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- Compacting datasets with bz2

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- Refactoring generate_flrg and train methods
 - Introducing batches and model saving on fit method
This commit is contained in:
Petrônio Cândido 2018-03-02 19:20:21 -03:00
parent cbc0974a3b
commit 00db6a30ad
40 changed files with 409 additions and 26313 deletions
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8
MANIFEST
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@ -1,6 +1,8 @@
include data/Enrollments.csv
include data/AirPassengers.csv
include data/NASDAQ.csv
include data/SP500.csv
include data/NASDAQ.csv.bz2
include data/SP500.csv.bz2
include data/sunspots.csv
include data/TAIEX.csv
include data/TAIEX.csv.bz2
include data/INMET.csv.bz2
include data/SONDA_BSB.csv.bz2

2
pyFTS/common/Composite.py
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@ -16,7 +16,7 @@ class FuzzySet(FuzzySet.FuzzySet):
Create an empty composite fuzzy set
:param name: fuzzy set name
"""
super(FuzzySet, self).__init__(self, name, None, None, None, type='composite')
super(FuzzySet, self).__init__(name, None, None, None, type='composite')
self.superset = superset
if self.superset:
self.sets = []

37
pyFTS/common/fts.py
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@ -1,6 +1,6 @@
import numpy as np
import pandas as pd
from pyFTS.common import FuzzySet, SortedCollection, tree
from pyFTS.common import FuzzySet, SortedCollection, tree, Util
class FTS(object):
@ -164,9 +164,42 @@ class FTS(object):
:param data:
:param kwargs:
:keyword
num_batches: split the training data in num_batches to save memory during the training process
save_model: save final model on disk
batch_save: save the model between each batch
file_path: path to save the model
:return:
"""
self.train(data, **kwargs)
num_batches = kwargs.get('num_batches', None)
save = kwargs.get('save_model', False) # save model on disk
batch_save = kwargs.get('batch_save', True) #save model between batches
file_path = kwargs.get('file_path', None)
if num_batches is not None:
n = len(data)
batch_size = round(n / num_batches, 0)
for ct in range(self.order, n, batch_size):
if self.is_multivariate:
ndata = data.iloc[ct - self.order:ct + batch_size]
else:
ndata = data[ct - self.order : ct + batch_size]
self.train(ndata, **kwargs)
if batch_save:
Util.persist_obj(self,file_path)
else:
self.train(data, **kwargs)
if save:
Util.persist_obj(self, file_path)
def append_transformation(self, transformation):
if transformation is not None:

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pyFTS/data/INMET.csv.bz2 Normal file
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26
pyFTS/data/INMET.py Normal file
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@ -0,0 +1,26 @@
#--------------------
#BDMEP - INMET
#--------------------
#Estação : BELO HORIZONTE - MG (OMM: 83587)
#Latitude (graus) : -19.93
#Longitude (graus) : -43.93
#Altitude (metros): 915.00
#Estação Operante
#Inicio de operação: 03/03/1910
#Periodo solicitado dos dados: 01/01/2000 a 31/12/2012
#Os dados listados abaixo são os que encontram-se digitados no BDMEP
#Hora em UTC
# http://www.inmet.gov.br
import pandas as pd
import numpy as np
import os
import pkg_resources
def get_dataframe():
filename = pkg_resources.resource_filename('pyFTS', 'data/INMET.csv.bz2')
dat = pd.read_csv(filename, sep=";", compression='bz2')
dat["DataHora"] = pd.to_datetime(dat["DataHora"], format='%d/%m/%Y %H:%M')
return dat

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pyFTS/data/NASDAQ.csv
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pyFTS/data/NASDAQ.py
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@ -5,7 +5,7 @@ import pkg_resources
def get_data():
filename = pkg_resources.resource_filename('pyFTS', 'data/NASDAQ.csv')
dat = pd.read_csv(filename, sep=";")
filename = pkg_resources.resource_filename('pyFTS', 'data/NASDAQ.csv.bz2')
dat = pd.read_csv(filename, sep=";", compression='bz2')
dat = np.array(dat["avg"])
return dat

18
pyFTS/data/SONDA.py Normal file
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@ -0,0 +1,18 @@
import pandas as pd
import numpy as np
import os
import pkg_resources
def get_data(field):
filename = pkg_resources.resource_filename('pyFTS', 'data/SONDA_BSB.csv.bz2')
dat = pd.read_csv(filename, sep=";", compression='bz2')
dat = np.array(dat[field])
return dat
def get_dataframe():
filename = pkg_resources.resource_filename('pyFTS', 'data/SONDA_BSB.csv.bz2')
dat = pd.read_csv(filename, sep=";", compression='bz2')
dat["datahora"] = pd.to_datetime(dat["datahora"], format='%Y-%m-%d %H:%M:%S')
return dat

