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Several bugfixes in models and benchmarks

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Petrônio Cândido de Lima e Silva 2017-01-24 10:40:48 -02:00
parent b909bdac5d
commit f53eded47e
10 changed files with 108 additions and 59 deletions
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108
benchmarks/benchmarks.py
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@ -14,52 +14,100 @@ from pyFTS.common import Membership, FuzzySet, FLR, Transformations, Util
from pyFTS import fts, chen, yu, ismailefendi, sadaei, hofts, hwang, pfts, ifts
def allPointForecasters(data_train, data_test, partitions, max_order=2,save=False, file=None, tam=[20, 5]):
models = [chen.ConventionalFTS, yu.WeightedFTS, ismailefendi.ImprovedWeightedFTS, sadaei.ExponentialyWeightedFTS,
hwang.HighOrderFTS, hofts.HighOrderFTS, pfts.ProbabilisticFTS ]
def allPointForecasters(data_train, data_test, partitions, max_order=3,save=False, file=None, tam=[20, 5]):
models = [chen.ConventionalFTS, yu.WeightedFTS, ismailefendi.ImprovedWeightedFTS,
sadaei.ExponentialyWeightedFTS, hwang.HighOrderFTS, hofts.HighOrderFTS,
pfts.ProbabilisticFTS]
objects = []
objs = []
all_colors = [clr for clr in pltcolors.cnames.keys() ]
data_train_fs = Grid.GridPartitionerTrimf(data_train,partitions)
count = 1
colors = []
for model in models:
fts = model("")
if not fts.isHighOrder:
fts.train(data_train, data_train_fs)
objects.append(fts)
#print(model)
mfts = model("")
if not mfts.isHighOrder:
mfts.train(data_train, data_train_fs)
objs.append(mfts)
colors.append( all_colors[count] )
else:
for order in np.arange(1,max_order+1):
fts.train(data_train, data_train_fs, order=order)
fts.shortname += str(order)
objects.append(fts)
mfts = model(" n = " + str(order))
mfts.train(data_train, data_train_fs, order=order)
objs.append(mfts)
colors.append(all_colors[count])
count += 5
print(getPointStatistics(data_test, objects))
print(getPointStatistics(data_test, objs))
plotComparedSeries(data_test, objs, colors, typeonlegend=False, save=save, file=file, tam=tam, intervals=False)
def getPointStatistics(original, models, externalmodels = None, externalforecasts = None):
ret = "Model & RMSE & MAPE \\ \n"
def getPointStatistics(data, models, externalmodels = None, externalforecasts = None):
ret = "Model & Order & RMSE & MAPE \\\\ \n"
for fts in models:
forecasts = fts.forecast(original)
forecasts = fts.forecast(data)
ret += fts.shortname + " & "
ret += str(round(Measures.rmse(original[fts.order:], forecasts[:-1]), 2)) + " & "
ret += str(round(Measures.mape(original[fts.order:], forecasts[:-1]), 2)) + " & "
ret += " \\ \n"
l = len(externalmodels)
for k in np.arange(0,l):
ret += externalmodels[k] + " & "
ret += str(round(Measures.rmse(original[fts.order:], externalforecasts[k][:-1]), 2)) + " & "
ret += str(round(Measures.mape(original[fts.order:], externalforecasts[k][:-1]), 2)) + " & "
ret += " \\ \n"
ret += str(fts.order) + " & "
ret += str(round(Measures.rmse(np.array(data[fts.order:]), np.array(forecasts[:-1])), 2)) + " & "
ret += str(round(Measures.mape(np.array(data[fts.order:]), np.array(forecasts[:-1])), 2))
ret += " \\\\ \n"
if externalmodels is not None:
l = len(externalmodels)
for k in np.arange(0,l):
ret += externalmodels[k] + " & "
ret += " 1 & "
ret += str(round(Measures.rmse(data[fts.order:], externalforecasts[k][:-1]), 2)) + " & "
ret += str(round(Measures.mape(data[fts.order:], externalforecasts[k][:-1]), 2))
ret += " \\\\ \n"
return ret
def allIntervalForecasters(data_train, data_test, partitions, max_order=3,save=False, file=None, tam=[20, 5]):
models = [ifts.IntervalFTS, pfts.ProbabilisticFTS]
objs = []
all_colors = [clr for clr in pltcolors.cnames.keys() ]
data_train_fs = Grid.GridPartitionerTrimf(data_train,partitions)
count = 1
colors = []
for model in models:
#print(model)
mfts = model("")
if not mfts.isHighOrder:
mfts.train(data_train, data_train_fs)
objs.append(mfts)
colors.append( all_colors[count] )
else:
for order in np.arange(1,max_order+1):
mfts = model(" n = " + str(order))
mfts.train(data_train, data_train_fs, order=order)
objs.append(mfts)
colors.append(all_colors[count])
count += 5
print(getIntervalStatistics(data_test, objs))
plotComparedSeries(data_test, objs, colors, typeonlegend=False, save=save, file=file, tam=tam, intervals=True)
def getIntervalStatistics(original, models):
ret = "Model & RMSE & MAPE & Sharpness & Resolution & Coverage \\ \n"
ret = "Model & Order & Sharpness & Resolution & Coverage \\ \n"
for fts in models:
forecasts = fts.forecastInterval(original)
ret += fts.shortname + " & "
ret += str(round(Measures.rmse_interval(original[fts.order:], forecasts[:-1]), 2)) + " & "
ret += str(round(Measures.mape_interval(original[fts.order:], forecasts[:-1]), 2)) + " & "
ret += str(fts.order) + " & "
ret += str(round(Measures.sharpness(forecasts), 2)) + " & "
ret += str(round(Measures.resolution(forecasts), 2)) + " & "
ret += str(round(Measures.coverage(original[fts.order:], forecasts[:-1]), 2)) + " \\ \n"
@ -86,7 +134,7 @@ def plotComparedSeries(original, models, colors, typeonlegend=False, save=False,
ax.plot(original, color='black', label="Original", linewidth=1.5)
count = 0
for fts in models:
if fts.hasPointForecasting:
if fts.hasPointForecasting and not intervals:
forecasted = fts.forecast(original)
mi.append(min(forecasted) * 0.95)
ma.append(max(forecasted) * 1.05)
@ -107,8 +155,8 @@ def plotComparedSeries(original, models, colors, typeonlegend=False, save=False,
upper.insert(0, None)
lbl = fts.shortname
if typeonlegend: lbl += " (Interval)"
ax.plot(lower, color=colors[count], label=lbl, ls="--")
ax.plot(upper, color=colors[count], ls="--")
ax.plot(lower, color=colors[count], label=lbl, ls="-")
ax.plot(upper, color=colors[count], ls="-")
handles0, labels0 = ax.get_legend_handles_labels()
ax.legend(handles0, labels0, loc=2)

