Bugfix in mvfts and seasonal.common.FuzzySet

pyFTS/common/FuzzySet.py

@@ -200,10 +200,13 @@ def get_fuzzysets(inst, fuzzy_sets, ordered_sets=None, alpha_cut=0.0):
     if ordered_sets is None:
         ordered_sets = set_ordered(fuzzy_sets)
 
-    fs = [ordered_sets[ix]
+    try:
-          for ix in __binary_search(inst, fuzzy_sets, ordered_sets)
+        fs = [ordered_sets[ix]
-          if fuzzy_sets[ordered_sets[ix]].membership(inst) > alpha_cut]
+              for ix in __binary_search(inst, fuzzy_sets, ordered_sets)
-    return fs
+              if fuzzy_sets[ordered_sets[ix]].membership(inst) > alpha_cut]
+        return fs
+    except Exception as ex:
+        raise ex
 
 
 def get_maximum_membership_fuzzyset(inst, fuzzy_sets, ordered_sets=None):

pyFTS/models/seasonal/common.py

@@ -89,10 +89,9 @@ 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=kwargs.get('type', 'seasonal'),
                                        **kwargs)
         self.datepart = datepart
-        self.type = 'seasonal'
+        self.type = kwargs.get('type', 'seasonal')
 
     def transform(self, x):
         if self.type == 'seasonal':

pyFTS/models/seasonal/partitioner.py

@@ -30,6 +30,8 @@ class TimeGridPartitioner(partitioner.Partitioner):
             self.min = tmp
             self.max = self.season.value + tmp
 
+        self.type = kwargs.get('type','seasonal')
+
         self.sets = self.build(None)
 
         if self.ordered_sets is None and self.setnames is not None:
@@ -40,7 +42,7 @@ class TimeGridPartitioner(partitioner.Partitioner):
     def build(self, data):
         sets = {}
 
-        kwargs = {'variable': self.variable}
+        kwargs = {'variable': self.variable, 'type': self.type }
 
         if self.season == DateTime.year:
             dlen = (self.max - self.min)

pyFTS/tests/general.py

@@ -21,10 +21,14 @@ from pyFTS.data import TAIEX, SP500, NASDAQ, Malaysia
 
 dataset = Malaysia.get_data('temperature')[:1000]
 
-p = Entropy.EntropyPartitioner(data=dataset, npart=19)
+p = Entropy.EntropyPartitioner(data=dataset, npart=3)
 
 print(p)
 
+model = hofts.HighOrderFTS(partitioner=p, order=2, lags=[34, 47], alpha_cut=0.31390672707694006)
+
+model.fit(dataset)
+
 '''
 #dataset = SP500.get_data()[11500:16000]
 #dataset = NASDAQ.get_data()

pyFTS/tests/multivariate.py

@@ -3,13 +3,6 @@ import matplotlib.pylab as plt
 from pyFTS.data import TAIEX as tx
 from pyFTS.common import Transformations
 
-
-from pyFTS.data import Malaysia
-
-dataset = Malaysia.get_dataframe()
-
-dataset["date"] = pd.to_datetime(dataset["time"], format='%m/%d/%y %I:%M %p')
-
 from pyFTS.partitioners import Grid, Util as pUtil
 from pyFTS.common import Transformations, Util
 from pyFTS.models.multivariate import common, variable, mvfts
@@ -23,6 +16,13 @@ from pyFTS.models.multivariate import common, variable, mvfts
 from pyFTS.models.seasonal import partitioner as seasonal
 from pyFTS.models.seasonal.common import DateTime
 
+#"""
+from pyFTS.data import Malaysia
+
+dataset = Malaysia.get_dataframe()
+
+dataset["date"] = pd.to_datetime(dataset["time"], format='%m/%d/%y %I:%M %p')
+
 mv_train = dataset.iloc[:100000]
 
 sp = {'seasonality': DateTime.month , 'names': ['Jan','Feb','Mar','Apr','May','Jun','Jul', 'Aug','Sep','Oct','Nov','Dec']}
@@ -71,3 +71,37 @@ for k in [itertools.combinations(var_list, r) for r in range(2,len(var_list))]:
       model.target_variable = vload
       model.fit(mv_train)
       models.append(model)
+#"""
+
+"""
+dataset =  pd.read_csv('/home/petronio/Downloads/priceHong')
+dataset['hour'] = dataset.index.values % 24
+
+data = dataset['price'].values.flatten()
+
+train_split = 24 * 800
+
+# Multivariate time series
+
+train_mv = dataset.iloc[:train_split]
+test_mv = dataset.iloc[train_split:]
+
+vhour = variable.Variable("Hour", data_label="hour", partitioner=seasonal.TimeGridPartitioner, npart=24,
+                          data=dataset,
+                          partitioner_specific={'seasonality': DateTime.hour_of_day, 'type': 'common'})
+vprice = variable.Variable("Price", data_label="price", partitioner=Grid.GridPartitioner, npart=25,
+                            data=train_mv)
+
+model1 = mvfts.MVFTS()
+model1.shortname += "1"
+model1.append_variable(vhour)
+model1.append_variable(vprice)
+model1.target_variable = vprice
+model1.fit(train_mv)
+
+model2 = mvfts.MVFTS()
+model2.shortname += "2"
+model2.append_variable(vhour)
+model2.target_variable = vprice
+model2.fit(train_mv)
+"""