stop'][0]
#Colors = [kBlue, kRed, kMagenta, kBlack, kCyan, kGreen]
lines['mse'].append(
line(x['mse'], y['mse'][0], x['mse'], y['mse'][1], Colors[3], 1,
2))
lines['sp'].append(
line(x['sp'], y['sp'][0], x['sp'], y['sp'][1], Colors[3], 1, 2))
if ref == 'Pd':
lines['det'].append(
line(x['det'], y['det'][0], x['det'], y['det'][1], Colors[2],
1, 2))
lines['fa'].append(
line(x['det'], y['fa'][0], x['det'], y['fa'][1], Colors[2], 2,
2))
if ref == 'Pf':
lines['det'].append(
line(x['fa'], y['det'][0], x['fa'], y['det'][1], Colors[2], 2,
2))
lines['fa'].append(
line(x['fa'], y['fa'][0], x['fa'], y['fa'][1], Colors[2], 1,
2))
#Start to build all ROOT objects
from ROOT import TCanvas, gROOT, kTRUE
gROOT.SetBatch(kTRUE)
canvas = TCanvas('canvas', 'canvas', 1600, 1300)
canvas.Divide(1, 4)
def __plot_curves(tpad, curves, y_limits, **kw):
from ROOT import kCyan, kRed, kGreen, kBlue, kBlack, kMagenta, kGray
title = kw.pop('title', '')
xlabel = kw.pop('xlabel', '')
ylabel = kw.pop('ylabel', '')
paintCurves = kw.pop('paintCurves', None)
colorCurves = kw.pop('colorCurves', kGray)
lines = kw.pop('lines', [])
#create dummy graph
x_max = 0
dummy = None
for i in range(len(curves)):
curves[i].SetLineColor(colorCurves)
x = curves[i].GetXaxis().GetXmax()
if x > x_max:
x_max = x
dummy = curves[i]
dummy.SetTitle(title)
dummy.GetXaxis().SetTitle(xlabel)
dummy.GetYaxis().SetTitle(ylabel)
dummy.GetHistogram().SetAxisRange(y_limits[0], y_limits[1], 'Y')
dummy.Draw('AL')
#Plot curves
for c in curves:
c.SetLineWidth(1)
c.SetLineStyle(3)
c.Draw('same')
#Paint a specifical curve
if paintCurves:
if len(paintCurves) > len(curves):
for idx, c in enumerate(curves):
c.SetLineWidth(1)
c.SetLineColor(paintCurves[idx].second)
c.SetLineStyle(1)
c.Draw('same')
else:
for pair in paintCurves:
curves[pair.first].SetLineWidth(1)
curves[pair.first].SetLineStyle(1)
curves[pair.first].SetLineColor(pair.second)
curves[pair.first].Draw('same')
#Plot lines
for l in lines:
l.Draw()
#Update TPad
tpad.Modified()
tpad.Update()
return x_max
#__plot_curves end
xlimits = list()
for idx, key in enumerate(['mse', 'sp', 'det', 'fa']):
#There are more plots
x_max = __plot_curves(canvas.cd(idx + 1),
curves[key],
minmax(curves[key], 8, pmask[idx] * percent),
xlabel='Epoch',
ylabel=ylabel[key],
paintCurves=paint_curves,
colorCurves=kGray + 1,
lines=lines[key])
xlimits.append(x_max)
#Loop over plots
#Check if there is any label
if 'label' in kwargs.keys():
tpad = canvas.cd(1)
from TuningStyle import Label
Label(0.6, 0.7, kwargs['label'], 1, 0.15)
tpad.Modified()
tpad.Update()
# Reference base line
if ref == 'Pd':
tpad = canvas.cd(3)
lines['ref'] = line(0.0, refVal, xlimits[2], refVal, kGreen, 2, 1)
lines['ref'].Draw()
tpad.Modified()
tpad.Update()
if ref == 'Pf':
tpad = canvas.cd(4)
lines['ref'] = line(0.0, refVal, xlimits[3], refVal, kGreen, 2, 1)
lines['ref'].Draw()
tpad.Modified()
tpad.Update()
canvas.Modified()
canvas.Update()
canvas.SaveAs(savename)
del canvas
return savename
def test_misc(self):
self.assertEqual(util.hr(width=10, char='#'), "##########")
self.assertEqual(util.line(width=11, char='@'), "@@@@@@@@@@@")
setup(600, 600, 370, 0)
hideturtle()
tracer(False)
listen()
#stage setting
