Bugfix in models.multivariate.grid

pyFTS/common/FuzzySet.py

@@ -126,7 +126,7 @@ def fuzzyfy(data, partitioner, **kwargs):
     :keyword mode: the fuzzyfication mode (sets: return the fuzzy sets names, vector: return a vector with the membership
     values for all fuzzy sets, both: return a list with tuples (fuzzy set, membership value) )
     :returns a list with the fuzzyfied values, depending on the mode
-    
+
     """
     alpha_cut = kwargs.get('alpha_cut', 0.)
     mode = kwargs.get('mode', 'sets')

pyFTS/models/multivariate/common.py

@@ -43,7 +43,7 @@ def membership(self, x):
 
 
 def fuzzyfy_instance(data_point, var, tuples=True):
-    fsets = var.partitioner(data_point, mode='sets', method='fuzzy', alpha_cut=var.alpha_cut)
+    fsets = var.partitioner.fuzzyfy(data_point, mode='sets', method='fuzzy', alpha_cut=var.alpha_cut)
     if tuples:
         return [(var.name, fs) for fs in fsets]
     else:

pyFTS/tests/general.py

@@ -14,17 +14,73 @@
 from pyFTS.models import chen, yu, cheng, ismailefendi, hofts, pwfts, tsaur, song, sadaei
 from pyFTS.common import Transformations, Membership
 
-from pyFTS.data import TAIEX
+dataset = pd.read_csv('https://query.data.world/s/2bgegjggydd3venttp3zlosh3wpjqj', sep=';')
 
-data = TAIEX.get_data()
+dataset['data'] = pd.to_datetime(dataset["data"], format='%Y-%m-%d %H:%M:%S')
 
-fs = Grid.GridPartitioner(data=data, npart=23)
+train_mv = dataset.iloc[:24505]
+test_mv = dataset.iloc[24505:]
+
+from itertools import product
+
+levels = ['VL', 'L', 'M', 'H', 'VH']
+sublevels = [str(k) for k in np.arange(0, 7)]
+names = []
+for combination in product(*[levels, sublevels]):
+    names.append(combination[0] + combination[1])
+
+print(names)
+
+from pyFTS.models.multivariate import common, variable, mvfts
+from pyFTS.models.seasonal import partitioner as seasonal
+from pyFTS.models.seasonal.common import DateTime
+
+
+
+sp = {'seasonality': DateTime.day_of_year , 'names': ['Jan','Feb','Mar','Apr','May',
+                                                      'Jun','Jul', 'Aug','Sep','Oct',
+                                                      'Nov','Dec']}
+
+vmonth = variable.Variable("Month", data_label="data", partitioner=seasonal.TimeGridPartitioner, npart=12,
+                           data=train_mv, partitioner_specific=sp)
+
+
+
+sp = {'seasonality': DateTime.minute_of_day, 'names': [str(k)+'hs' for k in range(0,24)]}
+
+vhour = variable.Variable("Hour", data_label="data", partitioner=seasonal.TimeGridPartitioner, npart=24,
+                          data=train_mv, partitioner_specific=sp)
+
+
+vavg = variable.Variable("Radiation", data_label="glo_avg", alias='rad',
+                         partitioner=Grid.GridPartitioner, npart=35, partitioner_specific={'names': names},
+                         data=train_mv)
+
+from pyFTS.models.multivariate import mvfts, wmvfts, cmvfts, grid
+
+parameters = [
+    {}, {},
+    {'order': 2, 'knn': 1},
+    {'order': 2, 'knn': 2},
+    {'order': 2, 'knn': 3},
+]
+
+for ct, method in enumerate([mvfts.MVFTS, wmvfts.WeightedMVFTS,
+                             cmvfts.ClusteredMVFTS, cmvfts.ClusteredMVFTS, cmvfts.ClusteredMVFTS]):
+
+    if method != cmvfts.ClusteredMVFTS:
+        model = method(explanatory_variables=[vmonth, vhour, vavg], target_variable=vavg, **parameters[ct])
+    else:
+        fs = grid.GridCluster(explanatory_variables=[vmonth, vhour, vavg], target_variable=vavg)
+        model = method(explanatory_variables=[vmonth, vhour, vavg], target_variable=vavg, partitioner=fs,
+                       **parameters[ct])
+
+    model.shortname += str(ct)
+    model.fit(train_mv)
+
+    forecasts = model.predict(test_mv.iloc[:100])
+
+    print(model.shortname, forecasts)
 
-test = [2000, 5000, 5500, 12000]
 
-for method in [yu.WeightedFTS, tsaur.MarkovWeightedFTS, song.ConventionalFTS, sadaei.ExponentialyWeightedFTS, ismailefendi.ImprovedWeightedFTS,
-               chen.ConventionalFTS, cheng.TrendWeightedFTS, hofts.HighOrderFTS, pwfts.ProbabilisticWeightedFTS]:
-    model = method(partitioner=fs)
-    model.fit(data)
-    print(model.forecast(test))