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- Bugfix on interval forecast of quantreg

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This commit is contained in:
Petrônio Cândido de Lima e Silva 2017-05-13 22:32:40 -03:00
parent f8ac95d24e
commit 0b7799a9bb
3 changed files with 26 additions and 10 deletions
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4
benchmarks/distributed_benchmarks.py
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@ -219,7 +219,7 @@ def run_interval(mfts, partitioner, train_data, test_data, window_key=None, tran
import time
from pyFTS import hofts,ifts,pwfts
from pyFTS.partitioners import Grid, Entropy, FCM
from pyFTS.benchmarks import Measures
from pyFTS.benchmarks import Measures, arima, quantreg
tmp = [hofts.HighOrderFTS, ifts.IntervalFTS, pwfts.ProbabilisticWeightedFTS]
@ -291,7 +291,7 @@ def interval_sliding_window(data, windowsize, train=0.8, inc=0.1, models=None,
if benchmark_models_parameters is None:
benchmark_models_parameters = [(1, 0, 0), (1, 0, 1), (2, 0, 1), (2, 0, 2), 1, 2]
cluster = dispy.JobCluster(run_point, nodes=nodes) #, depends=dependencies)
cluster = dispy.JobCluster(run_interval, nodes=nodes) #, depends=dependencies)
http_server = dispy.httpd.DispyHTTPServer(cluster)

4
benchmarks/quantreg.py
View File

@ -38,8 +38,8 @@ class QuantileRegression(fts.FTS):
self.mean_qt = [k for k in mqt.params]
if self.alpha is not None:
self.upper_qt = [uqt.params[k] for k in uqt.params.keys()]
self.lower_qt = [lqt.params[k] for k in lqt.params.keys()]
self.upper_qt = [k for k in uqt.params]
self.lower_qt = [k for k in lqt.params]
self.shortname = "QAR(" + str(self.order) + ")"

28
tests/general.py
View File

@ -29,11 +29,11 @@ DATASETS
#gauss = random.normal(0,1.0,5000)
#gauss_teste = random.normal(0,1.0,400)
taiexpd = pd.read_csv("DataSets/TAIEX.csv", sep=",")
taiex = np.array(taiexpd["avg"][:5000])
#taiexpd = pd.read_csv("DataSets/TAIEX.csv", sep=",")
#taiex = np.array(taiexpd["avg"][:5000])
#nasdaqpd = pd.read_csv("DataSets/NASDAQ_IXIC.csv", sep=",")
#nasdaq = np.array(nasdaqpd["avg"][0:5000])
nasdaqpd = pd.read_csv("DataSets/NASDAQ_IXIC.csv", sep=",")
nasdaq = np.array(nasdaqpd["avg"][0:5000])
#sp500pd = pd.read_csv("DataSets/S&P500.csv", sep=",")
#sp500 = np.array(sp500pd["Avg"][11000:])
@ -73,7 +73,7 @@ from pyFTS.benchmarks import arima, quantreg
#bchmk.teste(taiex,['192.168.0.109', '192.168.0.101'])
from pyFTS import song, chen, yu, cheng
diff = Transformations.Differential(1)
"""
bchmk.point_sliding_window(sonda, 9000, train=0.8, inc=0.4,#models=[yu.WeightedFTS], # #
@ -82,7 +82,7 @@ bchmk.point_sliding_window(sonda, 9000, train=0.8, inc=0.4,#models=[yu.WeightedF
dump=True, save=True, file="experiments/sondaws_point_analytic.csv",
nodes=['192.168.0.103', '192.168.0.106', '192.168.0.108', '192.168.0.109']) #, depends=[hofts, ifts])
diff = Transformations.Differential(1)
bchmk.point_sliding_window(sonda, 9000, train=0.8, inc=0.4, #models=[yu.WeightedFTS], # #
partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
@ -91,6 +91,21 @@ bchmk.point_sliding_window(sonda, 9000, train=0.8, inc=0.4, #models=[yu.Weighted
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], # #
partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
partitions= np.arange(10,200,step=10), #transformation=diff,
dump=True, save=True, file="experiments/nasdaq_interval_analytic.csv",
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], # #
partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
partitions= np.arange(3,20,step=2), #transformation=diff,
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])
"""
from pyFTS.partitioners import Grid
from pyFTS import pwfts
@ -108,3 +123,4 @@ x = tmp.forecastInterval(taiex[1600:1610])
print(taiex[1600:1610])
print(x)
"""