574 lines
20 KiB
Python
574 lines
20 KiB
Python
#!/usr/bin/python
|
|
# -*- coding: utf8 -*-
|
|
|
|
import numpy as np
|
|
import pandas as pd
|
|
import math
|
|
from operator import itemgetter
|
|
from pyFTS.common import FLR, FuzzySet, SortedCollection
|
|
from pyFTS import hofts, ifts, tree
|
|
|
|
|
|
class ProbabilisticWeightedFLRG(hofts.HighOrderFLRG):
|
|
def __init__(self, order):
|
|
super(ProbabilisticWeightedFLRG, self).__init__(order)
|
|
self.RHS = {}
|
|
self.frequencyCount = 0.0
|
|
|
|
def appendRHS(self, c):
|
|
self.frequencyCount += 1.0
|
|
if c.name in self.RHS:
|
|
self.RHS[c.name] += 1.0
|
|
else:
|
|
self.RHS[c.name] = 1.0
|
|
|
|
def appendRHSFuzzy(self, c, mv):
|
|
self.frequencyCount += mv
|
|
if c.name in self.RHS:
|
|
self.RHS[c.name] += mv
|
|
else:
|
|
self.RHS[c.name] = mv
|
|
|
|
def get_probability(self, c):
|
|
return self.RHS[c] / self.frequencyCount
|
|
|
|
def __str__(self):
|
|
tmp2 = ""
|
|
for c in sorted(self.RHS):
|
|
if len(tmp2) > 0:
|
|
tmp2 = tmp2 + ", "
|
|
tmp2 = tmp2 + "(" + str(round(self.RHS[c] / self.frequencyCount, 3)) + ")" + c
|
|
return self.strLHS() + " -> " + tmp2
|
|
|
|
|
|
class ProbabilisticWeightedFTS(ifts.IntervalFTS):
|
|
def __init__(self, name, update=True):
|
|
super(ProbabilisticWeightedFTS, self).__init__("PWFTS")
|
|
self.shortname = "PWFTS " + name
|
|
self.name = "Probabilistic FTS"
|
|
self.detail = "Silva, P.; Guimarães, F.; Sadaei, H."
|
|
self.flrgs = {}
|
|
self.globalFrequency = 0
|
|
self.hasPointForecasting = True
|
|
self.hasIntervalForecasting = True
|
|
self.hasDistributionForecasting = True
|
|
self.isHighOrder = True
|
|
self.auto_update = update
|
|
|
|
def train(self, data, sets, order=1,parameters=None):
|
|
|
|
data = self.doTransformations(data, updateUoD=True)
|
|
|
|
self.order = order
|
|
self.sets = sets
|
|
for s in self.sets: self.setsDict[s.name] = s
|
|
tmpdata = FuzzySet.fuzzySeries(data, sets)
|
|
flrs = FLR.generateRecurrentFLRs(tmpdata)
|
|
self.flrgs = self.generateFLRG(flrs)
|
|
#self.flrgs = self.generateFLRG2(data)
|
|
|
|
def generateFLRG2(self, data):
|
|
flrgs = {}
|
|
l = len(data)
|
|
for k in np.arange(self.order, l):
|
|
if self.dump: print("FLR: " + str(k))
|
|
flrg = ProbabilisticWeightedFLRG(self.order)
|
|
|
|
sample = data[k - self.order: k]
|
|
|
|
mvs = FuzzySet.fuzzyInstances(sample, self.sets)
|
|
lags = {}
|
|
|
|
for o in np.arange(0, self.order):
|
|
_sets = [self.sets[kk] for kk in np.arange(0, len(self.sets)) if mvs[o][kk] > 0]
|
|
|
|
lags[o] = _sets
|
|
|
|
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))))
|
|
|
|
lhs_mv = []
|
|
for c, e in enumerate(path, start=0):
|
|
lhs_mv.append( e.membership( sample[c] ) )
|
|
flrg.appendLHS(e)
|
|
|
|
if flrg.strLHS() not in flrgs:
|
|
flrgs[flrg.strLHS()] = flrg;
|
|
|
|
