common.Util.plot_rules; CVFTS bugfixes and other nonstationary sets improvements
This commit is contained in:
Petrônio Cândido
parent
aa45d7e38e
commit
b058ed5daa
@ -22,7 +22,7 @@ class FLR(object):
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self.RHS = RHS
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def __str__(self):
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return self.LHS + " -> " + self.RHS
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return str(self.LHS) + " -> " + str(self.RHS)
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class IndexedFLR(FLR):
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@ -59,6 +59,7 @@ class FuzzySet(object):
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def set_ordered(fuzzySets):
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if len(fuzzySets) > 0:
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tmp1 = [fuzzySets[k] for k in fuzzySets.keys()]
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return [k.name for k in sorted(tmp1, key=lambda x: x.centroid)]
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@ -8,6 +8,43 @@ import dill
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import numpy as np
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def plot_rules(model, size=[5, 5], axis=None):
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if axis is None:
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fig, axis = plt.subplots(nrows=1, ncols=1, figsize=size)
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for ct, key in enumerate(model.partitioner.ordered_sets):
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fs = model.sets[key]
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axis.plot([0, 1, 0], fs.parameters, label=fs.name)
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axis.axhline(fs.centroid, c="lightgray", alpha=0.5)
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axis.set_xlim([0, len(model.partitioner.ordered_sets)])
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axis.set_xticks(range(0,len(model.partitioner.ordered_sets)))
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tmp = ['']
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tmp.extend(model.partitioner.ordered_sets)
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axis.set_xticklabels(tmp)
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axis.set_ylim([model.partitioner.min, model.partitioner.max])
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axis.set_yticks([model.sets[k].centroid for k in model.partitioner.ordered_sets])
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axis.set_yticklabels([str(round(model.sets[k].centroid,1)) + " - " + k
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for k in model.partitioner.ordered_sets])
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if not model.is_high_order:
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for ct, key in enumerate(model.partitioner.ordered_sets):
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if key in model.flrgs:
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flrg = model.flrgs[key]
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orig = model.sets[key].centroid
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axis.plot([ct+1],[orig],'o')
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for rhs in flrg.RHS:
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dest = model.sets[rhs].centroid
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axis.arrow(ct+1.1, orig, 0.8, dest - orig, #length_includes_head=True,
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head_width=0.1, head_length=0.1, shape='full', overhang=0,
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fc='k', ec='k')
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plt.tight_layout()
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plt.show()
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print("fim")
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current_milli_time = lambda: int(round(time.time() * 1000))
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@ -38,6 +38,7 @@ class FTS(object):
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self.auto_update = False
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self.benchmark_only = False
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self.indexer = None
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self.uod_clip = kwargs.get("uod_clip", True)
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def fuzzy(self, data):
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"""
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@ -75,6 +76,7 @@ class FTS(object):
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else:
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ndata = self.apply_transformations(data)
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if self.uod_clip:
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ndata = np.clip(ndata, self.original_min, self.original_max)
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if 'distributed' in kwargs:
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@ -56,7 +56,8 @@ class FuzzySet(FS.FuzzySet):
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def perform_location(self, t, param):
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if self.location is None:
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return param
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inc = t
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else:
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l = len(self.location)
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@ -76,7 +77,8 @@ class FuzzySet(FS.FuzzySet):
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def perform_width(self, t, param):
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if self.width is None:
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return param
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inc = t
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else:
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l = len(self.width)
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@ -5,26 +5,20 @@ from pyFTS.common import FLR
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class ConditionalVarianceFTS(chen.ConventionalFTS):
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def __init__(self, name, **kwargs):
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super(ConditionalVarianceFTS, self).__init__("CVFTS " + name, **kwargs)
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def __init__(self, **kwargs):
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super(ConditionalVarianceFTS, self).__init__(**kwargs)
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self.name = "Conditional Variance FTS"
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self.shortname = "CVFTS "
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self.detail = ""
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self.flrgs = {}
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#self.append_transformation(Transformations.Differential(1))
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if self.partitioner is None:
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self.min_tx = None
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self.max_tx = None
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else:
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self.min_tx = self.partitioner.min
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self.max_tx = self.partitioner.max
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if self.partitioner is not None:
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self.append_transformation(self.partitioner.transformation)
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self.min_stack = [0,0,0]
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self.max_stack = [0,0,0]
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self.uod_clip = False
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def train(self, ndata, **kwargs):
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self.min_tx = min(ndata)
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self.max_tx = max(ndata)
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tmpdata = common.fuzzySeries(ndata, self.sets, self.partitioner.ordered_sets, method='fuzzy', const_t=0)
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flrs = FLR.generate_non_recurrent_flrs(tmpdata)
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@ -44,10 +38,10 @@ class ConditionalVarianceFTS(chen.ConventionalFTS):
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def perturbation_factors(self, data):
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_max = 0
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_min = 0
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if data < self.min_tx:
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_min = data - self.min_tx if data < 0 else self.min_tx - data
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elif data > self.max_tx:
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_max = data - self.max_tx if data > 0 else self.max_tx - data
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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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@ -96,21 +90,22 @@ class ConditionalVarianceFTS(chen.ConventionalFTS):
