Refactoring to help tasks automotion

benchmarks/benchmarks.py

@@ -75,11 +75,16 @@ def allPointForecasters(data_train, data_test, partitions, max_order=3, statisti
 def getPointStatistics(data, models, externalmodels = None, externalforecasts = None, indexers=None):
     ret = "Model		& Order     & RMSE		& SMAPE      & Theil's U		\\\\ \n"
     for count,model in enumerate(models,start=0):
-        if indexers is not None:
+        if indexers is not None and indexers[count] is not None:
             ndata = np.array(indexers[count].get_data(data[model.order:]))
         else:
             ndata = np.array(data[model.order:])
+
+        if model.isMultivariate or indexers is None:
             forecasts = model.forecast(data)
+        elif not model.isMultivariate and indexers is not None:
+            forecasts = model.forecast( indexers[count].get_data(data) )
+
         if model.hasSeasonality:
             nforecasts = np.array(forecasts)
         else:

models/cmsfts.py

@@ -5,7 +5,6 @@ from pyFTS import fts, sfts, chen
 
 class ContextualSeasonalFLRG(object):
     def __init__(self, seasonality):
-        super(ContextualSeasonalFLRG, self).__init__(None,None)
         self.season = seasonality
         self.flrgs = {}
 
@@ -43,7 +42,7 @@ class ContextualMultiSeasonalFTS(sfts.SeasonalFTS):
 
         for flr in flrs:
 
-            if str(flr.index) not in self.flrgs:
+            if str(flr.index) not in flrgs:
                 flrgs[str(flr.index)] = ContextualSeasonalFLRG(flr.index)
 
             flrgs[str(flr.index)].append(flr)
@@ -57,8 +56,11 @@ class ContextualMultiSeasonalFTS(sfts.SeasonalFTS):
         self.flrgs = self.generateFLRG(flrs)
 
     def getMidpoints(self, flrg, data):
-        ret = np.array([s.centroid for s in flrg.flrgs[data].RHS])
+        if data.name in flrg.flrgs:
+            ret = np.array([s.centroid for s in flrg.flrgs[data.name].RHS])
             return ret
+        else:
+            return  np.array([data.centroid])
 
     def forecast(self, data):
 

tests/cmsfts.py

@@ -0,0 +1,62 @@
+#!/usr/bin/python
+# -*- coding: utf8 -*-
+
+import os
+import numpy as np
+import pandas as pd
+import matplotlib as plt
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+
+import datetime
+
+import pandas as pd
+from pyFTS.partitioners import Grid, CMeans, FCM, Entropy
+from pyFTS.common import FLR,FuzzySet,Membership,Transformations,Util
+from pyFTS import fts,sfts
+from pyFTS.models import msfts
+from pyFTS.benchmarks import benchmarks as bchmk
+from pyFTS.benchmarks import Measures
+
+os.chdir("/home/petronio/dados/Dropbox/Doutorado/Disciplinas/AdvancedFuzzyTimeSeriesModels/")
+
+sonda = pd.read_csv("DataSets/SONDA_BSB_MOD.csv", sep=";")
+
+sonda['data'] = pd.to_datetime(sonda['data'])
+
+sonda = sonda[:][527041:]
+
+sonda.index = np.arange(0,len(sonda.index))
+
+
+sonda_treino = sonda[:1051200]
+sonda_teste = sonda[1051201:]
+
+
+#res = bchmk.simpleSearch_RMSE(sonda_treino, sonda_teste,
+#                              sfts.SeasonalFTS,np.arange(3,30),[1],parameters=1440,
+#                              tam=[15,8], plotforecasts=False,elev=45, azim=40,
+#                               save=False,file="pictures/sonda_sfts_error_surface", intervals=False)
+
+from pyFTS.common import Util
+from pyFTS.models import cmsfts
+
+partitions = ['grid', 'entropy']
+
+indexers = ['m15', 'Mh', 'Mhm15']
+
+for max_part in [40, 50]:
+    for part in partitions:
+        fs = Util.load_obj("models/sonda_fs_" + part + "_" + str(max_part) + ".pkl")
+
+        for ind in indexers:
+            ix = Util.load_obj("models/sonda_ix_" + ind + ".pkl")
+
+            model = cmsfts.ContextualMultiSeasonalFTS(part + " " + ind, ix)
+
+            model.train(sonda_treino, fs)
+
+            Util.persist_obj(model, "models/sonda_cmsfts_" + part + "_" + str(max_part) + "_" + ind + ".pkl")
+
+
+