mv = FuzzySet.fuzzyInstance(data[k], self.sets)
|
|
|
|
rhs_mv = [mv[kk] for kk in np.arange(0, len(self.sets)) if mv[kk] > 0]
|
|
_sets = [self.sets[kk] for kk in np.arange(0, len(self.sets)) if mv[kk] > 0]
|
|
|
|
for c, e in enumerate(_sets, start=0):
|
|
flrgs[flrg.strLHS()].appendRHSFuzzy(e,rhs_mv[c]*max(lhs_mv))
|
|
|
|
self.globalFrequency += max(lhs_mv)
|
|
|
|
return (flrgs)
|
|
|
|
def generateFLRG(self, flrs):
|
|
flrgs = {}
|
|
l = len(flrs)
|
|
for k in np.arange(self.order, l+1):
|
|
if self.dump: print("FLR: " + str(k))
|
|
flrg = ProbabilisticWeightedFLRG(self.order)
|
|
|
|
for kk in np.arange(k - self.order, k):
|
|
flrg.appendLHS(flrs[kk].LHS)
|
|
if self.dump: print("LHS: " + str(flrs[kk]))
|
|
|
|
if flrg.strLHS() in flrgs:
|
|
flrgs[flrg.strLHS()].appendRHS(flrs[k-1].RHS)
|
|
else:
|
|
flrgs[flrg.strLHS()] = flrg
|
|
flrgs[flrg.strLHS()].appendRHS(flrs[k-1].RHS)
|
|
if self.dump: print("RHS: " + str(flrs[k-1]))
|
|
|
|
self.globalFrequency += 1
|
|
return (flrgs)
|
|
|
|
def update_model(self,data):
|
|
|
|
fzzy = FuzzySet.fuzzySeries(data, self.sets)
|
|
|
|
flrg = ProbabilisticWeightedFLRG(self.order)
|
|
|
|
for k in np.arange(0, self.order): flrg.appendLHS(fzzy[k])
|
|
|
|
if flrg.strLHS() in self.flrgs:
|
|
self.flrgs[flrg.strLHS()].appendRHS(fzzy[self.order])
|
|
else:
|
|
self.flrgs[flrg.strLHS()] = flrg
|
|
self.flrgs[flrg.strLHS()].appendRHS(fzzy[self.order])
|
|
|
|
self.globalFrequency += 1
|
|
|
|
def add_new_PWFLGR(self, flrg):
|
|
if flrg.strLHS() not in self.flrgs:
|
|
tmp = ProbabilisticWeightedFLRG(self.order)
|
|
for fs in flrg.LHS: tmp.appendLHS(fs)
|
|
tmp.appendRHS(flrg.LHS[-1])
|
|
self.flrgs[tmp.strLHS()] = tmp;
|
|
self.globalFrequency += 1
|
|
|
|
def get_probability(self, flrg):
|
|
if flrg.strLHS() in self.flrgs:
|
|
return self.flrgs[flrg.strLHS()].frequencyCount / self.globalFrequency
|
|
else:
|
|
self.add_new_PWFLGR(flrg)
|
|
return self.get_probability(flrg)
|
|
|
|
def getMidpoints(self, flrg):
|
|
if flrg.strLHS() in self.flrgs:
|
|
tmp = self.flrgs[flrg.strLHS()]
|
|
ret = sum(np.array([tmp.get_probability(s) * self.setsDict[s].centroid for s in tmp.RHS]))
|
|
else:
|
|
pi = 1 / len(flrg.LHS)
|
|
ret = sum(np.array([pi * s.centroid for s in flrg.LHS]))
|
|
return ret
|
|
|
|
def getUpper(self, flrg):
|
|
if flrg.strLHS() in self.flrgs:
|
|
tmp = self.flrgs[flrg.strLHS()]
|
|
ret = sum(np.array([tmp.get_probability(s) * self.setsDict[s].upper for s in tmp.RHS]))
|
|
else:
|
|
pi = 1 / len(flrg.LHS)
|
|
ret = sum(np.array([pi * s.upper for s in flrg.LHS]))
|
|
return ret
|
|
|
|
def getLower(self, flrg):
|
|
if flrg.strLHS() in self.flrgs:
|
|
tmp = self.flrgs[flrg.strLHS()]
|
|
ret = sum(np.array([tmp.get_probability(s) * self.setsDict[s].lower for s in tmp.RHS]))
|
|
else:
|
|
pi = 1 / len(flrg.LHS)
|
|
