High Order Nostationary Fuzzy Time Series - HONSFTS

This commit is contained in:
Petrônio Cândido 2017-10-17 00:18:12 -02:00
parent 6f455f3215
commit 2ea599fdb8
7 changed files with 213 additions and 93 deletions

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@ -124,18 +124,18 @@ class FuzzySet(FS.FuzzySet):
def get_midpoint(self, t):
self.perturbate_parameters(t)
param = self.perturbated_parameters[t]
if self.mf == Membership.gaussmf:
return self.perturbated_parameters[t][0]
return param[0]
elif self.mf == Membership.sigmf:
return self.perturbated_parameters[t][1]
return param[1]
elif self.mf == Membership.trimf:
return self.perturbated_parameters[t][1]
return param[1]
elif self.mf == Membership.trapmf:
param = self.perturbated_parameters[t]
return (param[2] - param[1]) / 2
else:
return self.perturbated_parameters[t]
return param
def get_lower(self, t):

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@ -13,7 +13,7 @@ class NonStationaryFLRG(flrg.FLRG):
def get_membership(self, data, t, window_size=1):
ret = 0.0
if isinstance(self.LHS, (list, set)):
assert len(self.LHS) == len(data)
#assert len(self.LHS) == len(data)
ret = min([self.LHS[ct].membership(dat, common.window_index(t - (self.order - ct), window_size))
for ct, dat in enumerate(data)])
@ -31,20 +31,20 @@ class NonStationaryFLRG(flrg.FLRG):
else:
return self.LHS[-1].get_midpoint(common.window_index(t, window_size))
def get_lower(self, t, window_size=1):
if self.lower is None:
if len(self.RHS) > 0:
self.lower = min([r.get_lower(common.window_index(t, window_size)) for r in self.RHS])
else:
self.lower = self.LHS[-1].get_lower(common.window_index(t, window_size))
return self.lower
if len(self.RHS) > 0:
if isinstance(self.RHS, (list, set)):
return min([r.get_lower(common.window_index(t, window_size)) for r in self.RHS])
elif isinstance(self.RHS, dict):
return min([self.RHS[r].get_lower(common.window_index(t, window_size)) for r in self.RHS.keys()])
else:
return self.LHS[-1].get_lower(common.window_index(t, window_size))
def get_upper(self, t, window_size=1):
if self.upper is None:
if len(self.RHS) > 0:
self.upper = min([r.get_upper(common.window_index(t, window_size)) for r in self.RHS])
else:
self.upper = self.LHS[-1].get_upper(common.window_index(t, window_size))
return self.upper
if len(self.RHS) > 0:
if isinstance(self.RHS, (list, set)):
return max([r.get_upper(common.window_index(t, window_size)) for r in self.RHS])
elif isinstance(self.RHS, dict):
return max([self.RHS[r].get_upper(common.window_index(t, window_size)) for r in self.RHS.keys()])
else:
return self.LHS[-1].get_upper(common.window_index(t, window_size))

