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Bugfixes on nonstationary methods

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This commit is contained in:
Petrônio Cândido 2018-04-11 15:03:21 -03:00
parent b65af00526
commit 4ba6c16a2f
7 changed files with 70 additions and 185 deletions
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24
pyFTS/models/nonstationary/common.py
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@ -205,11 +205,11 @@ def fuzzify(inst, t, fuzzySets):
return ret return ret
def fuzzySeries(data, fuzzySets, window_size=1, method='fuzzy', const_t= None): def fuzzySeries(data, fuzzySets, ordered_sets, window_size=1, method='fuzzy', const_t= None):
fts = [] fts = []
for t, i in enumerate(data): for t, i in enumerate(data):
tdisp = window_index(t, window_size) if const_t is None else const_t tdisp = window_index(t, window_size) if const_t is None else const_t
mv = np.array([fs.membership(i, tdisp) for fs in fuzzySets]) mv = np.array([fuzzySets[fs].membership(i, tdisp) for fs in ordered_sets])
if len(mv) == 0: if len(mv) == 0:
sets = [check_bounds(i, fuzzySets, tdisp)] sets = [check_bounds(i, fuzzySets, tdisp)]
else: else:
@ -218,7 +218,7 @@ def fuzzySeries(data, fuzzySets, window_size=1, method='fuzzy', const_t= None):
elif method == 'maximum': elif method == 'maximum':
mx = max(mv) mx = max(mv)
ix = np.ravel(np.argwhere(mv == mx)) ix = np.ravel(np.argwhere(mv == mx))
sets = [fuzzySets[i] for i in ix] sets = [fuzzySets[ordered_sets[i]] for i in ix]
fts.append(sets) fts.append(sets)
return fts return fts
@ -229,15 +229,15 @@ def window_index(t, window_size):
return t - (t % window_size) return t - (t % window_size)
def check_bounds(data, sets, t): def check_bounds(data, partitioner, t):
if data < sets[0].get_lower(t): if data < partitioner.lower_set().get_lower(t):
return sets[0] return partitioner.lower_set()
elif data > sets[-1].get_upper(t): elif data > partitioner.upper_set().get_upper(t):
return sets[-1] return partitioner.upper_set()
def check_bounds_index(data, sets, t): def check_bounds_index(data, partitioner, t):
if data < sets[0].get_lower(t): if data < partitioner.lower_set().get_lower(t):
return 0 return 0
elif data > sets[-1].get_upper(t): elif data > partitioner.upper_set().get_upper(t):
return len(sets) -1 return len(partitioner.sets) -1

15
pyFTS/models/nonstationary/cvfts.py
View File

@ -23,13 +23,10 @@ class ConditionalVarianceFTS(chen.ConventionalFTS):
self.max_stack = [0,0,0] self.max_stack = [0,0,0]
def train(self, ndata, **kwargs): def train(self, ndata, **kwargs):
if kwargs.get('sets', None) is not None:
self.sets = kwargs.get('sets', None)
self.min_tx = min(ndata) self.min_tx = min(ndata)
self.max_tx = max(ndata) self.max_tx = max(ndata)
tmpdata = common.fuzzySeries(ndata, self.sets, method='fuzzy', const_t=0) tmpdata = common.fuzzySeries(ndata, self.sets, self.partitioner.ordered_sets, method='fuzzy', const_t=0)
flrs = FLR.generate_non_recurrent_flrs(tmpdata) flrs = FLR.generate_non_recurrent_flrs(tmpdata)
self.generate_flrg(flrs) self.generate_flrg(flrs)
@ -69,14 +66,14 @@ class ConditionalVarianceFTS(chen.ConventionalFTS):
def _affected_sets(self, sample, perturb): def _affected_sets(self, sample, perturb):
affected_sets = [[ct, set.membership(sample, perturb[ct])] affected_sets = [[ct, self.sets[key].membership(sample, perturb[ct])]
for ct, set in enumerate(self.sets) for ct, key in enumerate(self.partitioner.ordered_sets)
if set.membership(sample, perturb[ct]) > 0.0] if self.sets[key].membership(sample, perturb[ct]) > 0.0]
if len(affected_sets) == 0: if len(affected_sets) == 0:
if sample < self.sets[0].get_lower(perturb[0]): if sample < self.partitioner.lower_set().get_lower(perturb[0]):
affected_sets.append([0, 1]) affected_sets.append([0, 1])
elif sample < self.sets[-1].get_lower(perturb[-1]): elif sample > self.partitioner.upper_set().get_upper(perturb[-1]):
affected_sets.append([len(self.sets) - 1, 1]) affected_sets.append([len(self.sets) - 1, 1])

