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PWFTS new m-step-ahead probabilistic forecasting method

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Petrônio Cândido 2017-09-22 21:08:26 -03:00
parent 1ec03d34de
commit 9fb79ea080
5 changed files with 83 additions and 137 deletions
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7
pyFTS/ensemble/multiseasonal.py
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@ -77,6 +77,8 @@ class SeasonalEnsembleFTS(ensemble.EnsembleFTS):
smooth = kwargs.get("smooth", "KDE") smooth = kwargs.get("smooth", "KDE")
alpha = kwargs.get("alpha", None) alpha = kwargs.get("alpha", None)
uod = self.get_UoD()
for k in data.index: for k in data.index:
tmp = self.get_models_forecasts(data.ix[k]) tmp = self.get_models_forecasts(data.ix[k])
@ -88,9 +90,8 @@ class SeasonalEnsembleFTS(ensemble.EnsembleFTS):
name = str(self.indexer.get_index(data.ix[k])) name = str(self.indexer.get_index(data.ix[k]))
dist = ProbabilityDistribution.ProbabilityDistribution(smooth, dist = ProbabilityDistribution.ProbabilityDistribution(smooth, uod=uod, data=tmp,
uod=[self.original_min, self.original_max], name=name, **kwargs)
data=tmp, name=name, **kwargs)
ret.append(dist) ret.append(dist)

2
pyFTS/probabilistic/ProbabilityDistribution.py
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@ -17,7 +17,7 @@ class ProbabilityDistribution(object):
self.type = type self.type = type
if self.type == "KDE": if self.type == "KDE":
self.kde = kde.KernelSmoothing(kwargs.get("h", 10), kwargs.get("method", "epanechnikov")) self.kde = kde.KernelSmoothing(kwargs.get("h", 0.5), kwargs.get("kernel", "epanechnikov"))
self.nbins = kwargs.get("num_bins", 100) self.nbins = kwargs.get("num_bins", 100)

27
pyFTS/probabilistic/kde.py
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@ -8,39 +8,38 @@ Kernel Density Estimation
class KernelSmoothing(object): class KernelSmoothing(object):
"""Kernel Density Estimation""" """Kernel Density Estimation"""
def __init__(self,h, method="epanechnikov"): def __init__(self,h, kernel="epanechnikov"):
self.h = h self.h = h
self.method = method self.kernel = kernel
self.transf = Transformations.Scale(min=0,max=1) self.transf = Transformations.Scale(min=0,max=1)
def kernel(self, u): def kernel_function(self, u):
if self.method == "epanechnikov": if self.kernel == "epanechnikov":
tmp = (3/4)*(1.0 - u**2) tmp = (3/4)*(1.0 - u**2)
return tmp if tmp > 0 else 0 return tmp if tmp > 0 else 0
elif self.method == "gaussian": elif self.kernel == "gaussian":
return (1.0/np.sqrt(2*np.pi))*np.exp(-0.5*u**2) return (1.0/np.sqrt(2*np.pi))*np.exp(-0.5*u**2)
elif self.method == "uniform": elif self.kernel == "uniform":
return 0.5 return 0.5
elif self.method == "triangular": elif self.kernel == "triangular":
tmp = 1.0 - np.abs(u) tmp = 1.0 - np.abs(u)
return tmp if tmp > 0 else 0 return tmp if tmp > 0 else 0
elif self.method == "logistic": elif self.kernel == "logistic":
return 1.0/(np.exp(u)+2+np.exp(-u)) return 1.0/(np.exp(u)+2+np.exp(-u))
elif self.method == "cosine": elif self.kernel == "cosine":
return (np.pi/4.0)*np.cos((np.pi/2.0)*u) return (np.pi/4.0)*np.cos((np.pi/2.0)*u)
elif self.method == "sigmoid": elif self.kernel == "sigmoid":
return (2.0/np.pi)*(1.0/(np.exp(u)+np.exp(-u))) return (2.0/np.pi)*(1.0/(np.exp(u)+np.exp(-u)))
elif self.method == "tophat": elif self.kernel == "tophat":
return 1 if np.abs(u) < 0.5 else 0 return 1 if np.abs(u) < 0.5 else 0
elif self.method == "exponential": elif self.kernel == "exponential":
return 0.5 * np.exp(-np.abs(u)) return 0.5 * np.exp(-np.abs(u))
def probability(self, x, data): def probability(self, x, data):
l = len(data) l = len(data)
ndata = self.transf.apply(data) ndata = self.transf.apply(data)
nx = self.transf.apply(x) nx = self.transf.apply(x)
p = sum([self.kernel((nx - k)/self.h) for k in ndata]) / l*self.h p = sum([self.kernel_function((nx - k)/self.h) for k in ndata]) / l*self.h
return p return p

