Source code for pyFTS.models.multivariate.partitioner
from pyFTS.partitioners 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 MultivariatePartitioner(partitioner.Partitioner):
"""
Base class for partitioners which use the MultivariateFuzzySet
"""
def __init__(self, **kwargs):
super(MultivariatePartitioner, self).__init__(name="MultivariatePartitioner", preprocess=False, **kwargs)
self.type = 'multivariate'
self.sets = {}
self.kdtree = None
self.index = {}
self.explanatory_variables = kwargs.get('explanatory_variables', [])
self.target_variable = kwargs.get('target_variable', None)
self.neighbors = kwargs.get('neighbors', 2)
self.optimize = kwargs.get('optimize', True)
if self.optimize:
self.count = {}
data = kwargs.get('data', None)
self.build(data)
self.uod = {}
self.min = self.target_variable.partitioner.min
self.max = self.target_variable.partitioner.max
[docs] def build(self, data):
pass
[docs] def append(self, fset):
self.sets[fset.name] = fset
[docs] def prune(self):
if not self.optimize:
return
for fset in [fs for fs in self.sets.keys()]:
if fset not in self.count:
fs = self.sets.pop(fset)
del (fs)
self.build_index()
[docs] def search(self, data, **kwargs):
"""
Perform a search for the nearest fuzzy sets of the point 'data'. This function were designed to work with several
overlapped fuzzy sets.
:param data: the value to search for the nearest fuzzy sets
:param type: the return type: 'index' for the fuzzy set indexes or 'name' for fuzzy set names.
:return: a list with the nearest fuzzy sets
"""
if self.kdtree is None:
self.build_index()
type = kwargs.get('type', 'index')
ndata = [data[k.name] for k in self.explanatory_variables]
_, ix = self.kdtree.query(ndata, self.neighbors)
if not isinstance(ix, (list, np.ndarray)):
ix = [ix]
if self.optimize:
tmp = []
for k in ix:
tmp.append(self.index[k])
self.count[self.index[k]] = 1
if type == 'name':
return [self.index[k] for k in ix]
elif type == 'index':
return sorted(ix)
[docs] def fuzzyfy(self, data, **kwargs):
return fuzzyfy_instance_clustered(data, self, **kwargs)
[docs] def change_target_variable(self, variable):
self.target_variable = variable
for fset in self.sets.values():
fset.set_target_variable(variable)
self.min = variable.partitioner.min
self.max = variable.partitioner.max
[docs] def build_index(self):
midpoints = []
self.index = {}
for ct, fset in enumerate(self.sets.values()):
mp = []
for vr in self.explanatory_variables:
mp.append(fset.sets[vr.name].centroid)
midpoints.append(mp)
self.index[ct] = fset.name
import sys
sys.setrecursionlimit(100000)
self.kdtree = KDTree(midpoints)
sys.setrecursionlimit(1000)
