Improvements on pwfts to support GranularFTS; Improvementos on mvfts methods, fuzzy sets and partitioners to support interval and probabilistic forecasting
This commit is contained in:
Petrônio Cândido
parent
354a3131c9
commit
1237f3c2e3
@ -321,7 +321,7 @@ class FTS(object):
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if 'partitioner' in kwargs:
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self.partitioner = kwargs.pop('partitioner')
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if not self.is_wrapper and not self.benchmark_only:
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if not self.is_multivariate and not self.is_wrapper and not self.benchmark_only:
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if self.partitioner is None:
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raise Exception("Fuzzy sets were not provided for the model. Use 'partitioner' parameter. ")
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@ -34,12 +34,13 @@ class ClusteredMVFTS(mvfts.MVFTS):
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self.name = "Clustered Multivariate FTS"
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self.pre_fuzzyfy = kwargs.get('pre_fuzzyfy', True)
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self.fuzzyfy_mode = kwargs.get('fuzzyfy_mode', 'sets')
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def fuzzyfy(self,data):
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ndata = []
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for index, row in data.iterrows():
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data_point = self.format_data(row)
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ndata.append(self.partitioner.fuzzyfy(data_point, mode='sets'))
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ndata.append(self.partitioner.fuzzyfy(data_point, mode=self.fuzzyfy_mode))
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return ndata
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@ -71,6 +72,50 @@ class ClusteredMVFTS(mvfts.MVFTS):
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return self.model.forecast(ndata, fuzzyfied=pre_fuzz, **kwargs)
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def forecast_interval(self, data, **kwargs):
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if not self.model.has_interval_forecasting:
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raise Exception("The internal method does not support interval forecasting!")
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data = self.check_data(data)
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pre_fuzz = kwargs.get('pre_fuzzyfy', self.pre_fuzzyfy)
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return self.model.forecast_interval(data, fuzzyfied=pre_fuzz, **kwargs)
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def forecast_ahead_interval(self, data, steps, **kwargs):
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if not self.model.has_interval_forecasting:
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raise Exception("The internal method does not support interval forecasting!")
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data = self.check_data(data)
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pre_fuzz = kwargs.get('pre_fuzzyfy', self.pre_fuzzyfy)
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return self.model.forecast_ahead_interval(data, steps, fuzzyfied=pre_fuzz, **kwargs)
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def forecast_distribution(self, data, **kwargs):
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if not self.model.has_probability_forecasting:
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raise Exception("The internal method does not support probabilistic forecasting!")
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data = self.check_data(data)
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pre_fuzz = kwargs.get('pre_fuzzyfy', self.pre_fuzzyfy)
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return self.model.forecast_distribution(data, fuzzyfied=pre_fuzz, **kwargs)
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def forecast_ahead_distribution(self, data, steps, **kwargs):
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if not self.model.has_probability_forecasting:
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raise Exception("The internal method does not support probabilistic forecasting!")
