578 lines
20 KiB
Python
578 lines
20 KiB
Python
#!/usr/bin/python
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# -*- coding: utf8 -*-
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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 operator import itemgetter
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from pyFTS.common import FLR, FuzzySet, SortedCollection
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from pyFTS import hofts, ifts, tree
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class ProbabilisticWeightedFLRG(hofts.HighOrderFLRG):
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def __init__(self, order):
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super(ProbabilisticWeightedFLRG, self).__init__(order)
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self.RHS = {}
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self.frequencyCount = 0.0
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def appendRHS(self, c):
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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] += 1.0
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else:
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self.RHS[c.name] = 1.0
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def appendRHSFuzzy(self, c, mv):
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self.frequencyCount += mv
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if c.name in self.RHS:
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self.RHS[c.name] += mv
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else:
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self.RHS[c.name] = mv
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def get_probability(self, c):
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return self.RHS[c] / self.frequencyCount
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def __str__(self):
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tmp2 = ""
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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 + "(" + str(round(self.RHS[c] / self.frequencyCount, 3)) + ")" + c
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return self.strLHS() + " -> " + tmp2
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class ProbabilisticWeightedFTS(ifts.IntervalFTS):
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def __init__(self, name, **kwargs):
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super(ProbabilisticWeightedFTS, self).__init__(order=1, name=name)
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self.shortname = "PWFTS " + name
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self.name = "Probabilistic FTS"
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self.detail = "Silva, P.; Guimarães, F.; Sadaei, H."
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self.flrgs = {}
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self.globalFrequency = 0
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self.hasPointForecasting = True
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self.hasIntervalForecasting = True
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self.hasDistributionForecasting = True
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self.isHighOrder = True
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self.auto_update = kwargs.get('update',False)
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def train(self, data, sets, order=1,parameters=None):
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data = self.doTransformations(data, updateUoD=True)
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self.order = order
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self.sets = sets
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for s in self.sets: self.setsDict[s.name] = s
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tmpdata = FuzzySet.fuzzySeries(data, sets)
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flrs = FLR.generateRecurrentFLRs(tmpdata)
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self.flrgs = self.generateFLRG(flrs)
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#self.flrgs = self.generateFLRG2(data)
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def generateFLRG2(self, data):
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flrgs = {}
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l = len(data)
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for k in np.arange(self.order, l):
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if self.dump: print("FLR: " + str(k))
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flrg = ProbabilisticWeightedFLRG(self.order)
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sample = data[k - self.order: k]
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mvs = FuzzySet.fuzzyInstances(sample, self.sets)
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lags = {}
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for o in np.arange(0, self.order):
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_sets = [self.sets[kk] for kk in np.arange(0, len(self.sets)) if mvs[o][kk] > 0]
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lags[o] = _sets
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root = tree.FLRGTreeNode(None)
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self.buildTreeWithoutOrder(root, lags, 0)
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# Trace the possible paths
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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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lhs_mv = []
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for c, e in enumerate(path, start=0):
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lhs_mv.append( e.membership( sample[c] ) )
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flrg.appendLHS(e)
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if flrg.strLHS() not in flrgs:
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flrgs[flrg.strLHS()] = flrg;
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mv = FuzzySet.fuzzyInstance(data[k], self.sets)
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rhs_mv = [mv[kk] for kk in np.arange(0, len(self.sets)) if mv[kk] > 0]
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_sets = [self.sets[kk] for kk in np.arange(0, len(self.sets)) if mv[kk] > 0]
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for c, e in enumerate(_sets, start=0):
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flrgs[flrg.strLHS()].appendRHSFuzzy(e,rhs_mv[c]*max(lhs_mv))
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self.globalFrequency += max(lhs_mv)
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return (flrgs)
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def generateFLRG(self, flrs):
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flrgs = {}
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l = len(flrs)
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for k in np.arange(self.order, l+1):
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if self.dump: print("FLR: " + str(k))
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flrg = ProbabilisticWeightedFLRG(self.order)
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for kk in np.arange(k - self.order, k):
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flrg.appendLHS(flrs[kk].LHS)
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if self.dump: print("LHS: " + str(flrs[kk]))
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if flrg.strLHS() in flrgs:
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flrgs[flrg.strLHS()].appendRHS(flrs[k-1].RHS)
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else:
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flrgs[flrg.strLHS()] = flrg
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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 += 1
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return (flrgs)
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def update_model(self,data):
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fzzy = FuzzySet.fuzzySeries(data, self.sets)
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flrg = ProbabilisticWeightedFLRG(self.order)
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for k in np.arange(0, self.order): flrg.appendLHS(fzzy[k])
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if flrg.strLHS() in self.flrgs:
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self.flrgs[flrg.strLHS()].appendRHS(fzzy[self.order])
