Corrections and optimizations in IFTS and PIFTS codes
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Petrônio Cândido de Lima e Silva
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@ -1,6 +1,7 @@
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import numpy as np
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import pandas as pd
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import matplotlib as plt
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import matplotlib.colors as pltcolors
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import matplotlib.pyplot as plt
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from mpl_toolkits.mplot3d import Axes3D
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from sklearn.cross_validation import KFold
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@ -66,7 +67,7 @@ def plotDistribution(dist):
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alpha = np.array([dist[x][k] for x in dist])*100
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x = [k for x in np.arange(0,len(alpha))]
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y = dist.columns
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plt.scatter(x,y,c=alpha,marker='s',linewidths=0,cmap='Reds',edgecolors=None)
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plt.scatter(x,y,c=alpha,marker='s',linewidths=0,cmap='Oranges',norm=pltcolors.Normalize(vmin=0,vmax=1),vmin=0,vmax=1,edgecolors=None)
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def plotComparedSeries(original,models, colors):
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fig = plt.figure(figsize=[25,10])
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@ -79,6 +80,7 @@ def plotComparedSeries(original,models, colors):
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count = 0
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for fts in models:
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forecasted = fts.forecast(original)
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if fts.isInterval:
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lower = [kk[0] for kk in forecasted]
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upper = [kk[1] for kk in forecasted]
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@ -106,20 +108,28 @@ def plotComparedSeries(original,models, colors):
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ax.set_xlim([0,len(original)])
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def plotComparedIntervalsAhead(original,models, colors, time_from, time_to):
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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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ax = fig.add_subplot(111)
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mi = []
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ma = []
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ax.plot(original,color='black',label="Original")
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count = 0
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for fts in models:
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if fts.isDensity and distributions[count]:
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density = fts.forecastDistributionAhead(original[:time_from],time_to,25)
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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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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 fts.isInterval:
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forecasted = fts.forecastAhead(original[:time_from],time_to)
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lower = [kk[0] for kk in forecasted]
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upper = [kk[1] for kk in forecasted]
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forecasts = fts.forecastAhead(original[: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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@ -129,15 +139,17 @@ def plotComparedIntervalsAhead(original,models, colors, time_from, time_to):
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ax.plot(upper,color=colors[count])
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else:
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forecasted = fts.forecast(original)
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mi.append(min(forecasted))
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ma.append(max(forecasted))
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forecasted.insert(0,None)
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ax.plot(forecasted,color=colors[count],label=fts.shortname)
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forecasts = fts.forecast(original)
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mi.append(min(forecasts))
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ma.append(max(forecasts))
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for k in np.arange(0,time_from):
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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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#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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@ -8,12 +8,13 @@ def differential(original):
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return np.array(diff)
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def trimf(x,parameters):
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if(x < parameters[0]):
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xx = round(x,3)
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if(xx < parameters[0]):
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return 0
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elif(x >= parameters[0] and x < parameters[1]):
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elif(xx >= parameters[0] and xx < parameters[1]):
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return (x-parameters[0])/(parameters[1]-parameters[0])
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elif(x >= parameters[1] and x <= parameters[2]):
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return (parameters[2]-x)/(parameters[2]-parameters[1])
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elif(xx >= parameters[1] and xx <= parameters[2]):
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return (parameters[2]-xx)/(parameters[2]-parameters[1])
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else:
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return 0
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1
fts.py
1
fts.py
@ -11,6 +11,7 @@ class FTS:
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self.detail = name
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self.isSeasonal = False
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self.isInterval = False
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self.isDensity = False
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def fuzzy(self,data):
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best = {"fuzzyset":"", "membership":0.0}
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2
ifts.py
2
ifts.py
@ -27,6 +27,8 @@ class IntervalFTS(hofts.HighOrderFTS):
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return ret
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def getSequenceMembership(self, data, fuzzySets):
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#print(data)
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#print(fuzzySets)
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mb = [ fuzzySets[k].membership( data[k] ) for k in np.arange(0,len(data)) ]
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return mb
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@ -1,4 +1,5 @@
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import numpy as np
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import math
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from pyFTS import *
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#print(common.__dict__)
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@ -10,10 +11,10 @@ def GridPartitionerTrimf(data,npart,names = None,prefix = "A"):
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dmin = min(data)
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dmin = dmin - dmin*0.10
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dlen = dmax - dmin
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partlen = dlen / npart
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partition = dmin
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partlen = math.ceil(dlen / npart)
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partition = math.ceil(dmin)
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for c in range(npart):
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sets.append(common.FuzzySet(prefix+str(c),common.trimf,[partition-partlen, partition, partition+partlen], partition ) )
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sets.append(common.FuzzySet(prefix+str(c),common.trimf,[round(partition-partlen,3), partition, partition+partlen], partition ) )
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partition = partition + partlen
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return sets
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96
pifts.py
96
pifts.py
@ -1,5 +1,6 @@
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import numpy as np
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import pandas as pd
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import math
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from pyFTS import *
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class ProbabilisticFLRG(hofts.HighOrderFLRG):
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@ -35,6 +36,7 @@ class ProbabilisticIntervalFTS(ifts.IntervalFTS):
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self.flrgs = {}
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self.globalFrequency = 0
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self.isInterval = True
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self.isDensity = True
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def generateFLRG(self, flrs):
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flrgs = {}
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@ -58,14 +60,16 @@ class ProbabilisticIntervalFTS(ifts.IntervalFTS):
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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/ self.globalFrequency
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return 1.0 / self.globalFrequency
