- Several bugfixes

benchmarks/benchmarks.py

@@ -144,7 +144,7 @@ def point_sliding_window(data, windowsize, train=0.8,models=None,partitioners=[G
                         times[_key].append(_end - _start)
 
                         _start = time.time()
-                        _rmse, _smape, _u = get_point_statistics(test, mfts, indexer)
+                        _rmse, _smape, _u = Measures.get_point_statistics(test, mfts, indexer)
                         _end = time.time()
                         rmse[_key].append(_rmse)
                         smape[_key].append(_smape)
@@ -271,7 +271,7 @@ def all_point_forecasters(data_train, data_test, partitions, max_order=3, statis
 def print_point_statistics(data, models, externalmodels = None, externalforecasts = None, indexers=None):
     ret = "Model		& Order     & RMSE		& SMAPE      & Theil's U		\\\\ \n"
     for count,model in enumerate(models,start=0):
-        _rmse, _smape, _u = get_point_statistics(data, model, indexers)
+        _rmse, _smape, _u = Measures.get_point_statistics(data, model, indexers)
         ret += model.shortname + "		& "
         ret += str(model.order) + "		& "
         ret += str(_rmse) + "		& "
@@ -765,7 +765,7 @@ def all_ahead_forecasters(data_train, data_test, partitions, start, steps, resol
 
     print_distribution_statistics(data_test[start:], objs, steps, resolution)
 
-    plotComparedIntervalsAhead(data_test, objs, lcolors, distributions=distributions, time_from=start, time_to=steps,
+    plot_compared_intervals_ahead(data_test, objs, lcolors, distributions=distributions, time_from=start, time_to=steps,
                                interpol=False, save=save, file=file, tam=tam, resolution=resolution, option=option)
 
 
@@ -806,7 +806,7 @@ def print_distribution_statistics(original, models, steps, resolution):
     print(ret)
 
 
-def plotComparedIntervalsAhead(original, models, colors, distributions, time_from, time_to,
+def plot_compared_intervals_ahead(original, models, colors, distributions, time_from, time_to,
                                interpol=False, save=False, file=None, tam=[20, 5], resolution=None,
                                cmap='Blues',option=2):
     fig = plt.figure(figsize=tam)
@@ -824,7 +824,7 @@ def plotComparedIntervalsAhead(original, models, colors, distributions, time_fro
     for count, fts in enumerate(models, start=0):
         if fts.hasDistributionForecasting and distributions[count]:
             density = fts.forecastAheadDistribution(original[time_from - fts.order:time_from], time_to,
-                                                    parameters=option)
+                                                    resolution=resolution, method=option)
 
             Y = []
             X = []

hofts.py

@@ -39,7 +39,7 @@ class HighOrderFLRG(object):
 
 
 class HighOrderFTS(fts.FTS):
-    def __init__(self, order, **kwargs):
+    def __init__(self, order, name, **kwargs):
         super(HighOrderFTS, self).__init__(1, "HOFTS" + name)
         self.name = "High Order FTS"
         self.shortname = "HOFTS" + name

ifts.py

@@ -7,8 +7,8 @@ from pyFTS import hofts, fts, tree
 
 
 class IntervalFTS(hofts.HighOrderFTS):
-    def __init__(self, order, **kwargs):
+    def __init__(self, order, name, **kwargs):
-        super(IntervalFTS, self).__init__("IFTS " + name)
+        super(IntervalFTS, self).__init__(order=1, name="IFTS " + name)
         self.shortname = "IFTS " + name
         self.name = "Interval FTS"
         self.detail = "Silva, P.; Guimarães, F.; Sadaei, H. (2016)"

partitioners/Entropy.py

@@ -98,7 +98,7 @@ class EntropyPartitioner(partitioner.Partitioner):
                 b2 = (partitions[c + 1] - partitions[c]) / 2
                 sets.append(FuzzySet.FuzzySet(self.prefix + str(c), Membership.trapmf,
                                               [partitions[c - 1], partitions[c] - b1,
-                                               partitions[c] - b2, partitions[c + 1]],
+                                               partitions[c] + b2, partitions[c + 1]],
                                               partitions[c]))
 
         return sets

partitioners/FCM.py

@@ -107,12 +107,22 @@ class FCMPartitioner(partitioner.Partitioner):
     def build(self,data):
         sets = []
 
