CVFTS improvements on perturbation_factors
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@ -58,7 +58,6 @@ class FuzzySet(FS.FuzzySet):
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if self.location is None:
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if self.location is None:
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inc = t
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inc = t
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else:
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else:
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l = len(self.location)
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l = len(self.location)
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inc = sum([self.location[k](t + self.location_roots[k], self.location_params[k]) for k in np.arange(0, l)])
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inc = sum([self.location[k](t + self.location_roots[k], self.location_params[k]) for k in np.arange(0, l)])
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@ -30,6 +30,7 @@ class HighOrderNonstationaryFLRG(hofts.HighOrderFTS):
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def __len__(self):
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def __len__(self):
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return len(self.RHS)
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return len(self.RHS)
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class ConditionalVarianceFTS(hofts.HighOrderFTS):
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class ConditionalVarianceFTS(hofts.HighOrderFTS):
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def __init__(self, **kwargs):
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def __init__(self, **kwargs):
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super(ConditionalVarianceFTS, self).__init__(**kwargs)
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super(ConditionalVarianceFTS, self).__init__(**kwargs)
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@ -45,6 +46,12 @@ class ConditionalVarianceFTS(hofts.HighOrderFTS):
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self.uod_clip = False
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self.uod_clip = False
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self.order = 1
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self.order = 1
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self.min_order = 1
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self.min_order = 1
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self.inputs = []
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self.forecasts = []
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self.residuals = []
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self.variance_residual = 0.
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self.mean_residual = 0.
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self.memory_window = kwargs.get("memory_window",5)
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def train(self, ndata, **kwargs):
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def train(self, ndata, **kwargs):
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@ -52,6 +59,21 @@ class ConditionalVarianceFTS(hofts.HighOrderFTS):
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flrs = FLR.generate_non_recurrent_flrs(tmpdata)
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flrs = FLR.generate_non_recurrent_flrs(tmpdata)
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self.generate_flrg(flrs)
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self.generate_flrg(flrs)
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self.forecasts = self.forecast(ndata, no_update=True)
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self.residuals = np.array(ndata[1:]) - np.array(self.forecasts[:-1])
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self.variance_residual = np.var(self.residuals) # np.max(self.residuals
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self.mean_residual = np.mean(self.residuals)
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self.residuals = self.residuals[-self.memory_window:].tolist()
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self.forecasts = self.forecasts[-self.memory_window:]
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self.inputs = np.array(ndata[-self.memory_window:]).tolist()
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print(self.mean_residual)
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print(self.variance_residual)
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print([self.original_min,self.original_max])
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def generate_flrg(self, flrs, **kwargs):
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def generate_flrg(self, flrs, **kwargs):
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for flr in flrs:
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for flr in flrs:
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if flr.LHS.name in self.flrgs:
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if flr.LHS.name in self.flrgs:
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@ -64,7 +86,38 @@ class ConditionalVarianceFTS(hofts.HighOrderFTS):
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def _smooth(self, a):
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def _smooth(self, a):
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return .1 * a[0] + .3 * a[1] + .6 * a[2]
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return .1 * a[0] + .3 * a[1] + .6 * a[2]
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def perturbation_factors(self, data):
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def perturbation_factors(self, data, **kwargs):
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_max = 0
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_min = 0
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if data < self.original_min:
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_min = data - self.original_min if data < 0 else self.original_min - data
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elif data > self.original_max:
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_max = data - self.original_max if data > 0 else self.original_max - data
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self.min_stack.pop(2)
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self.min_stack.insert(0, _min)
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_min = min(self.min_stack)
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self.max_stack.pop(2)
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self.max_stack.insert(0, _max)
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_max = max(self.max_stack)
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_range = (_max - _min)/2
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translate = np.linspace(_min, _max, self.partitioner.partitions)
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var = np.std(self.residuals)
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var = 0 if var < 1 else var
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loc = (self.mean_residual + np.mean(self.residuals))
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location = [_range + w + loc + k for k in np.linspace(-var,var) for w in translate]
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perturb = [[location[k], var] for k in np.arange(0, self.partitioner.partitions)]
