- Compacting datasets with bz2

- Refactoring generate_flrg and train methods
 - Introducing batches and model saving on fit method

MANIFEST

@@ -1,6 +1,8 @@
 include data/Enrollments.csv
 include data/AirPassengers.csv
-include data/NASDAQ.csv
+include data/NASDAQ.csv.bz2
-include data/SP500.csv
+include data/SP500.csv.bz2
 include data/sunspots.csv
-include data/TAIEX.csv
+include data/TAIEX.csv.bz2
+include data/INMET.csv.bz2
+include data/SONDA_BSB.csv.bz2

pyFTS/common/Composite.py

@@ -16,7 +16,7 @@ class FuzzySet(FuzzySet.FuzzySet):
         Create an empty composite fuzzy set
         :param name: fuzzy set name
         """
-        super(FuzzySet, self).__init__(self, name, None, None, None, type='composite')
+        super(FuzzySet, self).__init__(name, None, None, None, type='composite')
         self.superset = superset
         if self.superset:
             self.sets = []

pyFTS/common/fts.py

@@ -1,6 +1,6 @@
 import numpy as np
 import pandas as pd
-from pyFTS.common import FuzzySet, SortedCollection, tree
+from pyFTS.common import FuzzySet, SortedCollection, tree, Util
 
 
 class FTS(object):
@@ -164,9 +164,42 @@ class FTS(object):
 
         :param data:
         :param kwargs:
+
+        :keyword
+        num_batches: split the training data in num_batches to save memory during the training process
+        save_model: save final model on disk
+        batch_save: save the model between each batch
+        file_path: path to save the model
         :return:
         """
-        self.train(data, **kwargs)
+
+        num_batches = kwargs.get('num_batches', None)
+
+        save = kwargs.get('save_model', False)  # save model on disk
+
+        batch_save = kwargs.get('batch_save', True) #save model between batches
+
+        file_path = kwargs.get('file_path', None)
+
+        if num_batches is not None:
+            n = len(data)
+            batch_size = round(n / num_batches, 0)
+            for ct in range(self.order, n, batch_size):
+                if self.is_multivariate:
+                    ndata = data.iloc[ct - self.order:ct + batch_size]
+                else:
+                    ndata = data[ct - self.order : ct + batch_size]
+
+                self.train(ndata, **kwargs)
+
+                if batch_save:
+                    Util.persist_obj(self,file_path)
+
+        else:
+            self.train(data, **kwargs)
+
+        if save:
+            Util.persist_obj(self, file_path)
 
     def append_transformation(self, transformation):
         if transformation is not None:

pyFTS/data/INMET.py

@@ -0,0 +1,26 @@
+#--------------------
+#BDMEP - INMET
+#--------------------
+#Estação           : BELO HORIZONTE - MG (OMM: 83587)
+#Latitude  (graus) : -19.93
+#Longitude (graus) : -43.93
+#Altitude  (metros): 915.00
+#Estação Operante
+#Inicio de operação: 03/03/1910
+#Periodo solicitado dos dados: 01/01/2000 a 31/12/2012
+#Os dados listados abaixo são os que encontram-se digitados no BDMEP
+#Hora em UTC
+
+# http://www.inmet.gov.br
+
+import pandas as pd
+import numpy as np
+import os
+import pkg_resources
+
+
+def get_dataframe():
+    filename = pkg_resources.resource_filename('pyFTS', 'data/INMET.csv.bz2')
+    dat = pd.read_csv(filename, sep=";", compression='bz2')
+    dat["DataHora"] = pd.to_datetime(dat["DataHora"], format='%d/%m/%Y %H:%M')
+    return dat

pyFTS/data/NASDAQ.py

@@ -5,7 +5,7 @@ import pkg_resources
 
 
 def get_data():
-    filename = pkg_resources.resource_filename('pyFTS', 'data/NASDAQ.csv')
+    filename = pkg_resources.resource_filename('pyFTS', 'data/NASDAQ.csv.bz2')
-    dat = pd.read_csv(filename, sep=";")
+    dat = pd.read_csv(filename, sep=";", compression='bz2')
     dat = np.array(dat["avg"])
     return dat

pyFTS/data/SONDA.py

@@ -0,0 +1,18 @@
+import pandas as pd
+import numpy as np
+import os
+import pkg_resources
+
+
+def get_data(field):
+    filename = pkg_resources.resource_filename('pyFTS', 'data/SONDA_BSB.csv.bz2')
+    dat = pd.read_csv(filename, sep=";", compression='bz2')
+    dat = np.array(dat[field])
+    return dat
+
+
+def get_dataframe():
+    filename = pkg_resources.resource_filename('pyFTS', 'data/SONDA_BSB.csv.bz2')
+    dat = pd.read_csv(filename, sep=";", compression='bz2')
+    dat["datahora"] = pd.to_datetime(dat["datahora"], format='%Y-%m-%d %H:%M:%S')
+    return dat

pyFTS/data/SP500.py

@@ -5,7 +5,7 @@ import pkg_resources
 
 
 def get_data():
-    filename = pkg_resources.resource_filename('pyFTS', 'data/SP500.csv')
+    filename = pkg_resources.resource_filename('pyFTS', 'data/SP500.csv.bz2')
-    dat = pd.read_csv(filename, sep=",")
+    dat = pd.read_csv(filename, sep=",", compression='bz2')
     dat = np.array(dat["Avg"])
     return dat