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pyFTS/data/SP500.csv
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pyFTS/data/SP500.py
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@ -5,7 +5,7 @@ import pkg_resources
def get_data():
filename = pkg_resources.resource_filename('pyFTS', 'data/SP500.csv')
dat = pd.read_csv(filename, sep=",")
filename = pkg_resources.resource_filename('pyFTS', 'data/SP500.csv.bz2')
dat = pd.read_csv(filename, sep=",", compression='bz2')
dat = np.array(dat["Avg"])
return dat

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pyFTS/data/TAIEX.csv
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8
pyFTS/data/TAIEX.py
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@ -5,14 +5,14 @@ import pkg_resources
def get_data():
filename = pkg_resources.resource_filename('pyFTS', 'data/TAIEX.csv')
dat = pd.read_csv(filename, sep=",")
filename = pkg_resources.resource_filename('pyFTS', 'data/TAIEX.csv.bz2')
dat = pd.read_csv(filename, sep=",", compression='bz2')
dat = np.array(dat["avg"])
return dat
def get_dataframe():
filename = pkg_resources.resource_filename('pyFTS', 'data/TAIEX.csv')
dat = pd.read_csv(filename, sep=",")
filename = pkg_resources.resource_filename('pyFTS', 'data/TAIEX.csv.bz2')
dat = pd.read_csv(filename, sep=",", compression='bz2')
dat["Date"] = pd.to_datetime(dat["Date"])
return dat

12
pyFTS/models/chen.py
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@ -37,14 +37,12 @@ class ConventionalFTS(fts.FTS):
self.flrgs = {}
def generate_flrg(self, flrs):
flrgs = {}
for flr in flrs:
if flr.LHS.name in flrgs:
flrgs[flr.LHS.name].append(flr.RHS)
if flr.LHS.name in self.flrgs:
self.flrgs[flr.LHS.name].append(flr.RHS)
else:
flrgs[flr.LHS.name] = ConventionalFLRG(flr.LHS)
flrgs[flr.LHS.name].append(flr.RHS)
return (flrgs)
self.flrgs[flr.LHS.name] = ConventionalFLRG(flr.LHS)
self.flrgs[flr.LHS.name].append(flr.RHS)
def train(self, data, **kwargs):
if kwargs.get('sets', None) is not None:
@ -52,7 +50,7 @@ class ConventionalFTS(fts.FTS):
ndata = self.apply_transformations(data)
tmpdata = FuzzySet.fuzzyfy_series_old(ndata, self.sets)
flrs = FLR.generate_non_recurrent_flrs(tmpdata)
self.flrgs = self.generate_flrg(flrs)
self.generate_flrg(flrs)
def forecast(self, data, **kwargs):

10
pyFTS/models/cheng.py
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@ -53,11 +53,9 @@ class TrendWeightedFTS(yu.WeightedFTS):
self.is_high_order = False
def generate_FLRG(self, flrs):
flrgs = {}
for flr in flrs:
if flr.LHS.name in flrgs:
flrgs[flr.LHS.name].append(flr.RHS)
if flr.LHS.name in self.flrgs:
self.flrgs[flr.LHS.name].append(flr.RHS)
else:
flrgs[flr.LHS.name] = TrendWeightedFLRG(flr.LHS)
flrgs[flr.LHS.name].append(flr.RHS)
return (flrgs)
self.flrgs[flr.LHS.name] = TrendWeightedFLRG(flr.LHS)
self.flrgs[flr.LHS.name].append(flr.RHS)

16
pyFTS/models/ensemble/ensemble.py
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@ -32,12 +32,12 @@ class EnsembleFTS(fts.FTS):
self.point_method = kwargs.get('point_method', 'mean')
self.interval_method = kwargs.get('interval_method', 'quantile')
def appendModel(self, model):
def append_model(self, model):
self.models.append(model)
if model.order > self.order:
self.order = model.order
def train(self, data, sets, order=1,parameters=None):
def train(self, data, **kwargs):
self.original_max = max(data)
self.original_min = min(data)
@ -228,10 +228,12 @@ class AllMethodEnsembleFTS(EnsembleFTS):
for t in self.transformations:
model.append_transformation(t)
def train(self, data, sets, order=1, parameters=None):
def train(self, data, **kwargs):
self.original_max = max(data)
self.original_min = min(data)
order = kwargs.get('order',2)
fo_methods = [song.ConventionalFTS, chen.ConventionalFTS, yu.WeightedFTS, cheng.TrendWeightedFTS,
sadaei.ExponentialyWeightedFTS, ismailefendi.ImprovedWeightedFTS]
@ -240,16 +242,16 @@ class AllMethodEnsembleFTS(EnsembleFTS):
for method in fo_methods:
model = method("")
self.set_transformations(model)
model.train(data, sets)
self.appendModel(model)
model.train(data, **kwargs)
self.append_model(model)
for method in ho_methods:
for o in np.arange(1, order+1):
model = method("")
if model.min_order >= o:
self.set_transformations(model)
model.train(data, sets, order=o)
self.appendModel(model)
model.train(data, **kwargs)
self.append_model(model)

