- Bugfixes and improvements on ProbabilityDistribution and KDE, msfts
- Scale Transformation
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Petrônio Cândido de Lima e Silva
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@ -234,7 +234,7 @@ def get_point_statistics(data, model, indexer=None):
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"""Condensate all measures for point forecasters"""
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if indexer is not None:
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ndata = np.array(indexer.get_data(data[model.order:]))
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ndata = np.array(indexer.get_data(data))
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else:
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ndata = np.array(data[model.order:])
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@ -62,7 +62,7 @@ def generateRecurrentFLRs(fuzzyData):
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return flrs
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def generateIndexedFLRs(sets, indexer, data):
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def generateIndexedFLRs(sets, indexer, data, transformation=None):
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"""
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Create a season-indexed ordered FLR set from a list of fuzzy sets with recurrence
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:param sets: fuzzy sets
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@ -73,7 +73,9 @@ def generateIndexedFLRs(sets, indexer, data):
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flrs = []
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index = indexer.get_season_of_data(data)
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ndata = indexer.get_data(data)
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for k in np.arange(1,len(data)):
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if transformation is not None:
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ndata = transformation.apply(ndata)
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for k in np.arange(1,len(ndata)):
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lhs = FuzzySet.getMaxMembershipFuzzySet(ndata[k-1],sets)
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rhs = FuzzySet.getMaxMembershipFuzzySet(ndata[k], sets)
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season = index[k]
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@ -73,6 +73,41 @@ class Differential(Transformation):
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return inc
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class Scale(Transformation):
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def __init__(self, min=0, max=1):
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super(Scale, self).__init__([min, max])
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self.data_max = None
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self.data_min = None
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self.transf_max = max
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self.transf_min = min
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def apply(self, data, param=None,**kwargs):
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if self.data_max is None:
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self.data_max = np.nanmax(data)
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self.data_min = np.nanmin(data)
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data_range = self.data_max - self.data_min
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transf_range = self.transf_max - self.transf_min
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if isinstance(data, list):
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tmp = [(k + (-1 * self.data_min)) / data_range for k in data]
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tmp2 = [ (k * transf_range) + self.transf_min for k in tmp]
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else:
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tmp = (data + (-1 * self.data_min)) / data_range
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tmp2 = (tmp * transf_range) + self.transf_min
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return tmp2
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def inverse(self, data, param, **kwargs):
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data_range = self.data_max - self.data_min
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transf_range = self.transf_max - self.transf_min
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if isinstance(data, list):
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tmp2 = [(k - self.transf_min) / transf_range for k in data]
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tmp = [(k * data_range) + self.data_min for k in tmp2]
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else:
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tmp2 = (data - self.transf_min) / transf_range
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tmp = (tmp2 * data_range) + self.data_min
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return tmp
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class AdaptiveExpectation(Transformation):
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"""
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Adaptive Expectation post processing
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@ -82,7 +82,8 @@ class SeasonalEnsembleFTS(ensemble.EnsembleFTS):
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tmp = np.ravel(tmp).tolist()
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dist = ProbabilityDistribution.ProbabilityDistribution("KDE",h=h,uod=[self.original_min, self.original_max], data=tmp)
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dist = ProbabilityDistribution.ProbabilityDistribution("KDE", uod=[self.original_min, self.original_max], data=tmp,
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**kwargs)
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ret.append(dist)
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@ -13,10 +13,10 @@ class MultiSeasonalFTS(sfts.SeasonalFTS):
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self.shortname = "MSFTS " + name
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self.detail = ""
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self.seasonality = 1
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self.hasSeasonality = True
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self.hasPointForecasting = True
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self.isHighOrder = True
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self.isMultivariate = True
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self.has_seasonality = True
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self.has_point_forecasting = True
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self.is_high_order = False
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self.is_multivariate = True
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self.indexer = indexer
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self.flrgs = {}
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@ -123,8 +123,11 @@ class ProbabilityDistribution(object):
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ys = [self.distribution[k]/self.count for k in self.bins]
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else:
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ys = [self.distribution[k] for k in self.bins]
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yp = [0 for k in self.data]
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axis.plot(self.data, yp, c="red")
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axis.plot(self.bins, ys,c=color, label=self.name)
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axis.plot(self.bins, ys, c=color, label=self.name)
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axis.set_xlabel('Universe of Discourse')
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axis.set_ylabel('Probability')
