- Bugfixes caused by FLRG improvements and refactorings on affected classes

pyFTS/ensemble/ensemble.py

@@ -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]
 

pyFTS/flrg.py

@@ -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()])

pyFTS/hofts.py

@@ -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"

pyFTS/ismailefendi.py

@@ -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.rhs_counts[c.name] += 1.0
-        self.count = self.count + 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):

pyFTS/pwfts.py

@@ -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."

pyFTS/sadaei.py

@@ -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)
 

pyFTS/seasonal/msfts.py

@@ -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):

pyFTS/tests/ensemble.py

@@ -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,

pyFTS/tests/pwfts.py

@@ -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])
 

pyFTS/tests/sfts.py

@@ -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 import fts
-from pyFTS.models import msfts
+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=";")