4
pyFTS/models/ensemble/multiseasonal.py
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@ -41,7 +41,7 @@ class SeasonalEnsembleFTS(ensemble.EnsembleFTS):
self.original_max = max(self.indexer.get_data(data))
self.original_min = min(self.indexer.get_data(data))
def train(self, data, sets, order=1, parameters=None):
def train(self, data, **kwargs):
self.original_max = max(self.indexer.get_data(data))
self.original_min = min(self.indexer.get_data(data))
@ -59,7 +59,7 @@ class SeasonalEnsembleFTS(ensemble.EnsembleFTS):
for m in pool.keys())
for tmp in results:
self.appendModel(tmp)
self.append_model(tmp)
cUtil.persist_obj(self, "models/"+self.name+".pkl")

20
pyFTS/models/hofts.py
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@ -77,7 +77,6 @@ class HighOrderFTS(fts.FTS):
self.build_tree_without_order(child, lags, level + 1)
def generateFLRG(self, flrs):
flrgs = {}
l = len(flrs)
for k in np.arange(self.order + 1, l):
flrg = HighOrderFLRG(self.order)
@ -85,15 +84,13 @@ class HighOrderFTS(fts.FTS):
for kk in np.arange(k - self.order, k):
flrg.append_lhs(flrs[kk].LHS)
if flrg.str_lhs() in flrgs:
flrgs[flrg.str_lhs()].append_rhs(flrs[k].RHS)
if flrg.str_lhs() in self.flrgs:
self.flrgs[flrg.str_lhs()].append_rhs(flrs[k].RHS)
else:
flrgs[flrg.str_lhs()] = flrg;
flrgs[flrg.str_lhs()].append_rhs(flrs[k].RHS)
return (flrgs)
self.flrgs[flrg.str_lhs()] = flrg;
self.flrgs[flrg.str_lhs()].append_rhs(flrs[k].RHS)
def generate_flrg(self, data):
flrgs = {}
l = len(data)
for k in np.arange(self.order, l):
if self.dump: print("FLR: " + str(k))
@ -121,13 +118,12 @@ class HighOrderFTS(fts.FTS):
for lhs in path:
flrg.append_lhs(lhs)
if flrg.str_lhs() not in flrgs:
flrgs[flrg.str_lhs()] = flrg;
if flrg.str_lhs() not in self.flrgs:
self.flrgs[flrg.str_lhs()] = flrg;
for st in rhs:
flrgs[flrg.str_lhs()].append_rhs(st)
self.flrgs[flrg.str_lhs()].append_rhs(st)
return flrgs
def train(self, data, **kwargs):
@ -138,7 +134,7 @@ class HighOrderFTS(fts.FTS):
if kwargs.get('sets', None) is not None:
self.sets = kwargs.get('sets', None)
for s in self.sets: self.setsDict[s.name] = s
self.flrgs = self.generate_flrg(data)
self.generate_flrg(data)
def forecast(self, data, **kwargs):

12
pyFTS/models/ismailefendi.py
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@ -54,14 +54,12 @@ class ImprovedWeightedFTS(fts.FTS):
self.setsDict = {}
def generate_flrg(self, flrs):
flrgs = {}
for flr in flrs:
if flr.LHS.name in flrgs:
flrgs[flr.LHS.name].append(flr.RHS)
if flr.LHS.name in self.flrgs:
self.flrgs[flr.LHS.name].append(flr.RHS)
else:
flrgs[flr.LHS.name] = ImprovedWeightedFLRG(flr.LHS);
flrgs[flr.LHS.name].append(flr.RHS)
return (flrgs)
self.flrgs[flr.LHS.name] = ImprovedWeightedFLRG(flr.LHS);
self.flrgs[flr.LHS.name].append(flr.RHS)
def train(self, data, **kwargs):
if kwargs.get('sets', None) is not None:
@ -73,7 +71,7 @@ class ImprovedWeightedFTS(fts.FTS):
tmpdata = FuzzySet.fuzzyfy_series_old(ndata, self.sets)
flrs = FLR.generate_recurrent_flrs(tmpdata)
self.flrgs = self.generate_flrg(flrs)
self.generate_flrg(flrs)
def forecast(self, data, **kwargs):
l = 1