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data = self.check_data(data)
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pre_fuzz = kwargs.get('pre_fuzzyfy', self.pre_fuzzyfy)
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return self.model.forecast_ahead_distribution(data, steps, fuzzyfied=pre_fuzz, **kwargs)
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def forecast_multivariate(self, data, **kwargs):
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ndata = self.check_data(data)
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@ -27,18 +27,26 @@ class MultivariateFuzzySet(Composite.FuzzySet):
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if variable == self.target_variable.name:
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self.centroid = set.centroid
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self.upper = set.upper
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self.lower = set.lower
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self.name += set.name
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def set_target_variable(self, variable):
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self.target_variable = variable
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self.centroid = self.sets[variable.name].centroid
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self.upper = self.sets[variable.name].upper
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self.lower = self.sets[variable.name].lower
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def membership(self, x):
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mv = []
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for var in self.sets.keys():
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data = x[var]
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mv.append(self.sets[var].membership(data))
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if isinstance(x, (dict, pd.DataFrame)):
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for var in self.sets.keys():
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data = x[var]
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mv.append(self.sets[var].membership(data))
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else:
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mv = [self.sets[self.target_variable.name].membership(x)]
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return np.nanmin(mv)
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@ -10,18 +10,19 @@ class GranularWMVFTS(cmvfts.ClusteredMVFTS):
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def __init__(self, **kwargs):
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super(GranularWMVFTS, self).__init__(**kwargs)
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self.fts_method = hofts.WeightedHighOrderFTS
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self.fts_method = kwargs.get('fts_method', hofts.WeightedHighOrderFTS)
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self.model = None
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"""The most recent trained model"""
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self.knn = kwargs.get('knn', 2)
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self.order = kwargs.get("order", 2)
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self.shortname = "GranularWMVFTS"
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self.name = "Granular Weighted Multivariate FTS"
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self.mode = kwargs.get('mode','sets')
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def train(self, data, **kwargs):
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self.partitioner = grid.IncrementalGridCluster(
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explanatory_variables=self.explanatory_variables,
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target_variable=self.target_variable,
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neighbors=self.knn)
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super(GranularWMVFTS, self).train(data,**kwargs)
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super(GranularWMVFTS, self).train(data, mode=self.mode, **kwargs)
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@ -31,6 +31,34 @@ class GridCluster(partitioner.MultivariatePartitioner):
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self.build_index()
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def defuzzyfy(self, values, mode='both'):
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if not isinstance(values, list):
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values = [values]
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ret = []
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for val in values:
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if mode == 'both':
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num = []
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den = []
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for fset, mv in val:
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num.append(self.sets[fset].centroid * mv)
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den.append(mv)
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ret.append(np.sum(num) / np.sum(den))
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elif mode == 'both':
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num = np.mean([self.sets[fset].centroid for fset in val])
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ret.append(num)
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elif mode == 'vector':
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num = []
