- General refactoring to include **kwargs on methods signatures

- Ensemble FTS

benchmarks/arima.py

@@ -3,12 +3,11 @@
 
 import numpy as np
 from statsmodels.tsa.arima_model import ARIMA as stats_arima
-from statsmodels.tsa.arima_model import ARMA
 from pyFTS import fts
 
 
 class ARIMA(fts.FTS):
-    def __init__(self, name):
+    def __init__(self, order, **kwargs):
         super(ARIMA, self).__init__(1, "ARIMA")
         self.name = "ARIMA"
         self.detail = "Auto Regressive Integrated Moving Average"
@@ -23,8 +22,6 @@ class ARIMA(fts.FTS):
         self.minOrder = 1
 
     def train(self, data, sets, order=1, parameters=None):
-        ndata = np.array(self.doTransformations(data))
-
         if parameters is not None:
             self.p = parameters[0]
             self.d = parameters[1]
@@ -33,44 +30,25 @@ class ARIMA(fts.FTS):
             self.shortname = "ARIMA(" + str(self.p) + "," + str(self.d) + "," + str(self.q) + ")"
 
         old_fit = self.model_fit
-        self.model =  stats_arima(ndata, order=(self.p, self.d, self.q))
+        self.model =  stats_arima(data, order=(self.p, self.d, self.q))
-        #try:
+        try:
-        self.model_fit = self.model.fit(disp=0)
+            self.model_fit = self.model.fit(disp=0)
-        #except:
+        except:
-        #    try:
+            try:
-        #        self.model = stats_arima(data, order=(self.p, self.d, self.q))
+                self.model = stats_arima(data, order=(self.p, self.d, self.q))
-        #        self.model_fit = self.model.fit(disp=1)
+                self.model_fit = self.model.fit(disp=1)
-        #    except:
+            except:
-        #        self.model_fit = old_fit
+                self.model_fit = old_fit
 
-        #self.trained_data = data #.tolist()
+        self.trained_data = data #.tolist()
 
-    def ar(self, data):
+    def forecast(self, data, **kwargs):
-        return data.dot(self.model_fit.arparams)
-
-    def ma(self, data):
-        return data.dot(self.model_fit.maparams)
-
-    def forecast(self, data):
         if self.model_fit is None:
             return np.nan
-
-        order = self.p
-
-        ndata = np.array(self.doTransformations(data))
-
-        l = len(ndata)
-
         ret = []
-
+        for t in data:
-        ar = np.array([self.ar(ndata[k - self.p: k]) for k in np.arange(self.p, l)])
+            output = self.model_fit.forecast()
-
+            ret.append( output[0] )
-        residuals = np.array([ar[k - self.p] - ndata[k] for k in np.arange(self.p, l)])
+            self.trained_data = np.append(self.trained_data, t) #.append(t)
-
+            self.train(self.trained_data,None,order=self.order, parameters=(self.p, self.d, self.q))
-        ma = np.array([self.ma(residuals[k - self.q : k]) for k in np.arange(self.q, len(ar)+1)])
-
-        ret = ar + ma
-
-        ret = self.doInverseTransformations(ret, params=[data[order - 1:]])
-
         return ret

benchmarks/benchmarks.py

@@ -773,7 +773,7 @@ def get_distribution_statistics(original, model, steps, resolution):
     ret = list()
     try:
         _s1 = time.time()
-        densities1 = model.forecastAheadDistribution(original,steps,resolution, parameters=3)
+        densities1 = model.forecastAheadDistribution(original, steps, parameters=3)
         _e1 = time.time()
         ret.append(round(Measures.crps(original, densities1), 3))
         ret.append(round(_e1 - _s1, 3))
@@ -784,7 +784,7 @@ def get_distribution_statistics(original, model, steps, resolution):
 
     try:
         _s2 = time.time()
-        densities2 = model.forecastAheadDistribution(original, steps, resolution, parameters=2)
+        densities2 = model.forecastAheadDistribution(original, steps, parameters=2)
         _e2 = time.time()
         ret.append( round(Measures.crps(original, densities2), 3))
         ret.append(round(_e2 - _s2, 3))
@@ -823,8 +823,8 @@ 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],
+            density = fts.forecastAheadDistribution(original[time_from - fts.order:time_from], time_to,
-                                                    time_to, resolution, parameters=option)
+                                                    parameters=option)
 
             Y = []
             X = []

benchmarks/naive.py

@@ -5,13 +5,13 @@ from pyFTS import fts
 
 
 class Naive(fts.FTS):
-    def __init__(self, name):
+    def __init__(self, order, name, **kwargs):
         super(Naive, self).__init__(1, "Naive " + name)
         self.name = "Naïve Model"
         self.detail = "Naïve Model"
         self.benchmark_only = True
         self.isHighOrder = False
 
