Source code for pyFTS.models.multivariate.mvfts

from pyFTS.common import fts, FuzzySet, FLR, Membership, tree
from pyFTS.partitioners import Grid
from pyFTS.models.multivariate import FLR as MVFLR, common, flrg as mvflrg

import numpy as np
import pandas as pd


[docs]class MVFTS(fts.FTS):
    """
    Multivariate extension of Chen's ConventionalFTS method
    """
    def __init__(self, **kwargs):
        super(MVFTS, self).__init__(order=1, **kwargs)
        self.explanatory_variables = []
        self.target_variable = None
        self.flrgs = {}
        self.is_multivariate = True
        self.shortname = "MVFTS"
        self.name = "Multivariate FTS"

[docs]    def append_variable(self, var):
        """
        Append a new endogenous variable to the model

        :param var: variable object
        :return:
        """
        self.explanatory_variables.append(var)

[docs]    def format_data(self, data):
        ndata = {}
        for var in self.explanatory_variables:
            ndata[var.name] = data[var.data_label]

        return ndata

[docs]    def apply_transformations(self, data, params=None, updateUoD=False, **kwargs):
        ndata = data.copy(deep=True)
        for var in self.explanatory_variables:
            ndata[var.data_label] = var.apply_transformations(data[var.data_label].values)

        return ndata

[docs]    def generate_lhs_flrs(self, data):
        flrs = []
        lags = {}
        for vc, var in enumerate(self.explanatory_variables):
            data_point = data[var.data_label]
            lags[vc] = common.fuzzyfy_instance(data_point, var)

        root = tree.FLRGTreeNode(None)

        tree.build_tree_without_order(root, lags, 0)

        for p in root.paths():
            path = list(reversed(list(filter(None.__ne__, p))))

            flr = MVFLR.FLR()

            for v, s in path:
                flr.set_lhs(v, s)

            if len(flr.LHS.keys()) == len(self.explanatory_variables):
                flrs.append(flr)

        return flrs

[docs]    def generate_flrs(self, data):
        flrs = []
        for ct in range(1, len(data.index)):
            ix = data.index[ct-1]
            data_point = data.loc[ix]

            tmp_flrs = self.generate_lhs_flrs(data_point)

            target_ix = data.index[ct]
            target_point = data[self.target_variable.data_label][target_ix]
            target = common.fuzzyfy_instance(target_point, self.target_variable)

            for flr in tmp_flrs:
                for v, s in target:
                    flr.set_rhs(s)
                    flrs.append(flr)

        return flrs

[docs]    def generate_flrg(self, flrs):
        for flr in flrs:
            flrg = mvflrg.FLRG(lhs=flr.LHS)

            if flrg.get_key() not in self.flrgs:
                self.flrgs[flrg.get_key()] = flrg

            self.flrgs[flrg.get_key()].append_rhs(flr.RHS)


[docs]    def train(self, data, **kwargs):

        ndata = self.apply_transformations(data)

        flrs = self.generate_flrs(ndata)
        self.generate_flrg(flrs)

[docs]    def forecast(self, data, **kwargs):
        ret = []
        ndata = self.apply_transformations(data)
        for ix in ndata.index:
            data_point = ndata.loc[ix]
            flrs = self.generate_lhs_flrs(data_point)
            mvs = []
            mps = []
            for flr in flrs:
                flrg = mvflrg.FLRG(lhs=flr.LHS)
                if flrg.get_key() not in self.flrgs:
                    #print('hit')
                    mvs.append(0.)
                    mps.append(0.)
                else:
                    mvs.append(self.flrgs[flrg.get_key()].get_membership(self.format_data(data_point), self.explanatory_variables))
                    mps.append(self.flrgs[flrg.get_key()].get_midpoint(self.target_variable.partitioner.sets))

            #print('mv', mvs)
            #print('mp', mps)
            mv = np.array(mvs)
            mp = np.array(mps)

            ret.append(np.dot(mv,mp.T)/np.sum(mv))

        ret = self.target_variable.apply_inverse_transformations(ret,
                                                           params=data[self.target_variable.data_label].values)
        return ret

[docs]    def clone_parameters(self, model):
        super(MVFTS, self).clone_parameters(model)

        self.explanatory_variables = model.explanatory_variables
        self.target_variable = model.target_variable

    def __str__(self):
        _str = self.name + ":\n"
        for k in self.flrgs.keys():
            _str += str(self.flrgs[k]) + "\n"

        return _str