2
chen.py
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@ -3,7 +3,7 @@ from pyFTS.common import FuzzySet, FLR
from pyFTS import fts
class ConventionalFLRG:
class ConventionalFLRG(object):
def __init__(self, LHS):
self.LHS = LHS
self.RHS = set()

2
common/Transformations.py
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@ -6,7 +6,7 @@ from pyFTS import *
def differential(original, lags=1):
n = len(original)
diff = [original[t - lags] - original[t] for t in np.arange(lags, n)]
for t in np.arange(0, lags): diff.insert(0, None)
for t in np.arange(0, lags): diff.insert(0, 0)
return np.array(diff)

2
fts.py
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@ -2,7 +2,7 @@ import numpy as np
from pyFTS import *
class FTS:
class FTS(object):
def __init__(self, order, name):
self.sets = {}
self.flrgs = {}

3
hofts.py
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@ -38,6 +38,7 @@ class HighOrderFTS(fts.FTS):
def __init__(self, name):
super(HighOrderFTS, self).__init__(1, "HOFTS" + name)
self.name = "High Order FTS"
self.shortname = "HOFTS" + name
self.detail = "Chen"
self.order = 1
self.setsDict = {}
@ -80,7 +81,7 @@ class HighOrderFTS(fts.FTS):
if l <= self.order:
return data
for k in np.arange(self.order, l):
for k in np.arange(self.order, l+1):
tmpdata = FuzzySet.fuzzySeries(data[k - self.order: k], self.sets)
tmpflrg = HighOrderFLRG(self.order)

