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- Bugfixes caused by FLRG improvements and refactorings on affected classes

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This commit is contained in:
Petrônio Cândido 2017-10-06 19:36:03 -03:00
parent 04b1ffea85
commit ce71dc20cb
10 changed files with 31 additions and 23 deletions
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8
pyFTS/ensemble/ensemble.py
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@ -6,12 +6,12 @@ import pandas as pd
import math
from operator import itemgetter
from pyFTS.common import FLR, FuzzySet, SortedCollection
from pyFTS import fts, chen, cheng, hofts, hwang, ismailefendi, sadaei, song, yu, sfts
from pyFTS import fts, chen, cheng, hofts, hwang, ismailefendi, sadaei, song, yu
from pyFTS.benchmarks import arima, quantreg
from pyFTS.common import Transformations
import scipy.stats as st
from pyFTS import tree
from pyFTS.models import msfts
from pyFTS.seasonal import sfts, msfts
from pyFTS.probabilistic import ProbabilityDistribution, kde
def sampler(data, quantiles):
@ -226,7 +226,7 @@ class EnsembleFTS(fts.FTS):
class AllMethodEnsembleFTS(EnsembleFTS):
def __init__(self, name, **kwargs):
def __init__(self, **kwargs):
super(AllMethodEnsembleFTS, self).__init__(name="Ensemble FTS", **kwargs)
self.min_order = 3
@ -239,7 +239,7 @@ class AllMethodEnsembleFTS(EnsembleFTS):
self.original_min = min(data)
fo_methods = [song.ConventionalFTS, chen.ConventionalFTS, yu.WeightedFTS, cheng.TrendWeightedFTS,
sadaei.ExponentialyWeightedFTS, ismailefendi.ImprovedWeightedFTS, sfts.SeasonalFTS]
sadaei.ExponentialyWeightedFTS, ismailefendi.ImprovedWeightedFTS]
ho_methods = [hofts.HighOrderFTS, hwang.HighOrderFTS]

2
pyFTS/flrg.py
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@ -17,7 +17,7 @@ class FLRG(object):
return self.midpoint
def get_midpoints(self):
if isinstance(self.RHS, list):
if isinstance(self.RHS, (list, set)):
return np.array([s.centroid for s in self.RHS])
elif isinstance(self.RHS, dict):
return np.array([self.RHS[s].centroid for s in self.RHS.keys()])

2
pyFTS/hofts.py
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@ -49,7 +49,7 @@ class HighOrderFLRG(flrg.FLRG):
class HighOrderFTS(fts.FTS):
"""Conventional High Order Fuzzy Time Series"""
def __init__(self, name, **kwargs):
super(HighOrderFTS, self).__init__(name="HOFTS" + name, **kwargs)
super(HighOrderFTS, self).__init__(1, name="HOFTS" + name, **kwargs)
self.name = "High Order FTS"
self.shortname = "HOFTS" + name
self.detail = "Chen"

14
pyFTS/ismailefendi.py
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@ -16,25 +16,27 @@ class ImprovedWeightedFLRG(flrg.FLRG):
super(ImprovedWeightedFLRG, self).__init__(1, **kwargs)
self.LHS = LHS
self.RHS = {}
self.rhs_counts = {}
self.count = 0.0
def append(self, c):
if c.name not in self.RHS:
self.RHS[c.name] = 1.0
self.RHS[c.name] = c
self.rhs_counts[c.name] = 1.0
else:
self.RHS[c.name] = self.RHS[c.name] + 1.0
self.count = self.count + 1.0
self.rhs_counts[c.name] += 1.0
self.count += 1.0
def weights(self):
return np.array([self.RHS[c] / self.count for c in self.RHS.keys()])
return np.array([self.rhs_counts[c] / self.count for c in self.RHS.keys()])
def __str__(self):
tmp = self.LHS.name + " -> "
tmp2 = ""
for c in sorted(self.RHS):
for c in sorted(self.RHS.keys()):
if len(tmp2) > 0:
tmp2 = tmp2 + ","
tmp2 = tmp2 + c + "(" + str(round(self.RHS[c] / self.count, 3)) + ")"
tmp2 = tmp2 + c + "(" + str(round(self.rhs_counts[c] / self.count, 3)) + ")"
return tmp + tmp2
def __len__(self):