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return perturb
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def perturbation_factors__old(self, data):
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_max = 0
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_max = 0
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_min = 0
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_min = 0
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if data < self.original_min:
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if data < self.original_min:
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@ -107,39 +160,59 @@ class ConditionalVarianceFTS(hofts.HighOrderFTS):
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ret = []
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ret = []
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no_update = kwargs.get("no_update",False)
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for k in np.arange(0, l):
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for k in np.arange(0, l):
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sample = ndata[k]
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sample = ndata[k]
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if not no_update:
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perturb = self.perturbation_factors(sample)
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perturb = self.perturbation_factors(sample)
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else:
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perturb = [[0, 1] for k in np.arange(0, self.partitioner.partitions)]
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affected_sets = self._affected_sets(sample, perturb)
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affected_sets = self._affected_sets(sample, perturb)
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tmp = []
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numerator = []
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denominator = []
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if len(affected_sets) == 1:
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if len(affected_sets) == 1:
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ix = affected_sets[0][0]
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ix = affected_sets[0][0]
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aset = self.partitioner.ordered_sets[ix]
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aset = self.partitioner.ordered_sets[ix]
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if aset in self.flrgs:
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if aset in self.flrgs:
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tmp.append(self.flrgs[aset].get_midpoint(perturb[ix]))
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numerator.append(self.flrgs[aset].get_midpoint(perturb[ix]))
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else:
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else:
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fuzzy_set = self.sets[aset]
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fuzzy_set = self.sets[aset]
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tmp.append(fuzzy_set.get_midpoint(perturb[ix]))
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numerator.append(fuzzy_set.get_midpoint(perturb[ix]))
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denominator.append(1)
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else:
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else:
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for aset in affected_sets:
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for aset in affected_sets:
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ix = aset[0]
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ix = aset[0]
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fs = self.partitioner.ordered_sets[ix]
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fs = self.partitioner.ordered_sets[ix]
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tdisp = perturb[ix]
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tdisp = perturb[ix]
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if fs in self.flrgs:
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if fs in self.flrgs:
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tmp.append(self.flrgs[fs].get_midpoint(tdisp) * aset[1])
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numerator.append(self.flrgs[fs].get_midpoint(tdisp) * aset[1])
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else:
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else:
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fuzzy_set = self.sets[fs]
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fuzzy_set = self.sets[fs]
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tmp.append(fuzzy_set.get_midpoint(tdisp) * aset[1])
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numerator.append(fuzzy_set.get_midpoint(tdisp) * aset[1])
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denominator.append(aset[1])
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pto = sum(tmp)
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if sum(denominator) > 0:
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pto = sum(numerator) /sum(denominator)
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else:
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pto = sum(numerator)
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ret.append(pto)
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ret.append(pto)
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if not no_update:
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self.forecasts.append(pto)
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self.residuals.append(self.inputs[-1] - self.forecasts[-1])
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self.inputs.append(sample)
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self.inputs.pop(0)
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self.forecasts.pop(0)
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self.residuals.pop(0)
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return ret
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return ret
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@ -62,6 +62,7 @@ def plot_sets_conditional(model, data, step=1, size=[5, 5], colors=None,
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fig, axes = plt.subplots(nrows=1, ncols=1, figsize=size)
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fig, axes = plt.subplots(nrows=1, ncols=1, figsize=size)
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for t in range:
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for t in range:
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model.forecast([data[t]])
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perturb = model.perturbation_factors(data[t])
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perturb = model.perturbation_factors(data[t])
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for ct, key in enumerate(model.partitioner.ordered_sets):
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for ct, key in enumerate(model.partitioner.ordered_sets):
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@ -19,7 +19,7 @@ dataset = TAIEX.get_data()
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#print(len(dataset))
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#print(len(dataset))
