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- Optimizations and bugfixes on Multi Seasonal Ensemble

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- Bugfixes on ProbabilityDistribution
 - Indexers on Partitioners
This commit is contained in:
Petrônio Cândido de Lima e Silva 2017-07-04 16:30:53 -03:00
parent 9861189d50
commit 962ef89bcf
9 changed files with 68 additions and 36 deletions
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3
ensemble/ensemble.py
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@ -59,6 +59,9 @@ class EnsembleFTS(fts.FTS):
forecast = int(forecast[-1])
elif isinstance(forecast, (list,np.ndarray)) and len(forecast) == 0:
forecast = np.nan
if isinstance(forecast, list):
tmp.extend(forecast)
else:
tmp.append(forecast)
return tmp

12
ensemble/multiseasonal.py
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@ -44,9 +44,13 @@ class SeasonalEnsembleFTS(ensemble.EnsembleFTS):
self.has_seasonality = True
self.has_probability_forecasting = True
def update_uod(self, data):
self.original_max = max(self.indexer.get_data(data))
self.original_min = min(self.indexer.get_data(data))
def train(self, data, sets, order=1, parameters=None):
self.original_max = max(data)
self.original_min = min(data)
self.original_max = max(self.indexer.get_data(data))
self.original_min = min(self.indexer.get_data(data))
num_cores = multiprocessing.cpu_count()
@ -76,7 +80,9 @@ class SeasonalEnsembleFTS(ensemble.EnsembleFTS):
tmp = self.get_models_forecasts(data.ix[k])
dist = ProbabilityDistribution.ProbabilityDistribution("KDE",h=h,uod=[self.original_min, self.original_max])
tmp = np.ravel(tmp).tolist()
dist = ProbabilityDistribution.ProbabilityDistribution("KDE",h=h,uod=[self.original_min, self.original_max], data=tmp)
ret.append(dist)

4
partitioners/CMeans.py
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@ -78,8 +78,8 @@ def c_means(k, dados, tam):
class CMeansPartitioner(partitioner.Partitioner):
def __init__(self, data, npart, func = Membership.trimf, transformation=None):
super(CMeansPartitioner, self).__init__("CMeans", data, npart, func=func, transformation=transformation)
def __init__(self, data, npart, func = Membership.trimf, transformation=None, indexer=None):
super(CMeansPartitioner, self).__init__("CMeans", data, npart, func=func, transformation=transformation, indexer=indexer)
def build(self, data):
sets = []

4
partitioners/Entropy.py
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@ -79,8 +79,8 @@ def bestSplit(data, npart):
class EntropyPartitioner(partitioner.Partitioner):
"""Huarng Entropy Partitioner"""
def __init__(self, data, npart, func = Membership.trimf, transformation=None):
super(EntropyPartitioner, self).__init__("Entropy", data, npart, func=func, transformation=transformation)
def __init__(self, data, npart, func = Membership.trimf, transformation=None, indexer=None):
super(EntropyPartitioner, self).__init__("Entropy", data, npart, func=func, transformation=transformation, indexer=indexer)
def build(self, data):
sets = []

4
partitioners/FCM.py
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@ -104,8 +104,8 @@ class FCMPartitioner(partitioner.Partitioner):
"""
"""
def __init__(self, data,npart,func = Membership.trimf, transformation=None):
super(FCMPartitioner, self).__init__("FCM", data, npart, func=func, transformation=transformation)
def __init__(self, data,npart,func = Membership.trimf, transformation=None, indexer=None):
super(FCMPartitioner, self).__init__("FCM", data, npart, func=func, transformation=transformation, indexer=indexer)
def build(self,data):
sets = []

4
partitioners/Grid.py
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@ -8,8 +8,8 @@ from pyFTS.partitioners import partitioner
class GridPartitioner(partitioner.Partitioner):
"""Even Length Grid Partitioner"""
def __init__(self, data, npart, func = Membership.trimf, transformation=None):
super(GridPartitioner, self).__init__("Grid", data, npart, func=func, transformation=transformation)
def __init__(self, data, npart, func = Membership.trimf, transformation=None, indexer=None):
super(GridPartitioner, self).__init__("Grid", data, npart, func=func, transformation=transformation, indexer=indexer)
def build(self, data):
sets = []

4
partitioners/Huarng.py
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@ -12,8 +12,8 @@ from pyFTS.partitioners import partitioner
class HuarngPartitioner(partitioner.Partitioner):
"""Huarng Empirical Partitioner"""
def __init__(self, data,npart,func = Membership.trimf, transformation=None):
super(HuarngPartitioner, self).__init__("Huarng", data, npart, func=func, transformation=transformation)
def __init__(self, data,npart,func = Membership.trimf, transformation=None, indexer=None):
super(HuarngPartitioner, self).__init__("Huarng", data, npart, func=func, transformation=transformation, indexer=indexer)
def build(self, data):
diff = Transformations.Differential(1)