ret = sum(np.array([pi * s.lower for s in flrg.LHS]))
|
|
return ret
|
|
|
|
def forecast(self, data):
|
|
|
|
ndata = np.array(self.doTransformations(data))
|
|
|
|
l = len(ndata)
|
|
|
|
ret = []
|
|
|
|
for k in np.arange(self.order - 1, l):
|
|
|
|
# print(k)
|
|
|
|
affected_flrgs = []
|
|
affected_rhs = []
|
|
affected_flrgs_memberships = []
|
|
norms = []
|
|
|
|
mp = []
|
|
|
|
# Find the sets which membership > 0 for each lag
|
|
count = 0
|
|
lags = {}
|
|
if self.order > 1:
|
|
subset = ndata[k - (self.order - 1): k + 1]
|
|
|
|
for instance in subset:
|
|
mb = FuzzySet.fuzzyInstance(instance, self.sets)
|
|
tmp = np.argwhere(mb)
|
|
idx = np.ravel(tmp) # flatten the array
|
|
|
|
if idx.size == 0: # the element is out of the bounds of the Universe of Discourse
|
|
if instance <= self.sets[0].lower:
|
|
idx = [0]
|
|
elif instance >= self.sets[-1].upper:
|
|
idx = [len(self.sets) - 1]
|
|
else:
|
|
raise Exception(instance)
|
|
|
|
lags[count] = idx
|
|
count = count + 1
|
|
|
|
# Build the tree with all possible paths
|
|
|
|
root = tree.FLRGTreeNode(None)
|
|
|
|
self.buildTree(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 = hofts.HighOrderFLRG(self.order)
|
|
for kk in path: flrg.appendLHS(self.sets[kk])
|
|
|
|
assert len(flrg.LHS) == subset.size, str(subset) + " -> " + str([s.name for s in flrg.LHS])
|
|
|
|
##
|
|
affected_flrgs.append(flrg)
|
|
|
|
# Find the general membership of FLRG
|
|
affected_flrgs_memberships.append(min(self.getSequenceMembership(subset, flrg.LHS)))
|
|
|
|
else:
|
|
|
|
mv = FuzzySet.fuzzyInstance(ndata[k], self.sets) # get all membership values
|
|
tmp = np.argwhere(mv) # get the indices of values > 0
|
|
idx = np.ravel(tmp) # flatten the array
|
|
|
|
if idx.size == 0: # the element is out of the bounds of the Universe of Discourse
|
|
if ndata[k] <= self.sets[0].lower:
|
|
idx = [0]
|
|
elif ndata[k] >= self.sets[-1].upper:
|
|
idx = [len(self.sets) - 1]
|
|
else:
|
|
raise Exception(ndata[k])
|
|
|
|
for kk in idx:
|
|
flrg = hofts.HighOrderFLRG(self.order)
|
|
flrg.appendLHS(self.sets[kk])
|
|
affected_flrgs.append(flrg)
|
|
affected_flrgs_memberships.append(mv[kk])
|
|
|
|
count = 0
|
|
for flrg in affected_flrgs:
|
|
# achar o os bounds de cada FLRG, ponderados pela probabilidade e pertinência
|
|
norm = self.get_probability(flrg) * affected_flrgs_memberships[count]
|
|
if norm == 0:
|
|
norm = self.get_probability(flrg) # * 0.001
|
|
mp.append(norm * self.getMidpoints(flrg))
|
|
norms.append(norm)
|
|
count = count + 1
|
|
|
|
# gerar o intervalo
|
|
norm = sum(norms)
|
|
if norm == 0:
|
|
ret.append(0)
|
|
else:
|
|
ret.append(sum(mp) / norm)
|
|
|
|
if self.auto_update and k > self.order+1: self.update_model(ndata[k - self.order - 1 : k])
|
|
|
|
ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]])
|
|
|
|
return ret
|
|
|