-    def forecast(self, data):
+    def forecast(self, data, **kwargs):
         return [k for k in data]
 

benchmarks/quantreg.py

@@ -8,7 +8,7 @@ from pyFTS import fts
 
 
 class QuantileRegression(fts.FTS):
-    def __init__(self, name):
+    def __init__(self, order, **kwargs):
         super(QuantileRegression, self).__init__(1, "QR")
         self.name = "QR"
         self.detail = "Quantile Regression"
@@ -38,7 +38,7 @@ class QuantileRegression(fts.FTS):
     def linearmodel(self,data,params):
         return params[0] + sum([ data[k] * params[k+1] for k in np.arange(0, self.order) ])
 
-    def forecast(self, data):
+    def forecast(self, data, **kwargs):
         ndata = np.array(self.doTransformations(data))
         l = len(ndata)
 
@@ -53,7 +53,7 @@ class QuantileRegression(fts.FTS):
 
         return ret
 
-    def forecastInterval(self, data):
+    def forecastInterval(self, data, **kwargs):
 
         ndata = np.array(self.doTransformations(data))
 

chen.py

@@ -25,7 +25,7 @@ class ConventionalFLRG(object):
 
 
 class ConventionalFTS(fts.FTS):
-    def __init__(self, name):
+    def __init__(self, order, **kwargs):
         super(ConventionalFTS, self).__init__(1, "CFTS " + name)
         self.name = "Conventional FTS"
         self.detail = "Chen"
@@ -48,7 +48,7 @@ class ConventionalFTS(fts.FTS):
         flrs = FLR.generateNonRecurrentFLRs(tmpdata)
         self.flrgs = self.generateFLRG(flrs)
 
-    def forecast(self, data):
+    def forecast(self, data, **kwargs):
 
         ndata = np.array(self.doTransformations(data))
 

ensemble.py

@@ -9,7 +9,7 @@ from pyFTS.common import FLR, FuzzySet, SortedCollection
 from pyFTS import fts
 
 class EnsembleFTS(fts.FTS):
-    def __init__(self, name, update=True):
+    def __init__(self, order, name, **kwargs):
         super(EnsembleFTS, self).__init__("Ensemble FTS")
         self.shortname = "Ensemble FTS " + name
         self.name = "Ensemble FTS"
@@ -25,7 +25,7 @@ class EnsembleFTS(fts.FTS):
 
         pass
 
-    def forecast(self, data):
+    def forecast(self, data, **kwargs):
 
         ndata = np.array(self.doTransformations(data))
 
@@ -40,7 +40,7 @@ class EnsembleFTS(fts.FTS):
 
         return ret
 
-    def forecastInterval(self, data):
+    def forecastInterval(self, data, **kwargs):
 
         ndata = np.array(self.doTransformations(data))
 
@@ -53,10 +53,10 @@ class EnsembleFTS(fts.FTS):
 
         return ret
 
-    def forecastAhead(self, data, steps):
+    def forecastAhead(self, data, steps, **kwargs):
         pass
 
-    def forecastAheadInterval(self, data, steps):
+    def forecastAheadInterval(self, data, steps, **kwargs):
         pass
 
 
@@ -88,6 +88,6 @@ class EnsembleFTS(fts.FTS):
         grid[k] += 1
         return grid
 
-    def forecastAheadDistribution(self, data, steps, resolution, parameters=2):
+    def forecastAheadDistribution(self, data, steps, **kwargs):
         pass
 

fts.py

@@ -4,7 +4,7 @@ from pyFTS import tree
 from pyFTS.common import FuzzySet, SortedCollection
 
 class FTS(object):
-    def __init__(self, order, name):
+    def __init__(self, order, name, **kwargs):
         self.sets = {}
         self.flrgs = {}
         self.order = order
@@ -38,22 +38,22 @@ class FTS(object):
 
         return best
 
-    def forecast(self, data):
+    def forecast(self, data, **kwargs):
         pass
 
-    def forecastInterval(self, data):
+    def forecastInterval(self, data, **kwargs):
         pass
 
-    def forecastDistribution(self, data):
+    def forecastDistribution(self, data, **kwargs):
         pass
 
-    def forecastAhead(self, data, steps):
+    def forecastAhead(self, data, steps, **kwargs):
         pass
 
-    def forecastAheadInterval(self, data, steps):
+    def forecastAheadInterval(self, data, steps, **kwargs):
         pass
 