12
pyFTS/models/multivariate/mvfts.py
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@ -15,6 +15,7 @@ class MVFTS(fts.FTS):
self.explanatory_variables = []
self.target_variable = None
self.flrgs = {}
self.is_multivariate = True
def append_variable(self, var):
self.explanatory_variables.append(var)
@ -76,24 +77,21 @@ class MVFTS(fts.FTS):
return flrs
def generate_flrg(self, flrs):
flrgs = {}
for flr in flrs:
flrg = mvflrg.FLRG(lhs=flr.LHS)
if flrg.get_key() not in flrgs:
flrgs[flrg.get_key()] = flrg
if flrg.get_key() not in self.flrgs:
self.flrgs[flrg.get_key()] = flrg
flrgs[flrg.get_key()].append_rhs(flr.RHS)
self.flrgs[flrg.get_key()].append_rhs(flr.RHS)
return flrgs
def train(self, data, **kwargs):
ndata = self.apply_transformations(data)
flrs = self.generate_flrs(ndata)
self.flrgs = self.generate_flrg(flrs)
self.generate_flrg(flrs)
def forecast(self, data, **kwargs):
ret = []

3
pyFTS/models/multivariate/variable.py
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@ -33,9 +33,10 @@ class Variable:
mf = kwargs.get('func', Membership.trimf)
np = kwargs.get('npart', 10)
data = kwargs.get('data', None)
kw = kwargs.get('partitioner_specific', {})
self.partitioner = fs(data=data[self.data_label].values, npart=np, func=mf,
transformation=self.transformation, prefix=self.alias,
variable=self.name)
variable=self.name, **kw)
self.partitioner.name = self.name + " " + self.partitioner.name

3
pyFTS/models/nonstationary/common.py
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@ -31,7 +31,7 @@ class FuzzySet(FS.FuzzySet):
- noise: Pertubation function that adds noise on the membership function
- noise_params: Parameters for noise pertubation function
"""
super(FuzzySet, self).__init__(name=name, mf=mf, parameters=parameters, centroid=None)
super(FuzzySet, self).__init__(name=name, mf=mf, parameters=parameters, centroid=None, alpha=1.0, **kwargs)
self.location = kwargs.get("location", None)
self.location_params = kwargs.get("location_params", None)
@ -42,6 +42,7 @@ class FuzzySet(FS.FuzzySet):
self.noise = kwargs.get("noise", None)
self.noise_params = kwargs.get("noise_params", None)
self.perturbated_parameters = {}
self.type = 'nonstationary'
if self.location is not None and not isinstance(self.location, (list, set)):
self.location = [self.location]

20
pyFTS/models/nonstationary/cvfts.py
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@ -22,11 +22,9 @@ class ConditionalVarianceFTS(chen.ConventionalFTS):
self.min_stack = [0,0,0]
self.max_stack = [0,0,0]
def train(self, data, sets = None, order=1,parameters=None):
if sets is not None:
self.sets = sets
else:
self.sets = self.partitioner.sets
def train(self, data, **kwargs):
if kwargs.get('sets', None) is not None:
self.sets = kwargs.get('sets', None)
ndata = self.apply_transformations(data)
@ -35,17 +33,15 @@ class ConditionalVarianceFTS(chen.ConventionalFTS):
tmpdata = common.fuzzySeries(ndata, self.sets, method='fuzzy', const_t=0)
flrs = FLR.generate_non_recurrent_flrs(tmpdata)
self.flrgs = self.generate_flrg(flrs)
self.generate_flrg(flrs)
def generate_flrg(self, flrs, **kwargs):
flrgs = {}
for flr in flrs:
if flr.LHS.name in flrgs:
flrgs[flr.LHS.name].append(flr.RHS)
if flr.LHS.name in self.flrgs:
self.flrgs[flr.LHS.name].append(flr.RHS)
else:
flrgs[flr.LHS.name] = nsfts.ConventionalNonStationaryFLRG(flr.LHS)
flrgs[flr.LHS.name].append(flr.RHS)
return flrgs
self.flrgs[flr.LHS.name] = nsfts.ConventionalNonStationaryFLRG(flr.LHS)
self.flrgs[flr.LHS.name].append(flr.RHS)
def _smooth(self, a):
return .1 * a[0] + .3 * a[1] + .6 * a[2]