|
def forecastInterval(self, data):
|
|
|
|
ndata = np.array(self.doTransformations(data))
|
|
|
|
l = len(ndata)
|
|
|
|
ret = []
|
|
|
|
for k in np.arange(self.order - 1, l):
|
|
|
|
# print(k)
|
|
|
|
affected_flrgs = []
|
|
affected_flrgs_memberships = []
|
|
norms = []
|
|
|
|
up = []
|
|
lo = []
|
|
|
|
# Find the sets which membership > 0 for each lag
|
|
count = 0
|
|
lags = {}
|
|
if self.order > 1:
|
|
subset = ndata[k - (self.order - 1): k + 1]
|
|
|
|
for instance in subset:
|
|
mb = FuzzySet.fuzzyInstance(instance, self.sets)
|
|
tmp = np.argwhere(mb)
|
|
idx = np.ravel(tmp) # flatten the array
|
|
|
|
if idx.size == 0: # the element is out of the bounds of the Universe of Discourse
|
|
if math.isclose(instance, self.sets[0].lower) or instance < self.sets[0].lower:
|
|
idx = [0]
|
|
elif math.isclose(instance, self.sets[-1].upper) or instance > self.sets[-1].upper:
|
|
idx = [len(self.sets) - 1]
|
|
else:
|
|
raise Exception("Data exceed the known bounds [%s, %s] of universe of discourse: %s" %
|
|
(self.sets[0].lower, self.sets[-1].upper, instance))
|
|
|
|
lags[count] = idx
|
|
count += 1
|
|
|
|
# Build the tree with all possible paths
|
|
|
|
root = tree.FLRGTreeNode(None)
|
|
|
|
self.buildTree(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 = hofts.HighOrderFLRG(self.order)
|
|
for kk in path: flrg.appendLHS(self.sets[kk])
|
|
|
|
assert len(flrg.LHS) == subset.size, str(subset) + " -> " + str([s.name for s in flrg.LHS])
|
|
|
|
##
|
|
affected_flrgs.append(flrg)
|
|
|
|
# Find the general membership of FLRG
|
|
affected_flrgs_memberships.append(min(self.getSequenceMembership(subset, flrg.LHS)))
|
|
|
|
else:
|
|
|
|
mv = FuzzySet.fuzzyInstance(ndata[k], self.sets) # get all membership values
|
|
tmp = np.argwhere(mv) # get the indices of values > 0
|
|
idx = np.ravel(tmp) # flatten the array
|
|
|
|
if idx.size == 0: # the element is out of the bounds of the Universe of Discourse
|
|
if math.isclose(ndata[k], self.sets[0].lower) or ndata[k] < self.sets[0].lower:
|
|
idx = [0]
|
|
elif math.isclose(ndata[k], self.sets[-1].upper) or ndata[k] > self.sets[-1].upper:
|
|
idx = [len(self.sets) - 1]
|
|
else:
|
|
raise Exception("Data exceed the known bounds [%s, %s] of universe of discourse: %s" %
|
|
(self.sets[0].lower, self.sets[-1].upper, ndata[k]))
|
|
|
|
for kk in idx:
|
|
flrg = hofts.HighOrderFLRG(self.order)
|
|
flrg.appendLHS(self.sets[kk])
|
|
affected_flrgs.append(flrg)
|
|
affected_flrgs_memberships.append(mv[kk])
|
|
|
|
count = 0
|
|
for flrg in affected_flrgs:
|
|
# achar o os bounds de cada FLRG, ponderados pela probabilidade e pertinência
|
|
norm = self.get_probability(flrg) * affected_flrgs_memberships[count]
|
|
if norm == 0:
|
|
norm = self.get_probability(flrg) # * 0.001
|
|
up.append(norm * self.getUpper(flrg))
|
|