24
probabilistic/ProbabilityDistribution.py
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@ -11,25 +11,20 @@ class ProbabilityDistribution(object):
If type is histogram, the PDF is discrete
If type is KDE the PDF is continuous
"""
def __init__(self,type, **kwargs):
def __init__(self,type = "KDE", **kwargs):
self.uod = kwargs.get("uod", None)
if type is None:
self.type = "KDE"
self.kde = kde.KernelSmoothing(kwargs.get("h", 10), kwargs.get("method", "epanechnikov"))
else:
self.type = type
if self.type == "KDE":
self.kde = kde.KernelSmoothing(kwargs.get("h", 10), kwargs.get("method", "epanechnikov"))
self.description = kwargs.get("description", None)
self.nbins = kwargs.get("num_bins", 100)
if self.type == "histogram":
self.bins = kwargs.get("bins", None)
self.labels = kwargs.get("bins_labels", None)
if self.bins is None:
self.bins = np.linspace(self.uod[0], self.uod[1], self.nbins).tolist()
self.bins = np.linspace(int(self.uod[0]), int(self.uod[1]), int(self.nbins)).tolist()
self.labels = [str(k) for k in self.bins]
self.index = SortedCollection.SortedCollection(iterable=sorted(self.bins))
@ -37,7 +32,14 @@ class ProbabilityDistribution(object):
self.count = 0
for k in self.bins: self.distribution[k] = 0
self.data = kwargs.get("data",None)
self.data = []
data = kwargs.get("data",None)
if data is not None:
self.append(data)
self.name = kwargs.get("name", "")
def append(self, values):
if self.type == "histogram":
@ -50,7 +52,7 @@ class ProbabilityDistribution(object):
self.distribution = {}
dens = self.density(self.bins)
for v,d in enumerate(dens):
self.distribution[v] = d
self.distribution[self.bins[v]] = d
def density(self, values):
ret = []

39
tests/general.py
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@ -20,7 +20,7 @@ from pyFTS.models.seasonal import SeasonalIndexer
os.chdir("/home/petronio/dados/Dropbox/Doutorado/Codigos/")
#diff = Transformations.Differential(1)
diff = Transformations.Differential(1)
#ix = SeasonalIndexer.LinearSeasonalIndexer([12, 24], [720, 1],[False, False])
"""
@ -72,22 +72,27 @@ sonda = sonda[:][527041:]
sonda.index = np.arange(0,len(sonda.index))
sonda_treino = sonda[:1051200]
sonda_teste = sonda[1051201:]
sonda_teste = sonda[1051901:1051910]
ix_m15 = SeasonalIndexer.DateTimeSeasonalIndexer('data',[SeasonalIndexer.DateTime.minute],[15],'glo_avg', name='m15')
fs1 = Grid.GridPartitioner(sonda_treino,50,transformation=diff, indexer=ix_m15)
'''
from pyFTS.models.seasonal import SeasonalIndexer
indexers = []
for i in ["models/sonda_ix_m15.pkl", "models/sonda_ix_Mh.pkl", "models/sonda_ix_Mhm15.pkl"]:
for i in ["models/sonda_ix_Mhm15.pkl"]: #, "models/sonda_ix_m15.pkl", "models/sonda_ix_Mh.pkl", ]:
obj = cUtil.load_obj(i)
indexers.append( obj )
print(obj)
partitioners = []
transformations = ["", "_diff"]
for max_part in [10, 20, 30, 40, 50, 60]:
transformations = [""] #, "_diff"]
for max_part in [30, 40, 50, 60, 70, 80, 90]:
for t in transformations:
obj = cUtil.load_obj("models/sonda_fs_grid_" + str(max_part) + t + ".pkl")
partitioners.append( obj )
@ -96,17 +101,33 @@ for max_part in [10, 20, 30, 40, 50, 60]:
from pyFTS.ensemble import ensemble, multiseasonal
fts = multiseasonal.SeasonalEnsembleFTS("")
fts = multiseasonal.SeasonalEnsembleFTS("sonda_msfts_Mhm15")
fts.indexers = indexers
fts.partitioners = partitioners
fts.indexer = indexers[0]
fts.train(sonda_treino, sets=None)
'''
ftse = cUtil.load_obj("models/sonda_msfts_ensemble.pkl")
#'''
tmp = ftse.forecastDistribution(sonda_teste)
#ix = cUtil.load_obj("models/sonda_ix_m15.pkl")
#ftse = cUtil.load_obj("models/msfts_Grid40_diff_Mhm15.pkl")
#ftse.indexer = ix
#ftse.update_uod(sonda_treino)
#tmp = ftse.forecastDistribution(sonda_teste,h=1)
#tmp = ftse.forecast(sonda_teste,h=1)
#tmp[5].plot()
#'''
'''
from pyFTS.benchmarks import benchmarks as bchmk
#from pyFTS.benchmarks import distributed_benchmarks as bchmk
#from pyFTS.benchmarks import parallel_benchmarks as bchmk
@ -299,7 +320,7 @@ diff = Transformations.Differential(1)
fs = Grid.GridPartitioner(sonda[:9000], 10, transformation=diff)
'''
tmp = sfts.SeasonalFTS("")
tmp.indexer = ix
tmp.appendTransformation(diff)