Bugfixes in FTS, HOFTS and CVFTS

pyFTS/benchmarks/Measures.py

@@ -332,11 +332,11 @@ def get_point_statistics(data, model, **kwargs):
         if not isinstance(forecasts, (list, np.ndarray)):
             forecasts = [forecasts]
 
-        nforecasts = np.array(forecasts[:-1])
+        forecasts = np.array(forecasts[:-1])
 
-        ret.append(np.round(rmse(ndata[model.max_lag:], nforecasts), 2))
+        ret.append(np.round(rmse(ndata[model.max_lag:], forecasts), 2))
-        ret.append(np.round(mape(ndata[model.max_lag:], nforecasts), 2))
+        ret.append(np.round(mape(ndata[model.max_lag:], forecasts), 2))
-        ret.append(np.round(UStatistic(ndata[model.max_lag:], nforecasts), 2))
+        ret.append(np.round(UStatistic(ndata[model.max_lag:], forecasts), 2))
     else:
         steps_ahead_sampler = kwargs.get('steps_ahead_sampler', 1)
         nforecasts = []

pyFTS/benchmarks/benchmarks.py

@@ -7,6 +7,7 @@
 import datetime
 import time
 from copy import deepcopy
+import traceback
 
 import matplotlib as plt
 import matplotlib.cm as cmx
@@ -245,8 +246,8 @@ def sliding_window_benchmarks(data, windowsize, train=0.8, **kwargs):
                             job = experiment_method(deepcopy(model), None, train, test, **kwargs)
                             synthesis_method(dataset, tag, job, conn)
                         except Exception as ex:
-                            print("Error evaluating model " + model.shortname)
+                            print('EXCEPTION! ', model.shortname, model.order)
-                            print(ex)
+                            traceback.print_exc()
                     else:
                         job = cluster.submit(deepcopy(model), None, train, test, **kwargs)
                         jobs.append(job)
@@ -279,11 +280,14 @@ def sliding_window_benchmarks(data, windowsize, train=0.8, **kwargs):
                         if progress:
                             progressbar.update(1)
                         try:
+                            print(model.shortname, model.order, partitioner.name,
+                                  partitioner.partitions, str(partitioner.transformation))
                             job = experiment_method(deepcopy(model), deepcopy(partitioner), train, test, **kwargs)
                             synthesis_method(dataset, tag, job, conn)
                         except Exception as ex:
-                            print("Error evaluating model " + model.shortname + " " + str(partitioner))
+                            print('EXCEPTION! ',model.shortname, model.order, partitioner.name,
-                            print(ex)
+                                  partitioner.partitions, str(partitioner.transformation))
+                            traceback.print_exc()
                     else:
                         job = cluster.submit(deepcopy(model), deepcopy(partitioner), train, test, **kwargs)
                         job.id = id  # associate an ID to identify jobs (if needed later)

pyFTS/common/fts.py

@@ -144,7 +144,7 @@ class FTS(object):
 
         if not self.is_multivariate:
             kwargs['type'] = type
-            ret = self.apply_inverse_transformations(ret, params=[data[self.order - 1:]], **kwargs)
+            ret = self.apply_inverse_transformations(ret, params=[data[self.max_lag - 1:]], **kwargs)
 
         return ret
 

pyFTS/models/nonstationary/cvfts.py

@@ -38,6 +38,7 @@ class ConditionalVarianceFTS(hofts.HighOrderFTS):
         self.shortname = "CVFTS "
         self.detail = ""
         self.flrgs = {}
+        self.is_high_order = False
         if self.partitioner is not None:
             self.append_transformation(self.partitioner.transformation)
 

pyFTS/models/nonstationary/partitioners.py

@@ -132,3 +132,19 @@ class SimpleNonStationaryPartitioner(partitioner.Partitioner):
 
     def build(self, data):
         return {}
+
+
+
+def simplenonstationary_gridpartitioner_builder(data, npart, transformation):
+    from pyFTS.partitioners import Grid
+    from pyFTS.models.nonstationary import perturbation, partitioners
+
+    tmp_fs = Grid.GridPartitioner(data=data, npart=npart, transformation=transformation)
+    fs = partitioners.SimpleNonStationaryPartitioner(data, tmp_fs,
+                                                     location=perturbation.polynomial,
+                                                     location_params=[1, 0],
+                                                     location_roots=0,
+                                                     width=perturbation.polynomial,
+                                                     width_params=[1, 0],
+                                                     width_roots=0)
+    return fs

pyFTS/tests/nonstationary.py

@@ -1,7 +1,7 @@
 import os
 import numpy as np
 from pyFTS.common import Membership, Transformations
-from pyFTS.models.nonstationary import common, perturbation, partitioners, util
+from pyFTS.models.nonstationary import common, perturbation, partitioners as nspart, util
 from pyFTS.models.nonstationary import nsfts, cvfts
 from pyFTS.partitioners import Grid, Entropy
 import matplotlib.pyplot as plt
@@ -33,36 +33,47 @@ tdiff = Transformations.Differential(1)
 
 boxcox = Transformations.BoxCox(0)
 