pyFTS/data/TAIEX.py

@@ -5,14 +5,14 @@ import pkg_resources
 
 
 def get_data():
-    filename = pkg_resources.resource_filename('pyFTS', 'data/TAIEX.csv')
+    filename = pkg_resources.resource_filename('pyFTS', 'data/TAIEX.csv.bz2')
-    dat = pd.read_csv(filename, sep=",")
+    dat = pd.read_csv(filename, sep=",", compression='bz2')
     dat = np.array(dat["avg"])
     return dat
 
 
 def get_dataframe():
-    filename = pkg_resources.resource_filename('pyFTS', 'data/TAIEX.csv')
+    filename = pkg_resources.resource_filename('pyFTS', 'data/TAIEX.csv.bz2')
-    dat = pd.read_csv(filename, sep=",")
+    dat = pd.read_csv(filename, sep=",", compression='bz2')
     dat["Date"] = pd.to_datetime(dat["Date"])
     return dat

pyFTS/models/chen.py

@@ -37,14 +37,12 @@ class ConventionalFTS(fts.FTS):
         self.flrgs = {}
 
     def generate_flrg(self, flrs):
-        flrgs = {}
         for flr in flrs:
-            if flr.LHS.name in flrgs:
+            if flr.LHS.name in self.flrgs:
-                flrgs[flr.LHS.name].append(flr.RHS)
+                self.flrgs[flr.LHS.name].append(flr.RHS)
             else:
-                flrgs[flr.LHS.name] = ConventionalFLRG(flr.LHS)
+                self.flrgs[flr.LHS.name] = ConventionalFLRG(flr.LHS)
-                flrgs[flr.LHS.name].append(flr.RHS)
+                self.flrgs[flr.LHS.name].append(flr.RHS)
-        return (flrgs)
 
     def train(self, data, **kwargs):
         if kwargs.get('sets', None) is not None:
@@ -52,7 +50,7 @@ class ConventionalFTS(fts.FTS):
         ndata = self.apply_transformations(data)
         tmpdata = FuzzySet.fuzzyfy_series_old(ndata, self.sets)
         flrs = FLR.generate_non_recurrent_flrs(tmpdata)
-        self.flrgs = self.generate_flrg(flrs)
+        self.generate_flrg(flrs)
 
     def forecast(self, data, **kwargs):
 

pyFTS/models/cheng.py

@@ -53,11 +53,9 @@ class TrendWeightedFTS(yu.WeightedFTS):
         self.is_high_order = False
 
     def generate_FLRG(self, flrs):
-        flrgs = {}
         for flr in flrs:
-            if flr.LHS.name in flrgs:
+            if flr.LHS.name in self.flrgs:
-                flrgs[flr.LHS.name].append(flr.RHS)
+                self.flrgs[flr.LHS.name].append(flr.RHS)
             else:
-                flrgs[flr.LHS.name] = TrendWeightedFLRG(flr.LHS)
+                self.flrgs[flr.LHS.name] = TrendWeightedFLRG(flr.LHS)
-                flrgs[flr.LHS.name].append(flr.RHS)
+                self.flrgs[flr.LHS.name].append(flr.RHS)
-        return (flrgs)

pyFTS/models/ensemble/ensemble.py

@@ -32,12 +32,12 @@ class EnsembleFTS(fts.FTS):
         self.point_method = kwargs.get('point_method', 'mean')
         self.interval_method = kwargs.get('interval_method', 'quantile')
 
-    def appendModel(self, model):
+    def append_model(self, model):
         self.models.append(model)
         if model.order > self.order:
             self.order = model.order
 
-    def train(self, data, sets, order=1,parameters=None):
+    def train(self, data, **kwargs):
         self.original_max = max(data)
         self.original_min = min(data)
 
@@ -228,10 +228,12 @@ class AllMethodEnsembleFTS(EnsembleFTS):
         for t in self.transformations:
             model.append_transformation(t)
 
-    def train(self, data, sets, order=1, parameters=None):
+    def train(self, data, **kwargs):
         self.original_max = max(data)
         self.original_min = min(data)
 
+        order = kwargs.get('order',2)
+
         fo_methods = [song.ConventionalFTS, chen.ConventionalFTS, yu.WeightedFTS, cheng.TrendWeightedFTS,
                       sadaei.ExponentialyWeightedFTS, ismailefendi.ImprovedWeightedFTS]
 
@@ -240,16 +242,16 @@ class AllMethodEnsembleFTS(EnsembleFTS):
         for method in fo_methods:
             model = method("")
             self.set_transformations(model)
-            model.train(data, sets)
+            model.train(data, **kwargs)
-            self.appendModel(model)
+            self.append_model(model)
 
         for method in ho_methods:
             for o in np.arange(1, order+1):
                 model = method("")
                 if model.min_order >= o:
                     self.set_transformations(model)
-                    model.train(data, sets, order=o)
+                    model.train(data, **kwargs)
-                    self.appendModel(model)
+                    self.append_model(model)
 
