Minor bugfixes on benchmarking methods
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@ -43,7 +43,7 @@ class ARIMA(fts.FTS):
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self.order = self.p + self.q + (self.q - 1 if self.q > 0 else 0)
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self.max_lag = self.order
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self.d = len(self.transformations)
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self.shortname = "ARIMA(" + str(self.p) + "," + str(self.d) + "," + str(self.q) + ") - " + str(self.alpha)
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self.shortname = "BSTS({},{},{})-{}".format(self.p,self.d,self.q,self.alpha)
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def train(self, data, **kwargs):
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@ -42,7 +42,7 @@ class ARIMA(fts.FTS):
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self.order = self.p + self.q + (self.q - 1 if self.q > 0 else 0)
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self.max_lag = self.order
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self.d = len(self.transformations)
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self.shortname = "ARIMA(" + str(self.p) + "," + str(self.d) + "," + str(self.q) + ") - " + str(self.alpha)
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self.shortname = "ARIMA({},{},{})-{}".format(self.p, self.d, self.q, self.alpha)
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def train(self, data, **kwargs):
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@ -61,6 +61,8 @@ class KNearestNeighbors(fts.FTS):
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self.kdtree = KDTree(np.array(X))
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self.values = Y
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self.shortname = "kNN({})-{}".format(self.order, self.alpha)
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def knn(self, sample):
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X = self._prepare_x(sample)
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_, ix = self.kdtree.query(np.array(X), self.k)
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@ -54,7 +54,7 @@ class QuantileRegression(fts.FTS):
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up_qt = [k for k in uqt.params]
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self.dist_qt.append([lo_qt, up_qt])
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self.shortname = "QAR(" + str(self.order) + ") - " + str(self.alpha)
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self.shortname = "QAR({})-{}".format(self.order,self.alpha)
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def linearmodel(self,data,params):
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#return params[0] + sum([ data[k] * params[k+1] for k in np.arange(0, self.order) ])
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@ -19,7 +19,7 @@ from pyFTS.fcm import fts, common, GA
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from pyFTS.data import TAIEX, NASDAQ, SP500
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#'''
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'''
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train = TAIEX.get_data()[:800]
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test = TAIEX.get_data()[800:1000]
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@ -52,13 +52,22 @@ datasets['TAIEX'] = TAIEX.get_data()[:5000]
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datasets['NASDAQ'] = NASDAQ.get_data()[:5000]
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datasets['SP500'] = SP500.get_data()[10000:15000]
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methods = [arima.ARIMA, quantreg.QuantileRegression, BSTS.ARIMA, knn.KNearestNeighbors]
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methods = [
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arima.ARIMA,arima.ARIMA,
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quantreg.QuantileRegression,quantreg.QuantileRegression,
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BSTS.ARIMA,BSTS.ARIMA,
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knn.KNearestNeighbors,knn.KNearestNeighbors
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]
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methods_parameters = [
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{'order':(2,0,0)},
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{'order':2, 'dist': True},
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{'order':(2,0,0)},
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{'order':2 }
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{'order':(2,0,0), 'alpha':.05},
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{'order':(2,0,0), 'alpha':.25},
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{'order':2, 'alpha':.05},
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{'order':2, 'alpha':.25},
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{'order': (2, 0, 0), 'alpha': .05},
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{'order': (2, 0, 0), 'alpha': .25},
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{'order': 2, 'alpha': .05},
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{'order': 2, 'alpha': .25}
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]
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for dataset_name, dataset in datasets.items():
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@ -72,8 +81,8 @@ for dataset_name, dataset in datasets.items():
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orders=[],
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steps_ahead=[10],
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partitions=[],
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type='distribution',
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type='interval',
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distributed=True, nodes=['192.168.0.110', '192.168.0.107','192.168.0.106'],
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file="experiments.db", dataset=dataset_name, tag="experiments")
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# file="tmp.db", dataset='TAIEX', tag="experiments")
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'''
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#'''
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