-        centroides = fuzzy_cmeans(self.partitions, data, 1, 2)
+        centroids = fuzzy_cmeans(self.partitions, data, 1, 2)
-        centroides.append(self.max)
+        centroids.append(self.max)
-        centroides.append(self.min)
+        centroids.append(self.min)
-        centroides = list(set(centroides))
+        centroids = list(set(centroids))
-        centroides.sort()
+        centroids.sort()
-        for c in np.arange(1,len(centroides)-1):
+        for c in np.arange(1,len(centroids)-1):
-            sets.append(FuzzySet.FuzzySet(self.prefix+str(c),Membership.trimf,[round(centroides[c-1],3), round(centroides[c],3), round(centroides[c+1],3)], round(centroides[c],3) ) )
+            if self.membership_function == Membership.trimf:
+                sets.append(FuzzySet.FuzzySet(self.prefix+str(c),Membership.trimf,
+                                              [round(centroids[c-1],3), round(centroids[c],3), round(centroids[c+1],3)],
+                                              round(centroids[c],3) ) )
+            elif self.membership_function == Membership.trapmf:
+                q1 = (round(centroids[c], 3) - round(centroids[c - 1], 3))/2
+                q2 = (round(centroids[c+1], 3) - round(centroids[c], 3)) / 2
+                sets.append(FuzzySet.FuzzySet(self.prefix + str(c), Membership.trimf,
+                                              [round(centroids[c - 1], 3),  round(centroids[c], 3) - q1,
+                                               round(centroids[c], 3) + q2, round(centroids[c + 1], 3)],
+                                              round(centroids[c], 3)))
 
         return sets

partitioners/Huarng.py

@@ -34,8 +34,17 @@ class HuarngPartitioner(partitioner.Partitioner):
         npart = math.ceil(dlen / base)
         partition = math.ceil(self.min)
         for c in range(npart):
-            sets.append(
+            if self.membership_function == Membership.trimf:
-                FuzzySet.FuzzySet(self.prefix + str(c), Membership.trimf, [partition - base, partition, partition + base], partition))
+                sets.append( FuzzySet.FuzzySet(self.prefix + str(c), Membership.trimf,
+                                               [partition - base, partition, partition + base], partition))
+            elif self.membership_function == Membership.gaussmf:
+                sets.append(FuzzySet.FuzzySet(self.prefix + str(c), Membership.gaussmf,
+                                              [partition, base/2], partition))
+            elif self.membership_function == Membership.trapmf:
+                sets.append(FuzzySet.FuzzySet(self.prefix + str(c), Membership.trapmf,
+                                              [partition - base, partition - (base/2),
+                                               partition + (base / 2), partition + base], partition))
+
             partition += base
 
         return sets

partitioners/Util.py

@@ -7,6 +7,9 @@ from mpl_toolkits.mplot3d import Axes3D
 from pyFTS.common import Membership, Util
 from pyFTS.partitioners import Grid,Huarng,FCM,Entropy
 
+all_methods = [Grid.GridPartitioner, Entropy.EntropyPartitioner, FCM.FCMPartitioner, Huarng.HuarngPartitioner]
+
+mfs = [Membership.trimf, Membership.gaussmf, Membership.trapmf]
 
 def plot_sets(data, sets, titles, tam=[12, 10], save=False, file=None):
     num = len(sets)
@@ -20,7 +23,7 @@ def plot_sets(data, sets, titles, tam=[12, 10], save=False, file=None):
         #ax = fig.add_axes([0.05, 1-(k*h), 0.9, h*0.7])  # left, bottom, width, height
         ax = axes[k]
         ax.set_title(titles[k])
-        ax.set_ylim([0, 1])
+        ax.set_ylim([0, 1.1])
         ax.set_xlim([minx, maxx])
         for s in sets[k]:
             if s.mf == Membership.trimf:
@@ -29,7 +32,7 @@ def plot_sets(data, sets, titles, tam=[12, 10], save=False, file=None):
                 tmpx = [ kk for kk in np.arange(s.lower, s.upper)]
                 tmpy = [s.membership(kk) for kk in np.arange(s.lower, s.upper)]
                 ax.plot(tmpx, tmpy)
-            elif s.mf == Membership.gaussmf:
+            elif s.mf == Membership.trapmf:
                 ax.plot(s.parameters, [0, 1, 1, 0])
 
     plt.tight_layout()
@@ -44,9 +47,6 @@ def plot_partitioners(data, objs, tam=[12, 10], save=False, file=None):
 
 
 def explore_partitioners(data, npart, methods=None, mf=None, tam=[12, 10], save=False, file=None):
-    all_methods = [Grid.GridPartitioner, Entropy.EntropyPartitioner, FCM.FCMPartitioner, Huarng.HuarngPartitioner]
-    mfs = [Membership.trimf, Membership.gaussmf, Membership.trapmf]
-
     if methods is None:
         methods = all_methods
 

pwfts.py

@@ -42,8 +42,8 @@ class ProbabilisticWeightedFLRG(hofts.HighOrderFLRG):
 
 
 class ProbabilisticWeightedFTS(ifts.IntervalFTS):
-    def __init__(self, order, name, **kwargs):
+    def __init__(self, name, **kwargs):
-        super(ProbabilisticWeightedFTS, self).__init__("PWFTS")
+        super(ProbabilisticWeightedFTS, self).__init__(order=1, name=name)
         self.shortname = "PWFTS " + name
         self.name = "Probabilistic FTS"
         self.detail = "Silva, P.; Guimarães, F.; Sadaei, H."
@@ -53,7 +53,7 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
         self.hasIntervalForecasting = True
         self.hasDistributionForecasting = True
         self.isHighOrder = True
-        self.auto_update = update
+        self.auto_update = kwargs.get('update',False)
 
     def train(self, data, sets, order=1,parameters=None):
 
