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Bugfixes

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This commit is contained in:
Petrônio Cândido 2018-05-14 00:16:19 -03:00
parent 7d5dae21e8
commit 5563af3079
3 changed files with 34 additions and 22 deletions
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1
pyFTS/benchmarks/Measures.py
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@ -276,6 +276,7 @@ def heavyside_cdf(bins, targets):
df = pd.DataFrame(ret, columns=bins) df = pd.DataFrame(ret, columns=bins)
return df return df
def crps(targets, densities): def crps(targets, densities):
''' '''
Continuous Ranked Probability Score Continuous Ranked Probability Score

15
pyFTS/models/pwfts.py
View File

@ -393,14 +393,19 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
return point <= lower_set.lower or point >= upper_set.upper return point <= lower_set.lower or point >= upper_set.upper
def forecast_ahead(self, data, steps, **kwargs): def forecast_ahead(self, data, steps, **kwargs):
ret = [data[k] for k in np.arange(len(data) - self.order, len(data))]
for k in np.arange(self.order - 1, steps): l = len(data)
start = kwargs.get('start', self.order)
ret = data[start - self.order: start].tolist()
for k in np.arange(self.order, steps+self.order):
if self.__check_point_bounds(ret[-1]) : if self.__check_point_bounds(ret[-1]) :
ret.append(ret[-1]) ret.append(ret[-1])
else: else:
mp = self.forecast([ret[x] for x in np.arange(k - self.order, k)], **kwargs) mp = self.forecast(ret[k - self.order: k], **kwargs)
ret.append(mp[0]) ret.append(mp[0])
return ret[self.order:] return ret[self.order:]
@ -423,7 +428,9 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
ret = [[k, k] for k in sample] ret = [[k, k] for k in sample]
for k in np.arange(self.order, steps+self.order): ret.append(self.forecast_interval(sample)[0])
for k in np.arange(self.order+1, steps+self.order):
if len(ret) > 0 and self.__check_interval_bounds(ret[-1]): if len(ret) > 0 and self.__check_interval_bounds(ret[-1]):
ret.append(ret[-1]) ret.append(ret[-1])

40
pyFTS/tests/general.py
View File

@ -50,7 +50,7 @@ bchmk.sliding_window_benchmarks(dataset, 1000, train=0.8, inc=0.2,
progress=False, type="point", progress=False, type="point",
#steps_ahead=[1,2,4,6,8,10], #steps_ahead=[1,2,4,6,8,10],
distributed=True, nodes=['192.168.0.110', '192.168.0.107', '192.168.0.106'], distributed=True, nodes=['192.168.0.110', '192.168.0.107', '192.168.0.106'],
file="benchmarks.db", dataset="NASDAQ", tag="comparisons") file="benchmarks.db", dataset="TAIEX", tag="comparisons")
@ -80,7 +80,7 @@ print(Measures.get_distribution_statistics(dataset[800:1000], model, steps_ahead
''' '''
#''' #'''
types = ['interval']#['point','interval','distribution'] types = ['point','interval','distribution']
benchmark_methods=[[arima.ARIMA for k in range(8)] + [quantreg.QuantileRegression for k in range(4)]] benchmark_methods=[[arima.ARIMA for k in range(8)] + [quantreg.QuantileRegression for k in range(4)]]
''' '''
benchmark_methods=[ benchmark_methods=[
@ -136,31 +136,35 @@ benchmark_methods_parameters= [
] ]
]''' ]'''
dataset_name = "SP500" dataset_name = "SP500"
tag = "comparisons" tag = "ahead2"
from pyFTS.benchmarks import arima, naive, quantreg from pyFTS.benchmarks import arima, naive, quantreg
for ct, type in enumerate(types): for ct, type in enumerate(types):
bchmk.sliding_window_benchmarks(dataset, 1000, train=0.8, inc=0.2,
benchmark_models=True,
benchmark_methods=benchmark_methods[ct],
benchmark_methods_parameters=benchmark_methods_parameters[ct],
transformations=[None],
orders=[1,2,3],
partitions=np.arange(15, 85, 5),
progress=False, type=type,
distributed=True, nodes=['192.168.0.110', '192.168.0.107','192.168.0.106'],
file="benchmarks.db", dataset=dataset_name, tag=tag)
bchmk.sliding_window_benchmarks(dataset, 1000, train=0.8, inc=0.2, bchmk.sliding_window_benchmarks(dataset, 1000, train=0.8, inc=0.2,
benchmark_models=True, methods=[pwfts.ProbabilisticWeightedFTS],
benchmark_methods=benchmark_methods[ct], benchmark_models=False,
benchmark_methods_parameters=benchmark_methods_parameters[ct], #benchmark_methods=benchmark_methods[ct],
#benchmark_methods_parameters=benchmark_methods_parameters[ct],
transformations=[tdiff], transformations=[tdiff],
orders=[1, 2, 3], orders=[1], #, 2, 3],
partitiTAIEXons=np.arange(3, 35, 2), partitions=[5], #np.arange(3, 35, 2),
progress=False, type=type, progress=False, type=type,
steps_ahead=[2, 4, 6, 8, 10],
distributed=True, nodes=['192.168.0.110', '192.168.0.107', '192.168.0.106'],
file="benchmarks.db", dataset=dataset_name, tag=tag)
bchmk.sliding_window_benchmarks(dataset, 1000, train=0.8, inc=0.2,
methods=[pwfts.ProbabilisticWeightedFTS],
benchmark_models=False,
#benchmark_methods=benchmark_methods[ct],
#benchmark_methods_parameters=benchmark_methods_parameters[ct],
transformations=[None],
orders=[1], #,2,3],
partitions=[30], #np.arange(15, 85, 5),
progress=False, type=type,
steps_ahead=[2, 4, 6, 8, 10],
distributed=True, nodes=['192.168.0.110', '192.168.0.107','192.168.0.106'], distributed=True, nodes=['192.168.0.110', '192.168.0.107','192.168.0.106'],
file="benchmarks.db", dataset=dataset_name, tag=tag) file="benchmarks.db", dataset=dataset_name, tag=tag)