diff --git a/benchmarks/benchmarks.py b/benchmarks/benchmarks.py index 36729eb..f499273 100644 --- a/benchmarks/benchmarks.py +++ b/benchmarks/benchmarks.py @@ -12,7 +12,7 @@ from mpl_toolkits.mplot3d import Axes3D from pyFTS.benchmarks import Measures, naive, ResidualAnalysis, ProbabilityDistribution from pyFTS.partitioners import Grid from pyFTS.common import Membership, FuzzySet, FLR, Transformations, Util -from pyFTS import fts, chen, yu, ismailefendi, sadaei, hofts, hwang, pfts, ifts +from pyFTS import fts, chen, yu, ismailefendi, sadaei, hofts, hwang, pwfts, ifts colors = ['grey', 'rosybrown', 'maroon', 'red','orange', 'yellow', 'olive', 'green', 'cyan', 'blue', 'darkblue', 'purple', 'darkviolet'] @@ -29,7 +29,7 @@ def allPointForecasters(data_train, data_test, partitions, max_order=3, statisti if models is None: models = [naive.Naive, chen.ConventionalFTS, yu.WeightedFTS, ismailefendi.ImprovedWeightedFTS, - sadaei.ExponentialyWeightedFTS, hofts.HighOrderFTS, pfts.ProbabilisticFTS] + sadaei.ExponentialyWeightedFTS, hofts.HighOrderFTS, pwfts.ProbabilisticWeightedFTS] objs = [] @@ -76,16 +76,16 @@ def allPointForecasters(data_train, data_test, partitions, max_order=3, statisti lcolors.insert(0,'black') pmfs = [] pmfs.append( - ProbabilityDistribution.ProbabilityDistribution("Original", 100, [min(data_train), max(data_train)], data=data_train) ) + ProbabilityDistribution.ProbabilityDistribution("Original", 100, [min(data_test), max(data_test)], data=data_test) ) for m in objs: - forecasts = m.forecast(data_train) + forecasts = m.forecast(data_test) pmfs.append( - ProbabilityDistribution.ProbabilityDistribution(m.shortname, 100, [min(data_train), max(data_train)], + ProbabilityDistribution.ProbabilityDistribution(m.shortname, 100, [min(data_test), max(data_test)], data=forecasts)) - print(getProbabilityDistributionStatistics(pmfs,data_train)) + print(getProbabilityDistributionStatistics(pmfs,data_test)) - plotProbabilityDistributions(pmfs, lcolors) + plotProbabilityDistributions(pmfs, lcolors,tam=tam) def getPointStatistics(data, models, externalmodels = None, externalforecasts = None, indexers=None): @@ -138,7 +138,7 @@ def getProbabilityDistributionStatistics(pmfs, data): def allIntervalForecasters(data_train, data_test, partitions, max_order=3,save=False, file=None, tam=[20, 5], models=None, transformation=None): if models is None: - models = [ifts.IntervalFTS, pfts.ProbabilisticFTS] + models = [ifts.IntervalFTS, pwfts.ProbabilisticWeightedFTS] objs = [] @@ -243,8 +243,8 @@ def plotComparedSeries(original, models, colors, typeonlegend=False, save=False, Util.showAndSaveImage(fig, file, save, lgd=legends) -def plotProbabilityDistributions(pmfs,lcolors): - fig = plt.figure(figsize=[15, 7]) +def plotProbabilityDistributions(pmfs,lcolors,tam=[15, 7]): + fig = plt.figure(figsize=tam) ax = fig.add_subplot(111) for k,m in enumerate(pmfs,start=0): @@ -257,7 +257,7 @@ def plotProbabilityDistributions(pmfs,lcolors): def allAheadForecasters(data_train, data_test, partitions, start, steps, resolution = None, max_order=3,save=False, file=None, tam=[20, 5], models=None, transformation=None, option=2): if models is None: - models = [pfts.ProbabilisticFTS] + models = [pwfts.ProbabilisticWeightedFTS] if resolution is None: resolution = (max(data_train) - min(data_train)) / 100 @@ -775,7 +775,7 @@ def pftsExploreOrderAndPartitions(data,save=False, file=None): axes[2].set_title('Interval Forecasts by Order') for order in np.arange(1, 6): - fts = pfts.ProbabilisticFTS("") + fts = pwfts.ProbabilisticWeightedFTS("") fts.shortname = "n = " + str(order) fts.train(data, data_fs1, order=order) point_forecasts = fts.forecast(data) @@ -797,7 +797,7 @@ def pftsExploreOrderAndPartitions(data,save=False, file=None): for partitions in np.arange(5, 11): data_fs = Grid.GridPartitionerTrimf(data, partitions) - fts = pfts.ProbabilisticFTS("") + fts = pwfts.ProbabilisticWeightedFTS("") fts.shortname = "q = " + str(partitions) fts.train(data, data_fs, 1) point_forecasts = fts.forecast(data) diff --git a/pfts.py b/pwfts.py similarity index 97% rename from pfts.py rename to pwfts.py index 5e08d3c..2c4db5e 100644 --- a/pfts.py +++ b/pwfts.py @@ -9,9 +9,9 @@ from pyFTS.common import FLR, FuzzySet, SortedCollection from pyFTS import hofts, ifts, tree -class ProbabilisticFLRG(hofts.HighOrderFLRG): +class ProbabilisticWeightedFLRG(hofts.HighOrderFLRG): def __init__(self, order): - super(ProbabilisticFLRG, self).__init__(order) + super(ProbabilisticWeightedFLRG, self).__init__(order) self.RHS = {} self.frequencyCount = 0.0 @@ -41,9 +41,9 @@ class ProbabilisticFLRG(hofts.HighOrderFLRG): return self.strLHS() + " -> " + tmp2 -class ProbabilisticFTS(ifts.IntervalFTS): +class ProbabilisticWeightedFTS(ifts.IntervalFTS): def __init__(self, name): - super(ProbabilisticFTS, self).__init__("PFTS") + super(ProbabilisticWeightedFTS, self).__init__("PFTS") self.shortname = "PFTS " + name self.name = "Probabilistic FTS" self.detail = "Silva, P.; Guimarães, F.; Sadaei, H." @@ -71,7 +71,7 @@ class ProbabilisticFTS(ifts.IntervalFTS): l = len(data) for k in np.arange(self.order, l): if self.dump: print("FLR: " + str(k)) - flrg = ProbabilisticFLRG(self.order) + flrg = ProbabilisticWeightedFLRG(self.order) sample = data[k - self.order: k] @@ -116,7 +116,7 @@ class ProbabilisticFTS(ifts.IntervalFTS): l = len(flrs) for k in np.arange(self.order, l+1): if self.dump: print("FLR: " + str(k)) - flrg = ProbabilisticFLRG(self.order) + flrg = ProbabilisticWeightedFLRG(self.order) for kk in np.arange(k - self.order, k): flrg.appendLHS(flrs[kk].LHS) @@ -134,7 +134,7 @@ class ProbabilisticFTS(ifts.IntervalFTS): def addNewPFLGR(self,flrg): if flrg.strLHS() not in self.flrgs: - tmp = ProbabilisticFLRG(self.order) + tmp = ProbabilisticWeightedFLRG(self.order) for fs in flrg.LHS: tmp.appendLHS(fs) tmp.appendRHS(flrg.LHS[-1]) self.flrgs[tmp.strLHS()] = tmp; diff --git a/tests/pfts.py b/tests/pwfts.py similarity index 91% rename from tests/pfts.py rename to tests/pwfts.py index 1321e11..180aca9 100644 --- a/tests/pfts.py +++ b/tests/pwfts.py @@ -11,7 +11,7 @@ from mpl_toolkits.mplot3d import Axes3D import pandas as pd from pyFTS.partitioners import Grid from pyFTS.common import FLR,FuzzySet,Membership,Transformations -from pyFTS import fts,hofts,ifts,pfts,tree, chen +from pyFTS import fts,hofts,ifts,pwfts,tree, chen from pyFTS.benchmarks import benchmarks as bchmk from pyFTS.benchmarks import Measures from numpy import random @@ -39,7 +39,7 @@ fs = Grid.GridPartitionerTrimf(taiex_treino,10) #tmp = chen.ConventionalFTS("") -pfts1 = pfts.ProbabilisticFTS("1") +pfts1 = pwfts.ProbabilisticWeightedFTS("1") #pfts1.appendTransformation(diff) pfts1.train(taiex_treino,fs,1) @@ -63,11 +63,11 @@ print(pmf1.averageloglikelihood(taiex_treino)) print(pmf2.averageloglikelihood(taiex_treino)) -#pfts2 = pfts.ProbabilisticFTS("n = 2") +#pfts2 = pfts.ProbabilisticWeightedFTS("n = 2") #pfts2.appendTransformation(diff) #pfts2.train(gauss_treino,fs,2) -#pfts3 = pfts.ProbabilisticFTS("n = 3") +#pfts3 = pfts.ProbabilisticWeightedFTS("n = 3") #pfts3.appendTransformation(diff) #pfts3.train(gauss_treino,fs,3)