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def getUpper(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].upper for s in tmp.RHS]))
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else:
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ret = flrg.LHS[-1].upper
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#print("hit" + flrg.strLHS())
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#ret = flrg.LHS[-1].upper
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ret = sum(np.array([ 0.33 * 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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@ -73,7 +77,9 @@ class ProbabilisticIntervalFTS(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 = flrg.LHS[-1].lower
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#print("hit" + flrg.strLHS())
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#ret = flrg.LHS[-1].lower
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ret = sum(np.array([ 0.33 * 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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@ -88,6 +94,8 @@ class ProbabilisticIntervalFTS(ifts.IntervalFTS):
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for k in np.arange(self.order-1,l):
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#print(k)
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affected_flrgs = []
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affected_flrgs_memberships = []
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norms = []
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@ -107,15 +115,18 @@ class ProbabilisticIntervalFTS(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 instance <= self.sets[0].lower:
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#print("high order - idx.size == 0 - " + str(instance))
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if math.ceil(instance) <= self.sets[0].lower:
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idx = [0]
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if instance >= self.sets[-1].upper:
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elif math.ceil(instance) >= self.sets[-1].upper:
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idx = [len(self.sets)-1]
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#print(idx)
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else:
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raise Exception( instance )
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#print(idx)
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lags[count] = idx
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count = count + 1
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# Build the tree with all possible paths
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root = tree.FLRGTreeNode(None)
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@ -129,26 +140,43 @@ class ProbabilisticIntervalFTS(ifts.IntervalFTS):
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flrg = hofts.HighOrderFLRG(self.order)
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for kk in path: flrg.appendLHS(self.sets[ kk ])
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assert len(flrg.LHS) == subset.size, str(subset) + " -> " + str([s.name for s in flrg.LHS])
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##
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affected_flrgs.append( flrg )
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# Find the general membership of FLRG
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affected_flrgs_memberships.append(min(self.getSequenceMembership(subset, flrg.LHS)))
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#print(self.getSequenceMembership(subset, flrg.LHS))
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else:
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mv = common.fuzzyInstance(ndata[k],self.sets) # get all membership values
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tmp = np.argwhere( mv ) # get the indices of values > 0
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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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#print("idx.size == 0")
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if math.ceil(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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idx = [len(self.sets)-1]
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#print(idx)
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else:
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raise Exception( ndata[k] )
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#print(idx)
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for kk in idx:
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flrg = hofts.HighOrderFLRG(self.order)
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flrg.appendLHS(self.sets[ kk ])
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affected_flrgs.append( flrg )
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#print(mv[kk])
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affected_flrgs_memberships.append(mv[kk])
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count = 0
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for flrg in affected_flrgs:
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# achar o os bounds de cada FLRG, ponderados pela probabilidade e pertinência
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norm = self.getProbability(flrg) * affected_flrgs_memberships[count]
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if norm == 0:
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norm = self.getProbability(flrg) # * 0.001
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up.append( norm * self.getUpper(flrg) )
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lo.append( norm * self.getLower(flrg) )
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norms.append(norm)
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@ -158,6 +186,7 @@ class ProbabilisticIntervalFTS(ifts.IntervalFTS):
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norm = sum(norms)
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if norm == 0:
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ret.append( [ 0, 0 ] )
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print("disparou")
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else:
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ret.append( [ sum(lo)/norm, sum(up)/norm ] )
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@ -165,12 +194,16 @@ class ProbabilisticIntervalFTS(ifts.IntervalFTS):
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def forecastAhead(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,steps):
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#print(ret)
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for k in np.arange(self.order-1,steps):
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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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#print("disparou")
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else:
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lower = self.forecast( [ret[x][0] for x in np.arange(k-self.order,k)] )
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upper = self.forecast( [ret[x][1] for x in np.arange(k-self.order,k)] )
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ret.append([np.min(lower),np.max(upper)])
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return ret
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@ -187,6 +220,53 @@ class ProbabilisticIntervalFTS(ifts.IntervalFTS):
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if sbin >= interval[0] and (sbin + resolution) <= interval[1]:
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grid[sbin] = grid[sbin] + 1
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return grid
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def forecastDistributionAhead2(self,data,steps,resolution):
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ret = []
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intervals = self.forecastAhead(data,steps)
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for k in np.arange(self.order,steps):
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grid = self.getGridClean(resolution)
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grid = self.gridCount(grid,resolution, intervals[k])
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lags = {}
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cc = 0
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for x in np.arange(k-self.order,k):
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tmp = []
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for qt in np.arange(0,100,5):
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tmp.append(intervals[x][0] + qt*(intervals[x][1]-intervals[x][0])/100)
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tmp.append(intervals[x][1] - qt*(intervals[x][1]-intervals[x][0])/100)
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tmp.append(intervals[x][0] + (intervals[x][1]-intervals[x][0])/2)
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lags[cc] = tmp
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cc = cc + 1
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# Build the tree with all possible paths
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root = tree.FLRGTreeNode(None)
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self.buildTree(root,lags,0)
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# Trace the possible paths and build the PFLRG's
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for p in root.paths():
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path = list(reversed(list(filter(None.__ne__, p))))
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subset = [kk for kk in path]
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qtle = self.forecast(subset)
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grid = self.gridCount(grid,resolution, np.ravel(qtle))
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tmp = np.array([ grid[k] for k in sorted(grid) ])
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ret.append( tmp/sum(tmp) )
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grid = self.getGridClean(resolution)
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df = pd.DataFrame(ret, columns=sorted(grid))
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return df
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def forecastDistributionAhead(self,data,steps,resolution):
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