Bugfix in benchmarks.mv_run methods

pyFTS/benchmarks/benchmarks.py

@@ -770,7 +770,9 @@ def mv_run_point2(mfts, train_data, test_data, window_key=None, **kwargs):
         _end = time.time()
         times += _end - _start
 
-        eval = Measures.get_point_ahead_statistics(test_data[mfts.order:mfts.order + steps_ahead], forecasts)
+        tmp_test = test_data[mfts.target_variable.data_label].values[mfts.order:mfts.order + steps_ahead]
+
+        eval = Measures.get_point_ahead_statistics(tmp_test, forecasts)
 
         for key in eval.keys():
             eval[key]["time"] = times
@@ -898,7 +900,9 @@ def mv_run_interval2(mfts,train_data, test_data, window_key=None, **kwargs):
         _end = time.time()
         times += _end - _start
 
-        eval = Measures.get_interval_ahead_statistics(test_data[mfts.order:mfts.order+steps_ahead], intervals)
+        tmp_test = test_data[mfts.target_variable.data_label].values[mfts.order:mfts.order + steps_ahead]
+
+        eval = Measures.get_interval_ahead_statistics(tmp_test, intervals)
 
         for key in eval.keys():
             eval[key]["time"] = times
@@ -1017,7 +1021,9 @@ def mv_run_probabilistic2(mfts, train_data, test_data, window_key=None, **kwargs
         _end = time.time()
         times += _end - _start
 
-        eval = Measures.get_distribution_ahead_statistics(test_data[mfts.order:mfts.order+steps_ahead], distributions)
+        tmp_test = test_data[mfts.target_variable.data_label].values[mfts.order:mfts.order + steps_ahead]
+
+        eval = Measures.get_distribution_ahead_statistics(tmp_test, distributions)
 
         for key in eval.keys():
             eval[key]["time"] = times

pyFTS/tests/transformations.py

@@ -69,9 +69,9 @@ detrend = trend.apply(data)
 plt.plot(trend.inverse(detrend, data))
 '''
 
-dataset = pd.read_csv('https://query.data.world/s/nxst4hzhjrqld4bxhbpn6twmjbwqk7')
+#dataset = pd.read_csv('https://query.data.world/s/nxst4hzhjrqld4bxhbpn6twmjbwqk7')
-dataset['data'] = pd.to_datetime([str(y)+'-'+str(m) for y,m in zip(dataset['Ano'].values, dataset['Mes'].values)],
+#dataset['data'] = pd.to_datetime([str(y)+'-'+str(m) for y,m in zip(dataset['Ano'].values, dataset['Mes'].values)],
-                                  format='%Y-%m')
+#                                  format='%Y-%m')
 roi = Transformations.ROI()
 
 '''
@@ -93,6 +93,8 @@ model.fit(train)
 fig, ax = plt.subplots(nrows=1, ncols=1, figsize=[10,5])
 ax.plot(test)
 
+'''
+
 '''
 train = dataset.iloc[:30]
 test = dataset.iloc[30:]
@@ -129,3 +131,47 @@ ax.plot(forecast)
 plt.show()
 
 print(dataset)
+'''
+
+eto = pd.read_csv('https://raw.githubusercontent.com/PatriciaLucas/Evapotranspiracao/master/ETo_setelagoas.csv', sep=',')
+eto['Data'] = pd.to_datetime(eto["Data"], format='%Y-%m-%d')
+
+from pyFTS.models.multivariate import common, variable, mvfts, wmvfts, granular
+from pyFTS.models import hofts, pwfts
+from pyFTS.partitioners import Grid, Entropy
+from pyFTS.common import Membership
+from pyFTS.models.seasonal.common import DateTime
+from pyFTS.models.seasonal import partitioner as seasonal
+from pyFTS.benchmarks import Measures
+from pyFTS.benchmarks import arima, quantreg, knn, benchmarks as bchmk
+
+variables = {
+    "Month": dict(data_label="Data", partitioner=seasonal.TimeGridPartitioner, npart=6),
+    "Eto": dict(data_label="Eto", alias='eto',
+                         partitioner=Grid.GridPartitioner, npart=50)
+}
+
+methods = [mvfts.MVFTS, wmvfts.WeightedMVFTS, granular.GranularWMVFTS]
+
+time_generator = lambda x : pd.to_datetime(x) + pd.to_timedelta(1, unit='d')
+
+parameters = [
+    {},{},
+    dict(fts_method=pwfts.ProbabilisticWeightedFTS, fuzzyfy_mode='both',
+                order=1, knn=3)
+]
+
+
+
+bchmk.multivariate_sliding_window_benchmarks2(eto, 2000, train=0.8, inc=0.2,
+                                              methods=methods,
+                                              methods_parameters=parameters,
+                                              variables=variables,
+                                              target_variable='Eto',
+                                              type='point',
+                                              steps_ahead=[7],
+                                              file="hyperparam.db", dataset='Eto',
+                                              tag="experiments",
+                                              generators= {'Data': time_generator}
+                                              )
+