lo.append(norm * self.getLower(flrg))
|
|
norms.append(norm)
|
|
count = count + 1
|
|
|
|
# gerar o intervalo
|
|
norm = sum(norms)
|
|
if norm == 0:
|
|
ret.append([0, 0])
|
|
else:
|
|
lo_ = self.doInverseTransformations(sum(lo) / norm, params=[data[k - (self.order - 1): k + 1]])
|
|
up_ = self.doInverseTransformations(sum(up) / norm, params=[data[k - (self.order - 1): k + 1]])
|
|
ret.append([lo_, up_])
|
|
|
|
return ret
|
|
|
|
def forecastAhead(self, data, steps):
|
|
ret = [data[k] for k in np.arange(len(data) - self.order, len(data))]
|
|
|
|
for k in np.arange(self.order - 1, steps):
|
|
|
|
if ret[-1] <= self.sets[0].lower or ret[-1] >= self.sets[-1].upper:
|
|
ret.append(ret[-1])
|
|
else:
|
|
mp = self.forecast([ret[x] for x in np.arange(k - self.order, k)])
|
|
|
|
ret.append(mp)
|
|
|
|
return ret
|
|
|
|
def forecastAheadInterval(self, data, steps):
|
|
|
|
l = len(data)
|
|
|
|
ret = [[data[k], data[k]] for k in np.arange(l - self.order, l)]
|
|
|
|
for k in np.arange(self.order, steps+self.order):
|
|
|
|
if (len(self.transformations) > 0 and ret[-1][0] <= self.sets[0].lower and ret[-1][1] >= self.sets[
|
|
-1].upper) or (len(self.transformations) == 0 and ret[-1][0] <= self.original_min and ret[-1][
|
|
1] >= self.original_max):
|
|
ret.append(ret[-1])
|
|
else:
|
|
lower = self.forecastInterval([ret[x][0] for x in np.arange(k - self.order, k)])
|
|
upper = self.forecastInterval([ret[x][1] for x in np.arange(k - self.order, k)])
|
|
|
|
ret.append([np.min(lower), np.max(upper)])
|
|
|
|
return ret
|
|
|
|
def getGridClean(self, resolution):
|
|
grid = {}
|
|
|
|
if len(self.transformations) == 0:
|
|
_min = self.sets[0].lower
|
|
_max = self.sets[-1].upper
|
|
else:
|
|
_min = self.original_min
|
|
_max = self.original_max
|
|
|
|
for sbin in np.arange(_min,_max, resolution):
|
|
grid[sbin] = 0
|
|
|
|
return grid
|
|
|
|
def gridCount(self, grid, resolution, index, interval):
|
|
#print(interval)
|
|
for k in index.inside(interval[0],interval[1]):
|
|
#print(k)
|
|
grid[k] += 1
|
|
return grid
|
|
|
|
def gridCountPoint(self, grid, resolution, index, point):
|
|
k = index.find_ge(point)
|
|
# print(k)
|
|
grid[k] += 1
|
|
return grid
|
|
|
|
def forecastAheadDistribution(self, data, steps, resolution, parameters=2):
|
|
|
|
ret = []
|
|
|
|
intervals = self.forecastAheadInterval(data, steps)
|
|
|
|
grid = self.getGridClean(resolution)
|
|
|
|
index = SortedCollection.SortedCollection(iterable=grid.keys())
|
|
|
|
if parameters == 1:
|
|
|
|
grids = []
|
|
for k in np.arange(0, steps):
|
|
grids.append(self.getGridClean(resolution))
|
|
|
|
for k in np.arange(self.order, steps + self.order):
|
|
|
|
lags = {}
|
|
|
|
cc = 0
|
|
|
|
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 parameters == 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
|