Multivariate grid.IncrementalGridCluster and improvements on CMVFTS
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@ -38,8 +38,9 @@ class ClusteredMVFTS(mvfts.MVFTS):
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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(common.fuzzyfy_instance_clustered(data_point, self.partitioner,
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alpha_cut=self.alpha_cut))
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#ndata.append(common.fuzzyfy_instance_clustered(data_point, self.partitioner,
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# alpha_cut=self.alpha_cut))
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ndata.append(self.partitioner.fuzzyfy(data_point, mode='sets'))
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return ndata
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@ -31,3 +31,52 @@ class GridCluster(partitioner.MultivariatePartitioner):
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self.build_index()
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class IncrementalGridCluster(partitioner.MultivariatePartitioner):
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def __init__(self, **kwargs):
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super(IncrementalGridCluster, self).__init__(**kwargs)
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self.name="IncrementalGridCluster"
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self.build(None)
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def fuzzyfy(self, data, **kwargs):
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if isinstance(data, pd.DataFrame):
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ret = []
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for inst in data.iterrows():
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mv = self.fuzzyfy(inst, **kwargs)
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ret.append(mv)
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return ret
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alpha_cut = kwargs.get('alpha_cut', 0.)
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mode = kwargs.get('mode', 'sets')
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fsets = {}
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ret = []
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for var in self.explanatory_variables:
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fsets[var.name] = var.partitioner.fuzzyfy(data[var.name], mode='sets')
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fset = [val for key, val in fsets.items()]
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for p in product(*fset):
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key = ''.join(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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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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if mode=='sets':
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ret.append(key)
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elif mode=='vector':
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raise NotImplementedError()
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elif mode == 'both':
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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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def prune(self):
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pass
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@ -19,9 +19,9 @@ class DateTime(Enum):
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day_of_month = 30
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day_of_year = 364
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day_of_week = 7
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hour = 6
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minute = 7
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second = 8
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hour = 24
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minute = 60
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second = 60
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hour_of_day = 24
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hour_of_week = 168
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hour_of_month = 744
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@ -4,6 +4,7 @@ from pyFTS.partitioners import partitioner, Grid
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from pyFTS.models.seasonal.common import DateTime, FuzzySet, strip_datepart
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import numpy as np
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import matplotlib.pylab as plt
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from scipy.spatial import KDTree
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class TimeGridPartitioner(partitioner.Partitioner):
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@ -56,6 +57,8 @@ class TimeGridPartitioner(partitioner.Partitioner):
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partlen = dlen / self.partitions
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elif self.season == DateTime.day_of_week:
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self.min, self.max, partlen, pl2 = 0, 7, 1, 1
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elif self.season == DateTime.minute:
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self.min, self.max, partlen, pl2 = 0, 60, 1, 1
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elif self.season == DateTime.hour:
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self.min, self.max, partlen, pl2 = 0, 24, 1, 1
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elif self.season == DateTime.month:
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@ -77,7 +80,7 @@ class TimeGridPartitioner(partitioner.Partitioner):
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self.season.value + 0.0000001], self.season.value, alpha=.5,
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**kwargs))
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tmp.append_set(FuzzySet(self.season, set_name, Membership.trimf,
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[c - partlen, c, c + partlen], c,
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[c - 0.0000001, c, c + partlen], c,
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**kwargs))
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tmp.centroid = c
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sets[set_name] = tmp
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@ -88,7 +91,7 @@ class TimeGridPartitioner(partitioner.Partitioner):
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pl2], 0.0, alpha=.5,
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**kwargs))
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tmp.append_set(FuzzySet(self.season, set_name, Membership.trimf,
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[c - partlen, c, c + partlen], c,
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[c - partlen, c, c + 0.0000001], c,
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**kwargs))
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tmp.centroid = c
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sets[set_name] = tmp
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@ -122,6 +125,51 @@ class TimeGridPartitioner(partitioner.Partitioner):
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return sets
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def build_index(self):
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points = []
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fset = self.sets[self.ordered_sets[0]]
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points.append([fset.centroid, fset.centroid, fset.centroid])
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for ct, key in enumerate(self.ordered_sets[1:-2]):
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fset = self.sets[key]
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points.append([fset.lower, fset.centroid, fset.upper])
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fset = self.sets[self.ordered_sets[-1]]
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points.append([fset.centroid, fset.centroid, fset.centroid])
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import sys
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sys.setrecursionlimit(100000)
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self.kdtree = KDTree(points)
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sys.setrecursionlimit(1000)
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def search(self, data, type='index', results=3):
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'''
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Perform a search for the nearest fuzzy sets of the point 'data'. This function were designed to work with several
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overlapped fuzzy sets.
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:param data: the value to search for the nearest fuzzy sets
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:param type: the return type: 'index' for the fuzzy set indexes or 'name' for fuzzy set names.
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:param results: the number of nearest fuzzy sets to return
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:return: a list with the nearest fuzzy sets
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'''
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if self.kdtree is None:
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self.build_index()
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_, ix = self.kdtree.query([data, data, data], results)
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if 0 in ix:
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ix[-1] = self.partitions-1
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elif self.partitions-1 in ix:
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ix[-1] = 0
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if type == 'name':
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return [self.ordered_sets[k] for k in sorted(ix)]
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else:
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return sorted(ix)
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def plot(self, ax):
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"""
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@ -182,9 +182,14 @@ vavg = variable.Variable("Radiation", data_label="glo_avg", alias='rad',
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partitioner=Grid.GridPartitioner, npart=30, alpha_cut=.3,
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data=train)
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from pyFTS.models.multivariate import mvfts, wmvfts, cmvfts
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from pyFTS.models.multivariate import mvfts, wmvfts, cmvfts, grid
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fs = grid.IncrementalGridCluster(explanatory_variables=[vmonth, vhour, vavg], target_variable=vavg)
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model = cmvfts.ClusteredMVFTS(explanatory_variables=[vmonth, vhour, vavg], target_variable=vavg,
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partitioner=fs, knn=3)
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model = wmvfts.WeightedMVFTS(explanatory_variables=[vmonth, vhour, vavg], target_variable=vavg)
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model.fit(train)
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forecasts = model.predict(test, type='interval')
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print(fs)
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print(model)
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