3
pyFTS/models/nonstationary/flrg.py
View File

@ -11,6 +11,9 @@ class NonStationaryFLRG(flrg.FLRG):
self.RHS = set() self.RHS = set()
def get_key(self): def get_key(self):
if isinstance(self.LHS, list):
return str([k.name for k in self.LHS])
else:
return self.LHS.name return self.LHS.name
def get_membership(self, data, t, window_size=1): def get_membership(self, data, t, window_size=1):

32
pyFTS/models/nonstationary/honsfts.py
View File

@ -46,25 +46,27 @@ class HighOrderNonStationaryFTS(hofts.HighOrderFTS):
disp = common.window_index(k, window_size) disp = common.window_index(k, window_size)
rhs = [set for set in self.sets if set.membership(data[k], disp) > 0.0] rhs = [self.sets[key] for key in self.partitioner.ordered_sets
if self.sets[key].membership(data[k], disp) > 0.0]
if len(rhs) == 0: if len(rhs) == 0:
rhs = [common.check_bounds(data[k], self.sets, disp)] rhs = [common.check_bounds(data[k], self.partitioner, disp)]
lags = {} lags = {}
for o in np.arange(0, self.order): for o in np.arange(0, self.order):
tdisp = common.window_index(k - (self.order - o), window_size) tdisp = common.window_index(k - (self.order - o), window_size)
lhs = [set for set in self.sets if set.membership(sample[o], tdisp) > 0.0] lhs = [self.sets[key] for key in self.partitioner.ordered_sets
if self.sets[key].membership(sample[o], tdisp) > 0.0]
if len(lhs) == 0: if len(lhs) == 0:
lhs = [common.check_bounds(sample[o], self.sets, tdisp)] lhs = [common.check_bounds(sample[o], self.partitioner, tdisp)]
lags[o] = lhs lags[o] = lhs
root = tree.FLRGTreeNode(None) root = tree.FLRGTreeNode(None)
self.build_tree_without_order(root, lags, 0) tree.build_tree_without_order(root, lags, 0)
# Trace the possible paths # Trace the possible paths
for p in root.paths(): for p in root.paths():
@ -103,10 +105,12 @@ class HighOrderNonStationaryFTS(hofts.HighOrderFTS):
for ct, dat in enumerate(sample): for ct, dat in enumerate(sample):
tdisp = common.window_index((k + time_displacement) - (self.order - ct), window_size) 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]
sel = [ct for ct, key in enumerate(self.partitioner.ordered_sets)
if self.sets[key].membership(dat, tdisp) > 0.0]
if len(sel) == 0: if len(sel) == 0:
sel.append(common.check_bounds_index(dat, self.sets, tdisp)) sel.append(common.check_bounds_index(dat, self.partitioner, tdisp))
lags[ct] = sel lags[ct] = sel
@ -114,7 +118,7 @@ class HighOrderNonStationaryFTS(hofts.HighOrderFTS):
root = tree.FLRGTreeNode(None) root = tree.FLRGTreeNode(None)
self.build_tree(root, lags, 0) tree.build_tree_without_order(root, lags, 0)
# Trace the possible paths and build the PFLRG's # Trace the possible paths and build the PFLRG's
@ -123,7 +127,7 @@ class HighOrderNonStationaryFTS(hofts.HighOrderFTS):
flrg = HighOrderNonStationaryFLRG(self.order) flrg = HighOrderNonStationaryFLRG(self.order)
for kk in path: for kk in path:
flrg.append_lhs(self.sets[kk]) flrg.append_lhs(self.sets[self.partitioner.ordered_sets[kk]])
affected_flrgs.append(flrg) affected_flrgs.append(flrg)
# affected_flrgs_memberships.append_rhs(flrg.get_membership(sample, disp)) # affected_flrgs_memberships.append_rhs(flrg.get_membership(sample, disp))
@ -135,16 +139,8 @@ class HighOrderNonStationaryFTS(hofts.HighOrderFTS):
for ct, dat in enumerate(sample): for ct, dat in enumerate(sample):
td = common.window_index((k + time_displacement) - (self.order - ct), window_size) td = common.window_index((k + time_displacement) - (self.order - ct), window_size)
tmp = flrg.LHS[ct].membership(dat, td) 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) mv.append(tmp)
# print(mv) # print(mv)