snake = [vector(20, 20)]
walls = {vector(100, 0), vector(-100, 0)}
pwalls = {vector(150, 0): 5}
thorns = {vector(10, 10)}
lasers = [[vector(-100, 0), vector(100, 0)]]
teledict = {vector(0, 100): vector(0, -100)}
foods = {vector(0, 0), vector(50, 50)}
for body in snake:
square(body.x, body.y, 9, 'black')
for telep in teledict:
square(telep.x, telep.y, 9, 'magenta')
square(teledict[telep].x, teledict[telep].y, 9, 'purple')
for food in foods:
square(food.x, food.y, 9, 'green')
for i in range(len(lasers)):
line(lasers[i][0].x, lasers[i][0].y + 4.5, lasers[i][1].x,
lasers[i][1].y + 4.5)
for wall in walls:
square(wall.x, wall.y, 9, 'gray')
for pwall in pwalls:
squarenum(pwall.x, pwall.y, 9, 'blue', pwalls[pwall])
for thorn in thorns:
triangle(thorn.x, thorn.y, 9, 'red')
done()
def plot_4c(plotObjects, kwargs):
"""
kwargs is a dict with all kwargsion needed to config the figure and the
curves. The kwargsions will be:
reference: Pd, SP or Pf
operation: True or False
set: tst or val
plot 4 curves
"""
from ROOT import kCyan, kRed, kGreen, kBlue, kBlack, kMagenta, kGray
Colors = [kBlue, kRed, kMagenta, kBlack, kCyan, kGreen]
from RingerCore import StdPair as std_pair
from util import line
ref = kwargs["reference"]
refVal = kwargs["refVal"]
dset = kwargs["set"]
isOperation = kwargs["operation"]
detailed = True if plotObjects.size() == 1 else False
percent = 0.03 # (default for now)
savename = kwargs["cname"] + ".pdf"
lines = []
# Some protection
if not ("val" in dset or "tst" in dset):
raise ValueError("Option set must be: tst (test) or val (validation)")
if not ("SP" in ref or "Pd" in ref or "Pf" in ref):
raise ValueError("Option reference must be: SP, Pd or Pf")
ylabel = {
"mse": "MSE (" + dset + ")",
"sp": "SP (" + dset + ")",
"det": "Det (" + dset + ")",
"fa": "FA (" + dset + ")",
}
# Create dict to hold all list plots
curves = dict()
# list of dicts to dict of lists
for name in plotObjects.keys():
curves[name] = plotObjects.tolist(name)
# Adapt reference into the validation set
# mse_trn, mse_val, mse_tst
if ref == "Pd":
curves["sp_val"] = curves["det_point_sp_val"]
curves["det_val"] = curves["det_point_det_val"] # det_fitted
curves["fa_val"] = curves["det_point_fa_val"]
curves["sp_tst"] = curves["det_point_sp_tst"]
curves["det_tst"] = curves["det_point_det_tst"]
curves["fa_tst"] = curves["det_point_fa_tst"]
elif ref == "Pf":
curves["sp_val"] = curves["fa_point_sp_val"]
curves["det_val"] = curves["fa_point_det_val"]
curves["fa_val"] = curves["fa_point_fa_val"] # fa_fitted
curves["sp_tst"] = curves["fa_point_sp_tst"]
curves["det_tst"] = curves["fa_point_det_tst"]
curves["fa_tst"] = curves["fa_point_fa_tst"]
else: # ref == 'SP'
curves["sp_val"] = curves["bestsp_point_sp_val"] # best SP curve
curves["det_val"] = curves["bestsp_point_det_val"]
curves["fa_val"] = curves["bestsp_point_fa_val"]
curves["sp_tst"] = curves["bestsp_point_sp_tst"]
curves["det_tst"] = curves["bestsp_point_det_tst"]
curves["fa_tst"] = curves["bestsp_point_fa_tst"]
from util import minmax
# check if the test set is zeros
hasTst = True if curves["mse_tst"][0].GetMean(2) > 1e-10 else False
if dset == "tst" and not hasTst:
print "We dont have test set into plotObjects, abort plot!"