24
pyFTS/models/nonstationary/honsfts.py
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@ -46,7 +46,6 @@ class HighOrderNonStationaryFTS(hofts.HighOrderFTS):
self.flrgs = {}
def generate_flrg(self, data, **kwargs):
flrgs = {}
l = len(data)
window_size = kwargs.get("window_size", 1)
for k in np.arange(self.order, l):
@ -84,30 +83,27 @@ class HighOrderNonStationaryFTS(hofts.HighOrderFTS):
for c, e in enumerate(path, start=0):
flrg.appendLHS(e)
if flrg.strLHS() not in flrgs:
flrgs[flrg.strLHS()] = flrg;
if flrg.strLHS() not in self.flrgs:
self.flrgs[flrg.strLHS()] = flrg;
for st in rhs:
flrgs[flrg.strLHS()].append_rhs(st)
self.flrgs[flrg.strLHS()].append_rhs(st)
# flrgs = sorted(flrgs, key=lambda flrg: flrg.get_midpoint(0, window_size=1))
return flrgs
def train(self, data, **kwargs):
def train(self, data, sets=None, order=2, parameters=None):
if kwargs.get('order', None) is not None:
self.order = kwargs.get('order', 1)
self.order = order
if sets is not None:
self.sets = sets
else:
self.sets = self.partitioner.sets
if kwargs.get('sets', None) is not None:
self.sets = kwargs.get('sets', None)
ndata = self.apply_transformations(data)
#tmpdata = common.fuzzyfy_series_old(ndata, self.sets)
#flrs = FLR.generate_recurrent_flrs(ndata)
window_size = parameters if parameters is not None else 1
self.flrgs = self.generate_flrg(ndata, window_size=window_size)
window_size = kwargs.get('parameters', 1)
self.generate_flrg(ndata, window_size=window_size)
def _affected_flrgs(self, sample, k, time_displacement, window_size):
# print("input: " + str(ndata[k]))

24
pyFTS/models/nonstationary/nsfts.py
View File

@ -1,6 +1,6 @@
import numpy as np
from pyFTS.common import FLR, fts
from pyFTS.nonstationary import common, flrg
from pyFTS.models.nonstationary import common, flrg
class ConventionalNonStationaryFLRG(flrg.NonStationaryFLRG):
@ -34,29 +34,25 @@ class NonStationaryFTS(fts.FTS):
self.method = kwargs.get("method",'fuzzy')
def generate_flrg(self, flrs, **kwargs):
flrgs = {}
for flr in flrs:
if flr.LHS.name in flrgs:
flrgs[flr.LHS.name].append(flr.RHS)
if flr.LHS.name in self.flrgs:
self.flrgs[flr.LHS.name].append(flr.RHS)
else:
flrgs[flr.LHS.name] = ConventionalNonStationaryFLRG(flr.LHS)
flrgs[flr.LHS.name].append(flr.RHS)
return flrgs
self.flrgs[flr.LHS.name] = ConventionalNonStationaryFLRG(flr.LHS)
self.flrgs[flr.LHS.name].append(flr.RHS)
def train(self, data, sets=None, order=1, parameters=None):
def train(self, data, **kwargs):
if sets is not None:
self.sets = sets
else:
self.sets = self.partitioner.sets
if kwargs.get('sets', None) is not None:
self.sets = kwargs.get('sets', None)
ndata = self.apply_transformations(data)
window_size = parameters if parameters is not None else 1
window_size = kwargs.get('parameters', 1)
tmpdata = common.fuzzySeries(ndata, self.sets, window_size, method=self.method)
#print([k[0].name for k in tmpdata])
flrs = FLR.generate_recurrent_flrs(tmpdata)
#print([str(k) for k in flrs])
self.flrgs = self.generate_flrg(flrs)
self.generate_flrg(flrs)
def forecast(self, data, **kwargs):

23
pyFTS/models/pwfts.py
View File

@ -128,12 +128,11 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
if parameters == 'Monotonic':
tmpdata = FuzzySet.fuzzyfy_series_old(data, self.sets)
flrs = FLR.generate_recurrent_flrs(tmpdata)
self.flrgs = self.generateFLRG(flrs)
self.generateFLRG(flrs)
else:
self.flrgs = self.generate_flrg(data)
self.generate_flrg(data)
def generate_flrg(self, data):
flrgs = {}
l = len(data)
for k in np.arange(self.order, l):
if self.dump: print("FLR: " + str(k))
@ -168,20 +167,17 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
lhs_mv = np.prod(tmp_path)
if flrg.str_lhs() not in flrgs:
flrgs[flrg.str_lhs()] = flrg;
if flrg.str_lhs() not in self.flrgs:
self.flrgs[flrg.str_lhs()] = flrg;
for st in idx:
flrgs[flrg.str_lhs()].appendRHSFuzzy(self.sets[st], lhs_mv * mv[st])
self.flrgs[flrg.str_lhs()].appendRHSFuzzy(self.sets[st], lhs_mv * mv[st])
tmp_fq = sum([lhs_mv*kk for kk in mv if kk > 0])
self.global_frequency_count = self.global_frequency_count + tmp_fq
return flrgs
def generateFLRG(self, flrs):
flrgs = {}
l = len(flrs)
for k in np.arange(self.order, l+1):
if self.dump: print("FLR: " + str(k))
@ -191,15 +187,14 @@ class ProbabilisticWeightedFTS(ifts.IntervalFTS):
flrg.append_lhs(flrs[kk].LHS)
if self.dump: print("LHS: " + str(flrs[kk]))
if flrg.str_lhs() in flrgs:
flrgs[flrg.str_lhs()].append_rhs(flrs[k - 1].RHS)
if flrg.str_lhs() in self.flrgs:
self.flrgs[flrg.str_lhs()].append_rhs(flrs[k - 1].RHS)
else:
flrgs[flrg.str_lhs()] = flrg
flrgs[flrg.str_lhs()].append_rhs(flrs[k - 1].RHS)
self.flrgs[flrg.str_lhs()] = flrg
self.flrgs[flrg.str_lhs()].append_rhs(flrs[k - 1].RHS)
if self.dump: print("RHS: " + str(flrs[k-1]))
self.global_frequency_count += 1
return (flrgs)
def update_model(self,data):