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den = []
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for fset, mv in enumerate(val):
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num.append(self.sets[self.ordered_sets[fset]].centroid * mv)
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den.append(mv)
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ret.append(np.sum(num) / np.sum(den))
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else:
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raise Exception('Unknown deffuzyfication mode')
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return ret
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class IncrementalGridCluster(partitioner.MultivariatePartitioner):
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"""
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@ -67,7 +95,8 @@ class IncrementalGridCluster(partitioner.MultivariatePartitioner):
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for key in fsets:
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mvfset = self.sets[key]
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ret.append((key, mvfset.membership(data)))
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return ret
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return ret
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def incremental_search(self, data, **kwargs):
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alpha_cut = kwargs.get('alpha_cut', 0.)
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@ -77,21 +106,30 @@ class IncrementalGridCluster(partitioner.MultivariatePartitioner):
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ret = []
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for var in self.explanatory_variables:
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ac = alpha_cut if alpha_cut > 0. else var.alpha_cut
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fsets[var.name] = var.partitioner.fuzzyfy(data[var.name], mode='sets', alpha_cut=ac)
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fsets[var.name] = var.partitioner.fuzzyfy(data[var.name], mode=mode, alpha_cut=ac)
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fset = [val for key, val in fsets.items()]
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fsets_by_var = [fsets for var, fsets in fsets.items()]
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for p in product(*fset):
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key = ''.join(p)
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for p in product(*fsets_by_var):
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if mode == 'both':
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path = [fset for fset, mv in p]
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mv = [mv for fset, mv in p]
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key = ''.join(path)
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elif mode == 'sets':
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key = ''.join(p)
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path = p
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if key not in self.sets:
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mvfset = MultivariateFuzzySet(target_variable=self.target_variable)
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for ct, fs in enumerate(p):
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for ct, fs in enumerate(path):
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mvfset.append_set(self.explanatory_variables[ct].name,
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self.explanatory_variables[ct].partitioner[fs])
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mvfset.name = key
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self.sets[key] = mvfset
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ret.append(key)
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if mode == 'sets':
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ret.append(key)
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elif mode == 'both':
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ret.append( tuple(key,np.nanmin(mv)) )
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return ret
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@ -302,7 +302,6 @@ class MVFTS(fts.FTS):
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return ret[-steps]
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def clone_parameters(self, model):
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super(MVFTS, self).clone_parameters(model)
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@ -26,6 +26,11 @@ class MultivariatePartitioner(partitioner.Partitioner):
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self.count = {}
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data = kwargs.get('data', None)
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self.build(data)
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self.uod = {}
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self.min = self.target_variable.partitioner.min
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self.max = self.target_variable.partitioner.max
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def format_data(self, data):
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ndata = {}
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@ -88,8 +93,11 @@ class MultivariatePartitioner(partitioner.Partitioner):
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return fuzzyfy_instance_clustered(data, self, **kwargs)
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def change_target_variable(self, variable):
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self.target_variable = variable