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else:
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self.flrgs[flrg.strLHS()] = flrg
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self.flrgs[flrg.strLHS()].appendRHS(fzzy[self.order])
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self.globalFrequency += 1
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def add_new_PWFLGR(self, flrg):
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if flrg.strLHS() not in self.flrgs:
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tmp = ProbabilisticWeightedFLRG(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 get_probability(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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self.add_new_PWFLGR(flrg)
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return self.get_probability(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.get_probability(s) * self.setsDict[s].centroid for s in tmp.RHS]))
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else:
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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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if flrg.strLHS() in self.flrgs:
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tmp = self.flrgs[flrg.strLHS()]
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ret = sum(np.array([tmp.get_probability(s) * self.setsDict[s].upper for s in tmp.RHS]))
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else:
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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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if flrg.strLHS() in self.flrgs:
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tmp = self.flrgs[flrg.strLHS()]
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ret = sum(np.array([tmp.get_probability(s) * self.setsDict[s].lower for s in tmp.RHS]))
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else:
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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, **kwargs):
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ndata = np.array(self.doTransformations(data))
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l = len(ndata)
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ret = []
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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_rhs = []
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affected_flrgs_memberships = []
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norms = []
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mp = []
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# Find the sets which membership > 0 for each lag
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count = 0
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lags = {}
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if self.order > 1:
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subset = ndata[k - (self.order - 1): k + 1]
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for instance in subset:
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mb = FuzzySet.fuzzyInstance(instance, self.sets)
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tmp = np.argwhere(mb)
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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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idx = [0]
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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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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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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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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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else:
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mv = FuzzySet.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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if ndata[k] <= self.sets[0].lower:
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idx = [0]
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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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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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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.get_probability(flrg) * affected_flrgs_memberships[count]
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if norm == 0:
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norm = self.get_probability(flrg) # * 0.001
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mp.append(norm * self.getMidpoints(flrg))
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norms.append(norm)
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count = count + 1
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# gerar o intervalo
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norm = sum(norms)
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if norm == 0:
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ret.append(0)
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else:
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ret.append(sum(mp) / norm)
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if self.auto_update and k > self.order+1: self.update_model(ndata[k - self.order - 1 : k])
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ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]])
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return ret
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def forecastInterval(self, data, **kwargs):
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ndata = np.array(self.doTransformations(data))
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l = len(ndata)
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ret = []
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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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up = []
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lo = []
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# Find the sets which membership > 0 for each lag
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count = 0
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lags = {}
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if self.order > 1:
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subset = ndata[k - (self.order - 1): k + 1]
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for instance in subset:
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mb = FuzzySet.fuzzyInstance(instance, self.sets)
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tmp = np.argwhere(mb)
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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.isclose(instance, self.sets[0].lower) or instance < self.sets[0].lower:
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idx = [0]
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elif math.isclose(instance, self.sets[-1].upper) or 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("Data exceed the known bounds [%s, %s] of universe of discourse: %s" %
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(self.sets[0].lower, self.sets[-1].upper, instance))
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lags[count] = idx
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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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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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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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else:
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mv = FuzzySet.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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if math.isclose(ndata[k], self.sets[0].lower) or ndata[k] < self.sets[0].lower:
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idx = [0]
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elif math.isclose(ndata[k], self.sets[-1].upper) or 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("Data exceed the known bounds [%s, %s] of universe of discourse: %s" %
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(self.sets[0].lower, self.sets[-1].upper, ndata[k]))