12
pyFTS/models/sadaei.py
View File

@ -58,14 +58,12 @@ class ExponentialyWeightedFTS(fts.FTS):
self.c = kwargs.get('c', default_c)
def generate_flrg(self, flrs, c):
flrgs = {}
for flr in flrs:
if flr.LHS.name in flrgs:
flrgs[flr.LHS.name].append(flr.RHS)
if flr.LHS.name in self.flrgs:
self.flrgs[flr.LHS.name].append(flr.RHS)
else:
flrgs[flr.LHS.name] = ExponentialyWeightedFLRG(flr.LHS, c=c);
flrgs[flr.LHS.name].append(flr.RHS)
return (flrgs)
self.flrgs[flr.LHS.name] = ExponentialyWeightedFLRG(flr.LHS, c=c);
self.flrgs[flr.LHS.name].append(flr.RHS)
def train(self, data, **kwargs):
self.c = kwargs.get('parameters', default_c)
@ -74,7 +72,7 @@ class ExponentialyWeightedFTS(fts.FTS):
ndata = self.apply_transformations(data)
tmpdata = FuzzySet.fuzzyfy_series_old(ndata, self.sets)
flrs = FLR.generate_recurrent_flrs(tmpdata)
self.flrgs = self.generate_flrg(flrs, self.c)
self.generate_flrg(flrs, self.c)
def forecast(self, data, **kwargs):
l = 1

30
pyFTS/models/seasonal/cmsfts.py
View File

@ -44,25 +44,23 @@ class ContextualMultiSeasonalFTS(sfts.SeasonalFTS):
self.indexer = indexer
self.flrgs = {}
def generateFLRG(self, flrs):
flrgs = {}
def generate_flrg(self, flrs):
for flr in flrs:
if str(flr.index) not in flrgs:
flrgs[str(flr.index)] = ContextualSeasonalFLRG(flr.index)
if str(flr.index) not in self.flrgs:
self.flrgs[str(flr.index)] = ContextualSeasonalFLRG(flr.index)
flrgs[str(flr.index)].append(flr)
self.flrgs[str(flr.index)].append(flr)
return (flrgs)
def train(self, data, sets, order=1, parameters=None):
self.sets = sets
self.seasonality = parameters
def train(self, data, **kwargs):
if kwargs.get('sets', None) is not None:
self.sets = kwargs.get('sets', None)
if kwargs.get('parameters', None) is not None:
self.seasonality = kwargs.get('parameters', None)
flrs = FLR.generate_indexed_flrs(self.sets, self.indexer, data)
self.flrgs = self.generateFLRG(flrs)
self.generate_flrg(flrs)
def getMidpoints(self, flrg, data):
def get_midpoints(self, flrg, data):
if data.name in flrg.flrgs:
ret = np.array([s.centroid for s in flrg.flrgs[data.name].RHS])
return ret
@ -82,7 +80,7 @@ class ContextualMultiSeasonalFTS(sfts.SeasonalFTS):
d = FuzzySet.get_maximum_membership_fuzzyset(ndata[k], self.sets)
mp = self.getMidpoints(flrg, d)
mp = self.get_midpoints(flrg, d)
ret.append(sum(mp) / len(mp))
@ -90,12 +88,12 @@ class ContextualMultiSeasonalFTS(sfts.SeasonalFTS):
return ret
def forecastAhead(self, data, steps, **kwargs):
def forecast_ahead(self, data, steps, **kwargs):
ret = []
for i in steps:
flrg = self.flrgs[str(i)]
mp = self.getMidpoints(flrg)
mp = self.get_midpoints(flrg)
ret.append(sum(mp) / len(mp))

5
pyFTS/models/seasonal/common.py
View File

@ -28,7 +28,7 @@ class DateTime(Enum):
second_of_day = 86400
def strip_datepart(self, date, date_part):
def strip_datepart(date, date_part):
if date_part == DateTime.year:
tmp = date.year
elif date_part == DateTime.month:
@ -90,7 +90,8 @@ class FuzzySet(FuzzySet.FuzzySet):
def __init__(self, datepart, name, mf, parameters, centroid, alpha=1.0, **kwargs):
super(FuzzySet, self).__init__(name, mf, parameters, centroid, alpha, type = 'datetime', **kwargs)
self.datepart = datepart
self.type = 'seasonal'
def membership(self, x):
dp = strip_datepart(x, self.datepart)
return self.mf.membership(dp)
return self.mf(dp, self.parameters) * self.alpha