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@ -134,5 +137,8 @@ class ProbabilityDistribution(object):
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body = '|'
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for k in sorted(self.distribution.keys()):
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head += str(round(k,2)) + '\t|'
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body += str(round(self.distribution[k] / self.count,3)) + '\t|'
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if self.type == "histogram":
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body += str(round(self.distribution[k] / self.count,3)) + '\t|'
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else:
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body += str(round(self.distribution[k], 3)) + '\t|'
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return head + '\n' + body
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@ -1,3 +1,6 @@
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from pyFTS.common import Transformations
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import numpy as np
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"""
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Kernel Density Estimation
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"""
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@ -8,17 +11,21 @@ class KernelSmoothing(object):
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def __init__(self,h, method="epanechnikov"):
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self.h = h
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self.method = method
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self.transf = Transformations.Scale(min=0,max=1)
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def kernel(self, u):
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if self.method == "epanechnikov":
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return (3/4) * (1 - u**2)
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return (3/4)*(1 - u**2)
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elif self.method == "gaussian":
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return 0.5
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return (1/np.sqrt(2*np.pi))*np.exp(-0.5*u**2)
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elif self.method == "uniform":
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return 0.5
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def probability(self, x, data):
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l = len(data)
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p = sum([self.kernel((x - k)/self.h) for k in data]) / l*self.h
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ndata = self.transf.apply(data)
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nx = self.transf.apply(x)
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p = sum([self.kernel((nx - k)/self.h) for k in ndata]) / l*self.h
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return p
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@ -62,22 +62,43 @@ DATASETS
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#print(lag)
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#print(a)
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sonda = pd.read_csv("DataSets/SONDA_BSB_MOD.csv", sep=";")
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#'''
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sonda = pd.read_csv("DataSets/SONDA_BSB_15MIN_AVG.csv", sep=";")
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sonda['data'] = pd.to_datetime(sonda['data'])
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sonda = sonda[:][527041:]
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#sonda = sonda[:][527041:].dropna()
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sonda.index = np.arange(0,len(sonda.index))
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sonda_treino = sonda[:1051200]
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sonda_teste = sonda[1051901:1051910]
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sonda_treino = sonda[:105313].dropna()
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sonda_teste = sonda[105314:].dropna()
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ix_m15 = SeasonalIndexer.DateTimeSeasonalIndexer('data',[SeasonalIndexer.DateTime.minute],[15],'glo_avg', name='m15')
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#ix_m15 = SeasonalIndexer.DateTimeSeasonalIndexer('data',[SeasonalIndexer.DateTime.minute],[15],'glo_avg', name='m15')
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fs1 = Grid.GridPartitioner(sonda_treino,50,transformation=diff, indexer=ix_m15)
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#fs1 = Grid.GridPartitioner(sonda_treino, 50, transformation=diff, indexer=ix_m15)
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#ix = cUtil.load_obj("models/sonda_ix_Mhm15.pkl")
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#fs = cUtil.load_obj("models/sonda_fs_Entropy40_diff.pkl")
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#from pyFTS.models import msfts
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#obj = msfts.MultiSeasonalFTS("sonda_msfts_Entropy40_Mhm15", indexer=ix)
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#obj.appendTransformation(diff)
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#obj.train(sonda_treino, fs.sets)
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#cUtil.persist_obj(obj, "models/sonda_msfts_Entropy40_Mhm15.pkl")
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ftse = cUtil.load_obj("models/sonda_ensemble_msfts.pkl")
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tmp = ftse.forecastDistribution(sonda_teste[850:860], h=0.5, method="gaussian")
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print(tmp[0])
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#'''
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'''
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from pyFTS.models.seasonal import SeasonalIndexer
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@ -223,7 +244,7 @@ bchmk.ahead_sliding_window(sonda, 10000, steps=10, resolution=10, train=0.2, inc
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dump=True, save=True, file="experiments/sondawind_ahead_analytic_diff.csv",
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nodes=['192.168.0.106', '192.168.0.108', '192.168.0.109']) #, depends=[hofts, ifts])
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"""
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from pyFTS import pwfts
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from pyFTS.common import Transformations
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@ -240,7 +261,7 @@ from pyFTS.partitioners import Grid
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#bchmk.plot_compared_intervals_ahead(best[1600:1700],[model], ['blue','red'],
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# distributions=[True], save=True, file="pictures/best_ahead_forecasts",
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# time_from=40, time_to=60, resolution=100)
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'''
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experiments = [
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["experiments/taiex_point_synthetic_diff.csv","experiments/taiex_point_analytic_diff.csv",16],
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["experiments/nasdaq_point_synthetic_diff.csv","experiments/nasdaq_point_analytic_diff.csv", 11],
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@ -320,7 +341,6 @@ diff = Transformations.Differential(1)
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fs = Grid.GridPartitioner(sonda[:9000], 10, transformation=diff)
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'''
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tmp = sfts.SeasonalFTS("")
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tmp.indexer = ix
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tmp.appendTransformation(diff)
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