 
 

pyFTS/models/ensemble/multiseasonal.py

@@ -41,7 +41,7 @@ class SeasonalEnsembleFTS(ensemble.EnsembleFTS):
         self.original_max = max(self.indexer.get_data(data))
         self.original_min = min(self.indexer.get_data(data))
 
-    def train(self, data, sets, order=1, parameters=None):
+    def train(self, data, **kwargs):
         self.original_max = max(self.indexer.get_data(data))
         self.original_min = min(self.indexer.get_data(data))
 
@@ -59,7 +59,7 @@ class SeasonalEnsembleFTS(ensemble.EnsembleFTS):
             for m in pool.keys())
 
         for tmp in results:
-            self.appendModel(tmp)
+            self.append_model(tmp)
 
         cUtil.persist_obj(self, "models/"+self.name+".pkl")
 

pyFTS/models/hofts.py

@@ -77,7 +77,6 @@ class HighOrderFTS(fts.FTS):
             self.build_tree_without_order(child, lags, level + 1)
 
     def generateFLRG(self, flrs):
-        flrgs = {}
         l = len(flrs)
         for k in np.arange(self.order + 1, l):
             flrg = HighOrderFLRG(self.order)
@@ -85,15 +84,13 @@ class HighOrderFTS(fts.FTS):
             for kk in np.arange(k - self.order, k):
                 flrg.append_lhs(flrs[kk].LHS)
 
-            if flrg.str_lhs() in flrgs:
+            if flrg.str_lhs() in self.flrgs:
-                flrgs[flrg.str_lhs()].append_rhs(flrs[k].RHS)
+                self.flrgs[flrg.str_lhs()].append_rhs(flrs[k].RHS)
             else:
-                flrgs[flrg.str_lhs()] = flrg;
+                self.flrgs[flrg.str_lhs()] = flrg;
-                flrgs[flrg.str_lhs()].append_rhs(flrs[k].RHS)
+                self.flrgs[flrg.str_lhs()].append_rhs(flrs[k].RHS)
-        return (flrgs)
 
     def generate_flrg(self, data):
-        flrgs = {}
         l = len(data)
         for k in np.arange(self.order, l):
             if self.dump: print("FLR: " + str(k))
@@ -121,13 +118,12 @@ class HighOrderFTS(fts.FTS):
                 for lhs in path:
                     flrg.append_lhs(lhs)
 
-                if flrg.str_lhs() not in flrgs:
+                if flrg.str_lhs() not in self.flrgs:
-                    flrgs[flrg.str_lhs()] = flrg;
+                    self.flrgs[flrg.str_lhs()] = flrg;
 
                 for st in rhs:
-                    flrgs[flrg.str_lhs()].append_rhs(st)
+                    self.flrgs[flrg.str_lhs()].append_rhs(st)
 
-        return flrgs
 
     def train(self, data, **kwargs):
 
@@ -138,7 +134,7 @@ class HighOrderFTS(fts.FTS):
         if kwargs.get('sets', None) is not None:
             self.sets = kwargs.get('sets', None)
         for s in self.sets:    self.setsDict[s.name] = s
-        self.flrgs = self.generate_flrg(data)
+        self.generate_flrg(data)
 
     def forecast(self, data, **kwargs):
 

pyFTS/models/ismailefendi.py

@@ -54,14 +54,12 @@ class ImprovedWeightedFTS(fts.FTS):
         self.setsDict = {}
 
     def generate_flrg(self, flrs):
-        flrgs = {}
         for flr in flrs:
-            if flr.LHS.name in flrgs:
+            if flr.LHS.name in self.flrgs:
-                flrgs[flr.LHS.name].append(flr.RHS)
+                self.flrgs[flr.LHS.name].append(flr.RHS)
             else:
-                flrgs[flr.LHS.name] = ImprovedWeightedFLRG(flr.LHS);
+                self.flrgs[flr.LHS.name] = ImprovedWeightedFLRG(flr.LHS);
-                flrgs[flr.LHS.name].append(flr.RHS)
+                self.flrgs[flr.LHS.name].append(flr.RHS)
-        return (flrgs)
 
     def train(self, data, **kwargs):
         if kwargs.get('sets', None) is not None:
@@ -73,7 +71,7 @@ class ImprovedWeightedFTS(fts.FTS):
 
         tmpdata = FuzzySet.fuzzyfy_series_old(ndata, self.sets)
         flrs = FLR.generate_recurrent_flrs(tmpdata)
-        self.flrgs = self.generate_flrg(flrs)
+        self.generate_flrg(flrs)
 
     def forecast(self, data, **kwargs):
         l = 1

pyFTS/models/multivariate/mvfts.py

@@ -15,6 +15,7 @@ class MVFTS(fts.FTS):
         self.explanatory_variables = []
         self.target_variable = None
         self.flrgs = {}
+        self.is_multivariate = True
 
     def append_variable(self, var):
         self.explanatory_variables.append(var)
@@ -76,24 +77,21 @@ class MVFTS(fts.FTS):
         return flrs
 
     def generate_flrg(self, flrs):
-        flrgs = {}
-
         for flr in flrs:
             flrg = mvflrg.FLRG(lhs=flr.LHS)
 
