Otimizações nos Benchmarks e correções de erros em PFTS
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Petrônio Cândido de Lima e Silva
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ba1b4fbae6
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53029681d8
@ -32,8 +32,8 @@ def plotDistribution(dist):
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vmin=0, vmax=1, edgecolors=None)
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def plotComparedSeries(original, models, colors):
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fig = plt.figure(figsize=[15, 5])
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def plotComparedSeries(original, models, colors, typeonlegend=False, save=False, file=None,tam=[20, 5]):
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fig = plt.figure(figsize=tam)
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ax = fig.add_subplot(111)
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mi = []
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@ -48,7 +48,9 @@ def plotComparedSeries(original, models, colors):
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ma.append(max(forecasted))
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for k in np.arange(0, fts.order):
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forecasted.insert(0, None)
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ax.plot(forecasted, color=colors[count], label=fts.shortname, ls="-")
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lbl = fts.shortname
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if typeonlegend: lbl += " (Point)"
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ax.plot(forecasted, color=colors[count], label=lbl, ls="-")
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if fts.hasIntervalForecasting:
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forecasted = fts.forecastInterval(original)
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@ -59,7 +61,9 @@ def plotComparedSeries(original, models, colors):
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for k in np.arange(0, fts.order):
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lower.insert(0, None)
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upper.insert(0, None)
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ax.plot(lower, color=colors[count], label=fts.shortname,ls="--")
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lbl = fts.shortname
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if typeonlegend: lbl += " (Interval)"
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ax.plot(lower, color=colors[count], label=lbl,ls="--")
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ax.plot(upper, color=colors[count],ls="--")
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handles0, labels0 = ax.get_legend_handles_labels()
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@ -71,32 +75,58 @@ def plotComparedSeries(original, models, colors):
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ax.set_xlabel('T')
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ax.set_xlim([0, len(original)])
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if save:
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fig.savefig(file)
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plt.close(fig)
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def plotComparedIntervalsAhead(original, models, colors, distributions, time_from, time_to):
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fig = plt.figure(figsize=[25, 10])
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def plotComparedIntervalsAhead(original, models, colors, distributions, time_from, time_to, interpol=False, save=False, file=None,tam=[20, 5]):
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fig = plt.figure(figsize=tam)
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ax = fig.add_subplot(111)
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percentile = (max(original) - min(original))/100
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mi = []
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ma = []
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count = 0
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for fts in models:
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if fts.hasDistributionForecasting and distributions[count]:
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density = fts.forecastDistributionAhead(original[:time_from], time_to, 25)
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density = fts.forecastAheadDistribution(original[time_from - fts.order:time_from], time_to, percentile)
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y = density.columns
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t = len(y)
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# interpol between time_from and time_from+1
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if interpol:
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diffs = [density[q][0] / 50 for q in density]
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for p in np.arange(0, 50):
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xx = [(time_from - 1) + 0.02 * p for q in np.arange(0, t)]
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alpha2 = np.array([diffs[q] * p for q in np.arange(0, t)]) * 100
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ax.scatter(xx, y, c=alpha2, marker='s', linewidths=0, cmap='Oranges',
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norm=pltcolors.Normalize(vmin=0, vmax=1), vmin=0, vmax=1, edgecolors=None)
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for k in density.index:
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alpha = np.array([density[x][k] for x in density]) * 100
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x = [time_from + fts.order + k for x in np.arange(0, len(alpha))]
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y = density.columns
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alpha = np.array([density[q][k] for q in density]) * 100
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x = [time_from + k for x in np.arange(0, t)]
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ax.scatter(x, y, c=alpha, marker='s', linewidths=0, cmap='Oranges',
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norm=pltcolors.Normalize(vmin=0, vmax=1), vmin=0, vmax=1, edgecolors=None)
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if interpol and k < max(density.index):
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diffs = [(density[q][k + 1] - density[q][k])/50 for q in density]
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for p in np.arange(0,50):
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xx = [time_from + k + 0.02*p for q in np.arange(0, t)]
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alpha2 = np.array([density[density.columns[q]][k] + diffs[q]*p for q in np.arange(0, t)]) * 100
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ax.scatter(xx, y, c=alpha2, marker='s', linewidths=0, cmap='Oranges',
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norm=pltcolors.Normalize(vmin=0, vmax=1), vmin=0, vmax=1, edgecolors=None)
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if fts.hasIntervalForecasting:
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forecasts = fts.forecastAhead(original[:time_from], time_to)
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forecasts = fts.forecastAheadInterval(original[time_from - fts.order:time_from], time_to)
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lower = [kk[0] for kk in forecasts]
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upper = [kk[1] for kk in forecasts]
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mi.append(min(lower))
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ma.append(max(upper))
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for k in np.arange(0, time_from):
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for k in np.arange(0, time_from-fts.order):
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lower.insert(0, None)
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upper.insert(0, None)
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ax.plot(lower, color=colors[count], label=fts.shortname)
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@ -110,16 +140,20 @@ def plotComparedIntervalsAhead(original, models, colors, distributions, time_fro
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forecasts.insert(0, None)
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ax.plot(forecasts, color=colors[count], label=fts.shortname)
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handles0, labels0 = ax.get_legend_handles_labels()
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ax.legend(handles0, labels0)
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count = count + 1
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ax.plot(original, color='black', label="Original")
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handles0, labels0 = ax.get_legend_handles_labels()
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ax.legend(handles0, labels0, loc=2)
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# ax.set_title(fts.name)
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ax.set_ylim([min(mi), max(ma)])
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ax.set_ylabel('F(T)')
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ax.set_xlabel('T')
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ax.set_xlim([0, len(original)])