-transformations = {'None': None, 'Differential(1)': tdiff, 'BoxCox(0)': boxcox }
+transformations = {
+    'None': None,
+    'Differential(1)': tdiff,
+    'BoxCox(0)': boxcox
+}
 
 from pyFTS.partitioners import Grid, Util as pUtil
 from pyFTS.benchmarks import benchmarks as bchmk
-from pyFTS.models import chen
+from pyFTS.models import chen, hofts, pwfts, hwang
 
-partitions = {'CMIV': {'BoxCox(0)': 17, 'Differential(1)': 7, 'None': 13},
+partitions = {'CMIV': {'BoxCox(0)': 36, 'Differential(1)': 11, 'None': 8},
- 'IMCV': {'BoxCox(0)': 22, 'Differential(1)': 9, 'None': 25},
+ 'IMCV': {'BoxCox(0)': 36, 'Differential(1)': 20, 'None': 16},
- 'IMIV': {'BoxCox(0)': 27, 'Differential(1)': 11, 'None': 6},
+ 'IMIV': {'BoxCox(0)': 39, 'Differential(1)': 12, 'None': 6},
- 'NASDAQ': {'BoxCox(0)': 39, 'Differential(1)': 10, 'None': 34},
+ 'IMIV0': {'BoxCox(0)': 39, 'Differential(1)': 12, 'None': 3},
- 'SP500': {'BoxCox(0)': 38, 'Differential(1)': 15, 'None': 39},
+ 'NASDAQ': {'BoxCox(0)': 39, 'Differential(1)': 13, 'None': 36},
- 'TAIEX': {'BoxCox(0)': 36, 'Differential(1)': 18, 'None': 38}}
+ 'SP500': {'BoxCox(0)': 33, 'Differential(1)': 7, 'None': 33},
+ 'TAIEX': {'BoxCox(0)': 39, 'Differential(1)': 31, 'None': 33}}
 
 
 tag = 'benchmarks'
+'''
+for ds in datasets.keys():
+    dataset = datasets[ds]
 
-def nsfts_partitioner_builder(data, npart, transformation):
+    for tf in transformations.keys():
-    from pyFTS.partitioners import Grid
+        transformation = transformations[tf]
-    from pyFTS.models.nonstationary import perturbation, partitioners
 
-    tmp_fs = Grid.GridPartitioner(data=data, npart=npart, transformation=transformation)
+        partitioning = partitions[ds][tf]
-    fs = partitioners.SimpleNonStationaryPartitioner(data, tmp_fs,
-                                                     location=perturbation.polynomial,
-                                                     location_params=[1, 0],
-                                                     location_roots=0,
-                                                     width=perturbation.polynomial,
-                                                     width_params=[1, 0],
-                                                     width_roots=0)
-    return fs
 
+        bchmk.sliding_window_benchmarks(dataset, 2000, train=0.2, inc=0.2,
+                                        methods=[
+                                            hwang.HighOrderFTS,
+                                            hofts.HighOrderFTS,
+                                            pwfts.ProbabilisticWeightedFTS],
+                                        #orders = [3],
+                                        benchmark_models=False,
+                                        transformations=[transformation],
+                                        partitions=[partitioning],
+                                        progress=False, type='point',
+                                        file="nsfts_benchmarks.db", dataset=ds, tag=tag)
+'''
 
 for ds in datasets.keys():
     dataset = datasets[ds]
@@ -75,7 +86,7 @@ for ds in datasets.keys():
         bchmk.sliding_window_benchmarks(dataset, 2000, train=0.2, inc=0.2,
                                         benchmark_models=False,
                                         methods=[cvfts.ConditionalVarianceFTS],
-                                        partitioners_methods=[nsfts_partitioner_builder],
+                                        partitioners_methods=[nspart.simplenonstationary_gridpartitioner_builder],
                                         transformations=[transformation],
                                         partitions=[partitioning],
                                         progress=False, type='point',