return False
# If only one plot per key, enabled analysis using all sets
if detailed:
# train, validation and test
paint_curves = [std_pair(i, Colors[i]) for i in range(3)]
curves["mse"] = [curves["mse_trn"][0], curves["mse_val"][0]]
curves["sp"] = [curves["sp_val"][0]]
curves["det"] = [curves["det_val"][0]]
curves["fa"] = [curves["fa_val"][0]]
if hasTst:
for key in ["mse", "sp", "det", "fa"]:
curves[key].append(curves[key + "_tst"][0])
ylabel = {"mse": "MSE", "sp": "SP", "det": "Det", "fa": "FA"}
else: # Do analysis for each set type
paintIdx = kwargs["paintListIdx"] # [best, worst]
paint_curves = [
std_pair(plotObjects.index_correction(paintIdx[0]), kBlack),
std_pair(plotObjects.index_correction(paintIdx[1]), kRed),
]
curves["mse"] = curves["mse_" + dset]
curves["sp"] = curves["sp_" + dset]
curves["det"] = curves["det_" + dset]
curves["fa"] = curves["fa_" + dset]
# Adapt the legend and percent vec
pmask = [1, 1, 1, 1]
if ref == "Pd":
ylabel["det"] = ylabel["det"] + " [Reference]"
ylabel["fa"] = ylabel["fa"] + " [benchmark]"
pmask = [1, 1, 0, 1]
elif ref == "Pf":
ylabel["det"] = ylabel["det"] + " [benchmark]"
ylabel["fa"] = ylabel["fa"] + " [Reference]"
pmask = [1, 1, 1, 0]
else:
ylabel["sp"] = ylabel["sp"] + " [benchmark]"
# Build lines
lines = {"mse": [], "sp": [], "det": [], "fa": [], "ref": None}
if detailed: # Hard code setting lines
y = dict()
x = dict()
for idx, key in enumerate(["mse", "sp", "det", "fa"]):
y[key] = minmax(curves[key], 8, pmask[idx] * percent)
x[key] = curves[key + "_stop"][0]
# Colors = [kBlue, kRed, kMagenta, kBlack, kCyan, kGreen]
lines["mse"].append(line(x["mse"], y["mse"][0], x["mse"], y["mse"][1], Colors[3], 1, 2))
lines["sp"].append(line(x["sp"], y["sp"][0], x["sp"], y["sp"][1], Colors[3], 1, 2))
if ref == "Pd":
lines["det"].append(line(x["det"], y["det"][0], x["det"], y["det"][1], Colors[2], 1, 2))
lines["fa"].append(line(x["det"], y["fa"][0], x["det"], y["fa"][1], Colors[2], 2, 2))
if ref == "Pf":
lines["det"].append(line(x["fa"], y["det"][0], x["fa"], y["det"][1], Colors[2], 2, 2))
lines["fa"].append(line(x["fa"], y["fa"][0], x["fa"], y["fa"][1], Colors[2], 1, 2))
# Start to build all ROOT objects
from ROOT import TCanvas, gROOT, kTRUE
gROOT.SetBatch(kTRUE)
canvas = TCanvas("canvas", "canvas", 1600, 1300)
canvas.Divide(1, 4)
def __plot_curves(tpad, curves, y_limits, **kw):
from ROOT import kCyan, kRed, kGreen, kBlue, kBlack, kMagenta, kGray
title = kw.pop("title", "")
xlabel = kw.pop("xlabel", "")
ylabel = kw.pop("ylabel", "")
paintCurves = kw.pop("paintCurves", None)
colorCurves = kw.pop("colorCurves", kGray)
lines = kw.pop("lines", [])
# create dummy graph
x_max = 0
dummy = None
for i in range(len(curves)):
curves[i].SetLineColor(colorCurves)
x = curves[i].GetXaxis().GetXmax()
if x > x_max:
x_max = x
dummy = curves[i]
dummy.SetTitle(title)
dummy.GetXaxis().SetTitle(xlabel)