163
pyFTS/pwfts.py
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@ -566,24 +566,28 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
ret = [] ret = []
resolution = kwargs.get('resolution',100) method = kwargs.get('method', 2)
smooth = "KDE" if method != 4 else "none"
nbins = kwargs.get("num_bins", 100)
method = kwargs.get('method',2) uod = self.get_UoD()
_bins = np.linspace(uod[0], uod[1], nbins).tolist()
if method != 4:
intervals = self.forecastAheadInterval(data, steps) intervals = self.forecastAheadInterval(data, steps)
else:
grid = self.getGridClean(resolution) l = len(data)
for k in np.arange(l - self.order, l):
index = SortedCollection.SortedCollection(iterable=grid.keys()) dist = ProbabilityDistribution.ProbabilityDistribution(smooth, uod=uod, bins=_bins, **kwargs)
dist.set(data[k], 1.0)
if method == 1: ret.append(dist)
grids = []
for k in np.arange(0, steps):
grids.append(self.getGridClean(resolution))
for k in np.arange(self.order, steps + self.order): for k in np.arange(self.order, steps + self.order):
data = []
if method == 1:
lags = {} lags = {}
cc = 0 cc = 0
@ -612,139 +616,74 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
self.buildTreeWithoutOrder(root, lags, 0) self.buildTreeWithoutOrder(root, lags, 0)
# Trace the possible paths # Trace the possible paths
for p in root.paths(): for p in root.paths():
path = list(reversed(list(filter(None.__ne__, p)))) path = list(reversed(list(filter(None.__ne__, p))))
qtle = self.forecastInterval(path) qtle = np.ravel(self.forecastInterval(path))
grids[k - self.order] = self.gridCount(grids[k - self.order], resolution, index, np.ravel(qtle)) data.extend(np.linspace(qtle[0],qtle[1],100).tolist())
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: 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): for qt in np.arange(0, 50, 1):
# print(qt) # print(qt)
qtle_lower = self.forecastInterval( qtle_lower = self.forecastInterval(
[intervals[x][0] + qt * ((intervals[x][1] - intervals[x][0]) / 100) for x in [intervals[x][0] + qt * ((intervals[x][1] - intervals[x][0]) / 100) for x in
np.arange(k - self.order, k)]) np.arange(k - self.order, k)])
grid = self.gridCount(grid, resolution, index, np.ravel(qtle_lower)) qtle_lower = np.ravel(qtle_lower)
data.extend(np.linspace(qtle_lower[0], qtle_lower[1], 100).tolist())
qtle_upper = self.forecastInterval( qtle_upper = self.forecastInterval(
[intervals[x][1] - qt * ((intervals[x][1] - intervals[x][0]) / 100) for x in [intervals[x][1] - qt * ((intervals[x][1] - intervals[x][0]) / 100) for x in
np.arange(k - self.order, k)]) np.arange(k - self.order, k)])
grid = self.gridCount(grid, resolution, index, np.ravel(qtle_upper)) qtle_upper = np.ravel(qtle_upper)
data.extend(np.linspace(qtle_upper[0], qtle_upper[1], 100).tolist())
qtle_mid = self.forecastInterval( qtle_mid = self.forecastInterval(
[intervals[x][0] + (intervals[x][1] - intervals[x][0]) / 2 for x in np.arange(k - self.order, k)]) [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)) qtle_mid = np.ravel(qtle_mid)
data.extend(np.linspace(qtle_mid[0], qtle_mid[1], 100).tolist())
tmp = np.array([grid[k] for k in sorted(grid) if not np.isnan(grid[k])]) elif method == 3:
try: i = intervals[k]
ret.append(tmp / sum(tmp))
except Exception as ex: data = np.linspace(i[0],i[1],100).tolist()
ret.append(0)
else: else:
ret = [] dist = ProbabilityDistribution.ProbabilityDistribution(smooth, bins=_bins,
uod=uod, **kwargs)
lags = {}
for k in np.arange(self.order, steps + self.order): cc = 0
grid = self.getGridClean(resolution)
grid = self.gridCount(grid, resolution, index, intervals[k])
tmp = np.array([grid[k] for k in sorted(grid)]) for dd in ret[k - self.order: k]:
vals = [float(v) for v in dd.bins if round(dd.density(v),4) > 0]
lags[cc] = sorted(vals)
cc += 1
ret.append(tmp / sum(tmp)) root = tree.FLRGTreeNode(None)
grid = self.getGridClean(resolution) self.buildTreeWithoutOrder(root, lags, 0)
df = pd.DataFrame(ret, columns=sorted(grid))
return df
def density(self, x, num_bins=100): # Trace the possible paths
affected_flrgs = [] for p in root.paths():
affected_flrgs_memberships = [] path = list(reversed(list(filter(None.__ne__, p))))
mv = FuzzySet.fuzzyInstance(x, self.sets)
tmp = np.argwhere(mv)
idx = np.ravel(tmp)
if idx.size == 0: # the element is out of the bounds of the Universe of Discourse pk = np.prod([ret[k - self.order + o].density(path[o])
if x <= self.sets[0].lower: for o in np.arange(0,self.order)])
idx = [0]
elif x >= self.sets[-1].upper:
idx = [len(self.sets) - 1]
else:
raise Exception(x)
for kk in idx: d = self.forecastDistribution(path)[0]
flrg = ProbabilisticWeightedFLRG(self.order)
flrg.appendLHS(self.sets[kk])
affected_flrgs.append(flrg)
affected_flrgs_memberships.append(mv[kk])
total_prob = 0.0 for bin in _bins:
dist.set(bin, dist.density(bin) + pk * d.density(bin))
for count, flrg in enumerate(affected_flrgs): if method != 4:
_flrg = self.flrgs[flrg.strLHS()] dist = ProbabilityDistribution.ProbabilityDistribution(smooth, bins=_bins, data=data,
pk = _flrg.frequency_count / self.global_frequency_count uod=uod, **kwargs)
priori = pk * (affected_flrgs_memberships[count]) # / _flrg.partition_function(uod=self.get_UoD(), nbins=num_bins))
#print(flrg.strLHS() + ": PK=" + str(pk) + " Priori=" + str(priori))
#posteriori = self.get_conditional_probability(k, flrg) * priori
total_prob += priori
return total_prob ret.append(dist)
def AprioriPDF(self, **kwargs): return ret
nbins = kwargs.get('num_bins', 100)
pdf = ProbabilityDistribution.ProbabilityDistribution(uod=[self.original_min, self.original_max], num_bins=nbins)
t = 0.0
for k in pdf.bins:
#print("BIN: " + str(k) )
affected_flrgs = []
affected_flrgs_memberships = []
mv = FuzzySet.fuzzyInstance(k, self.sets)
tmp = np.argwhere(mv)
idx = np.ravel(tmp)
if idx.size == 0: # the element is out of the bounds of the Universe of Discourse
if k <= self.sets[0].lower:
idx = [0]
elif k >= self.sets[-1].upper:
idx = [len(self.sets) - 1]
else:
raise Exception(k)
for kk in idx:
flrg = ProbabilisticWeightedFLRG(self.order)
flrg.appendLHS(self.sets[kk])
affected_flrgs.append(flrg)
affected_flrgs_memberships.append(mv[kk])
total_prob = 0.0
for count, flrg in enumerate(affected_flrgs):
_flrg = self.flrgs[flrg.strLHS()]
pk = _flrg.frequency_count / self.global_frequency_count
priori = pk * (affected_flrgs_memberships[count] / _flrg.partition_function(uod=self.get_UoD()))
#print(flrg.strLHS() + ": PK=" + str(pk) + " Priori=" + str(priori))
posteriori = self.get_conditional_probability(k, flrg) * priori
total_prob += posteriori
t += total_prob
pdf.set(k, total_prob)
print(t)
return pdf
def __str__(self): def __str__(self):
tmp = self.name + ":\n" tmp = self.name + ":\n"