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if len(affected_sets) == 1:
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ix = affected_sets[0][0]
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aset = self.sets[ix]
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if aset.name in self.flrgs:
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tmp.append(self.flrgs[aset.name].get_midpoint(perturb[ix]))
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aset = self.partitioner.ordered_sets[ix]
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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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else:
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print('naive')
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tmp.append(aset.get_midpoint(perturb[ix]))
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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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else:
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for aset in affected_sets:
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ix = aset[0]
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fs = self.sets[ix]
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fs = self.partitioner.ordered_sets[ix]
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tdisp = perturb[ix]
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if fs.name in self.flrgs:
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tmp.append(self.flrgs[fs.name].get_midpoint(tdisp) * aset[1])
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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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else:
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tmp.append(fs.get_midpoint(tdisp) * aset[1])
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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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pto = sum(tmp)
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@ -137,24 +132,26 @@ class ConditionalVarianceFTS(chen.ConventionalFTS):
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if len(affected_sets) == 1:
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ix = affected_sets[0][0]
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aset = self.sets[ix]
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if aset.name in self.flrgs:
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lower.append(self.flrgs[aset.name].get_lower(perturb[ix]))
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upper.append(self.flrgs[aset.name].get_upper(perturb[ix]))
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aset = self.partitioner.ordered_sets[ix]
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if aset in self.flrgs:
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lower.append(self.flrgs[aset].get_lower(perturb[ix]))
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upper.append(self.flrgs[aset].get_upper(perturb[ix]))
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else:
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lower.append(aset.get_lower(perturb[ix]))
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upper.append(aset.get_upper(perturb[ix]))
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fuzzy_set = self.sets[aset]
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lower.append(fuzzy_set.get_lower(perturb[ix]))
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upper.append(fuzzy_set.get_upper(perturb[ix]))
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else:
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for aset in affected_sets:
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ix = aset[0]
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fs = self.sets[ix]
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fs = self.partitioner.ordered_sets[ix]
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tdisp = perturb[ix]
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if fs.name in self.flrgs:
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lower.append(self.flrgs[fs.name].get_lower(tdisp) * aset[1])
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upper.append(self.flrgs[fs.name].get_upper(tdisp) * aset[1])
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if fs in self.flrgs:
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lower.append(self.flrgs[fs].get_lower(tdisp) * aset[1])
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upper.append(self.flrgs[fs].get_upper(tdisp) * aset[1])
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else:
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lower.append(fs.get_lower(tdisp) * aset[1])
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upper.append(fs.get_upper(tdisp) * aset[1])
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fuzzy_set = self.sets[fs]
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lower.append(fuzzy_set.get_lower(tdisp) * aset[1])
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upper.append(fuzzy_set.get_upper(tdisp) * aset[1])
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itvl = [sum(lower), sum(upper)]
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@ -109,22 +109,26 @@ class PolynomialNonStationaryPartitioner(partitioner.Partitioner):
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pass
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class ConstantNonStationaryPartitioner(partitioner.Partitioner):
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class SimpleNonStationaryPartitioner(partitioner.Partitioner):
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"""
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Non Stationary Universe of Discourse Partitioner
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"""
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def __init__(self, data, part, **kwargs):
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""""""
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super(ConstantNonStationaryPartitioner, self).__init__(name=part.name, data=data, npart=part.partitions,
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super(SimpleNonStationaryPartitioner, self).__init__(name=part.name, data=data, npart=part.partitions,
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func=part.membership_function, names=part.setnames,
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prefix=part.prefix, transformation=part.transformation,
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indexer=part.indexer)
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self.sets = {}
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indexer=part.indexer)#, preprocess=False)
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for key in part.sets.keys():
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set = part.sets[key]
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tmp = common.FuzzySet(set.name, set.mf, set.parameters, **kwargs)
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tmp.centroid = set.centroid
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self.sets[key] =tmp
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self.ordered_sets = stationary_fs.set_ordered(self.sets)
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def build(self, data):
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return {}
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@ -54,23 +54,23 @@ def plot_sets(partitioner, start=0, end=10, step=1, tam=[5, 5], colors=None,
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Util.show_and_save_image(fig, file, save)
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def plot_sets_conditional(model, data, start=0, end=10, step=1, tam=[5, 5], colors=None,
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def plot_sets_conditional(model, data, step=1, size=[5, 5], colors=None,
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save=False, file=None, axes=None):
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range = np.arange(start,end,step)
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range = np.arange(0, len(data), step)
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ticks = []
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if axes is None:
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fig, axes = plt.subplots(nrows=1, ncols=1, figsize=tam)
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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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perturb = model.perturbation_factors(data[t])
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for ct, key in enumerate(model.partitioner.ordered_sets):
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set = model.partitioner.sets[key]
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for t in range:
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tdisp = model.perturbation_factors(data[t])
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set.perturbate_parameters(tdisp[ct])
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param = set.perturbated_parameters[str(tdisp[ct])]
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set.perturbate_parameters(perturb[ct])
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param = set.perturbated_parameters[str(perturb[ct])]