-            if flrg.get_key() not in flrgs:
+            if flrg.get_key() not in self.flrgs:
-                flrgs[flrg.get_key()] = flrg
+                self.flrgs[flrg.get_key()] = flrg
 
-            flrgs[flrg.get_key()].append_rhs(flr.RHS)
+            self.flrgs[flrg.get_key()].append_rhs(flr.RHS)
 
-        return flrgs
 
     def train(self, data, **kwargs):
 
         ndata = self.apply_transformations(data)
 
         flrs = self.generate_flrs(ndata)
-        self.flrgs = self.generate_flrg(flrs)
+        self.generate_flrg(flrs)
 
     def forecast(self, data, **kwargs):
         ret = []

pyFTS/models/multivariate/variable.py

@@ -33,9 +33,10 @@ class Variable:
         mf = kwargs.get('func', Membership.trimf)
         np = kwargs.get('npart', 10)
         data = kwargs.get('data', None)
+        kw = kwargs.get('partitioner_specific', {})
         self.partitioner = fs(data=data[self.data_label].values, npart=np, func=mf,
                               transformation=self.transformation, prefix=self.alias,
-                              variable=self.name)
+                              variable=self.name, **kw)
 
         self.partitioner.name = self.name + " " + self.partitioner.name
 

pyFTS/models/nonstationary/common.py

@@ -31,7 +31,7 @@ class FuzzySet(FS.FuzzySet):
             - noise: Pertubation function that adds noise on the membership function
             - noise_params: Parameters for noise pertubation function
         """
-        super(FuzzySet, self).__init__(name=name, mf=mf, parameters=parameters, centroid=None)
+        super(FuzzySet, self).__init__(name=name, mf=mf, parameters=parameters, centroid=None, alpha=1.0, **kwargs)
     
         self.location = kwargs.get("location", None)
         self.location_params = kwargs.get("location_params", None)
@@ -42,6 +42,7 @@ class FuzzySet(FS.FuzzySet):
         self.noise = kwargs.get("noise", None)
         self.noise_params = kwargs.get("noise_params", None)
         self.perturbated_parameters = {}
+        self.type = 'nonstationary'
     
         if self.location is not None and not isinstance(self.location, (list, set)):
             self.location = [self.location]

pyFTS/models/nonstationary/cvfts.py

@@ -22,11 +22,9 @@ class ConditionalVarianceFTS(chen.ConventionalFTS):
         self.min_stack = [0,0,0]
         self.max_stack = [0,0,0]
 
-    def train(self, data, sets = None, order=1,parameters=None):
+    def train(self, data, **kwargs):
-        if sets is not None:
+        if kwargs.get('sets', None) is not None:
-            self.sets = sets
+            self.sets = kwargs.get('sets', None)
-        else:
-            self.sets = self.partitioner.sets
 
         ndata = self.apply_transformations(data)
 
@@ -35,17 +33,15 @@ class ConditionalVarianceFTS(chen.ConventionalFTS):
 
         tmpdata = common.fuzzySeries(ndata, self.sets, method='fuzzy', const_t=0)
         flrs = FLR.generate_non_recurrent_flrs(tmpdata)
-        self.flrgs = self.generate_flrg(flrs)
+        self.generate_flrg(flrs)
 
     def generate_flrg(self, flrs, **kwargs):
-        flrgs = {}
         for flr in flrs:
-            if flr.LHS.name in flrgs:
+            if flr.LHS.name in self.flrgs:
-                flrgs[flr.LHS.name].append(flr.RHS)
+                self.flrgs[flr.LHS.name].append(flr.RHS)
             else:
-                flrgs[flr.LHS.name] = nsfts.ConventionalNonStationaryFLRG(flr.LHS)
+                self.flrgs[flr.LHS.name] = nsfts.ConventionalNonStationaryFLRG(flr.LHS)
-                flrgs[flr.LHS.name].append(flr.RHS)
+                self.flrgs[flr.LHS.name].append(flr.RHS)
-        return flrgs
 
     def _smooth(self, a):
         return .1 * a[0] + .3 * a[1] + .6 * a[2]

pyFTS/models/nonstationary/honsfts.py

@@ -46,7 +46,6 @@ class HighOrderNonStationaryFTS(hofts.HighOrderFTS):
         self.flrgs = {}
 
     def generate_flrg(self, data, **kwargs):
-        flrgs = {}
         l = len(data)
         window_size = kwargs.get("window_size", 1)
         for k in np.arange(self.order, l):
@@ -84,30 +83,27 @@ class HighOrderNonStationaryFTS(hofts.HighOrderFTS):
                 for c, e in enumerate(path, start=0):
                     flrg.appendLHS(e)
 
-                if flrg.strLHS() not in flrgs:
+                if flrg.strLHS() not in self.flrgs:
-                    flrgs[flrg.strLHS()] = flrg;
+                    self.flrgs[flrg.strLHS()] = flrg;
 
                 for st in rhs:
-                    flrgs[flrg.strLHS()].append_rhs(st)
+                    self.flrgs[flrg.strLHS()].append_rhs(st)
 
         # flrgs = sorted(flrgs, key=lambda flrg: flrg.get_midpoint(0, window_size=1))
 
-        return flrgs
+    def train(self, data, **kwargs):
 
