- Optimizations and bugfixes on Multi Seasonal Ensemble

- Bugfixes on ProbabilityDistribution
 - Indexers on Partitioners

ensemble/ensemble.py

@@ -59,7 +59,10 @@ class EnsembleFTS(fts.FTS):
                     forecast = int(forecast[-1])
                 elif isinstance(forecast, (list,np.ndarray)) and len(forecast) == 0:
                     forecast = np.nan
-            tmp.append(forecast)
+            if isinstance(forecast, list):
+                tmp.extend(forecast)
+            else:
+                tmp.append(forecast)
         return tmp
 
     def get_point(self,forecasts, **kwargs):

ensemble/multiseasonal.py

@@ -44,9 +44,13 @@ class SeasonalEnsembleFTS(ensemble.EnsembleFTS):
         self.has_seasonality = True
         self.has_probability_forecasting = True
 
+    def update_uod(self, data):
+        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):
-        self.original_max = max(data)
+        self.original_max = max(self.indexer.get_data(data))
-        self.original_min = min(data)
+        self.original_min = min(self.indexer.get_data(data))
 
         num_cores = multiprocessing.cpu_count()
 
@@ -76,7 +80,9 @@ class SeasonalEnsembleFTS(ensemble.EnsembleFTS):
 
             tmp = self.get_models_forecasts(data.ix[k])
 
-            dist = ProbabilityDistribution.ProbabilityDistribution("KDE",h=h,uod=[self.original_min, self.original_max])
+            tmp = np.ravel(tmp).tolist()
+
+            dist = ProbabilityDistribution.ProbabilityDistribution("KDE",h=h,uod=[self.original_min, self.original_max], data=tmp)
 
             ret.append(dist)
 

partitioners/CMeans.py

@@ -78,8 +78,8 @@ def c_means(k, dados, tam):
 
 
 class CMeansPartitioner(partitioner.Partitioner):
-    def __init__(self, data, npart, func = Membership.trimf, transformation=None):
+    def __init__(self, data, npart, func = Membership.trimf, transformation=None, indexer=None):
-        super(CMeansPartitioner, self).__init__("CMeans", data, npart, func=func, transformation=transformation)
+        super(CMeansPartitioner, self).__init__("CMeans", data, npart, func=func, transformation=transformation, indexer=indexer)
 
     def build(self, data):
         sets = []

partitioners/Entropy.py

@@ -79,8 +79,8 @@ def bestSplit(data, npart):
 
 class EntropyPartitioner(partitioner.Partitioner):
     """Huarng Entropy Partitioner"""
-    def __init__(self, data, npart, func = Membership.trimf, transformation=None):
+    def __init__(self, data, npart, func = Membership.trimf, transformation=None, indexer=None):
-        super(EntropyPartitioner, self).__init__("Entropy", data, npart, func=func, transformation=transformation)
+        super(EntropyPartitioner, self).__init__("Entropy", data, npart, func=func, transformation=transformation, indexer=indexer)
 
     def build(self, data):
         sets = []

partitioners/FCM.py

@@ -104,8 +104,8 @@ class FCMPartitioner(partitioner.Partitioner):
     """
     
     """
-    def __init__(self, data,npart,func = Membership.trimf, transformation=None):
+    def __init__(self, data,npart,func = Membership.trimf, transformation=None, indexer=None):
-        super(FCMPartitioner, self).__init__("FCM", data, npart, func=func, transformation=transformation)
+        super(FCMPartitioner, self).__init__("FCM", data, npart, func=func, transformation=transformation, indexer=indexer)
 
     def build(self,data):
         sets = []

partitioners/Grid.py

@@ -8,8 +8,8 @@ from pyFTS.partitioners import partitioner
 
 class GridPartitioner(partitioner.Partitioner):
     """Even Length Grid Partitioner"""
-    def __init__(self, data, npart, func = Membership.trimf, transformation=None):
+    def __init__(self, data, npart, func = Membership.trimf, transformation=None, indexer=None):
-        super(GridPartitioner, self).__init__("Grid", data, npart, func=func, transformation=transformation)
+        super(GridPartitioner, self).__init__("Grid", data, npart, func=func, transformation=transformation, indexer=indexer)
 
     def build(self, data):
         sets = []

partitioners/Huarng.py

@@ -12,8 +12,8 @@ from pyFTS.partitioners import partitioner
 
 class HuarngPartitioner(partitioner.Partitioner):
     """Huarng Empirical Partitioner"""
-    def __init__(self, data,npart,func = Membership.trimf, transformation=None):
+    def __init__(self, data,npart,func = Membership.trimf, transformation=None, indexer=None):
-        super(HuarngPartitioner, self).__init__("Huarng", data, npart, func=func, transformation=transformation)
+        super(HuarngPartitioner, self).__init__("Huarng", data, npart, func=func, transformation=transformation, indexer=indexer)
 
     def build(self, data):
         diff = Transformations.Differential(1)

probabilistic/ProbabilityDistribution.py

@@ -11,25 +11,20 @@ class ProbabilityDistribution(object):
     If type is histogram, the PDF is discrete
     If type is KDE the PDF is continuous
     """
-    def __init__(self,type, **kwargs):
+    def __init__(self,type = "KDE", **kwargs):
         self.uod = kwargs.get("uod", None)
 