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if set.mf == Membership.trimf:
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if t == start:
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if t == 0:
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line = axes.plot([t, t+1, t], param, label=set.name)
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set.metadata['color'] = line[0].get_color()
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else:
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@ -86,7 +86,7 @@ def plot_sets_conditional(model, data, start=0, end=10, step=1, tam=[5, 5], colo
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lgd = axes.legend(handles0, labels0, loc=2, bbox_to_anchor=(1, 1))
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if data is not None:
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axes.plot(np.arange(start, start + len(data), 1), data,c="black")
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axes.plot(np.arange(0, len(data), 1), data,c="black")
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plt.tight_layout()
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@ -13,20 +13,27 @@ tdiff = Transformations.Differential(1)
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from pyFTS.data import TAIEX, SP500, NASDAQ
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#dataset = TAIEX.get_data()
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dataset = SP500.get_data()[11500:16000]
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dataset = TAIEX.get_data()
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#dataset = SP500.get_data()[11500:16000]
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#dataset = NASDAQ.get_data()
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#print(len(dataset))
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'''
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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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'''
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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.benchmarks import benchmarks as bchmk, Util as bUtil, Measures, knn, quantreg, arima, naive
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from pyFTS.models import pwfts, song, 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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model = chen.ConventionalFTS(partitioner=partitioner)
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model.append_transformation(tdiff)
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model.fit(dataset[:800])
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cUtil.plot_rules(model)
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'''
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model = knn.KNearestNeighbors(order=3)
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#model = ensemble.AllMethodEnsembleFTS("", partitioner=partitioner)
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@ -78,17 +85,16 @@ print(Measures.get_distribution_statistics(dataset[800:1000], model, steps_ahead
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#for tmp2 in tmp:
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# print(tmp2)
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'''
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#'''
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'''
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types = ['point','interval','distribution']
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benchmark_methods=[[arima.ARIMA for k in range(8)] + [quantreg.QuantileRegression for k in range(4)]]
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'''
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benchmark_methods=[
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[arima.ARIMA for k in range(4)] + [naive.Naive],
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[arima.ARIMA for k in range(8)] + [quantreg.QuantileRegression for k in range(4)],
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[arima.ARIMA for k in range(4)] + [quantreg.QuantileRegression for k in range(2)]
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+ [knn.KNearestNeighbors for k in range(3)]
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]'''
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]
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benchmark_methods_parameters= [
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[
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{'order': (1, 0, 0), 'alpha': .05},
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@ -105,7 +111,7 @@ benchmark_methods_parameters= [
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{'order': 2, 'alpha': .25}
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]
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]
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'''benchmark_methods_parameters= [
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benchmark_methods_parameters= [
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[
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{'order': (1, 0, 0)},
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{'order': (1, 0, 1)},
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@ -134,7 +140,7 @@ benchmark_methods_parameters= [
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{'order': 2, 'dist': True},
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{'order': 1}, {'order': 2}, {'order': 3},
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]
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]'''
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]
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dataset_name = "SP500"
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tag = "ahead2"
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@ -169,7 +175,7 @@ for ct, type in enumerate(types):
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file="benchmarks.db", dataset=dataset_name, tag=tag)
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#'''
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'''
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'''
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dat = pd.read_csv('pwfts_taiex_partitioning.csv', sep=';')
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print(bUtil.analytic_tabular_dataframe(dat))
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@ -1,13 +1,14 @@
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import os
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import numpy as np
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from pyFTS.common import Membership, Transformations
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from pyFTS.models.nonstationary import common, perturbation, partitioners, util, honsfts, cvfts
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from pyFTS.models.nonstationary import nsfts
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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.partitioners import Grid
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import matplotlib.pyplot as plt
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from pyFTS.common import Util as cUtil
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import pandas as pd
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'''
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from pyFTS.data import artificial
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lmv1 = artificial.generate_gaussian_linear(1,0.2,0.2,0.05)
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@ -32,3 +33,35 @@ print(nsfts1.predict(test1))
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print(nsfts1)
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util.plot_sets(fs1, tam=[10, 5], start=0, end=100, step=2, data=lmv1[:100], window_size=35)
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'''
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from pyFTS.common import Transformations
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tdiff = Transformations.Differential(1)
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from pyFTS.common import Util
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from pyFTS.data import TAIEX
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taiex = TAIEX.get_data()
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taiex_diff = tdiff.apply(taiex)
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train = taiex_diff[:600]
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test = taiex_diff[600:1500]
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fs_tmp = Grid.GridPartitioner(data=train, npart=20) #, transformation=tdiff)
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fs = partitioners.SimpleNonStationaryPartitioner(train, fs_tmp)
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print(fs)
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model = cvfts.ConditionalVarianceFTS(partitioner=fs)
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model.fit(train)
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print(model)
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#tmpp4 = model.predict(test, type='point')
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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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