-    def train(self, data, sets=None, order=2, parameters=None):
+        if kwargs.get('order', None) is not None:
+            self.order = kwargs.get('order', 1)
 
-        self.order = order
+        if kwargs.get('sets', None) is not None:
-
+            self.sets = kwargs.get('sets', None)
-        if sets is not None:
-            self.sets = sets
-        else:
-            self.sets = self.partitioner.sets
 
         ndata = self.apply_transformations(data)
         #tmpdata = common.fuzzyfy_series_old(ndata, self.sets)
         #flrs = FLR.generate_recurrent_flrs(ndata)
-        window_size = parameters if parameters is not None else 1
+        window_size = kwargs.get('parameters', 1)
-        self.flrgs = self.generate_flrg(ndata, window_size=window_size)
+        self.generate_flrg(ndata, window_size=window_size)
 
     def _affected_flrgs(self, sample, k, time_displacement, window_size):
         # print("input: " + str(ndata[k]))

pyFTS/models/nonstationary/nsfts.py

@@ -1,6 +1,6 @@
 import numpy as np
 from pyFTS.common import FLR, fts
-from pyFTS.nonstationary import common, flrg
+from pyFTS.models.nonstationary import common, flrg
 
 
 class ConventionalNonStationaryFLRG(flrg.NonStationaryFLRG):
@@ -34,29 +34,25 @@ class NonStationaryFTS(fts.FTS):
         self.method = kwargs.get("method",'fuzzy')
 
     def generate_flrg(self, flrs, **kwargs):
-        flrgs = {}
         for flr in flrs:
-            if flr.LHS.name in flrgs:
+            if flr.LHS.name in self.flrgs:
-                flrgs[flr.LHS.name].append(flr.RHS)
+                self.flrgs[flr.LHS.name].append(flr.RHS)
             else:
-                flrgs[flr.LHS.name] = ConventionalNonStationaryFLRG(flr.LHS)
+                self.flrgs[flr.LHS.name] = ConventionalNonStationaryFLRG(flr.LHS)
-                flrgs[flr.LHS.name].append(flr.RHS)
+                self.flrgs[flr.LHS.name].append(flr.RHS)
-        return flrgs
 
-    def train(self, data, sets=None, order=1, parameters=None):
+    def train(self, data, **kwargs):
 
-        if sets is not None:
+        if kwargs.get('sets', None) is not None:
-            self.sets = sets
+            self.sets = kwargs.get('sets', None)
-        else:
-            self.sets = self.partitioner.sets
 
         ndata = self.apply_transformations(data)
-        window_size = parameters if parameters is not None else 1
+        window_size = kwargs.get('parameters', 1)
         tmpdata = common.fuzzySeries(ndata, self.sets, window_size, method=self.method)
         #print([k[0].name for k in tmpdata])
         flrs = FLR.generate_recurrent_flrs(tmpdata)
         #print([str(k) for k in flrs])
-        self.flrgs = self.generate_flrg(flrs)
+        self.generate_flrg(flrs)
 
     def forecast(self, data, **kwargs):
 

pyFTS/models/pwfts.py

@@ -128,12 +128,11 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
         if parameters == 'Monotonic':
             tmpdata = FuzzySet.fuzzyfy_series_old(data, self.sets)
             flrs = FLR.generate_recurrent_flrs(tmpdata)
-            self.flrgs = self.generateFLRG(flrs)
+            self.generateFLRG(flrs)
         else:
-            self.flrgs = self.generate_flrg(data)
+            self.generate_flrg(data)
 
     def generate_flrg(self, data):
-        flrgs = {}
         l = len(data)
         for k in np.arange(self.order, l):
             if self.dump: print("FLR: " + str(k))
@@ -168,20 +167,17 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
 
                 lhs_mv = np.prod(tmp_path)
 
-                if flrg.str_lhs() not in flrgs:
+                if flrg.str_lhs() not in self.flrgs:
-                    flrgs[flrg.str_lhs()] = flrg;
+                    self.flrgs[flrg.str_lhs()] = flrg;
 
                 for st in idx:
-                    flrgs[flrg.str_lhs()].appendRHSFuzzy(self.sets[st], lhs_mv * mv[st])
+                    self.flrgs[flrg.str_lhs()].appendRHSFuzzy(self.sets[st], lhs_mv * mv[st])
 
                 tmp_fq = sum([lhs_mv*kk for kk in mv if kk > 0])
 
                 self.global_frequency_count = self.global_frequency_count + tmp_fq
 
-        return flrgs
-
     def generateFLRG(self, flrs):
-        flrgs = {}
         l = len(flrs)
         for k in np.arange(self.order, l+1):
             if self.dump: print("FLR: " + str(k))
@@ -191,15 +187,14 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
                 flrg.append_lhs(flrs[kk].LHS)
                 if self.dump: print("LHS: " + str(flrs[kk]))
 