-        if type is None:
+        self.type = type
-            self.type = "KDE"
+        if self.type == "KDE":
             self.kde = kde.KernelSmoothing(kwargs.get("h", 10), kwargs.get("method", "epanechnikov"))
-        else:
-            self.type = type
 
-        self.description = kwargs.get("description", None)
         self.nbins = kwargs.get("num_bins", 100)
 
-        if self.type == "histogram":
+        self.bins = kwargs.get("bins", None)
-
+        self.labels = kwargs.get("bins_labels", None)
-            self.bins = kwargs.get("bins", None)
-            self.labels = kwargs.get("bins_labels", None)
 
         if self.bins is None:
-            self.bins = np.linspace(self.uod[0], self.uod[1], self.nbins).tolist()
+            self.bins = np.linspace(int(self.uod[0]), int(self.uod[1]), int(self.nbins)).tolist()
             self.labels = [str(k) for k in self.bins]
 
         self.index = SortedCollection.SortedCollection(iterable=sorted(self.bins))
@@ -37,7 +32,14 @@ class ProbabilityDistribution(object):
         self.count = 0
         for k in self.bins: self.distribution[k] = 0
 
-        self.data = kwargs.get("data",None)
+        self.data = []
+
+        data = kwargs.get("data",None)
+
+        if data is not None:
+            self.append(data)
+
+        self.name = kwargs.get("name", "")
 
     def append(self, values):
         if self.type == "histogram":
@@ -50,7 +52,7 @@ class ProbabilityDistribution(object):
             self.distribution = {}
             dens = self.density(self.bins)
             for v,d in enumerate(dens):
-                self.distribution[v] = d
+                self.distribution[self.bins[v]] = d
 
     def density(self, values):
         ret = []

tests/general.py

@@ -20,7 +20,7 @@ from pyFTS.models.seasonal import SeasonalIndexer
 
 os.chdir("/home/petronio/dados/Dropbox/Doutorado/Codigos/")
 
-#diff = Transformations.Differential(1)
+diff = Transformations.Differential(1)
 #ix = SeasonalIndexer.LinearSeasonalIndexer([12, 24], [720, 1],[False, False])
 
 """
@@ -72,22 +72,27 @@ sonda = sonda[:][527041:]
 sonda.index = np.arange(0,len(sonda.index))
 
 sonda_treino = sonda[:1051200]
-sonda_teste = sonda[1051201:]
+sonda_teste = sonda[1051901:1051910]
+
+ix_m15 = SeasonalIndexer.DateTimeSeasonalIndexer('data',[SeasonalIndexer.DateTime.minute],[15],'glo_avg', name='m15')
+
+fs1 = Grid.GridPartitioner(sonda_treino,50,transformation=diff, indexer=ix_m15)
+
 
 '''
 from pyFTS.models.seasonal import SeasonalIndexer
 
 indexers = []
 
-for i in ["models/sonda_ix_m15.pkl", "models/sonda_ix_Mh.pkl", "models/sonda_ix_Mhm15.pkl"]:
+for i in ["models/sonda_ix_Mhm15.pkl"]: #, "models/sonda_ix_m15.pkl", "models/sonda_ix_Mh.pkl", ]:
     obj = cUtil.load_obj(i)
     indexers.append( obj )
     print(obj)
 
 partitioners = []
 
-transformations = ["", "_diff"]
+transformations = [""] #, "_diff"]
-for max_part in [10, 20, 30, 40, 50, 60]:
+for max_part in [30, 40, 50, 60, 70, 80, 90]:
     for t in transformations:
         obj = cUtil.load_obj("models/sonda_fs_grid_" + str(max_part) + t + ".pkl")
         partitioners.append( obj )
@@ -96,17 +101,33 @@ for max_part in [10, 20, 30, 40, 50, 60]:
 
 from pyFTS.ensemble import ensemble, multiseasonal
 
-fts = multiseasonal.SeasonalEnsembleFTS("")
+fts = multiseasonal.SeasonalEnsembleFTS("sonda_msfts_Mhm15")
 
 fts.indexers = indexers
 fts.partitioners = partitioners
 
+fts.indexer = indexers[0]
+
 fts.train(sonda_treino, sets=None)
 '''
-ftse = cUtil.load_obj("models/sonda_msfts_ensemble.pkl")
+#'''
 
-tmp = ftse.forecastDistribution(sonda_teste)
+#ix = cUtil.load_obj("models/sonda_ix_m15.pkl")
 
+#ftse = cUtil.load_obj("models/msfts_Grid40_diff_Mhm15.pkl")
+
+#ftse.indexer = ix
+
+#ftse.update_uod(sonda_treino)
+
+#tmp = ftse.forecastDistribution(sonda_teste,h=1)
+
+#tmp = ftse.forecast(sonda_teste,h=1)
+
+#tmp[5].plot()
+#'''
+
+'''
 from pyFTS.benchmarks import benchmarks as bchmk
 #from pyFTS.benchmarks import distributed_benchmarks as bchmk
 #from pyFTS.benchmarks import parallel_benchmarks as bchmk
@@ -299,7 +320,7 @@ diff = Transformations.Differential(1)
 fs = Grid.GridPartitioner(sonda[:9000], 10, transformation=diff)
 
 
-
+'''
 tmp = sfts.SeasonalFTS("")
 tmp.indexer = ix
 tmp.appendTransformation(diff)