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Improvements on cmvfts and pwfts for multivariate forecasting

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This commit is contained in:
Petrônio Cândido 2018-12-11 22:21:19 -02:00
parent 9a66da2d5a
commit 0d033c3ac2
7 changed files with 54 additions and 16 deletions
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4
pyFTS/common/flrg.py
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@ -106,5 +106,9 @@ class FLRG(object):
def __len__(self):
return len(self.RHS)
def reset_calculated_values(self):
self.midpoint = None
self.upper = None
self.lower = None

14
pyFTS/common/fts.py
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@ -507,8 +507,22 @@ class FTS(object):
return len(self.flrgs)
def len_total(self):
"""
Total length of the model, adding the number of terms in all rules
:return:
"""
return sum([len(k) for k in self.flrgs])
def reset_calculated_values(self):
"""
Reset all pre-calculated values on the FLRG's
:return:
"""
for flrg in self.flrgs.keys():
self.flrgs[flrg].reset_calculated_values()

10
pyFTS/models/multivariate/cmvfts.py
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@ -82,11 +82,15 @@ class ClusteredMVFTS(mvfts.MVFTS):
ret = {}
for var in self.explanatory_variables:
self.cluster.change_target_variable(var)
if self.target_variable.name != var.name:
self.target_variable = var
self.cluster.change_target_variable(var)
self.model.partitioner = self.cluster
self.reset_calculated_values()
ret[var.name] = self.model.forecast(ndata, fuzzyfied=self.pre_fuzzyfy, **kwargs)
columns = ret.keys()
return pd.DataFrame(ret, columns=columns)
return pd.DataFrame(ret, columns=ret.keys())
def __str__(self):
"""String representation of the model"""

6
pyFTS/models/multivariate/common.py
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@ -28,6 +28,12 @@ class MultivariateFuzzySet(Composite.FuzzySet):
if variable == self.target_variable.name:
self.centroid = set.centroid
def set_target_variable(self, variable):
#print(self.target_variable, variable)
self.target_variable = variable
#print(self.centroid,self.sets[variable.name].centroid)
self.centroid = self.sets[variable.name].centroid
def membership(self, x):
mv = []
for var in self.sets.keys():

2
pyFTS/models/multivariate/grid.py
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@ -108,4 +108,4 @@ class GridCluster(partitioner.Partitioner):
def change_target_variable(self, variable):
for fset in self.sets:
self.sets[fset].target_variable = variable
self.sets[fset].set_target_variable(variable)

18
pyFTS/models/pwfts.py
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@ -202,18 +202,18 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
#return self.flrg_lhs_unconditional_probability(flrg)
def flrg_lhs_conditional_probability(self, x, flrg):
mv = flrg.get_membership(x, self.sets)
mv = flrg.get_membership(x, self.partitioner.sets)
pb = self.flrg_lhs_unconditional_probability(flrg)
return mv * pb
def get_midpoint(self, flrg):
if flrg.get_key() in self.flrgs:
tmp = self.flrgs[flrg.get_key()]
ret = tmp.get_midpoint(self.sets) #sum(np.array([tmp.rhs_unconditional_probability(s) * self.setsDict[s].centroid for s in tmp.RHS]))
ret = tmp.get_midpoint(self.partitioner.sets) #sum(np.array([tmp.rhs_unconditional_probability(s) * self.setsDict[s].centroid for s in tmp.RHS]))
else:
if len(flrg.LHS) > 0:
pi = 1 / len(flrg.LHS)
ret = sum(np.array([pi * self.sets[s].centroid for s in flrg.LHS]))
ret = sum(np.array([pi * self.partitioner.sets[s].centroid for s in flrg.LHS]))
else:
ret = np.nan
return ret
@ -224,19 +224,19 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
_flrg = self.flrgs[flrg.get_key()]
cond = []
for s in _flrg.RHS.keys():
_set = self.sets[s]
_set = self.partitioner.sets[s]
tmp = _flrg.rhs_unconditional_probability(s) * (_set.membership(x) / _set.partition_function(uod=self.get_UoD()))
cond.append(tmp)
ret = sum(np.array(cond))
else:
pi = 1 / len(flrg.LHS)
ret = sum(np.array([pi * self.sets[s].membership(x) for s in flrg.LHS]))
ret = sum(np.array([pi * self.partitioner.sets[s].membership(x) for s in flrg.LHS]))
return ret
def get_upper(self, flrg):
if flrg.get_key() in self.flrgs:
tmp = self.flrgs[flrg.get_key()]
ret = tmp.get_upper(self.sets)
ret = tmp.get_upper(self.partitioner.sets)
else:
ret = 0
return ret
@ -244,7 +244,7 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
def get_lower(self, flrg):
if flrg.get_key() in self.flrgs:
tmp = self.flrgs[flrg.get_key()]
ret = tmp.get_lower(self.sets)
ret = tmp.get_lower(self.partitioner.sets)
else:
ret = 0
return ret
@ -398,8 +398,8 @@ 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.sets, self.global_frequency_count, uod, nbins)
wi = flrg.rhs_conditional_probability(bin, self.sets, uod, nbins)
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)
num.append(wi * pk)
den.append(pk)
else:

16
pyFTS/tests/multivariate.py
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@ -26,13 +26,23 @@ from pyFTS.models.multivariate import variable, cmvfts
vx = variable.Variable("x", data_label="x", partitioner=Grid.GridPartitioner, npart=15, data=df)
vy = variable.Variable("y", data_label="y", partitioner=Grid.GridPartitioner, npart=15, data=df)
model = cmvfts.ClusteredMVFTS(pre_fuzzyfy=False, knn=3, fts_method=pwfts.ProbabilisticWeightedFTS)
model = cmvfts.ClusteredMVFTS(pre_fuzzyfy=False, knn=3, order=2, fts_method=pwfts.ProbabilisticWeightedFTS)
model.append_variable(vx)
model.append_variable(vy)
model.target_variable = vx
model.fit(df.iloc[:800])
df = model.predict(df.iloc[800:], type='multivariate')
test = df.iloc[800:]
print(df)
forecasts = model.predict(test, type='multivariate')
fig, ax = plt.subplots(nrows=2, ncols=2, figsize=[15,7])
ax[0][0].plot(test['x'].values)
ax[0][0].plot(forecasts['x'].values)
ax[0][1].scatter(test['x'].values,test['y'].values)
ax[0][1].scatter(forecasts['x'].values,forecasts['y'].values)
ax[1][0].scatter(test['y'].values,test['x'].values)
ax[1][0].scatter(forecasts['y'].values,forecasts['x'].values)
ax[1][1].plot(test['y'].values)
ax[1][1].plot(forecasts['y'].values)