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from pyFTS.partitioners import Grid, Util as pUtil
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from pyFTS.partitioners import Grid, Util as pUtil
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partitioner = Grid.GridPartitioner(data=dataset[:800], npart=10)#, transformation=tdiff)
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partitioner = Grid.GridPartitioner(data=dataset[:800], npart=10, transformation=tdiff)
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from pyFTS.common import Util as cUtil
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from pyFTS.common import Util as cUtil
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@ -28,9 +28,9 @@ from pyFTS.benchmarks import benchmarks as bchmk, Util as bUtil, Measures, knn,
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from pyFTS.models import pwfts, song, chen, ifts, hofts
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from pyFTS.models import pwfts, song, chen, ifts, hofts
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from pyFTS.models.ensemble import ensemble
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from pyFTS.models.ensemble import ensemble
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#model = chen.ConventionalFTS(partitioner=partitioner)
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model = chen.ConventionalFTS(partitioner=partitioner)
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model = hofts.HighOrderFTS(partitioner=partitioner,order=2)
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#model = hofts.HighOrderFTS(partitioner=partitioner,order=2)
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#model.append_transformation(tdiff)
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model.append_transformation(tdiff)
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model.fit(dataset[:800])
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model.fit(dataset[:800])
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cUtil.plot_rules(model, size=[20,20], rules_by_axis=5, columns=1)
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cUtil.plot_rules(model, size=[20,20], rules_by_axis=5, columns=1)
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@ -3,7 +3,7 @@ import numpy as np
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from pyFTS.common import Membership, Transformations
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from pyFTS.common import Membership, Transformations
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from pyFTS.models.nonstationary import common, perturbation, partitioners, util
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from pyFTS.models.nonstationary import common, perturbation, partitioners, util
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from pyFTS.models.nonstationary import nsfts, cvfts
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from pyFTS.models.nonstationary import nsfts, cvfts
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from pyFTS.partitioners import Grid
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from pyFTS.partitioners import Grid, Entropy
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import matplotlib.pyplot as plt
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import matplotlib.pyplot as plt
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from pyFTS.common import Util as cUtil
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from pyFTS.common import Util as cUtil
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import pandas as pd
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import pandas as pd
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@ -45,23 +45,44 @@ from pyFTS.common import Util
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from pyFTS.data import TAIEX
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from pyFTS.data import TAIEX
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taiex = TAIEX.get_data()
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taiex = TAIEX.get_data()
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taiex_diff = tdiff.apply(taiex)
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#taiex_diff = tdiff.apply(taiex)
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train = taiex_diff[:600]
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train = taiex[:600]
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test = taiex_diff[600:1500]
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test = taiex[600:800]
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fs_tmp = Grid.GridPartitioner(data=train, npart=20) #, transformation=tdiff)
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#fs_tmp = Grid.GridPartitioner(data=train, npart=7, transformation=tdiff)
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#fs_tmp = Entropy.EntropyPartitioner(data=train, npart=7, transformation=tdiff)
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fs_tmp = Grid.GridPartitioner(data=train, npart=20)
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fs = partitioners.SimpleNonStationaryPartitioner(train, fs_tmp)
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fs = partitioners.SimpleNonStationaryPartitioner(train, fs_tmp)
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print(fs)
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print(fs)
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model = cvfts.ConditionalVarianceFTS(partitioner=fs)
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model = cvfts.ConditionalVarianceFTS(partitioner=fs,memory_window=3)
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model.fit(train)
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model.fit(train)
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print(model)
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print(model)
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#tmpp4 = model.predict(test, type='point')
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#tmpp4 = model.predict(test, type='point')
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tmp = model.predict(test, type='interval')
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#tmp = model.predict(test, type='interval')
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#util.plot_sets_conditional(model, test, step=1, tam=[10, 5])
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#util.plot_sets_conditional(model, tdiff.apply(test), step=5, size=[10, 5])
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#util.plot_sets_conditional(model, test, step=5, size=[10, 5])
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fig, axes = plt.subplots(nrows=2, ncols=1, figsize=[10, 5])
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axes[0].plot(test[1:], label="Test Data")
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forecasts = model.predict(test, type='point')
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axes[0].plot(forecasts[:-1], label="CVFTS Forecasts")
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handles0, labels0 = axes[0].get_legend_handles_labels()
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lgd = axes[0].legend(handles0, labels0, loc=2)
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residuals = np.array(test[1:]) - np.array(forecasts[:-1])
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axes[1].plot(residuals)
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axes[1].set_title("Residuals")
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print("fim")
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