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@ -91,6 +91,8 @@ class HighOrderNonStationaryFTS(hofts.HighOrderFTS):
for st in rhs:
flrgs[flrg.strLHS()].appendRHS(st)
# flrgs = sorted(flrgs, key=lambda flrg: flrg.get_midpoint(0, window_size=1))
return flrgs
def train(self, data, sets=None, order=2, parameters=None):
@ -108,6 +110,65 @@ class HighOrderNonStationaryFTS(hofts.HighOrderFTS):
window_size = parameters if parameters is not None else 1
self.flrgs = self.generate_flrg(ndata, window_size=window_size)
def _affected_flrgs(self, sample, k, time_displacement, window_size):
# print("input: " + str(ndata[k]))
affected_flrgs = []
affected_flrgs_memberships = []
lags = {}
for ct, dat in enumerate(sample):
tdisp = common.window_index((k + time_displacement) - (self.order - ct), window_size)
sel = [ct for ct, set in enumerate(self.sets) if set.membership(dat, tdisp) > 0.0]
if len(sel) == 0:
sel.append(common.check_bounds_index(dat, self.sets, tdisp))
lags[ct] = sel
# Build the tree with all possible paths
root = tree.FLRGTreeNode(None)
self.build_tree(root, lags, 0)
# Trace the possible paths and build the PFLRG's
for p in root.paths():
path = list(reversed(list(filter(None.__ne__, p))))
flrg = HighOrderNonStationaryFLRG(self.order)
for kk in path:
flrg.appendLHS(self.sets[kk])
affected_flrgs.append(flrg)
# affected_flrgs_memberships.append(flrg.get_membership(sample, disp))
# print(flrg.strLHS())
# the FLRG is here because of the bounds verification
mv = []
for ct, dat in enumerate(sample):
td = common.window_index((k + time_displacement) - (self.order - ct), window_size)
tmp = flrg.LHS[ct].membership(dat, td)
# print('td',td)
# print('dat',dat)
# print(flrg.LHS[ct].name, flrg.LHS[ct].perturbated_parameters[td])
# print(tmp)
if (tmp == 0.0 and flrg.LHS[ct].name == self.sets[0].name and dat < self.sets[0].get_lower(td)) \
or (tmp == 0.0 and flrg.LHS[ct].name == self.sets[-1].name and dat > self.sets[-1].get_upper(
td)):
mv.append(1.0)
else:
mv.append(tmp)
# print(mv)
affected_flrgs_memberships.append(np.prod(mv))
return [affected_flrgs, affected_flrgs_memberships]
def forecast(self, data, **kwargs):
time_displacement = kwargs.get("time_displacement",0)
@ -122,54 +183,32 @@ class HighOrderNonStationaryFTS(hofts.HighOrderFTS):
for k in np.arange(self.order, l+1):
#print("input: " + str(ndata[k]))
disp = common.window_index(k + time_displacement, window_size)
affected_flrgs = []
affected_flrgs_memberships = []
lags = {}
sample = ndata[k - self.order: k]
for ct, dat in enumerate(sample):
tdisp = common.window_index((k + time_displacement) - (self.order - ct), window_size)
sel = [ct for ct, set in enumerate(self.sets) if set.membership(dat, tdisp) > 0.0]
affected_flrgs, affected_flrgs_memberships = self._affected_flrgs(sample, k,
time_displacement, window_size)
if len(sel) == 0:
sel.append(common.check_bounds_index(dat, self.sets, tdisp))
lags[ct] = sel
# Build the tree with all possible paths
root = tree.FLRGTreeNode(None)
self.build_tree(root, lags, 0)
# Trace the possible paths and build the PFLRG's
for p in root.paths():
path = list(reversed(list(filter(None.__ne__, p))))
flrg = HighOrderNonStationaryFLRG(self.order)
for kk in path:
flrg.appendLHS(self.sets[kk])
affected_flrgs.append(flrg)
affected_flrgs_memberships.append(flrg.get_membership(ndata[k - self.order: k], disp))
#print(affected_sets)
#print([str(k) for k in affected_flrgs])
#print(affected_flrgs_memberships)
tmp = []
for ct, aset in enumerate(affected_flrgs):
if aset.strLHS() in self.flrgs:
tmp.append(self.flrgs[aset.strLHS()].get_midpoint(tdisp) *
affected_flrgs_memberships[ct])
tdisp = common.window_index(k + time_displacement, window_size)
if len(affected_flrgs) == 0:
tmp.append(common.check_bounds(sample[-1], self.sets, tdisp))
elif len(affected_flrgs) == 1:
if affected_flrgs[0].strLHS() in self.flrgs:
flrg = affected_flrgs[0]
tmp.append(self.flrgs[flrg.strLHS()].get_midpoint(tdisp))
else:
tmp.append(aset.LHS[-1].get_midpoint(tdisp))
tmp.append(flrg.LHS[-1].get_midpoint(tdisp))
else:
for ct, aset in enumerate(affected_flrgs):
if aset.strLHS() in self.flrgs:
tmp.append(self.flrgs[aset.strLHS()].get_midpoint(tdisp) *
affected_flrgs_memberships[ct])
else:
tmp.append(aset.LHS[-1].get_midpoint(tdisp)*
affected_flrgs_memberships[ct])
pto = sum(tmp)
#print(pto)
@ -182,7 +221,9 @@ class HighOrderNonStationaryFTS(hofts.HighOrderFTS):
def forecastInterval(self, data, **kwargs):
time_displacement = kwargs.get("time_displacement",0)
time_displacement = kwargs.get("time_displacement", 0)
window_size = kwargs.get("window_size", 1)
ndata = np.array(self.doTransformations(data))
@ -190,21 +231,48 @@ class HighOrderNonStationaryFTS(hofts.HighOrderFTS):
ret = []
for k in np.arange(0, l):
for k in np.arange(self.order, l + 1):
tdisp = k + time_displacement
sample = ndata[k - self.order: k]
affected_sets = [ [set.name, set.membership(ndata[k], tdisp)]
for set in self.sets if set.membership(ndata[k], tdisp) > 0.0]
affected_flrgs, affected_flrgs_memberships = self._affected_flrgs(sample, k,
time_displacement, window_size)
# print([str(k) for k in affected_flrgs])
# print(affected_flrgs_memberships)
upper = []
lower = []
for aset in affected_sets:
lower.append(self.flrgs[aset[0]].get_lower(tdisp) * aset[1])
upper.append(self.flrgs[aset[0]].get_upper(tdisp) * aset[1])
tdisp = common.window_index(k + time_displacement, window_size)
if len(affected_flrgs) == 0:
aset = common.check_bounds(sample[-1], self.sets, tdisp)
lower.append(aset.get_lower(tdisp))
upper.append(aset.get_upper(tdisp))
elif len(affected_flrgs) == 1:
if affected_flrgs[0].strLHS() in self.flrgs:
flrg = affected_flrgs[0]
lower.append(self.flrgs[flrg.strLHS()].get_lower(tdisp))
upper.append(self.flrgs[flrg.strLHS()].get_upper(tdisp))
else:
lower.append(flrg.LHS[-1].get_lower(tdisp))
upper.append(flrg.LHS[-1].get_upper(tdisp))
else:
for ct, aset in enumerate(affected_flrgs):
if aset.strLHS() in self.flrgs:
lower.append(self.flrgs[aset.strLHS()].get_lower(tdisp) *
affected_flrgs_memberships[ct])
upper.append(self.flrgs[aset.strLHS()].get_upper(tdisp) *
affected_flrgs_memberships[ct])
else:
lower.append(aset.LHS[-1].get_lower(tdisp) *
affected_flrgs_memberships[ct])
upper.append(aset.LHS[-1].get_upper(tdisp) *
affected_flrgs_memberships[ct])
ret.append([sum(lower), sum(upper)])
ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]])
return ret