20
pyFTS/models/seasonal/msfts.py
View File

@ -20,24 +20,22 @@ class MultiSeasonalFTS(sfts.SeasonalFTS):
self.indexer = indexer
self.flrgs = {}
def generateFLRG(self, flrs):
flrgs = {}
def generate_flrg(self, flrs):
for flr in flrs:
if str(flr.index) not in self.flrgs:
flrgs[str(flr.index)] = sfts.SeasonalFLRG(flr.index)
self.flrgs[str(flr.index)] = sfts.SeasonalFLRG(flr.index)
flrgs[str(flr.index)].append(flr.RHS)
self.flrgs[str(flr.index)].append(flr.RHS)
return (flrgs)
def train(self, data, sets, order=1, parameters=None):
self.sets = sets
self.seasonality = parameters
def train(self, data, **kwargs):
if kwargs.get('sets', None) is not None:
self.sets = kwargs.get('sets', None)
if kwargs.get('parameters', None) is not None:
self.seasonality = kwargs.get('parameters', None)
#ndata = self.indexer.set_data(data,self.doTransformations(self.indexer.get_data(data)))
flrs = FLR.generate_indexed_flrs(self.sets, self.indexer, data)
self.flrgs = self.generateFLRG(flrs)
self.generate_flrg(flrs)
def forecast(self, data, **kwargs):

15
pyFTS/models/seasonal/partitioner.py
View File

@ -17,7 +17,7 @@ class TimeGridPartitioner(partitioner.Partitioner):
:param npart: The number of universe of discourse partitions, i.e., the number of fuzzy sets that will be created
:param func: Fuzzy membership function (pyFTS.common.Membership)
"""
super(TimeGridPartitioner, self).__init__(name="TimeGrid", **kwargs)
super(TimeGridPartitioner, self).__init__(name="TimeGrid", preprocess=False, **kwargs)
self.season = kwargs.get('seasonality', DateTime.day_of_year)
data = kwargs.get('data', None)
@ -101,11 +101,12 @@ class TimeGridPartitioner(partitioner.Partitioner):
ticks = []
x = []
for s in self.sets:
if s.type == 'common':
self.plot_set(ax, s)
elif s.type == 'composite':
if s.type == 'composite':
for ss in s.sets:
self.plot_set(ax, ss)
# ticks.append(str(round(s.centroid, 0)) + '\n' + s.name)
# x.append(s.centroid)
# plt.xticks(x, ticks)
else:
self.plot_set(ax, s)
ticks.append(str(round(s.centroid, 0)) + '\n' + s.name)
x.append(s.centroid)
ax.xaxis.set_ticklabels(ticks)
ax.xaxis.set_ticks(x)

22
pyFTS/models/seasonal/sfts.py
View File

@ -43,29 +43,29 @@ class SeasonalFTS(fts.FTS):
self.has_seasonality = True
self.has_point_forecasting = True
self.is_high_order = False
self.flrgs = {}
def generate_flrg(self, flrs):
def generateFLRG(self, flrs):
flrgs = {}
for ct, flr in enumerate(flrs, start=1):
season = self.indexer.get_season_by_index(ct)[0]
ss = str(season)
if ss not in flrgs:
flrgs[ss] = SeasonalFLRG(season)
if ss not in self.flrgs:
self.flrgs[ss] = SeasonalFLRG(season)
#print(season)
flrgs[ss].append(flr.RHS)
self.flrgs[ss].append(flr.RHS)
return (flrgs)
def train(self, data, sets, order=1, parameters=None):
self.sets = sets
def train(self, data, **kwargs):
if kwargs.get('sets', None) is not None:
self.sets = kwargs.get('sets', None)
ndata = self.apply_transformations(data)
tmpdata = FuzzySet.fuzzyfy_series_old(ndata, sets)
tmpdata = FuzzySet.fuzzyfy_series_old(ndata, self.sets)
flrs = FLR.generate_recurrent_flrs(tmpdata)
self.flrgs = self.generateFLRG(flrs)
self.generate_flrg(flrs)
def forecast(self, data, **kwargs):