-    def forecastAheadDistribution(self, data, steps):
+    def forecastAheadDistribution(self, data, steps, **kwargs):
         pass
 
     def train(self, data, sets, order=1, parameters=None):

hofts.py

@@ -39,7 +39,7 @@ class HighOrderFLRG(object):
 
 
 class HighOrderFTS(fts.FTS):
-    def __init__(self, name):
+    def __init__(self, order, **kwargs):
         super(HighOrderFTS, self).__init__(1, "HOFTS" + name)
         self.name = "High Order FTS"
         self.shortname = "HOFTS" + name
@@ -79,7 +79,7 @@ class HighOrderFTS(fts.FTS):
         ret = np.array([self.setsDict[s].centroid for s in flrg.RHS])
         return ret
 
-    def forecast(self, data):
+    def forecast(self, data, **kwargs):
 
         ret = []
 

hwang.py

@@ -4,7 +4,7 @@ from pyFTS import fts
 
 
 class HighOrderFTS(fts.FTS):
-    def __init__(self, name):
+    def __init__(self, order, **kwargs):
         super(HighOrderFTS, self).__init__(1, name)
         self.isHighOrder = True
         self.minOrder = 2
@@ -12,7 +12,7 @@ class HighOrderFTS(fts.FTS):
         self.shortname = "Hwang" + name
         self.detail = "Hwang"
 
-    def forecast(self, data):
+    def forecast(self, data, **kwargs):
 
         ndata = self.doTransformations(data)
 

ifts.py

@@ -7,7 +7,7 @@ from pyFTS import hofts, fts, tree
 
 
 class IntervalFTS(hofts.HighOrderFTS):
-    def __init__(self, name):
+    def __init__(self, order, **kwargs):
         super(IntervalFTS, self).__init__("IFTS " + name)
         self.shortname = "IFTS " + name
         self.name = "Interval FTS"
@@ -47,7 +47,7 @@ class IntervalFTS(hofts.HighOrderFTS):
         for child in node.getChildren():
             self.buildTree(child, lags, level + 1)
 
-    def forecastInterval(self, data):
+    def forecastInterval(self, data, **kwargs):
 
         ndata = np.array(self.doTransformations(data))
 

ismailefendi.py

@@ -33,7 +33,7 @@ class ImprovedWeightedFLRG(object):
 
 
 class ImprovedWeightedFTS(fts.FTS):
-    def __init__(self, name):
+    def __init__(self, order, **kwargs):
         super(ImprovedWeightedFTS, self).__init__(1, "IWFTS " + name)
         self.name = "Improved Weighted FTS"
         self.detail = "Ismail & Efendi"
@@ -64,7 +64,7 @@ class ImprovedWeightedFTS(fts.FTS):
         ret = np.array([self.setsDict[s].centroid for s in flrg.RHS])
         return ret
 
-    def forecast(self, data):
+    def forecast(self, data, **kwargs):
         l = 1
 
         data = np.array(data)

models/cmsfts.py

@@ -24,7 +24,7 @@ class ContextualSeasonalFLRG(object):
 
 
 class ContextualMultiSeasonalFTS(sfts.SeasonalFTS):
-    def __init__(self, name, indexer):
+    def __init__(self, order, name, **kwargs):
         super(ContextualMultiSeasonalFTS, self).__init__("CMSFTS")
         self.name = "Contextual Multi Seasonal FTS"
         self.shortname = "CMSFTS " + name
@@ -62,7 +62,7 @@ class ContextualMultiSeasonalFTS(sfts.SeasonalFTS):
         else:
             return  np.array([data.centroid])
 
-    def forecast(self, data):
+    def forecast(self, data, **kwargs):
 
         ret = []
 
@@ -83,7 +83,7 @@ class ContextualMultiSeasonalFTS(sfts.SeasonalFTS):
 
         return ret
 
-    def forecastAhead(self, data, steps):
+    def forecastAhead(self, data, steps, **kwargs):
         ret = []
         for i in steps:
             flrg = self.flrgs[str(i)]

models/msfts.py

@@ -4,7 +4,7 @@ from pyFTS import fts, sfts
 
 
 class MultiSeasonalFTS(sfts.SeasonalFTS):
-    def __init__(self, name, indexer):
+    def __init__(self, order, name, **kwargs):
         super(MultiSeasonalFTS, self).__init__("MSFTS")
         self.name = "Multi Seasonal FTS"
         self.shortname = "MSFTS " + name
@@ -36,7 +36,7 @@ class MultiSeasonalFTS(sfts.SeasonalFTS):
         flrs = FLR.generateIndexedFLRs(self.sets, self.indexer, data)
         self.flrgs = self.generateFLRG(flrs)
 
