- Bugfix on interval forecast of quantreg
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@ -219,7 +219,7 @@ def run_interval(mfts, partitioner, train_data, test_data, window_key=None, tran
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import time
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from pyFTS import hofts,ifts,pwfts
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from pyFTS.partitioners import Grid, Entropy, FCM
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from pyFTS.benchmarks import Measures
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from pyFTS.benchmarks import Measures, arima, quantreg
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tmp = [hofts.HighOrderFTS, ifts.IntervalFTS, pwfts.ProbabilisticWeightedFTS]
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@ -291,7 +291,7 @@ def interval_sliding_window(data, windowsize, train=0.8, inc=0.1, models=None,
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if benchmark_models_parameters is None:
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benchmark_models_parameters = [(1, 0, 0), (1, 0, 1), (2, 0, 1), (2, 0, 2), 1, 2]
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cluster = dispy.JobCluster(run_point, nodes=nodes) #, depends=dependencies)
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cluster = dispy.JobCluster(run_interval, nodes=nodes) #, depends=dependencies)
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http_server = dispy.httpd.DispyHTTPServer(cluster)
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@ -38,8 +38,8 @@ class QuantileRegression(fts.FTS):
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self.mean_qt = [k for k in mqt.params]
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if self.alpha is not None:
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self.upper_qt = [uqt.params[k] for k in uqt.params.keys()]
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self.lower_qt = [lqt.params[k] for k in lqt.params.keys()]
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self.upper_qt = [k for k in uqt.params]
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self.lower_qt = [k for k in lqt.params]
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self.shortname = "QAR(" + str(self.order) + ")"
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@ -29,11 +29,11 @@ DATASETS
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#gauss = random.normal(0,1.0,5000)
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#gauss_teste = random.normal(0,1.0,400)
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taiexpd = pd.read_csv("DataSets/TAIEX.csv", sep=",")
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taiex = np.array(taiexpd["avg"][:5000])
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#taiexpd = pd.read_csv("DataSets/TAIEX.csv", sep=",")
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#taiex = np.array(taiexpd["avg"][:5000])
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#nasdaqpd = pd.read_csv("DataSets/NASDAQ_IXIC.csv", sep=",")
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#nasdaq = np.array(nasdaqpd["avg"][0:5000])
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nasdaqpd = pd.read_csv("DataSets/NASDAQ_IXIC.csv", sep=",")
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nasdaq = np.array(nasdaqpd["avg"][0:5000])
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#sp500pd = pd.read_csv("DataSets/S&P500.csv", sep=",")
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#sp500 = np.array(sp500pd["Avg"][11000:])
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@ -73,7 +73,7 @@ from pyFTS.benchmarks import arima, quantreg
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#bchmk.teste(taiex,['192.168.0.109', '192.168.0.101'])
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from pyFTS import song, chen, yu, cheng
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diff = Transformations.Differential(1)
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"""
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bchmk.point_sliding_window(sonda, 9000, train=0.8, inc=0.4,#models=[yu.WeightedFTS], # #
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@ -82,7 +82,7 @@ bchmk.point_sliding_window(sonda, 9000, train=0.8, inc=0.4,#models=[yu.WeightedF
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dump=True, save=True, file="experiments/sondaws_point_analytic.csv",
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nodes=['192.168.0.103', '192.168.0.106', '192.168.0.108', '192.168.0.109']) #, depends=[hofts, ifts])
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diff = Transformations.Differential(1)
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bchmk.point_sliding_window(sonda, 9000, train=0.8, inc=0.4, #models=[yu.WeightedFTS], # #
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partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
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@ -91,6 +91,21 @@ bchmk.point_sliding_window(sonda, 9000, train=0.8, inc=0.4, #models=[yu.Weighted
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nodes=['192.168.0.103', '192.168.0.106', '192.168.0.108', '192.168.0.109']) #, depends=[hofts, ifts])
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#"""
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bchmk.interval_sliding_window(nasdaq, 2000, train=0.8, inc=0.1,#models=[yu.WeightedFTS], # #
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partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
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partitions= np.arange(10,200,step=10), #transformation=diff,
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dump=True, save=True, file="experiments/nasdaq_interval_analytic.csv",
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nodes=['192.168.0.103', '192.168.0.106', '192.168.0.108', '192.168.0.109']) #, depends=[hofts, ifts])
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bchmk.interval_sliding_window(nasdaq, 2000, train=0.8, inc=0.1, #models=[yu.WeightedFTS], # #
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partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
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partitions= np.arange(3,20,step=2), #transformation=diff,
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dump=True, save=True, file="experiments/nasdaq_interval_analytic_diff.csv",
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nodes=['192.168.0.103', '192.168.0.106', '192.168.0.108', '192.168.0.109']) #, depends=[hofts, ifts])
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"""
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from pyFTS.partitioners import Grid
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from pyFTS import pwfts
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@ -108,3 +123,4 @@ x = tmp.forecastInterval(taiex[1600:1610])
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print(taiex[1600:1610])
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print(x)
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"""
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