Contextual Multi Season FTS
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Petrônio Cândido de Lima e Silva
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55d3deadfc
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26656153f4
@ -72,15 +72,23 @@ def allPointForecasters(data_train, data_test, partitions, max_order=3, statisti
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intervals=False)
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intervals=False)
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def getPointStatistics(data, models, externalmodels = None, externalforecasts = None):
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def getPointStatistics(data, models, externalmodels = None, externalforecasts = None, indexers=None):
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ret = "Model & Order & RMSE & MAPE & Theil's U \\\\ \n"
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ret = "Model & Order & RMSE & SMAPE & Theil's U \\\\ \n"
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for fts in models:
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for count,model in enumerate(models,start=0):
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forecasts = fts.forecast(data)
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if indexers is not None:
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ret += fts.shortname + " & "
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ndata = np.array(indexers[count].get_data(data[model.order:]))
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ret += str(fts.order) + " & "
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else:
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ret += str(round(Measures.rmse(np.array(data[fts.order:]), np.array(forecasts[:-1])), 2)) + " & "
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ndata = np.array(data[model.order:])
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ret += str(round(Measures.smape(np.array(data[fts.order:]), np.array(forecasts[:-1])), 2))+ " & "
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forecasts = model.forecast(data)
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ret += str(round(Measures.UStatistic(np.array(data[fts.order:]), np.array(forecasts[:-1])), 2))
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if model.hasSeasonality:
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nforecasts = np.array(forecasts)
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else:
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nforecasts = np.array(forecasts[:-1])
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ret += model.shortname + " & "
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ret += str(model.order) + " & "
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ret += str(round(Measures.rmse(ndata, nforecasts), 2)) + " & "
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ret += str(round(Measures.smape(ndata, nforecasts), 2))+ " & "
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ret += str(round(Measures.UStatistic(ndata, nforecasts), 2))
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#ret += str(round(Measures.TheilsInequality(np.array(data[fts.order:]), np.array(forecasts[:-1])), 4))
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#ret += str(round(Measures.TheilsInequality(np.array(data[fts.order:]), np.array(forecasts[:-1])), 4))
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ret += " \\\\ \n"
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ret += " \\\\ \n"
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if externalmodels is not None:
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if externalmodels is not None:
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@ -88,9 +96,9 @@ def getPointStatistics(data, models, externalmodels = None, externalforecasts =
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for k in np.arange(0,l):
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for k in np.arange(0,l):
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ret += externalmodels[k] + " & "
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ret += externalmodels[k] + " & "
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ret += " 1 & "
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ret += " 1 & "
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ret += str(round(Measures.rmse(data[fts.order:], externalforecasts[k][:-1]), 2)) + " & "
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ret += str(round(Measures.rmse(data, externalforecasts[k][:-1]), 2)) + " & "
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ret += str(round(Measures.smape(data[fts.order:], externalforecasts[k][:-1]), 2))+ " & "
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ret += str(round(Measures.smape(data, externalforecasts[k][:-1]), 2))+ " & "
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ret += str(round(Measures.UStatistic(np.array(data[fts.order:]), np.array(forecasts[:-1])), 2))
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ret += str(round(Measures.UStatistic(np.array(data), np.array(forecasts[:-1])), 2))
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ret += " \\\\ \n"
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ret += " \\\\ \n"
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return ret
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return ret
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2
chen.py
2
chen.py
@ -34,7 +34,7 @@ class ConventionalFTS(fts.FTS):
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if flr.LHS.name in flrgs:
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if flr.LHS.name in flrgs:
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flrgs[flr.LHS.name].append(flr.RHS)
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flrgs[flr.LHS.name].append(flr.RHS)
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else:
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else:
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flrgs[flr.LHS.name] = ConventionalFLRG(flr.LHS);
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flrgs[flr.LHS.name] = ConventionalFLRG(flr.LHS)
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flrgs[flr.LHS.name].append(flr.RHS)
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flrgs[flr.LHS.name].append(flr.RHS)
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return (flrgs)
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return (flrgs)
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95
models/cmsfts.py
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95
models/cmsfts.py
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@ -0,0 +1,95 @@
