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Bugfixes

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This commit is contained in:
Petrônio Cândido 2018-04-25 13:03:14 -03:00
parent abe9a45a47
commit a446cec232
2 changed files with 29 additions and 30 deletions
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10
pyFTS/models/ensemble/ensemble.py
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@ -18,7 +18,7 @@ def sampler(data, quantiles):
class EnsembleFTS(fts.FTS):
def __init__(self, name, **kwargs):
super(EnsembleFTS, self).__init__(1, "Ensemble FTS")
super(EnsembleFTS, self).__init__(1, "Ensemble FTS", **kwargs)
self.shortname = "Ensemble FTS " + name
self.name = "Ensemble FTS"
self.flrgs = {}
@ -266,17 +266,17 @@ class AllMethodEnsembleFTS(EnsembleFTS):
ho_methods = [hofts.HighOrderFTS, hwang.HighOrderFTS]
for method in fo_methods:
model = method("")
model = method("", partitioner=self.partitioner)
self.set_transformations(model)
model.train(data, **kwargs)
model.fit(data, **kwargs)
self.append_model(model)
for method in ho_methods:
for o in np.arange(1, order+1):
model = method("")
model = method("", partitioner=self.partitioner)
if model.min_order >= o:
self.set_transformations(model)
model.train(data, **kwargs)
model.fit(data, **kwargs)
self.append_model(model)

49
pyFTS/tests/general.py
View File

@ -14,17 +14,22 @@ tdiff = Transformations.Differential(1)
from pyFTS.data import TAIEX
dataset = TAIEX.get_data()
'''
from pyFTS.partitioners import Grid, Util as pUtil
partitioner = Grid.GridPartitioner(data=dataset[:800], npart=10) #, transformation=tdiff)
'''
from pyFTS.benchmarks import benchmarks as bchmk, Util as bUtil, Measures, knn, quantreg, arima
'''
from pyFTS.models import pwfts, song, ifts
from pyFTS.models.ensemble import ensemble
model = arima.ARIMA("", order=(1,0,0))
model = ensemble.AllMethodEnsembleFTS(partitioner=partitioner)
model.fit(dataset[:800])
tmp = model.predict(dataset[800:1000], type='distribution')
for tmp2 in tmp:
print(tmp2)
'''
'''
@ -40,39 +45,33 @@ print(Measures.get_distribution_statistics(dataset[800:1000], model, steps_ahead
# print(tmp2)
'''
'''
#'''
from pyFTS.benchmarks import arima, naive, quantreg
bchmk.sliding_window_benchmarks(dataset, 1000, train=0.8, inc=0.2,
methods=[ifts.IntervalFTS], #[pwfts.ProbabilisticWeightedFTS],
benchmark_models=True,
benchmark_methods=[arima.ARIMA for k in range(8)]
+ [quantreg.QuantileRegression for k in range(4)],
#methods=[ifts.IntervalFTS], #[pwfts.ProbabilisticWeightedFTS],
benchmark_models=False,
benchmark_methods=[arima.ARIMA for k in range(4)]
+ [quantreg.QuantileRegression for k in range(2)],
benchmark_methods_parameters=[
{'order': (1, 0, 0), 'alpha': .05},
{'order': (1, 0, 0), 'alpha': .25},
{'order': (1, 0, 1), 'alpha': .05},
{'order': (1, 0, 1), 'alpha': .25},
{'order': (2, 0, 1), 'alpha': .05},
{'order': (2, 0, 1), 'alpha': .25},
{'order': (2, 0, 2), 'alpha': .05},
{'order': (2, 0, 2), 'alpha': .25},
{'order': 1, 'alpha': .05},
{'order': 1, 'alpha': .25},
{'order': 2, 'alpha': .05},
{'order': 2, 'alpha': .25},
{'order': (1, 0, 0)},
{'order': (1, 0, 1)},
{'order': (2, 0, 1)},
{'order': (2, 0, 2)},
{'order': 1, 'dist': True},
{'order': 2, 'dist': True},
],
transformations=[None, tdiff],
orders=[1], #2, 3],
partitions=[3], #np.arange(3, 25, 2),
progress=False, type='interval',
#transformations=[None, tdiff],
orders=[1, 2, 3],
partitions=np.arange(30, 80, 5),
progress=False, type='distribution',
#steps_ahead=[1,4,7,10], #steps_ahead=[1]
distributed=True, nodes=['192.168.0.110', '192.168.0.107','192.168.0.106'],
file="benchmarks.db", dataset="TAIEX", tag="comparisons")
'''
#'''
'''
dat = pd.read_csv('pwfts_taiex_partitioning.csv', sep=';')
print(bUtil.analytic_tabular_dataframe(dat))