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for fset in self.sets.values():
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fset.set_target_variable(variable)
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self.min = variable.partitioner.min
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self.max = variable.partitioner.max
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def build_index(self):
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@ -41,6 +41,13 @@ class ProbabilisticWeightedFLRG(hofts.HighOrderFLRG):
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return tmp
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def lhs_conditional_probability_fuzzyfied(self, lhs_mv, sets, norm, uod, nbins):
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pk = self.frequency_count / norm
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tmp = pk * (lhs_mv / self.partition_function(sets, uod, nbins=nbins))
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return tmp
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def rhs_unconditional_probability(self, c):
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return self.RHS[c] / self.frequency_count
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@ -114,14 +121,54 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
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def train(self, data, **kwargs):
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self.configure_lags(**kwargs)
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parameters = kwargs.get('parameters','fuzzy')
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if parameters == 'monotonic':
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tmpdata = self.partitioner.fuzzyfy(data, mode='sets', method='maximum')
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flrs = FLR.generate_recurrent_flrs(tmpdata)
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self.generate_flrg(flrs)
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if not kwargs.get('fuzzyfied',False):
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self.generate_flrg2(data)
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else:
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self.generate_flrg(data)
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self.generate_flrg_fuzzyfied(data)
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def generate_flrg2(self, data):
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fuzz = []
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l = len(data)
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for k in np.arange(0, l):
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fuzz.append(self.partitioner.fuzzyfy(data[k], mode='both', method='fuzzy',
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alpha_cut=self.alpha_cut))
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self.generate_flrg_fuzzyfied(fuzz)
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def generate_flrg_fuzzyfied(self, data):
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l = len(data)
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for k in np.arange(self.max_lag, l):
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sample = data[k - self.max_lag: k]
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set_sample = []
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for instance in sample:
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set_sample.append([k for k, v in instance])
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flrgs = self.generate_lhs_flrg_fuzzyfied(set_sample)
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for flrg in flrgs:
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if flrg.get_key() not in self.flrgs:
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self.flrgs[flrg.get_key()] = flrg;
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lhs_mv = self.pwflrg_lhs_memberhip_fuzzyfied(flrg, sample)
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mvs = []
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inst = data[k]
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for set, mv in inst:
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self.flrgs[flrg.get_key()].append_rhs(set, count=lhs_mv * mv)
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mvs.append(mv)
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tmp_fq = sum([lhs_mv * kk for kk in mvs if kk > 0])
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self.global_frequency_count += tmp_fq
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def pwflrg_lhs_memberhip_fuzzyfied(self, flrg, sample):
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vals = []
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for ct, fuzz in enumerate(sample):
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vals.append([mv for fset, mv in fuzz if fset == flrg.LHS[ct]])
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return np.nanprod(vals)
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def generate_lhs_flrg(self, sample, explain=False):
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nsample = [self.partitioner.fuzzyfy(k, mode="sets", alpha_cut=self.alpha_cut)
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@ -206,6 +253,11 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
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pb = self.flrg_lhs_unconditional_probability(flrg)
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return mv * pb
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def flrg_lhs_conditional_probability_fuzzyfied(self, x, flrg):
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mv = self.pwflrg_lhs_memberhip_fuzzyfied(flrg, x)