30
pyFTS/models/nonstationary/nsfts.py
View File

@ -34,7 +34,6 @@ class NonStationaryFTS(fts.FTS):
self.name = "Non Stationary FTS" self.name = "Non Stationary FTS"
self.detail = "" self.detail = ""
self.flrgs = {} self.flrgs = {}
self.method = kwargs.get("method",'fuzzy')
def generate_flrg(self, flrs, **kwargs): def generate_flrg(self, flrs, **kwargs):
for flr in flrs: for flr in flrs:
@ -46,11 +45,9 @@ class NonStationaryFTS(fts.FTS):
def train(self, data, **kwargs): def train(self, data, **kwargs):
if kwargs.get('sets', None) is not None:
self.sets = kwargs.get('sets', None)
window_size = kwargs.get('parameters', 1) window_size = kwargs.get('parameters', 1)
tmpdata = common.fuzzySeries(data, self.sets, window_size, method=self.method) tmpdata = common.fuzzySeries(data, self.sets, self.partitioner.ordered_sets,
window_size, method='fuzzy')
flrs = FLR.generate_recurrent_flrs(tmpdata) flrs = FLR.generate_recurrent_flrs(tmpdata)
self.generate_flrg(flrs) self.generate_flrg(flrs)
@ -68,23 +65,16 @@ class NonStationaryFTS(fts.FTS):
tdisp = common.window_index(k + time_displacement, window_size) tdisp = common.window_index(k + time_displacement, window_size)
if self.method == 'fuzzy': affected_sets = [ [self.sets[key], self.sets[key].membership(ndata[k], tdisp)]
affected_sets = [ [set, set.membership(ndata[k], tdisp)] for key in self.partitioner.ordered_sets
for set in self.sets if set.membership(ndata[k], tdisp) > 0.0] if self.sets[key].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 len(affected_sets) == 0:
if self.method == 'fuzzy': affected_sets.append([common.check_bounds(ndata[k], self.partitioner, tdisp), 1.0])
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))
tmp = [] tmp = []
if len(affected_sets) == 1 and self.method == 'fuzzy': if len(affected_sets) == 1:
aset = affected_sets[0][0] aset = affected_sets[0][0]
if aset.name in self.flrgs: if aset.name in self.flrgs:
tmp.append(self.flrgs[aset.name].get_midpoint(tdisp)) tmp.append(self.flrgs[aset.name].get_midpoint(tdisp))
@ -92,16 +82,10 @@ class NonStationaryFTS(fts.FTS):
tmp.append(aset.get_midpoint(tdisp)) tmp.append(aset.get_midpoint(tdisp))
else: else:
for aset in affected_sets: for aset in affected_sets:
if self.method == 'fuzzy':
if aset[0].name in self.flrgs: if aset[0].name in self.flrgs:
tmp.append(self.flrgs[aset[0].name].get_midpoint(tdisp) * aset[1]) tmp.append(self.flrgs[aset[0].name].get_midpoint(tdisp) * aset[1])
else: else:
tmp.append(aset[0].get_midpoint(tdisp) * aset[1]) 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))
else:
tmp.append(aset.get_midpoint(tdisp))
pto = sum(tmp) pto = sum(tmp)

10
pyFTS/models/nonstationary/partitioners.py
View File

@ -1,6 +1,7 @@
import numpy as np import numpy as np
from pyFTS.partitioners import partitioner from pyFTS.partitioners import partitioner
from pyFTS.models.nonstationary import common, perturbation from pyFTS.models.nonstationary import common, perturbation
from pyFTS.common import FuzzySet as stationary_fs
class PolynomialNonStationaryPartitioner(partitioner.Partitioner): class PolynomialNonStationaryPartitioner(partitioner.Partitioner):
@ -13,13 +14,18 @@ class PolynomialNonStationaryPartitioner(partitioner.Partitioner):
super(PolynomialNonStationaryPartitioner, self).__init__(name=part.name, data=data, npart=part.partitions, super(PolynomialNonStationaryPartitioner, self).__init__(name=part.name, data=data, npart=part.partitions,
func=part.membership_function, names=part.setnames, func=part.membership_function, names=part.setnames,
prefix=part.prefix, transformation=part.transformation, prefix=part.prefix, transformation=part.transformation,
indexer=part.indexer) indexer=part.indexer, preprocess=False)
self.sets = {} self.sets = {}
loc_params, wid_params = self.get_polynomial_perturbations(data, **kwargs) loc_params, wid_params = self.get_polynomial_perturbations(data, **kwargs)
for ct, key in enumerate(part.sets.keys()): if self.ordered_sets is None and self.setnames is not None:
self.ordered_sets = part.setnames
else:
self.ordered_sets = stationary_fs.set_ordered(part.sets)
for ct, key in enumerate(self.ordered_sets):
set = part.sets[key] set = part.sets[key]
loc_roots = np.roots(loc_params[ct])[0] loc_roots = np.roots(loc_params[ct])[0]
wid_roots = np.roots(wid_params[ct])[0] wid_roots = np.roots(wid_params[ct])[0]

