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- Correction of inverse transformation on interval forecasts

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This commit is contained in:
Petrônio Cândido de Lima e Silva 2017-05-13 21:37:10 -03:00
parent 2c0e685e87
commit f8ac95d24e
8 changed files with 47 additions and 80 deletions
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2
benchmarks/arima.py
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@ -103,7 +103,7 @@ class ARIMA(fts.FTS):
ret.append(tmp)
ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]])
ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]], interval=True)
return ret

2
benchmarks/distributed_benchmarks.py
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@ -55,6 +55,7 @@ def run_point(mfts, partitioner, train_data, test_data, window_key=None, transfo
pttr = str(partitioner.__module__).split('.')[-1]
_key = mfts.shortname + " n = " + str(mfts.order) + " " + pttr + " q = " + str(partitioner.partitions)
mfts.partitioner = partitioner
if transformation is not None:
mfts.appendTransformation(transformation)
@ -234,6 +235,7 @@ def run_interval(mfts, partitioner, train_data, test_data, window_key=None, tran
pttr = str(partitioner.__module__).split('.')[-1]
_key = mfts.shortname + " n = " + str(mfts.order) + " " + pttr + " q = " + str(partitioner.partitions)
mfts.partitioner = partitioner
if transformation is not None:
mfts.appendTransformation(transformation)

2
benchmarks/quantreg.py
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@ -76,6 +76,6 @@ class QuantileRegression(fts.FTS):
down = self.linearmodel(sample, self.down_qt)
ret.append([up, down])
ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]])
ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]], interval=True)
return ret

17
common/Transformations.py
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@ -13,10 +13,10 @@ class Transformation(object):
self.parameters = parameters
self.minimalLength = 1
def apply(self,data,param):
def apply(self,data,param,**kwargs):
pass
def inverse(self,data, param):
def inverse(self,data, param,**kwargs):
pass
def __str__(self):
@ -32,7 +32,7 @@ class Differential(Transformation):
self.lag = parameters
self.minimalLength = 2
def apply(self, data, param=None):
def apply(self, data, param=None,**kwargs):
if param is not None:
self.lag = param
@ -47,7 +47,9 @@ class Differential(Transformation):
for t in np.arange(0, self.lag): diff.insert(0, 0)
return diff
def inverse(self,data, param):
def inverse(self,data, param, **kwargs):
interval = kwargs.get("interval",False)
if isinstance(data, (np.ndarray, np.generic)):
data = data.tolist()
@ -57,7 +59,10 @@ class Differential(Transformation):
n = len(data)
if not interval:
inc = [data[t] + param[t] for t in np.arange(0, n)]
else:
inc = [[data[t][0] + param[t], data[t][0] + param[t]] for t in np.arange(0, n)]
if n == 1:
return inc[0]
@ -73,10 +78,10 @@ class AdaptiveExpectation(Transformation):
super(AdaptiveExpectation, self).__init__(parameters)
self.h = parameters
def apply(self, data, param=None):
def apply(self, data, param=None,**kwargs):
return data
def inverse(self, data, param):
def inverse(self, data, param,**kwargs):
n = len(data)
inc = [param[t] + self.h*(data[t] - param[t]) for t in np.arange(0, n)]

4
fts.py
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@ -135,7 +135,7 @@ class FTS(object):
if transformation is not None:
self.transformations.append(transformation)
def doTransformations(self,data,params=None,updateUoD=False):
def doTransformations(self,data,params=None,updateUoD=False, **kwargs):
ndata = data
if updateUoD:
if min(data) < 0:
@ -157,7 +157,7 @@ class FTS(object):
return ndata
def doInverseTransformations(self, data, params=None):
def doInverseTransformations(self, data, params=None, **kwargs):
ndata = data
if len(self.transformations) > 0:
if params is None:

6
ifts.py
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@ -133,8 +133,10 @@ class IntervalFTS(hofts.HighOrderFTS):
# gerar o intervalo
norm = sum(affected_flrgs_memberships)
lo_ = self.doInverseTransformations(sum(lo) / norm, params=[data[k - (self.order - 1): k + 1]])
up_ = self.doInverseTransformations(sum(up) / norm, params=[data[k - (self.order - 1): k + 1]])
lo_ = sum(lo) / norm
up_ = sum(up) / norm
ret.append([lo_, up_])
ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]], interval=True)
return ret

6
pwfts.py
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@ -398,10 +398,12 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
if norm == 0:
ret.append([0, 0])
else:
lo_ = self.doInverseTransformations(sum(lo) / norm, params=[data[k - (self.order - 1): k + 1]])
up_ = self.doInverseTransformations(sum(up) / norm, params=[data[k - (self.order - 1): k + 1]])
lo_ = sum(lo) / norm
up_ = sum(up) / norm
ret.append([lo_, up_])
ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]], interval=True)
return ret
def forecastAhead(self, data, steps, **kwargs):