-            if flrg.str_lhs() in flrgs:
+            if flrg.str_lhs() in self.flrgs:
-                flrgs[flrg.str_lhs()].append_rhs(flrs[k - 1].RHS)
+                self.flrgs[flrg.str_lhs()].append_rhs(flrs[k - 1].RHS)
             else:
-                flrgs[flrg.str_lhs()] = flrg
+                self.flrgs[flrg.str_lhs()] = flrg
-                flrgs[flrg.str_lhs()].append_rhs(flrs[k - 1].RHS)
+                self.flrgs[flrg.str_lhs()].append_rhs(flrs[k - 1].RHS)
             if self.dump: print("RHS: " + str(flrs[k-1]))
 
             self.global_frequency_count += 1
-        return (flrgs)
 
     def update_model(self,data):
 

pyFTS/models/sadaei.py

@@ -58,14 +58,12 @@ class ExponentialyWeightedFTS(fts.FTS):
         self.c = kwargs.get('c', default_c)
 
     def generate_flrg(self, flrs, c):
-        flrgs = {}
         for flr in flrs:
-            if flr.LHS.name in flrgs:
+            if flr.LHS.name in self.flrgs:
-                flrgs[flr.LHS.name].append(flr.RHS)
+                self.flrgs[flr.LHS.name].append(flr.RHS)
             else:
-                flrgs[flr.LHS.name] = ExponentialyWeightedFLRG(flr.LHS, c=c);
+                self.flrgs[flr.LHS.name] = ExponentialyWeightedFLRG(flr.LHS, c=c);
-                flrgs[flr.LHS.name].append(flr.RHS)
+                self.flrgs[flr.LHS.name].append(flr.RHS)
-        return (flrgs)
 
     def train(self, data, **kwargs):
         self.c = kwargs.get('parameters', default_c)
@@ -74,7 +72,7 @@ class ExponentialyWeightedFTS(fts.FTS):
         ndata = self.apply_transformations(data)
         tmpdata = FuzzySet.fuzzyfy_series_old(ndata, self.sets)
         flrs = FLR.generate_recurrent_flrs(tmpdata)
-        self.flrgs = self.generate_flrg(flrs, self.c)
+        self.generate_flrg(flrs, self.c)
 
     def forecast(self, data, **kwargs):
         l = 1

pyFTS/models/seasonal/cmsfts.py

@@ -44,25 +44,23 @@ class ContextualMultiSeasonalFTS(sfts.SeasonalFTS):
         self.indexer = indexer
         self.flrgs = {}
 
-    def generateFLRG(self, flrs):
+    def generate_flrg(self, flrs):
-        flrgs = {}
-
         for flr in flrs:
 
-            if str(flr.index) not in flrgs:
+            if str(flr.index) not in self.flrgs:
-                flrgs[str(flr.index)] = ContextualSeasonalFLRG(flr.index)
+                self.flrgs[str(flr.index)] = ContextualSeasonalFLRG(flr.index)
 
-            flrgs[str(flr.index)].append(flr)
+            self.flrgs[str(flr.index)].append(flr)
 
-        return (flrgs)
+    def train(self, data,  **kwargs):
-
+        if kwargs.get('sets', None) is not None:
-    def train(self, data, sets, order=1, parameters=None):
+            self.sets = kwargs.get('sets', None)
-        self.sets = sets
+        if kwargs.get('parameters', None) is not None:
-        self.seasonality = parameters
+            self.seasonality = kwargs.get('parameters', None)
         flrs = FLR.generate_indexed_flrs(self.sets, self.indexer, data)
-        self.flrgs = self.generateFLRG(flrs)
+        self.generate_flrg(flrs)
 
-    def getMidpoints(self, flrg, data):
+    def get_midpoints(self, flrg, data):
         if data.name in flrg.flrgs:
             ret = np.array([s.centroid for s in flrg.flrgs[data.name].RHS])
             return ret
@@ -82,7 +80,7 @@ class ContextualMultiSeasonalFTS(sfts.SeasonalFTS):
 
             d = FuzzySet.get_maximum_membership_fuzzyset(ndata[k], self.sets)
 
-            mp = self.getMidpoints(flrg, d)
+            mp = self.get_midpoints(flrg, d)
 
             ret.append(sum(mp) / len(mp))
 
@@ -90,12 +88,12 @@ class ContextualMultiSeasonalFTS(sfts.SeasonalFTS):
 
         return ret
 
-    def forecastAhead(self, data, steps, **kwargs):
+    def forecast_ahead(self, data, steps, **kwargs):
         ret = []
         for i in steps:
             flrg = self.flrgs[str(i)]
 
-            mp = self.getMidpoints(flrg)
+            mp = self.get_midpoints(flrg)
 
             ret.append(sum(mp) / len(mp))
 

pyFTS/models/seasonal/common.py

@@ -28,7 +28,7 @@ class DateTime(Enum):
     second_of_day = 86400
 
 
-def strip_datepart(self, date, date_part):
+def strip_datepart(date, date_part):
     if date_part == DateTime.year:
         tmp = date.year
     elif date_part == DateTime.month:
@@ -90,7 +90,8 @@ class FuzzySet(FuzzySet.FuzzySet):
     def __init__(self, datepart, name, mf, parameters, centroid, alpha=1.0, **kwargs):
         super(FuzzySet, self).__init__(name, mf, parameters, centroid, alpha, type = 'datetime', **kwargs)
         self.datepart = datepart
+        self.type = 'seasonal'
 
     def membership(self, x):
         dp = strip_datepart(x, self.datepart)
-        return self.mf.membership(dp)
+        return self.mf(dp, self.parameters) * self.alpha

pyFTS/models/seasonal/msfts.py

@@ -20,24 +20,22 @@ class MultiSeasonalFTS(sfts.SeasonalFTS):
         self.indexer = indexer
         self.flrgs = {}
 