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pb = self.flrg_lhs_unconditional_probability(flrg)
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return mv * pb
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def get_midpoint(self, flrg):
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if flrg.get_key() in self.flrgs:
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tmp = self.flrgs[flrg.get_key()]
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@ -273,11 +325,16 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
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def point_heuristic(self, sample, **kwargs):
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explain = kwargs.get('explain', False)
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fuzzyfied = kwargs.get('fuzzyfied', False)
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if explain:
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print("Fuzzyfication \n")
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flrgs = self.generate_lhs_flrg(sample, explain)
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if not fuzzyfied:
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flrgs = self.generate_lhs_flrg(sample, explain)
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else:
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fsets = self.get_sets_from_both_fuzzyfication(sample)
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flrgs = self.generate_lhs_flrg_fuzzyfied(fsets, explain)
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mp = []
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norms = []
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@ -286,16 +343,17 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
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print("Rules:\n")
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for flrg in flrgs:
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norm = self.flrg_lhs_conditional_probability(sample, flrg)
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if not fuzzyfied:
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norm = self.flrg_lhs_conditional_probability(sample, flrg)
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else:
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norm = self.flrg_lhs_conditional_probability_fuzzyfied(sample, flrg)
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if norm == 0:
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norm = self.flrg_lhs_unconditional_probability(flrg)
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if explain:
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print("\t {} \t Midpoint: {}\t Norm: {}\n".format(str(self.flrgs[flrg.get_key()]),
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self.get_midpoint(flrg), norm))
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mp.append(norm * self.get_midpoint(flrg))
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norms.append(norm)
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@ -307,10 +365,13 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
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print("Deffuzyfied value: {} \n".format(final))
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return final
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def get_sets_from_both_fuzzyfication(self, sample):
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return [[k for k, v in inst] for inst in sample]
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def point_expected_value(self, sample, **kwargs):
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explain = kwargs.get('explain', False)
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dist = self.forecast_distribution(sample)[0]
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dist = self.forecast_distribution(sample, **kwargs)[0]
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final = dist.expected_value()
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return final
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@ -329,28 +390,37 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
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sample = ndata[k - (self.max_lag - 1): k + 1]
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if method == 'heuristic':
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ret.append(self.interval_heuristic(sample))
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ret.append(self.interval_heuristic(sample, **kwargs))
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elif method == 'quantile':
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ret.append(self.interval_quantile(sample, alpha))
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ret.append(self.interval_quantile(sample, alpha, **kwargs))
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else:
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raise ValueError("Unknown interval forecasting method!")
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return ret
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def interval_quantile(self, ndata, alpha):
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dist = self.forecast_distribution(ndata)
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def interval_quantile(self, ndata, alpha, **kwargs):
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dist = self.forecast_distribution(ndata, **kwargs)
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itvl = dist[0].quantile([alpha, 1.0 - alpha])
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return itvl
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def interval_heuristic(self, sample):