@@ -468,13 +468,17 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
 
         ret = []
 
+        resolution = kwargs.get('resolution',100)
+
+        method = kwargs.get('method',2)
+
         intervals = self.forecastAheadInterval(data, steps)
 
         grid = self.getGridClean(resolution)
 
         index = SortedCollection.SortedCollection(iterable=grid.keys())
 
-        if parameters == 1:
+        if method == 1:
 
             grids = []
             for k in np.arange(0, steps):
@@ -522,7 +526,7 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
                 tmp = np.array([grids[k][q] for q in sorted(grids[k])])
                 ret.append(tmp / sum(tmp))
 
-        elif parameters == 2:
+        elif method == 2:
 
             ret = []
 

tests/pwfts.py

@@ -20,60 +20,43 @@ from numpy import random
 #gauss_teste = random.normal(0,1.0,400)
 
 
-os.chdir("/home/petronio/dados/Dropbox/Doutorado/Disciplinas/AdvancedFuzzyTimeSeriesModels/")
+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"])
 
-taiex = pd.read_csv("DataSets/TAIEX.csv", sep=",")
+import importlib
-taiex_treino = np.array(taiex["avg"][2500:3900])
+import pandas as pd
-taiex_teste = np.array(taiex["avg"][3901:4500])
+from pyFTS.partitioners import Grid
+from pyFTS.common import FLR, FuzzySet, Membership, SortedCollection
+from pyFTS import fts
+from pyFTS import hofts
+from pyFTS import pwfts
+from pyFTS import tree
+from pyFTS.benchmarks import benchmarks as bchmk
 
-#nasdaq = pd.read_csv("DataSets/NASDAQ_IXIC.csv", sep=",")
+enrollments_fs1 = Grid.GridPartitioner(enrollments, 6).sets
-#nasdaq_treino = np.array(nasdaq["avg"][0:1600])
+for s in enrollments_fs1:
-#nasdaq_teste = np.array(nasdaq["avg"][1601:2000])
+    print(s)
+
+pfts1_enrollments = pwfts.ProbabilisticWeightedFTS("1")
+pfts1_enrollments.train(enrollments, enrollments_fs1, 1)
+pfts1_enrollments.shortname = "1st Order"
+pfts2_enrollments = pwfts.ProbabilisticWeightedFTS("2")
+pfts2_enrollments.dump = False
+pfts2_enrollments.shortname = "2nd Order"
+pfts2_enrollments.train(enrollments, enrollments_fs1, 2)
+pfts3_enrollments = pwfts.ProbabilisticWeightedFTS("3")
+pfts3_enrollments.dump = False
+pfts3_enrollments.shortname = "3rd Order"
+pfts3_enrollments.train(enrollments, enrollments_fs1, 3)
+
+bchmk.plot_compared_series(enrollments,[pfts1_enrollments,pfts2_enrollments, pfts3_enrollments],
+                           ["red","blue","green"], linewidth=2,
+                         typeonlegend=True,save=True,file="pictures/pwfts_enrollments_interval.png",
+                           tam=[20,7],points=False, intervals=False)
 
-diff = Transformations.Differential(1)
 
-fs = Grid.GridPartitionerTrimf(taiex_treino,10)
-
-#tmp = chen.ConventionalFTS("")
-
-pfts1 = pwfts.ProbabilisticWeightedFTS("1")
-#pfts1.appendTransformation(diff)
-pfts1.train(taiex_treino,fs,1)
-
-from pyFTS.benchmarks import ProbabilityDistribution as dist
-
-forecasts = pfts1.forecast(taiex_treino)
-
-pmf1 = dist.ProbabilityDistribution("Original",100,[min(taiex_treino),max(taiex_treino)],data=taiex_treino)
-
-#print(pmf1.entropy())
-
-pmf2 = dist.ProbabilityDistribution("Original",100,[min(taiex_treino),max(taiex_treino)],data=forecasts)
-
-#print(pmf2.entropy())
-
-#print(pmf2.kullbackleiblerdivergence(pmf1))
-
-#print(pmf2.crossentropy(pmf1))
-
-print(pmf1.averageloglikelihood(taiex_treino))
-
-print(pmf2.averageloglikelihood(taiex_treino))
-
-#pfts2 = pfts.ProbabilisticWeightedFTS("n = 2")
-#pfts2.appendTransformation(diff)
-#pfts2.train(gauss_treino,fs,2)
-
-#pfts3 = pfts.ProbabilisticWeightedFTS("n = 3")
-#pfts3.appendTransformation(diff)
-#pfts3.train(gauss_treino,fs,3)
-
-#densities1 = pfts1.forecastAheadDistribution(gauss_teste[:50],2,1.50, parameters=2)
-
-#print(bchmk.getDistributionStatistics(gauss_teste[:50], [pfts1,pfts2,pfts3], 20, 1.50))