78
tests/general.py
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@ -29,8 +29,8 @@ 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])
@ -39,10 +39,10 @@ DATASETS
#sp500 = np.array(sp500pd["Avg"][11000:])
#del(sp500pd)
sondapd = pd.read_csv("DataSets/SONDA_BSB_HOURLY_AVG.csv", sep=";")
sondapd = sondapd.dropna(axis=0, how='any')
sonda = np.array(sondapd["ws_10m"])
del(sondapd)
#sondapd = pd.read_csv("DataSets/SONDA_BSB_HOURLY_AVG.csv", sep=";")
#sondapd = sondapd.dropna(axis=0, how='any')
#sonda = np.array(sondapd["ws_10m"])
#del(sondapd)
#bestpd = pd.read_csv("DataSets/BEST_TAVG.csv", sep=";")
#best = np.array(bestpd["Anomaly"])
@ -75,7 +75,7 @@ from pyFTS.benchmarks import arima, quantreg
from pyFTS import song, chen, yu, cheng
"""
bchmk.point_sliding_window(sonda, 9000, train=0.8, inc=0.4,#models=[yu.WeightedFTS], # #
partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
partitions= np.arange(10,200,step=10), #transformation=diff,
@ -90,65 +90,21 @@ bchmk.point_sliding_window(sonda, 9000, train=0.8, inc=0.4, #models=[yu.Weighted
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])
#"""
#bchmk.testa(taiex,[10,20],partitioners=[Grid.GridPartitioner], nodes=['192.168.0.109', '192.168.0.101'])
#parallel_util.explore_partitioners(taiex,20)
from pyFTS.partitioners import Grid
from pyFTS import pwfts
#nasdaqpd = pd.read_csv("DataSets/NASDAQ_IXIC.csv", sep=",")
#nasdaq = np.array(nasdaqpd["avg"][:5000])
diff = Transformations.Differential(1)
#taiex = pd.read_csv("DataSets/TAIEX.csv", sep=",")
#taiex_treino = np.array(taiex["avg"][2500:3900])
#taiex_teste = np.array(taiex["avg"][3901:4500])
fs = Grid.GridPartitioner(taiex[:2000], 10, transformation=diff)
#print(len(taiex))
tmp = pwfts.ProbabilisticWeightedFTS("")
#from pyFTS.common import Util
tmp.appendTransformation(diff)
#, ,
tmp.train(taiex[:1600], fs.sets, order=1)
#diff = Transformations.Differential(1)
x = tmp.forecastInterval(taiex[1600:1610])
#bchmk.external_point_sliding_window([naive.Naive, arima.ARIMA, arima.ARIMA, arima.ARIMA, arima.ARIMA, arima.ARIMA, arima.ARIMA],
# [None, (1,0,0),(1,1,0),(2,0,0), (2,1,0), (1,1,1), (1,0,1)],
# gauss,2000,train=0.8, dump=True, save=True, file="experiments/arima_gauss.csv")
#bchmk.interval_sliding_window(gauss,2000,train=0.8, #transformation=diff, #models=[pwfts.ProbabilisticWeightedFTS], # #
# partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
# partitions= np.arange(10,200,step=5), #
# dump=True, save=False, file="experiments/nasdaq_interval.csv")
#3bchmk.ahead_sliding_window(taiex,2000,train=0.8, steps=20, resolution=250, #transformation=diff, #models=[pwfts.ProbabilisticWeightedFTS], # #
# partitioners=[Grid.GridPartitioner], #Entropy.EntropyPartitioner], # FCM.FCMPartitioner, ],
# partitions= np.arange(10,200,step=10), #
# dump=True, save=True, file="experiments/taiex_ahead.csv")
#bchmk.allPointForecasters(taiex_treino, taiex_treino, 95, #transformation=diff,
# models=[ naive.Naive, pfts.ProbabilisticFTS, pwfts.ProbabilisticWeightedFTS],
# statistics=True, residuals=False, series=False)
#data_train_fs = Grid.GridPartitioner(nasdaq[:1600], 95).sets
#fts1 = pwfts.ProbabilisticWeightedFTS("")
#fts1.appendTransformation(diff)
#fts1.train(nasdaq[:1600], data_train_fs, order=1)
#_crps1, _crps2, _t1, _t2 = bchmk.get_distribution_statistics(nasdaq[1600:2000], fts1, steps=20, resolution=200)
#print(_crps1, _crps2, _t1, _t2)
#print(fts1.forecast([5000, 5000]))
#fts2 = pwfts.ProbabilisticWeightedFTS("")
#fts2.appendTransformation(diff)
#fts2.train(taiex_treino, data_train_fs, order=1)
#print(fts2.forecast([5000, 5000]))
#tmp = Grid.GridPartitioner(taiex_treino,7,transformation=diff)
#for s in tmp.sets: print(s)
print(taiex[1600:1610])
print(x)