sonda.index = np.arange(0, len(sonda.index))
sonda_treino = sonda[:1051200]
sonda_teste = sonda[1051201:]
#res = bchmk.simpleSearch_RMSE(sonda_treino, sonda_teste,
# sfts.SeasonalFTS,np.arange(3,30),[1],parameters=1440,
# tam=[15,8], plotforecasts=False,elev=45, azim=40,
# save=False,file="pictures/sonda_sfts_error_surface", intervals=False)
partitions = ['grid', 'entropy']
indexers = ['m15', 'Mh', 'Mhm15']
models = []
ixs = []
sample = sonda_teste[0:4300]
for max_part in [10, 20, 30, 40, 50]:
for part in partitions:
for ind in indexers:
ix = Util.load_obj("models/sonda_ix_" + ind + ".pkl")
model = Util.load_obj("models/sonda_msfts_" + part + "_" +
str(max_part) + "_" + ind + ".pkl")
model.shortname = part + "_" + str(max_part) + "_" + ind
models.append(model)
ixs.append(ix)
print(bchmk.print_point_statistics(sample, models, indexers=ixs))
data=train,
partitioner_specific=sp)
vhour.partitioner.plot(axes[1])
vavg = variable.Variable("Radiance",
data_label="glo_avg",
partitioner=Grid.GridPartitioner,
npart=30,
data=train)
model1 = mvfts.MVFTS("")
model1.append_variable(vmonth)
model1.append_variable(vhour)
model1.append_variable(vavg)
model1.target_variable = vavg
#model1.fit(train, num_batches=60, save=True, batch_save=True, file_path='mvfts_sonda')
#model.fit(train, num_batches=60, save=True, batch_save=True, file_path='mvfts_sonda')
#model1.fit(train, num_batches=200, save=True, batch_save=True, file_path='mvfts_sonda', distributed=False,
# nodes=['192.168.0.110'], batch_save_interval=10)
model = Util.load_obj('mvfts_sonda')
forecasts = model.predict(test)
taiex = TAIEX.get_data()
train = taiex[:3000]
test = taiex[3000:3200]
from pyFTS.common import Transformations
tdiff = Transformations.Differential(1)
'''
from pyFTS.partitioners import Grid, Util as pUtil
from pyFTS.common import FLR,FuzzySet,Membership,SortedCollection
taiex_fs1 = Grid.GridPartitioner(data=train, npart=30)
taiex_fs2 = Grid.GridPartitioner(data=train, npart=10, transformation=tdiff)
#pUtil.plot_partitioners(train, [taiex_fs1,taiex_fs2], tam=[15,7])
from pyFTS.common import fts,tree
from pyFTS.models import hofts, pwfts
pfts1_taiex = pwfts.ProbabilisticWeightedFTS("1", partitioner=taiex_fs1)
#pfts1_taiex.appendTransformation(diff)
pfts1_taiex.fit(train, save_model=True, file_path='pwfts')
pfts1_taiex.shortname = "1st Order"
print(pfts1_taiex)
'''
model = Util.load_obj('pwfts')
model.predict(test, type='distribution')
#'''
sonda_treino = sonda[:1051200]
sonda_teste = sonda[1051201:]
#res = bchmk.simpleSearch_RMSE(sonda_treino, sonda_teste,
# sfts.SeasonalFTS,np.arange(3,30),[1],parameters=1440,
# tam=[15,8], plotforecasts=False,elev=45, azim=40,
# save=False,file="pictures/sonda_sfts_error_surface", intervals=False)
from pyFTS.common import Util
from pyFTS.models import cmsfts
partitions = ['grid', 'entropy']
indexers = ['m15', 'Mh', 'Mhm15']
for max_part in [40, 50]:
for part in partitions:
fs = Util.load_obj("models/sonda_fs_" + part + "_" + str(max_part) +
".pkl")
for ind in indexers:
ix = Util.load_obj("models/sonda_ix_" + ind + ".pkl")
model = cmsfts.ContextualMultiSeasonalFTS(part + " " + ind, ix)
model.train(sonda_treino, fs)
Util.persist_obj(
model, "models/sonda_cmsfts_" + part + "_" + str(max_part) +
"_" + ind + ".pkl")
from pyFTS.benchmarks import Measures
from pyFTS.partitioners import Grid, Util as pUtil
from pyFTS.common import Transformations, Util
from pyFTS.models.multivariate import common, variable, mvfts, wmvfts
from pyFTS.models.seasonal import partitioner as seasonal
from pyFTS.models.seasonal.common import DateTime
bc = Transformations.BoxCox(0)
tdiff = Transformations.Differential(1)
from pyFTS.models.multivariate import common, variable, mvfts, cmvfts
from pyFTS.models.seasonal import partitioner as seasonal
from pyFTS.models.seasonal.common import DateTime
model = Util.load_obj(
'/home/petronio/Downloads/ClusteredMVFTS1solarorder2knn3')
data = [[12, 100], [13, 200]]
for k in data:
k[0] = pd.to_datetime('2018-01-01 {}:00:00'.format(k[0]),
format='%Y-%m-%d %H:%M:%S')
df = pd.DataFrame(data, columns=['data', 'glo_avg'])
#forecasts = model.predict(df, steps_ahead=24, generators={'Hour': lambda x: x + pd.to_timedelta(1, unit='h')})
#print(forecasts)
f = lambda x: x + pd.to_timedelta(1, unit='h')
order = 3
nparts = 20
fuzzysets = []
fuzzysets.append(Grid.GridPartitioner(fln_train.glo_avg,nparts))
fuzzysets.append(Grid.GridPartitioner(joi_train.glo_avg,nparts))
fuzzysets.append(Grid.GridPartitioner(sbr_train.glo_avg,nparts))
d = {'fln_glo_avg':fln_train.glo_avg,'sbr_glo_avg':sbr_train.glo_avg,'joi_glo_avg':joi_train.glo_avg}
data_train = pd.DataFrame(d)
data_train = data_train.dropna(axis=0, how='any')
model_file = "models/fts/multivariate/mvhofts-"+str(order)+"-"+str(nparts)+".pkl"
mvhofts = mvhofts.MultivariateHighOrderFTS("")
mvhofts.train(data_train,fuzzysets,order)
cUtil.persist_obj(mvhofts, model_file)
obj = cUtil.load_obj(model_file)
dt = {'fln_glo_avg':fln_test.glo_avg,'sbr_glo_avg':sbr_test.glo_avg,'joi_glo_avg':joi_test.glo_avg}
data_test = pd.DataFrame(dt)
data_test = data_test.dropna(axis=0, how='any')
ret = obj.forecast(data_test)
print("RMSE: " + str(Measures.rmse(list(data_test.fln_glo_avg[order:]), ret[:-1])))
#print(mvhofts)
