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Bugfixes at pwfts.forecast_ahead_distribution

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Petrônio Cândido 2018-04-09 14:52:25 -03:00
parent 326d1b4f40
commit 1312d96246
6 changed files with 48 additions and 16 deletions
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1
pyFTS/benchmarks/Util.py
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@ -2,7 +2,6 @@
Benchmark utility functions
"""
import numba
import matplotlib as plt
import matplotlib.cm as cmx
import matplotlib.colors as pltcolors

1
pyFTS/benchmarks/benchmarks.py
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@ -6,7 +6,6 @@
import datetime
import time
import numba
from copy import deepcopy
import matplotlib as plt

1
pyFTS/common/Util.py
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@ -1,5 +1,4 @@
import time
import numba
import matplotlib.pyplot as plt
import dill
import numpy as np

23
pyFTS/models/pwfts.py
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@ -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()]
pk = flrg.lhs_conditional_probability(sample, self.sets, self.global_frequency_count, uod, nbins)
wi = flrg.rhs_conditional_probability(bin, self.sets, uod, nbins)
num.append(wi * pk)
den.append(pk)
if s.get_key() in self.flrgs:
flrg = self.flrgs[s.get_key()]
pk = flrg.lhs_conditional_probability(sample, self.sets, self.global_frequency_count, uod, nbins)
wi = flrg.rhs_conditional_probability(bin, self.sets, uod, nbins)
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

2
pyFTS/partitioners/Util.py
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@ -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

34
pyFTS/tests/general.py
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@ -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")
'''
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')