+
+
Source code for pyFTS.models.multivariate.grid
+from pyFTS.models.multivariate import partitioner
+from pyFTS.models.multivariate.common import MultivariateFuzzySet, fuzzyfy_instance_clustered
+from itertools import product
+from scipy.spatial import KDTree
+import numpy as np
+import pandas as pd
+
+
+[docs]class GridCluster(partitioner.MultivariatePartitioner):
+
"""
+
A cartesian product of all fuzzy sets of all variables
+
"""
+
+
def __init__(self, **kwargs):
+
super(GridCluster, self).__init__(**kwargs)
+
self.name="GridCluster"
+
self.build(None)
+
+
[docs] def build(self, data):
+
+
fsets = [[x for x in k.partitioner.sets.values()]
+
for k in self.explanatory_variables]
+
c = 0
+
for k in product(*fsets):
+
mvfset = MultivariateFuzzySet(target_variable=self.target_variable)
+
for fset in k:
+
mvfset.append_set(fset.variable, fset)
+
+
self.sets[mvfset.name] = mvfset
+
c += 1
+
+
self.build_index()
+
+
+[docs]class IncrementalGridCluster(partitioner.MultivariatePartitioner):
+
"""
+
Create combinations of fuzzy sets of the variables on demand, incrementally increasing the
+
multivariate fuzzy set base.
+
"""
+
def __init__(self, **kwargs):
+
super(IncrementalGridCluster, self).__init__(**kwargs)
+
self.name="IncrementalGridCluster"
+
self.build(None)
+
+
[docs] def fuzzyfy(self, data, **kwargs):
+
+
if isinstance(data, pd.DataFrame):
+
ret = []
+
for index, inst in data.iterrows():
+
mv = self.fuzzyfy(inst, **kwargs)
+
ret.append(mv)
+
return ret
+
+
if self.kdtree is not None:
+
fsets = self.search(data, type='name')
+
else:
+
fsets = self.incremental_search(data, type='name')
+
+
mode = kwargs.get('mode', 'sets')
+
if mode == 'sets':
+
return fsets
+
elif mode == 'vector':
+
raise NotImplementedError()
+
elif mode == 'both':
+
ret = []
+
for key in fsets:
+
mvfset = self.sets[key]
+
ret.append((key, mvfset.membership(data)))
+
return ret
+
+
[docs] def incremental_search(self, data, **kwargs):
+
alpha_cut = kwargs.get('alpha_cut', 0.)
+
mode = kwargs.get('mode', 'sets')
+
+
fsets = {}
+
ret = []
+
for var in self.explanatory_variables:
+
ac = alpha_cut if alpha_cut > 0. else var.alpha_cut
+
fsets[var.name] = var.partitioner.fuzzyfy(data[var.name], mode='sets', alpha_cut=ac)
+
+
fset = [val for key, val in fsets.items()]
+
+
for p in product(*fset):
+
key = ''.join(p)
+
if key not in self.sets:
+
mvfset = MultivariateFuzzySet(target_variable=self.target_variable)
+
for ct, fs in enumerate(p):
+
mvfset.append_set(self.explanatory_variables[ct].name,
+
self.explanatory_variables[ct].partitioner[fs])
+
mvfset.name = key
+
self.sets[key] = mvfset
+
ret.append(key)
+
+
+
return ret
+
+
[docs] def prune(self):
+
self.build_index()
+
+
+
+
+