-    def generateFLRG(self, flrs):
+    def generate_flrg(self, flrs):
-        flrgs = {}
-
         for flr in flrs:
 
             if str(flr.index) not in self.flrgs:
-                flrgs[str(flr.index)] = sfts.SeasonalFLRG(flr.index)
+                self.flrgs[str(flr.index)] = sfts.SeasonalFLRG(flr.index)
 
-            flrgs[str(flr.index)].append(flr.RHS)
+            self.flrgs[str(flr.index)].append(flr.RHS)
 
-        return (flrgs)
+    def train(self, data,  **kwargs):
-
+        if kwargs.get('sets', None) is not None:
-    def train(self, data, sets, order=1, parameters=None):
+            self.sets = kwargs.get('sets', None)
-        self.sets = sets
+        if kwargs.get('parameters', None) is not None:
-        self.seasonality = parameters
+            self.seasonality = kwargs.get('parameters', None)
         #ndata = self.indexer.set_data(data,self.doTransformations(self.indexer.get_data(data)))
         flrs = FLR.generate_indexed_flrs(self.sets, self.indexer, data)
-        self.flrgs = self.generateFLRG(flrs)
+        self.generate_flrg(flrs)
 
     def forecast(self, data, **kwargs):
 

pyFTS/models/seasonal/partitioner.py

@@ -17,7 +17,7 @@ class TimeGridPartitioner(partitioner.Partitioner):
         :param npart: The number of universe of discourse partitions, i.e., the number of fuzzy sets that will be created
         :param func: Fuzzy membership function (pyFTS.common.Membership)
         """
-        super(TimeGridPartitioner, self).__init__(name="TimeGrid", **kwargs)
+        super(TimeGridPartitioner, self).__init__(name="TimeGrid", preprocess=False, **kwargs)
 
         self.season = kwargs.get('seasonality', DateTime.day_of_year)
         data = kwargs.get('data', None)
@@ -101,11 +101,12 @@ class TimeGridPartitioner(partitioner.Partitioner):
         ticks = []
         x = []
         for s in self.sets:
-            if s.type == 'common':
+            if s.type == 'composite':
-                self.plot_set(ax, s)
-            elif s.type == 'composite':
                 for ss in s.sets:
                     self.plot_set(ax, ss)
-#            ticks.append(str(round(s.centroid, 0)) + '\n' + s.name)
+            else:
-#            x.append(s.centroid)
+                self.plot_set(ax, s)
-#        plt.xticks(x, ticks)
+            ticks.append(str(round(s.centroid, 0)) + '\n' + s.name)
+            x.append(s.centroid)
+        ax.xaxis.set_ticklabels(ticks)
+        ax.xaxis.set_ticks(x)

pyFTS/models/seasonal/sfts.py

@@ -43,29 +43,29 @@ class SeasonalFTS(fts.FTS):
         self.has_seasonality = True
         self.has_point_forecasting = True
         self.is_high_order = False
+        self.flrgs = {}
+
+    def generate_flrg(self, flrs):
 
-    def generateFLRG(self, flrs):
-        flrgs = {}
         for ct, flr in enumerate(flrs, start=1):
 
             season = self.indexer.get_season_by_index(ct)[0]
 
             ss = str(season)
 
-            if ss not in flrgs:
+            if ss not in self.flrgs:
-                flrgs[ss] = SeasonalFLRG(season)
+                self.flrgs[ss] = SeasonalFLRG(season)
 
             #print(season)
-            flrgs[ss].append(flr.RHS)
+            self.flrgs[ss].append(flr.RHS)
 
-        return (flrgs)
+    def train(self, data,  **kwargs):
-
+        if kwargs.get('sets', None) is not None:
-    def train(self, data, sets, order=1, parameters=None):
+            self.sets = kwargs.get('sets', None)
-        self.sets = sets
         ndata = self.apply_transformations(data)
-        tmpdata = FuzzySet.fuzzyfy_series_old(ndata, sets)
+        tmpdata = FuzzySet.fuzzyfy_series_old(ndata, self.sets)
         flrs = FLR.generate_recurrent_flrs(tmpdata)
-        self.flrgs = self.generateFLRG(flrs)
+        self.generate_flrg(flrs)
 
     def forecast(self, data, **kwargs):
 

pyFTS/models/song.py

@@ -14,8 +14,15 @@ class ConventionalFTS(fts.FTS):
         super(ConventionalFTS, self).__init__(1, "FTS " + name, **kwargs)
         self.name = "Traditional FTS"
         self.detail = "Song & Chissom"
+        if self.sets is not None and self.partitioner is not None:
+            self.sets = self.partitioner.sets
+
         self.R = None
 