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import numpy as np
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from pyFTS.common import FuzzySet,FLR
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from pyFTS import fts, sfts, chen
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class ContextualSeasonalFLRG(object):
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def __init__(self, seasonality):
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super(ContextualSeasonalFLRG, self).__init__(None,None)
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self.season = seasonality
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self.flrgs = {}
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def append(self, flr):
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if flr.LHS.name in self.flrgs:
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self.flrgs[flr.LHS.name].append(flr.RHS)
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else:
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self.flrgs[flr.LHS.name] = chen.ConventionalFLRG(flr.LHS)
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self.flrgs[flr.LHS.name].append(flr.RHS)
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def __str__(self):
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tmp = str(self.season) + ": \n "
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tmp2 = "\t"
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for r in sorted(self.flrgs):
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tmp2 += str(self.flrgs[r]) + "\n\t"
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return tmp + tmp2 + "\n"
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class ContextualMultiSeasonalFTS(sfts.SeasonalFTS):
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def __init__(self, name, indexer):
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super(ContextualMultiSeasonalFTS, self).__init__("CMSFTS")
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self.name = "Contextual Multi Seasonal FTS"
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self.shortname = "CMSFTS " + name
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self.detail = ""
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self.seasonality = 1
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self.hasSeasonality = True
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self.hasPointForecasting = True
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self.isHighOrder = True
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self.isMultivariate = True
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self.indexer = indexer
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self.flrgs = {}
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def generateFLRG(self, flrs):
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flrgs = {}
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for flr in flrs:
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if str(flr.index) not in self.flrgs:
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flrgs[str(flr.index)] = ContextualSeasonalFLRG(flr.index)
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flrgs[str(flr.index)].append(flr)
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return (flrgs)
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def train(self, data, sets, order=1, parameters=None):
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self.sets = sets
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self.seasonality = parameters
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flrs = FLR.generateIndexedFLRs(self.sets, self.indexer, data)
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self.flrgs = self.generateFLRG(flrs)
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def getMidpoints(self, flrg, data):
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ret = np.array([s.centroid for s in flrg.flrgs[data].RHS])
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return ret
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def forecast(self, data):
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ret = []
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index = self.indexer.get_season_of_data(data)
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ndata = self.indexer.get_data(data)
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for k in np.arange(1, len(data)):
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flrg = self.flrgs[str(index[k])]
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d = FuzzySet.getMaxMembershipFuzzySet(ndata[k], self.sets)
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mp = self.getMidpoints(flrg, d)
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ret.append(sum(mp) / len(mp))
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ret = self.doInverseTransformations(ret, params=[ndata[self.order - 1:]])
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return ret
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def forecastAhead(self, data, steps):
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ret = []
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for i in steps:
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flrg = self.flrgs[str(i)]
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mp = self.getMidpoints(flrg)
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ret.append(sum(mp) / len(mp))
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ret = self.doInverseTransformations(ret, params=data)
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return ret
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@ -7,6 +7,7 @@ class MultiSeasonalFTS(sfts.SeasonalFTS):
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def __init__(self, name, indexer):
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def __init__(self, name, indexer):
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super(MultiSeasonalFTS, self).__init__("MSFTS")
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super(MultiSeasonalFTS, self).__init__("MSFTS")
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self.name = "Multi Seasonal FTS"
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self.name = "Multi Seasonal FTS"
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self.shortname = "MSFTS " + name
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self.detail = ""
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self.detail = ""
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self.seasonality = 1
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self.seasonality = 1
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self.hasSeasonality = True