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def interval_heuristic(self, sample, **kwargs):
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fuzzyfied = kwargs.get('fuzzyfied', False)
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flrgs = self.generate_lhs_flrg(sample)
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if not fuzzyfied:
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flrgs = self.generate_lhs_flrg(sample)
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else:
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fsets = self.get_sets_from_both_fuzzyfication(sample)
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flrgs = self.generate_lhs_flrg_fuzzyfied(fsets)
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up = []
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lo = []
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norms = []
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for flrg in flrgs:
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norm = self.flrg_lhs_conditional_probability(sample, flrg)
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if not fuzzyfied:
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norm = self.flrg_lhs_conditional_probability(sample, flrg)
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else:
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norm = self.flrg_lhs_conditional_probability_fuzzyfied(sample, flrg)
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if norm == 0:
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norm = self.flrg_lhs_unconditional_probability(flrg)
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up.append(norm * self.get_upper(flrg))
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@ -370,6 +440,8 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
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smooth = kwargs.get("smooth", "none")
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fuzzyfied = kwargs.get('fuzzyfied', False)
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l = len(ndata)
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uod = self.get_UoD()
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@ -385,7 +457,11 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
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for k in np.arange(self.max_lag - 1, l):
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sample = ndata[k - (self.max_lag - 1): k + 1]
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flrgs = self.generate_lhs_flrg(sample)
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if not fuzzyfied:
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flrgs = self.generate_lhs_flrg(sample)
|
||||
else:
|
||||
fsets = self.get_sets_from_both_fuzzyfication(sample)
|
||||
flrgs = self.generate_lhs_flrg_fuzzyfied(fsets)
|
||||
|
||||
if 'type' in kwargs:
|
||||
kwargs.pop('type')
|
||||
@ -398,8 +474,14 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
|
||||
for s in flrgs:
|
||||
if s.get_key() in self.flrgs:
|
||||
flrg = self.flrgs[s.get_key()]
|
||||
pk = flrg.lhs_conditional_probability(sample, self.partitioner.sets, self.global_frequency_count, uod, nbins)
|
||||
wi = flrg.rhs_conditional_probability(bin, self.partitioner.sets, uod, nbins)
|
||||
if not fuzzyfied:
|
||||
pk = flrg.lhs_conditional_probability(sample, self.partitioner.sets, self.global_frequency_count, uod, nbins)
|
||||
else:
|
||||
lhs_mv = self.pwflrg_lhs_memberhip_fuzzyfied(flrg, sample)
|
||||
pk = flrg.lhs_conditional_probability_fuzzyfied(lhs_mv, self.partitioner.sets,
|
||||
self.global_frequency_count, uod, nbins)
|
||||
|
||||
num.append(wi * pk)
|
||||
den.append(pk)
|
||||
else:
|
||||
@ -422,13 +504,15 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
|
||||
|
||||
l = len(data)
|
||||
|
||||
fuzzyfied = kwargs.get('fuzzyfied', False)
|
||||
|
||||
start = kwargs.get('start_at', 0)
|
||||
|
||||
ret = data[start: start+self.max_lag].tolist()
|
||||
|
||||
for k in np.arange(self.max_lag, steps+self.max_lag):
|
||||
|
||||
if self.__check_point_bounds(ret[-1]) :
|
||||
if self.__check_point_bounds(ret[-1]) and not fuzzyfied:
|
||||
ret.append(ret[-1])
|
||||
else:
|
||||
mp = self.forecast(ret[k - self.max_lag: k], **kwargs)
|
||||
@ -448,11 +532,19 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
|
||||
|
||||
start = kwargs.get('start_at', 0)
|
||||
|
||||
fuzzyfied = kwargs.get('fuzzyfied', False)
|
||||
|
||||
sample = data[start: start + self.max_lag]
|
||||
|
||||
ret = [[k, k] for k in sample]
|
||||
if not fuzzyfied:
|
||||
ret = [[k, k] for k in sample]
|
||||
else:
|
||||
ret = []
|
||||
for k in sample:
|
||||
kv = self.partitioner.deffuzyfy(k,mode='both')
|
||||
ret.append([kv,kv])
|
||||
|
||||
ret.append(self.forecast_interval(sample)[0])
|
||||
ret.append(self.forecast_interval(sample, **kwargs)[0])
|
||||
|
||||
for k in np.arange(self.max_lag+1, steps+self.max_lag):
|
||||
|
||||
@ -492,7 +584,7 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
|
||||
tmp.set(dat, 1.0)
|
||||
ret.append(tmp)
|
||||
|
||||
dist = self.forecast_distribution(sample, bins=_bins)[0]
|
||||
dist = self.forecast_distribution(sample, bins=_bins, **kwargs)[0]
|
||||
|
||||
ret.append(dist)
|
||||
|
||||
|
||||
@ -181,6 +181,34 @@ class Partitioner(object):
|
||||
sets = [(self.ordered_sets[i], mv[i]) for i in ix]
|
||||
return sets
|
||||
|
||||
def defuzzyfy(self, values, mode='both'):
|
||||
if not isinstance(values, list):
|
||||
values = [values]
|
||||
|
||||
ret = []
|
||||
for val in values:
|
||||
if mode == 'both':
|
||||
num = []
|
||||
den = []
|
||||
for fset, mv in val:
|
||||
num.append( self.sets[fset].centroid * mv )
|
||||
den.append(mv)
|
||||
ret.append(np.sum(num)/np.sum(den))
|
||||
elif mode == 'both':
|