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if save:
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fig.savefig(file)
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plt.close(fig)
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def plotCompared(original, forecasts, labels, title):
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fig = plt.figure(figsize=[13, 6])
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40
pfts.py
40
pfts.py
@ -12,9 +12,9 @@ class ProbabilisticFLRG(hofts.HighOrderFLRG):
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self.frequencyCount = 0.0
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def appendRHS(self, c):
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self.frequencyCount += 1
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self.frequencyCount += 1.0
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if c.name in self.RHS:
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self.RHS[c.name] += 1
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self.RHS[c.name] += 1.0
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else:
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self.RHS[c.name] = 1.0
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@ -26,7 +26,7 @@ class ProbabilisticFLRG(hofts.HighOrderFLRG):
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for c in sorted(self.RHS):
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if len(tmp2) > 0:
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tmp2 = tmp2 + ", "
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tmp2 = tmp2 + c + "(" + str(round(self.RHS[c] / self.frequencyCount, 3)) + ")"
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tmp2 = tmp2 + "(" + str(round(self.RHS[c] / self.frequencyCount, 3)) + ")" + c
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return self.strLHS() + " -> " + tmp2
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@ -60,21 +60,31 @@ class ProbabilisticFTS(ifts.IntervalFTS):
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flrgs[flrg.strLHS()].appendRHS(flrs[k-1].RHS)
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if self.dump: print("RHS: " + str(flrs[k-1]))
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self.globalFrequency = self.globalFrequency + 1
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self.globalFrequency += 1
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return (flrgs)
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def addNewPFLGR(self,flrg):
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if flrg.strLHS() not in self.flrgs:
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tmp = ProbabilisticFLRG(self.order)
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for fs in flrg.LHS: tmp.appendLHS(fs)
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tmp.appendRHS(flrg.LHS[-1])
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self.flrgs[tmp.strLHS()] = tmp;
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self.globalFrequency += 1
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def getProbability(self, flrg):
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if flrg.strLHS() in self.flrgs:
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return self.flrgs[flrg.strLHS()].frequencyCount / self.globalFrequency
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else:
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return 1.0 / self.globalFrequency
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self.addNewPFLGR(flrg)
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return self.getProbability(flrg)
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def getMidpoints(self, flrg):
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if flrg.strLHS() in self.flrgs:
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tmp = self.flrgs[flrg.strLHS()]
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ret = sum(np.array([tmp.getProbability(s) * self.setsDict[s].centroid for s in tmp.RHS]))
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else:
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ret = sum(np.array([0.33 * s.centroid for s in flrg.LHS]))
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pi = 1 / len(flrg.LHS)
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ret = sum(np.array([pi * s.centroid for s in flrg.LHS]))
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return ret
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def getUpper(self, flrg):
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@ -82,7 +92,8 @@ class ProbabilisticFTS(ifts.IntervalFTS):
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tmp = self.flrgs[flrg.strLHS()]
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ret = sum(np.array([tmp.getProbability(s) * self.setsDict[s].upper for s in tmp.RHS]))
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else:
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ret = sum(np.array([0.33 * s.upper for s in flrg.LHS]))
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pi = 1 / len(flrg.LHS)
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ret = sum(np.array([pi * s.upper for s in flrg.LHS]))
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return ret
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def getLower(self, flrg):
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@ -90,7 +101,8 @@ class ProbabilisticFTS(ifts.IntervalFTS):
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tmp = self.flrgs[flrg.strLHS()]
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ret = sum(np.array([tmp.getProbability(s) * self.setsDict[s].lower for s in tmp.RHS]))
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else:
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ret = sum(np.array([0.33 * s.lower for s in flrg.LHS]))
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pi = 1 / len(flrg.LHS)
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ret = sum(np.array([pi * s.lower for s in flrg.LHS]))
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return ret
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def forecast(self, data):
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@ -224,9 +236,9 @@ class ProbabilisticFTS(ifts.IntervalFTS):
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idx = np.ravel(tmp) # flatten the array
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if idx.size == 0: # the element is out of the bounds of the Universe of Discourse
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if math.ceil(instance) <= self.sets[0].lower:
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if instance <= self.sets[0].lower:
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idx = [0]
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elif math.ceil(instance) >= self.sets[-1].upper:
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elif instance >= self.sets[-1].upper:
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idx = [len(self.sets) - 1]
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else:
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raise Exception(instance)
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@ -262,9 +274,9 @@ class ProbabilisticFTS(ifts.IntervalFTS):
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idx = np.ravel(tmp) # flatten the array
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if idx.size == 0: # the element is out of the bounds of the Universe of Discourse
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if math.ceil(ndata[k]) <= self.sets[0].lower:
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if ndata[k] <= self.sets[0].lower:
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idx = [0]
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elif math.ceil(ndata[k]) >= self.sets[-1].upper:
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elif ndata[k] >= self.sets[-1].upper:
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idx = [len(self.sets) - 1]
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else:
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raise Exception(ndata[k])
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@ -312,7 +324,7 @@ class ProbabilisticFTS(ifts.IntervalFTS):
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def forecastAheadInterval(self, data, steps):
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ret = [[data[k], data[k]] for k in np.arange(len(data) - self.order, len(data))]
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for k in np.arange(self.order - 1, steps):
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for k in np.arange(self.order, steps+self.order):
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if ret[-1][0] <= self.sets[0].lower and ret[-1][1] >= self.sets[-1].upper:
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ret.append(ret[-1])
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@ -390,7 +402,7 @@ class ProbabilisticFTS(ifts.IntervalFTS):
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intervals = self.forecastAheadInterval(data, steps)
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for k in np.arange(self.order, steps):
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for k in np.arange(self.order, steps+self.order):
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grid = self.getGridClean(resolution)
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grid = self.gridCount(grid, resolution, intervals[k])
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