13
hwang.py
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@ -4,9 +4,12 @@ from pyFTS import fts
class HighOrderFTS(fts.FTS):
def __init__(self, order, name):
super(HighOrderFTS, self).__init__(order, name)
def __init__(self, name):
super(HighOrderFTS, self).__init__(1, name)
self.isHighOrder = True
self.name = "Hwang High Order FTS"
self.shortname = "Hwang" + name
self.detail = "Hwang"
def forecast(self, data):
cn = np.array([0.0 for k in range(len(self.sets))])
@ -16,7 +19,7 @@ class HighOrderFTS(fts.FTS):
ret = []
for t in np.arange(self.order, len(data)):
for t in np.arange(self.order-1, len(data)):
for s in range(len(self.sets)):
cn[s] = self.sets[s].membership(data[t])
@ -30,11 +33,11 @@ class HighOrderFTS(fts.FTS):
for s in range(len(self.sets)):
if ft[s] == mft:
out = out + self.sets[s].centroid
count = count + 1.0
count += 1.0
ret.append(out / count)
return ret
def train(self, data, sets, order=2, parameters=None):
def train(self, data, sets, order=1, parameters=None):
self.sets = sets
self.order = order

2
ifts.py
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@ -47,7 +47,7 @@ class IntervalFTS(hofts.HighOrderFTS):
for child in node.getChildren():
self.buildTree(child, lags, level + 1)
def forecast(self, data):
def forecastInterval(self, data):
ndata = np.array(data)

3
pfts.py
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@ -5,11 +5,10 @@ import numpy as np
import pandas as pd
import math
from operator import itemgetter
from pyFTS.common import FuzzySet, FLR, SortedCollection
from pyFTS.common import FuzzySet, SortedCollection
from pyFTS import hofts, ifts, tree
class ProbabilisticFLRG(hofts.HighOrderFLRG):
def __init__(self, order):
super(ProbabilisticFLRG, self).__init__(order)

4
sadaei.py
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@ -16,7 +16,7 @@ class ExponentialyWeightedFLRG:
def weights(self):
wei = [self.c ** k for k in np.arange(0.0, self.count, 1.0)]
tot = sum(wei)
return np.iarray([k / tot for k in wei])
return np.array([k / tot for k in wei])
def __str__(self):
tmp = self.LHS.name + " -> "
@ -54,7 +54,7 @@ class ExponentialyWeightedFTS(fts.FTS):
self.sets = sets
tmpdata = FuzzySet.fuzzySeries(data, sets)
flrs = FLR.generateRecurrentFLRs(tmpdata)
self.flrgs = self.generateFLRG(flrs, c)
self.flrgs = self.generateFLRG(flrs, self.c)
def forecast(self, data):
l = 1

28
tests/pfts.py
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@ -11,28 +11,26 @@ from mpl_toolkits.mplot3d import Axes3D
import pandas as pd
from pyFTS.partitioners import Grid
from pyFTS.common import FLR,FuzzySet,Membership
from pyFTS import fts
from pyFTS import hofts
from pyFTS import ifts
from pyFTS import pfts
from pyFTS import tree
from pyFTS import fts,hofts,ifts,pfts,tree, chen
from pyFTS.benchmarks import benchmarks as bchmk
os.chdir("/home/petronio/dados/Dropbox/Doutorado/Disciplinas/AdvancedFuzzyTimeSeriesModels/")
enrollments = pd.read_csv("DataSets/Enrollments.csv", sep=";")
enrollments = np.array(enrollments["Enrollments"])
#enrollments = pd.read_csv("DataSets/Enrollments.csv", sep=";")
#enrollments = np.array(enrollments["Enrollments"])
enrollments_fs1 = Grid.GridPartitionerTrimf(enrollments,6)
#enrollments_fs1 = Grid.GridPartitionerTrimf(enrollments,6)
#tmp = chen.ConventionalFTS("")
pfts1_enrollments = pfts.ProbabilisticFTS("1")
pfts1_enrollments.train(enrollments,enrollments_fs1,1)
pfts1_enrollments.shortname = "1st Order"
pfts2_enrollments = pfts.ProbabilisticFTS("2")
pfts2_enrollments.dump = False
pfts2_enrollments.shortname = "2nd Order"
pfts2_enrollments.train(enrollments,enrollments_fs1,2)
#pfts1_enrollments.train(enrollments,enrollments_fs1,1)
#pfts1_enrollments.shortname = "1st Order"
#pfts2_enrollments = pfts.ProbabilisticFTS("2")
#pfts2_enrollments.dump = False
#pfts2_enrollments.shortname = "2nd Order"
#pfts2_enrollments.train(enrollments,enrollments_fs1,2)
pfts1_enrollments.forecastAheadDistribution2(enrollments[:15],5,100)
#pfts1_enrollments.forecastAheadDistribution2(enrollments[:15],5,100)