dummy.GetYaxis().SetTitle(ylabel)
dummy.GetHistogram().SetAxisRange(y_limits[0], y_limits[1], "Y")
dummy.Draw("AL")
# Plot curves
for c in curves:
c.SetLineWidth(1)
c.SetLineStyle(3)
c.Draw("same")
# Paint a specifical curve
if paintCurves:
if len(paintCurves) > len(curves):
for idx, c in enumerate(curves):
c.SetLineWidth(1)
c.SetLineColor(paintCurves[idx].second)
c.SetLineStyle(1)
c.Draw("same")
else:
for pair in paintCurves:
curves[pair.first].SetLineWidth(1)
curves[pair.first].SetLineStyle(1)
curves[pair.first].SetLineColor(pair.second)
curves[pair.first].Draw("same")
# Plot lines
for l in lines:
l.Draw()
# Update TPad
tpad.Modified()
tpad.Update()
return x_max
# __plot_curves end
xlimits = list()
for idx, key in enumerate(["mse", "sp", "det", "fa"]):
# There are more plots
x_max = __plot_curves(
canvas.cd(idx + 1),
curves[key],
minmax(curves[key], 8, pmask[idx] * percent),
xlabel="Epoch",
ylabel=ylabel[key],
paintCurves=paint_curves,
colorCurves=kGray + 1,
lines=lines[key],
)
xlimits.append(x_max)
# Loop over plots
# Check if there is any label
if "label" in kwargs.keys():
tpad = canvas.cd(1)
from TuningStyle import Label
Label(0.6, 0.7, kwargs["label"], 1, 0.15)
tpad.Modified()
tpad.Update()
# Reference base line
if ref == "Pd":
tpad = canvas.cd(3)
lines["ref"] = line(0.0, refVal, xlimits[2], refVal, kGreen, 2, 1)
lines["ref"].Draw()
tpad.Modified()
tpad.Update()
if ref == "Pf":
tpad = canvas.cd(4)
lines["ref"] = line(0.0, refVal, xlimits[3], refVal, kGreen, 2, 1)
lines["ref"].Draw()
tpad.Modified()
tpad.Update()
canvas.Modified()
canvas.Update()
canvas.SaveAs(savename)
del canvas
return savename
def plot_rocs(plotObjects, kwargs):
from ROOT import kCyan, kRed, kGreen, kBlue, kBlack, kMagenta, kGray, kWhite, kYellow
Colors = [kBlue, kRed, kMagenta, kBlack, kCyan, kGreen]
from RingerCore import StdPair as std_pair
from util import line, minmax
dset = kwargs["set"]
ref = kwargs["reference"]
refVal = kwargs["refVal"]
eps = kwargs["eps"]
savename = kwargs["cname"] + ".pdf"
# Some protection
if not ("op" in dset or "tst" in dset):
raise ValueError("Option set must be: tst (test) or val (validation)")
if not ("SP" in ref or "Pd" in ref or "Pf" in ref):
raise ValueError("Option reference must be: SP, Pd or Pf")
# Create dict to hold all list plots
curves = dict()
# list of dicts to dict of lists
for name in plotObjects.keys():
curves[name] = plotObjects.tolist(name)
paintIdx = kwargs["paintListIdx"] # [best, worst]
paintCurves = [
std_pair(plotObjects.index_correction(paintIdx[0]), kBlack),
std_pair(plotObjects.index_correction(paintIdx[1]), kRed),
]
curves["roc"] = curves["roc_" + dset]
# Start to build all ROOT objects
from ROOT import TCanvas, gROOT, kTRUE
gROOT.SetBatch(kTRUE)
canvas = TCanvas("canvas", "canvas", 1600, 1300)
x_limits = [0.00, 0.40]
y_limits = [0.6, 1.03]
# create dummy graph
dummy = curves["roc"][0]
