Bugfixes at pwfts.forecast_ahead_distribution

pyFTS/benchmarks/Util.py

@@ -2,7 +2,6 @@
 Benchmark utility functions
 """
 
-import numba
 import matplotlib as plt
 import matplotlib.cm as cmx
 import matplotlib.colors as pltcolors

pyFTS/benchmarks/benchmarks.py

@@ -6,7 +6,6 @@
 
 import datetime
 import time
-import numba
 from copy import deepcopy
 
 import matplotlib as plt

pyFTS/common/Util.py

@@ -1,5 +1,4 @@
 import time
-import numba
 import matplotlib.pyplot as plt
 import dill
 import numpy as np

pyFTS/models/pwfts.py

@@ -363,6 +363,7 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
 
         if 'bins' in kwargs:
             _bins = kwargs.pop('bins')
+            nbins = len(_bins)
         else:
             nbins = kwargs.get("num_bins", 100)
             _bins = np.linspace(uod[0], uod[1], nbins)
@@ -380,11 +381,15 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
                 num = []
                 den = []
                 for s in flrgs:
-                    flrg = self.flrgs[s.get_key()]
+                    if s.get_key() in self.flrgs:
-                    pk = flrg.lhs_conditional_probability(sample, self.sets, self.global_frequency_count, uod, nbins)
+                        flrg = self.flrgs[s.get_key()]
-                    wi = flrg.rhs_conditional_probability(bin, self.sets, uod, nbins)
+                        pk = flrg.lhs_conditional_probability(sample, self.sets, self.global_frequency_count, uod, nbins)
-                    num.append(wi * pk)
+                        wi = flrg.rhs_conditional_probability(bin, self.sets, uod, nbins)
-                    den.append(pk)
+                        num.append(wi * pk)
+                        den.append(pk)
+                    else:
+                        num.append(0.0)
+                        den.append(0.000000001)
                 pf = sum(num) / sum(den)
 
                 dist.set(bin, pf)
@@ -452,11 +457,11 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
             tmp.set(dat, 1.0)
             ret.append(tmp)
 
-        dist = self.forecast_distribution(sample, bins=_bins)
+        dist = self.forecast_distribution(sample, bins=_bins)[0]
 
         ret.append(dist)
 
-        for k in np.arange(self.order, steps+self.order):
+        for k in np.arange(self.order+1, steps+self.order+1):
             dist = ProbabilityDistribution.ProbabilityDistribution(smooth, uod=uod, bins=_bins, **kwargs)
 
             lags = {}
@@ -469,7 +474,7 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
 
             root = tree.FLRGTreeNode(None)
 
-            self.build_tree_without_order(root, lags, 0)
+            tree.build_tree_without_order(root, lags, 0)
 
             # Trace all possible combinations between the bins of past distributions
 
@@ -487,6 +492,8 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
                 for bin in _bins:
                     dist.set(bin, dist.density(bin) + pk * d.density(bin))
 
+            ret.append(dist)
+
         ret = ret[self.order:]
 
         return ret

pyFTS/partitioners/Util.py

@@ -1,4 +1,3 @@
-import numba
 import numpy as np
 import pandas as pd
 import matplotlib as plt
@@ -15,7 +14,6 @@ all_methods = [Grid.GridPartitioner, Entropy.EntropyPartitioner, FCM.FCMPartitio
 mfs = [Membership.trimf, Membership.gaussmf, Membership.trapmf]
 
 
-@numba.jit()
 def sliding_window_simple_search(data, windowsize, model, partitions, orders, **kwargs):
 
     _3d = len(orders) > 1

pyFTS/tests/general.py

@@ -3,7 +3,7 @@
 
 import os
 import numpy as np
-#import matplotlib.pyplot as plt
+import matplotlib.pylab as plt
 #from mpl_toolkits.mplot3d import Axes3D
 
 import pandas as pd
@@ -17,9 +17,39 @@ from pyFTS.benchmarks import benchmarks as bchmk
 
 from pyFTS.models import pwfts
 
-
+'''
 bchmk.sliding_window_benchmarks(dataset, 1000, train=0.8, inc=0.2, methods=[pwfts.ProbabilisticWeightedFTS],
                                 benchmark_models=False, orders=[1], partitions=[10], #np.arange(10,100,2),
                                 progress=False, type='distribution',
                                 distributed=False, nodes=['192.168.0.106', '192.168.0.105', '192.168.0.110'],
-                                save=True, file="pwfts_taiex_interval.csv")
+                                save=True, file="pwfts_taiex_interval.csv")
+'''
+
+train_split = 2000
+test_length = 200
+
+from pyFTS.partitioners import Grid, Util as pUtil
+partitioner = Grid.GridPartitioner(data=dataset[:train_split], npart=30)
+
+from pyFTS.common import fts,tree
+from pyFTS.models import hofts, pwfts
+
+pfts1_taiex = pwfts.ProbabilisticWeightedFTS("1", partitioner=partitioner)
+pfts1_taiex.fit(dataset[:train_split], save_model=True, file_path='pwfts')
+pfts1_taiex.shortname = "1st Order"
+
+print(pfts1_taiex)
+
+tmp = pfts1_taiex.predict(dataset[train_split:train_split+200], type='distribution', steps_ahead=20)
+f, ax = plt.subplots(3, 4, figsize=[20,15])
+tmp[0].plot(ax[0][0], title='t=1')
+tmp[2].plot(ax[0][1], title='t=20')
+tmp[4].plot(ax[0][2], title='t=40')
+tmp[6].plot(ax[0][3], title='t=60')
+tmp[8].plot(ax[1][0], title='t=80')
+tmp[10].plot(ax[1][1], title='t=100')
+tmp[12].plot(ax[1][2], title='t=120')
+tmp[14].plot(ax[1][3], title='t=140')
+tmp[16].plot(ax[2][0], title='t=160')
+tmp[18].plot(ax[2][1], title='t=180')
+tmp[20].plot(ax[2][2], title='t=200')