Improvements on multivariate and multivariate ahead forecasting methods for CMVFTS

pyFTS/common/fts.py

@@ -148,7 +148,7 @@ class FTS(object):
                 elif type == 'distribution':
                     ret = self.forecast_ahead_distribution(ndata, steps_ahead, **kwargs)
                 elif type == 'multivariate':
-                    ret = self.forecast_ahead_multivariate(ndata, **kwargs)
+                    ret = self.forecast_ahead_multivariate(ndata, steps_ahead, **kwargs)
 
             if not ['point', 'interval', 'distribution', 'multivariate'].__contains__(type):
                 raise ValueError('The argument \'type\' has an unknown value.')

pyFTS/models/multivariate/cmvfts.py

@@ -4,6 +4,7 @@ import pandas as pd
 from pyFTS.common import FuzzySet, FLR, fts, flrg
 from pyFTS.models import hofts
 from pyFTS.models.multivariate import mvfts, grid, common
+from types import LambdaType
 
 
 class ClusteredMVFTS(mvfts.MVFTS):
@@ -72,18 +73,53 @@ class ClusteredMVFTS(mvfts.MVFTS):
 
         ndata = self.check_data(data)
 
-        ret = {}
+        generators = kwargs.get('generators', {})
-        for var in self.explanatory_variables:
-            if self.target_variable.name != var.name:
-                self.target_variable = var
-                self.partitioner.change_target_variable(var)
-                self.model.partitioner = self.partitioner
-                self.model.reset_calculated_values()
 
-            ret[var.name] = self.model.forecast(ndata, fuzzyfied=self.pre_fuzzyfy, **kwargs)
+        already_processed_cols = []
+
+        ret = {}
+
+        ret[self.target_variable.data_label] = self.model.forecast(ndata, fuzzyfied=self.pre_fuzzyfy, **kwargs)
+
+        for var in self.explanatory_variables:
+            if var.data_label not in already_processed_cols:
+                if var.data_label in generators:
+                    if isinstance(generators[var.data_label], LambdaType):
+                        fx = generators[var.data_label]
+                        if len(data[var.data_label].values) > self.order:
+                            ret[var.data_label] = [fx(k) for k in data[var.data_label].values[self.order:]]
+                        else:
+                            ret[var.data_label] = [fx(data[var.data_label].values[-1])]
+                    elif isinstance(generators[var.data_label], fts.FTS):
+                        model = generators[var.data_label]
+                        if not model.is_multivariate:
+                            ret[var.data_label] = model.forecast(data[var.data_label].values)
+                        else:
+                            ret[var.data_label] = model.forecast(data)
+                elif self.target_variable.name != var.name:
+                    self.target_variable = var
+                    self.partitioner.change_target_variable(var)
+                    self.model.partitioner = self.partitioner
+                    self.model.reset_calculated_values()
+                    ret[var.data_label] = self.model.forecast(ndata, fuzzyfied=self.pre_fuzzyfy, **kwargs)
+
+                already_processed_cols.append(var.data_label)
 
         return pd.DataFrame(ret, columns=ret.keys())
 
+    def forecast_ahead_multivariate(self, data, steps, **kwargs):
+
+        ndata = self.apply_transformations(data)
+
+        ret = ndata.iloc[:self.order]
+
+        for k in np.arange(0, steps):
+            sample = ret.iloc[k:self.order+k]
+            tmp = self.forecast_multivariate(sample, **kwargs)
+            ret = ret.append(tmp, ignore_index=True)
+
+        return ret
+
     def __str__(self):
         """String representation of the model"""
         return str(self.model)

pyFTS/tests/general.py

@@ -14,12 +14,13 @@ from pyFTS.benchmarks import benchmarks as bchmk, Measures
 from pyFTS.models import chen, yu, cheng, ismailefendi, hofts, pwfts, tsaur, song, sadaei
 from pyFTS.common import Transformations, Membership
 
+'''
 dataset = pd.read_csv('https://query.data.world/s/2bgegjggydd3venttp3zlosh3wpjqj', sep=';')
 
 dataset['data'] = pd.to_datetime(dataset["data"], format='%Y-%m-%d %H:%M:%S')
 
 train_mv = dataset.iloc[:24505]
-test_mv = dataset.iloc[24505:]
+test_mv = dataset.iloc[24505:24605]
 
 from itertools import product
 
@@ -65,22 +66,41 @@ parameters = [
     {'order': 2, 'knn': 3},
 ]
 
-for ct, method in enumerate([mvfts.MVFTS, wmvfts.WeightedMVFTS,
+from pyFTS.models.multivariate import mvfts, wmvfts, cmvfts, grid, granular
-                             cmvfts.ClusteredMVFTS, cmvfts.ClusteredMVFTS, cmvfts.ClusteredMVFTS]):
+from pyFTS.benchmarks import Measures
 
-    if method != cmvfts.ClusteredMVFTS:
+time_generator = lambda x : pd.to_datetime(x) + pd.to_timedelta(1, unit='h')
-        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 = granular.GranularWMVFTS(explanatory_variables=[vmonth, vhour, vavg], target_variable=vavg, order=2, knn=2)
-    model.fit(train_mv)
 
-    forecasts = model.predict(test_mv.iloc[:100])
+model.fit(train_mv)
 
-    print(model.shortname, forecasts)
+forecasts = model.predict(test_mv, type='multivariate', generators={'data': time_generator}, steps_ahead=24 )
 
+print(forecasts)
 
+'''
 
+from pyFTS.data import lorentz
+df = lorentz.get_dataframe(iterations=5000)
+
+train = df.iloc[:4000]
+test = df.iloc[4000:]
+
+from pyFTS.models.multivariate import common, variable, mvfts
+from pyFTS.partitioners import Grid
+
+vx = variable.Variable("x", data_label="x", alias='x', partitioner=Grid.GridPartitioner, npart=45, data=train)
+vy = variable.Variable("y", data_label="y", alias='y', partitioner=Grid.GridPartitioner, npart=45, data=train)
+vz = variable.Variable("z", data_label="z", alias='z', partitioner=Grid.GridPartitioner, npart=45, data=train)
+
+from pyFTS.models.multivariate import mvfts, wmvfts, cmvfts, grid, granular
+from pyFTS.benchmarks import Measures
+
+model = granular.GranularWMVFTS(explanatory_variables=[vx, vy, vz], target_variable=vx, order=5, knn=2)
+
+model.fit(train)
+
+forecasts = model.predict(test, type='multivariate', steps_ahead=20)
+
+print(forecasts)