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@ -96,12 +96,18 @@ class NonStationaryFTS(fts.FTS):
tmp = []
if len(affected_sets) == 1 and self.method == 'fuzzy':
tmp.append(affected_sets[0][0].get_midpoint(tdisp))
aset = affected_sets[0][0]
if aset.name in self.flrgs:
tmp.append(self.flrgs[aset.name].get_midpoint(tdisp))
else:
tmp.append(aset.get_midpoint(tdisp))
else:
for aset in affected_sets:
if self.method == 'fuzzy':
if aset[0].name in self.flrgs:
tmp.append(self.flrgs[aset[0].name].get_midpoint(tdisp) * aset[1])
else:
tmp.append(aset[0].get_midpoint(tdisp) * aset[1])
elif self.method == 'maximum':
if aset.name in self.flrgs:
tmp.append(self.flrgs[aset.name].get_midpoint(tdisp))
@ -120,7 +126,7 @@ class NonStationaryFTS(fts.FTS):
def forecastInterval(self, data, **kwargs):
time_displacement = kwargs.get("time_displacement",0)
time_displacement = kwargs.get("time_displacement", 0)
window_size = kwargs.get("window_size", 1)
@ -132,16 +138,46 @@ class NonStationaryFTS(fts.FTS):
for k in np.arange(0, l):
# print("input: " + str(ndata[k]))
tdisp = common.window_index(k + time_displacement, window_size)
affected_sets = [ [set.name, set.membership(ndata[k], tdisp)]
for set in self.sets if set.membership(ndata[k], tdisp) > 0.0]
if self.method == 'fuzzy':
affected_sets = [[set, set.membership(ndata[k], tdisp)]
for set in self.sets if set.membership(ndata[k], tdisp) > 0.0]
elif self.method == 'maximum':
mv = [set.membership(ndata[k], tdisp) for set in self.sets]
ix = np.ravel(np.argwhere(mv == max(mv)))
affected_sets = [self.sets[x] for x in ix]
if len(affected_sets) == 0:
if self.method == 'fuzzy':
affected_sets.append([common.check_bounds(ndata[k], self.sets, tdisp), 1.0])
else:
affected_sets.append(common.check_bounds(ndata[k], self.sets, tdisp))
upper = []
lower = []
for aset in affected_sets:
lower.append(self.flrgs[aset[0]].get_lower(tdisp) * aset[1])
upper.append(self.flrgs[aset[0]].get_upper(tdisp) * aset[1])
if len(affected_sets) == 1:
#print(2)
aset = affected_sets[0][0]
if aset.name in self.flrgs:
lower.append(self.flrgs[aset.name].get_lower(tdisp))
upper.append(self.flrgs[aset.name].get_upper(tdisp))
else:
lower.append(aset.get_lower(tdisp))
upper.append(aset.get_upper(tdisp))
else:
for aset in affected_sets:
#print(aset)
if aset[0].name in self.flrgs:
lower.append(self.flrgs[aset[0].name].get_lower(tdisp) * aset[1])
upper.append(self.flrgs[aset[0].name].get_upper(tdisp) * aset[1])
else:
lower.append(aset[0].get_lower(tdisp) * aset[1])
upper.append(aset[0].get_upper(tdisp) * aset[1])
ret.append([sum(lower), sum(upper)])