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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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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.get_probability(flrg) * affected_flrgs_memberships[count]
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if norm == 0:
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norm = self.get_probability(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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count = count + 1
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# gerar o intervalo
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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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else:
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lo_ = self.doInverseTransformations(sum(lo) / norm, params=[data[k - (self.order - 1): k + 1]])
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up_ = self.doInverseTransformations(sum(up) / norm, params=[data[k - (self.order - 1): k + 1]])
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ret.append([lo_, up_])
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return ret
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def forecastAhead(self, data, steps, **kwargs):
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ret = [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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if ret[-1] <= self.sets[0].lower or ret[-1] >= self.sets[-1].upper:
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ret.append(ret[-1])
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else:
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mp = self.forecast([ret[x] for x in np.arange(k - self.order, k)])
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ret.append(mp)
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return ret
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def forecastAheadInterval(self, data, steps, **kwargs):
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l = len(data)
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ret = [[data[k], data[k]] for k in np.arange(l - self.order, l)]
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for k in np.arange(self.order, steps+self.order):
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if (len(self.transformations) > 0 and ret[-1][0] <= self.sets[0].lower and ret[-1][1] >= self.sets[
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-1].upper) or (len(self.transformations) == 0 and ret[-1][0] <= self.original_min and ret[-1][
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1] >= self.original_max):
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ret.append(ret[-1])
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else:
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lower = self.forecastInterval([ret[x][0] for x in np.arange(k - self.order, k)])
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upper = self.forecastInterval([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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def getGridClean(self, resolution):
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grid = {}
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if len(self.transformations) == 0:
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_min = self.sets[0].lower
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_max = self.sets[-1].upper
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else:
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_min = self.original_min
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_max = self.original_max
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for sbin in np.arange(_min,_max, resolution):
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grid[sbin] = 0
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return grid
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def gridCount(self, grid, resolution, index, interval):
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#print(interval)
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for k in index.inside(interval[0],interval[1]):
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#print(k)
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grid[k] += 1
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return grid
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def gridCountPoint(self, grid, resolution, index, point):
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k = index.find_ge(point)
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# print(k)
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grid[k] += 1
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return grid
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def forecastAheadDistribution(self, data, steps, **kwargs):
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ret = []
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resolution = kwargs.get('resolution',100)
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method = kwargs.get('method',2)
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intervals = self.forecastAheadInterval(data, steps)
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grid = self.getGridClean(resolution)
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index = SortedCollection.SortedCollection(iterable=grid.keys())
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if method == 1:
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grids = []
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for k in np.arange(0, steps):
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grids.append(self.getGridClean(resolution))
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for k in np.arange(self.order, steps + self.order):
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lags = {}
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cc = 0
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for i in intervals[k - self.order : k]:
|
|
|
|
quantiles = []
|
|
|
|
for qt in np.arange(0, 50, 2):
|
|
quantiles.append(i[0] + qt * ((i[1] - i[0]) / 100))
|
|
quantiles.append(i[1] - qt * ((i[1] - i[0]) / 100))
|
|
quantiles.append(i[0] + ((i[1] - i[0]) / 2))
|
|
|
|
quantiles = list(set(quantiles))
|
|
|
|
quantiles.sort()
|
|
|
|
lags[cc] = quantiles
|
|
|
|
cc += 1
|
|
|
|
# Build the tree with all possible paths
|
|
|
|
root = tree.FLRGTreeNode(None)
|
|
|
|
self.buildTreeWithoutOrder(root, lags, 0)
|
|
|
|
# Trace the possible paths
|
|
|
|
for p in root.paths():
|
|
path = list(reversed(list(filter(None.__ne__, p))))
|
|
|
|
qtle = self.forecastInterval(path)
|
|
|
|
grids[k - self.order] = self.gridCount(grids[k - self.order], resolution, index, np.ravel(qtle))
|
|
|
|
for k in np.arange(0, steps):
|
|
tmp = np.array([grids[k][q] for q in sorted(grids[k])])
|
|
ret.append(tmp / sum(tmp))
|
|
|
|
elif method == 2:
|
|
|
|
ret = []
|
|
|
|
for k in np.arange(self.order, steps + self.order):
|
|
|
|
grid = self.getGridClean(resolution)
|
|
grid = self.gridCount(grid, resolution, index, intervals[k])
|
|
|
|
for qt in np.arange(0, 50, 1):
|
|
# print(qt)
|
|
qtle_lower = self.forecastInterval(
|
|
[intervals[x][0] + qt * ((intervals[x][1] - intervals[x][0]) / 100) for x in
|
|
np.arange(k - self.order, k)])
|
|
grid = self.gridCount(grid, resolution, index, np.ravel(qtle_lower))
|
|
qtle_upper = self.forecastInterval(
|
|
[intervals[x][1] - qt * ((intervals[x][1] - intervals[x][0]) / 100) for x in
|
|
np.arange(k - self.order, k)])
|
|
grid = self.gridCount(grid, resolution, index, np.ravel(qtle_upper))
|
|
qtle_mid = self.forecastInterval(
|
|
[intervals[x][0] + (intervals[x][1] - intervals[x][0]) / 2 for x in np.arange(k - self.order, k)])
|
|
grid = self.gridCount(grid, resolution, index, np.ravel(qtle_mid))
|
|
|
|
tmp = np.array([grid[k] for k in sorted(grid)])
|
|
|
|
ret.append(tmp / sum(tmp))
|
|
|
|
else:
|
|
ret = []
|
|
|
|
for k in np.arange(self.order, steps + self.order):
|
|
grid = self.getGridClean(resolution)
|
|
grid = self.gridCount(grid, resolution, index, intervals[k])
|
|
|
|
tmp = np.array([grid[k] for k in sorted(grid)])
|
|
|
|
ret.append(tmp / sum(tmp))
|
|
|
|
grid = self.getGridClean(resolution)
|
|
df = pd.DataFrame(ret, columns=sorted(grid))
|
|
return df
|
|
|
|
|
|
def __str__(self):
|
|
tmp = self.name + ":\n"
|
|
for r in sorted(self.flrgs):
|
|
p = round(self.flrgs[r].frequencyCount / self.globalFrequency, 3)
|
|
tmp = tmp + "(" + str(p) + ") " + str(self.flrgs[r]) + "\n"
|
|
return tmp
|