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- Bugfix on interval forecast of quantreg

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Petrônio Cândido de Lima e Silva 2017-05-14 01:19:49 -03:00
parent 8bc4b966b6
commit a0f49ea151
3 changed files with 18 additions and 10 deletions
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2
benchmarks/arima.py
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@ -33,7 +33,7 @@ class ARIMA(fts.FTS):
self.d = order[1] self.d = order[1]
self.q = order[2] self.q = order[2]
self.order = self.p + self.q self.order = self.p + self.q
self.shortname = "ARIMA(" + str(self.p) + "," + str(self.d) + "," + str(self.q) + ")" self.shortname = "ARIMA(" + str(self.p) + "," + str(self.d) + "," + str(self.q) + ") - " + str(self.alpha)
old_fit = self.model_fit old_fit = self.model_fit
try: try:

4
benchmarks/quantreg.py
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@ -41,7 +41,7 @@ class QuantileRegression(fts.FTS):
self.upper_qt = [k for k in uqt.params] self.upper_qt = [k for k in uqt.params]
self.lower_qt = [k for k in lqt.params] self.lower_qt = [k for k in lqt.params]
self.shortname = "QAR(" + str(self.order) + ")" self.shortname = "QAR(" + str(self.order) + ") - " + str(self.alpha)
def linearmodel(self,data,params): def linearmodel(self,data,params):
#return params[0] + sum([ data[k] * params[k+1] for k in np.arange(0, self.order) ]) #return params[0] + sum([ data[k] * params[k+1] for k in np.arange(0, self.order) ])
@ -74,7 +74,7 @@ class QuantileRegression(fts.FTS):
sample = ndata[k - self.order: k] sample = ndata[k - self.order: k]
up = self.linearmodel(sample, self.upper_qt) up = self.linearmodel(sample, self.upper_qt)
down = self.linearmodel(sample, self.lower_qt) down = self.linearmodel(sample, self.lower_qt)
ret.append([up, down]) ret.append([down, up])
ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]], interval=True) ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]], interval=True)

22
tests/general.py
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@ -54,7 +54,7 @@ nasdaq = np.array(nasdaqpd["avg"][0:5000])
from pyFTS.benchmarks import distributed_benchmarks as bchmk from pyFTS.benchmarks import distributed_benchmarks as bchmk
#from pyFTS.benchmarks import parallel_benchmarks as bchmk #from pyFTS.benchmarks import parallel_benchmarks as bchmk
from pyFTS.benchmarks import Util from pyFTS.benchmarks import Util
from pyFTS.benchmarks import arima, quantreg from pyFTS.benchmarks import arima, quantreg, Measures
#Util.cast_dataframe_to_synthetic_point("experiments/taiex_point_analitic.csv","experiments/taiex_point_sintetic.csv",11) #Util.cast_dataframe_to_synthetic_point("experiments/taiex_point_analitic.csv","experiments/taiex_point_sintetic.csv",11)
@ -64,13 +64,18 @@ from pyFTS.benchmarks import arima, quantreg
#tmp.train(taiex[:1600], None, order=(2,0,2)) #tmp.train(taiex[:1600], None, order=(2,0,2))
#teste = tmp.forecastInterval(taiex[1600:1605]) #teste = tmp.forecastInterval(taiex[1600:1605])
#tmp = quan#treg.QuantileRegression("") """
#tmp.train(taiex[:1600], None, order=2) tmp = quantreg.QuantileRegression("", alpha=0.25)
#teste = tmp.forecast(taiex[1600:1605]) tmp.train(taiex[:1600], None, order=1)
teste = tmp.forecastInterval(taiex[1600:1605])
#print(taiex[1600:1605]) print(taiex[1600:1605])
#print(teste) print(teste)
kk = Measures.get_interval_statistics(taiex[1600:1605], tmp)
print(kk)
"""
#bchmk.teste(taiex,['192.168.0.109', '192.168.0.101']) #bchmk.teste(taiex,['192.168.0.109', '192.168.0.101'])
diff = Transformations.Differential(1) diff = Transformations.Differential(1)
@ -89,6 +94,7 @@ bchmk.point_sliding_window(sonda, 9000, train=0.8, inc=0.4, #models=[yu.Weighted
partitions= np.arange(3,20,step=2), #transformation=diff, partitions= np.arange(3,20,step=2), #transformation=diff,
dump=True, save=True, file="experiments/sondaws_point_analytic_diff.csv", dump=True, save=True, file="experiments/sondaws_point_analytic_diff.csv",
nodes=['192.168.0.103', '192.168.0.106', '192.168.0.108', '192.168.0.109']) #, depends=[hofts, ifts]) nodes=['192.168.0.103', '192.168.0.106', '192.168.0.108', '192.168.0.109']) #, depends=[hofts, ifts])
"""
#""" #"""
bchmk.interval_sliding_window(nasdaq, 2000, train=0.8, inc=0.1,#models=[yu.WeightedFTS], # # bchmk.interval_sliding_window(nasdaq, 2000, train=0.8, inc=0.1,#models=[yu.WeightedFTS], # #
@ -105,6 +111,8 @@ bchmk.interval_sliding_window(nasdaq, 2000, train=0.8, inc=0.1, #models=[yu.Weig
dump=True, save=True, file="experiments/nasdaq_interval_analytic_diff.csv", dump=True, save=True, file="experiments/nasdaq_interval_analytic_diff.csv",
nodes=['192.168.0.103', '192.168.0.106', '192.168.0.108', '192.168.0.109']) #, depends=[hofts, ifts]) nodes=['192.168.0.103', '192.168.0.106', '192.168.0.108', '192.168.0.109']) #, depends=[hofts, ifts])
#"""
""" """
from pyFTS.partitioners import Grid from pyFTS.partitioners import Grid
from pyFTS import pwfts from pyFTS import pwfts
@ -123,4 +131,4 @@ x = tmp.forecastInterval(taiex[1600:1610])
print(taiex[1600:1610]) print(taiex[1600:1610])
print(x) print(x)
""" #"""