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@ -60,7 +60,7 @@ class ProbabilisticWeightedFLRG(hofts.HighOrderFLRG):
for count, set in enumerate(self.LHS):
mv.append(set.membership(x[count]))
min_mv = np.prod(mv)
min_mv = np.min(mv)
return min_mv
def partition_function(self, uod, nbins=100):
@ -73,6 +73,7 @@ class ProbabilisticWeightedFLRG(hofts.HighOrderFLRG):
return self.Z
def get_midpoint(self):
'''Return the expectation of the PWFLRG, the weighted sum'''
return sum(np.array([self.get_RHSprobability(s) * self.RHS[s].centroid
for s in self.RHS.keys()]))
@ -495,6 +496,9 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
def forecastDistribution(self, data, **kwargs):
if not isinstance(data, (list, set, np.ndarray)):
data = [data]
smooth = kwargs.get("smooth", "none")
nbins = kwargs.get("num_bins", 100)

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@ -56,24 +56,28 @@ ws=12
trainp = passengers[:ts]
testp = passengers[ts:]
tmp_fsp = Grid.GridPartitioner(trainp[:ws], 15)
tmp_fsp = Grid.GridPartitioner(trainp[:50], 10)
fsp = common.PolynomialNonStationaryPartitioner(trainp, tmp_fsp, window_size=ws, degree=1)
#nsftsp = honsfts.HighOrderNonStationaryFTS("", partitioner=fsp)
nsftsp = nsfts.NonStationaryFTS("", partitioner=fsp, method='fuzzy')
nsftsp = honsfts.HighOrderNonStationaryFTS("", partitioner=fsp)
#nsftsp = nsfts.NonStationaryFTS("", partitioner=fsp, method='fuzzy')
#nsftsp.train(trainp, order=1, parameters=ws)
nsftsp.train(trainp, order=2, parameters=ws)
print(fsp)
#print(fsp)
#print(nsftsp)
#tmpp = nsftsp.forecast(passengers[55:65], time_displacement=55, window_size=ws)
tmpp = nsftsp.forecast(passengers[101:104], time_displacement=101, window_size=ws)
tmpi = nsftsp.forecastInterval(passengers[101:104], time_displacement=101, window_size=ws)
#print(passengers[100:120])
#print(tmpp)
#print(passengers[101:104])
print([k[0] for k in tmpi])
print(tmpp)
print([k[1] for k in tmpi])
#util.plot_sets(fsp.sets,tam=[10, 5], start=0, end=100, step=2, data=passengers[:100],
# window_size=ws, only_lines=False)

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@ -54,20 +54,28 @@ pfts1.shortname = "1st Order"
#print(pfts1_enrollments)
tmp = pfts1.forecast(data[3000:3020])
#tmp = pfts1.forecast(data[3000:3020])
tmp = pfts1.forecastInterval(data[3000:3020])
#tmp = pfts1.forecastInterval(data[3000:3020])
tmp = pfts1.forecastAheadInterval(data[3000:3020],20)
tmp = pfts1.forecastDistribution(data[3500])
tmp = pfts1.forecastAheadDistribution(data[3000:3020],20, method=3, h=0.45, kernel="gaussian")
print(tmp[0])
p = 0
for b in tmp[0].bins:
p += tmp[0].density(b)
print(p)
#tmp = pfts1.forecastAheadInterval(data[3000:3020],20)
#tmp = pfts1.forecastAheadDistribution(data[3000:3020],20, method=3, h=0.45, kernel="gaussian")
#print(tmp[0])
#print(tmp[0].quantile([0.05, 0.95]))
#pfts1_enrollments.AprioriPDF
#norm = pfts1_enrollments.global_frequency_count
#uod = pfts1_enrollments.get_UoD()
#uod = pfts1.get_UoD()
#for k in sorted(pfts1_enrollments.flrgs.keys())
# flrg = pfts1_enrollments.flrgs[k]