dummy.SetTitle("Receive Operation Curve")
dummy.GetXaxis().SetTitle("False Alarm")
dummy.GetYaxis().SetTitle("Detection")
dummy.GetHistogram().SetAxisRange(y_limits[0], y_limits[1], "Y")
dummy.GetHistogram().SetAxisRange(x_limits[0], x_limits[1], "X")
dummy.Draw("AL")
corredor = None
target = None
from ROOT import TBox
if ref == "Pf":
corredor = TBox(refVal - eps, y_limits[0], refVal + eps, y_limits[1])
target = line(refVal, y_limits[0], refVal, y_limits[1], kBlack, 2, 1, "")
elif ref == "Pd":
corredor = TBox(x_limits[0], refVal - eps, x_limits[1], refVal + eps)
target = line(x_limits[0], refVal, x_limits[1], refVal, kBlack, 2, 1, "")
if ref != "SP":
corredor.SetFillColor(kYellow - 9)
corredor.Draw("same")
target.Draw("same")
canvas.Modified()
canvas.Update()
# Plot curves
for c in curves["roc"]:
c.SetLineColor(kGray + 1)
# c.SetMarkerStyle(7)
# c.SetMarkerColor(kBlue)
c.SetLineWidth(1)
c.SetLineStyle(3)
# c.Draw('PLsame')
c.Draw("same")
marker = list()
# Paint a specifical curve
for pair in paintCurves:
curves["roc"][pair.first].SetLineWidth(1)
curves["roc"][pair.first].SetLineStyle(1)
# curves['roc'][pair.first].SetMarkerStyle(7)
# curves['roc'][pair.first].SetMarkerColor(kBlue)
curves["roc"][pair.first].SetLineColor(pair.second)
# curves['roc'][pair.first].Draw('PLsame')
curves["roc"][pair.first].Draw("same")
if ref == "SP":
faVec = curves["roc"][pair.first].GetX()
detVec = curves["roc"][pair.first].GetY()
from RingerCore import calcSP
spVec = [calcSP(detVec[i], 1 - faVec[i]) for i in range(curves["roc"][pair.first].GetN())]
imax = spVec.index(max(spVec))
from ROOT import TMarker
marker.append(TMarker(faVec[imax], detVec[imax], 4))
marker[-1].SetMarkerColor(pair.second)
marker[-1].Draw("same")
# Update Canvas
canvas.Modified()
canvas.Update()
canvas.SaveAs(savename)
del canvas
return savename
comma.scale(1.3, 1.3).rotate(-30).move(5.5,
0).rotate(0).fill(ax, facecolor='black')
circle.scale(0.7, 0.7).move(5.5, 0).rotate(0).fill(ax, facecolor='white')
comma.scale(1.3, 1.3).rotate(-30).move(5.5,
0).rotate(120).fill(ax,
facecolor='black')
circle.scale(0.7, 0.7).move(5.5, 0).rotate(120).fill(ax, facecolor='white')
comma.scale(1.3, 1.3).rotate(-30).move(5.5,
0).rotate(240).fill(ax,
facecolor='black')
circle.scale(0.7, 0.7).move(5.5, 0).rotate(240).fill(ax, facecolor='white')
#block
theta = 15 / 180 * np.pi
block = Shape()
block.add_array(line([-np.sin(theta), 0], [np.sin(theta), 0]))
block.add_array(line([np.sin(theta), 0], [np.sin(theta), -np.cos(theta)]))
block.add_array(
arc([np.sin(theta), -np.cos(theta)], [-np.sin(theta), -np.cos(theta)], 1))
block.add_array(line([-np.sin(theta), -np.cos(theta)], [-np.sin(theta), 0]))
block.loop()
#moon
moon = Shape()
moon.add_array(arc([0, 0], [0, 1], 0.53))
moon.add_array(arc([0, 1], [0, 0], 1, out=False))
moon.loop()
#2, 2
ax = axs[5]
circle.scale(11, 11).fill(ax, facecolor='black')
