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- SeasonalIndexer on sfts

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Petrônio Cândido de Lima e Silva 2017-05-22 00:44:07 -03:00
parent 4cfc2bced4
commit 849cd74bff
3 changed files with 53 additions and 36 deletions
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25
models/seasonal/SeasonalIndexer.py
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@ -25,9 +25,11 @@ class SeasonalIndexer(object):
class LinearSeasonalIndexer(SeasonalIndexer):
def __init__(self,seasons):
def __init__(self,seasons,units,ignore=None):
super(LinearSeasonalIndexer, self).__init__(len(seasons))
self.seasons = seasons
self.units = units
self.ignore = ignore
def get_season_of_data(self,data):
return self.get_season_by_index(np.arange(0, len(data)).tolist())
@ -35,20 +37,25 @@ class LinearSeasonalIndexer(SeasonalIndexer):
def get_season_by_index(self,index):
ret = []
if not isinstance(index, (list, np.ndarray)):
season = (index % self.seasons[0]) + 1
if self.num_seasons == 1:
season = (index // self.units[0]) % self.seasons[0]
else:
season = []
for ct, seasonality in enumerate(self.seasons, start=0):
tmp = (index // self.units[ct]) % self.seasons[ct]
if not self.ignore[ct]:
season.append(tmp)
ret.append(season)
else:
for ix in index:
if self.num_seasons == 1:
season = (ix % self.seasons[0])
season = (ix // self.units[0]) % self.seasons[0]
else:
season = []
for seasonality in self.seasons:
#print("S ", seasonality)
tmp = ix // seasonality
#print("T ", tmp)
for ct, seasonality in enumerate(self.seasons, start=0):
tmp = (ix // self.units[ct]) % self.seasons[ct]
if not self.ignore[ct]:
season.append(tmp)
#season.append(rest)
ret.append(season)
return ret

14
sfts.py
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@ -51,11 +51,13 @@ class SeasonalFTS(fts.FTS):
season = self.indexer.get_season_by_index(ct)[0]
if season not in flrgs:
flrgs[season] = SeasonalFLRG(season)
ss = str(season)
if ss not in flrgs:
flrgs[ss] = SeasonalFLRG(season)
#print(season)
flrgs[season].append(flr.RHS)
flrgs[ss].append(flr.RHS)
return (flrgs)
@ -63,7 +65,7 @@ class SeasonalFTS(fts.FTS):
self.sets = sets
ndata = self.doTransformations(data)
tmpdata = FuzzySet.fuzzySeries(ndata, sets)
flrs = FLR.generateNonRecurrentFLRs(tmpdata)
flrs = FLR.generateRecurrentFLRs(tmpdata)
self.flrgs = self.generateFLRG(flrs)
def forecast(self, data, **kwargs):
@ -78,11 +80,11 @@ class SeasonalFTS(fts.FTS):
season = self.indexer.get_season_by_index(k)[0]
flrg = self.flrgs[season]
flrg = self.flrgs[str(season)]
mp = self.getMidpoints(flrg)
ret.append(sum(mp) / len(mp))
ret.append(np.percentile(mp, 50))
ret = self.doInverseTransformations(ret, params=[data[self.order - 1:]])

46
tests/general.py
View File

@ -16,10 +16,12 @@ from pyFTS import fts,hofts,ifts,pwfts,tree, chen
from pyFTS.benchmarks import naive, arima
from pyFTS.benchmarks import Measures
from numpy import random
from pyFTS.models.seasonal import SeasonalIndexer
os.chdir("/home/petronio/dados/Dropbox/Doutorado/Codigos/")
diff = Transformations.Differential(1)
ix = SeasonalIndexer.LinearSeasonalIndexer([12, 24], [720, 1],[False, False])
"""
DATASETS
@ -47,14 +49,14 @@ 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["glo_avg"])
#del(sondapd)
sondapd = pd.read_csv("DataSets/SONDA_BSB_HOURLY_AVG.csv", sep=";")
sondapd = sondapd.dropna(axis=0, how='any')
sonda = np.array(sondapd["glo_avg"])
del(sondapd)
bestpd = pd.read_csv("DataSets/BEST_TAVG.csv", sep=";")
best = np.array(bestpd["Anomaly"])
del(bestpd)
#bestpd = pd.read_csv("DataSets/BEST_TAVG.csv", sep=";")
#best = np.array(bestpd["Anomaly"])
#del(bestpd)
#print(lag)
#print(a)
@ -137,35 +139,41 @@ bchmk.interval_sliding_window(sp500, 2000, train=0.8, inc=0.2, #models=[yu.Weigh
"""
"""
#"""
bchmk.ahead_sliding_window(best, 4000, steps=10, resolution=100, train=0.8, inc=0.5,
bchmk.ahead_sliding_window(sonda, 10000, steps=10, resolution=10, train=0.2, inc=0.5,
partitioners=[Grid.GridPartitioner],
partitions= np.arange(10,200,step=10),
dump=True, save=True, file="experiments/best_ahead_analytic.csv",
partitions= np.arange(10,200,step=10), indexer=ix,
dump=True, save=True, file="experiments/sondasolar_ahead_analytic.csv",
nodes=['192.168.0.106', '192.168.0.108', '192.168.0.109']) #, depends=[hofts, ifts])
bchmk.ahead_sliding_window(best, 4000, steps=10, resolution=100, train=0.8, inc=0.5,
bchmk.ahead_sliding_window(sonda, 10000, steps=10, resolution=10, train=0.2, inc=0.5,
partitioners=[Grid.GridPartitioner],
partitions= np.arange(3,20,step=2), transformation=diff,
dump=True, save=True, file="experiments/best_ahead_analytic_diff.csv",
partitions= np.arange(3,20,step=2), transformation=diff, indexer=ix,
dump=True, save=True, file="experiments/sondasolar_ahead_analytic_diff.csv",
nodes=['192.168.0.106', '192.168.0.108', '192.168.0.109']) #, depends=[hofts, ifts])
"""
from pyFTS.partitioners import Grid
from pyFTS.models.seasonal import SeasonalIndexer
from pyFTS import sfts
ix = SeasonalIndexer.LinearSeasonalIndexer([10])
#print(ix.get_season_of_data(best[:2000]))
#print(ix.get_season_by_index(45))
#ix = SeasonalIndexer.LinearSeasonalIndexer([720,24],[False,True,False])
#print(ix.get_season_of_data(sonda[6500:9000])[-20:])
diff = Transformations.Differential(1)
fs = Grid.GridPartitioner(best[:2000], 10, transformation=diff)
fs = Grid.GridPartitioner(sonda[:9000], 10, transformation=diff)
tmp = sfts.SeasonalFTS("")
tmp.indexer = ix
@ -175,9 +183,9 @@ tmp.appendTransformation(diff)
#tmp.appendTransformation(diff)
tmp.train(best[:1600], fs.sets, order=1)
tmp.train(sonda[:9000], fs.sets, order=1)
x = tmp.forecast(best[1600:1610])
x = tmp.forecast(sonda[:1610])
#print(taiex[1600:1610])
print(x)