129
pyFTS/tests/nonstationary.py
View File

@ -1,133 +1,32 @@
import os import os
import numpy as np import numpy as np
from pyFTS.common import Membership, Transformations from pyFTS.common import Membership, Transformations
from pyFTS.nonstationary import common,perturbation, partitioners, util, honsfts, cvfts from pyFTS.models.nonstationary import common, perturbation, partitioners, util, honsfts, cvfts
from pyFTS.models.nonstationary import nsfts from pyFTS.models.nonstationary import nsfts
from pyFTS.partitioners import Grid from pyFTS.partitioners import Grid
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
from pyFTS.common import Util as cUtil from pyFTS.common import Util as cUtil
import pandas as pd import pandas as pd
os.chdir("/home/petronio/Dropbox/Doutorado/Codigos/")
data = pd.read_csv("DataSets/synthetic_nonstationary_dataset_A.csv", sep=";") from pyFTS.data import artificial
data = np.array(data["0"][:])
for ct, train, test in cUtil.sliding_window(data, 300): lmv1 = artificial.generate_gaussian_linear(1,0.2,0.2,0.05)
for partition in np.arange(10,50):
print(partition)
tmp_fsp = Grid.GridPartitioner(train, partition)
print(len(tmp_fsp.sets))
fsp = partitioners.PolynomialNonStationaryPartitioner(train, tmp_fsp, window_size=35, degree=1) ts=200
ws=35
train1 = lmv1[:ts]
test1 = lmv1[ts:]
''' tmp_fs1 = Grid.GridPartitioner(data=train1[:50], npart=10)
diff = Transformations.Differential(1)
def generate_heteroskedastic_linear(mu_ini, sigma_ini, mu_inc, sigma_inc, it=10, num=35): fs1 = partitioners.PolynomialNonStationaryPartitioner(train1, tmp_fs1, window_size=ws, degree=1)
mu = mu_ini
sigma = sigma_ini
ret = []
for k in np.arange(0,it):
ret.extend(np.random.normal(mu, sigma, num))
mu += mu_inc
sigma += sigma_inc
return ret
nsfts1 = honsfts.HighOrderNonStationaryFTS("", partitioner=fs1)
#lmv1 = generate_heteroskedastic_linear(1,0.1,1,0.3) nsfts1.fit(train1, order=2, parameters=ws)
lmv1 = generate_heteroskedastic_linear(5,0.1,0,0.2)
#lmv1 = generate_heteroskedastic_linear(1,0.3,1,0)
lmv1 = diff.apply(lmv1) print(fs1)
ns = 10 #number of fuzzy sets print(nsfts1.predict(test1))
ts = 200
train = lmv1[:ts]
test = lmv1[ts:]
w = 25
deg = 4
tmp_fs = Grid.GridPartitioner(train, 10) print(nsfts1)
#fs = partitioners.PolynomialNonStationaryPartitioner(train, tmp_fs, window_size=35, degree=1)
fs = partitioners.ConstantNonStationaryPartitioner(train, tmp_fs,
location=perturbation.polynomial,
location_params=[1,0],
location_roots=0,
width=perturbation.polynomial,
width_params=[1,0],
width_roots=0)
'''
"""
perturb = [0.5, 0.25]
for i in [0,1]:
print(fs.sets[i].parameters)
fs.sets[i].perturbate_parameters(perturb[i])
for i in [0,1]:
print(fs.sets[i].perturbated_parameters[perturb[i]])
"""
'''
#nsfts1 = nsfts.NonStationaryFTS("", partitioner=fs)
nsfts1 = cvfts.ConditionalVarianceFTS("", partitioner=fs)
nsfts1.train(train)
#print(fs)
#print(nsfts1)
#tmp = nsfts1.forecast(test[50:60])
#print(tmp)
#print(test[50:60])
util.plot_sets_conditional(nsfts1, test, end=150, step=1,tam=[10, 5])
print('')
"""
passengers = pd.read_csv("DataSets/AirPassengers.csv", sep=",")
passengers = np.array(passengers["Passengers"])
ts = 100
ws=12
trainp = passengers[:ts]
testp = passengers[ts:]
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.train(trainp, order=2, parameters=ws)
#print(fsp)
#print(nsftsp)
tmpp = nsftsp.forecast(passengers[101:104], time_displacement=101, window_size=ws)
tmpi = nsftsp.forecast_interval(passengers[101:104], time_displacement=101, window_size=ws)
#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)
#fig, axes = plt.subplots(nrows=1, ncols=1, figsize=[15,5])
"""
"""
axes.plot(testp, label="Original")
#axes.plot(tmpp, label="NSFTS")
handles0, labels0 = axes.get_legend_handles_labels()
lgd = axes.legend(handles0, labels0, loc=2)
"""
'''