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self.hasSeasonality = True
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4
sfts.py
4
sfts.py
@ -2,8 +2,10 @@ import numpy as np
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from pyFTS.common import FuzzySet,FLR
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from pyFTS.common import FuzzySet,FLR
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from pyFTS import fts
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from pyFTS import fts
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class SeasonalFLRG(fts.FTS):
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class SeasonalFLRG(FLR.FLR):
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def __init__(self, seasonality):
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def __init__(self, seasonality):
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super(SeasonalFLRG, self).__init__(None,None)
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self.LHS = seasonality
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self.LHS = seasonality
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self.RHS = []
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self.RHS = []
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@ -28,27 +28,6 @@ sonda = sonda[:][527041:]
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sonda.index = np.arange(0,len(sonda.index))
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sonda.index = np.arange(0,len(sonda.index))
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#data = []
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#for i in sonda.index:
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#inst = []
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#year = int( sonda["year"][i] )
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#day_of_year = int( sonda["day"][i] )
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#minute = int (sonda["min"][i] )
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#glo_avg = sonda["glo_avg"][i]
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#inst.append( datetime.datetime(year, 1, 1) + datetime.timedelta(day_of_year - 1, minutes=minute) )
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#inst.append( glo_avg )
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#data.append(inst)
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#nov = pd.DataFrame(data,columns=["data","glo_avg"])
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#nov.to_csv("DataSets/SONDA_BSB_MOD.csv", sep=";")
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sonda_treino = sonda[:1051200]
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sonda_treino = sonda[:1051200]
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sonda_teste = sonda[1051201:]
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sonda_teste = sonda[1051201:]
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@ -63,42 +42,23 @@ from pyFTS.models.seasonal import SeasonalIndexer
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from pyFTS.models import msfts
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from pyFTS.models import msfts
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from pyFTS.common import FLR
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from pyFTS.common import FLR
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ix = SeasonalIndexer.DateTimeSeasonalIndexer('data',[SeasonalIndexer.DateTime.month,
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partitions = ['grid','entropy']
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SeasonalIndexer.DateTime.hour, SeasonalIndexer.DateTime.minute],
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[None, None,15],'glo_avg')
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indexers = ['m15','Mh','Mhm15']
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models = []
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ixs = []
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sample = sonda_teste[0:4300]
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tmp = ix.get_data(sonda_treino)
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for max_part in [10, 20, 30, 40, 50]:
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for max_part in [10, 20, 30, 40, 50]:
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for part in partitions:
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for ind in indexers:
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ix = Util.load_obj("models/sonda_ix_" + ind + ".pkl")
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model = Util.load_obj("models/sonda_msfts_" + part + "_" + str(max_part) + "_" + ind + ".pkl")
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model.shortname = part + "_" + str(max_part) + "_" + ind
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fs1 = Grid.GridPartitionerTrimf(tmp,max_part)
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models.append(model)
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ixs.append(ix)
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Util.persist_obj(fs1,"models/sonda_fs_grid_" + str(max_part) + ".pkl")
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print(bchmk.getPointStatistics(sample, models, indexers=ixs))
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fs2 = FCM.FCMPartitionerTrimf(tmp, max_part)
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Util.persist_obj(fs2, "models/sonda_fs_fcm_" + str(max_part) + ".pkl")
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fs3 = Entropy.EntropyPartitionerTrimf(tmp, max_part)
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Util.persist_obj(fs3, "models/sonda_fs_entropy_" + str(max_part) + ".pkl")
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#fs = Util.load_obj("models/sonda_fs_grid_50.pkl")
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#for f in fs:
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# print(f)
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#mfts = msfts.MultiSeasonalFTS("",ix)
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#mfts.train(sonda_treino,fs)
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#print(str(mfts))
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#plt.plot(mfts.forecast(sonda_teste))
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#[10, 508]
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#flrs = FLR.generateIndexedFLRs(fs, ix, sonda_treino[110000:111450])
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#for i in mfts.forecast(sonda_teste):
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# print(i)
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