||||
num = np.mean([self.sets[fset].centroid for fset in val ])
|
||||
ret.append(num)
|
||||
elif mode == 'vector':
|
||||
num = []
|
||||
den = []
|
||||
for fset, mv in enumerate(val):
|
||||
num.append(self.sets[self.ordered_sets[fset]].centroid * mv)
|
||||
den.append(mv)
|
||||
ret.append(np.sum(num) / np.sum(den))
|
||||
else:
|
||||
raise Exception('Unknown deffuzyfication mode')
|
||||
|
||||
return ret
|
||||
|
||||
def check_bounds(self, data):
|
||||
"""
|
||||
Check if the input data is outside the known Universe of Discourse and, if it is, round it to the closest
|
||||
|
||||
@ -31,7 +31,7 @@ datasets['Malaysia.load'] = malaysia["load"].values
|
||||
|
||||
windows = [600000, 600000, 10000, 10000]
|
||||
|
||||
cpus = 3
|
||||
cpus = 7
|
||||
|
||||
for ct, (dataset_name, dataset) in enumerate(datasets.items()):
|
||||
bchmk.train_test_time(dataset, windowsize=windows[ct], train=0.9, inc=.5,
|
||||
@ -40,6 +40,6 @@ for ct, (dataset_name, dataset) in enumerate(datasets.items()):
|
||||
partitions=50,
|
||||
steps=cpus,
|
||||
num_batches=cpus,
|
||||
distributed='dispy', nodes=['192.168.0.110'], #, '192.168.0.107','192.168.0.106'],
|
||||
distributed='dispy', nodes=['192.168.0.110', '192.168.0.107','192.168.0.106'],
|
||||
file="experiments.db", dataset=dataset_name,
|
||||
tag="speedup")
|
||||
|
||||
@ -9,70 +9,60 @@ from statsmodels.tsa.stattools import adfuller
|
||||
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
|
||||
|
||||
from pyFTS.common import Util
|
||||
|
||||
from pyFTS.data import TAIEX
|
||||
|
||||
taiex = TAIEX.get_data()
|
||||
|
||||
train = taiex[:3000]
|
||||
test = taiex[3000:3200]
|
||||
|
||||
from pyFTS.common import Transformations
|
||||
tdiff = Transformations.Differential(1)
|
||||
|
||||
from pyFTS.benchmarks import benchmarks as bchmk, Measures
|
||||
from pyFTS.models import pwfts,hofts,ifts
|
||||
from pyFTS.models.multivariate import granular, grid
|
||||
from pyFTS.partitioners import Grid, Util as pUtil
|
||||
|
||||
fs = Grid.GridPartitioner(data=train, npart=30) #, transformation=tdiff)
|
||||
from pyFTS.models.multivariate import common, variable, mvfts
|
||||
from pyFTS.models.seasonal import partitioner as seasonal
|
||||
from pyFTS.models.seasonal.common import DateTime
|
||||
from pyFTS.common import Membership
|
||||
|
||||
model1 = hofts.HighOrderFTS(partitioner=fs, lags=[1,2])#lags=[0,1])
|
||||
model1.shortname = "1"
|
||||
model2 = pwfts.ProbabilisticWeightedFTS(partitioner=fs, lags=[1,2])
|
||||
#model2.append_transformation(tdiff)
|
||||
model2.shortname = "2"
|
||||
#model = pwfts.ProbabilisticWeightedFTS(partitioner=fs, order=2)# lags=[1,2])
|
||||
from pyFTS.data import SONDA, Malaysia
|
||||
|
||||
model1.fit(train)
|
||||
model2.fit(train)
|
||||
df = Malaysia.get_dataframe()
|
||||
df['time'] = pd.to_datetime(df["time"], format='%m/%d/%y %I:%M %p')
|
||||
|
||||
#print(model1)
|
||||
train_mv = df.iloc[:8000]
|
||||
test_mv = df.iloc[8000:10000]
|
||||
|
||||
#print(model2)
|
||||
sp = {'seasonality': DateTime.minute_of_day, 'names': [str(k)+'hs' for k in range(0,24)]}
|
||||
|
||||
for model in [model1, model2]:
|
||||
#forecasts = model.predict(test)
|
||||
print(model.shortname)
|
||||
print(Measures.get_point_statistics(test, model))
|
||||
vhour = variable.Variable("Hour", data_label="time", partitioner=seasonal.TimeGridPartitioner, npart=24,
|
||||
data=train_mv, partitioner_specific=sp, alpha_cut=.3)
|
||||
vtemp = variable.Variable("Temperature", data_label="temperature", alias='temp',
|
||||
partitioner=Grid.GridPartitioner, npart=5, func=Membership.gaussmf,
|
||||
data=train_mv, alpha_cut=.3)
|
||||
vload = variable.Variable("Load", data_label="load", alias='load',
|
||||
partitioner=Grid.GridPartitioner, npart=5, func=Membership.gaussmf,
|
||||
data=train_mv, alpha_cut=.3)
|
||||
|
||||
#handles, labels = ax.get_legend_handles_labels()
|
||||
#ax.legend(handles, labels, loc=2, bbox_to_anchor=(1, 1))
|
||||
order = 1
|
||||
knn = 1
|
||||
|
||||
#print(Measures.get_point_statistics(test,model))
|
||||
model = granular.GranularWMVFTS(explanatory_variables=[vhour, vtemp, vload], target_variable=vload,
|
||||
fts_method=pwfts.ProbabilisticWeightedFTS, fuzzyfy_mode='both',
|
||||
order=order, knn=knn)
|
||||
|
||||
model.fit(train_mv)
|
||||
|
||||
print(model)
|
||||
|
||||
|
||||
print(model.predict(test_mv.iloc[:10], type='point'))
|
||||
print(model.predict(test_mv.iloc[:10], type='interval'))
|
||||
print(model.predict(test_mv.iloc[:10], type='distribution'))
|
||||
|
||||
'''
|
||||
bchmk.sliding_window_benchmarks(train,1000,0.8,
|
||||
methods=[pwfts.ProbabilisticWeightedFTS], #,ifts.IntervalFTS],
|
||||
orders=[1,2,3],
|
||||
partitions=[10])
|
||||
'''
|
||||
'''
|
||||
from pyFTS.data import Enrollments
|
||||
train = Enrollments.get_data()
|
||||
|
||||
from pyFTS.common import FLR,FuzzySet,Membership,SortedCollection
|
||||
taiex_fs1 = Grid.GridPartitioner(data=train, npart=30)
|
||||
taiex_fs2 = Grid.GridPartitioner(data=train, npart=10, transformation=tdiff)
|
||||
fs = Grid.GridPartitioner(data=train, npart=10) #, transformation=tdiff)
|
||||
|
||||
#pUtil.plot_partitioners(train, [taiex_fs1,taiex_fs2], tam=[15,7])
|
||||
|
||||
from pyFTS.common import fts,tree
|
||||
from pyFTS.models import hofts, pwfts
|
||||
|
||||
pfts1_taiex = pwfts.ProbabilisticWeightedFTS("1", partitioner=taiex_fs1)
|
||||
#pfts1_taiex.appendTransformation(diff)
|
||||
pfts1_taiex.fit(train, save_model=True, file_path='pwfts')
|
||||
pfts1_taiex.shortname = "1st Order"
|
||||
print(pfts1_taiex)
|
||||
|
||||
'''
|
||||
model = pwfts.ProbabilisticWeightedFTS(partitioner=fs, order=2)
|
||||
model.fit(train)
|
||||
print(model)
|
||||
|
||||
print(model.predict(train))
|
||||
'''
|
||||
Loading…
Reference in New Issue
Block a user