+        if self.sets is not None:
+            self.R = np.zeros((len(self.sets),len(self.sets)))
+
+
     def flr_membership_matrix(self, flr):
         lm = [flr.LHS.membership(k.centroid) for k in self.sets]
         rm = [flr.RHS.membership(k.centroid) for k in self.sets]
@@ -28,14 +35,14 @@ class ConventionalFTS(fts.FTS):
         return r
 
     def operation_matrix(self, flrs):
-        r = np.zeros((len(self.sets),len(self.sets)))
+        if self.R is None:
+            self.R = np.zeros((len(self.sets), len(self.sets)))
         for k in flrs:
             mm = self.flr_membership_matrix(k)
             for k in range(0, len(self.sets)):
                 for l in range(0, len(self.sets)):
-                    r[k][l] = max(r[k][l], mm[k][l])
+                    self.R[k][l] = max(r[k][l], mm[k][l])
 
-        return r
 
     def train(self, data, **kwargs):
         if kwargs.get('sets', None) is not None:
@@ -43,7 +50,7 @@ class ConventionalFTS(fts.FTS):
         ndata = self.apply_transformations(data)
         tmpdata = FuzzySet.fuzzyfy_series_old(ndata, self.sets)
         flrs = FLR.generate_non_recurrent_flrs(tmpdata)
-        self.R = self.operation_matrix(flrs)
+        self.operation_matrix(flrs)
 
     def forecast(self, data, **kwargs):
 

pyFTS/models/yu.py

@@ -47,14 +47,12 @@ class WeightedFTS(fts.FTS):
         self.detail = "Yu"
 
     def generate_FLRG(self, flrs):
-        flrgs = {}
         for flr in flrs:
-            if flr.LHS.name in flrgs:
+            if flr.LHS.name in self.flrgs:
-                flrgs[flr.LHS.name].append(flr.RHS)
+                self.flrgs[flr.LHS.name].append(flr.RHS)
             else:
-                flrgs[flr.LHS.name] = WeightedFLRG(flr.LHS);
+                self.flrgs[flr.LHS.name] = WeightedFLRG(flr.LHS);
-                flrgs[flr.LHS.name].append(flr.RHS)
+                self.flrgs[flr.LHS.name].append(flr.RHS)
-        return (flrgs)
 
     def train(self, data, **kwargs):
         if kwargs.get('sets', None) is not None:
@@ -62,7 +60,7 @@ class WeightedFTS(fts.FTS):
         ndata = self.apply_transformations(data)
         tmpdata = FuzzySet.fuzzyfy_series_old(ndata, self.sets)
         flrs = FLR.generate_recurrent_flrs(tmpdata)
-        self.flrgs = self.generate_FLRG(flrs)
+        self.generate_FLRG(flrs)
 
     def forecast(self, data, **kwargs):
         l = 1

pyFTS/tests/ensemble.py

@@ -37,7 +37,7 @@ for method in fo_methods:
     model = method("")
     model.append_transformation(diff)
     model.train(passengers, fs.sets)
-    e.appendModel(model)
+    e.append_model(model)
 
 
 for method in ho_methods:
@@ -45,7 +45,7 @@ for method in ho_methods:
         model = method("")
         model.append_transformation(diff)
         model.train(passengers, fs.sets, order=order)
-        e.appendModel(model)
+        e.append_model(model)
 
 
 arima100 = arima.ARIMA("", alpha=0.25)
@@ -65,10 +65,10 @@ arima201 = arima.ARIMA("", alpha=0.25)
 arima201.train(passengers, None, order=(2,0,1))
 
 
-e.appendModel(arima100)
+e.append_model(arima100)
-e.appendModel(arima101)
+e.append_model(arima101)
-e.appendModel(arima200)
+e.append_model(arima200)
-e.appendModel(arima201)
+e.append_model(arima201)
 
 e.train(passengers, None)
 

pyFTS/tests/seasonal.py

@@ -4,16 +4,33 @@ from pyFTS.partitioners import Util
 from pyFTS.common import Membership
 
 
-#fs = partitioner.TimeGridPartitioner(None, 12, common.DateTime.day_of_year, func=Membership.trapmf,
+#fs = partitioner.TimeGridPartitioner(data=None, npart=12, seasonality=common.DateTime.day_of_year,
+#                                     func=Membership.trapmf,
 #                                     names=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec'])
 
 
 #fs = partitioner.TimeGridPartitioner(None, 24, common.DateTime.minute_of_day, func=Membership.trapmf)
 
-fs = partitioner.TimeGridPartitioner(None, 7, common.DateTime.hour_of_week, func=Membership.trapmf)
+#fs = partitioner.TimeGridPartitioner(None, 7, common.DateTime.hour_of_week, func=Membership.trapmf)
 
 
-fig, ax = plt.subplots(nrows=1, ncols=1, figsize=[6, 8])
+#fig, ax = plt.subplots(nrows=1, ncols=1, figsize=[6, 8])
 
-fs.plot(ax)
+#fs.plot(ax)
-plt.show()
+#plt.show()
+
+"""
+import pandas as pd
+
+from pyFTS.data import SONDA
+df = SONDA.get_dataframe()
+
+df = df.drop(df[df.rain.values > 100].index)
+df = df.drop(df[df.press.values < 800].index)
+df = df.drop(df[df.humid.values < 15].index)
+
+df.to_csv("SONDA_BSB_MOD.csv", sep=";", index=False)
+"""
+
+import os
+print(os.getcwd())