-    def forecast(self, data):
+    def forecast(self, data, **kwargs):
 
         ret = []
 
@@ -55,7 +55,7 @@ class MultiSeasonalFTS(sfts.SeasonalFTS):
 
         return ret
 
-    def forecastAhead(self, data, steps):
+    def forecastAhead(self, data, steps, **kwargs):
         ret = []
         for i in steps:
             flrg = self.flrgs[str(i)]

pwfts.py

@@ -42,7 +42,7 @@ class ProbabilisticWeightedFLRG(hofts.HighOrderFLRG):
 
 
 class ProbabilisticWeightedFTS(ifts.IntervalFTS):
-    def __init__(self, name, update=True):
+    def __init__(self, order, name, **kwargs):
         super(ProbabilisticWeightedFTS, self).__init__("PWFTS")
         self.shortname = "PWFTS " + name
         self.name = "Probabilistic FTS"
@@ -191,7 +191,7 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
             ret = sum(np.array([pi * s.lower for s in flrg.LHS]))
         return ret
 
-    def forecast(self, data):
+    def forecast(self, data, **kwargs):
 
         ndata = np.array(self.doTransformations(data))
 
@@ -296,7 +296,7 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
 
         return ret
 
-    def forecastInterval(self, data):
+    def forecastInterval(self, data, **kwargs):
 
         ndata = np.array(self.doTransformations(data))
 
@@ -402,7 +402,7 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
 
         return ret
 
-    def forecastAhead(self, data, steps):
+    def forecastAhead(self, data, steps, **kwargs):
         ret = [data[k] for k in np.arange(len(data) - self.order, len(data))]
 
         for k in np.arange(self.order - 1, steps):
@@ -416,7 +416,7 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
 
         return ret
 
-    def forecastAheadInterval(self, data, steps):
+    def forecastAheadInterval(self, data, steps, **kwargs):
 
         l = len(data)
 
@@ -464,7 +464,7 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
         grid[k] += 1
         return grid
 
-    def forecastAheadDistribution(self, data, steps, resolution, parameters=2):
+    def forecastAheadDistribution(self, data, steps, **kwargs):
 
         ret = []
 

sadaei.py

@@ -36,7 +36,7 @@ class ExponentialyWeightedFLRG(object):
 
 
 class ExponentialyWeightedFTS(fts.FTS):
-    def __init__(self, name):
+    def __init__(self, order, **kwargs):
         super(ExponentialyWeightedFTS, self).__init__(1, "EWFTS")
         self.name = "Exponentialy Weighted FTS"
         self.detail = "Sadaei"
@@ -60,7 +60,7 @@ class ExponentialyWeightedFTS(fts.FTS):
         flrs = FLR.generateRecurrentFLRs(tmpdata)
         self.flrgs = self.generateFLRG(flrs, self.c)
 
-    def forecast(self, data):
+    def forecast(self, data, **kwargs):
         l = 1
 
         data = np.array(data)

sfts.py

@@ -26,7 +26,7 @@ class SeasonalFLRG(FLR.FLR):
 
 
 class SeasonalFTS(fts.FTS):
-    def __init__(self, name):
+    def __init__(self, order, **kwargs):
         super(SeasonalFTS, self).__init__(1, "SFTS")
         self.name = "Seasonal FTS"
         self.detail = "Chen"
@@ -60,7 +60,7 @@ class SeasonalFTS(fts.FTS):
         flrs = FLR.generateRecurrentFLRs(tmpdata)
         self.flrgs = self.generateFLRG(flrs)
 
-    def forecast(self, data):
+    def forecast(self, data, **kwargs):
 
         ndata = np.array(self.doTransformations(data))
 

yu.py

@@ -4,7 +4,7 @@ from pyFTS import fts
 
 
 class WeightedFLRG(fts.FTS):
-    def __init__(self, LHS):
+    def __init__(self, order, **kwargs):
         self.LHS = LHS
         self.RHS = []
         self.count = 1.0
@@ -31,7 +31,7 @@ class WeightedFLRG(fts.FTS):
 
 
 class WeightedFTS(fts.FTS):
-    def __init__(self, name):
+    def __init__(self, order, **kwargs):
         super(WeightedFTS, self).__init__(1, "WFTS " + name)
         self.name = "Weighted FTS"
         self.detail = "Yu"
@@ -53,7 +53,7 @@ class WeightedFTS(fts.FTS):
         flrs = FLR.generateRecurrentFLRs(tmpdata)
         self.flrgs = self.generateFLRG(flrs)
 
-    def forecast(self, data):
+    def forecast(self, data, **kwargs):
         l = 1
 
         data = np.array(data)