21
pyFTS/tests/pwfts.py
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@ -22,8 +22,15 @@ from numpy import random
os.chdir("/home/petronio/dados/Dropbox/Doutorado/Codigos/") os.chdir("/home/petronio/dados/Dropbox/Doutorado/Codigos/")
'''
enrollments = pd.read_csv("DataSets/Enrollments.csv", sep=";") enrollments = pd.read_csv("DataSets/Enrollments.csv", sep=";")
enrollments = np.array(enrollments["Enrollments"]) enrollments = np.array(enrollments["Enrollments"])
'''
taiexpd = pd.read_csv("DataSets/TAIEX.csv", sep=",")
data = np.array(taiexpd["avg"][:5000])
del(taiexpd)
import importlib import importlib
import pandas as pd import pandas as pd
@ -37,20 +44,20 @@ from pyFTS.benchmarks import benchmarks as bchmk
#uod = [10162, 21271] #uod = [10162, 21271]
enrollments_fs1 = Grid.GridPartitioner(enrollments, 6) fs1 = Grid.GridPartitioner(data[:3000], 30)
#for s in enrollments_fs1.sets: #for s in enrollments_fs1.sets:
# print(s) #.partition_function(uod, 100)) # print(s) #.partition_function(uod, 100))
pfts1_enrollments = pwfts.ProbabilisticWeightedFTS("1", partitioner=enrollments_fs1) pfts1 = pwfts.ProbabilisticWeightedFTS("1", partitioner=fs1)
pfts1_enrollments.train(enrollments, None, 1) pfts1.train(data, None, 1)
pfts1_enrollments.shortname = "1st Order" pfts1.shortname = "1st Order"
#print(pfts1_enrollments) #print(pfts1_enrollments)
tmp = pfts1_enrollments.forecastDistribution([15000]) tmp = pfts1.forecastAheadDistribution(data[3000:3020],20, method=3, h=0.45, kernel="gaussian")
#print(tmp[0]) print(tmp[0])
print(tmp[0].quantile([0.05, 0.95])) #print(tmp[0].quantile([0.05, 0.95]))
#pfts1_enrollments.AprioriPDF #pfts1_enrollments.AprioriPDF
#norm = pfts1_enrollments.global_frequency_count #norm = pfts1_enrollments.global_frequency_count