15
pyFTS/models/song.py
View File

@ -14,8 +14,15 @@ class ConventionalFTS(fts.FTS):
super(ConventionalFTS, self).__init__(1, "FTS " + name, **kwargs)
self.name = "Traditional FTS"
self.detail = "Song & Chissom"
if self.sets is not None and self.partitioner is not None:
self.sets = self.partitioner.sets
self.R = None
if self.sets is not None:
self.R = np.zeros((len(self.sets),len(self.sets)))
def flr_membership_matrix(self, flr):
lm = [flr.LHS.membership(k.centroid) for k in self.sets]
rm = [flr.RHS.membership(k.centroid) for k in self.sets]
@ -28,14 +35,14 @@ class ConventionalFTS(fts.FTS):
return r
def operation_matrix(self, flrs):
r = np.zeros((len(self.sets),len(self.sets)))
if self.R is None:
self.R = np.zeros((len(self.sets), len(self.sets)))
for k in flrs:
mm = self.flr_membership_matrix(k)
for k in range(0, len(self.sets)):
for l in range(0, len(self.sets)):
r[k][l] = max(r[k][l], mm[k][l])
self.R[k][l] = max(r[k][l], mm[k][l])
return r
def train(self, data, **kwargs):
if kwargs.get('sets', None) is not None:
@ -43,7 +50,7 @@ class ConventionalFTS(fts.FTS):
ndata = self.apply_transformations(data)
tmpdata = FuzzySet.fuzzyfy_series_old(ndata, self.sets)
flrs = FLR.generate_non_recurrent_flrs(tmpdata)
self.R = self.operation_matrix(flrs)
self.operation_matrix(flrs)
def forecast(self, data, **kwargs):

12
pyFTS/models/yu.py
View File

@ -47,14 +47,12 @@ class WeightedFTS(fts.FTS):
self.detail = "Yu"
def generate_FLRG(self, flrs):
flrgs = {}
for flr in flrs:
if flr.LHS.name in flrgs:
flrgs[flr.LHS.name].append(flr.RHS)
if flr.LHS.name in self.flrgs:
self.flrgs[flr.LHS.name].append(flr.RHS)
else:
flrgs[flr.LHS.name] = WeightedFLRG(flr.LHS);
flrgs[flr.LHS.name].append(flr.RHS)
return (flrgs)
self.flrgs[flr.LHS.name] = WeightedFLRG(flr.LHS);
self.flrgs[flr.LHS.name].append(flr.RHS)
def train(self, data, **kwargs):
if kwargs.get('sets', None) is not None:
@ -62,7 +60,7 @@ class WeightedFTS(fts.FTS):
ndata = self.apply_transformations(data)
tmpdata = FuzzySet.fuzzyfy_series_old(ndata, self.sets)
flrs = FLR.generate_recurrent_flrs(tmpdata)
self.flrgs = self.generate_FLRG(flrs)
self.generate_FLRG(flrs)
def forecast(self, data, **kwargs):
l = 1

150
pyFTS/notebooks/multivariate.ipynb
View File

File diff suppressed because one or more lines are too long

12
pyFTS/tests/ensemble.py
View File

@ -37,7 +37,7 @@ for method in fo_methods:
model = method("")
model.append_transformation(diff)
model.train(passengers, fs.sets)
e.appendModel(model)
e.append_model(model)
for method in ho_methods:
@ -45,7 +45,7 @@ for method in ho_methods:
model = method("")
model.append_transformation(diff)
model.train(passengers, fs.sets, order=order)
e.appendModel(model)
e.append_model(model)
arima100 = arima.ARIMA("", alpha=0.25)
@ -65,10 +65,10 @@ arima201 = arima.ARIMA("", alpha=0.25)
arima201.train(passengers, None, order=(2,0,1))
e.appendModel(arima100)
e.appendModel(arima101)
e.appendModel(arima200)
e.appendModel(arima201)
e.append_model(arima100)
e.append_model(arima101)
e.append_model(arima200)
e.append_model(arima201)
e.train(passengers, None)

27
pyFTS/tests/seasonal.py
View File

@ -4,16 +4,33 @@ from pyFTS.partitioners import Util
from pyFTS.common import Membership
#fs = partitioner.TimeGridPartitioner(None, 12, common.DateTime.day_of_year, func=Membership.trapmf,
#fs = partitioner.TimeGridPartitioner(data=None, npart=12, seasonality=common.DateTime.day_of_year,
# func=Membership.trapmf,
# names=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec'])
#fs = partitioner.TimeGridPartitioner(None, 24, common.DateTime.minute_of_day, func=Membership.trapmf)
fs = partitioner.TimeGridPartitioner(None, 7, common.DateTime.hour_of_week, func=Membership.trapmf)
#fs = partitioner.TimeGridPartitioner(None, 7, common.DateTime.hour_of_week, func=Membership.trapmf)
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=[6, 8])
#fig, ax = plt.subplots(nrows=1, ncols=1, figsize=[6, 8])
fs.plot(ax)
plt.show()
#fs.plot(ax)
#plt.show()
"""
import pandas as pd
from pyFTS.data import SONDA
df = SONDA.get_dataframe()
df = df.drop(df[df.rain.values > 100].index)
df = df.drop(df[df.press.values < 800].index)
df = df.drop(df[df.humid.values < 15].index)
df.to_csv("SONDA_BSB_MOD.csv", sep=";", index=False)
"""
import os
print(os.getcwd())