8
pyFTS/pwfts.py
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@ -73,15 +73,15 @@ class ProbabilisticWeightedFLRG(hofts.HighOrderFLRG):
return self.Z
def get_midpoint(self):
return sum(np.array([self.get_RHSprobability(s.name) * self.RHS[s].centroid
return sum(np.array([self.get_RHSprobability(s) * self.RHS[s].centroid
for s in self.RHS.keys()]))
def get_upper(self):
return sum(np.array([self.get_RHSprobability(s.name) * self.RHS[s].upper
return sum(np.array([self.get_RHSprobability(s) * self.RHS[s].upper
for s in self.RHS.keys()]))
def get_lower(self):
return sum(np.array([self.get_RHSprobability(s.name) * self.RHS[s].lower
return sum(np.array([self.get_RHSprobability(s) * self.RHS[s].lower
for s in self.RHS.keys()]))
def __str__(self):
@ -96,7 +96,7 @@ class ProbabilisticWeightedFLRG(hofts.HighOrderFLRG):
class ProbabilisticWeightedFTS(ifts.IntervalFTS):
"""High Order Probabilistic Weighted Fuzzy Time Series"""
def __init__(self, name, **kwargs):
super(ProbabilisticWeightedFTS, self).__init__(order=1, name=name, **kwargs)
super(ProbabilisticWeightedFTS, self).__init__(name=name, **kwargs)
self.shortname = "PWFTS " + name
self.name = "Probabilistic FTS"
self.detail = "Silva, P.; Guimarães, F.; Sadaei, H."

2
pyFTS/sadaei.py
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@ -59,7 +59,7 @@ class ExponentialyWeightedFTS(fts.FTS):
if flr.LHS.name in flrgs:
flrgs[flr.LHS.name].append(flr.RHS)
else:
flrgs[flr.LHS.name] = ExponentialyWeightedFLRG(flr.LHS, c);
flrgs[flr.LHS.name] = ExponentialyWeightedFLRG(flr.LHS, c=c);
flrgs[flr.LHS.name].append(flr.RHS)
return (flrgs)

2
pyFTS/seasonal/msfts.py
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@ -1,6 +1,6 @@
import numpy as np
from pyFTS.common import FuzzySet,FLR
from pyFTS import fts, sfts
from pyFTS.seasonal import sfts
class MultiSeasonalFTS(sfts.SeasonalFTS):

4
pyFTS/tests/ensemble.py
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@ -11,11 +11,12 @@ from mpl_toolkits.mplot3d import Axes3D
import pandas as pd
from pyFTS.partitioners import Grid, Entropy, FCM, Huarng
from pyFTS.common import FLR,FuzzySet,Membership,Transformations
from pyFTS import fts,hofts,ifts,pwfts,tree, chen, ensemble, song, yu, cheng, ismailefendi, sadaei, hwang
from pyFTS import fts,hofts,ifts,pwfts,tree, chen, song, yu, cheng, ismailefendi, sadaei, hwang
from pyFTS.benchmarks import naive, arima
from numpy import random
from pyFTS.benchmarks import benchmarks as bchmk
from pyFTS.benchmarks import arima, quantreg, Measures
from pyFTS.ensemble import ensemble
os.chdir("/home/petronio/dados/Dropbox/Doutorado/Codigos/")
@ -24,7 +25,6 @@ diff = Transformations.Differential(1)
passengers = pd.read_csv("DataSets/AirPassengers.csv", sep=",")
passengers = np.array(passengers["Passengers"])
e = ensemble.AllMethodEnsembleFTS(alpha=0.25, point_method="median", interval_method='quantile')
fo_methods = [song.ConventionalFTS, chen.ConventionalFTS, yu.WeightedFTS, cheng.TrendWeightedFTS, sadaei.ExponentialyWeightedFTS,

6
pyFTS/tests/pwfts.py
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@ -54,6 +54,12 @@ pfts1.shortname = "1st Order"
#print(pfts1_enrollments)
tmp = pfts1.forecast(data[3000:3020])
tmp = pfts1.forecastInterval(data[3000:3020])
tmp = pfts1.forecastAheadInterval(data[3000:3020],20)
tmp = pfts1.forecastAheadDistribution(data[3000:3020],20, method=3, h=0.45, kernel="gaussian")
print(tmp[0])

6
pyFTS/tests/sfts.py
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@ -13,12 +13,12 @@ import datetime
import pandas as pd
from pyFTS.partitioners import Grid, CMeans, FCM, Entropy
from pyFTS.common import FLR,FuzzySet,Membership,Transformations,Util
from pyFTS import fts,sfts
from pyFTS.models import msfts
from pyFTS import fts
from pyFTS.seasonal import sfts, msfts
from pyFTS.benchmarks import benchmarks as bchmk
from pyFTS.benchmarks import Measures
os.chdir("/home/petronio/dados/Dropbox/Doutorado/Disciplinas/AdvancedFuzzyTimeSeriesModels/")
os.chdir("/home/petronio/dados/Dropbox/Doutorado/Codigos/")
sonda = pd.read_csv("DataSets/SONDA_BSB_MOD.csv", sep=";")